Global Mapping of the Yeast Genetic Interaction Network

Deciphering the Genetic Basis of Kernel Composition in a Maize Association Panel

The primary objective in contemporary maize breeding is to pursue high quality alongside high yield. Deciphering the genetic basis of natural variation in starch, protein, oil, and fiber contents is essential for manipulating kernel composition, thereby enhancing the kernel quality and meeting growing demands. Here, we identified 12 to 88 statistically significant loci associated with kernel composition traits through a genome-wide association study (GWAS) using a panel of 212 diverse inbred lines. A regional association study pinpointed numerous causal candidate genes at these loci. Coexpression and protein-protein interaction network analyses of candidate genes revealed several causal genes directly or indirectly involved in the metabolic processes related to kernel composition traits. Subsequent mutant experiment revealed that nonsense mutations in ZmTIFY12 affect starch, protein, and fiber content, whereas nonsense mutations in ZmTT12 affect starch, protein, and oil content. These findings provide valuable guidance for improving kernel quality in maize breeding efforts.

CONSTANS alters the circadian clock in Arabidopsis thaliana

Plants are sessile organisms that have acquired highly plastic developmental strategies to adapt to the environment. Among these processes, the floral transition is essential to ensure reproductive success and is finely regulated by several internal and external genetic networks. The photoperiodic pathway, which controls plant response to day length, is one of the most important pathways controlling flowering. In Arabidopsis photoperiodic flowering, CONSTANS (CO) is the central gene activating the expression of the florigen FLOWERING LOCUS T (FT) in the leaves at the end of a long day. The circadian clock strongly regulates CO expression. However, to date, no evidence has been reported regarding a feedback loop from the photoperiod pathway back to the circadian clock. Using transcriptional networks, we have identified relevant network motifs regulating the interplay between the circadian clock and the photoperiod pathway. Gene expression, chromatin immunoprecipitation experiments, and phenotypic analysis allowed us to elucidate the role of CO over the circadian clock. Plants with altered CO expression showed a different internal clock period, measured by daily leaf rhythmic movements. We showed that CO upregulates the expression of key genes related to the circadian clock, such as CCA1, LHY, PRR5, and GI, at the end of a long day by binding to specific sites on their promoters. Moreover, a high number of PRR5-repressed target genes are upregulated by CO, and this could explain the phase transition promoted by CO. The CO-PRR5 complex interacts with the bZIP transcription factor HY5 and helps to localize the complex in the promoters of clock genes. Taken together, our results indicate that there may be a feedback loop in which CO communicates back to the circadian clock, providing seasonal information to the circadian system.

Oxidative stress-induced YAP1 expression is regulated by NCE102, CDA2, and BCS1

Maintaining cellular homeostasis in the face of stress conditions is vital for the overall well-being of an organism. Reactive oxygen species (ROS) are among the most potent cellular stressors and can disrupt the internal redox balance, giving rise to oxidative stress. Elevated levels of ROS can severely affect biomolecules and have been associated with a range of pathophysiological conditions. In response to oxidative stress, yeast activator protein-1 (Yap1p) undergoes post-translation modification that results in its nuclear accumulation. YAP1 has a key role in oxidative detoxification by promoting transcription of numerous antioxidant genes. In this study, we identified previously undescribed functions for NCE102, CDA2, and BCS1 in YAP1 expression in response to oxidative stress induced by hydrogen peroxide (H2O2). Deletion mutant strains for these candidates demonstrated increased sensitivity to H2O2. Our follow-up investigation linked the activity of these genes to YAP1 expression at the level of translation. Under oxidative stress, global cap-dependent translation is inhibited, prompting stress-responsive genes like YAP1 to employ alternative modes of translation. We provide evidence that NCE102, CDA2, and BCS1 contribute to cap-independent translation of YAP1 under oxidative stress.

Unlocking biological mechanisms with integrative functional genomics approaches

Reverse genetics offers precise functional insights into genes through the targeted manipulation of gene expression followed by phenotypic assessment. While these approaches have proven effective in model organisms such as Saccharomyces cerevisiae, large-scale genetic manipulations in human cells were historically unfeasible due to methodological limitations. However, recent advancements in functional genomics, particularly clustered regularly interspaced short palindromic repeats (CRISPR)-based screening technologies and next-generation sequencing platforms, have enabled pooled screening technologies that allow massively parallel, unbiased assessments of biological phenomena in human cells. This review provides a comprehensive overview of cutting-edge functional genomic screening technologies applicable to human cells, ranging from short hairpin RNA screens to modern CRISPR screens. Additionally, we explore the integration of CRISPR platforms with single-cell approaches to monitor gene expression, chromatin accessibility, epigenetic regulation, and chromatin architecture following genetic perturbations at the omics level. By offering an in-depth understanding of these genomic screening methods, this review aims to provide insights into more targeted and effective strategies for genomic research and personalized medicine.

Synthetic lethality and the minimal genome size problem

Gene knockout studies suggest that ~300 genes in a bacterial genome and ~1,100 genes in a yeast genome cannot be deleted without loss of viability. These single-gene knockout experiments do not account for negative genetic interactions, when two or more genes can each be deleted without effect, but their joint deletion is lethal. Thus, large-scale single-gene deletion studies underestimate the size of a minimal gene set compatible with cell survival. In yeast Saccharomyces cerevisiae, the viability of all possible deletions of gene pairs (2-tuples), and of some deletions of gene triplets (3-tuples), has been experimentally tested. To estimate the size of a yeast minimal genome from that data, we first established that finding the size of a minimal gene set is equivalent to finding the minimum vertex cover in the lethality (hyper)graph, where the vertices are genes and (hyper)edges connect k-tuples of genes whose joint deletion is lethal. Using the Lovász-Johnson-Chvatal greedy approximation algorithm, we computed the minimum vertex cover of the synthetic-lethal 2-tuples graph to be 1,723 genes. We next simulated the genetic interactions in 3-tuples, extrapolating from the existing triplet sample, and again estimated minimum vertex covers. The size of a minimal gene set in yeast rapidly approaches the size of the entire genome even when considering only synthetic lethalities in k-tuples with small k. In contrast, several studies reported successful experimental reductions of yeast and bacterial genomes by simultaneous deletions of hundreds of genes, without eliciting synthetic lethality. We discuss possible reasons for this apparent contradiction.IMPORTANCEHow can we estimate the smallest number of genes sufficient for a unicellular organism to survive on a rich medium? One approach is to remove genes one at a time and count how many of such deletion strains are unable to grow. However, the single-gene knockout data are insufficient, because joint gene deletions may result in negative genetic interactions, also known as synthetic lethality. We used a technique from graph theory to estimate the size of minimal yeast genome from partial data on synthetic lethality. The number of potential synthetic lethal interactions grows very fast when multiple genes are deleted, revealing a paradoxical contrast with the experimental reductions of yeast genome by ~100 genes, and of bacterial genomes by several hundreds of genes.

On knowing a gene: A distributional hypothesis of gene function

As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as "is-a" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.

The Ser/Thr protein kinase FonKin4-poly(ADP-ribose) polymerase FonPARP1 phosphorylation cascade is required for the pathogenicity of watermelon fusarium wilt fungus Fusarium oxysporum f. sp. niveum

Poly(ADP-ribosyl)ation (PARylation), catalyzed by poly(ADP-ribose) polymerases (PARPs) and hydrolyzed by poly(ADP-ribose) glycohydrolase (PARG), is a kind of post-translational protein modification that is involved in various cellular processes in fungi, plants, and mammals. However, the function of PARPs in plant pathogenic fungi remains unknown. The present study investigated the roles and mechanisms of FonPARP1 in watermelon Fusarium wilt fungus Fusarium oxysporum f. sp. niveum (Fon). Fon has a single PARP FonPARP1 and one PARG FonPARG1. FonPARP1 is an active PARP and contributes to Fon pathogenicity through regulating its invasive growth within watermelon plants, while FonPARG1 is not required for Fon pathogenicity. A serine/threonine protein kinase, FonKin4, was identified as a FonPARP1-interacting partner by LC-MS/MS. FonKin4 is required for vegetative growth, conidiation, macroconidia morphology, abiotic stress response and pathogenicity of Fon. The S_TKc domain is sufficient for both enzyme activity and pathogenicity function of FonKin4 in Fon. FonKin4 phosphorylates FonPARP1 in vitro to enhance its poly(ADP-ribose) polymerase activity; however, FonPARP1 does not PARylate FonKin4. These results establish the FonKin4-FonPARP1 phosphorylation cascade that positively contributes to Fon pathogenicity. The present study highlights the importance of PARP-catalyzed protein PARylation in regulating the pathogenicity of Fon and other plant pathogenic fungi.

Histone variant H2A.Z and linker histone H1 influence chromosome condensation in Saccharomyces cerevisiae

Chromosome condensation is essential for the fidelity of chromosome segregation during mitosis and meiosis. Condensation is associated both with local changes in nucleosome structure and larger-scale alterations in chromosome topology mediated by the condensin complex. We examined the influence of linker histone H1 and variant histone H2A.Z on chromosome condensation in budding yeast cells. Linker histone H1 has been implicated in local and global compaction of chromatin in multiple eukaryotes, but we observe normal condensation of the rDNA locus in yeast strains lacking H1. However, deletion of the yeast HTZ1 gene, coding for variant histone H2A.Z, causes a significant defect in rDNA condensation. Loss of H2A.Z does not change condensin association with the rDNA locus or significantly affect condensin mRNA levels. Prior studies reported that several phenotypes caused by loss of H2A.Z are suppressed by eliminating Swr1, a key component of the SWR complex that deposits H2A.Z in chromatin. We observe that an htz1Δ swr1Δ strain has near-normal rDNA condensation. Unexpectedly, we find that elimination of the linker histone H1 can also suppress the rDNA condensation defect of htz1Δ strains. Our experiments demonstrate that histone H2A.Z promotes chromosome condensation, in part by counteracting activities of histone H1 and the SWR complex.

PROTACs: Current and Future Potential as a Precision Medicine Strategy to Combat Cancer

Proteolysis targeting chimeras (PROTAC) are an emerging precision medicine strategy, which targets key proteins for proteolytic degradation to ultimately induce cancer cell killing. These hetero-bifunctional molecules hijack the ubiquitin proteasome system to selectively add polyubiquitin chains onto a specific protein target to induce proteolytic degradation. Importantly, PROTACs have the capacity to target virtually any intracellular and transmembrane protein for degradation, including oncoproteins previously considered undruggable, which strategically positions PROTACs at the crossroads of multiple cancer research areas. In this review, we present normal functions of the ubiquitin regulation proteins and describe the application of PROTACs to improve the efficacy of current broad-spectrum therapeutics. We subsequently present the potential for PROTACs to exploit specific cancer vulnerabilities through synthetic genetic approaches, which may expedite the development, translation, and utility of novel synthetic genetic therapies in cancer. Finally, we describe the challenges associated with PROTACs and the ongoing efforts to overcome these issues to streamline clinical translation. Ultimately, these efforts may lead to their routine clinical use, which is expected to revolutionize cancer treatment strategies, delay familial cancer onset, and ultimately improve the lives and outcomes of those living with cancer.

Hydrogen peroxide sensitivity connects the activity of COX5A and NPR3 to the regulation of YAP1 expression

Reactive oxygen species (ROS) are among the most severe types of cellular stressors with the ability to damage essential cellular biomolecules. Excess levels of ROS are correlated with multiple pathophysiological conditions including neurodegeneration, diabetes, atherosclerosis, and cancer. Failure to regulate the severely imbalanced levels of ROS can ultimately lead to cell death, highlighting the importance of investigating the molecular mechanisms involved in the detoxification procedures that counteract the effects of these compounds in living organisms. One of the most abundant forms of ROS is H2 O2 , mainly produced by the electron transport chain in the mitochondria. Numerous genes have been identified as essential to the process of cellular detoxification. Yeast YAP1, which is homologous to mammalian AP-1 type transcriptional factors, has a key role in oxidative detoxification by upregulating the expression of antioxidant genes in yeast. The current study reveals novel functions for COX5A and NPR3 in H2 O2 -induced stress by demonstrating that their deletions result in a sensitive phenotype. Our follow-up investigations indicate that COX5A and NPR3 regulate the expression of YAP1 through an alternative mode of translation initiation. These novel gene functions expand our understanding of the regulation of gene expression and defense mechanism of yeast against oxidative stress.

Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.

The Involvement of YNR069C in Protein Synthesis in the Baker's Yeast, Saccharomyces cerevisiae

Maintaining translation fidelity is a critical step within the process of gene expression. It requires the involvement of numerous regulatory elements to ensure the synthesis of functional proteins. The efficient termination of protein synthesis can play a crucial role in preserving this fidelity. Here, we report on investigating a protein of unknown function, YNR069C (also known as BSC5), for its activity in the process of translation. We observed a significant increase in the bypass of premature stop codons upon the deletion of YNR069C. Interestingly, the genomic arrangement of this ORF suggests a compatible mode of expression reliant on translational readthrough, incorporating the neighboring open reading frame. We also showed that the deletion of YNR069C results in an increase in the rate of translation. Based on our results, we propose that YNR069C may play a role in translation fidelity, impacting the overall quantity and quality of translation. Our genetic interaction analysis supports our hypothesis, associating the role of YNR069C to the regulation of protein synthesis.

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population's mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn.

Nuclear Hsp104 safeguards the dormant translation machinery during quiescence

The resilience of cellular proteostasis declines with age, which drives protein aggregation and compromises viability. The nucleus has emerged as a key quality control compartment that handles misfolded proteins produced by the cytosolic protein biosynthesis system. Here, we find that age-associated metabolic cues target the yeast protein disaggregase Hsp104 to the nucleus to maintain a functional nuclear proteome during quiescence. The switch to respiratory metabolism and the accompanying decrease in translation rates direct cytosolic Hsp104 to the nucleus to interact with latent translation initiation factor eIF2 and to suppress protein aggregation. Hindering Hsp104 from entering the nucleus in quiescent cells results in delayed re-entry into the cell cycle due to compromised resumption of protein synthesis. In sum, we report that cytosolic-nuclear partitioning of the Hsp104 disaggregase is a critical mechanism to protect the latent protein synthesis machinery during quiescence in yeast, ensuring the rapid restart of translation once nutrients are replenished.

From beer to breadboards: yeast as a force for biological innovation

The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.

The separation pin distinguishes the pro- and anti-recombinogenic functions of Saccharomyces cerevisiae Srs2

Srs2 is an Sf1a helicase that helps maintain genome stability in Saccharomyces cerevisiae through its ability to regulate homologous recombination. Srs2 downregulates HR by stripping Rad51 from single-stranded DNA, and Srs2 is also thought to promote synthesis-dependent strand annealing by unwinding D-loops. However, it has not been possible to evaluate the relative contributions of these two distinct activities to any aspect of recombination. Here, we used a structure-based approach to design an Srs2 separation-of-function mutant that can dismantle Rad51-ssDNA filaments but is incapable of disrupting D-loops, allowing us to assess the relative contributions of these pro- and anti-recombinogenic functions. We show that this separation-of-function mutant phenocopies wild-type SRS2 in vivo, suggesting that the ability of Srs2 to remove Rad51 from ssDNA is its primary role during HR.

A Protein-Protein Interaction Analysis Suggests a Wide Range of New Functions for the p21-Activated Kinase (PAK) Ste20

The p21-activated kinases (PAKs) are important signaling proteins. They contribute to a surprisingly wide range of cellular processes and play critical roles in a number of human diseases including cancer, neurological disorders and cardiac diseases. To get a better understanding of PAK functions, mechanisms and integration of various cellular activities, we screened for proteins that bind to the budding yeast PAK Ste20 as an example, using the split-ubiquitin technique. We identified 56 proteins, most of them not described previously as Ste20 interactors. The proteins fall into a small number of functional categories such as vesicle transport and translation. We analyzed the roles of Ste20 in glucose metabolism and gene expression further. Ste20 has a well-established role in the adaptation to changing environmental conditions through the stimulation of mitogen-activated protein kinase (MAPK) pathways which eventually leads to transcription factor activation. This includes filamentous growth, an adaptation to nutrient depletion. Here we show that Ste20 also induces filamentous growth through interaction with nuclear proteins such as Sac3, Ctk1 and Hmt1, key regulators of gene expression. Combining our observations and the data published by others, we suggest that Ste20 has several new and unexpected functions.

Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in 'non-home' environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

Mapping temperature-sensitive mutations at a genome scale to engineer growth switches in Escherichia coli

Temperature-sensitive (TS) mutants are a unique tool to perturb and engineer cellular systems. Here, we constructed a CRISPR library with 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. 1,269 of these mutants showed temperature-sensitive growth in a time-resolved competition assay. We reconstructed 94 TS mutants and measured their metabolism under growth arrest at 42°C using metabolomics. Metabolome changes were strong and mutant-specific, showing that metabolism of nongrowing E. coli is perturbation-dependent. For example, 24 TS mutants of metabolic enzymes overproduced the direct substrate metabolite due to a bottleneck in their associated pathway. A strain with TS homoserine kinase (ThrBF267D ) produced homoserine for 24 h, and production was tunable by temperature. Finally, we used a TS subunit of DNA polymerase III (DnaXL289Q ) to decouple growth from arginine overproduction in engineered E. coli. These results provide a strategy to identify TS mutants en masse and demonstrate their large potential to produce bacterial metabolites with nongrowing cells.

Joint embedding of biological networks for cross-species functional alignment

MOTIVATION

Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.

RESULTS

We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA's embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ylaboratory/ETNA.

A new layer of regulation of chromosomal passenger complex (CPC) translocation in budding yeast

The conserved chromosomal passenger complex (CPC) consists of Ipl1Aurora-B, Sli15INCENP, Bir1Survivin, and Nbl1Borealin, and localizes at the kinetochore/centromere to correct kinetochore attachment errors and to prevent checkpoint silencing. After anaphase entry, the CPC moves from the kinetochore/centromere to the spindle. In budding yeast, CPC subunit Sli15 is phosphorylated by both cyclin-dependent kinase (CDK) and Ipl1 kinase. Following anaphase onset, activated Cdc14 phosphatase reverses Sli15 phosphorylation imposed by CDK to promote CPC translocation. Although abolished Sli15 phosphorylation imposed by Ipl1 also causes CPC translocation, the regulation of Ipl1-imposed Sli15 phosphorylation remains unclear. In addition to Sli15, Cdc14 also dephosphorylates Fin1, a regulatory subunit of protein phosphatase 1 (PP1), to enable kinetochore localization of Fin1-PP1. Here, we present evidence supporting the notion that kinetochore-localized Fin1-PP1 likely reverses Ipl1-imposed Sli15 phosphorylation to promote CPC translocation from the kinetochore/centromere to the spindle. Importantly, premature Fin1 kinetochore localization or phospho-deficient sli15 mutation causes checkpoint defects in response to tensionless attachments, resulting in chromosome missegregation. In addition, our data indicate that reversion of CDK- and Ipl1-imposed Sli15 phosphorylation shows an additive effect on CPC translocation. Together, these results reveal a previously unidentified pathway to regulate CPC translocation, which is important for accurate chromosome segregation.

The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.

Mapping the Genetic Interaction Network of PARP inhibitor Response

Genetic interactions have long informed our understanding of the coordinated proteins and pathways that respond to DNA damage in mammalian cells, but systematic interrogation of the genetic network underlying that system has yet to be achieved. Towards this goal, we measured 147,153 pairwise interactions among genes implicated in PARP inhibitor (PARPi) response. Evaluating genetic interactions at this scale, with and without exposure to PARPi, revealed hierarchical organization of the pathways and complexes that maintain genome stability during normal growth and defined changes that occur upon accumulation of DNA lesions due to cytotoxic doses of PARPi. We uncovered unexpected relationships among DNA repair genes, including context-specific buffering interactions between the minimally characterized AUNIP and BRCA1-A complex genes. Our work thus establishes a foundation for mapping differential genetic interactions in mammalian cells and provides a comprehensive resource for future studies of DNA repair and PARP inhibitors.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling

MOTIVATION

The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks.

RESULTS

To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains.

AVAILABILITY AND IMPLEMENTATION

Gemini can be accessed at: https://github.com/MinxZ/Gemini.

The GET pathway is a major bottleneck for maintaining proteostasis in Saccharomyces cerevisiae

A hallmark of aging in a variety of organisms is a breakdown of proteostasis and an ensuing accumulation of protein aggregates and inclusions. However, it is not clear if the proteostasis network suffers from a uniform breakdown during aging or if some distinct components act as bottlenecks especially sensitive to functional decline. Here, we report on a genome-wide, unbiased, screen for single genes in young cells of budding yeast required to keep the proteome aggregate-free under non-stress conditions as a means to identify potential proteostasis bottlenecks. We found that the GET pathway, required for the insertion of tail-anchored (TA) membrane proteins in the endoplasmic reticulum, is such a bottleneck as single mutations in either GET3, GET2 or GET1 caused accumulation of cytosolic Hsp104- and mitochondria-associated aggregates in nearly all cells when growing at 30 °C (non-stress condition). Further, results generated by a second screen identifying proteins aggregating in GET mutants and analyzing the behavior of cytosolic reporters of misfolding, suggest that there is a general collapse in proteostasis in GET mutants that affects other proteins than TA proteins.

The Ycx1 protein encoded by the yeast YDL206W gene plays a role in calcium and calcineurin signaling

Calcium is ubiquitously present in all living cells and plays important regulatory roles in a wide variety of biological processes. In yeast, many effects of calcium are mediated via the action of calcineurin, a calcium/calmodulin-dependent protein phosphatase. Proper signaling of calcium and calcineurin is important in yeast, and the calcineurin pathway has emerged as a valuable target for developing novel antifungal drugs. Here, we report a role of YDL206W in calcium and calcineurin signaling in yeast. YDL206W is an uncharacterized gene in yeast, encoding a protein with two sodium/calcium exchange domains. Disrupting the YDL206W gene leads to a diminished level of calcium-induced activation of calcineurin and a reduced accumulation of cytosolic calcium. Consistent with a role of calcineurin in regulating pheromone and cell wall integrity signaling, the ydl206wΔ mutants display an enhanced growth arrest induced by pheromone treatment and poor growth at elevated temperature. Subcellular localization studies indicate that YDL206W is localized in endoplasmic reticulum and Golgi. Together, our results reveal YDL206W as a new regulator for calcineurin signaling in yeast and suggest a role of the endoplasmic reticulum and Golgi in regulating cytosolic calcium in yeast.

Integrative structure determination of histones H3 and H4 using genetic interactions

Integrative structure modeling is increasingly used for determining the architectures of biological assemblies, especially those that are structurally heterogeneous. Recently, we reported on how to convert in vivo genetic interaction measurements into spatial restraints for structural modeling: first, phenotypic profiles are generated for each point mutation and thousands of gene deletions or environmental perturbations. Following, the phenotypic profile similarities are converted into distance restraints on the pairs of mutated residues. We illustrate the approach by determining the structure of the histone H3-H4 complex. The method is implemented in our open-source IMP program, expanding the structural biology toolbox by allowing structural characterization based on in vivo data without the need to purify the target system. We compare genetic interaction measurements to other sources of structural information, such as residue coevolution and deep-learning structure prediction of complex subunits. We also suggest that determining genetic interactions could benefit from new technologies, such as CRISPR-Cas9 approaches to gene editing, especially for mammalian cells. Finally, we highlight the opportunity for using genetic interactions to determine recalcitrant biomolecular structures, such as those of disordered proteins, transient protein assemblies, and host-pathogen protein complexes.

A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

Functional tug of war between kinases, phosphatases, and the Gcn5 acetyltransferase in chromatin and cell cycle checkpoint controls

Covalent modifications of chromatin regulate genomic structure and accessibility in diverse biological processes such as transcriptional regulation, cell cycle progression, and DNA damage repair. Many histone modifications have been characterized, yet understanding the interactions between these and their combinatorial effects remains an active area of investigation, including dissecting functional interactions between enzymes mediating these modifications. In budding yeast, the histone acetyltransferase Gcn5 interacts with Rts1, a regulatory subunit of protein phosphatase 2A (PP2A). Implicated in the interaction is the potential for the dynamic phosphorylation of conserved residues on histone H2B and the Cse4 centromere-specific histone H3 variant. To probe these dynamics, we sought to identify kinases which contribute to the phosphorylated state. In a directed screen beginning with in silico analysis of the 127 members of yeast kinome, we have now identified 16 kinases with genetic interactions with GCN5 and specifically found distinct roles for the Hog1 stress-activated protein kinase. Deletion of HOG1 (hog1Δ) rescues gcn5Δ sensitivity to the microtubule poison nocodazole and the lethality of the gcn5Δ rts1Δ double mutant. The Hog1-Gcn5 interaction requires the conserved H2B-T91 residue, which is phosphorylated in vertebrate species. Furthermore, deletion of HOG1 decreases aneuploidy and apoptotic populations in gcn5Δ cells. Together, these results introduce Hog1 as a kinase that functionally opposes Gcn5 and Rts1 in the context of the spindle assembly checkpoint and suggest further kinases may also influence GCN5's functions.

Trimming the genomic fat: minimising and re-functionalising genomes using synthetic biology

Naturally evolved organisms typically have large genomes that enable their survival and growth under various conditions. However, the complexity of genomes often precludes our complete understanding of them, and limits the success of biotechnological designs. In contrast, minimal genomes have reduced complexity and therefore improved engineerability, increased biosynthetic capacity through the removal of unnecessary genetic elements, and less recalcitrance to complete characterisation. Here, we review the past and current genome minimisation and re-functionalisation efforts, with an emphasis on the latest advances facilitated by synthetic genomics, and provide a critical appraisal of their potential for industrial applications.

Whole Exome Sequencing of a Patient with a Milder Phenotype of Xeroderma Pigmentosum Group C

A 17-year-old female Korean patient (XP115KO) was previously diagnosed with Xeroderma pigmentosum group C (XPC) by Direct Sanger sequencing, which revealed a homozygous nonsense mutation in the XPC gene (rs121965088: c.1735C > T, p.Arg579Ter). While rs121965088 is associated with a poor prognosis, our patient presented with a milder phenotype. Hence, we conducted whole-exome sequencing in the patient and her family members to detect coexisting mutations that may have resulted in a milder phenotype of rs121965088 through genetic interaction. Materials and Methods: the whole-exome sequencing analysis of samples obtained from the patient and her family members (father, mother, and brother) was performed. To identify the underlying genetic cause of XPC, the extracted DNA was analyzed using Agilent's SureSelect XT Human All Exon v5. The functional effects of the resultant variants were predicted using the SNPinfo web server, and structural changes in the XPC protein using the 3D protein modeling program SWISS-MODEL. Results: Eight biallelic variants, homozygous in the patient and heterozygous in her parents, were detected. Four were found in the XPC gene: one nonsense variant (rs121965088: c.1735C > T, p.Arg579Ter) and three silent variants (rs2227998: c.2061G > A, p. Arg687Arg; rs2279017: c.2251-6A > C, intron; rs2607775: c.-27G > C, 5'UTR). The remaining four variants were found in non-XP genes, including one frameshift variant [rs72452004 of olfactory receptor family 2 subfamily T member 35 (OR2T35)], three missense variants [rs202089462 of ALF transcription elongation factor 3 (AFF3), rs138027161 of TCR gamma alternate reading frame protein (TARP), and rs3750575 of annexin A7 (ANXA7)]. Conclusions: potential candidates for genetic interactions with rs121965088 were found. The rs2279017 and rs2607775 of XPC involved mutations in the intron region, which affected RNA splicing and protein translation. The genetic variants of AFF3, TARP, and ANXA7 are all frameshift or missense mutations, inevitably disturbing the translation and function of the resultant proteins. Further research on their functions in DNA repair pathways may reveal undiscovered cellular relationships within xeroderma pigmentosum.

The conserved yeast protein Knr4 involved in cell wall integrity is a multi-domain intrinsically disordered protein

Knr4/Smi1 proteins are specific to the fungal kingdom and their deletion in the model yeast Saccharomyces cerevisiae and the human pathogen Candida albicans results in hypersensitivity to specific antifungal agents and a wide range of parietal stresses. In S. cerevisiae, Knr4 is located at the crossroads of several signalling pathways, including the conserved cell wall integrity and calcineurin pathways. Knr4 interacts genetically and physically with several protein members of those pathways. Its sequence suggests that it contains large intrinsically disordered regions. Here, a combination of small-angle X-ray scattering (SAXS) and crystallographic analysis led to a comprehensive structural view of Knr4. This experimental work unambiguously showed that Knr4 comprises two large intrinsically disordered regions flanking a central globular domain whose structure has been established. The structured domain is itself interrupted by a disordered loop. Using the CRISPR/Cas9 genome editing technique, strains expressing KNR4 genes deleted from different domains were constructed. The N-terminal domain and the loop are essential for optimal resistance to cell wall-binding stressors. The C-terminal disordered domain, on the other hand, acts as a negative regulator of this function of Knr4. The identification of molecular recognition features, the possible presence of secondary structure in these disordered domains and the functional importance of the disordered domains revealed here designate these domains as putative interacting spots with partners in either pathway. Targeting these interacting regions is a promising route to the discovery of inhibitory molecules that could increase the susceptibility of pathogens to the antifungals currently in clinical use.

PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel

Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.

Fungal resilience and host-pathogen interactions: Future perspectives and opportunities

We are constantly exposed to the threat of fungal infection. The outcome-clearance, commensalism or infection-depends largely on the ability of our innate immune defences to clear infecting fungal cells versus the success of the fungus in mounting compensatory adaptive responses. As each seeks to gain advantage during these skirmishes, the interactions between host and fungal pathogen are complex and dynamic. Nevertheless, simply compromising the physiological robustness of fungal pathogens reduces their ability to evade antifungal immunity, their virulence, and their tolerance against antifungal therapy. In this article I argue that this physiological robustness is based on a 'Resilience Network' which mechanistically links and controls fungal growth, metabolism, stress resistance and drug tolerance. The elasticity of this network probably underlies the phenotypic variability of fungal isolates and the heterogeneity of individual cells within clonal populations. Consequently, I suggest that the definition of the fungal Resilience Network represents an important goal for the future which offers the clear potential to reveal drug targets that compromise drug tolerance and synergise with current antifungal therapies.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene co-expression estimation methods on simulation datasets with known ground truth co-expression networks. We generate these novel datasets using two simulation processes that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate potentially caused by high-sparsity levels in the data. Finally, we find that commonly used pre-processing approaches, such as normalization and imputation, do not improve the co-expression estimation. Overall, our benchmark setup contributes to the co-expression estimator development, and our study provides valuable insights for the community of single-cell data analyses.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Top-Down, Knowledge-Based Genetic Reduction of Yeast Central Carbon Metabolism

Saccharomyces cerevisiae, whose evolutionary past includes a whole-genome duplication event, is characterized by a mosaic genome configuration with substantial apparent genetic redundancy. This apparent redundancy raises questions about the evolutionary driving force for genomic fixation of “minor” paralogs and complicates modular and combinatorial metabolic engineering strategies. While isoenzymes might be important in specific environments, they could be dispensable in controlled laboratory or industrial contexts. The present study explores the extent to which the genetic complexity of the central carbon metabolism (CCM) in S. cerevisiae, here defined as the combination of glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and a limited number of related pathways and reactions, can be reduced by elimination of (iso)enzymes without major negative impacts on strain physiology. Cas9-mediated, groupwise deletion of 35 of the 111 genes yielded a “minimal CCM” strain which, despite the elimination of 32% of CCM-related proteins, showed only a minimal change in phenotype on glucose-containing synthetic medium in controlled bioreactor cultures relative to a congenic reference strain. Analysis under a wide range of other growth and stress conditions revealed remarkably few phenotypic changes from the reduction of genetic complexity. Still, a well-documented context-dependent role of GPD1 in osmotolerance was confirmed. The minimal CCM strain provides a model system for further research into genetic redundancy of yeast genes and a platform for strategies aimed at large-scale, combinatorial remodeling of yeast CCM.

Genomic Approaches to Antifungal Drug Target Identification and Validation

The last several decades have witnessed a surge in drug-resistant fungal infections that pose a serious threat to human health. While there is a limited arsenal of drugs that can be used to treat systemic infections, scientific advances have provided renewed optimism for the discovery of novel antifungals. The development of chemical-genomic assays using Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of molecules in a living cell. Advances in molecular biology techniques have enabled complementary assays to be developed in fungal pathogens, including Candida albicans and Cryptococcus neoformans. These approaches enable the identification of target genes for drug candidates, as well as genes involved in buffering drug target pathways. Here, we examine yeast chemical-genomic assays and highlight how such resources can be utilized to predict the mechanisms of action of compounds, to study virulence attributes of diverse fungal pathogens, and to bolster the antifungal pipeline.

Repression of essential cell cycle genes increases cellular fitness

A network of transcription factors (TFs) coordinates transcription with cell cycle events in eukaryotes. Most TFs in the network are phosphorylated by cyclin-dependent kinase (CDK), which limits their activities during the cell cycle. Here, we investigate the physiological consequences of disrupting CDK regulation of the paralogous repressors Yhp1 and Yox1 in yeast. Blocking Yhp1/Yox1 phosphorylation increases their levels and decreases expression of essential cell cycle regulatory genes which, unexpectedly, increases cellular fitness in optimal growth conditions. Using synthetic genetic interaction screens, we find that Yhp1/Yox1 mutations improve the fitness of mutants with mitotic defects, including condensin mutants. Blocking Yhp1/Yox1 phosphorylation simultaneously accelerates the G1/S transition and delays mitotic exit, without decreasing proliferation rate. This mitotic delay partially reverses the chromosome segregation defect of condensin mutants, potentially explaining their increased fitness when combined with Yhp1/Yox1 phosphomutants. These findings reveal how altering expression of cell cycle genes leads to a redistribution of cell cycle timing and confers a fitness advantage to cells.

Genetic-interaction screens uncover novel biological roles and regulators of transcription factors in fission yeast

In Schizosaccharomyces pombe, systematic analyses of single transcription factor deletion or overexpression strains have made substantial advances in determining the biological roles and target genes of transcription factors, yet these characteristics are still relatively unknown for over a quarter of them. Moreover, the comprehensive list of proteins that regulate transcription factors remains incomplete. To further characterize Schizosaccharomyces pombe transcription factors, we performed synthetic sick/lethality and synthetic dosage lethality screens by synthetic genetic array. Examination of 2,672 transcription factor double deletion strains revealed a sick/lethality interaction frequency of 1.72%. Phenotypic analysis of these sick/lethality strains revealed potential cell cycle roles for several poorly characterized transcription factors, including SPBC56F2.05, SPCC320.03, and SPAC3C7.04. In addition, we examined synthetic dosage lethality interactions between 14 transcription factors and a miniarray of 279 deletion strains, observing a synthetic dosage lethality frequency of 4.99%, which consisted of known and novel transcription factor regulators. The miniarray contained deletions of genes that encode primarily posttranslational-modifying enzymes to identify putative upstream regulators of the transcription factor query strains. We discovered that ubiquitin ligase Ubr1 and its E2/E3-interacting protein, Mub1, degrade the glucose-responsive transcriptional repressor Scr1. Loss of ubr1⁺ or mub1⁺ increased Scr1 protein expression, which resulted in enhanced repression of flocculation through Scr1. The synthetic dosage lethality screen also captured interactions between Scr1 and 2 of its known repressors, Sds23 and Amk2, each affecting flocculation through Scr1 by influencing its nuclear localization. Our study demonstrates that sick/lethality and synthetic dosage lethality screens can be effective in uncovering novel functions and regulators of Schizosaccharomyces pombe transcription factors.

A role for the Saccharomyces cerevisiae Rtt109 histone acetyltransferase in R-loop homeostasis and associated genome instability

The stability of the genome is occasionally challenged by the formation of DNA–RNA hybrids and R-loops, which can be influenced by the chromatin context. This is mainly due to the fact that DNA–RNA hybrids hamper the progression of replication forks, leading to fork stalling and, ultimately, DNA breaks. Through a specific screening of chromatin modifiers performed in the yeast Saccharomyces cerevisiae, we have found that the Rtt109 histone acetyltransferase is involved in several steps of R-loop-metabolism and their associated genetic instability. On the one hand, Rtt109 prevents DNA–RNA hybridization by the acetylation of histone H3 lysines 14 and 23 and, on the other hand, it is involved in the repair of replication-born DNA breaks, such as those that can be caused by R-loops, by acetylating lysines 14 and 56. In addition, Rtt109 loss renders cells highly sensitive to replication stress in combination with R-loop-accumulating THO-complex mutants. Our data evidence that the chromatin context simultaneously influences the occurrence of DNA–RNA hybrid-associated DNA damage and its repair, adding complexity to the source of R-loop-associated genetic instability.

The N-Terminal Domain of Bfa1 Coordinates Mitotic Exit Independent of GAP Activity in Saccharomyces cerevisiae

The spindle position checkpoint (SPOC) of budding yeast delays mitotic exit in response to misaligned spindles to ensure cell survival and the maintenance of genomic stability. The GTPase-activating protein (GAP) complex Bfa1–Bub2, a key SPOC component, inhibits the GTPase Tem1 to induce mitotic arrest in response to DNA and spindle damage, as well as spindle misorientation. However, previous results strongly suggest that Bfa1 exerts a GAP-independent function in blocking mitotic exit in response to misaligned spindles. Thus, the molecular mechanism by which Bfa1 controls mitotic exit in response to misaligned spindles remains unclear. Here, we observed that overexpression of the N-terminal domain of Bfa1 (Bfa1-D16), which lacks GAP activity and cannot localize to the spindle pole body (SPB), induced cell cycle arrest along with hyper-elongation of astral microtubules (aMTs) as Bfa1 overexpression in Δbub2. We found that Δbub2 cells overexpressing Bfa1 or Bfa1-D16 inhibited activation of Mob1, which is responsible for mitotic exit. In anaphase-arrested cells, Bfa1-D16 overexpression inhibited Tem1 binding to the SPB as well as Bfa1 overexpression. Additionally, endogenous levels of Bfa1-D16 showed minor SPOC activity that was not regulated by Kin4. These results suggested that Bfa1-D16 may block mitotic exit through inhibiting Tem1 activity outside of SPBs. Alternatively, Bfa1-D16 dispersed out of SPBs may block Tem1 binding to SPBs by physically interacting with Tem1 as previously reported. Moreover, we observed hyper-elongated aMTs in tem1-3, cdc15-2, and dbf2-2 mutants that induce anaphase arrest and cannot undergo mitotic exit at restrictive temperatures, suggesting that aMT dynamics are closely related to the regulation of mitotic exit. Altogether, these observations suggest that Bfa1 can control the SPOC independent of its GAP activity and SPB localization.

Yeast Kinesin-5 Motor Protein CIN8 Promotes Accurate Chromosome Segregation

Accurate chromosome segregation depends on bipolar chromosome–microtubule attachment and tension generation on chromosomes. Incorrect chromosome attachment results in chromosome missegregation, which contributes to genome instability. The kinetochore is a protein complex that localizes at the centromere region of a chromosome and mediates chromosome–microtubule interaction. Incorrect chromosome attachment leads to checkpoint activation to prevent anaphase onset. Kinetochore detachment activates the spindle assembly checkpoint (SAC), while tensionless kinetochore attachment relies on both the SAC and tension checkpoint. In budding yeast Saccharomyces cerevisiae, kinesin-5 motor proteins Cin8 and Kip1 are needed to separate spindle pole bodies for spindle assembly, and deletion of CIN8 causes lethality in the absence of SAC. To study the function of Cin8 and Kip1 in chromosome segregation, we constructed an auxin-inducible degron (AID) mutant, cin8-AID. With this conditional mutant, we first confirmed that cin8-AID kip1∆ double mutants were lethal when Cin8 is depleted in the presence of auxin. These cells arrested in metaphase with unseparated spindle pole bodies and kinetochores. We further showed that the absence of either the SAC or tension checkpoint was sufficient to abolish the cell-cycle delay in cin8-AID mutants, causing chromosome missegregation and viability loss. The tension checkpoint-dependent phenotype in cells with depleted Cin8 suggests the presence of tensionless chromosome attachment. We speculate that the failed spindle pole body separation in cin8 mutants could increase the chance of tensionless syntelic chromosome attachments, which depends on functional tension checkpoint for survival.

Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p

The methylation of histidine is a post-translational modification whose function is poorly understood. Methyltransferase histidine protein methyltransferase 1 (Hpm1p) monomethylates H243 in the ribosomal protein Rpl3p and represents the only known histidine methyltransferase in Saccharomyces cerevisiae. Interestingly, the hpm1 deletion strain is highly pleiotropic, with many extraribosomal phenotypes including improved growth rates in alternative carbon sources. Here, we investigate how the loss of histidine methyltransferase Hpm1p results in diverse phenotypes, through use of targeted mass spectrometry (MS), growth assays, quantitative proteomics, and differential crosslinking MS. We confirmed the localization and stoichiometry of the H243 methylation site, found unreported sensitivities of Δhpm1 yeast to nonribosomal stressors, and identified differentially abundant proteins upon hpm1 knockout with clear links to the coordination of sugar metabolism. We adapted the emerging technique of quantitative large-scale stable isotope labeling of amino acids in cell culture crosslinking MS for yeast, which resulted in the identification of 1267 unique in vivo lysine-lysine crosslinks. By reproducibly monitoring over 350 of these in WT and Δhpm1, we detected changes to protein structure or protein-protein interactions in the ribosome, membrane proteins, chromatin, and mitochondria. Importantly, these occurred independently of changes in protein abundance and could explain a number of phenotypes of Δhpm1, not addressed by expression analysis. Further to this, some phenotypes were predicted solely from changes in protein structure or interactions and could be validated by orthogonal techniques. Taken together, these studies reveal a broad role for Hpm1p in yeast and illustrate how crosslinking MS will be an essential tool for understanding complex phenotypes.

Structural Thermokinetic Modelling

To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.

Genome-Wide Identification of Cellular Pathways and Key Genes That Respond to Sodium Bicarbonate Stress in Saccharomyces cerevisiae

Sodium bicarbonate (NaHCO₃) is an important inorganic salt. It is not only widely used in industrial production and daily life, but is also the main stress in alkaline saline soil. NaHCO₃ has a strong ability to inhibit the growth of fungi in both natural environment and daily application. However, the mechanism by which fungi respond to NaHCO₃ stress is not fully understood. To further clarify the toxic mechanisms of NaHCO₃ stress and identify the specific cellular genes and pathways involved in NaHCO₃ resistance, we performed genome-wide screening with NaHCO₃ using a Saccharomyces cerevisiae deletion mutant library. A total of 33 deletion mutants with NaHCO₃ sensitivity were identified. Compared with wild-type strains, these mutants had significant growth defects in the medium containing NaHCO₃. Bioinformatics analysis found that the corresponding genes of these mutants are mainly enriched in the cell cycle, mitophagy, cell wall integrity, and signaling pathways. Further study using transcriptomic analysis showed that 309 upregulated and 233 downregulated genes were only responded to NaHCO₃ stress, when compared with yeast transcriptomic data under alkaline and saline stress. Upregulated genes were mainly concentrated in amino acid metabolism, steroid biosynthesis, and cell wall, while downregulated genes were enriched in various cellular metabolisms. In summary, we have identified the cellular pathways and key genes that respond to NaHCO₃ stress in the whole genome, providing resource and direction for understanding NaHCO₃ toxicity and cellular resistance mechanisms.

Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow

The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.

Phenomics approaches to understand genetic networks and gene function in yeast

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.