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

Leishmania mexicana N-Acetyltransferease 10 Is Important for Polysome Formation and Cell Cycle Progression

Leishmania presents a complex life cycle that involves both invertebrate and vertebrate hosts. By regulating gene expression, protein synthesis, and metabolism, the parasite can adapt to various environmental conditions. This regulation occurs mainly at the post-transcriptional level and may involve epitranscriptomic modifications of RNAs. Recent studies have shown that mRNAs in humans undergo a modification known as N4-acetylcytidine (ac4C) catalyzed by the enzyme N-acetyltransferase (NAT10), impacting mRNAs stability and translation. Here, we characterized the NAT10 homologue of L. mexicana, finding that the enzyme exhibits all the conserved acetyltransferase domains although failed to functionally complement the Kre33 mutant in Saccharomyces cerevisiae. We also discovered that LmexNAT10 is nuclear, and seems essential, as evidenced by unsuccessful attempts to obtain null mutant parasites. Phenotypic characterization of single-knockout parasites revealed that LmexNAT10 affects the multiplication of procyclic forms and the promastigote-amastigote differentiation. Additionally, in vivo infection studies using the invertebrate vector Lutzomyia longipalpis showed a delay in the parasite differentiation into metacyclics. Finally, we observed changes in the cell cycle progression and protein synthesis in the mutant parasites. Together, these results suggest that LmexNAT10 might be important for parasite differentiation, potentially by regulating ac4C levels.

Experimental evolution of gene essentiality in bacteria

Essential gene products carry out fundamental cellular activities in interaction with other components. However, the lack of essential gene mutants and appropriate methodologies to link essential gene functions with their partners poses significant challenges. Here, we have generated deletion mutants in 32 genes previously identified as essential, with 23 mutants showing extremely slow growth in the SK36 strain of Streptococcus sanguinis. The 23 genes corresponding to these mutants encode components of diverse pathways, are widely conserved among bacteria, and are essential in many other bacterial species. Whole-genome sequencing of 243 independently evolved populations of these mutants has identified >1000 spontaneous suppressor mutations in experimental evolution. Many of these mutations define new gene and pathway relationships, such as F1Fo-ATPase/V1Vo-ATPase/TrkA1-H1 that were demonstrated across multiple Streptococcus species. Patterns of spontaneous mutations occurring in essential gene mutants differed from those found in wildtype. While gene duplications occurred rarely and appeared most often at later stages of evolution, substitutions, deletions, and insertions were prevalent in evolved populations. These essential gene deletion mutants and spontaneous mutations fixed in the mutant populations during evolution establish a foundation for understanding gene essentiality and the interaction of essential genes in networks.

Investigating the role of RNA-binding protein Ssd1 in aneuploidy tolerance through network analysis

RNA-binding proteins (RBPs) play critical cellular roles by mediating various stages of RNA life cycles. Ssd1, an RBP with pleiotropic effects, has been implicated in aneuploidy tolerance in Saccharomyces cerevisiae but its mechanistic role remains unclear. Here, we used a network-based approach to inform on Ssd1's role in aneuploidy tolerance, by identifying and experimentally perturbing a network of RBPs that share mRNA targets with Ssd1. We identified RBPs whose bound mRNA targets significantly overlap with Ssd1 targets. For 14 identified RBPs, we then used a genetic approach to generate all combinations of genotypes for euploid and aneuploid yeast with an extra copy of chromosome XII, with and without SSD1 and/or the RBP of interest. Deletion of 10 RBPs either exacerbated or alleviated the sensitivity of wild-type and/or ssd1Δ cells to chromosome XII duplication, in several cases indicating genetic interactions with SSD1 in the context of aneuploidy. We integrated these findings with results from a global overexpression screen that identified genes whose duplication complements ssd1Δ aneuploid sensitivity. The resulting network points to a subgroup of proteins with shared roles in translational repression and P-body formation, implicating these functions in aneuploidy tolerance. Our results reveal a role for new RBPs in aneuploidy tolerance and support a model in which Ssd1 mitigates translation-related stresses in aneuploid cells.

Chemical genomics informs antibiotic and essential gene function in Acinetobacter baumannii

The Gram-negative pathogen, Acinetobacter baumannii , poses a serious threat to human health due to its role in nosocomial infections that are resistant to treatment with current antibiotics. Despite this, our understanding of fundamental A. baumannii biology remains limited, as many essential genes have not been experimentally characterized. These essential genes are critical for bacterial survival and, thus, represent promising targets for drug discovery. Here, we systematically probe the function of essential genes by screening a CRISPR interference knockdown library against a diverse panel of chemical inhibitors, including antibiotics. We find that most essential genes show chemical-gene interactions, allowing insights into both inhibitor and gene function. For instance, knockdown of lipooligosaccharide (LOS) transport genes increased sensitivity to a broad range of chemicals. Cells with defective LOS transport showed cell envelope hyper-permeability that was dependent on continued LOS synthesis. Using phenotypes across our chemical-gene interaction dataset, we constructed an essential gene network linking poorly understood genes to well-characterized genes in cell division and other processes. Finally, our phenotype-structure analysis identified structurally related antibiotics with distinct cellular impacts and suggested potential targets for underexplored inhibitors. This study advances our understanding of essential gene and inhibitor function, providing a valuable resource for mechanistic studies, therapeutic strategies, and future key targets for antibiotic development.

Interstitial telomeric sequences promote gross chromosomal rearrangement via multiple mechanisms

Telomeric DNA sequences are difficult to replicate. Replication forks frequently pause or stall at telomeres, which can lead to telomere truncation and dysfunction. In addition to being at chromosome ends, telomere repeats are also present at internal locations within chromosomes, known as interstitial telomeric sequences (ITSs). These sequences are unstable and prone to triggering gross chromosomal rearrangements (GCRs). In this study, we quantitatively examined the effect of ITSs on the GCR rate in Saccharomyces cerevisiae using a genetic assay. We find that the GCR rate increases exponentially with ITS length. This increase can be attributed to the telomere repeat binding protein Rap1 impeding DNA replication and a bias of repairing DNA breaks at or distal to the ITS via de novo telomere addition. Additionally, we performed a genome-wide screen for genes that modulate the rate of ITS-induced GCRs. We find that mutation of core components of the DNA replication machinery leads to an increase in GCRs, but many mutants known to increase the GCR rate in the absence of an ITS do not significantly affect the GCR rate when an ITS is present. We also identified genes that promote the formation of ITS-induced GCRs, including genes with roles in telomere maintenance, nucleotide excision repair, and transcription. Our work thus uncovers multiple mechanisms by which an ITS promotes GCR.

Biochemistry and genetics are coming together to improve our understanding of genotype to phenotype relationships

Since genome sequencing became accessible, determining how specific differences in genotypes lead to complex phenotypes such as disease has become one of the key goals in biomedicine. Predicting effects of sequence variants on cellular or organismal phenotype faces several challenges. First, variants simultaneously affect multiple protein properties and predicting their combined effect is complex. Second, effects of changes in a single protein propagate through the cellular network, which we only partially understand. In this review, we emphasize the importance of both biochemistry and genetics in addressing these challenges. Moreover, we highlight work that blurs the distinction between biochemistry and genetics fields to provide new insights into the genotype-to-phenotype relationships.

Genetic Interaction Between F-Box Encoding UCC1 and RRM3 Regulates Growth Rate, Cell Size, and Stress Tolerance in Saccharomyces cerevisiae

Ucc1, an F-box motif-containing protein of Saccharomyces cerevisiae encoded by UCC1 regulates energy metabolism through proteasomal degradation of citrate synthase Cit2 and inactivation of the glyoxylate cycle when glucose is present as the main carbon source in the growth medium. Rrm3, a Pif1 family DNA helicase, encoded by RRM3 regulates the movement of the replication forks during the DNA replication process. Here in this study, we present evidence of binary genetic interaction between both the genes, UCC1 and RRM3, that determine the growth rate, cell morphology, cell size, apoptosis, and stress response. The absence of both genes UCC1 and RRM3 leads to altered cell morphology, increased growth rate, utilization of alternate carbon sources, resistance to hydrogen peroxide, and susceptibility to acetic acid-induced apoptosis. Further, the genetic interaction network analysis shows both the genes UCC1 and RRM3 interaction through the SGS1 and cross-link among metabolic, glyoxylate, DNA replication, and retrograde signaling pathways.

Identification of a Non-canonical Function of Prefoldin Subunit 5 in Proteasome Assembly

The prefoldin complex is a heterohexameric, evolutionarily conserved co-chaperone that assists in folding of polypeptides downstream of the protein translation machinery. Loss of prefoldin function leads to impaired solubility of cellular proteins. The degradation of proteins by the proteasome is an integral part of protein homeostasis. Failure of regulated protein degradation can lead to the accumulation of misfolded and defective proteins. We show that prefoldin subunit 5 is required for proteasome activity by contributing to the assembly of the 26S proteasome. In particular, we found that absence of the prefoldin subunit 5 impairs formation of the Rpt ring subcomplex of the proteasome. Concomitant deletion of PFD5 and HSM3, a chaperone for assembly of the ATPase subunits comprising the Rpt ring, exacerbates this effect, suggesting a synergistic relationship between the two factors in proteasome assembly. Thus, our findings reveal a regulatory mechanism wherein prefoldin subunit 5 plays a crucial role in maintaining proteasome integrity, thereby influencing the degradation of proteins.

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on residual graph convolutional neural network and contrastive learning for cell type annotation of single-cell RNA-seq data. scRGCL mainly consists of a residual graph convolutional neural network, contrastive learning, and weight freezing. A residual graph convolutional neural network is utilized to extract complex high-order features from data. Contrastive learning can help the model learn meaningful cell-to-cell differential features. Weight freezing can avoid overfitting and help the model discover the impact of specific gene expression on cell type annotation. To verify the effectiveness of scRGCL, we compared its performance with six methods (three shallow learning algorithms and three state-of-the-art DL-based methods) on eight single-cell benchmark datasets from two species (seven in human and one in mouse). Experimental results not only show that scRGCL outperforms competing methods but also demonstrate the generalizability of scRGCL for cell type annotation. scRGCL is available at https://github.com/nathanyl/scRGCL.

Considerations in the search for epistasis

Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely failed to recover this missing heritability. We identify key challenges and propose that future works need to leverage idealized systems, known biology and even previously identified epistatic interactions, in order to guide the search for new interactions.

Regulation of yeast polarized exocytosis by phosphoinositide lipids

Phosphoinositides help steer membrane trafficking routes within eukaryotic cells. In polarized exocytosis, which targets vesicular cargo to sites of polarized growth at the plasma membrane (PM), the two phosphoinositides phosphatidylinositol 4-phosphate (PI4P) and its derivative phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) pave the pathway for vesicle transport from the Golgi to the PM. PI4P is a critical regulator of mechanisms that shape late Golgi membranes for vesicle biogenesis and release. Although enriched in vesicle membranes, PI4P is inexplicably removed from post-Golgi vesicles during their transit to the PM, which drives subsequent steps in exocytosis. At the PM, PI(4,5)P2 recruits effectors that establish polarized membrane sites for targeting the vesicular delivery of secretory cargo. The budding yeast Saccharomyces cerevisiae provides an elegant model to unravel the complexities of phosphoinositide regulation during polarized exocytosis. Here, we review how PI4P and PI(4,5)P2 promote yeast vesicle biogenesis, exocyst complex assembly and vesicle docking at polarized cortical sites, and suggest how these steps might impact related mechanisms of human disease.

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

Epistasis and cryptic QTL identified using modified bulk segregant analysis of copper resistance in budding yeast

The contributions of genetic interactions to natural trait variation are challenging to estimate experimentally, as current approaches for detecting epistasis are often underpowered. Powerful mapping approaches such as bulk segregant analysis, wherein individuals with extreme phenotypes are pooled for genotyping, obscure epistasis by averaging over genotype combinations. To accurately characterize and quantify epistasis underlying natural trait variation, we have engineered strains of the budding yeast Saccharomyces cerevisiae to enable crosses where one parent's chromosome is fixed while the rest of the chromosomes segregate. These crosses allow us to use bulk segregant analysis to identify quantitative trait loci (QTL) whose effects depend on alleles on the fixed parental chromosome, indicating a genetic interaction with that chromosome. Our method, which we term epic-QTL (for epistatic-with-chromosome QTL) analysis, can thus identify interaction loci with high statistical power. Here we perform epic-QTL analysis of copper resistance with chromosome I or VIII fixed in a cross between divergent naturally derived strains. We find seven loci that interact significantly with chromosome VIII and none that interact with chromosome I, the smallest of the 16 budding yeast chromosomes. Each of the seven interactions alters the magnitude, rather than the direction, of an additive QTL effect. We also show that fixation of one source of variation - in this case chromosome VIII, which contains the large-effect QTL mapping to CUP1 - increases power to detect the contributions of other loci to trait differences.

The combined signatures of programmed cell death and immune landscape provide a prognostic and therapeutic biomarker in the hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks as the fourth most common cause of mortality globally among all cancer types. Programmed cell death (PCD) is a crucial biological mechanism governing cancer progression, tumor expansion, and metastatic dissemination. Furthermore, the tumor microenvironment (TME) is critical in influencing overall survival (OS) and immune responses to immunotherapeutic interventions. From a multi-omics perspective, the combination of PCD and TME could help to predict the survival of HCC patient survival and immunotherapy response. Our study analyzed variations in the PCD- and TME-classifier used in the classification of HCC patients into two subgroups: PCD high-TME low and PCD low-TME high. In the following step, we compared the tumor somatic mutation (TMB), immunotherapy response, and functional annotation of both groups of patients. Lastly, Western Blot (WB) were conducted. The immunohistochemistry (IHC) was performed on the Human Protein Atlas (HPA). In the PCD-TME classifier, 23 PCD-related genes and three immune cell types were identified. Patients' prognoses and responses to therapy could be accurately predicted using this model. The findings of this study provide a new instrument for the clinical management of HCC patients, and they contribute to the development of accurate treatment strategies for these patients.

Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks

MOTIVATION

Spatial Analysis of Functional Enrichment (SAFE) is a popular tool for biologists to investigate the functional organization of biological networks via highly intuitive 2D functional maps. To create these maps, SAFE uses Spring embedding to project a given network into a 2D space in which nodes connected in the network are near each other in space. However, many biological networks are scale-free, containing highly connected hub nodes. Because Spring embedding fails to separate hub nodes, it provides uninformative embeddings that resemble a 'hairball'. In addition, Spring embedding only captures direct node connectivity in the network and does not consider higher-order node wiring patterns, which are best captured by graphlets, small, connected, nonisomorphic, induced subgraphs. The scale-free structure of biological networks is hypothesized to stem from an underlying low-dimensional hyperbolic geometry, which novel hyperbolic embedding methods try to uncover. These include coalescent embedding, which projects a network onto a 2D disk.

RESULTS

To better capture the functional organization of scale-free biological networks, whilst also going beyond simple direct connectivity patterns, we introduce Graphlet Coalescent (GraCoal) embedding, which embeds nodes nearby on a disk if they frequently co-occur on a given graphlet together. We use GraCoal to extend SAFE-based network analysis. Through SAFE-enabled enrichment analysis, we show that GraCoal outperforms graphlet-based Spring embedding in capturing the functional organization of the genetic interaction networks of fruit fly, budding yeast, fission yeast and Escherichia coli. We show that depending on the underlying graphlet, GraCoal embeddings capture different topology-function relationships. We show that triangle-based GraCoal embedding captures functional redundancies between paralogs.

AVAILABILITY AND IMPLEMENTATION

https://gitlab.bsc.es/swindels/gracoal_embedding.

Minimisation of metabolic networks defines a new functional class of genes

Construction of minimal metabolic networks (MMNs) contributes both to our understanding of the origins of metabolism and to the efficiency of biotechnological processes by preventing the diversion of flux away from product formation. We have designed MMNs using a novel in silico synthetic biology pipeline that removes genes encoding enzymes and transporters from genome-scale metabolic models. The resulting minimal gene-set still ensures both viability and high growth rates. The composition of these MMNs has defined a new functional class of genes termed Network Efficiency Determinants (NEDs). These genes, whilst not essential, are very rarely eliminated in constructing an MMN, suggesting that it is difficult for metabolism to be re-routed to obviate the need for such genes. Moreover, the removal of NED genes from an MMN significantly reduces its global efficiency. Bioinformatic analyses of the NED genes have revealed that not only do these genes have more genetic interactions than the bulk of metabolic genes but their protein products also show more protein-protein interactions. In yeast, NED genes are predominantly single-copy and are highly conserved across evolutionarily distant organisms. These features confirm the importance of the NED genes to the metabolic network, including why they are so rarely excluded during minimisation.

The Drosophila ribonucleoprotein Clueless is required for ribosome biogenesis in vivo

As hubs of metabolism, mitochondria contribute critical processes to coordinate and optimize energy and intermediate metabolites. Drosophila Clueless (Clu) and vertebrate CLUH are ribonucleoproteins critical for supporting mitochondrial function; yet do so in multiple ways. Clu-CLUH bind mRNAs, and CLUH regulates mRNA localization and translation of mRNAs encoding proteins destined for mitochondrial import. In addition, Clu associates with ribosomal proteins and translation factors; yet whether it is required for fundamental ribosome function in vivo is not clear. In this study, we examine the Clu interactome and probe Clu's requirement in ribosome biogenesis. We previously showed that Clu associates with ribosomal proteins. In this study, we extend these observations to show that clu null mutants display a significant decrease in overall protein synthesis. In addition, Clu associates with ribosomal proteins in an mRNA-independent manner, suggesting Clu's core ribosomal function may be separate from its role in localizing and translating specific mRNAs. We find that Clu is present in the nucleus and associates with the rRNA processing protein fibrillarin but, surprisingly, that processed rRNA products are normal in the absence of Clu. Furthermore, Clu loss does not affect ribosomal protein levels but does result in a decrease in 40S and 60S ribosomal subunit abundance. Together, these results demonstrate that Clu is present in the nucleus and required for 40S and 60S biogenesis and global translation in vivo. These results highlight the multifaceted role of Clu in supporting cell function through regulation of mRNA encoding mitochondrial proteins and ribosome biogenesis.

ScRAPdb: an integrated pan-omics database for the Saccharomyces cerevisiae reference assembly panel

As a unicellular eukaryote, the budding yeast Saccharomyces cerevisiae strikes a unique balance between biological complexity and experimental tractability, serving as a long-standing classic model for both basic and applied studies. Recently, S. cerevisiae further emerged as a leading system for studying natural diversity of genome evolution and its associated functional implication at population scales. Having high-quality comparative and functional genomics data are critical for such efforts. Here, we exhaustively expanded the telomere-to-telomere (T2T) S. cerevisiae reference assembly panel (ScRAP) that we previously constructed for 142 strains to cover high-quality genome assemblies and annotations of 264 S. cerevisiae strains from diverse geographical and ecological niches and also 33 outgroup strains from all the other Saccharomyces species complex. We created a dedicated online database, ScRAPdb (https://www.evomicslab.org/db/ScRAPdb/), to host this expanded pangenome collection. Furthermore, ScRAPdb also integrates an array of population-scale pan-omics atlases (pantranscriptome, panproteome and panphenome) and extensive data exploration toolkits for intuitive genomics analyses. All curated data and downstream analysis results can be easily downloaded from ScRAPdb. We expect ScRAPdb to become a highly valuable platform for the yeast community and beyond, leading to a pan-omics understanding of the global genetic and phenotypic diversity.

A genome-to-proteome atlas charts natural variants controlling proteome diversity and forecasts their fitness effects

Despite abundant genomic and phenotypic data across individuals and environments, the functional impact of most mutations on phenotype remains unclear. Here, we bridge this gap by linking genome to proteome in 800 meiotic progeny from an intercross between two closely related Saccharomyces cerevisiae isolates adapted to distinct niches. Modest genetic distance between the parents generated remarkable proteomic diversity that was amplified in the progeny and captured by 6,476 genotype-protein associations, over 1,600 of which we resolved to single variants. Proteomic adaptation emerged through the combined action of numerous cis- and trans-regulatory mutations, a regulatory architecture that was conserved across the species. Notably, trans-regulatory variants often arose in proteins not traditionally associated with gene regulation, such as enzymes. Moreover, the proteomic consequences of mutations predicted fitness under various stresses. Our study demonstrates that the collective action of natural genetic variants drives dramatic proteome diversification, with molecular consequences that forecast phenotypic outcomes.

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 betwen MSL3 (male-specific lethal 3) and Set2-dependent histone marks like trimethylated H3 lysine-36 (H3K36me3). However, Set2 could affect DC via another target, or there could be redundancy between canonical H3.2 and variant H3.3 histones. Furthermore, it is important to parse male-specific effects from those that are 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 or H3K36 mutants, we observe heterogeneous effects. Interestingly, we identified groups of differentially expressed genes (DEGs) whose changes were in opposite directions following loss of H3K36 or Set2, suggesting that H3K36me states have reciprocal functions. In contrast to H4K16R controls, differential expression analysis of combined H3.2K36R/H3.3K36R mutants showed neither consistent reduction in X-gene expression, nor correlation with MSL3 binding. Motif analysis of the DEGs implicated BEAF-32 and other insulator proteins in Set2/H3K36-dependent regulation. Overall, the data are inconsistent with the prevailing model wherein H3K36me3 is essential for spreading the MSL complex to genes along the male X. Rather, we propose that Set2 and H3K36 support DC indirectly, via processes that are utilized by MSL but common to both sexes.

The Smc5/6 complex counteracts R-loop formation at highly transcribed genes in cooperation with RNase H2

The R-loop is a common transcriptional by-product that consists of an RNA-DNA duplex joined to a displaced strand of genomic DNA. While the effects of R-loops on health and disease are well established, there is still an incomplete understanding of the cellular processes responsible for their removal from eukaryotic genomes. Here, we show that a core regulator of chromosome architecture -the Smc5/6 complex- plays a crucial role in the removal of R-loop structures formed during gene transcription. Consistent with this, budding yeast mutants defective in the Smc5/6 complex and enzymes involved in R-loop resolution show strong synthetic interactions and accumulate high levels of RNA-DNA hybrid structures in their chromosomes. Importantly, we demonstrate that the Smc5/6 complex acts on specific types of RNA-DNA hybrid structures in vivo and promotes R-loop degradation by the RNase H2 enzyme in vitro. Collectively, our results reveal a crucial role for the Smc5/6 complex in the removal of toxic R-loops formed at highly transcribed genes and telomeres.

An automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.

Bayesian inference of sample-specific coexpression networks

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

Tdh3 and Rom2 are functional modulators of a conserved condensate-resident RNA-binding protein, Scd6, in Saccharomyces cerevisiae

Arginine-glycine-glycine motif proteins play a crucial role in determining mRNA fate. Suppressor of clathrin deficiency 6 (Scd6) is a conserved arginine-glycine-glycine motif containing ribonucleoprotein (RNP) condensate-resident, translation repressor, and decapping activator protein in Saccharomyces cerevisiae. Identifying protein factors that can modulate Scd6 function is critical to understanding the regulation of mRNA fate by Scd6. In this study, using an approach that combined mRNA tethering assay with flow cytometry, we screened 50 genes for their role in modulating the translation repression activity of Scd6. We identified 8 conserved modulators with human homologs. Of these, we further characterized in detail guanine nucleotide exchange factor Rho1 multicopy suppressor 2 (Rom2) and glycolytic enzyme triose phosphate dehydrogenase 3 (Tdh3), which, respectively, impede and promote translation repression activity of Scd6. Our study reveals that Rom2 negatively regulates the arginine methylation of Scd6 and antagonizes its localization to P-bodies. Tdh3, on the other hand, promotes Scd6 interaction with Hmt1, thereby promoting the arginine methylation of Scd6 and enhanced eIF4G1 interaction, which is known to promote its repression activity. Identifying these novel modulators provides exciting new insights into the role of a metabolic enzyme of the glycolytic pathway and guanine nucleotide exchange factor implicated in the cell wall integrity pathway in regulating Scd6 function and, thereby, cytoplasmic mRNA fate.

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish.

pSPObooster: A Plasmid System to Improve Sporulation Efficiency of Saccharomyces cerevisiae Lab Strains

Common Saccharomyces cerevisiae lab yeast strains derived from S288C have meiotic defects and therefore are poor sporulators. Here, we developed a plasmid system containing corrected alleles of the MKT1 and RME1 genes to rescue the meiotic defects and show that standard BY4741 and BY4742 strains containing the plasmid display faster and more efficient sporulation. The plasmid, pSPObooster, can be maintained as an episome and easily cured or stably integrated into the genome at a single locus. We demonstrate the use of pSPObooster in low- and high-throughput yeast genetic manipulations and show that it can expedite both procedures without impacting strain behavior.

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Non-canonical chromatin-based functions for the threonine metabolic pathway

The emerging class of multi-functional proteins known as moonlighters challenges the "one protein, one function" mentality by demonstrating crosstalk between biological pathways that were previously thought to be functionally discrete. Here, we present new links between amino acid metabolism and chromatin regulation, two biological pathways that are critical for cellular and organismal homeostasis. We discovered that the threonine biosynthetic pathway is required for the transcriptional silencing of ribosomal DNA (rDNA) in Saccharomyces cerevisiae. The enzymes in the pathway promote rDNA silencing through distinct mechanisms as a subset of silencing phenotypes was rescued with exogenous threonine. In addition, we found that a key pathway enzyme, homoserine dehydrogenase, promotes DNA repair through a mechanism involving the MRX complex, a major player in DNA double strand break repair. These data further the understanding of enzymes with non-canonical roles, here demonstrated within the threonine biosynthetic pathway, and provide insight into their roles as potential anti-fungal pharmaceutical targets.

Dysfunction of Telomeric Cdc13-Stn1-Ten1 Simultaneously Activates DNA Damage and Spindle Checkpoints

Telomeres, the ends of eukaryotic linear chromosomes, are composed of repeated DNA sequences and specialized proteins, with the conserved telomeric Cdc13/CTC1-Stn1-Ten1 (CST) complex providing chromosome stability via telomere end protection and the regulation of telomerase accessibility. In this study, SIZ1, coding for a SUMO E3 ligase, and TOP2 (a SUMO target for Siz1 and Siz2) were isolated as extragenic suppressors of Saccharomyces cerevisiae CST temperature-sensitive mutants. ten1-sz, stn1-sz and cdc13-sz mutants were isolated next due to being sensitive to intracellular Siz1 dosage. In parallel, strong negative genetic interactions between mutants of CST and septins were identified, with septins being noticeably sumoylated through the action of Siz1. The temperature-sensitive arrest in these new mutants of CST was dependent on the G2/M Mad2-mediated and Bub2-mediated spindle checkpoints as well as on the G2/M Mec1-mediated DNA damage checkpoint. Our data suggest the existence of yet unknown functions of the telomeric Cdc13-Stn1-Ten1 complex associated with mitotic spindle positioning and/or assembly that could be further elucidated by studying these new ten1-sz, stn1-sz and cdc13-sz mutants.

The rise and future of CRISPR-based approaches for high-throughput genomics

Clustered regularly interspaced short palindromic repeats (CRISPR) has revolutionized the field of genome editing. To circumvent the permanent modifications made by traditional CRISPR techniques and facilitate the study of both essential and nonessential genes, CRISPR interference (CRISPRi) was developed. This gene-silencing technique employs a deactivated Cas effector protein and a guide RNA to block transcription initiation or elongation. Continuous improvements and a better understanding of the mechanism of CRISPRi have expanded its scope, facilitating genome-wide high-throughput screens to investigate the genetic basis of phenotypes. Additionally, emerging CRISPR-based alternatives have further expanded the possibilities for genetic screening. This review delves into the mechanism of CRISPRi, compares it with other high-throughput gene-perturbation techniques, and highlights its superior capacities for studying complex microbial traits. We also explore the evolution of CRISPRi, emphasizing enhancements that have increased its capabilities, including multiplexing, inducibility, titratability, predictable knockdown efficacy, and adaptability to nonmodel microorganisms. Beyond CRISPRi, we discuss CRISPR activation, RNA-targeting CRISPR systems, and single-nucleotide resolution perturbation techniques for their potential in genome-wide high-throughput screens in microorganisms. Collectively, this review gives a comprehensive overview of the general workflow of a genome-wide CRISPRi screen, with an extensive discussion of strengths and weaknesses, future directions, and potential alternatives.

CRISPRi-TnSeq maps genome-wide interactions between essential and non-essential genes in bacteria

Genetic interactions identify functional connections between genes and pathways, establishing gene functions or druggable targets. Here we use CRISPRi-TnSeq, CRISPRi-mediated knockdown of essential genes alongside TnSeq-mediated knockout of non-essential genes, to map genome-wide interactions between essential and non-essential genes in Streptococcus pneumoniae. Transposon-mutant libraries constructed in 13 CRISPRi strains enabled screening of ~24,000 gene pairs. This identified 1,334 genetic interactions, including 754 negative and 580 positive interactions. Network analyses show that 17 non-essential genes pleiotropically interact with more than half the essential genes tested. Validation experiments confirmed that a 7-gene subset protects against perturbations. Furthermore, we reveal hidden redundancies that compensate for essential gene loss, relationships between cell wall synthesis, integrity and cell division, and show that CRISPRi-TnSeq identifies synthetic and suppressor-type relationships between both functionally linked and disparate genes and pathways. Importantly, in species where CRISPRi and Tn-Seq are established, CRISPRi-TnSeq should be straightforward to implement.

Functional genomic analysis of genes important for Candida albicans fitness in diverse environmental conditions

Fungal pathogens such as Candida albicans pose a significant threat to human health with limited treatment options available. One strategy to expand the therapeutic target space is to identify genes important for pathogen growth in host-relevant environments. Here, we leverage a pooled functional genomic screening strategy to identify genes important for fitness of C. albicans in diverse conditions. We identify an essential gene with no known Saccharomyces cerevisiae homolog, C1_09670C, and demonstrate that it encodes subunit 3 of replication factor A (Rfa3). Furthermore, we apply computational analyses to identify functionally coherent gene clusters and predict gene function. Through this approach, we predict the cell-cycle-associated function of C3_06880W, a previously uncharacterized gene required for fitness specifically at elevated temperatures, and follow-up assays confirm that C3_06880W encodes Iml3, a component of the C. albicans kinetochore with roles in virulence in vivo. Overall, this work reveals insights into the vulnerabilities of C. albicans.

Role of a novel endoplasmic reticulum-resident glycoprotein Mtc6/Ehg2 in high-pressure growth: stability of tryptophan permease Tat2 in Saccharomyces cerevisiae

Deep-sea organisms are subjected to extreme conditions; therefore, understanding their adaptive strategies is crucial. We utilize Saccharomyces cerevisiae as a model to investigate pressure-dependent protein regulation and piezo-adaptation. Using yeast deletion library analysis, we identified 6 poorly characterized genes that are crucial for high-pressure growth, forming novel functional modules associated with cell growth. In this study, we aimed to unravel the molecular mechanisms of high-pressure adaptation in S. cerevisiae, focusing on the role of MTC6. MTC6, the gene encoding the novel glycoprotein Mtc6/Ehg2, was found to stabilize tryptophan permease Tat2, ensuring efficient tryptophan uptake and growth under high pressure at 25 MPa. The loss of MTC6 led to promoted vacuolar degradation of Tat2, depending on the Rsp5-Bul1 ubiquitin ligase complex. These findings enhance our understanding of deep-sea adaptations and stress biology, with broad implications for biotechnology, environmental microbiology, and evolutionary insights across species.

Screen for temperature-sensitive mutants of non-essential yeast genes

Yeast deletion mutants of crucial genes are often associated with a number of secondary defects, which hamper the analysis of primary protein function. Therefore, temperature-sensitive mutants are valuable tools to evaluate protein function in a focused and often reversible manner. However, temperature-sensitive mutants are uncommon for non-essential genes that nevertheless may have strong defects. Here we describe a screening method for generating temperature-sensitive mutants of non-essential genes in synthetic lethal backgrounds of Saccharomyces cerevisiae. As proof of principle, we describe a successful screen for the yeast mitochondrial inner membrane protease iAAA subunit Yme1 utilizing two screening approaches: a random mutagenesis and rational design approach. We then describe how candidate temperature-sensitive mutants are validated.

Comprehensive double-mutant analysis of the Bacillus subtilis envelope using double-CRISPRi

Understanding bacterial gene function remains a major biological challenge. Double-mutant genetic interaction (GI) analysis addresses this challenge by uncovering the functional partners of targeted genes, allowing us to associate genes of unknown function with novel pathways and unravel connections between well-studied pathways, but is difficult to implement at the genome-scale. Here, we develop and use double-CRISPRi to systematically quantify genetic interactions at scale in the Bacillus subtilis envelope, including essential genes. We discover > 1000 known and novel genetic interactions. Our analysis pipeline and experimental follow-ups reveal the distinct roles of paralogous genes such as the mreB and mbl actin homologs, and identify new genes involved in the well-studied process of cell division. Overall, our study provides valuable insights into gene function and demonstrates the utility of double-CRISPRi for high-throughput dissection of bacterial gene networks, providing a blueprint for future studies in diverse bacterial species.

Analysis of mitochondrial biogenesis and protein localization by genetic screens and automated imaging

Budding yeast is a laboratory model of a simple eukaryotic cell. Its compact genome is very easy to edit. This allowed to create systematic collections (libraries) of yeast strains where every gene is either perturbed or tagged. Here we review how such collections were used to study mitochondrial biology by doing genetic screens. First, we introduce the principles of yeast genome editing and the basics of its life cycle that are useful for genetic experiments. Then we overview what yeast strain collections were created over the past years. We also describe the creation and the usage of the new generation of SWAP-Tag (SWAT) collections that allow to create custom libraries. We outline the principles of changing the genetic background of whole collections in parallel, and the basics of synthetic genetic array (SGA) approach. Then we review the discoveries that were made using different types of genetic screens focusing on general mitochondrial functions, proteome, and protein targeting pathways. The development of new collections and screening techniques will continue to bring valuable insight into the function of mitochondria and other organelles.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

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

The Ccr4-Not complex contains the poorly understood Not4 ubiquitin ligase that functions in transcription, mRNA decay, translation, proteostasis, and endolysosomal nutrient signaling. To gain further insight into the in vivo functions of the ligase, we performed quantitative proteomics in Saccharomyces cerevisiae using yeast cells lacking Not4, or cells overexpressing wild-type Not4 or an inactive Not4 mutant. Herein, we provide evidence that balanced Not4 activity maintains ribosomal protein (RP) homeostasis independent of changes to RP mRNA or known Not4 ribosomal substrates. Intriguingly, we also find that Not4 loss activates 40S ribosomal autophagy independently of canonical Atg7-dependent macroautophagy, indicating that microautophagy is responsible. We previously demonstrated that Ccr4-Not stimulates the target of rapamycin complex 1 (TORC1) signaling, which activates RP expression and inhibits autophagy, by maintaining vacuole V-ATPase H+ pump activity. Importantly, combining Not4 deficient cells with a mutant that blocks vacuole H+ export fully restores RP expression and increases 40S RP autophagy efficiency. In contrast, restoring TORC1 activity alone fails to rescue either process, indicating that Not4 loss disrupts additional endolysosomal functions that regulate RP expression and 40S autophagy. Analysis of the Not4-regulated proteome reveals increases in endolysosomal and autophagy-related factors that functionally interact with Not4 to control RP expression and affect 40S autophagy. Collectively, our data indicate that balanced Ccr4-Not ubiquitin ligase signaling maintains RP homeostasis and inhibits 40S autophagy via the ligase's emerging role as an endolysosomal regulator.

Unlocking biological mechanisms with integrative functional genomics approaches

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

Pros and cons of auxin-inducible degron as a tool for regulated depletion of telomeric proteins from Saccharomyces cerevisiae

To assess the immediate responses of the yeast cells to telomere defects, we employed the auxin-inducible degron (AID) enabling rapid depletion of essential (Rap1, Tbf1, Cdc13, Stn1) and non-essential (Est1, Est2, Est3) telomeric proteins. Using two variants of AID systems, we show that most of the studied proteins are depleted within 10-30 min after the addition of auxin. As expected, depletion of essential proteins yields nondividing cells, provided that the strains are cultivated in an appropriate carbon source and at temperatures lower than 28°C. Cells with depleted Cdc13 and Stn1 exhibit extension of the single-stranded overhang as early as 3 h after addition of auxin. Notably, prolonged incubation of strains carrying AID-tagged essential proteins in the presence of auxin resulted in the appearance of auxin-resistant clones, caused at least in part by mutations within the OsTIR1 gene. Upon assessing the length of telomeres in strains carrying AID-tagged non-essential telomeric proteins, we found that the depletion of Est1 and Est3 leads to auxin-dependent telomere shortening. However, the EST3-AID strain had slightly shorter telomeres even in the absence of auxin. Furthermore, a strain with the AID-tagged version of Est2 (catalytic subunit of telomerase) not only had shorter telomeres in the absence of auxin but also did not exhibit auxin-dependent telomere shortening. Our results demonstrate that while AID can be useful in assessing immediate cellular responses to telomere deprotection, each strain must be carefully evaluated for the effect of AID-tag on the properties of the protein of interest.

Characterizing interactions of endoplasmic reticulum resident proteins in situ through the YST-PPI method

The mutual interactions of endoplasmic reticulum (ER) resident proteins in the ER maintain its functions, prompting the protein folding, modification, and transportation. Here, a new method, named YST-PPI (YESS-based Split fast TEV protease system for Protein-Protein Interaction) was developed, targeting the characterization of protein interactions in ER. YST-PPI method integrated the YESS system, split-TEV technology, and endoplasmic reticulum retention signal peptide (ERS) to provide an effective strategy for studying ER in situ PPIs in a fast and quantitative manner. The interactions among 15 ER-resident proteins, most being identified molecular chaperones, of S. cerevisiae were explored using the YST-PPI system, and their interaction network map was constructed, in which more than 74 interacting resident protein pairs were identified. Our studies also showed that Lhs1p plays a critical role in regulating the interactions of most of the ER-resident proteins, except the Sil1p, indicating its potential role in controlling the ER molecular chaperones. Moreover, the mutual interaction revealed by our studies further confirmed that the ER-resident proteins perform their functions in a cooperative way and a multimer complex might be formed during the process.

Synthetic lethality and the minimal genome size problem

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

Vitamin B5 metabolism is essential for vacuolar and mitochondrial functions and drug detoxification in fungi

Fungal infections, a leading cause of mortality among eukaryotic pathogens, pose a growing global health threat due to the rise of drug-resistant strains. New therapeutic strategies are urgently needed to combat this challenge. The PCA pathway for biosynthesis of Co-enzyme A (CoA) and Acetyl-CoA (AcCoA) from vitamin B5 (pantothenic acid) has been validated as an excellent target for the development of new antimicrobials against fungi and protozoa. The pathway regulates key cellular processes including metabolism of fatty acids, amino acids, sterols, and heme. In this study, we provide genetic evidence that disruption of the PCA pathway in Saccharomyces cerevisiae results in a significant alteration in the susceptibility of fungi to a wide range of xenobiotics, including clinically approved antifungal drugs through alteration of vacuolar morphology and drug detoxification. The drug potentiation mediated by genetic regulation of genes in the PCA pathway could be recapitulated using the pantazine analog PZ-2891 as well as the celecoxib derivative, AR-12 through inhibition of fungal AcCoA synthase activity. Collectively, the data validate the PCA pathway as a suitable target for enhancing the efficacy and safety of current antifungal therapies.

Investigating the role of RNA-binding protein Ssd1 in aneuploidy tolerance through network analysis

RNA-binding proteins (RBPs) play critical cellular roles by mediating various stages of RNA life cycles. Ssd1, an RBP with pleiotropic effects, has been implicated in aneuploidy tolerance in Saccharomyces cerevisiae but its mechanistic role remains unclear. Here we used a network-based approach to inform on Ssd1's role in aneuploidy tolerance, by identifying and experimentally perturbing a network of RBPs that share mRNA targets with Ssd1. We identified RBPs whose bound mRNA targets significantly overlap with Ssd1 targets. For 14 identified RBPs, we then used a genetic approach to generate all combinations of genotypes for euploid and aneuploid yeast with an extra copy of chromosome XII, with and without SSD1 and/or the RBP of interest. Deletion of 10 RBPs either exacerbated or alleviated the sensitivity of wild-type and/or ssd1 Δ cells to chromosome XII duplication, in several cases indicating genetic interactions with SSD1 in the context of aneuploidy. We integrated these findings with results from a global over-expression screen that identified genes whose duplication complements ssd1 Δ aneuploid sensitivity. The resulting network points to a sub-group of proteins with shared roles in translational repression and p-body formation, implicating these functions in aneuploidy tolerance. Our results reveal a role for new RBPs in aneuploidy tolerance and support a model in which Ssd1 mitigates translation-related stresses in aneuploid cells.

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

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.

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.

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

What are the 100 most cited fungal genera?

The global diversity of fungi has been estimated between 2 to 11 million species, of which only about 155 000 have been named. Most fungi are invisible to the unaided eye, but they represent a major component of biodiversity on our planet, and play essential ecological roles, supporting life as we know it. Although approximately 20 000 fungal genera are presently recognised, the ecology of most remains undetermined. Despite all this diversity, the mycological community actively researches some fungal genera more commonly than others. This poses an interesting question: why have some fungal genera impacted mycology and related fields more than others? To address this issue, we conducted a bibliometric analysis to identify the top 100 most cited fungal genera. A thorough database search of the Web of Science, Google Scholar, and PubMed was performed to establish which genera are most cited. The most cited 10 genera are Saccharomyces, Candida, Aspergillus, Fusarium, Penicillium, Trichoderma, Botrytis, Pichia, Cryptococcus and Alternaria. Case studies are presented for the 100 most cited genera with general background, notes on their ecology and economic significance and important research advances. This paper provides a historic overview of scientific research of these genera and the prospect for further research. Citation: Bhunjun CS, Chen YJ, Phukhamsakda C, Boekhout T, Groenewald JZ, McKenzie EHC, Francisco EC, Frisvad JC, Groenewald M, Hurdeal VG, Luangsa-ard J, Perrone G, Visagie CM, Bai FY, Błaszkowski J, Braun U, de Souza FA, de Queiroz MB, Dutta AK, Gonkhom D, Goto BT, Guarnaccia V, Hagen F, Houbraken J, Lachance MA, Li JJ, Luo KY, Magurno F, Mongkolsamrit S, Robert V, Roy N, Tibpromma S, Wanasinghe DN, Wang DQ, Wei DP, Zhao CL, Aiphuk W, Ajayi-Oyetunde O, Arantes TD, Araujo JC, Begerow D, Bakhshi M, Barbosa RN, Behrens FH, Bensch K, Bezerra JDP, Bilański P, Bradley CA, Bubner B, Burgess TI, Buyck B, Čadež N, Cai L, Calaça FJS, Campbell LJ, Chaverri P, Chen YY, Chethana KWT, Coetzee B, Costa MM, Chen Q, Custódio FA, Dai YC, Damm U, de Azevedo Santiago ALCM, De Miccolis Angelini RM, Dijksterhuis J, Dissanayake AJ, Doilom M, Dong W, Alvarez-Duarte E, Fischer M, Gajanayake AJ, Gené J, Gomdola D, Gomes AAM, Hausner G, He MQ, Hou L, Iturrieta-González I, Jami F, Jankowiak R, Jayawardena RS, Kandemir H, Kiss L, Kobmoo N, Kowalski T, Landi L, Lin CG, Liu JK, Liu XB, Loizides M, Luangharn T, Maharachchikumbura SSN, Makhathini Mkhwanazi GJ, Manawasinghe IS, Marin-Felix Y, McTaggart AR, Moreau PA, Morozova OV, Mostert L, Osiewacz HD, Pem D, Phookamsak R, Pollastro S, Pordel A, Poyntner C, Phillips AJL, Phonemany M, Promputtha I, Rathnayaka AR, Rodrigues AM, Romanazzi G, Rothmann L, Salgado-Salazar C, Sandoval-Denis M, Saupe SJ, Scholler M, Scott P, Shivas RG, Silar P, Souza-Motta CM, Silva-Filho AGS, Spies CFJ, Stchigel AM, Sterflinger K, Summerbell RC, Svetasheva TY, Takamatsu S, Theelen B, Theodoro RC, Thines M, Thongklang N, Torres R, Turchetti B, van den Brule T, Wang XW, Wartchow F, Welti S, Wijesinghe SN, Wu F, Xu R, Yang ZL, Yilmaz N, Yurkov A, Zhao L, Zhao RL, Zhou N, Hyde KD, Crous PW (2024). What are the 100 most cited fungal genera? Studies in Mycology 108: 1-411. doi: 10.3114/sim.2024.108.01.