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

Qarles: a web server for the quick characterization of large sets of genes

The characterization of gene sets is a recurring task in computational biology. Identifying specific properties of a hit set compared to a reference set can reveal biological roles and mechanisms, and can lead to the prediction of new hits. However, collecting the features to evaluate can be time consuming, and implementing an informative but compact graphical representation of the multiple comparisons can be challenging, particularly for bench scientists. Here, I present Qarles (quick characterization of large sets of genes), a web server that annotates Saccharomyces cerevisiae gene sets by querying a database of 31 features widely used by the yeast community and that identifies their specific properties, providing publication-ready figures and reliable statistics. Qarles has a deliberately simple user interface with all the functionality in a single web page and a fast response time to facilitate adoption by the scientific community. Qarles provides a rich and compact graphical output, including up to five gene set comparisons across 31 features in a single dotplot, and interactive boxplots to enable the identification of outliers. Qarles can also predict new hit genes by using a random forest trained on the selected features. The web server is freely available at https://qarles.org.

A single-cell resolved genotype-phenotype map using genome-wide genetic and environmental perturbations

Heterogeneity is inherent to living organisms and it determines cell fate and phenotypic variability. Despite its ubiquity, the underlying molecular mechanisms and the genetic basis linking genotype to-phenotype heterogeneity remain a central challenge. Here we construct a yeast knockout library with a clone and genotype RNA barcoding structure suitable for genome-scale analyses to generate a high-resolution single-cell yeast transcriptome atlas of 3500 mutants under control and stress conditions. We find that transcriptional heterogeneity reflects the coordinated expression of specific gene programs, generating a continuous of cell states that can be responsive to external insults. Cell state plasticity can be genetically modulated with mutants that act as state attractors and disruption of state homeostasis results in decreased adaptive fitness. Leveraging on intra-genetic variability, we establish that regulators of transcriptional heterogeneity are functionally diverse and influenced by the environment. Our multimodal perturbation-based single-cell Genotype-to-Transcriptome Atlas in yeast provides insights into organism-level responses.

Equitable machine learning counteracts ancestral bias in precision medicine

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. Therapeutics and outcomes remain hidden because we lack insights that could be gained from analyzing ancestrally diverse genomic data. To address this significant gap, we present PhyloFrame, a machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating functional interaction networks and population genomics data with transcriptomic training data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. Validation in fourteen ancestrally diverse datasets demonstrates that PhyloFrame is better able to adjust for ancestry bias across all populations. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. Analysis of performance in the most diverse continental ancestry group, African, illustrates how phylogenetic distance from training data negatively impacts model performance, as well as PhyloFrame's capacity to mitigate these effects. These results demonstrate how equitable artificial intelligence (AI) approaches can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

Thirty Years of BRCA1: Mechanistic Insights and Their Impact on Mutation Carriers

SIGNIFICANCE

Here, we explore the impact of three decades of BRCA1 research on the lives of mutation carriers and propose strategies to improve the prevention and treatment of BRCA1-associated cancer.

Coordination between ESCRT function and Rab conversion during endosome maturation

The endosomal pathway is essential for regulating cell signaling and cellular homeostasis. Rab5 positive early endosomes receive proteins from the plasma membrane. Dependent on a ubiquitin mark on the protein, they will be either recycled or sorted into intraluminal vesicles (ILVs) by endosomal sorting complex required for transport (ESCRT) proteins. During endosome maturation Rab5 is replaced by Rab7 on endosomes that are able to fuse with lysosomes to form endolysosomes. However, whether ESCRT-driven ILV formation and Rab5-to-Rab7 conversion are coordinated remains unknown. Here we show that loss of early ESCRTs led to enlarged Rab5 positive endosomes and prohibited Rab conversion. Reduction of ubiquitinated cargo alleviated this phenotype. Moreover, ubiquitinated proteins on the endosomal limiting membrane prevented the displacement of the Rab5 guanine nucleotide exchange factor (GEF) RABX-5 by the GEF for Rab7, SAND-1/CCZ-1. Overexpression of Rab7 could partially overcome this block, even in the absence of SAND-1 or CCZ1, suggesting the presence of a second Rab7 GEF. Our data reveal a hierarchy of events in which cargo corralling by ESCRTs is upstream of Rab conversion, suggesting that ESCRT-0 and ubiquitinated cargo could act as timers that determine the onset of Rab conversion.

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.

The kinesin Kar3 is required for endoplasmic reticulum-associated degradation

Degradation of aberrant, excess, and regulatory proteins at the endoplasmic reticulum (ER) is a conserved feature of eukaryotic cells, disruption of which contributes to disease. While remarkable progress has been made in recent years, mechanisms and genetic requirements for ER-associated degradation (ERAD) remain incompletely understood. We recently conducted a screen for genes required for turnover of a model ER translocon-associated substrate of the Hrd1 ubiquitin ligase in Saccharomyces cerevisiae. This screen revealed loss of Kar3 impedes degradation of Deg1*-Sec62, which persistently and aberrantly engages the translocon. Kar3 is a microtubule-associated kinesin 14 family member that impacts multiple aspects of microtubule dynamics during cell division and karyogamy. We investigated involvement of Kar3 and its cofactors in ERAD. Loss of Kar3 hindered ERAD mediated by three ubiquitin ligases but did not impair turnover of a soluble nuclear protein. Further, KAR3 deletion caused hypersensitivity to conditions associated with proteotoxic stress. Kar3's cytoplasmic cofactor Vik1 was also required for efficient degradation of Deg1*-Sec62. Our results reveal a profound and underappreciated role for microtubule-associated proteins in ERAD.

PRODE recovers essential and context-essential genes through neighborhood-informed scores

Gene context-essentiality assessment supports precision oncology opportunities. The variability of gene effects inference from loss-of-function screenings across models and technologies limits identifying robust hits. We propose a computational framework named PRODE that integrates gene effects with protein-protein interactions to generate neighborhood-informed essential (NIE) and neighborhood-informed context essential (NICE) scores. It outperforms the canonical gene effect approach in recovering missed essential genes in shRNA screens and prioritizing context-essential hits from CRISPR-KO screens, as supported by in vitro validations. Applied to Her2 + breast cancer tumor samples, PRODE identifies oxidative phosphorylation genes as vulnerabilities with prognostic value, highlighting new therapeutic opportunities.

The integrated stress response regulates 18S nonfunctional rRNA decay in mammals

18S nonfunctional rRNA decay (NRD) detects and eliminates translationally nonfunctional 18S rRNA. Although this process is critical for ribosome quality control, the mechanisms underlying nonfunctional 18S rRNA turnover remain elusive, particularly in mammals. Here, we show that mammalian 18S NRD initiates through the integrated stress response (ISR) via GCN2. Nonfunctional 18S rRNA induces translational arrest at start sites. Biochemical analyses demonstrate that ISR activation limits translation initiation and attenuates collisions between scanning 43S preinitiation complexes and stalled nonfunctional ribosomes. The ISR promotes 18S NRD and 40S ribosomal protein turnover by RNF10-mediated ubiquitination. Ultimately, RIOK3 binds the resulting ubiquitinated 40S subunits and facilitates 18S rRNA decay. Overall, mammalian 18S NRD acts through GCN2, followed by ubiquitin-dependent 18S rRNA degradation involving the ubiquitin E3 ligase RNF10 and the atypical protein kinase RIOK3. These findings establish a dynamic feedback mechanism by which the GCN2-RNF10-RIOK3 axis surveils ribosome functionality at the translation initiation step.

A gene-based model of fitness and its implications for genetic variation: linkage disequilibrium

A widely used model of the effects of mutations on fitness (the "sites" model) assumes that heterozygous recessive or partially recessive deleterious mutations at different sites in a gene complement each other, similarly to mutations in different genes. However, the general lack of complementation between major effect allelic mutations suggests an alternative possibility, which we term the "gene" model. This assumes that a pair of heterozygous deleterious mutations in trans behave effectively as homozygotes, so that the fitnesses of trans heterozygotes are lower than those of cis heterozygotes. We examine the properties of the two different models, using both analytical and simulation methods. We show that the gene model predicts positive linkage disequilibrium (LD) between deleterious variants within the coding sequence, under conditions when the sites model predicts zero or slightly negative LD. We also show that focussing on rare variants when examining patterns of LD, especially with Lewontin's D ' measure, is likely to produce misleading results with respect to inferences concerning the causes of the sign of LD. Synergistic epistasis between pairs of mutations was also modeled; it is less likely to produce negative LD under the gene model than the sites model. The theoretical results are discussed in relation to patterns of LD in natural populations of several species.

Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis

Epistasis - the interaction between alleles at different genetic loci - plays a fundamental role in biology. However, several recent approaches quantify epistasis using a chimeric formula that measures deviations from a multiplicative fitness model on an additive scale, thus mixing two scales. Here, we show that for pairwise interactions, the chimeric formula yields a different magnitude but the same sign 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. We resolve these inconsistencies by deriving mathematical relationships between the different epistasis formulae and different parametrizations of the multivariate Bernoulli distribution. We argue that the chimeric formula does not appropriately model interactions between the Bernoulli random variables. In simulations, we show that the chimeric formula is less accurate than the classical multiplicative/additive epistasis formulae and may falsely detect higher-order epistasis. Analyzing multi-gene knockouts in yeast, multi-way drug interactions in E. coli, and deep mutational scanning of several proteins, we find that approximately 10% to 60% of inferred higher-order interactions change sign using the multiplicative/additive formula compared to the chimeric formula.

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

Paralogous genes are often functionally redundant for long periods of time. While their functions are preserved, paralogs accumulate cryptic changes in sequence and expression, which could modulate the impact of future mutations through epistasis. We examine the impact of mutations on redundant myosin proteins that have maintained the same binding preference despite having accumulated differences in expression levels and amino acid substitutions in the last 100 million years. By quantifying the impact of all single-amino acid substitutions in their SH3 domains on the physical interaction with their interaction partners, we show that the same mutations in the paralogous SH3s change binding in a paralog-specific and interaction partner-specific manner. This contingency is explained by the difference in promoter strength of the two paralogous myosin genes and epistatic interactions between the mutations introduced and cryptic divergent sites within the SH3s. One significant consequence of this contingency is that while some mutations would be sufficient to nonfunctionalize one paralog, they would have minimal impact on the other. Our results reveal how cryptic divergence, which accumulates while maintaining functional redundancy in cellular networks, could bias gene duplicates to specific fates.

SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction

BACKGROUND

A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships between distant proteins within PPI networks is essential for improving the accuracy of protein function prediction. Most current studies attempt to capture these distant relationships by stacking graph network layers, but performance gains diminish as the number of layers increases.

RESULTS

To further explore the potential functional relationships between multi-hop proteins in PPI networks, this paper proposes SEGT-GO, a Graph Transformer method based on PPI multi-hop neighborhood Serialization and Explainable artificial intelligence for large-scale multispecies protein function prediction. The multi-hop neighborhood serialization maps multi-hop information in the PPI Network into serialized feature embeddings, enabling the Graph Transformer to learn deeper functional features within the PPI Network. Based on game theory, the SHAP eXplainable Artificial Intelligence (XAI) framework optimizes model input and filters out feature noise, enhancing model performance.

CONCLUSIONS

Compared to the advanced network method DeepGraphGO, SEGT-GO achieves more competitive results in standard large-scale datasets and superior results on small ones, validating its ability to extract functional information from deep proteins. Furthermore, SEGT-GO achieves superior results in cross-species learning and prediction of the functions of unseen proteins, further proving the method's strong generalization.

Simplicity within biological complexity

Motivation

Heterogeneous, interconnected, systems-level, molecular (multi-omic) data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs.

Results

In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods (also called graph representation learning) map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications, and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics, focusing on precision medicine and personalized drug discovery. It will lead to a paradigm shift in the computational and biomedical understanding of data and diseases that will open up ways to solve some of the major bottlenecks in precision medicine and other domains.

Saturated Transposon Analysis in Yeast as a one-step method to quantify the fitness effects of gene disruptions on a genome-wide scale

Transposon insertion site sequencing (TIS) is a powerful tool that has significantly advanced our knowledge of functional genomics. For example, TIS has been used to identify essential genes of Saccharomyces cerevisiae, screen for antibiotic resistance genes in Klebsiella pneumoniae and determine the set of genes required for virulence of Mycobacterium tuberculosis. While providing valuable insights, these applications of TIS focus on (conditional) gene essentiality and neglect possibly interesting but subtle differences in the importance of genes for fitness. Notably, it has been demonstrated that data obtained from TIS experiments can be used for fitness quantification and the construction of genetic interaction maps, but this potential is only sporadically exploited. Here, we present a method to quantify the fitness of gene disruption mutants using data obtained from a TIS screen developed for the yeast Saccharomyces cerevisiae called SATAY. We show that the mean read count per transposon insertion site provides a metric for fitness that is robust across biological and technical replicate experiments. Importantly, the ability to resolve differences between gene disruption mutants with low fitness depends crucially on the inclusion of insertion sites that are not observed in the sequencing data to estimate the mean. While our method provides reproducible results between replicate SATAY datasets, the obtained fitness distribution differs substantially from those obtained using other techniques. It is currently unclear whether these inconsistencies are due to biological or technical differences between the methods. We end with suggestions for modifications of the SATAY procedure that could improve the resolution of the fitness estimates. Our analysis indicates that increasing the sequencing depth does very little to reduce the uncertainty in the estimates, while replacing the PCR amplification with methods that avoid or reduce the number of amplification cycles will likely be most effective in reducing noise.

Combinatorial In Vivo Genome Editing Identifies Widespread Epistasis and an Accessible Fitness Landscape 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 the evolution of cancer in vivo, largely due to a lack of methods for investigating genetic interactions in a high-throughput and quantitative manner. Here, we employed a novel platform to generate tumors with inactivation of pairs of ten diverse 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 synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. Sign epistasis was extremely rare, suggesting a surprisingly accessible fitness landscape during lung tumorigenesis. These findings expand our understanding of the interactions that drive tumorigenesis in vivo.

mTOR signaling controls protein aggregation during heat stress and cellular aging in a translation- and Hsf1-independent manner

The mechanistic target of rapamycin (mTOR) signaling pathway appears central to the aging process as genetic or pharmacological inhibition of mTOR extends lifespan in most eukaryotes tested. While the regulation of protein synthesis by mTOR has been studied in great detail, its impact on protein misfolding and aggregation during stress and aging is less explored. In this study, we identified the mTOR signaling pathway and the linked Seh1-associated complex as central nodes of protein aggregation during heat stress and cellular aging, using Saccharomyces cerevisiae as a model organism. Based on a synthetic genetic array screen, we found that reduced mTOR activity, achieved through deletion of TCO89, an mTORC1 subunit, almost completely prevents protein aggregation during heat stress and aging without reducing global translation rates and independently of an Hsf1-dependent stress response. Conversely, increased mTOR activity, achieved through deletion of NPR3, a Seh1-associated complex subunit, exacerbates protein aggregation, but not by overactivating translation. In summary, our work demonstrates that mTOR signaling is a central contributor to age-associated and heat shock-induced protein aggregation, and that this is unlinked to quantitatively discernable effects on translation and Hsf1.

TUDCA modulates drug bioavailability to regulate resistance to acute ER stress in Saccharomyces cerevisiae

Cells counter accumulation of misfolded secretory proteins in the endoplasmic reticulum (ER) through activation of the Unfolded Protein Response (UPR). Small molecules termed chemical chaperones can promote protein folding to alleviate ER stress. The bile acid tauroursodeoxycholic acid (TUDCA) has been described as a chemical chaperone. While promising in models of protein folding diseases, TUDCA's mechanism of action remains unclear. Here, we found TUDCA can rescue growth of yeast treated with the ER stressor tunicamycin (Tm), even in the absence of a functional UPR. In contrast, TUDCA failed to rescue growth on other ER stressors. Nor could TUDCA attenuate chronic UPR associated with specific gene deletions or overexpression of a misfolded mutant secretory protein. Neither pretreatment with nor delayed addition of TUDCA conferred protection against Tm. Importantly, attenuation of Tm-induced toxicity required TUDCA's critical micelle forming concentration, suggesting a mechanism where TUDCA directly sequesters drugs. Indeed, in several assays, TUDCA-treated cells closely resembled cells treated with lower doses of Tm. In addition, we found TUDCA can inhibit dyes from labeling intracellular compartments. Thus, our study challenges the model of TUDCA as a chemical chaperone and suggests that TUDCA decreases drug bioavailability, allowing cells to adapt to ER stress.

More than Just Protein Folding: The Epichaperome, Mastermind of the Cancer Cell

The epichaperome, a dynamic and integrated network of chaperone proteins, extends its roles beyond basic protein folding to protein stabilization and intracellular signal transduction to orchestrating a multitude of cellular processes critical for tumor survival. In this review, we explore the multifaceted roles of the epichaperome, delving into its diverse cellular locations, factors that modulate its formation and function, its liquid-liquid phase separation, and the key signaling and crosstalk pathways it regulates, including cellular metabolism and intracellular signal transduction. We further highlight techniques for isolating and identifying epichaperome networks, pitfalls, and opportunities. Further, we review the profound implications of the epichaperome for cancer treatment and therapy design, underscoring the need for strategic engineering that hinges on a comprehensive insight into the comprehensive structure and workings of the epichaperome across the heterogeneous cell subpopulations in the tumor milieu. By presenting a holistic view of the epichaperome's functions and mechanisms, we aim to underscore its potential as a key target for novel anti-cancer strategies, revealing that the epichaperome is not merely a piece of protein folding machinery but a mastermind that facilitates the malignant phenotype.

Diphthamide synthesis is linked to the eEF2-client chaperone machinery

The diphthamide modification of eukaryotic translation elongation factor (eEF2) is important for accurate protein synthesis. While the enzymes for diphthamide synthesis are known, coordination of eEF2 synthesis with the diphthamide modification to maintain only modified eEF2 is unknown. Physical and genetic interactions extracted from BioGRID show a connection between diphthamide synthesis enzymes and chaperones in yeast. This includes the Hsp90 co-chaperones Hgh1 and Cpr7. The respective co-chaperone deletion strains contained eEF2 without diphthamide. Notably, strains deficient in other co-chaperones showed no defect in the eEF2-diphthamide modification. Our results demonstrate that diphthamide synthesis involves not only Dph enzymes but also the eEF2-interacting co-chaperones Hgh1 and Cpr7 and may thus require a conformational state of eEF2 which is maintained by specific (co-)chaperones.

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.

Stress granule formation is regulated by signaling machinery involving Sch9/Ypk1, sphingolipids, and Ubi4

Rationale: Stress granules (SGs) are membraneless organelles that are formed in response to various stresses. Multiple cellular processes have been reported to be involved in SG formation. However, the signaling cascades that coordinate SG formation remain to be elucidated. Methods: By performing two high-content imaging-based phenomic screens, we identified multiple signaling components that form a possible signal transduction pathway that regulates SG formation. Results: We found that Sch9 and Ypk1 function in an early step of SG formation, leading to a decrease in intermediate long-chain base sphingolipids (LCBs). This further downregulates the polyubiquitin precursor protein Ubi4 through upregulating the deubiquitinase Ubp3. Decreased levels of cellular free ubiquitin may subsequently facilitate Lsm7 phase separation and thus trigger SG formation. Conclusion: The signaling pathway identified in this work, together with its conserved components, provides valuable clues for understanding the mechanisms underlying SG formation and SG-associated human diseases.

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.

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.

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.

Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales

All biological systems are subject to perturbations: due to thermal fluctuations, external environments, or mutations. Yet, while biological systems are composed of thousands of interacting components, recent high-throughput experiments show that their response to perturbations is surprisingly low-dimensional: confined to only a few stereotyped changes out of the many possible. Here, we explore a unifying dynamical systems framework - soft modes - to explain and analyze low-dimensionality in biology, from molecules to eco-systems. We argue that this one framework of soft modes makes non-trivial predictions that generalize classic ideas from developmental biology to disparate systems, namely: phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.

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.

ICoN: integration using co-attention across biological networks

Motivation

Molecular interaction networks are powerful tools for studying cellular functions. Integrating diverse types of networks enhances performance in downstream tasks such as gene module detection and protein function prediction. The challenge lies in extracting meaningful protein feature representations due to varying levels of sparsity and noise across these heterogeneous networks.

Results

We propose ICoN, a novel unsupervised graph neural network model that takes multiple protein-protein association networks as inputs and generates a feature representation for each protein that integrates the topological information from all the networks. A key contribution of ICoN is exploiting a mechanism called "co-attention" that enables cross-network communication during training. The model also incorporates a denoising training technique, introducing perturbations to each input network and training the model to reconstruct the original network from its corrupted version. Our experimental results demonstrate that ICoN surpasses individual networks across three downstream tasks: gene module detection, gene coannotation prediction, and protein function prediction. Compared to existing unsupervised network integration models, ICoN exhibits superior performance across the majority of downstream tasks and shows enhanced robustness against noise. This work introduces a promising approach for effectively integrating diverse protein-protein association networks, aiming to achieve a biologically meaningful representation of proteins.

Availability and implementation

The ICoN software is available under the GNU Public License v3 at https://github.com/Murali-group/ICoN.

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.

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.

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.