The genetic landscape of a cell

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.

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.

K29-linked free polyubiquitin chains affect ribosome biogenesis and direct ribosomal proteins to the intranuclear quality control compartment

Ribosome assembly requires precise coordination between the production and assembly of ribosomal components. Mutations in ribosomal proteins that inhibit the assembly process or ribosome function are often associated with ribosomopathies, some of which are linked to defects in proteostasis. In this study, we examine the interplay between several yeast proteostasis enzymes, including deubiquitylases (DUBs) Ubp2 and Ubp14, and E3 ligases Ufd4 and Hul5, and we explore their roles in the regulation of the cellular levels of K29-linked unanchored polyubiquitin (polyUb) chains. Accumulating K29-linked unanchored polyUb chains associate with maturing ribosomes to disrupt their assembly, activate the ribosome assembly stress response (RASTR), and lead to the sequestration of ribosomal proteins at the intranuclear quality control compartment (INQ). These findings reveal the physiological relevance of INQ and provide insights into mechanisms of cellular toxicity associated with ribosomopathies.

Exportin-1 functions as an adaptor for transcription factor-mediated docking of chromatin at the nuclear pore complex

Nuclear pore proteins (Nups) in yeast, flies and mammals physically interact with hundreds or thousands of chromosomal sites, which impacts transcriptional regulation. In budding yeast, transcription factors mediate interaction of Nups with enhancers of highly active genes. To define the molecular basis of this mechanism, we exploited a separation-of-function mutation in the Gcn4 transcription factor that blocks its interaction with the nuclear pore complex (NPC) without altering its DNA binding or activation domains. SILAC mass spectrometry revealed that this mutation reduces the interaction of Gcn4 with the highly conserved nuclear export factor Crm1/Xpo1. Crm1 both interacts with the same sites as Nups genome-wide and is required for Nup2 to interact with the yeast genome. In vivo, Crm1 undergoes extensive and stable interactions with the NPC. In vitro, Crm1 binds to Gcn4 and these proteins form a complex with the nuclear pore protein Nup2. Importantly, the interaction between Crm1 and Gcn4 does not require Ran-GTP, suggesting that it is not through the nuclear export sequence binding site. Finally, Crm1 stimulates DNA binding by Gcn4, supporting a model in which allosteric coupling between Crm1 binding and DNA binding permits docking of transcription factor-bound enhancers at the NPC.

ARV1 deficiency induces lipid bilayer stress and enhances rDNA stability by activating the unfolded protein response in Saccharomyces cerevisiae

The stability of ribosomal DNA (rDNA) is maintained through transcriptional silencing by the NAD+-dependent histone deacetylase Sir2 in Saccharomyces cerevisiae. Alongside proteostasis, rDNA stability is a crucial factor regulating the replicative lifespan of S. cerevisiae. The unfolded protein response (UPR) is induced by misfolding of proteins or an imbalance of membrane lipid composition and is responsible for degrading misfolded proteins and restoring endoplasmic reticulum (ER) membrane homeostasis. Recent investigations have suggested that the UPR can extend the replicative lifespan of yeast by enhancing protein quality control mechanisms, but the relationship between the UPR and rDNA stability remains unknown. In this study, we found that the deletion of ARV1, which encodes an ER protein of unknown molecular function, activates the UPR by inducing lipid bilayer stress. In arv1Δ cells, the UPR and the cell wall integrity pathway are activated independently of each other, and the high osmolarity glycerol (HOG) pathway is activated in a manner dependent on Ire1, which mediates the UPR. Activated Hog1 translocates the stress response transcription factor Msn2 to the nucleus, where it promotes the expression of nicotinamidase Pnc1, a well-known Sir2 activator. Following Sir2 activation, rDNA silencing and rDNA stability are promoted. Furthermore, the loss of other ER proteins, such as Pmt1 or Bst1, and ER stress induced by tunicamycin or inositol depletion also enhance rDNA stability in a Hog1-dependent manner. Collectively, these findings suggest that the induction of the UPR enhances rDNA stability in S. cerevisiae by promoting the Msn2-Pnc1-Sir2 pathway in a Hog1-dependent manner.

The distribution of fitness effects during adaptive walks using a simple genetic network

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Pungency related gene network in Allium sativum L., response to sulfur treatments

Pungency of garlic (Allium sativum L.) is generated from breakdown of the alk(en)yl cysteine sulphoxide (CSO), alliin and its subsequent breakdown to allicin under the activity of alliinase (All). Based on recent evidence, two other important genes including Sulfite reductase (SiR) and Superoxide dismutase (SOD) are thought to be related to sulfur metabolism. These three gene functions are in sulfate assimilation pathway. However, whether it is involved in stress response in crops is largely unknown. In this research, the order and priority of simultaneous expression of three genes including All, SiR and SOD were measured on some garlic ecotypes of Iran, collected from Zanjan, Hamedan and Gilan, provinces under sulfur concentrations (0, 6, 12, 24 and 60 g/ per experimental unit: pot) using real-time quantitative PCR (RT-qPCR) analysis. For understanding the network interactions between studied genes and other related genes, in silico gene network analysis was constructed to investigate various mechanisms underlying stimulation of A. sativum L. to cope with imposed sulfur. Complicated network including TF-TF, miRNA-TF, and miRNA-TF-gene, was split into sub-networks to have a deeper insight. Analysis of q-RT-PCR data revealed the highest expression in All and SiR genes respectively. To distinguish and select significant pathways in sulfur metabolism, RESNET Plant database of Pathway Studio software v.10 (Elsevier), and other relative data such as chemical reactions, TFs, miRNAs, enzymes, and small molecules were extracted. Complex sub-network exhibited plenty of routes between stress response and sulfate assimilation pathway. Even though Alliinase did not display any connectivity with other stress response genes, it showed binding relation with lectin functional class, as a result of which connected to leucine zipper, exocellulase, peroxidase and ARF functional class indirectly. Integration network of these genes revealed their involvement in various biological processes such as, RNA splicing, stress response, gene silencing by miRNAs, and epigenetic. The findings of this research can be used to extend further research on the garlic metabolic engineering, garlic stress related genes, and also reducing or enhancing the activity of the responsible genes for garlic pungency for health benefits and industry demands.

Reducing Epistasis and Pleiotropy Can Avoid the Survival of the Flattest Tragedy

This study investigates whether reducing epistasis and pleiotropy enhances mutational robustness in evolutionary adaptation, utilizing an indirect encoded model within the "survival of the flattest" (SoF) fitness landscape. By simulating genetic variations and their phenotypic consequences, we explore organisms' adaptive mechanisms to maintain positions on higher, narrower evolutionary peaks amidst environmental and genetic pressures. Our results reveal that organisms can indeed sustain their advantageous positions by minimizing the complexity of genetic interactions-specifically, by reducing the levels of epistasis and pleiotropy. This finding suggests a counterintuitive strategy for evolutionary stability: simpler genetic architectures, characterized by fewer gene interactions and multifunctional genes, confer a survival advantage by enhancing mutational robustness. This study contributes to our understanding of the genetic underpinnings of adaptability and robustness, challenging traditional views that equate complexity with fitness in dynamic environments.

An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.

Combinatorial in vivo genome editing identifies widespread epistasis during lung tumorigenesis

Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable tumorigenesis in vivo , largely due to a lack of methods for investigating genetic interactions in a high-throughput and multiplexed manner. Here, we employed a novel platform to generate tumors with all pairwise inactivation of ten tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including dramatically synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. This approach has the potential to expand the scope of genetic interactions that may be functionally characterized in vivo , which could lead to a better understanding of how complex tumor genotypes impact each step of carcinogenesis.

Biological mechanisms and clinical significance of endoplasmic reticulum oxidoreductase 1 alpha (ERO1α) in human cancer

A firm link between endoplasmic reticulum (ER) stress and tumors has been wildly reported. Endoplasmic reticulum oxidoreductase 1 alpha (ERO1α), an ER-resident thiol oxidoreductase, is confirmed to be highly upregulated in various cancer types and associated with a significantly worse prognosis. Of importance, under ER stress, the functional interplay of ERO1α/PDI axis plays a pivotal role to orchestrate proper protein folding and other key processes. Multiple lines of evidence propose ERO1α as an attractive potential target for cancer treatment. However, the unavailability of specific inhibitor for ERO1α, its molecular inter-relatedness with closely related paralog ERO1β and the tightly regulated processes with other members of flavoenzyme family of enzymes, raises several concerns about its clinical translation. Herein, we have provided a detailed description of ERO1α in human cancers and its vulnerability towards the aforementioned concerns. Besides, we have discussed a few key considerations that may improve our understanding about ERO1α in tumors.

Resistance mechanisms of Saccharomyces cerevisiae against silver nanoparticles with different sizes and coatings

To investigate the underlying resistance mechanisms of Saccharomyces cerevisiae against Ag-NPs with different particle sizes and coatings, transcriptome sequencing (RNA-seq) technology was used to characterize the transcriptomes from S. cerevisiae exposed to 20-PVP-Ag, 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag, respectively. The steroid biosynthesis was found as a general pathway for Ag-NPs stress responding, in which ERG6 and ERG3 were inhibited and ERG11, ERG25 and ERG5 were significantly up-regulated to resist the stress by supporting the later mutation and resistance and modulate drug efflux indirectly. The resistance mechanism of S. cerevisiae to 20-PVP-Ag seems different from that of 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag. Under the 20-PVP-Ag, transmembrane transporter activity, transition metal ion homeostasis and oxidative phosphorylation pathway were main resistance pathways to enhance cell transport processes. While 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag mainly impacted RNA binding, structural constituent of ribosome and ribosome pathway which can provide more energy to maintain the number and function of protein in cells. This study reveals the differences in resistance mechanisms of S. cerevisiae to Ag-NPs with different particle sizes and coatings, and explains several main regulatory mechanisms used to respond to silver stress. It will provide theoretical basis for the study of chemical risk assessment.

Differential Protein Expression in Set5p-Mediated Acetic Acid Stress Response and Novel Targets for Engineering Yeast Stress Tolerance

Acetic acid is a prevalent inhibitor in lignocellulosic hydrolysate, which represses microbial growth and bioproduction. Histone modification and chromatin remodeling have been revealed to be critical for regulating eukaryotic metabolism. However, related studies in chronic acetic acid stress responses remain unclear. Our previous studies revealed that overexpression of the histone H4 methyltransferase Set5p enhanced acetic acid stress tolerance of the budding yeast Saccharomyces cerevisiae. In this study, we examined the role of Set5p in acetic acid stress by analyzing global protein expression. Significant activation of intracellular protein expression under the stress was discovered, and the functions of the differential proteins were mainly involved in chromatin modification, signal transduction, and carbohydrate metabolism. Notably, a substantial increase of Set5p expression was observed in response to acetic acid stress. Functional studies demonstrated that the restriction of the telomere capping protein Rtc3p, as well as Ies3p and Taf14p, which are related to chromatin regulation, was critical for yeast stress response. This study enriches the understanding of the epigenetic regulatory mechanisms underlying yeast stress response mediated by histone-modifying enzymes. The results also benefit the development of robust yeast strains for lignocellulosic bioconversion.

Mind your marker: the effect of common auxotrophic markers on complex traits in yeast

Yeast cells are extensively used as a key model organism owing to their highly conserved genome, metabolic pathways, and cell biology processes. To assist in genetic engineering and analysis, laboratory yeast strains typically harbor auxotrophic selection markers. When uncompensated, auxotrophic markers cause significant phenotypic bias compared to prototrophic strains and have different combinatorial influences on the metabolic network. Here, we used BY4741, a laboratory strain commonly used as a "wild type" strain in yeast studies, to generate a set of revertant strains, containing all possible combinations of four common auxotrophic markers (leu2∆, ura3∆, his3∆1, met15∆). We examined the effect of the auxotrophic combinations on complex phenotypes such as resistance to rapamycin, acetic acid, and ethanol. Among the markers, we found that leucine auxotrophy most significantly affected the phenotype. We analyzed the phenotypic bias caused by auxotrophy at the genomic level using a prototrophic version of a genome-wide deletion library and a decreased mRNA perturbation (DAmP) library. Prototrophy was found to suppress rapamycin sensitivity in many mutants previously annotated for the phenotype, raising a possible need for reevaluation of the findings in a native metabolic context. These results reveal a significant phenotypic bias caused by common auxotrophic markers and support the use of prototrophic wild-type strains in yeast research.

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients. The increasing sizes of ultra-rare, "N-of-1" disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We apply existing and introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We make our gene-level findings and variant-level information across the cohort available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.

Proteins that carry dual targeting signals can act as tethers between peroxisomes and partner organelles

Peroxisomes are organelles with crucial functions in oxidative metabolism. To correctly target to peroxisomes, proteins require specialized targeting signals. A mystery in the field is the sorting of proteins that carry a targeting signal for peroxisomes and as well as for other organelles, such as mitochondria or the endoplasmic reticulum (ER). Exploring several of these proteins in fungal model systems, we observed that they can act as tethers bridging organelles together to create contact sites. We show that in Saccharomyces cerevisiae this mode of tethering involves the peroxisome import machinery, the ER-mitochondria encounter structure (ERMES) at mitochondria and the guided entry of tail-anchored proteins (GET) pathway at the ER. Our findings introduce a previously unexplored concept of how dual affinity proteins can regulate organelle attachment and communication.

The cell morphological diversity of Saccharomycotina yeasts

The ∼1 200 known species in subphylum Saccharomycotina are a highly diverse clade of unicellular fungi. During its lifecycle, a typical yeast exhibits multiple cell types with various morphologies; these morphologies vary across Saccharomycotina species. Here, we synthesize the evolutionary dimensions of variation in cellular morphology of yeasts across the subphylum, focusing on variation in cell shape, cell size, type of budding, and filament production. Examination of 332 representative species across the subphylum revealed that the most common budding cell shapes are ovoid, spherical, and ellipsoidal, and that their average length and width is 5.6 µm and 3.6 µm, respectively. 58.4% of yeast species examined can produce filamentous cells, and 87.3% of species reproduce asexually by multilateral budding, which does not require utilization of cell polarity for mitosis. Interestingly, ∼1.8% of species examined have not been observed to produce budding cells, but rather only produce filaments of septate hyphae and/or pseudohyphae. 76.9% of yeast species examined have sexual cycle descriptions, with most producing one to four ascospores that are most commonly hat-shaped (37.4%). Systematic description of yeast cellular morphological diversity and reconstruction of its evolution promises to enrich our understanding of the evolutionary cell biology of this major fungal lineage.

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.

Meta-analysis of dispensable essential genes and their interactions with bypass suppressors

Genes have been historically classified as essential or non-essential based on their requirement for viability. However, genomic mutations can sometimes bypass the requirement for an essential gene, challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability in Saccharomyces cerevisiae to comprehensively characterize these genes. In analyses spanning different evolutionary timescales, dispensable essential genes exhibited distinct phylogenetic properties compared with other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape of S. cerevisiae strains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressors reflect evolutionary outcomes.

Master regulators of biological systems in higher dimensions

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.

Diversity in Biological Function and Mechanism of the tRNA Methyltransferase Trm10

Transfer ribonucleic acid (tRNA) is the most highly modified RNA species in the cell, and loss of tRNA modifications can lead to growth defects in yeast as well as metabolic, neurological, and mitochondrial disorders in humans. Significant progress has been made toward identifying the enzymes that are responsible for installing diverse modifications in tRNA, revealing a landscape of fascinating biological and mechanistic diversity that remains to be fully explored. Most early discoveries of tRNA modification enzymes were in model systems, where many enzymes were not strictly required for viability, an observation somewhat at odds with the extreme conservation of many of the same enzymes throughout multiple domains of life. Moreover, many tRNA modification enzymes act on more than one type of tRNA substrate, which is not necessarily surprising given the similar overall secondary and tertiary structures of tRNA, yet biochemical characterization has revealed interesting patterns of substrate specificity that can be challenging to rationalize on a molecular level. Questions about how many enzymes efficiently select a precise set of target tRNAs from among a structurally similar pool of molecules persist.The tRNA methyltransferase Trm10 provides an exciting paradigm to study the biological and mechanistic questions surrounding tRNA modifications. Even though the enzyme was originally characterized in Saccharomyces cerevisiae where its deletion causes no detectable phenotype under standard lab conditions, several more recently identified phenotypes provide insight into the requirement for this modification in the overall quality control of the tRNA pool. Studies of Trm10 in yeast also revealed another characteristic feature that has turned out to be a conserved feature of enzymes throughout the Trm10 family tree. We were initially surprised to see that purified S. cerevisiae Trm10 was capable of modifying tRNA substrates that were not detectably modified by the enzyme in vivo in yeast. This pattern has continued to emerge as we and others have studied Trm10 orthologs from Archaea and Eukarya, with enzymes exhibiting in vitro substrate specificities that can differ significantly from in vivo patterns of modification. While this feature complicates efforts to predict substrate specificities of Trm10 enzymes in the absence of appropriate genetic systems, it also provides an exciting opportunity for studying how enzyme activities can be regulated to achieve dynamic patterns of biological tRNA modification, which have been shown to be increasingly important for stress responses and human disease. Finally, the intriguing diversity in target nucleotide modification that has been revealed among Trm10 orthologs is distinctive among known tRNA modifying enzymes and necessitates unusual and likely novel catalytic strategies for methylation that are being revealed by biochemical and structural studies directed toward various family members. These efforts will no doubt yield more surprising discoveries in terms of tRNA modification enzymology.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention

Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.

A Microfluidic Platform for Screening Gene Expression Dynamics across Yeast Strain Libraries

The relative ease of genetic manipulation in S. cerevisiae is one of its greatest strengths as a model eukaryotic organism. Researchers have leveraged this quality of the budding yeast to study the effects of a variety of genetic perturbations, such as deletion or overexpression, in a high-throughput manner. This has been accomplished by producing a number of strain libraries that can contain hundreds or even thousands of distinct yeast strains with unique genetic alterations. While these strategies have led to enormous increases in our understanding of the functions and roles that genes play within cells, the techniques used to screen genetically modified libraries of yeast strains typically rely on plate or sequencing-based assays that make it difficult to analyze gene expression changes over time. Microfluidic devices, combined with fluorescence microscopy, can allow gene expression dynamics of different strains to be captured in a continuous culture environment; however, these approaches often have significantly lower throughput compared to traditional techniques. To address these limitations, we have developed a microfluidic platform that uses an array pinning robot to allow for up to 48 different yeast strains to be transferred onto a single device. Here, we detail a validated methodology for constructing and setting up this microfluidic device, starting with the photolithography steps for constructing the wafer, then the soft lithography steps for making polydimethylsiloxane (PDMS) microfluidic devices, and finally the robotic arraying of strains onto the device for experiments. We have applied this device for dynamic screens of a protein aggregation library; however, this methodology has the potential to enable complex and dynamic screens of yeast libraries for a wide range of applications. Key features • Major steps of this protocol require access to specialized equipment (i.e., microfabrication tools typically found in a cleanroom facility and an array pinning robot). • Construction of microfluidic devices with multiple different feature heights using photolithography and soft lithography with PDMS. • Robotic spotting of up to 48 different yeast strains onto microfluidic devices.

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

Simple model systems reveal conserved mechanisms of Alzheimer's disease and related tauopathies

The lack of effective therapies that slow the progression of Alzheimer's disease (AD) and related tauopathies highlights the need for a more comprehensive understanding of the fundamental cellular mechanisms underlying these diseases. Model organisms, including yeast, worms, and flies, provide simple systems with which to investigate the mechanisms of disease. The evolutionary conservation of cellular pathways regulating proteostasis and stress response in these organisms facilitates the study of genetic factors that contribute to, or protect against, neurodegeneration. Here, we review genetic modifiers of neurodegeneration and related cellular pathways identified in the budding yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster, focusing on models of AD and related tauopathies. We further address the potential of simple model systems to better understand the fundamental mechanisms that lead to AD and other neurodegenerative disorders.

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.

Explainable Multilayer Graph Neural Network for cancer gene prediction

MOTIVATION

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction.

RESULTS

Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research.

AVAILABILITY AND IMPLEMENTATION

Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches.

N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity

Most eukaryotic proteins are N-terminally acetylated, but the functional impact on a global scale has remained obscure. Using genome-wide CRISPR knockout screens in human cells, we reveal a strong genetic dependency between a major N-terminal acetyltransferase and specific ubiquitin ligases. Biochemical analyses uncover that both the ubiquitin ligase complex UBR4-KCMF1 and the acetyltransferase NatC recognize proteins bearing an unacetylated N-terminal methionine followed by a hydrophobic residue. NatC KO-induced protein degradation and phenotypes are reversed by UBR knockdown, demonstrating the central cellular role of this interplay. We reveal that loss of Drosophila NatC is associated with male sterility, reduced longevity, and age-dependent loss of motility due to developmental muscle defects. Remarkably, muscle-specific overexpression of UbcE2M, one of the proteins targeted for NatC KO-mediated degradation, suppresses defects of NatC deletion. In conclusion, NatC-mediated N-terminal acetylation acts as a protective mechanism against protein degradation, which is relevant for increased longevity and motility.

Scalable genetic screening for regulatory circuits using compressed Perturb-seq

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

TDP-1 and FUST-1 co-inhibit exon inclusion and control fertility together with transcriptional regulation

Gene expression is a multistep process and crosstalk among regulatory layers plays an important role in coordinating gene expression. To identify functionally relevant gene expression coordination, we performed a systematic reverse-genetic interaction screen in C. elegans, combining RNA binding protein (RBP) and transcription factor (TF) mutants to generate over 100 RBP;TF double mutants. We identified many unexpected double mutant phenotypes, including two strong genetic interactions between the ALS-related RBPs, fust-1 and tdp-1, and the homeodomain TF ceh-14. Losing any one of these genes alone has no effect on the health of the organism. However, fust-1;ceh-14 and tdp-1;ceh-14 double mutants both exhibit strong temperature-sensitive fertility defects. Both double mutants exhibit defects in gonad morphology, sperm function, and oocyte function. RNA-Seq analysis of double mutants identifies ceh-14 as the main controller of transcript levels, while fust-1 and tdp-1 control splicing through a shared role in exon inhibition. A skipped exon in the polyglutamine-repeat protein pqn-41 is aberrantly included in tdp-1 mutants, and genetically forcing this exon to be skipped in tdp-1;ceh-14 double mutants rescues their fertility. Together our findings identify a novel shared physiological role for fust-1 and tdp-1 in promoting C. elegans fertility and a shared molecular role in exon inhibition.

Turning up the heat on essential E. coli genes

Temperature-sensitive (TS) alleles create tunable thermoswitches to deplete essential cellular activities and are used to dissect gene function. In their recent study, Link and colleagues (Schramm et al 2023) use a CRISPR-based approach to systematically create TS alleles across essential genes in E. coli.

Yeast Lsm Pro-Apoptotic Mutants Show Defects in Autophagy

LSM4 is an essential yeast gene encoding a component of different LSM complexes involved in the regulation of mRNA splicing, stability, and translation. In previous papers, we reported that the expression in S. cerevisiae of the K. lactis LSM4 gene lacking the C-terminal Q/N-rich domain in an Lsm4 null strain S. cerevisiae (Sclsm4Δ1) restored cell viability. Nevertheless, in this transformed strain, we observed some phenotypes that are typical markers of regulated cell death, reactive oxygen species (ROS), and oxidated RNA accumulation. In this paper, we report that a similar truncation operated in the S. cerevisiae LSM4 gene confers on cells the same phenotypes observed with the K. lactis lsm4Δ1 gene. Up until now, there was no evidence of the direct involvement of LSM4 in autophagy. Here we found that the Sclsm4Δ1 mutant showed a block in the autophagic process and was very sensitive to nitrogen starvation or treatment with low doses of rapamycin, an inducer of autophagy. Moreover, both during nitrogen starvation and aging, the Sclsm4Δ1 mutant accumulated cytoplasmic autophagy-related structures, suggesting a role of Lsm4 in a later step of the autophagy process.

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.

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Misregulation of cell cycle-dependent methylation of budding yeast CENP-A contributes to chromosomal instability

Centromere (CEN) identity is specified epigenetically by specialized nucleosomes containing evolutionarily conserved CEN-specific histone H3 variant CENP-A (Cse4 in Saccharomyces cerevisiae, CENP-A in humans), which is essential for faithful chromosome segregation. However, the epigenetic mechanisms that regulate Cse4 function have not been fully defined. In this study, we show that cell cycle-dependent methylation of Cse4-R37 regulates kinetochore function and high-fidelity chromosome segregation. We generated a custom antibody that specifically recognizes methylated Cse4-R37 and showed that methylation of Cse4 is cell cycle regulated with maximum levels of methylated Cse4-R37 and its enrichment at the CEN chromatin occur in the mitotic cells. Methyl-mimic cse4-R37F mutant exhibits synthetic lethality with kinetochore mutants, reduced levels of CEN-associated kinetochore proteins and chromosome instability (CIN), suggesting that mimicking the methylation of Cse4-R37 throughout the cell cycle is detrimental to faithful chromosome segregation. Our results showed that SPOUT methyltransferase Upa1 contributes to methylation of Cse4-R37 and overexpression of UPA1 leads to CIN phenotype. In summary, our studies have defined a role for cell cycle-regulated methylation of Cse4 in high-fidelity chromosome segregation and highlight an important role of epigenetic modifications such as methylation of kinetochore proteins in preventing CIN, an important hallmark of human cancers.

Inferring sparse structure in genotype-phenotype maps

Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple phenotypes (an effect known as pleiotropy), creating observable relationships between phenotypes. A natural hypothesis is that pleiotropic effects reflect a relatively small set of common "core" cellular processes: each genetic locus affects one or a few core processes, and these core processes in turn determine the observed phenotypes. Here, we propose a method to infer such structure in genotype-phenotype data. Our approach, sparse structure discovery (SSD) is based on a penalized matrix decomposition designed to identify latent structure that is low-dimensional (many fewer core processes than phenotypes and genetic loci), locus-sparse (each locus affects few core processes), and/or phenotype-sparse (each phenotype is influenced by few core processes). Our use of sparsity as a guide in the matrix decomposition is motivated by the results of a novel empirical test indicating evidence of sparse structure in several recent genotype-phenotype datasets. First, we use synthetic data to show that our SSD approach can accurately recover core processes if each genetic locus affects few core processes or if each phenotype is affected by few core processes. Next, we apply the method to three datasets spanning adaptive mutations in yeast, genotoxin robustness assay in human cell lines, and genetic loci identified from a yeast cross, and evaluate the biological plausibility of the core process identified. More generally, we propose sparsity as a guiding prior for resolving latent structure in empirical genotype-phenotype maps.

Ccr4-Not ubiquitin ligase signaling regulates ribosomal protein homeostasis and inhibits 40S ribosomal autophagy

The Ccr4-Not complex containing the Not4 ubiquitin ligase regulates gene transcription and mRNA decay, yet it also has poorly defined roles in translation, proteostasis, and endolysosomal-dependent nutrient signaling. To define how Ccr4-Not mediated ubiquitin signaling regulates these additional processes, we performed quantitative proteomics in the yeast Saccharomyces cerevisiae lacking the Not4 ubiquitin ligase, and also in cells overexpressing either wild-type or functionally inactive ligase. Herein, we provide evidence that both increased and decreased Ccr4-Not ubiquitin signaling disrupts ribosomal protein (RP) homeostasis independently of reduced RP mRNA changes or reductions in known Not4 ribosomal substrates. Surprisingly, we also find that both Not4-mediated ubiquitin signaling, and the Ccr4 subunit, actively inhibit 40S ribosomal autophagy. This 40S autophagy is independent of canonical Atg7-dependent macroautophagy, thus indicating microautophagy activation is responsible. Furthermore, the Not4 ligase genetically interacts with endolysosomal pathway effectors to control both RP expression and 40S autophagy efficiency. Overall, we demonstrate that balanced Ccr4-Not ligase activity maintains RP homeostasis, and that Ccr4-Not ubiquitin signaling interacts with the endolysosomal pathway to both regulate RP expression and inhibit 40S ribosomal autophagy.

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.

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches.

Whole genome sequencing of Canadian Saccharomyces cerevisiae strains isolated from spontaneous wine fermentations reveals a new Pacific West Coast Wine clade

Vineyards in wine regions around the world are reservoirs of yeast with oenological potential. Saccharomyces cerevisiae ferments grape sugars to ethanol and generates flavor and aroma compounds in wine. Wineries place a high-value on identifying yeast native to their region to develop a region-specific wine program. Commercial wine strains are genetically very similar due to a population bottleneck and in-breeding compared to the diversity of S. cerevisiae from the wild and other industrial processes. We have isolated and microsatellite-typed hundreds of S. cerevisiae strains from spontaneous fermentations of grapes from the Okanagan Valley wine region in British Columbia, Canada. We chose 75 S. cerevisiae strains, based on our microsatellite clustering data, for whole genome sequencing using Illumina paired-end reads. Phylogenetic analysis shows that British Columbian S. cerevisiae strains cluster into 4 clades: Wine/European, Transpacific Oak, Beer 1/Mixed Origin, and a new clade that we have designated as Pacific West Coast Wine. The Pacific West Coast Wine clade has high nucleotide diversity and shares genomic characteristics with wild North American oak strains but also has gene flow from Wine/European and Ecuadorian clades. We analyzed gene copy number variations to find evidence of domestication and found that strains in the Wine/European and Pacific West Coast Wine clades have gene copy number variation reflective of adaptations to the wine-making environment. The "wine circle/Region B", a cluster of 5 genes acquired by horizontal gene transfer into the genome of commercial wine strains is also present in the majority of the British Columbian strains in the Wine/European clade but in a minority of the Pacific West Coast Wine clade strains. Previous studies have shown that S. cerevisiae strains isolated from Mediterranean Oak trees may be the living ancestors of European wine yeast strains. This study is the first to isolate S. cerevisiae strains with genetic similarity to nonvineyard North American Oak strains from spontaneous wine fermentations.

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.

Environmental challenge rewires functional connections among human genes

A fundamental question in biology is how a limited number of genes combinatorially govern cellular responses to environmental changes. While the prevailing hypothesis is that relationships between genes, processes, and ontologies could be plastic to achieve this adaptability, quantitatively comparing human gene functional connections between specific environmental conditions at scale is very challenging. Therefore, it remains unclear whether and how human genetic interaction networks are rewired in response to changing environmental conditions. Here, we developed a framework for mapping context-specific genetic interactions, enabling us to measure the plasticity of human genetic architecture upon environmental challenge for ~250,000 interactions, using cell cycle interruption, genotoxic perturbation, and nutrient deprivation as archetypes. We discover large-scale rewiring of human gene relationships across conditions, highlighted by dramatic shifts in the functional connections of epigenetic regulators (TIP60), cell cycle regulators (PP2A), and glycolysis metabolism. Our study demonstrates that upon environmental perturbation, intra-complex genetic rewiring is rare while inter-complex rewiring is common, suggesting a modular and flexible evolutionary genetic strategy that allows a limited number of human genes to enable adaptation to a large number of environmental conditions.

Lipid biosynthesis perturbation impairs endoplasmic reticulum-associated degradation

The relationship between lipid homeostasis and protein homeostasis (proteostasis) is complex and remains incompletely understood. We conducted a screen for genes required for efficient degradation of Deg1-Sec62, a model aberrant translocon-associated substrate of the endoplasmic reticulum (ER) ubiquitin ligase Hrd1, in Saccharomyces cerevisiae. This screen revealed that INO4 is required for efficient Deg1-Sec62 degradation. INO4 encodes one subunit of the Ino2/Ino4 heterodimeric transcription factor, which regulates expression of genes required for lipid biosynthesis. Deg1-Sec62 degradation was also impaired by mutation of genes encoding several enzymes mediating phospholipid and sterol biosynthesis. The degradation defect in ino4Δ yeast was rescued by supplementation with metabolites whose synthesis and uptake are mediated by Ino2/Ino4 targets. Stabilization of a panel of substrates of the Hrd1 and Doa10 ER ubiquitin ligases by INO4 deletion indicates ER protein quality control is generally sensitive to perturbed lipid homeostasis. Loss of INO4 sensitized yeast to proteotoxic stress, suggesting a broad requirement for lipid homeostasis in maintaining proteostasis. A better understanding of the dynamic relationship between lipid homeostasis and proteostasis may lead to improved understanding and treatment of several human diseases associated with altered lipid biosynthesis.

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.

Quantitative approaches to study phenotypic effects of large-scale genetic perturbations

How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in microbial system biology, which is also important from a public health perspective. Large-scale studies of gene-gene, gene-drug, and drug-drug interactions have proven to be powerful tools for elucidating gene function and functional modules in the cell. Approaches that systematically quantify phenotypes in libraries of microbial strains with genome-wide genetic perturbations are crucial for progress in this area. Here, we review recent advances in this field, and point out applications to the study of gene-drug interactions. We highlight newly developed techniques for the rapid generation of genome-wide mutant libraries and the high-throughput measurement of more complex phenotypes and other observables, such as cell morphology or thermal stability of the proteome.

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.