The chemical genomic portrait of yeast: uncovering a phenotype for all genes

Systematic characterization of gene function in the photosynthetic alga Chlamydomonas reinhardtii

Most genes in photosynthetic organisms remain functionally uncharacterized. Here, using a barcoded mutant library of the model eukaryotic alga Chlamydomonas reinhardtii, we determined the phenotypes of more than 58,000 mutants under more than 121 different environmental growth conditions and chemical treatments. A total of 59% of genes are represented by at least one mutant that showed a phenotype, providing clues to the functions of thousands of genes. Mutant phenotypic profiles place uncharacterized genes into functional pathways such as DNA repair, photosynthesis, the CO₂-concentrating mechanism and ciliogenesis. We illustrate the value of this resource by validating phenotypes and gene functions, including three new components of an actin cytoskeleton defense pathway. The data also inform phenotype discovery in land plants; mutants in Arabidopsis thaliana genes exhibit phenotypes similar to those we observed in their Chlamydomonas homologs. We anticipate that this resource will guide the functional characterization of genes across the tree of life.

Systematic phenotyping of 58,101 mutants of the model eukaryotic alga Chlamydomonas reinhardtii under 121 environmental and chemical stress conditions provides a large resource for characterizing gene function.

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

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

The cellular response to drug perturbation is limited: comparison of large-scale chemogenomic fitness signatures

Background

Chemogenomic profiling is a powerful approach for understanding the genome-wide cellular response to small molecules. First developed in Saccharomyces cerevisiae, chemogenomic screens provide direct, unbiased identification of drug target candidates as well as genes required for drug resistance. While many laboratories have performed chemogenomic fitness assays, few have been assessed for reproducibility and accuracy. Here we analyze the two largest independent yeast chemogenomic datasets comprising over 35 million gene-drug interactions and more than 6000 unique chemogenomic profiles; the first from our own academic laboratory (HIPLAB) and the second from the Novartis Institute of Biomedical Research (NIBR).

Results

Despite substantial differences in experimental and analytical pipelines, the combined datasets revealed robust chemogenomic response signatures, characterized by gene signatures, enrichment for biological processes and mechanisms of drug action. We previously reported that the cellular response to small molecules is limited and can be described by a network of 45 chemogenomic signatures. In the present study, we show that the majority of these signatures (66%) are also found in the companion dataset, providing further support for their biological relevance as conserved systems-level, small molecule response systems.

Conclusions

Our results demonstrate the robustness of chemogenomic fitness profiling in yeast, while offering guidelines for performing other high-dimensional comparisons including parallel CRISPR screens in mammalian cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08395-x.

The physiology and genetics of bacterial responses to antibiotic combinations

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.

A human kinase yeast array for the identification of kinases modulating phosphorylation-dependent protein-protein interactions

Protein kinases play an important role in cellular signaling pathways and their dysregulation leads to multiple diseases, making kinases prime drug targets. While more than 500 human protein kinases are known to collectively mediate phosphorylation of over 290,000 S/T/Y sites, the activities have been characterized only for a minor, intensively studied subset. To systematically address this discrepancy, we developed a human kinase array in Saccharomyces cerevisiae as a simple readout tool to systematically assess kinase activities. For this array, we expressed 266 human kinases in four different S. cerevisiae strains and profiled ectopic growth as a proxy for kinase activity across 33 conditions. More than half of the kinases showed an activity-dependent phenotype across many conditions and in more than one strain. We then employed the kinase array to identify the kinase(s) that can modulate protein-protein interactions (PPIs). Two characterized, phosphorylation-dependent PPIs with unknown kinase-substrate relationships were analyzed in a phospho-yeast two-hybrid assay. CK2α1 and SGK2 kinases can abrogate the interaction between the spliceosomal proteins AAR2 and PRPF8, and NEK6 kinase was found to mediate the estrogen receptor (ERα) interaction with 14-3-3 proteins. The human kinase yeast array can thus be used for a variety of kinase activity-dependent readouts.

Systematic identification of genes encoding cell surface and secreted proteins that are essential for in vitro growth and infection in Leishmania donovani

Leishmaniasis is an infectious disease caused by protozoan parasites belonging to the genus Leishmania for which there are no approved human vaccines. Infections localise to different tissues in a species-specific manner with the visceral form of the disease caused by Leishmania donovani and L. infantum being the most deadly in humans. Although Leishmania spp. parasites are predominantly intracellular, the visceral disease can be prevented in dogs by vaccinating with a complex mixture of secreted products from cultures of L. infantum promastigotes. With the logic that extracellular parasite proteins make good subunit vaccine candidates because they are directly accessible to vaccine-elicited host antibodies, here we attempt to discover proteins that are essential for in vitro growth and host infection with the goal of identifying subunit vaccine candidates. Using an in silico analysis of the Leishmania donovani genome, we identified 92 genes encoding proteins that are predicted to be secreted or externally anchored to the parasite membrane by a single transmembrane region or a GPI anchor. By selecting a transgenic L. donovani parasite that expresses both luciferase and the Cas9 nuclease, we systematically attempted to target all 92 genes by CRISPR genome editing and identified four that were required for in vitro growth. For fifty-five genes, we infected cohorts of mice with each mutant parasite and by longitudinally quantifying parasitaemia with bioluminescent imaging, showed that nine genes had evidence of an attenuated infection although all ultimately established an infection. Finally, we expressed two genes as full-length soluble recombinant proteins and tested them as subunit vaccine candidates in a murine preclinical infection model. Both proteins elicited significant levels of protection against the uncontrolled development of a splenic infection warranting further investigation as subunit vaccine candidates against this deadly infectious tropical disease.

Leishmaniasis is a parasitic infectious disease that is responsible for many tens of thousands of human deaths per year, primarily in impoverished parts of the world. Although there are drugs to treat this parasite infection, resistance is emerging and there are no approved human vaccines. Extracellular parasite proteins can make good vaccine targets because they are directly accessible to host antibodies; however, not all parasite surface proteins can elicit protective immune responses. With the goal of identifying new vaccine targets, we selected over 90 genes that encode parasite cell surface and secreted proteins and used the latest CRISPR gene editing technology to individually target them. Using these mutant parasites, we identified four genes required for parasite growth in the laboratory. We expressed two of the proteins as subunit vaccines and a preclinical infection model was used to determine if they could elicit protective immune responses. We found that two of our candidates were able to confer significant levels of protection in a murine model of visceral leishmaniasis. Our study will contribute to the search for a highly effective vaccine against visceral leishmaniasis to improve the lives of people living in some of the poorest regions on the planet.

Coordinated Regulation of Membrane Homeostasis and Drug Accumulation by Novel Kinase STK-17 in Response to Antifungal Azole Treatment

The emergence of antifungal resistance, especially to the most widely used azole class of ergosterol biosynthesis inhibitors, makes fungal infections difficult to treat in clinics and agriculture. When exposed to azoles, fungi can make adaptive responses to alleviate azole toxicity and produce azole tolerance. However, except for azole efflux pumps and ergosterol biosynthesis genes, the role of most azole responsive genes in azole resistance is unknown. In this study, STK-17, whose transcription is upregulated by azoles, was characterized as a novel kinase that is required for azole resistance. Deletion or dysfunction of STK-17 led to azole hypersensitivity in Neurospora crassa and to other ergosterol biosynthesis inhibitors such as amorolfine, terbinafine, and amphotericin B, but not fatty acid and ceramide biosynthesis inhibitors. STK-17 was also required for oxidative stress resistance, but this was not connected to azole resistance. RNA-seq results showed that stk-17 deletion affected the basal expression and the response to ketoconazole of some membrane protein genes, indicating functional association of STK-17 with the membrane. Notably, deletion of stk-17 affected the normal response to azoles of erg genes, including the azole target-encoding gene erg11, and erg2, erg6, and erg24, and led to abnormal accumulation of sterols in the presence of azoles. HPLC-MS/MS analysis revealed increased intracellular azole accumulation in the stk-17 mutant, possibly due to enhanced azole influx and reduced azole efflux that was independent of the major efflux pump CDR4. Importantly, STK-17 was widely distributed and functionally conserved among fungi, thus providing a potential antifungal target.

IMPORTANCE Antifungal resistance is increasing worldwide, especially to the most widely used azole class of ergosterol biosynthesis inhibitors, making control of fungal infections more challenging. A lot of effort has been expended in elucidating the mechanism of azole resistance and revealing potential antifungal targets. In this study, by analyzing azole-responsive genes in Neurospora crassa, we discovered STK-17, a novel kinase, that is required for azole resistance in several types of fungi. It has a role in regulating membrane homeostasis, responses to azole by ergosterol biosynthesis genes and azole accumulation, thus, deepening our understanding on the mechanism of azole stress response. Additionally, STK-17 is conserved among fungi and plays important roles in fungal development and stress resistance. Kinase inhibitors are broadly used for treating diseases, and our study pinpoints a potential drug target for antifungal development.

In rice splice variants that restore the reading frame after frameshifting indel introduction are common, often induced by the indels and sometimes lead to organism-level rescue

The introduction of frameshifting non-3n indels enables the identification of gene-trait associations. However, it has been hypothesised that recovery of the original reading frame owing to usage of non-canonical splice forms could cause rescue. To date there is very little evidence for organism-level rescue by such a mechanism and it is unknown how commonly indels induce, or are otherwise associated with, frame-restoring splice forms. We perform CRISPR/Cas9 editing of randomly selected loci in rice to investigate these issues. We find that the majority of loci have a frame-restoring isoform. Importantly, three quarters of these isoforms are not seen in the absence of the indels, consistent with indels commonly inducing novel isoforms. This is supported by analysis in the context of NMD knockdowns. We consider in detail the two top rescue candidates, in wax deficient anther 1 (wda1) and brittle culm (bc10), finding that organismal-level rescue in both cases is strong but owing to different splice modification routes. More generally, however, as frame-restoring isoforms are low abundance and possibly too disruptive, such rescue we suggest to be the rare exception, not the rule. Nonetheless, assuming that indels commonly induce frame-restoring isoforms, these results emphasize the need to examine RNA level effects of non-3n indels and suggest that multiple non-3n indels in any given gene are advisable to probe a gene’s trait associations.

As protein coding genes are read in units of three (codons), insertions or deletions (indels) that are not a multiple of three long (non 3n) are expected to be especially harmful. Whether they are is important both for interpreting the results of non-3n indel experiments to probe a gene’s functional importance and for diagnostics. Particularly enigmatic are incidences where some non-3n changes in a gene compromise phenotypes while other seemingly comparable ones do not. One explanation for the latter is that a non-3n indel might be rescued via a frame-restoring splice form. Here we examine this hypothesis by inducing non-3n indels in many genes in rice and find that many non-3n indels are associated with a splice form that restores the reading frame. In the majority of these cases the indel appears to induce the potential rescuing splice form. We examine two top hit cases in detail and show functional rescue by splice modification. More generally, the frame-restoring forms are, however, low abundance and probably result in compromised proteins. We conclude then that splice mediated rescue is possible, but probably uncommon. Nonetheless it should not be overlooked in experimental design and interpretation.

Adaptive laboratory evolution in S. cerevisiae highlights role of transcription factors in fungal xenobiotic resistance

In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae. Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn₂C₆ transcription factors YRR1 and YRM1 (p < 1 × 10⁻¹⁰⁰). This remarkable enrichment for transcription factors as drug resistance genes highlights their important role in the evolution of antifungal xenobiotic resistance and underscores the challenge to develop antifungal treatments that maintain potency.

Ottilie et al. employ an experimental evolution approach to investigate the role of transcription factors in yeast chemical resistance. Most emergent mutations in resistant strains were enriched in transcription factor coding genes, highlighting their importance in drug resistance.

A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments

A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human cell lines offer the largest opportunity to understand the biology of cell specificity. The prevailing viewpoint, synthetic lethality, occurs when a genetic alteration creates a unique CRISPR dependency. Here, we use machine learning for an unbiased investigation of cell type specificity. Quantifying model accuracy, we find that most cell type specific phenotypes are predicted by the function of related genes of wild-type sequence, not synthetic lethal relationships. These models then identify unexpected sets of 100-300 genes where reduced CRISPR measurements can produce genome-scale loss-of-function predictions across >18,000 genes. Thus, it is possible to reduce in vitro CRISPR libraries by orders of magnitude—with some information loss—when we remove redundant genes and not redundant sgRNAs.

Context specificity confounds genetic analysis and prevents reproducible genome engineering. Here, the authors report a pan-CRISPR analysis of specificity in mammalian cells and identify ultra-compact sgRNA subsets for genome-scale screens.

Functional profiling of long intergenic non-coding RNAs in fission yeast

Eukaryotic genomes express numerous long intergenic non-coding RNAs (lincRNAs) that do not overlap any coding genes. Some lincRNAs function in various aspects of gene regulation, but it is not clear in general to what extent lincRNAs contribute to the information flow from genotype to phenotype. To explore this question, we systematically analysed cellular roles of lincRNAs in Schizosaccharomyces pombe. Using seamless CRISPR/Cas9-based genome editing, we deleted 141 lincRNA genes to broadly phenotype these mutants, together with 238 diverse coding-gene mutants for functional context. We applied high-throughput colony-based assays to determine mutant growth and viability in benign conditions and in response to 145 different nutrient, drug, and stress conditions. These analyses uncovered phenotypes for 47.5% of the lincRNAs and 96% of the protein-coding genes. For 110 lincRNA mutants, we also performed high-throughput microscopy and flow cytometry assays, linking 37% of these lincRNAs with cell-size and/or cell-cycle control. With all assays combined, we detected phenotypes for 84 (59.6%) of all lincRNA deletion mutants tested. For complementary functional inference, we analysed colony growth of strains ectopically overexpressing 113 lincRNA genes under 47 different conditions. Of these overexpression strains, 102 (90.3%) showed altered growth under certain conditions. Clustering analyses provided further functional clues and relationships for some of the lincRNAs. These rich phenomics datasets associate lincRNA mutants with hundreds of phenotypes, indicating that most of the lincRNAs analysed exert cellular functions in specific environmental or physiological contexts. This study provides groundwork to further dissect the roles of these lincRNAs in the relevant conditions.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

Yeast Double Transporter Gene Deletion Library for Identification of Xenobiotic Carriers in Low or High Throughput

The routes of uptake and efflux should be considered when developing new drugs so that they can effectively address their intracellular targets. As a general rule, drugs appear to enter cells via protein carriers that normally carry nutrients or metabolites. A previously developed pipeline that searched for drug transporters using Saccharomyces cerevisiae mutants carrying single-gene deletions identified import routes for most compounds tested. However, due to the redundancy of transporter functions, we propose that this methodology can be improved by utilizing double mutant strains in both low- and high-throughput screens. We constructed a library of over 14,000 strains harboring double deletions of genes encoding 122 nonessential plasma membrane transporters and performed low- and high-throughput screens identifying possible drug import routes for 23 compounds. In addition, the high-throughput assay enabled the identification of putative efflux routes for 21 compounds. Focusing on azole antifungals, we were able to identify the involvement of the myo-inositol transporter, Itr1p, in the uptake of these molecules and to confirm the role of Pdr5p in their export.

Retromer Complex and PI3K Complex II-Related Genes Mediate the Yeast (Saccharomyces cerevisiae) Sodium Metabisulfite Resistance Response

Sodium metabisulfite (Na₂S₂O₅) is widely used as a preservative in the food and wine industry. However, it causes varying degrees of cellular damage to organisms. In order to improve our knowledge regarding its cyto-toxicity, a genome-wide screen using the yeast single deletion collection was performed. Additionally, a total of 162 Na₂S₂O₅-sensitive strains and 16 Na₂S₂O₅-tolerant strains were identified. Among the 162 Na₂S₂O₅ tolerance-related genes, the retromer complex was the top enriched cellular component. Further analysis demonstrated that retromer complex deletion leads to increased sensitivity to Na₂S₂O₅, and that Na₂S₂O₅ can induce mislocalization of retromer complex proteins. Notably, phosphatidylinositol 3-monophosphate kinase (PI3K) complex II, which is important for retromer recruitment to the endosome, might be a potential regulator mediating retromer localization and the yeast Na₂S₂O₅ tolerance response. Na₂S₂O₅ can decrease the protein expressions of Vps34, which is the component of PI3K complex. Therefore, Na₂S₂O₅-mediated retromer redistribution might be caused by the effects of decreased Vps34 expression levels. Moreover, both pharmaceutical inhibition of Vps34 functions and deletions of PI3K complex II-related genes affect cell tolerance to Na₂S₂O₅. The results of our study provide a global picture of cellular components required for Na₂S₂O₅ tolerance and advance our understanding concerning Na₂S₂O₅-induced cytotoxicity effects.

Transposon insertion site sequencing (TIS) of Pseudomonas aeruginosa

Transposon insertion site sequencing (TIS) is a technique that determines the insertion profile of a transposon mutant library by massive parallel sequencing of transposon-genomic DNA junctions. Because the transposon insertion profile reflects the abundance of each mutant in the library, it provides information to assess the fitness contribution of each genetic locus of a bacterial genome in a specific growth condition or strain background. Although introduced only about a dozen years ago, TIS has become an important tool in bacterial genetics that provides clues to study biological functions and regulatory mechanisms. Here, I describe a protocol for generating high density transposon insertion mutant libraries and preparing Illumina sequencing samples for mapping the transposon junctions of the transposon mutant libraries using Pseudomonas aeruginosa as an example.

Identifying essential genes across eukaryotes by machine learning

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

Functional genomics and metabolomics advance the ethnobotany of the Samoan traditional medicine "matalafi"

The leaf homogenate of Psychotria insularum is widely used in Samoan traditional medicine to treat inflammation associated with fever, body aches, swellings, wounds, elephantiasis, incontinence, skin infections, vomiting, respiratory infections, and abdominal distress. However, the bioactive components and underlying mechanisms of action are unknown. We used chemical genomic analyses in the model organism Saccharomyces cerevisiae (baker's yeast) to identify and characterize an iron homeostasis mechanism of action in the traditional medicine as an unfractionated entity to emulate its traditional use. Bioactivity-guided fractionation of the homogenate identified two flavonol glycosides, rutin and nicotiflorin, each binding iron in an ion-dependent molecular networking metabolomics analysis. Translating results to mammalian immune cells and traditional application, the iron chelator activity of the P. insularum homogenate or rutin decreased proinflammatory and enhanced anti-inflammatory cytokine responses in immune cells. Together, the synergistic power of combining traditional knowledge with chemical genomics, metabolomics, and bioassay-guided fractionation provided molecular insight into a relatively understudied Samoan traditional medicine and developed methodology to advance ethnobotany.

Wisdom of the crowds: A suggested polygenic plan for small-RNA-mediated regulation in bacteria

The omnigenic/polygenic theory, which states that complex traits are not shaped by single/few genes, but by situation-specific large networks, offers an explanation for a major enigma in microbiology: deletion of specific small RNAs (sRNAs) playing key roles in various aspects of bacterial physiology, including virulence and antibiotic resistance, results in surprisingly subtle phenotypes. A recent study uncovered polar accumulation of most sRNAs upon osmotic stress, the majority not known to be involved in the applied stress. Here we show that cells deleted for a handful of pole-enriched sRNAs exhibit fitness defect in several stress conditions, as opposed to single, double, or triple sRNA-knockouts, implying that regulation by sRNA relies on sets of genes. Moreover, analysis of RNA-seq data of Escherichia coli and Salmonella typhimurium exposed to antibiotics and/or infection-relevant conditions reveals the involvement of multiple sRNAs in all cases, in line with the existence of a polygenic plan for sRNA-mediated regulation.

Decoding 'Unnecessary Complexity': A Law of Complexity and a Concept of Hidden Variation Behind "Missing Heritability" in Precision Medicine

The high hopes for the Human Genome Project and personalized medicine were not met because the relationship between genotypes and phenotypes turned out to be more complex than expected. In a previous study we laid the foundation of a theory of complexity and showed that because of the blind nature of evolution, and molecular and historical contingency, cells have accumulated unnecessary complexity, complexity beyond what is necessary and sufficient to describe an organism. Here we provide empirical evidence and show that unnecessary complexity has become integrated into the genome in the form of redundancy and is relevant to molecular evolution of phenotypic complexity. Unnecessary complexity creates uncertainty between molecular and phenotypic complexity, such that phenotypic complexity (CP) is higher than molecular complexity (CM), which is higher than DNA complexity (CD). The qualitative inequality in complexity is based on the following hierarchy: CP > CM > CD. This law-like relationship holds true for all complex traits, including complex diseases. We present a hypothesis of two types of variation, namely open and closed (hidden) systems, show that hidden variation provides a hitherto undiscovered "third source" of phenotypic variation, beside genotype and environment, and argue that "missing heritability" for some complex diseases is likely to be a case of "diluted heritability". There is a need for radically new ways of thinking about the principles of genotype-phenotype relationship. Understanding how cells use hidden, pathway variation to respond to stress can shed light on why two individuals who share the same risk factors may not develop the same disease, or how cancer cells escape death.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.

Genome-wide association studies (GWAS) have identified many important loci for common diseases and other traits. However, the loci identified by these studies are almost always many steps away from an understanding of underlying biological mechanisms. Here we develop an approach using hierarchical biological knowledge to identify genes and pathways responsible for phenotypic traits. Variants identified by the new method could explain a substantially greater fraction of heritability than previously reported. Moreover, we identified mechanistic pathways by which each causal variant affects cellular function. For example, we find that sensitivity to hydroxyurea is tied to genetic variants in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. The new approach is a potentially transformative concept for understanding the genetic drivers of phenotypic variance, with potential applications in understanding traits in biomedicine and agriculture.

Antimicrobial Properties of Antidepressants and Antipsychotics-Possibilities and Implications

The spreading of antibiotic resistance is responsible annually for over 700,000 deaths worldwide, and the prevision is that this number will increase exponentially. The identification of new antimicrobial treatments is a challenge that requires scientists all over the world to collaborate. Developing new drugs is an extremely long and costly process, but it could be paralleled by drug repositioning. The latter aims at identifying new clinical targets of an “old” drug that has already been tested, approved, and even marketed. This approach is very intriguing as it could reduce costs and speed up approval timelines, since data from preclinical studies and on pharmacokinetics, pharmacodynamics, and toxicity are already available. Antidepressants and antipsychotics have been described to inhibit planktonic and sessile growth of different yeasts and bacteria. The main findings in the field are discussed in this critical review, along with the description of the possible microbial targets of these molecules. Considering their antimicrobial activity, the manuscript highlights important implications that the administration of antidepressants and antipsychotics may have on the gut microbiome.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast

Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with query mutants related with RNA metabolism and estimated the growth rates of about 700 000 double mutant Saccharomyces cerevisiae strains. The tested targets included the gene deletion collection and 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results, we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1471 genes and 117 biological processes. In addition to these large-scale results, we validated both enhancement and suppression of slow growth measured for specific RNA-related pathways. Thus, negative genetic interactions identified a role for the OCA inositol polyphosphate hydrolase complex in mRNA translation initiation. By analysis of suppressors, we found that Puf4, a Pumilio family RNA binding protein, inhibits ribosomal protein Rpl9 function, by acting on a conserved UGUAcauUA motif located downstream the stop codon of the RPL9B mRNA. Altogether, the results and their analysis should represent a useful resource for discovery of gene function in yeast.

A CRISPR Interference Screen of Essential Genes Reveals that Proteasome Regulation Dictates Acetic Acid Tolerance in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) is a powerful tool to study cellular physiology under different growth conditions, and this technology provides a means for screening changed expression of essential genes. In this study, a Saccharomyces cerevisiae CRISPRi library was screened for growth in medium supplemented with acetic acid. Acetic acid is a growth inhibitor challenging the use of yeast for the industrial conversion of lignocellulosic biomasses. Tolerance to acetic acid that is released during biomass hydrolysis is crucial for cell factories to be used in biorefineries. The CRISPRi library screened consists of >9,000 strains, where >98% of all essential and respiratory growth-essential genes were targeted with multiple guide RNAs (gRNAs). The screen was performed using the high-throughput, high-resolution Scan-o-matic platform, where each strain is analyzed separately. Our study identified that CRISPRi targeting of genes involved in vesicle formation or organelle transport processes led to severe growth inhibition during acetic acid stress, emphasizing the importance of these intracellular membrane structures in maintaining cell vitality. In contrast, strains in which genes encoding subunits of the 19S regulatory particle of the 26S proteasome were downregulated had increased tolerance to acetic acid, which we hypothesize is due to ATP salvage through an increased abundance of the 20S core particle that performs ATP-independent protein degradation. This is the first study where high-resolution CRISPRi library screening paves the way to understanding and bioengineering the robustness of yeast against acetic acid stress.

IMPORTANCE Acetic acid is inhibitory to the growth of the yeast Saccharomyces cerevisiae, causing ATP starvation and oxidative stress, which leads to the suboptimal production of fuels and chemicals from lignocellulosic biomass. In this study, where each strain of a CRISPRi library was characterized individually, many essential and respiratory growth-essential genes that regulate tolerance to acetic acid were identified, providing a new understanding of the stress response of yeast and new targets for the bioengineering of industrial yeast. Our findings on the fine-tuning of the expression of proteasomal genes leading to increased tolerance to acetic acid suggest that this could be a novel strategy for increasing stress tolerance, leading to improved strains for the production of biobased chemicals.

Degree of Functional Divergence in Duplicates Is Associated with Distinct Roles in Plant Evolution

Gene duplication is a major mechanism to create new genes. After gene duplication, some duplicated genes undergo functionalization, whereas others largely maintain redundant functions. Duplicated genes comprise various degrees of functional diversification in plants. However, the evolutionary fate of high and low diversified duplicates is unclear at genomic scale. To infer high and low diversified duplicates in Arabidopsis thaliana genome, we generated a prediction method for predicting whether a pair of duplicate genes was subjected to high or low diversification based on the phenotypes of knock-out mutants. Among 4,017 pairs of recently duplicated A. thaliana genes, 1,052 and 600 are high and low diversified duplicate pairs, respectively. The predictions were validated based on the phenotypes of generated knock-down transgenic plants. We determined that the high diversified duplicates resulting from tandem duplications tend to have lineage-specific functions, whereas the low diversified duplicates produced by whole-genome duplications are related to essential signaling pathways. To assess the evolutionary impact of high and low diversified duplicates in closely related species, we compared the retention rates and selection pressures on the orthologs of A. thaliana duplicates in two closely related species. Interestingly, high diversified duplicates resulting from tandem duplications tend to be retained in multiple lineages under positive selection. Low diversified duplicates by whole-genome duplications tend to be retained in multiple lineages under purifying selection. Taken together, the functional diversities determined by different duplication mechanisms had distinct effects on plant evolution.

Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening in Saccharomyces cerevisiae

Since the earliest days of using natural remedies, combining therapies for disease treatment has been standard practice. Combination treatments exhibit synergistic effects, broadly defined as a greater-than-additive effect of two or more therapeutic agents. Clinicians often use their experience and expertise to tailor such combinations to maximize the therapeutic effect. Although understanding and predicting biophysical underpinnings of synergy have benefitted from high-throughput screening and computational studies, one challenge is how to best design and analyze the results of synergy studies, especially because the number of possible combinations to test quickly becomes unmanageable. Nevertheless, the benefits of such studies are clear-by combining multiple drugs in the treatment of infectious disease and cancer, for instance, one can lessen host toxicity and simultaneously reduce the likelihood of resistance to treatment. This study introduces a new approach to characterize drug synergy, in which we extend the widely validated chemogenomic HIP-HOP assay to drug combinations; this assay involves parallel screening of comprehensive collections of barcoded deletion mutants. We identify a class of "combination-specific sensitive strains" that introduces mechanisms for the synergies we observe and further suggest focused follow-up studies.

Cellular Darwinism: Regulatory networks, stochasticity, and selection in cancer development

There are strong parallels between the evolutionary origin of species within populations of organisms and new concepts for the origin of cancers within cell populations in the tissues of the body. The analogy is that cancers can be regarded as a new somatic species developing within the host organism. In both cases, understanding the processes involved requires a multi-scale analysis, including higher-level control of genetic and epigenetic changes. A key to developing successful therapeutic strategies will be to identify the processes that control heterogeneity in tissues. These include processes outside the currently dominant theory of evolution, i.e. the Modern Synthesis. Specifically, organisms can partially direct both genetic and epigenetic changes through the harnessing of chance. The loci and rates of mutation and of genome reorganisation are forms of targeted functional reorganisation of genomes. They are more likely to result in functional reorganisations compared to the slow accumulation of point mutations.

CRISPR screens in physiologic medium reveal conditionally essential genes in human cells

Forward genetic screens across hundreds of cancer cell lines have started to define the genetic dependencies of proliferating human cells and how these vary by genotype and lineage. Most screens, however, have been carried out in culture media that poorly reflect metabolite availability in human blood. Here, we performed CRISPR-based screens in traditional versus human plasma-like medium (HPLM). Sets of conditionally essential genes in human cancer cell lines span several cellular processes and vary with both natural cell-intrinsic diversity and the combination of basal and serum components that comprise typical media. Notably, we traced the causes for each of three conditional CRISPR phenotypes to the availability of metabolites uniquely defined in HPLM versus conventional media. Our findings reveal the profound impact of medium composition on gene essentiality in human cells, and also suggest general strategies for using genetic screens in HPLM to uncover new cancer vulnerabilities and gene-nutrient interactions.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Protein aggregation and the evolution of stress resistance in clinical yeast

Protein aggregation, particularly in its prion-like form, has long been thought to be detrimental. However, recent studies have identified multiple instances where protein aggregation is important for normal physiological functions. Combining mass spectrometry and cell biological approaches, we developed a strategy for the identification of protein aggregates in cell lysates. We used this approach to characterize prion-based traits in pathogenic strains of the yeast Saccharomyces cerevisiae isolated from immunocompromised human patients. The proteins that we found, including the metabolic enzyme Cdc19, the translation elongation factor Yef3 and the fibrillarin homologue Nop1, are known to assemble under certain physiological conditions. Yet, such assemblies have not been reported to be stable or heritable. Our data suggest that some proteins which aggregate in response to stress have the capacity to acquire diverse assembled states, certain ones of which can be propagated across generations in a form of protein-based epigenetics. This article is part of the theme issue 'How does epigenetics influence the course of evolution?'

Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS

Oxidative phosphorylation (OXPHOS) and glycolysis are the two major pathways for ATP production. The reliance on each varies across tissues and cell states, and can influence susceptibility to disease. At present, the full set of molecular mechanisms governing the relative expression and balance of these two pathways is unknown. Here, we focus on genes whose loss leads to an increase in OXPHOS activity. Unexpectedly, this class of genes is enriched for components of the pre-mRNA splicing machinery, in particular for subunits of the U1 snRNP. Among them, we show that LUC7L2 represses OXPHOS and promotes glycolysis by multiple mechanisms, including (1) splicing of the glycolytic enzyme PFKM to suppress glycogen synthesis, (2) splicing of the cystine/glutamate antiporter SLC7A11 (xCT) to suppress glutamate oxidation, and (3) secondary repression of mitochondrial respiratory supercomplex formation. Our results connect LUC7L2 expression and, more generally, the U1 snRNP to cellular energy metabolism.

Complex interactions of lovastatin with 10 chemotherapeutic drugs: a rigorous evaluation of synergism and antagonism

Background

Evidence bearing on the role of statins in the prevention and treatment of cancer is confounded by the diversity of statins, chemotherapeutic agents and cancer types included in the numerous published studies; consequently, the adjunctive value of statins with chemotherapy remains uncertain.

Methods

We assayed lovastatin in combination with each of ten commonly prescribed chemotherapy drugs in highly reproducible in vitro assays, using a neutral cellular substrate, Saccharomyces cerevisiae. Cell density (OD₆₀₀) data were analyzed for synergism and antagonism using the Loewe additivity model implemented with the Combenefit software.

Results

Four of the ten chemotherapy drugs – tamoxifen, doxorubicin, methotrexate and rapamycin – exhibited net synergism with lovastatin. The remaining six agents (5-fluorouracil, gemcitabine, epothilone, cisplatin, cyclophosphamide and etoposide) compiled neutral or antagonistic scores. Distinctive patterns of synergism and antagonism, often coexisting within the same concentration space, were documented with the various combinations, including those with net synergism scores. Two drug pairs, lovastatin combined with tamoxifen or cisplatin, were also assayed in human cell lines as proof of principle.

Conclusions

The synergistic interactions of tamoxifen, doxorubicin, methotrexate and rapamycin with lovastatin – because they suggest the possibility of clinical utility - merit further exploration and validation in cell lines and animal models. No less importantly, strong antagonistic interactions between certain agents and lovastatin argue for a cautious, data-driven approach before adding a statin to any chemotherapeutic regimen. We also urge awareness of adventitious statin usage by patients entering cancer treatment protocols.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07963-w.

FIREWORKS: a bottom-up approach to integrative coessentiality network analysis

Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug-gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of "undruggable" proteins, and context-specific rewiring of genetic networks.

Massively parallel assessment of human variants with base editor screens

Understanding the functional consequences of single-nucleotide variants is critical to uncovering the genetic underpinnings of diseases, but technologies to characterize variants are limiting. Here, we leverage CRISPR-Cas9 cytosine base editors in pooled screens to scalably assay variants at endogenous loci in mammalian cells. We benchmark the performance of base editors in positive and negative selection screens, identifying known loss-of-function mutations in BRCA1 and BRCA2 with high precision. To demonstrate the utility of base editor screens to probe small molecule-protein interactions, we screen against BH3 mimetics and PARP inhibitors, identifying point mutations that confer drug sensitivity or resistance. We also create a library of single guide RNAs (sgRNAs) predicted to generate 52,034 ClinVar variants in 3,584 genes and conduct screens in the presence of cellular stressors, identifying loss-of-function variants in numerous DNA damage repair genes. We anticipate that this screening approach will be broadly useful to readily and scalably functionalize genetic variants.

Responses of the Necrotrophic Fungus Alternaria brassisicola to the Indolic Phytoalexin Brassinin

Alternaria brassicicola causes black spot disease in Brassicaceae. During host infection, this necrotrophic fungus is exposed to various antimicrobial compounds, such as the phytoalexin brassinin which is produced by many cultivated Brassica species. To investigate the cellular mechanisms by which this compound causes toxicity and the corresponding fungal adaptive strategies, we first analyzed fungal transcriptional responses to short-term exposure to brassinin and then used additional functional approaches. This study supports the hypothesis that indolic phytoalexin primarily targets mitochondrial functions in fungal cells. Indeed, we notably observed that phytoalexin treatment of A. brassicicola disrupted the mitochondrial membrane potential and resulted in a significant and rapid decrease in the oxygen consumption rates. Secondary effects, such as Reactive oxygen species production, changes in lipid and endoplasmic reticulum homeostasis were then found to be induced. Consequently, the fungus has to adapt its metabolism to protect itself against the toxic effects of these molecules, especially via the activation of high osmolarity glycerol and cell wall integrity signaling pathways and by induction of the unfolded protein response.

A three-dimensional habitat for C. elegans environmental enrichment

As we learn more about the importance of gene-environment interactions and the effects of environmental enrichment, it becomes evident that minimalistic laboratory conditions can affect gene expression patterns and behaviors of model organisms. In the laboratory, Caenorhabditis elegans is generally cultured on two-dimensional, homogeneous agar plates abundantly covered with axenic bacteria culture as a food source. However, in the wild, this nematode thrives in rotting fruits and plant stems feeding on bacteria and small eukaryotes. This contrast in habitat complexity suggests that studying C. elegans in enriched laboratory conditions can deepen our understanding of its fundamental traits and behaviors. Here, we developed a protocol to create three-dimensional habitable scaffolds for trans-generational culture of C. elegans in the laboratory. Using decellularization and sterilization of fruit tissue, we created an axenic environment that can be navigated throughout and where the microbial environment can be strictly controlled. C. elegans were maintained over generations on this habitat, and showed a clear behavioral bias for the enriched environment. As an initial assessment of behavioral variations, we found that dauer populations in scaffolds exhibit high-frequency, complex nictation behavior including group towering and jumping behavior.

Biochemical and genetic analysis of Ecm14, a conserved fungal pseudopeptidase

Background

Like most major enzyme families, the M14 family of metallocarboxypeptidases (MCPs) contains a number of pseudoenzymes predicted to lack enzyme activity and with poorly characterized molecular function. The genome of the yeast Saccharomyces cerevisiae encodes one member of the M14 MCP family, a pseudoenzyme named Ecm14 proposed to function in the extracellular matrix. In order to better understand the function of such pseudoenzymes, we studied the structure and function of Ecm14 in S. cerevisiae.

Results

A phylogenetic analysis of Ecm14 in fungi found it to be conserved throughout the ascomycete phylum, with a group of related pseudoenzymes found in basidiomycetes. To investigate the structure and function of this conserved protein, His6-tagged Ecm14 was overexpressed in Sf9 cells and purified. The prodomain of Ecm14 was cleaved in vivo and in vitro by endopeptidases, suggesting an activation mechanism; however, no activity was detectable using standard carboxypeptidase substrates. In order to determine the function of Ecm14 using an unbiased screen, we undertook a synthetic lethal assay. Upon screening approximately 27,000 yeast colonies, twenty-two putative synthetic lethal clones were identified. Further analysis showed many to be synthetic lethal with auxotrophic marker genes and requiring multiple mutations, suggesting that there are few, if any, single S. cerevisiae genes that present synthetic lethal interactions with ecm14Δ.

Conclusions

We show in this study that Ecm14, although lacking detectable enzyme activity, is a conserved carboxypeptidase-like protein that is secreted from cells and is processed to a mature form by the action of an endopeptidase. Our study and datasets from other recent large-scale screens suggest a role for Ecm14 in processes such as vesicle-mediated transport and aggregate invasion, a fungal process that has been selected against in modern laboratory strains of S. cerevisiae.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12860-020-00330-w.

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq

Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells.

Identifying chemical-genetic interactions in mammalian cells is limited to low-throughput or computational methods. Here, the authors present QMAP-Seq, a broadly accessible and scalable approach that uses NGS for pooled high-throughput chemical-genetic profiling in mammalian cells.

Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens

BACKGROUND

Pooled library CRISPR/Cas9 knockout screening across hundreds of cell lines has identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens is low compared to the number of constitutively expressed genes in a cell.

RESULTS

Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observe that half of all constitutively expressed genes are never detected in any CRISPR screen and that these never-essentials are highly enriched for paralogs. We investigated functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening in three cell lines. We observe 24 synthetic lethal paralog pairs that have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions are present in at least two out of three cell lines and 14 of 24 (58%) are present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes.

CONCLUSIONS

Together, these observations strongly suggest that functionally redundant paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes in monogenic CRISPR-based loss of function screens.

Deep Collaborative Filtering for Prediction of Disease Genes

Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve such problems. Inductive Matrix Completion (IMC) is one of the most reliable models for its well-established framework and its superior performance in predicting gene-disease associations. However, the IMC method does not hierarchically extract deep features, which might limit the quality of recovery. In this case, the architecture of deep learning, which obtains high-level representations and handles noises and outliers presented in large-scale biological datasets, is introduced into the side information of genes in our Deep Collaborative Filtering (DCF) model. Further, for lack of negative examples, we also exploit Positive-Unlabeled (PU) learning formulation to low-rank matrix completion. Our approach achieves substantially improved performance over other state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database. Our approach is 10 percent more efficient than standard IMC in detecting a true association, and significantly outperforms other alternatives in terms of the precision-recall metric at the top-k predictions. Moreover, we also validate the disease with no previously known gene associations and newly reported OMIM associations. The experimental results show that DCF is still satisfactory for ranking novel disease phenotypes as well as mining unexplored relationships. The source code and the data are available at https://github.com/xzenglab/DCF.

Advances in fungal chemical genomics for the discovery of new antifungal agents

Invasive fungal infections have escalated from a rare curiosity to a major cause of human mortality around the globe. This is in part due to a scarcity in the number of antifungal drugs available to combat mycotic disease, making the discovery of novel bioactive compounds and determining their mode of action of utmost importance. The development and application of chemical genomic assays using the model yeast Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of diverse molecules in a living cell. Furthermore, complementary assays are continually being developed in fungal pathogens, most notably Candida albicans and Cryptococcus neoformans, to elucidate compound mechanism of action directly in the pathogen of interest. Collectively, the suite of chemical genetic assays that have been developed in multiple fungal species enables the identification of candidate drug target genes, as well as genes involved in buffering drug target pathways, and genes involved in general cellular responses to small molecules. In this review, we examine current yeast chemical genomic assays and highlight how such resources provide powerful tools that can be utilized to bolster the antifungal pipeline.

Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples

We give results from a detailed analysis of human Ribosomal Protein (RP) levels in normal and cancer samples and cell lines from large mRNA, copy number variation and ribosome profiling datasets. After normalizing total RP mRNA levels per sample, we find highly consistent tissue specific RP mRNA signatures in normal and tumor samples. Multiple RP mRNA-subtypes exist in several cancers, with significant survival and genomic differences. Some RP mRNA variations among subtypes correlate with copy number loss of RP genes. In kidney cancer, RP subtypes map to molecular subtypes related to cell-of-origin. Pan-cancer analysis of TCGA data showed widespread single/double copy loss of RP genes, without significantly affecting survival. In several cancer cell lines, CRISPR-Cas9 knockout of RP genes did not affect cell viability. Matched RP ribosome profiling and mRNA data in humans and rodents stratified by tissue and development stage and were strongly correlated, showing that RP translation rates were proportional to mRNA levels. In a small dataset of human adult and fetal tissues, RP protein levels showed development stage and tissue specific heterogeneity of RP levels. Our results suggest that heterogeneous RP levels play a significant functional role in cellular physiology, in both normal and disease states.

Essential Gene Profiles for Human Pluripotent Stem Cells Identify Uncharacterized Genes and Substrate Dependencies

Human pluripotent stem cells (hPSCs) provide an invaluable tool for modeling diseases and hold promise for regenerative medicine. For understanding pluripotency and lineage differentiation mechanisms, a critical first step involves systematically cataloging essential genes (EGs) that are indispensable for hPSC fitness, defined as cell reproduction in this study. To map essential genetic determinants of hPSC fitness, we performed genome-scale loss-of-function screens in an inducible Cas9 H1 hPSC line cultured on feeder cells and laminin to identify EGs. Among these, we found FOXH1 and VENTX, genes that encode transcription factors previously implicated in stem cell biology, as well as an uncharacterized gene, C22orf43/DRICH1. hPSC EGs are substantially different from other human model cell lines, and EGs in hPSCs are highly context dependent with respect to different growth substrates. Our CRISPR screens establish parameters for genome-wide screens in hPSCs, which will facilitate the characterization of unappreciated genetic regulators of hPSC biology.

Target identification of natural medicine with chemical proteomics approach: probe synthesis, target fishing and protein identification

Natural products are an important source of new drugs for the treatment of various diseases. However, developing natural product-based new medicines through random moiety modification is a lengthy and costly process, due in part to the difficulties associated with comprehensively understanding the mechanism of action and the side effects. Identifying the protein targets of natural products is an effective strategy, but most medicines interact with multiple protein targets, which complicate this process. In recent years, an increasing number of researchers have begun to screen the target proteins of natural products with chemical proteomics approaches, which can provide a more comprehensive array of the protein targets of active small molecules in an unbiased manner. Typically, chemical proteomics experiments for target identification consist of two key steps: (1) chemical probe design and synthesis and (2) target fishing and identification. In recent decades, five different types of chemical proteomic probes and their respective target fishing methods have been developed to screen targets of molecules with different structures, and a variety of protein identification approaches have been invented. Presently, we will classify these chemical proteomics approaches, the application scopes and characteristics of the different types of chemical probes, the different protein identification methods, and the advantages and disadvantages of these strategies.

Systems Biochemistry Approaches to Defining Mitochondrial Protein Function

Defining functions for the full complement of proteins is a grand challenge in the post-genomic era and is essential for our understanding of basic biology and disease pathogenesis. In recent times, this endeavor has benefitted from a combination of modern large-scale and classical reductionist approaches-a process we refer to as "systems biochemistry"-that helps surmount traditional barriers to the characterization of poorly understood proteins. This strategy is proving to be particularly effective for mitochondria, whose well-defined proteome has enabled comprehensive analyses of the full mitochondrial system that can position understudied proteins for fruitful mechanistic investigations. Recent systems biochemistry approaches have accelerated the identification of new disease-related mitochondrial proteins and of long-sought "missing" proteins that fulfill key functions. Collectively, these studies are moving us toward a more complete understanding of mitochondrial activities and providing a molecular framework for the investigation of mitochondrial pathogenesis.