A global genetic interaction network maps a wiring diagram of cellular function

Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder

Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict such data for any genomic context of interest. Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies and drug sensitivities, outperforming previous methods. Our models, data and implementation are available at: https://github.com/aijag/drugGraphNet.

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.

Nab3 nuclear granule accumulation is driven by respiratory capacity

Numerous biological processes involve proteins capable of transiently assembling into subcellular compartments necessary for cellular functions. One process is the RNA polymerase II transcription cycle which involves initiation, elongation, co-transcriptional modification of nascent RNA, and termination. The essential yeast transcription termination factor Nab3 is required for termination of small non-coding RNAs and accumulates into a compact nuclear granule upon glucose removal. Nab3 nuclear granule accumulation varies in penetrance across yeast strains and a higher Nab3 granule accumulation phenotype is associated with petite strains, suggesting a possible ATP-dependent mechanism for granule disassembly. Here, we demonstrate the uncoupling of mitochondrial oxidative phosphorylation by drug treatment or deletions of nuclear-encoded ATP synthase subunit genes were sufficient to increase Nab3 granule accumulation and led to an inability to proliferate during prolonged glucose deprivation, which requires respiration. Additionally, by enriching for respiration competent cells from a petite-prone strain, we generated a low granule-accumulating strain from a relatively high one, providing another link between respiratory competency and Nab3 granules. Consistent with the resulting idea that ATP is involved in granule accumulation, the addition of extracellular ATP to semi-permeabilized cells was sufficient to reduce Nab3 granule accumulation. Deleting the SKY1 gene, which encodes a kinase that phosphorylates nuclear SR repeat-containing proteins and is involved in efficient stress granule disassembly, also resulted in increased granule accumulation. This observation implicates Sky1 in Nab3 granule biogenesis. Taken together, these findings suggest there is normally an equilibrium between termination factor granule assembly and disassembly mediated by ATP-requiring nuclear machinery.

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

BACKGROUND

Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process.

RESULTS

In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step-essentiality scoring, sample variance and covariance normalization, and similarity measurement-to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners.

CONCLUSIONS

We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions.

Genome-Wide Screen for Enhanced Noncanonical Amino Acid Incorporation in Yeast

Numerous applications of noncanonical amino acids (ncAAs) in basic biology and therapeutic development require efficient protein biosynthesis using an expanded genetic code. However, achieving such incorporation at repurposed stop codons in cells is generally inefficient and limited by complex cellular processes that preserve the fidelity of protein synthesis. A more comprehensive understanding of the processes that contribute to ncAA incorporation would aid in the development of genomic engineering strategies for augmenting genetic code manipulation. In this work, we used a series of fluorescent reporters to screen a pooled Saccharomyces cerevisiae molecular barcoded yeast knockout (YKO) collection. Fluorescence-activated cell sorting enabled isolation of strains encoding single-gene deletions exhibiting improved ncAA incorporation efficiency in response to the amber (TAG) stop codon; 55 unique candidate deletions were identified. The deleted genes encoded for proteins that participate in diverse cellular processes, including many genes that have no known connection with protein translation. We then verified that two knockouts, yil014c-aΔ and alo1Δ, exhibited improved ncAA incorporation efficiency starting from independently acquired strains possessing the knockouts. Using additional orthogonal translation systems and ncAAs, we determined that yil014c-aΔ and alo1Δ enhance ncAA incorporation efficiency without loss of fidelity over a wide range of conditions. Our findings highlight opportunities for further modulating gene expression with genetic, genomic, and synthetic biology approaches to improve ncAA incorporation efficiency. In addition, these discoveries have the potential to enhance our fundamental understanding of protein translation. Ultimately, cells that efficiently biosynthesize ncAA-containing proteins will streamline the realization of applications utilizing expanded genetic codes ranging from basic biology to drug discovery.

A comparative analysis of telomere length maintenance circuits in fission and budding yeast

The natural ends of the linear eukaryotic chromosomes are protected by telomeres, which also play an important role in aging and cancer development. Telomere length varies between species, but it is strictly controlled in all organisms. The process of Telomere Length Maintenance (TLM) involves many pathways, protein complexes and interactions that were first discovered in budding and fission yeast model organisms (Saccharomyces cerevisiae, Schizosaccharomyces pombe). In particular, large-scale systematic genetic screens in budding yeast uncovered a network of ≈500 genes that, when mutated, cause telomeres to lengthen or to shorten. In contrast, the TLM network in fission yeast remains largely unknown and systematic data is still lacking. In this work we try to close this gap and develop a unified interpretable machine learning framework for TLM gene discovery and phenotype prediction in both species. We demonstrate the utility of our framework in pinpointing the pathways by which TLM homeostasis is maintained and predicting novel TLM genes in fission yeast. The results of this study could be used for better understanding of telomere biology and serve as a step towards the adaptation of computational methods based on telomeric data for human prognosis.

High-resolution secretory timeline from vesicle formation at the Golgi to fusion at the plasma membrane in S. cerevisiae

Most of the components in the yeast secretory pathway have been studied, yet a high-resolution temporal timeline of their participation is lacking. Here, we define the order of acquisition, lifetime, and release of critical components involved in late secretion from the Golgi to the plasma membrane. Of particular interest is the timing of the many reported effectors of the secretory vesicle Rab protein Sec4, including the myosin-V Myo2, the exocyst complex, the lgl homolog Sro7, and the small yeast-specific protein Mso1. At the trans-Golgi network (TGN) Sec4's GEF, Sec2, is recruited to Ypt31-positive compartments, quickly followed by Sec4 and Myo2 and vesicle formation. While transported to the bud tip, the entire exocyst complex, including Sec3, is assembled on to the vesicle. Before fusion, vesicles tether for 5 s, during which the vesicle retains the exocyst complex and stimulates lateral recruitment of Rho3 on the plasma membrane. Sec2 and Myo2 are rapidly lost, followed by recruitment of cytosolic Sro7, and finally the SM protein Sec1, which appears for just 2 s prior to fusion. Perturbation experiments reveal an ordered and robust series of events during tethering that provide insights into the function of Sec4 and effector exchange.

Genome-wide base editor screen identifies regulators of protein abundance in yeast

Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins as viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal (SL) partners of cancer-specific mutations. Similarly, SL interactions of viral-induced hypomorphs can potentially be targeted as host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for SL partners of GBF1 revealed ARF1 as the top hit, disruption of which selectively killed cells that synthesize 3A alone or in the context of a poliovirus replicon. Thus, viral protein interactions can induce hypomorphs that render host cells selectively vulnerable to perturbations that leave uninfected cells otherwise unscathed. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

APC/CCdc20-mediated degradation of Clb4 prompts astral microtubule stabilization at anaphase onset

Key for accurate chromosome partitioning to the offspring is the ability of mitotic spindle microtubules to respond to different molecular signals and remodel their dynamics accordingly. Spindle microtubules are conventionally divided into three classes: kinetochore, interpolar, and astral microtubules (kMTs, iMTs, and aMTs, respectively). Among all, aMT regulation remains elusive. Here, we show that aMT dynamics are tightly regulated. aMTs remain unstable up to metaphase and are stabilized at anaphase onset. This switch in aMT dynamics, important for proper spindle orientation, specifically requires the degradation of the mitotic cyclin Clb4 by the Anaphase Promoting Complex bound to its activator subunit Cdc20 (APC/CCdc20). These data highlight a unique role for mitotic cyclin Clb4 in controlling aMT regulating factors, of which Kip2 is a prime candidate, provide a framework to understand aMT regulation in vertebrates, and uncover mechanistic principles of how the APC/CCdc20 choreographs the timing of late mitotic events by sequentially impacting on the three classes of spindle microtubules.

MFDA: Multiview fusion based on dual-level attention for drug interaction prediction

Drug-drug interaction prediction plays an important role in pharmacology and clinical applications. Most traditional methods predict drug interactions based on drug attributes or network structure. They usually have three limitations: 1) failing to integrate drug features and network structures well, resulting in less informative drug embeddings; 2) being restricted to a single view of drug interaction relationships; 3) ignoring the importance of different neighbors. To tackle these challenges, this paper proposed a multiview fusion based on dual-level attention to predict drug interactions (called MFDA). The MFDA first constructed multiple views for the drug interaction relationship, and then adopted a cross-fusion strategy to deeply fuse drug features with the drug interaction network under each view. To distinguish the importance of different neighbors and views, MFDA adopted a dual-level attention mechanism (node level and view level) to obtain the unified drug embedding for drug interaction prediction. Extensive experiments were conducted on real datasets, and the MFDA demonstrated superior performance compared to state-of-the-art baselines. In the multitask analysis of new drug reactions, MFDA obtained higher scores on multiple metrics. In addition, its prediction results corresponded to specific drug reaction events, which achieved more accurate predictions.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

BIONIC: biological network integration using convolutions

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

Chaperone requirements for de novo folding of Saccharomyces cerevisiae septins

Polymers of septin protein complexes play cytoskeletal roles in eukaryotic cells. The specific subunit composition within complexes controls functions and higher-order structural properties. All septins have globular GTPase domains. The other eukaryotic cytoskeletal NTPases strictly require assistance from molecular chaperones of the cytosol, particularly the cage-like chaperonins, to fold into oligomerization-competent conformations. We previously identified cytosolic chaperones that bind septins and influence the oligomerization ability of septins carrying mutations linked to human disease, but it was unknown to what extent wild-type septins require chaperone assistance for their native folding. Here we use a combination of in vivo and in vitro approaches to demonstrate chaperone requirements for de novo folding and complex assembly by budding yeast septins. Individually purified septins adopted nonnative conformations and formed nonnative homodimers. In chaperonin- or Hsp70-deficient cells, septins folded slower and were unable to assemble posttranslationally into native complexes. One septin, Cdc12, was so dependent on cotranslational chaperonin assistance that translation failed without it. Our findings point to distinct translation elongation rates for different septins as a possible mechanism to direct a stepwise, cotranslational assembly pathway in which general cytosolic chaperones act as key intermediaries.

Sharing the wealth: The versatility of proteins targeted to peroxisomes and other organelles

Peroxisomes are eukaryotic organelles with critical functions in cellular energy and lipid metabolism. Depending on the organism, cell type, and developmental stage, they are involved in numerous other metabolic and regulatory pathways. Many peroxisomal functions require factors also relevant to other cellular compartments. Here, we review proteins shared by peroxisomes and at least one different site within the cell. We discuss the mechanisms to achieve dual targeting, their regulation, and functional consequences. Characterization of dual targeting is fundamental to understand how peroxisomes are integrated into the metabolic and regulatory circuits of eukaryotic cells.

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.

Loss-of-function mutation survey revealed that genes with background-dependent fitness are rare and functionally related in yeast

In different individuals, the same mutation can lead to different phenotypes due to genetic background effects. This is commonly observed in various systems, including many human diseases. While isolated examples of such background effects have been observed, a systematic view across a large number of individuals is still lacking. Here, we surveyed genetic background effects associated with gene loss-of-function mutations across a population of natural isolates of the yeast Saccharomyces cerevisiae. We found that ∼15% of genes can display a background-dependent fitness change. Genes related to mitochondrial functions are significantly overrepresented, and showed reversed patterns of fitness gain or loss with genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport, suggesting a potential functional rewiring.

In natural populations, the same mutation can lead to different phenotypic outcomes due to the genetic variation that exists among individuals. Such genetic background effects are commonly observed, including in the context of many human diseases. However, systematic characterization of these effects at the species level is still lacking to date. Here, we sought to comprehensively survey background-dependent traits associated with gene loss-of-function (LoF) mutations in 39 natural isolates of Saccharomyces cerevisiae using a transposon saturation strategy. By analyzing the modeled fitness variability of a total of 4,469 genes, we found that 15% of them, when impacted by a LoF mutation, exhibited a significant gain- or loss-of-fitness phenotype in certain natural isolates compared with the reference strain S288C. Out of these 632 genes with predicted background-dependent fitness effects, around 2/3 impact multiple backgrounds with a gradient of predicted fitness change while 1/3 are specific to a single genetic background. Genes related to mitochondrial function are significantly overrepresented in the set of genes showing a continuous variation and display a potential functional rewiring with other genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport. Such rewiring effects are likely modulated by both the genetic background and the environment. While background-specific cases are rare and span diverse cellular processes, they can be functionally related at the individual level. All genes with background-dependent fitness effects tend to have an intermediate connectivity in the global genetic interaction network and have shown relaxed selection pressure at the population level, highlighting their potential evolutionary characteristics.

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.

The mechanism of branched-chain amino acid transferases in different diseases: Research progress and future prospects

It is well known that the enzyme catalyzes the first step of branched-chain amino acid (BCAA) catabolism is branched-chain amino transferase (BCAT), which is involved in the synthesis and degradation of leucine, isoleucine and valine. There are two main subtypes of human branched chain amino transferase (hBCAT), including cytoplasmic BCAT (BCAT1) and mitochondrial BCAT (BCAT2). In recent years, the role of BCAT in tumors has attracted the attention of scientists, and there have been continuous research reports that BCAT plays a role in the tumor, Alzheimer’s disease, myeloid leukaemia and other diseases. It plays a significant role in the growth and development of diseases, and new discoveries about this gene in some diseases are made every year. BCAT usually promotes cancer proliferation and invasion by activating the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin pathway and activating Wnt/β-catenin signal transduction. This article reviews the role and mechanism of BCAT in different diseases, as well as the recent biomedical research progress. This review aims to make a comprehensive summary of the role and mechanism of BCAT in different diseases and to provide new research ideas for the treatment, prognosis and prevention of certain diseases.

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

Deep neural networks (DNNs) have been successfully utilized in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging due to their poor interpretability. Here we consider the problem of scalable, robust variable selection in DNNs for the identification of putative causal genetic variants in genome sequencing studies. We identified a pronounced randomness in feature selection in DNNs due to its stochastic nature, which may hinder interpretability and give rise to misleading results. We propose an interpretable neural network model, stabilized using ensembling, with controlled variable selection for genetic studies. The merit of the proposed method includes: flexible modelling of the nonlinear effect of genetic variants to improve statistical power; multiple knockoffs in the input layer to rigorously control the false discovery rate; hierarchical layers to substantially reduce the number of weight parameters and activations, and improve computational efficiency; and stabilized feature selection to reduce the randomness in identified signals. We evaluate the proposed method in extensive simulation studies and apply it to the analysis of Alzheimer's disease genetics. We show that the proposed method, when compared with conventional linear and nonlinear methods, can lead to substantially more discoveries.

Origins, evolution, and physiological implications of de novo genes in yeast

De novo gene birth is the process by which new genes emerge in sequences that were previously noncoding. Over the past decade, researchers have taken advantage of the power of yeast as a model and a tool to study the evolutionary mechanisms and physiological implications of de novo gene birth. We summarize the mechanisms that have been proposed to explicate how noncoding sequences can become protein-coding genes, highlighting the discovery of pervasive translation of the yeast transcriptome and its presumed impact on evolutionary innovation. We summarize current best practices for the identification and characterization of de novo genes. Crucially, we explain that the field is still in its nascency, with the physiological roles of most young yeast de novo genes identified thus far still utterly unknown. We hope this review inspires researchers to investigate the true contribution of de novo gene birth to cellular physiology and phenotypic diversity across yeast strains and species.

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.

Systematic mutagenesis of TFIIH subunit p52/Tfb2 identifies residues required for XPB/Ssl2 subunit function and genetic interactions with TFB6

TFIIH is an evolutionarily conserved complex that plays central roles in both RNA polymerase II (pol II) transcription and DNA repair. As an integral component of the pol II preinitiation complex, TFIIH regulates pol II enzyme activity in numerous ways. The TFIIH subunit XPB/Ssl2 is an ATP-dependent DNA translocase that stimulates promoter opening prior to transcription initiation. Crosslinking-mass spectrometry and cryo-EM results have shown a conserved interaction network involving XPB/Ssl2 and the C-terminal Hub region of the TFIIH p52/Tfb2 subunit, but the functional significance of specific residues is unclear. Here, we systematically mutagenized the HubA region of Tfb2 and screened for growth phenotypes in a TFB6 deletion background in Saccharomyces cerevisiae. We identified six lethal and 12 conditional mutants. Slow growth phenotypes of all but three conditional mutants were relieved in the presence of TFB6, thus identifying a functional interaction between Tfb2 HubA mutants and Tfb6, a protein that dissociates Ssl2 from TFIIH. Our biochemical analysis of Tfb2 mutants with severe growth phenotypes revealed defects in Ssl2 association, with similar results in human cells. Further characterization of these tfb2 mutant cells revealed defects in GAL gene induction, and reduced occupancy of TFIIH and pol II at GAL gene promoters, suggesting that functionally competent TFIIH is required for proper pol II recruitment to preinitiation complexes in vivo. Consistent with recent structural models of TFIIH, our results identify key residues in the p52/Tfb2 HubA domain that are required for stable incorporation of XPB/Ssl2 into TFIIH and for pol II transcription.

SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality.

Database URL http://www.tmliang.cn/SLOAD/

Rbp95 binds to 25S rRNA helix H95 and cooperates with the Npa1 complex during early pre-60S particle maturation

Eukaryotic ribosome synthesis involves more than 200 assembly factors, which promote ribosomal RNA (rRNA) processing, modification and folding, and assembly of ribosomal proteins. The formation and maturation of the earliest pre-60S particles requires structural remodeling by the Npa1 complex, but is otherwise still poorly understood. Here, we introduce Rbp95 (Ycr016w), a constituent of early pre-60S particles, as a novel ribosome assembly factor. We show that Rbp95 is both genetically and physically linked to most Npa1 complex members and to ribosomal protein Rpl3. We demonstrate that Rbp95 is an RNA-binding protein containing two independent RNA-interacting domains. In vivo, Rbp95 associates with helix H95 in the 3′ region of the 25S rRNA, in close proximity to the binding sites of Npa1 and Rpl3. Additionally, Rbp95 interacts with several snoRNAs. The absence of Rbp95 results in alterations in the protein composition of early pre-60S particles. Moreover, combined mutation of Rbp95 and Npa1 complex members leads to a delay in the maturation of early pre-60S particles. We propose that Rbp95 acts together with the Npa1 complex during early pre-60S maturation, potentially by promoting pre-rRNA folding events within pre-60S particles.

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.

Partitioned usage of chromatin remodelers by nucleosome-displacing factors

Nucleosome-displacing-factors (NDFs) in yeast, similar to pioneer factors in higher eukaryotes, can open closed chromatin and generate nucleosome-depleted regions (NDRs). NDRs in yeast are also affected by ATP-dependent chromatin remodelers (CRs). However, how NDFs and CRs coordinate in nucleosome invasion and NDR formation is still unclear. Here, we design a high-throughput method to systematically study the interplay between NDFs and CRs. By combining an integrated synthetic oligonucleotide library with DNA methyltransferase-based, single-molecule nucleosome mapping, we measure the impact of CRs on NDRs generated by individual NDFs. We find that CRs are dispensable for nucleosome invasion by NDFs, and they function downstream of NDF binding to modulate the NDR length. A few CRs show high specificity toward certain NDFs; however, in most cases, CRs are recruited in a factor-nonspecific and NDR length-dependent manner. Overall, our study provides a framework to investigate how NDFs and CRs cooperate to regulate chromatin opening.

Chromatin accessibility in yeast is regulated by nucleosome-displacing-factors (NDFs) and chromatin remodelers (CRs). Chen et al. show that NDFs first invade into nucleosomes and then recruit CRs to modulate the NDR length. NDF-specific and NDR length-dependent recruitment of CRs allow partitioned usage of CRs by NDFs.

Targeting the mRNA endonuclease CPSF73 inhibits breast cancer cell migration, invasion, and self-renewal

Cleavage by the endonuclease CPSF73 and polyadenylation of nascent RNA is an essential step in co-transcriptional mRNA maturation. Recent work has surprisingly identified CPSF73 as a promising drug target for inhibiting the growth of specific cancers, triggering further studies on understanding CPSF73 regulation and functions in cells. Here, we report that a HECT-like E3 ligase, UBE3D, participates in stabilizing CPFS73 protein by preventing its ubiquitin-mediated degradation by the proteasome. Depletion of UBE3D leads to CPSF73 downregulation, a pre-mRNA cleavage defect, and dysregulated gene expression in cells. UBE3D dysfunction or chemical inactivation of CPSF73 inhibited migration and invasion as well as stem cell renewal phenotypes in vitro in triple-negative breast cancer cells. In addition, genetic overexpression of CPSF73 promoted breast cancer stemness and knocking down CPSF73 inhibited stem cell renewal properties. Together, our findings indicate that targeting the pre-mRNA processing nuclease CPSF73 has potential for breast cancer therapy.

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

Exploring conditional gene essentiality through systems genetics approaches in yeast

An essential gene encodes for a cellular function that is required for viability. Although viability is a straightforward phenotype to analyze in yeast, defining a gene as essential is not always trivial. Gene essentiality has generally been studied in specific laboratory strains and under standard growth conditions, however, essentiality can vary across species, strains, and environments. Recent systematic studies of gene essentiality revealed that two sets of essential genes exist: core essential genes that are always required for viability and conditional essential genes that vary in essentiality in different genetic and environmental contexts. Here, we review recent advances made in the systematic analysis of gene essentiality in yeast and discuss the properties that distinguish core from context-dependent essential genes.

Predicting the Prognostic Value of POLI Expression in Different Cancers via a Machine Learning Approach

Translesion synthesis (TLS) is a cell signaling pathway that facilitates the tolerance of replication stress. Increased TLS activity, the particularly elevated expression of TLS polymerases, has been linked to resistance to cancer chemotherapeutics and significantly altered patient outcomes. Building upon current knowledge, we found that the expression of one of these TLS polymerases (POLI) is associated with significant differences in cervical and pancreatic cancer survival. These data led us to hypothesize that POLI expression is associated with cancer survival more broadly. However, when cancers were grouped cancer type, POLI expression did not have a significant prognostic value. We presented a binary cancer random forest classifier using 396 genes that influence the prognostic characteristics of POLI in cervical and pancreatic cancer selected via graphical least absolute shrinkage and selection operator. The classifier was then used to cluster patients with bladder, breast, colorectal, head and neck, liver, lung, ovary, melanoma, stomach, and uterus cancer when high POLI expression was associated with worsened survival (Group I) or with improved survival (Group II). This approach allowed us to identify cancers where POLI expression is a significant prognostic factor for survival (p = 0.028 in Group I and p = 0.0059 in Group II). Multiple independent validation approaches, including the gene ontology enrichment analysis and visualization tool and network visualization support the classification scheme. The functions of the selected genes involving mitochondrial translational elongation, Wnt signaling pathway, and tumor necrosis factor-mediated signaling pathway support their association with TLS and replication stress. Our multidisciplinary approach provides a novel way of identifying tumors where increased TLS polymerase expression is associated with significant differences in cancer survival.

Up-regulation of Osh6 boosts an anti-aging membrane trafficking pathway toward vacuoles

Members of the family of oxysterol-binding proteins mediate non-vesicular lipid transport between membranes and contribute to longevity in different manners. We previously found that a 2-fold up-regulation of Osh6, one of seven yeast oxysterol-binding proteins, remedies vacuolar morphology defects in mid-aged cells, partly down-regulates the target of rapamycin complex 1 (TORC1), and increases the replicative lifespan. At the molecular level, Osh6 transports phosphatidylserine (PS) and phosphatidylinositol-4-phosphate (PI4P) between the endoplasmic reticulum (ER) and the plasma membrane (PM). To decipher how an ER-PM working protein controls vacuolar morphology, we tested genetic interactions between OSH6 and DRS2, whose protein flips PS from the lumen to the cytosolic side of the Golgi, the organelle between ER and vacuoles in many pathways. Up-regulated OSH6 complemented vacuolar morphology of drs2Δ and enriched PI4P on the Golgi, indicating that Osh6 also works on the Golgi. This altered PI4P-enrichment led to a delay in the secretion of the proton ATPase Pma1 to the PM and a rerouting of Pma1 to vacuoles in a manner dependent on the trans-Golgi network (TGN) to late endosome (LE) trafficking pathway. Since the TGN-LE pathway controls endosomal and vacuolar TORC1, it may be the anti-aging pathway boosted by up-regulated Osh6.

Proteotoxicity caused by perturbed protein complexes underlies hybrid incompatibility in yeast

Dobzhansky–Muller incompatibilities represent a major driver of reproductive isolation between species. They are caused when interacting components encoded by alleles from different species cannot function properly when mixed. At incipient stages of speciation, complex incompatibilities involving multiple genetic loci with weak effects are frequently observed, but the underlying mechanisms remain elusive. Here we show perturbed proteostasis leading to compromised mitosis and meiosis in Saccharomyces cerevisiae hybrid lines carrying one or two chromosomes from Saccharomyces bayanus var. uvarum. Levels of proteotoxicity are correlated with the number of protein complexes on replaced chromosomes. Proteomic approaches reveal that multi-protein complexes with subunits encoded by replaced chromosomes tend to be unstable. Furthermore, hybrid defects can be alleviated or aggravated, respectively, by up- or down-regulating the ubiquitin-proteasomal degradation machinery, suggesting that destabilized complex subunits overburden the proteostasis machinery and compromise hybrid fitness. Our findings reveal the general role of impaired protein complex assembly in complex incompatibilities.

Hybrid incompatibility can be an important element of reproductive isolation and speciation. Using chromosome replacement lines of yeast, the authors show that perturbed proteostasis caused by destabilized hybrid protein complexes may represent a general mechanism of hybrid incompatibility.

An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases

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Numerous pathogenic interactions are yet to be revealed efficiently due to dimension burden.

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Existing methods are underpowered and inaccurate to estimate pathogenic interactions.

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We developed an accurate bioinformatic method to predict digenic interaction effects based on gene-level features.

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The new method created a valuable resource of genome-wide pathogenic digenic interactions.

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We found that known causal genes in Mendelian and Oligogenic diseases may be enriched with interactive effects for the first time.

Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases.

Mutational robustness changes during long-term adaptation in laboratory budding yeast populations

As an adapting population traverses the fitness landscape, its local neighborhood (i.e., the collection of fitness effects of single-step mutations) can change shape because of interactions with mutations acquired during evolution. These changes to the distribution of fitness effects can affect both the rate of adaptation and the accumulation of deleterious mutations. However, while numerous models of fitness landscapes have been proposed in the literature, empirical data on how this distribution changes during evolution remains limited. In this study, we directly measure how the fitness landscape neighborhood changes during laboratory adaptation. Using a barcode-based mutagenesis system, we measure the fitness effects of 91 specific gene disruption mutations in genetic backgrounds spanning 8000–10,000 generations of evolution in two constant environments. We find that the mean of the distribution of fitness effects decreases in one environment, indicating a reduction in mutational robustness, but does not change in the other. We show that these distribution-level patterns result from differences in the relative frequency of certain patterns of epistasis at the level of individual mutations, including fitness-correlated and idiosyncratic epistasis.

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability

MOTIVATION

Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict a binary outcome (viable or not, and negatively interacting or not). Third, most are uninterpretable black box machine learning models.

RESULTS

Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability.

AVAILABILITY AND IMPLEMENTATION

All code and processed datasets used to generate results and figures in this manuscript are available at our Github repository at https://github.com/KISRDevelopment/cell_viability_paper. The repository also contains a link to the GI prediction website that lets users search for GIs using the MN models.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery and identification of genes involved in DNA damage repair in yeast

DNA repair defects are common in tumour cells and can lead to misrepair of double-strand breaks (DSBs), posing a significant challenge to cellular integrity. The overall mechanisms of DSB have been known for decades. However, the list of the genes that affect the efficiency of DSB repair continues to grow. Additional factors that play a role in DSB repair pathways have yet to be identified. In this study, we present a computational approach to identify novel gene functions that are involved in DNA damage repair in Saccharomyces cerevisiae. Among the primary candidates, GAL7, YMR130W, and YHI9 were selected for further analysis since they had not previously been identified as being active in DNA repair pathways. Originally, GAL7 was linked to galactose metabolism. YHI9 and YMR130W encode proteins of unknown functions. Laboratory testing of deletion strains gal7Δ, ymr130wΔ, and yhi9Δ implicated all 3 genes in Homologous Recombination (HR) and/or Non-Homologous End Joining (NHEJ) repair pathways, and enhanced sensitivity to DNA damage-inducing drugs suggested involvement in the broader DNA damage repair machinery. A subsequent genetic interaction analysis revealed interconnections of these three genes, most strikingly through SIR2, SIR3 and SIR4 that are involved in chromatin regulation and DNA damage repair network.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

The bacterium E. coli has around 300 transcriptional factors, but the functions of many of them, and the interactions between their respective regulatory networks, are unclear. Here, the authors study genetic interactions among all transcription factor genes in E. coli, revealing condition-dependent interactions and roles for uncharacterized transcription factors.

Genetic background and mistranslation frequency determine the impact of mistranslating tRNASerUGG

Transfer RNA variants increase the frequency of mistranslation, the misincorporation of an amino acid not specified by the “standard” genetic code, to frequencies approaching 10% in yeast and bacteria. Cells cope with these variants by having multiple copies of each tRNA isodecoder and through pathways that deal with proteotoxic stress. In this study, we define the genetic interactions of the gene encoding tRNA^Ser_UGG,G26A, which mistranslates serine at proline codons. Using a collection of yeast temperature-sensitive alleles, we identify negative synthetic genetic interactions between the mistranslating tRNA and 109 alleles representing 91 genes, with nearly half of the genes having roles in RNA processing or protein folding and turnover. By regulating tRNA expression, we then compare the strength of the negative genetic interaction for a subset of identified alleles under differing amounts of mistranslation. The frequency of mistranslation correlated with the impact on cell growth for all strains analyzed; however, there were notable differences in the extent of the synthetic interaction at different frequencies of mistranslation depending on the genetic background. For many of the strains, the extent of the negative interaction with tRNA^Ser_UGG,G26A was proportional to the frequency of mistranslation or only observed at intermediate or high frequencies. For others, the synthetic interaction was approximately equivalent at all frequencies of mistranslation. As humans contain similar mistranslating tRNAs, these results are important when analyzing the impact of tRNA variants on disease, where both the individual’s genetic background and the expression of the mistranslating tRNA variant need to be considered.

Overexpression of MRX9 impairs processing of RNAs encoding mitochondrial oxidative phosphorylation factors COB and COX1 in yeast

Mitochondrial translation is a highly regulated process, and newly synthesized mitochondrial products must first associate with several nuclear-encoded auxiliary factors to form oxidative phosphorylation complexes. The output of mitochondrial products should therefore be in stoichiometric equilibrium with the nuclear-encoded products to prevent unnecessary energy expense or the accumulation of pro-oxidant assembly modules. In the mitochondrial DNA of Saccharomyces cerevisiae, COX1 encodes subunit 1 of the cytochrome c oxidase and COB the central core of the cytochrome bc1 electron transfer complex; however, factors regulating the expression of these mitochondrial products are not completely described. Here, we identified Mrx9p as a new factor that controls COX1 and COB expression. We isolated MRX9 in a screen for mitochondrial factors that cause poor accumulation of newly synthesized Cox1p and compromised transition to the respiratory metabolism. Northern analyses indicated lower levels of COX1 and COB mature mRNAs accompanied by an accumulation of unprocessed transcripts in the presence of excess Mrx9p. In a strain devoid of mitochondrial introns, MRX9 overexpression did not affect COX1 and COB translation or respiratory adaptation, implying Mrx9p regulates processing of COX1 and COB RNAs. In addition, we found Mrx9p was localized in the mitochondrial inner membrane, facing the matrix, as a portion of it cosedimented with mitoribosome subunits and its removal or overexpression altered Mss51p sedimentation. Finally, we showed accumulation of newly synthesized Cox1p in the absence of Mrx9p was diminished in cox14 null mutants. Taken together, these data indicate a regulatory role of Mrx9p in COX1 RNA processing.

Cvm1 is a component of multiple vacuolar contact sites required for sphingolipid homeostasis

Membrane contact sites are specialized platforms formed between most organelles that enable them to exchange metabolites and influence the dynamics of each other. The yeast vacuole is a degradative organelle equivalent to the lysosome in higher eukaryotes with important roles in ion homeostasis and metabolism. Using a high-content microscopy screen, we identified Ymr160w (Cvm1, for contact of the vacuole membrane 1) as a novel component of three different contact sites of the vacuole: with the nuclear endoplasmic reticulum, the mitochondria, and the peroxisomes. At the vacuole-mitochondria contact site, Cvm1 acts as a tether independently of previously known tethers. We show that changes in Cvm1 levels affect sphingolipid homeostasis, altering the levels of multiple sphingolipid classes and the response of sphingolipid-sensing signaling pathways. Furthermore, the contact sites formed by Cvm1 are induced upon a decrease in sphingolipid levels. Altogether, our work identifies a novel protein that forms multiple contact sites and supports a role of lysosomal contacts in sphingolipid homeostasis.

Functional analysis of the Candida albicans kinome reveals Hrr25 as a regulator of antifungal susceptibility

Candida albicans is a leading cause of death due to systemic fungal infections. Poor patient outcomes are attributable to the limited number of antifungal classes and the increasing prevalence of drug resistance. Protein kinases have emerged as rewarding targets in the development of drugs for diverse diseases, yet kinases remain untapped in the quest for new antifungals. Here, we performed a comprehensive analysis of the C. albicans kinome to identify genes for which loss-of-function confers hypersensitivity to the two most widely deployed antifungals, echinocandins and azoles. Through this analysis, we found a role for the casein kinase 1 (CK1) homologue Hrr25 in regulating tolerance to both antifungals as well as target-mediated echinocandin resistance. Follow-up investigations established that Hrr25 regulates these responses through its interaction with the SBF transcription factor. Thus, we provide insights into the circuitry governing cellular responses to antifungals and implicate Hrr25 as a key mediator of drug resistance.

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Screening Candida albicans kinase mutants reveals 47 regulators of antifungal tolerance

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Hrr25 is important for growth and cell wall/membrane stress tolerance

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Hrr25 enables target-mediated echinocandin resistance

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Hrr25 interacts with the SBF transcription factor complex

Contribution of Model Organisms to Investigating the Far-Reaching Consequences of PRPP Metabolism on Human Health and Well-Being

Phosphoribosyl pyrophosphate synthetase (PRS EC 2.7.6.1) is a rate-limiting enzyme that irreversibly catalyzes the formation of phosphoribosyl pyrophosphate (PRPP) from ribose-5-phosphate and adenosine triphosphate (ATP). This key metabolite is required for the synthesis of purine and pyrimidine nucleotides, the two aromatic amino acids histidine and tryptophan, the cofactors nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+), all of which are essential for various life processes. Despite its ubiquity and essential nature across the plant and animal kingdoms, PRPP synthetase displays species-specific characteristics regarding the number of gene copies and architecture permitting interaction with other areas of cellular metabolism. The impact of mutated PRS genes in the model eukaryote Saccharomyces cerevisiae on cell signalling and metabolism may be relevant to the human neuropathies associated with PRPS mutations. Human PRPS1 and PRPS2 gene products are implicated in drug resistance associated with recurrent acute lymphoblastic leukaemia and progression of colorectal cancer and hepatocellular carcinoma. The investigation of PRPP metabolism in accepted model organisms, e.g., yeast and zebrafish, has the potential to reveal novel drug targets for treating at least some of the diseases, often characterized by overlapping symptoms, such as Arts syndrome and respiratory infections, and uncover the significance and relevance of human PRPS in disease diagnosis, management, and treatment.

Complex mutation profiles in mismatch repair and ribonucleotide reductase mutants reveal novel repair substrate specificity of MutS homolog (MSH) complexes

Determining mutation signatures is standard for understanding the etiology of human tumors and informing cancer treatment. Multiple determinants of DNA replication fidelity prevent mutagenesis that leads to carcinogenesis, including the regulation of free deoxyribonucleoside triphosphate pools by ribonucleotide reductase and repair of replication errors by the mismatch repair system. We identified genetic interactions between rnr1 alleles that skew and/or elevate deoxyribonucleoside triphosphate levels and mismatch repair gene deletions. These defects indicate that the rnr1 alleles lead to increased mutation loads that are normally acted upon by mismatch repair. We then utilized a targeted deep-sequencing approach to determine mutational profiles associated with mismatch repair pathway defects. By combining rnr1 and msh mutations to alter and/or increase deoxyribonucleoside triphosphate levels and alter the mutational load, we uncovered previously unreported specificities of Msh2-Msh3 and Msh2-Msh6. Msh2-Msh3 is uniquely able to direct the repair of G/C single-base deletions in GC runs, while Msh2-Msh6 specifically directs the repair of substitutions that occur at G/C dinucleotides. We also identified broader sequence contexts that influence variant profiles in different genetic backgrounds. Finally, we observed that the mutation profiles in double mutants were not necessarily an additive relationship of mutation profiles in single mutants. Our results have implications for interpreting mutation signatures from human tumors, particularly when mismatch repair is defective.

A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance

Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We build a genome-wide atlas of drug susceptibility determinants and generated a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide atlas of cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in genes that decrease antibiotic sensitivity and increase tolerance readily evolve and are frequently associated with resistant strains, indicating such mutations could be harbingers for the emergence of antibiotic resistance.

Humanized yeast to model human biology, disease and evolution

For decades, budding yeast, a single-cellular eukaryote, has provided remarkable insights into human biology. Yeast and humans share several thousand genes despite morphological and cellular differences and over a billion years of separate evolution. These genes encode critical cellular processes, the failure of which in humans results in disease. Although recent developments in genome engineering of mammalian cells permit genetic assays in human cell lines, there is still a need to develop biological reagents to study human disease variants in a high-throughput manner. Many protein-coding human genes can successfully substitute for their yeast equivalents and sustain yeast growth, thus opening up doors for developing direct assays of human gene function in a tractable system referred to as 'humanized yeast'. Humanized yeast permits the discovery of new human biology by measuring human protein activity in a simplified organismal context. This Review summarizes recent developments showing how humanized yeast can directly assay human gene function and explore variant effects at scale. Thus, by extending the 'awesome power of yeast genetics' to study human biology, humanizing yeast reinforces the high relevance of evolutionarily distant model organisms to explore human gene evolution, function and disease.