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

Climate warming promotes collateral antibiotic resistance development in cyanobacteria

Both cyanobacterial blooms and antibiotic resistance have aggravated worldwide and posed a great threat to public health in recent years. As a significant source and reservoir of water environmental resistome, cyanobacteria exhibit confusing discrepancy between their reduced susceptibility and their chronic exposure to antibiotic mixtures at sub-inhibitory concentrations. How the increasing temperature affects the adaptive evolution of cyanobacteria-associated antibiotic resistance in response to low-level antibiotic combinations under climate change remains unclear. Here we profiled the antibiotic interaction and collateral susceptibility networks among 33 commonly detected antibiotics in 600 cyanobacterial strains isolated from 50 sites across four eutrophicated lakes in China. Cyanobacteria-associated antibiotic resistance level was found positively correlated to antibiotic heterogeneity across all sites. Among 528 antibiotic combinations, antagonism was observed for 62 % interactions and highly conserved within cyanobacterial species. Collateral resistance was detected in 78.5 % of pairwise antibiotic interaction, leading to a widened or shifted upwards mutant selection window for increased opportunity of acquiring second-step mutations. We quantified the interactive promoting effect of collateral resistance and increasing temperature on the evolution of both phenotypic and genotypic cyanobacteria-associated resistance under chronic exposure to environmental level of antibiotic combinations. With temperature increasing from 16 °C to 36 °C, the evolvability index and genotypic resistance level increased by 1.25 - 2.5 folds and 3 - 295 folds in the collateral-resistance-informed lineages, respectively. Emergence of resistance mutation pioneered by tolerance, which was jointly driven by mutation rate and persister fraction, was found to be accelerated by increased temperature and antibiotic switching rate. Our findings provided mechanic insights into the boosting effect of climate warming on the emergence and development of cyanobacteria-associated resistance against collateral antibiotic phenotypes.

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.

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Prime Editing and DNA Repair System: Balancing Efficiency with Safety

Prime editing (PE), a recent progression in CRISPR-based technologies, holds promise for precise genome editing without the risks associated with double-strand breaks. It can introduce a wide range of changes, including single-nucleotide variants, insertions, and small deletions. Despite these advancements, there is a need for further optimization to overcome certain limitations to increase efficiency. One such approach to enhance PE efficiency involves the inhibition of the DNA mismatch repair (MMR) system, specifically MLH1. The rationale behind this approach lies in the MMR system's role in correcting mismatched nucleotides during DNA replication. Inhibiting this repair pathway creates a window of opportunity for the PE machinery to incorporate the desired edits before permanent DNA repair actions. However, as the MMR system plays a crucial role in various cellular processes, it is important to consider the potential risks associated with manipulating this system. The new versions of PE with enhanced efficiency while blocking MLH1 are called PE4 and PE5. Here, we explore the potential risks associated with manipulating the MMR system. We pay special attention to the possible implications for human health, particularly the development of cancer.

GPSFun: geometry-aware protein sequence function predictions with language models

Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSite and SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locations and protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.

Hypothesis: evidence that the PRS gene products of Saccharomyces cerevisiae support both PRPP synthesis and maintenance of cell wall integrity

The gene products of PRS1-PRS5 in Saccharomyces cerevisiae are responsible for the production of PRPP (5-phospho-D-ribosyl-α-1-pyrophosphate). However, it has been demonstrated that they are also involved in the cell wall integrity (CWI) signalling pathway as shown by protein-protein interactions (PPIs) with, for example Slt2, the MAP kinase of the CWI pathway. The following databases: SGD, BioGRID and Hit Predict, which collate PPIs from various research papers, have been scrutinized for evidence of PPIs between Prs1-Prs5 and components of the CWI pathway. The level of certainty in PPIs was verified by interaction scores available in the Hit Predict database revealing that well-documented interactions correspond with higher interaction scores and can be graded as high confidence interactions based on a score > 0.28, an annotation score ≥ 0.5 and a method-based high confidence score level of  ≥ 0.485. Each of the Prs1-Prs5 polypeptides shows some degree of interaction with the CWI pathway. However, Prs5 has a vital role in the expression of FKS2 and Rlm1, previously only documented by reporter assay studies. This report emphasizes the importance of investigating interactions using more than one approach since every method has its limitations and the use of different methods, as described herein, provides complementary experimental and statistical data, thereby corroborating PPIs. Since the experimental data described so far are consistent with a link between PRPP synthetase and the CWI pathway, our aim was to demonstrate that these data are also supported by high-throughput bioinformatic analyses promoting our hypothesis that two of the five PRS-encoding genes contain information required for the maintenance of CWI by combining data from our targeted approach with relevant, unbiased data from high-throughput analyses.

Identification of candidate biomarkers for GBM based on WGCNA

Glioblastoma multiforme (GBM), the most aggressive form of primary brain tumor, poses a considerable challenge in neuro-oncology. Despite advancements in therapeutic approaches, the prognosis for GBM patients remains bleak, primarily attributed to its inherent resistance to conventional treatments and a high recurrence rate. The primary goal of this study was to acquire molecular insights into GBM by constructing a gene co-expression network, aiming to identify and predict key genes and signaling pathways associated with this challenging condition. To investigate differentially expressed genes between various grades of Glioblastoma (GBM), we employed Weighted Gene Co-expression Network Analysis (WGCNA) methodology. Through this approach, we were able to identify modules with specific expression patterns in GBM. Next, genes from these modules were performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using ClusterProfiler package. Our findings revealed a negative correlation between biological processes associated with neuronal development and functioning and GBM. Conversely, the processes related to the cell cycle, glomerular development, and ECM-receptor interaction exhibited a positive correlation with GBM. Subsequently, hub genes, including SYP, TYROBP, and ANXA5, were identified. This study offers a comprehensive overview of the existing research landscape on GBM, underscoring the challenges encountered by clinicians and researchers in devising effective therapeutic strategies.

Mapping of DDX11 genetic interactions defines sister chromatid cohesion as the major dependency

DDX11/Chl1R is a conserved DNA helicase with roles in genome maintenance, DNA replication, and chromatid cohesion. Loss of DDX11 in humans leads to the rare cohesinopathy Warsaw breakage syndrome. DDX11 has also been implicated in human cancer where it has been proposed to have an oncogenic role and possibly to constitute a therapeutic target. Given the multiple roles of DDX11 in genome stability and its potential as an anticancer target, we set out to define a complete genetic interaction profile of DDX11 loss in human cell lines. Screening the human genome with clustered regularly interspaced short palindromic repeats (CRISPR) guide RNA drop out screens in DDX11-wildtype (WT) or DDX11-deficient cells revealed a strong enrichment of genes with functions related to sister chromatid cohesion. We confirm synthetic lethal relationships between DDX11 and the tumor suppressor cohesin subunit STAG2, which is frequently mutated in several cancer types and the kinase HASPIN. This screen highlights the importance of cohesion in cells lacking DDX11 and suggests DDX11 may be a therapeutic target for tumors with mutations in STAG2.

Set2 and H3K36 regulate the Drosophila male X chromosome in a context-specific manner, independent from MSL complex spreading

Dosage compensation in Drosophila involves upregulating male X-genes two-fold. This process is carried out by the MSL (male-specific lethal) complex, which binds high-affinity sites and spreads to surrounding genes. Current models of MSL spreading focus on interactions of MSL3 (male-specific lethal 3) with histone marks; in particular, Set2- dependent H3 lysine-36 trimethylation (H3K36me3). However, Set2 might affect DC via another target, or there could be redundancy between canonical H3.2 and variant H3.3 histones. Further, it is difficult to parse male-specific effects from those that are simply X- specific. To discriminate among these possibilities, we employed genomic approaches in H3K36 (residue) and Set2 (writer) mutants. The results confirm a role for Set2 in X-gene regulation, but show that expression trends in males are often mirrored in females. Instead of global male-specific reduction of X-genes in Set2/H3K36 mutants, the effects were heterogeneous. We identified cohorts of genes whose expression was significantly altered following loss of H3K36 or Set2, but the changes were in opposite directions, suggesting that H3K36me states have reciprocal functions. In contrast to H4 K16R controls, analysis of combined H3.2 K36R /H3.3 K36R mutants neither showed consistent reduction in X-gene expression, nor any correlation with MSL3 binding. Examination of other developmental stages/tissues revealed additional layers of context-dependence. Our studies implicate BEAF-32 and other insulator proteins in Set2/H3K36-dependent regulation. Overall, the data are inconsistent with the prevailing model wherein H3K36me3 directly recruits the MSL complex. We propose that Set2 and H3K36 support DC indirectly, via processes that are utilized by MSL but common to both sexes.

BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

Genome-wide host-pathway interactions affecting cis-cis-muconic acid production in yeast

The success of forward metabolic engineering depends on a thorough understanding of the behaviour of a heterologous metabolic pathway within its host. We have recently described CRI-SPA, a high-throughput gene editing method enabling the delivery of a metabolic pathway to all strains of the Saccharomyces cerevisiae knock-out library. CRI-SPA systematically quantifies the effect of each modified gene present in the library on product synthesis, providing a complete map of host:pathway interactions. In its first version, CRI-SPA relied on the colour of the product betaxanthins to quantify strains synthesis ability. However, only a few compounds produce a visible or fluorescent phenotype limiting the scope of our approach. Here, we adapt CRI-SPA to onboard a biosensor reporting the interactions between host genes and the synthesis of the colourless product cis-cis-muconic acid (CCM). We phenotype >9,000 genotypes, including both gene knock-out and overexpression, by quantifying the fluorescence of yeast colonies growing in high-density agar arrays. We identify novel metabolic targets belonging to a broad range of cellular functions and confirm their positive impact on CCM biosynthesis. In particular, our data suggests a new interplay between CCM biosynthesis and cytosolic redox through their common interaction with the oxidative pentose phosphate pathway. Our genome-wide exploration of host:pathway interaction opens novel strategies for improved production of CCM in yeast cell factories.

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.

PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website ( https://thecellvision.org/pifia/ ), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

An evolutionarily conserved phosphoserine-arginine salt bridge in the interface between ribosomal proteins uS4 and uS5 regulates translational accuracy in Saccharomyces cerevisiae

Protein-protein and protein-rRNA interactions at the interface between ribosomal proteins uS4 and uS5 are thought to maintain the accuracy of protein synthesis by increasing selection of cognate aminoacyl-tRNAs. Selection involves a major conformational change-domain closure-that stabilizes aminoacyl-tRNA in the ribosomal acceptor (A) site. This has been thought a constitutive function of the ribosome ensuring consistent accuracy. Recently, the Saccharomyces cerevisiae Ctk1 cyclin-dependent kinase was demonstrated to ensure translational accuracy and Ser238 of uS5 proposed as its target. Surprisingly, Ser238 is outside the uS4-uS5 interface and no obvious mechanism has been proposed to explain its role. We show that the true target of Ctk1 regulation is another uS5 residue, Ser176, which lies in the interface opposite to Arg57 of uS4. Based on site specific mutagenesis, we propose that phospho-Ser176 forms a salt bridge with Arg57, which should increase selectivity by strengthening the interface. Genetic data show that Ctk1 regulates accuracy indirectly; the data suggest that the kinase Ypk2 directly phosphorylates Ser176. A second kinase pathway involving TORC1 and Pkc1 can inhibit this effect. The level of accuracy appears to depend on competitive action of these two pathways to regulate the level of Ser176 phosphorylation.

MEMO1 binds iron and modulates iron homeostasis in cancer cells

Mediator of ERBB2-driven cell motility 1 (MEMO1) is an evolutionary conserved protein implicated in many biological processes; however, its primary molecular function remains unknown. Importantly, MEMO1 is overexpressed in many types of cancer and was shown to modulate breast cancer metastasis through altered cell motility. To better understand the function of MEMO1 in cancer cells, we analyzed genetic interactions of MEMO1 using gene essentiality data from 1028 cancer cell lines and found multiple iron-related genes exhibiting genetic relationships with MEMO1. We experimentally confirmed several interactions between MEMO1 and iron-related proteins in living cells, most notably, transferrin receptor 2 (TFR2), mitoferrin-2 (SLC25A28), and the global iron response regulator IRP1 (ACO1). These interactions indicate that cells with high-MEMO1 expression levels are hypersensitive to the disruptions in iron distribution. Our data also indicate that MEMO1 is involved in ferroptosis and is linked to iron supply to mitochondria. We have found that purified MEMO1 binds iron with high affinity under redox conditions mimicking intracellular environment and solved MEMO1 structures in complex with iron and copper. Our work reveals that the iron coordination mode in MEMO1 is very similar to that of iron-containing extradiol dioxygenases, which also display a similar structural fold. We conclude that MEMO1 is an iron-binding protein that modulates iron homeostasis in cancer cells.

Mitochondrial Differentiation during Spermatogenesis: Lessons from Drosophila melanogaster

Numerous diseases can arise as a consequence of mitochondrial malfunction. Hence, there is a significant focus on studying the role of mitochondria in cancer, ageing, neurodegenerative diseases, and the field of developmental biology. Mitochondria could exist as discrete organelles in the cell; however, they have the ability to fuse, resulting in the formation of interconnected reticular structures. The dynamic changes between these forms correlate with mitochondrial function and mitochondrial health, and consequently, there is a significant scientific interest in uncovering the specific molecular constituents that govern these transitions. Moreover, the specialized mitochondria display a wide array of variable morphologies in their cristae formations. These inner mitochondrial structures are closely associated with the specific functions performed by the mitochondria. In multiple cases, the presence of mitochondrial dysfunction has been linked to male sterility, as it has been observed to cause a range of abnormal spermatogenesis and sperm phenotypes in different species. This review aims to elucidate the dynamic alterations and functions of mitochondria in germ cell development during the spermatogenesis of Drosophila melanogaster.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

A spectral framework to map QTLs affecting joint differential networks of gene co-expression

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network

Paralogous genes are often redundant for long periods of time before they diverge in function. While their functions are preserved, paralogous proteins can accumulate mutations that, through epistasis, could impact their fate in the future. By quantifying the impact of all single-amino acid substitutions on the binding of two myosin proteins to their interaction partners, we find that the future evolution of these proteins is highly contingent on their regulatory divergence and the mutations that have silently accumulated in their protein binding domains. Differences in the promoter strength of the two paralogs amplify the impact of mutations on binding in the lowly expressed one. While some mutations would be sufficient to non-functionalize one paralog, they would have minimal impact on the other. Our results reveal how functionally equivalent protein domains could be destined to specific fates by regulatory and cryptic coding sequence changes that currently have little to no functional impact.

Role of the small protein Mco6 in the mitochondrial sorting and assembly machinery

The majority of mitochondrial precursor proteins are imported through the Tom40 β-barrel channel of the translocase of the outer membrane (TOM). The sorting and assembly machinery (SAM) is essential for β-barrel membrane protein insertion into the outer membrane and thus required for the assembly of the TOM complex. Here, we demonstrate that the α-helical outer membrane protein Mco6 co-assembles with the mitochondrial distribution and morphology protein Mdm10 as part of the SAM machinery. MCO6 and MDM10 display a negative genetic interaction, and a mco6-mdm10 yeast double mutant displays reduced levels of the TOM complex. Cells lacking Mco6 affect the levels of Mdm10 and show assembly defects of the TOM complex. Thus, this work uncovers a role of the SAMMco6 complex for the biogenesis of the mitochondrial outer membrane.

Disorder in CENP-ACse4 tail-chaperone interaction facilitates binding with Ame1/Okp1 at the kinetochore

The centromere is epigenetically marked by a histone H3 variant-CENP-A. The budding yeast CENP-A called Cse4, consists of an unusually long N-terminus that is known to be involved in kinetochore assembly. Its disordered chaperone, Scm3 is responsible for the centromeric deposition of Cse4 as well as in the maintenance of a segregation-competent kinetochore. In this study, we show that the Cse4 N-terminus is intrinsically disordered and interacts with Scm3 at multiple sites, and the complex does not gain any substantial structure. Additionally, the complex forms a synergistic association with an essential inner kinetochore component (Ctf19-Mcm21-Okp1-Ame1), and a model has been suggested to this effect. Thus, our study provides mechanistic insights into the Cse4 N-terminus-chaperone interaction and also illustrates how intrinsically disordered proteins mediate assembly of complex multiprotein networks, in general.

Translation variation across genetic backgrounds reveals a post-transcriptional buffering signature in yeast

Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

The spatial organization of sphingofungin biosynthesis in Aspergillus fumigatus and its cross-interaction with sphingolipid metabolism

Sphingofungins are sphinganine analog mycotoxins acting as inhibitors of serine palmitoyl transferases, enzymes responsible for the first step in the sphingolipid biosynthesis. Eukaryotic cells are highly organized with various structures and organelles to facilitate cellular processes and chemical reactions, including the ones occurring as part of the secondary metabolism. We studied how sphingofungin biosynthesis is compartmentalized in the human-pathogenic fungus Aspergillus fumigatus, and we observed that it takes place in the endoplasmic reticulum (ER), ER-derived vesicles, and the cytosol. This implies that sphingofungin and sphingolipid biosynthesis colocalize to some extent. Automated analysis of confocal microscopy images confirmed the colocalization of the fluorescent proteins. Moreover, we demonstrated that the cluster-associated aminotransferase (SphA) and 3-ketoreductase (SphF) play a bifunctional role, supporting sphingolipid biosynthesis, and thereby antagonizing the toxic effects caused by sphingofungin production.IMPORTANCEA balanced sphingolipid homeostasis is critical for the proper functioning of eukaryotic cells. To this end, sphingolipid inhibitors have therapeutic potential against diseases related to the deregulation of sphingolipid balance. In addition, some of them have significant antifungal activity, suggesting that sphingolipid inhibitors-producing fungi have evolved mechanisms to escape self-poisoning. Here, we propose a novel self-defense mechanism, with cluster-associated genes coding for enzymes that play a dual role, being involved in both sphingofungin and sphingolipid production.

In vivo CRISPR screening directly targeting testicular cells

CRISPR-Cas9 short guide RNA (sgRNA) library screening is a powerful approach to understand the molecular mechanisms of biological phenomena. However, its in vivo application is currently limited. Here, we developed our previously established in vitro revival screening method into an in vivo one to identify factors involved in spermatogenesis integrity by utilizing sperm capacitation as an indicator. By introducing an sgRNA library into testicular cells, we successfully pinpointed the retinal degeneration 3 (Rd3) gene as a significant factor in spermatogenesis. Single-cell RNA sequencing (scRNA-seq) analysis highlighted the high expression of Rd3 in round spermatids, and proteomics analysis indicated that Rd3 interacts with mitochondria. To search for cell-type-specific signaling pathways based on scRNA-seq and proteomics analyses, we developed a computational tool, Hub-Explorer. Through this, we discovered that Rd3 modulates oxidative stress by regulating mitochondrial distribution upon ciliogenesis induction. Collectively, our screening system provides a valuable in vivo approach to decipher molecular mechanisms in biological processes.

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.

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cell behavior. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates to generate a model of the functional network dynamics that can be transferred to specific reprogramming goals. The approach additively combines transcriptional responses to gene perturbations (single-gene knockdowns and overexpressions) to minimize the transcriptional difference between a given pair of initial and target states. We demonstrate the flexibility of our approach by applying it to a microarray dataset comprising over 9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of over 10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an average AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases as the developmental relatedness decreases. We also show that fewer genes are needed to progress along developmental paths than to regress. Together, these findings establish a proof-of-concept for our approach to computationally design control strategies and demonstrate their ability to provide insights into the dynamics of gene regulatory networks.

Amino acid metabolism and MAP kinase signaling pathway play opposite roles in the regulation of ethanol production during fermentation of sugarcane molasses in budding yeast

Sugarcane molasses is one of the main raw materials for bioethanol production, and Saccharomyces cerevisiae is the major biofuel-producing organism. In this study, a batch fermentation model has been used to examine ethanol titers of deletion mutants for all yeast nonessential genes in this yeast genome. A total of 42 genes are identified to be involved in ethanol production during fermentation of sugarcane molasses. Deletion mutants of seventeen genes show increased ethanol titers, while deletion mutants for twenty-five genes exhibit reduced ethanol titers. Two MAP kinases Hog1 and Kss1 controlling the high osmolarity and glycerol (HOG) signaling and the filamentous growth, respectively, are negatively involved in the regulation of ethanol production. In addition, twelve genes involved in amino acid metabolism are crucial for ethanol production during fermentation. Our findings provide novel targets and strategies for genetically engineering industrial yeast strains to improve ethanol titer during fermentation of sugarcane molasses.

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

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

High-throughput characterization of the effect of sodium chloride and potassium chloride on 31 lactic acid bacteria and their co-cultures

Salt (NaCl) is associated with a risk of hypertension and the development of coronary heart disease, so its consumption should be limited. However, salt plays a key role in the quality and safety of food by controlling undesirable microorganisms. Since studies have focused primarily on the effect of salts on the overall counts of the lactic acid bacteria (LAB) group, we have not yet understood how salt stress individually affects the strains and the interactions between them. In this study, we characterized the effect of sodium chloride (NaCl) and potassium chloride (KCl) on the growth and acidification of 31 LAB strains. In addition, we evaluated the effect of salts on a total of 93 random pairwise strain combinations. Strains and co-cultures were tested at 3% NaCl, 5% NaCl, and 3% KCl on solid medium using an automated approach and image analysis. The results showed that the growth of LAB was significantly reduced by up to 68% at 5% NaCl (p < 0.0001). For the co-cultures, a reduction of up to 57% was observed at 5% NaCl (p < 0.0001). However, acidification was less affected by salt stress, whether for monocultures or co-cultures. Furthermore, KCl had a lesser impact on both growth and acidification compared to NaCl. Indeed, some strains showed a significant increase in growth at 3% KCl, such as Lactococcus lactis subsp. lactis 74310 (23%, p = 0.01). More importantly, co-cultures appeared to be more resilient and had more varied responses to salt stress than the monocultures, as several cases of suppression of the significant effect of salts on acidification and growth were detected. Our results highlight that while salts can modulate microbial interactions, these latter can also attenuate the effect of salts on LAB.

Genetic response analysis of Beauveria bassiana Z1 under high concentration Cd(II) stress

Cadmium (Cd(II)) has carcinogenic and teratogenic toxicity, which can be accumulated in the human body through the food chain, endangering human health and life. In this study, a highly Cd(II)-tolerant fungus named Beauveria bassiana Z1 was studied, and its Cd(Ⅱ) removal efficiency was 71.2% when the Cd(II) concentration was 10 mM. Through bioanalysis and experimental verification of the transcriptome data, it was found that cadmium entered the cells through calcium ion channels, and then complexed with intracellular glutathione (GSH) and stored in vacuoles or excluded extracellular by ABC transporters. Cytochrome P450 was significantly upregulated in many pathways and actively participated in detoxification related reactions. The addition of cytochrome inhibitor taxifolin reduced the removal efficiency of Cd(II) by 45%. In the analysis, it demonstrated that ACOX1 gene and OPR gene of jasmonic acid (JA) synthesis pathway were significantly up-regulated, and were correlated with bZIP family transcription factors cpc-1_0 and pa p1_0. The results showed that exogenous JA could improve the removal efficiency of Cd(II) by strain Z1.

Mutations in yeast Pcf11, a conserved protein essential for mRNA 3' end processing and transcription termination, elicit the Environmental Stress Response

The Pcf11 protein is an essential subunit of the large complex that cleaves and polyadenylates eukaryotic mRNA precursor. It has also been functionally linked to gene-looping, termination of RNA Polymerase II (Pol II) transcripts, and mRNA export. We have examined a poorly characterized but conserved domain (amino acids 142-225) of the Saccharomyces cerevisiae  Pcf11 and found that while it is not needed for mRNA 3' end processing or termination downstream of the poly(A) sites of protein-coding genes, its presence improves the interaction with Pol II and the use of transcription terminators near gene promoters. Analysis of genome-wide Pol II occupancy in cells with Pcf11 missing this region, as well as Pcf11 mutated in the Pol II CTD Interacting Domain, indicates that systematic changes in mRNA expression are mediated primarily at the level of transcription. Global expression analysis also shows that a general stress response, involving both activation and suppression of specific gene sets known to be regulated in response to a wide variety of stresses, is induced in the two pcf11 mutants, even though cells are grown in optimal conditions. The mutants also cause an unbalanced expression of cell wall-related genes that does not activate the Cell Wall Integrity pathway but is associated with strong caffeine sensitivity. Based on these findings, we propose that Pcf11 can modulate the expression level of specific functional groups of genes in ways that do not involve its well-characterized role in mRNA 3' end processing.

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.

Calcineurin-dependent contributions to fitness in the opportunistic pathogen Candida glabrata

The protein phosphatase calcineurin is vital for the virulence of the opportunistic fungal pathogen Candida glabrata. The host-induced stresses that activate calcineurin signaling are unknown, as are the targets of calcineurin relevant to virulence. To potentially shed light on these processes, millions of transposon insertion mutants throughout the genome of C. glabrata were profiled en masse for fitness defects in the presence of FK506, a specific inhibitor of calcineurin. Eighty-seven specific gene deficiencies depended on calcineurin signaling for full viability in vitro both in wild-type and pdr1∆ null strains lacking pleiotropic drug resistance. Three genes involved in cell wall biosynthesis (FKS1, DCW1, FLC1) possess co-essential paralogs whose expression depended on calcineurin and Crz1 in response to micafungin, a clinical antifungal that interferes with cell wall biogenesis. Interestingly, 80% of the FK506-sensitive mutants were deficient in different aspects of vesicular trafficking, such as endocytosis, exocytosis, sorting, and biogenesis of secretory proteins in the endoplasmic reticulum (ER). In response to the experimental antifungal manogepix that blocks GPI-anchor biosynthesis in the ER, calcineurin signaling increased and strongly prevented cell death independent of Crz1, one of its major targets. Comparisons between manogepix, micafungin, and the ER-stressing tunicamycin reveal a correlation between the degree of calcineurin signaling and the degree of cell survival. These findings suggest that calcineurin plays major roles in mitigating stresses of vesicular trafficking. Such stresses may arise during host infection and in response to antifungal therapies.IMPORTANCECalcineurin plays critical roles in the virulence of most pathogenic fungi. This study sheds light on those roles in the opportunistic pathogen Candida glabrata using a genome-wide analysis in vitro. The findings could lead to antifungal developments that also avoid immunosuppression.

Yeast eIF2A has a minimal role in translation initiation and uORF-mediated translational control in vivo

Initiating translation of most eukaryotic mRNAs depends on recruitment of methionyl initiator tRNA (Met-tRNAi) in a ternary complex (TC) with GTP-bound eukaryotic initiation factor 2 (eIF2) to the small (40S) ribosomal subunit, forming a 43S preinitiation complex (PIC) that attaches to the mRNA and scans the 5'-untranslated region (5' UTR) for an AUG start codon. Previous studies have implicated mammalian eIF2A in GTP-independent binding of Met-tRNAi to the 40S subunit and its recruitment to specialized mRNAs that do not require scanning, and in initiation at non-AUG start codons, when eIF2 function is attenuated by phosphorylation of its α-subunit during stress. The role of eIF2A in translation in vivo is poorly understood however, and it was unknown whether the conserved ortholog in budding yeast can functionally substitute for eIF2. We performed ribosome profiling of a yeast deletion mutant lacking eIF2A and isogenic wild-type (WT) cells in the presence or absence of eIF2α phosphorylation induced by starvation for amino acids isoleucine and valine. Whereas starvation of WT confers changes in translational efficiencies (TEs) of hundreds of mRNAs, the eIF2AΔ mutation conferred no significant TE reductions for any mRNAs in non-starved cells, and it reduced the TEs of only a small number of transcripts in starved cells containing phosphorylated eIF2α. We found no evidence that eliminating eIF2A altered the translation of mRNAs containing putative internal ribosome entry site (IRES) elements, or harboring uORFs initiated by AUG or near-cognate start codons, in non-starved or starved cells. Thus, very few mRNAs (possibly only one) appear to employ eIF2A for Met-tRNAi recruitment in yeast cells, even when eIF2 function is attenuated by stress.

Mobile CRISPRi moves through the complexity of bacterial genetics

In this issue of Cell Reports Methods, Rachwalski et al. describe a high-throughput method to screen genetic interactions in bacteria using a conjugative CRISPR interference (CRISPRi) plasmid. The method enables systematic studies of gene essentiality in diverse genomic and environmental contexts and is applicable to Escherichia coli as well as other bacteria.

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 7,700 CRISPRi perturbations targeting 1,712 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes

Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.

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.

Truncating the spliceosomal 'rope protein' Prp45 results in Htz1 dependent phenotypes

Spliceosome assembly contributes an important but incompletely understood aspect of splicing regulation. Prp45 is a yeast splicing factor which runs as an extended fold through the spliceosome, and which may be important for bringing its components together. We performed a whole genome analysis of the genetic interaction network of the truncated allele of PRP45 (prp45(1-169)) using synthetic genetic array technology and found chromatin remodellers and modifiers as an enriched category. In agreement with related studies, H2A.Z-encoding HTZ1, and the components of SWR1, INO80, and SAGA complexes represented prominent interactors, with htz1 conferring the strongest growth defect. Because the truncation of Prp45 disproportionately affected low copy number transcripts of intron-containing genes, we prepared strains carrying intronless versions of SRB2, VPS75, or HRB1, the most affected cases with transcription-related function. Intron removal from SRB2, but not from the other genes, partly repaired some but not all the growth phenotypes identified in the genetic screen. The interaction of prp45(1-169) and htz1Δ was detectable even in cells with SRB2 intron deleted (srb2Δi). The less truncated variant, prp45(1-330), had a synthetic growth defect with htz1Δ at 16°C, which also persisted in the srb2Δi background. Moreover, htz1Δ enhanced prp45(1-330) dependent pre-mRNA hyper-accumulation of both high and low efficiency splicers, genes ECM33 and COF1, respectively. We conclude that while the expression defects of low expression intron-containing genes contribute to the genetic interactome of prp45(1-169), the genetic interactions between prp45 and htz1 alleles demonstrate the sensitivity of spliceosome assembly, delayed in prp45(1-169), to the chromatin environment.

Orphan quality control by an SCF ubiquitin ligase directed to pervasive C-degrons

Selective protein degradation typically involves substrate recognition via short linear motifs known as degrons. Various degrons can be found at protein termini from bacteria to mammals. While N-degrons have been extensively studied, our understanding of C-degrons is still limited. Towards a comprehensive understanding of eukaryotic C-degron pathways, here we perform an unbiased survey of C-degrons in budding yeast. We identify over 5000 potential C-degrons by stability profiling of random peptide libraries and of the yeast C‑terminome. Combining machine learning, high-throughput mutagenesis and genetic screens reveals that the SCF ubiquitin ligase targets ~40% of degrons using a single F-box substrate receptor Das1. Although sequence-specific, Das1 is highly promiscuous, recognizing a variety of C-degron motifs. By screening for full-length substrates, we implicate SCFDas1 in degradation of orphan protein complex subunits. Altogether, this work highlights the variety of C-degron pathways in eukaryotes and uncovers how an SCF/C-degron pathway of broad specificity contributes to proteostasis.

Rapid evolution of pre-zygotic reproductive barriers in allopatric populations

IMPORTANCE

A population diversifies into two or more species-such a process is known as speciation. In sexually reproducing microorganisms, which barriers arise first-pre-mating or post-mating? In this work, we quantify the relative strengths of these barriers and demonstrate that pre-mating barriers arise first in allopatrically evolving populations of yeast, Saccharomyces cerevisiae. These defects arise because of the altered kinetics of mating of the participating groups. Thus, our work provides an understanding of how adaptive changes can lead to diversification among microbial populations.

Yeast eIF2A has a minimal role in translation initiation and uORF-mediated translational control in vivo

Initiating translation of most eukaryotic mRNAs depends on recruitment of methionyl initiator tRNA (Met-tRNAi) in a ternary complex (TC) with GTP-bound eukaryotic initiation factor 2 (eIF2) to the small (40S) ribosomal subunit, forming a 43S preinitiation complex (PIC) that attaches to the mRNA and scans the 5'-untranslated region (5' UTR) for an AUG start codon. Previous studies have implicated mammalian eIF2A in GTP-independent binding of Met-tRNAi to the 40S subunit and its recruitment to specialized mRNAs that do not require scanning, and in initiation at non-AUG start codons, when eIF2 function is attenuated by phosphorylation of its α-subunit during stress. The role of eIF2A in translation in vivo is poorly understood however, and it was unknown whether the conserved ortholog in budding yeast can functionally substitute for eIF2. We performed ribosome profiling of a yeast deletion mutant lacking eIF2A and isogenic wild-type (WT) cells in the presence or absence of eIF2α phosphorylation induced by starvation for amino acids isoleucine and valine. Whereas starvation of WT confers changes in translational efficiencies (TEs) of hundreds of mRNAs, the eIF2AΔ mutation conferred no significant TE reductions for any mRNAs in non-starved cells, and it reduced the TEs of only a small number of transcripts in starved cells containing phosphorylated eIF2α. We found no evidence that eliminating eIF2A altered the translation of mRNAs containing putative IRES elements, or harboring uORFs initiated by AUG or near-cognate start codons, in non-starved or starved cells. Thus, very few mRNAs (possibly only one) appear to employ eIF2A for Met-tRNAi recruitment in yeast cells, even when eIF2 function is attenuated by stress.

Synthetic Lethality Screening with Recursive Feature Machines

Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic vulnerabilities. The rise of large-scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPR gene knockout from cell line embeddings consisting of gene expression and mutation features. After training, RFMs use a statistical operator known as average gradient outer product to provide weights for each feature indicating the importance of each feature in predicting cellular viability. We subsequently apply correlation-based filters to re-weight RFM feature importances and identify those features that are most indicative of low cellular viability. Our resulting pipeline is computationally efficient, taking under 3 minutes for analyzing all 17, 453 knockouts from DepMap for candidate SL pairs. We show that our pipeline more accurately recovers experimentally verified SL pairs than prior approaches. Moreover, our pipeline finds new candidate SL pairs, thereby opening novel avenues for identifying genetic vulnerabilities in cancer.

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

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.

Saccharomyces cerevisiae survival against heat stress entails a communication between CCT and cell wall integrity pathway

The chaperonin TRiC/CCT is cytosolic cylindrical complex of 16 subunits encoded by eight essential genes CCT1-8. It contributes to folding 10% of cellular polypeptides in yeast. The strain carrying substitution point mutation G412E in the equatorial domain of Cct7p resulted in the improper folding of substrates. In this study, the Cct7p mutant exhibited sensitivity to non-optimal growth temperatures and cell wall stressors. Heat shock is known to disrupt cell wall and protein stability in budding yeast. Mitogen-activated protein kinase-mediated cell wall integrity pathway gets activated to compensate the perturbed cell wall. Overexpression of the PKC1 and SLT2 genes of MAPK signaling pathway in mutant rescued the growth and cell division defects. Additionally, the genes of the CWI pathway such as SED1, GFA1, PIR1, and RIM21 are down-regulated. The Cct7p mutant strain (G412E) is unable to withstand the heat stress due to the underlying defects in protein folding and cell wall maintenance. Taken together, our results strongly indicate the interaction between CCT and cell wall integrity pathway.

Building a eukaryotic chromosome arm by de novo design and synthesis

The genome of an organism is inherited from its ancestor and continues to evolve over time, however, the extent to which the current version could be altered remains unknown. To probe the genome plasticity of Saccharomyces cerevisiae, here we replace the native left arm of chromosome XII (chrXIIL) with a linear artificial chromosome harboring small sets of reconstructed genes. We find that as few as 12 genes are sufficient for cell viability, whereas 25 genes are required to recover the partial fitness defects observed in the 12-gene strain. Next, we demonstrate that these genes can be reconstructed individually using synthetic regulatory sequences and recoded open-reading frames with a "one-amino-acid-one-codon" strategy to remain functional. Finally, a synthetic neochromsome with the reconstructed genes is assembled which could substitute chrXIIL for viability. Together, our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional eukaryotic chromosomes from entirely artificial sequences.

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.