Systematic screen for human disease genes in yeast

Extreme positive epistasis for fitness in monosomic yeast strains

The loss of a single chromosome in a diploid organism halves the dosage of many genes and is usually accompanied by a substantial decrease in fitness. We asked whether this decrease simply reflects the joint damage caused by individual gene dosage deficiencies. We measured the fitness effects of single heterozygous gene deletions in yeast and combined them for each chromosome. This predicted a negative growth rate, that is, lethality, for multiple monosomies. However, monosomic strains remained alive and grew as if much (often most) of the damage caused by single mutations had disappeared, revealing an exceptionally large and positive epistatic component of fitness. We looked for functional explanations by analyzing the transcriptomes. There was no evidence of increased (compensatory) gene expression on the monosomic chromosomes. Nor were there signs of the cellular stress response that would be expected if monosomy led to protein destabilization and thus cytotoxicity. Instead, all monosomic strains showed extensive upregulation of genes encoding ribosomal proteins, but in an indiscriminate manner that did not correspond to their altered dosage. This response did not restore the stoichiometry required for efficient biosynthesis, which probably became growth limiting, making all other mutation-induced metabolic defects much less important. In general, the modular structure of the cell leads to an effective fragmentation of the total mutational load. Defects outside the module(s) currently defining fitness lose at least some of their relevance, producing the epiphenomenon of positive interactions between individually negative effects.

Revisiting Dominance in Population Genetics

Dominance refers to the effect of a heterozygous genotype relative to that of the two homozygous genotypes. The degree of dominance of mutations for fitness can have a profound impact on how deleterious and beneficial mutations change in frequency over time as well as on the patterns of linked neutral genetic variation surrounding such selected alleles. Since dominance is such a fundamental concept, it has received immense attention throughout the history of population genetics. Early work from Fisher, Wright, and Haldane focused on understanding the conceptual basis for why dominance exists. More recent work has attempted to test these theories and conceptual models by estimating dominance effects of mutations. However, estimating dominance coefficients has been notoriously challenging and has only been done in a few species in a limited number of studies. In this review, we first describe some of the early theoretical and conceptual models for understanding the mechanisms for the existence of dominance. Second, we discuss several approaches used to estimate dominance coefficients and summarize estimates of dominance coefficients. We note trends that have been observed across species, types of mutations, and functional categories of genes. By comparing estimates of dominance coefficients for different types of genes, we test several hypotheses for the existence of dominance. Lastly, we discuss how dominance influences the dynamics of beneficial and deleterious mutations in populations and how the degree of dominance of deleterious mutations influences the impact of inbreeding on fitness.

Fresh Insights Into SLC25A26: Potential New Therapeutic Target for Cancers: A Review

SLC25A26 is the only known human mitochondrial S-adenosylmethionine carrier encoding gene. Recent studies have shown that SLC25A26 is abnormally expressed in some cancers, such as cervical cancer, low-grade glioma, non-small cell lung cancer, and liver cancer, which suggests SLC25A26 can affect the occurrence and development of some cancers. This article in brief briefly reviewed mitochondrial S-adenosylmethionine carrier in different species and its encoding gene, focused on the association of SLC25A26 aberrant expression and some cancers as well as potential mechanisms, summarized its potential for cancer prognosis, and characteristics of mitochondrial diseases caused by SLC25A26 mutation. Finally, we provide a brief expectation that needs to be further investigated. We speculate that SLC25A26 will be a potential new therapeutic target for some cancers.

Loss of mitochondrial DNA is associated with reduced DNA content variability in Saccharomyces cerevisiae

DNA content measurement by fluorescence-assisted cell sorting (FACS) provides information on cell cycle progression and DNA content variability. Saccharomyces cerevisiae mutants with DNA content variability that was reduced relative to wild-type strains had defects in mitochondrial DNA (mtDNA) maintenance and mitochondrial gene expression and were correlated with strains found to lack mtDNA ([ rho 0 ] cells) by genome sequencing and fluorescence microscopy. In contrast, mutants with increased variability had defects in cell cycle progression, which may indicate a loss of coordination between mtDNA and nuclear DNA replication. Thus, FACS measurement of DNA content variability can provide insight into cell-to-cell heterogeneity in mtDNA copy number.

Sua5 catalyzing universal t6A tRNA modification is responsible for multifaceted functions of the KEOPS complex in Cryptococcus neoformans

The N6-threonylcarbamoyl adenosine (t6A) tRNA modification is critical for ensuring translation fidelity across three domains of life. Our prior work highlighted the KEOPS complex, organized in a Pcc1-Kae1-Bud32-Cgi121 linear arrangement, not only serves an evolutionarily conserved role in t6A tRNA modification but also exerts diverse functional impacts on pathobiological characteristics in Cryptococcus neoformans, a leading cause of fungal meningitis worldwide. However, the extent to which the pleiotropic functions of the KEOPS complex are specifically tied to tRNA modification remains uncertain. To address this, we undertook a functional characterization of Sua5, responsible for generating the precursor threonylcarbamoyl-adenylate (TC-AMP) for t6A tRNA modification, using a reverse genetics approach. Comparative phenotypic analyses with KEOPS mutants revealed that Sua5 plays a vital role in multiple cellular processes, such as t6A tRNA modification, growth, sexual development, stress response, and virulence factor production, thus reflecting the multifaceted functions of the KEOPS complex. In support of this, sua5Δ bud32Δ double mutants showed phenotypes comparable to those of the corresponding single mutants. Intriguingly, a SUA5 allele lacking a mitochondria targeting sequence (SUA5MTSΔ) was sufficient to restore the wild-type phenotypes in the sua5Δ mutant, suggesting that Sua5's primary functional locus may be cytosolic, akin to the KEOPS complex. Further supporting this, the deletion of Qri7, a mitochondrial paralog of Kae1, had no discernible phenotypic impact on C. neoformans. We concluded that cytosolic t6A tRNA modifications, orchestrated by Sua5 and the KEOPS complex, are central to the regulation of diverse pathobiological functions in C. neoformans.IMPORTANCEUnderstanding cellular functions at the molecular level is crucial for advancing disease treatments. Our research reveals a critical connection between the KEOPS complex and Sua5 in Cryptococcus neoformans, a significant cause of fungal meningitis. While the KEOPS complex is known for its versatile roles in cellular processes, Sua5 is specialized in t6A tRNA modification. Our key finding is that the diverse roles of the KEOPS complex, ranging from cell growth and stress response to virulence, are fundamentally linked to its function in t6A tRNA modification. This conclusion is supported by the remarkable similarities between the impacts of Sua5 and KEOPS on these processes, despite their roles in different steps of the t6A modification pathway. This newfound understanding deepens our insight into fungal biology and opens new avenues for developing potential therapies against dangerous fungal diseases.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

BACKGROUND

In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.

RESULTS

We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset.

CONCLUSIONS

Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

From beer to breadboards: yeast as a force for biological innovation

The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in 'non-home' environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

Andrographolide Induces ROS-Mediated Cytotoxicity, Lipid Peroxidation, and Compromised Cell Integrity in Saccharomyces cerevisiae

Andrographolide, a bioactive compound found in Andrographis paniculata, has gained significant attention for its potential therapeutic properties. Despite its promising benefits, the understanding of its side effects and underlying mechanisms remains limited. Here, we investigated the impact of andrographolide in Saccharomyces cerevisiae and observed that andrographolide induced cytotoxicity, particularly when oxidative phosphorylation was active. Furthermore, andrographolide affected various cellular processes, including vacuole fragmentation, endoplasmic reticulum stress, lipid droplet accumulation, reactive oxygen species levels, and compromised cell integrity. Moreover, we unexpectedly observed that andrographolide induced the precipitation of biomolecules secreted from yeast cells, adding an additional source of stress. Overall, this study provides insights into the cellular effects and potential mechanisms of andrographolide in yeast, shedding light on its side effects and underlying cytotoxicity pathways.

Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays

The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.

Global analysis of the yeast knockout phenome

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

Yeast UPS1 deficiency leads to UVC radiation sensitivity and shortened lifespan

UPS1/YLR193C of Saccharomyces cerevisiae (S. cerevisiae) encodes a mitochondrial intermembrane space protein. A previous study found that Ups1p is needed for normal mitochondrial morphology and that UPS1 deficiency disrupts the intramitochondrial transport of phosphatidic acid in yeast cells and leads to an altered unfolded protein response and mTORC1 signaling activation. In this paper, we first provide evidence showing that the UPS1 gene is involved in the UVC-induced DNA damage response and aging. We show that UPS1 deficiency leads to sensitivity to ultraviolet C (UVC) radiation and that this effect is accompanied by elevated DNA damage, increased intracellular ROS levels, abnormal mitochondrial respiratory function, an increased early apoptosis rate, and shortened replicative lifespan and chronological lifespan. Moreover, we show that overexpression of the DNA damage-induced checkpoint gene RAD9 effectively eliminates the senescence-related defects observed in the UPS1-deficient strain. Collectively, these results suggest a novel role for UPS1 in the UVC-induced DNA damage response and aging.

Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

Large-scale genomic and transcriptomic profiles of rice hybrids reveal a core mechanism underlying heterosis

BACKGROUND

Heterosis is widely used in agriculture. However, its molecular mechanisms are still unclear in plants. Here, we develop, sequence, and record the phenotypes of 418 hybrids from crosses between two testers and 265 rice varieties from a mini-core collection.

RESULTS

Phenotypic analysis shows that heterosis is dependent on genetic backgrounds and environments. By genome-wide association study of 418 hybrids and their parents, we find that nonadditive QTLs are the main genetic contributors to heterosis. We show that nonadditive QTLs are more sensitive to the genetic background and environment than additive ones. Further simulations and experimental analysis support a novel mechanism, homo-insufficiency under insufficient background (HoIIB), underlying heterosis. We propose heterosis in most cases is not due to heterozygote advantage but homozygote disadvantage under the insufficient genetic background.

CONCLUSION

The HoIIB model elucidates that genetic background insufficiency is the intrinsic mechanism of background dependence, and also the core mechanism of nonadditive effects and heterosis. This model can explain most known hypotheses and phenomena about heterosis, and thus provides a novel theory for hybrid rice breeding in future.

Loss of Num1-mediated cortical dynein anchoring negatively impacts respiratory growth

Num1 is a multifunctional protein that both tethers mitochondria to the plasma membrane and anchors dynein to the cell cortex during nuclear inheritance. Previous work has examined the impact loss of Num1-based mitochondrial tethering has on dynein function in Saccharomyces cerevisiae; here, we elucidate its impact on mitochondrial function. We find that like mitochondria, Num1 is regulated by changes in metabolic state, with the protein levels and cortical distribution of Num1 differing between fermentative and respiratory growth conditions. In cells lacking Num1, we observe a reproducible respiratory growth defect, suggesting a role for Num1 in not only maintaining mitochondrial morphology, but also function. A structure-function approach revealed that, unexpectedly, Num1-mediated cortical dynein anchoring is important for normal growth under respiratory conditions. The severe respiratory growth defect in Δnum1 cells is not specifically due to the canonical functions of dynein in nuclear migration but is dependent on the presence of dynein, as deletion of DYN1 in Δnum1 cells partially rescues respiratory growth. We hypothesize that misregulated dynein present in cells that lack Num1 negatively impacts mitochondrial function resulting in defects in respiratory growth.

A multiplexed, three-dimensional pooling and next-generation sequencing strategy for creating barcoded mutant arrays: construction of a Schizosaccharomyces pombe transposon insertion library

Arrayed libraries of defined mutants have been used to elucidate gene function in the post-genomic era. Yeast haploid gene deletion libraries have pioneered this effort, but are costly to construct, do not reveal phenotypes that may occur with partial gene function and lack essential genes required for growth. We therefore devised an efficient method to construct a library of barcoded insertion mutants with a wider range of phenotypes that can be generalized to other organisms or collections of DNA samples. We developed a novel but simple three-dimensional pooling and multiplexed sequencing approach that leveraged sequence information to reduce the number of required sequencing reactions by orders of magnitude, and were able to identify the barcode sequences and DNA insertion sites of 4391 Schizosaccharomyces pombe insertion mutations with only 40 sequencing preparations. The insertion mutations are in the genes and untranslated regions of nonessential, essential and noncoding RNA genes, and produced a wider range of phenotypes compared to the cognate deletion mutants, including novel phenotypes. This mutant library represents both a proof of principle for an efficient method to produce novel mutant libraries and a valuable resource for the S. pombe research community.

Convergent evolution of polyploid genomes from across the eukaryotic tree of life

By modeling the homoeologous gene losses that occurred in 50 genomes deriving from ten distinct polyploidy events, we show that the evolutionary forces acting on polyploids are remarkably similar, regardless of whether they occur in flowering plants, ciliates, fishes, or yeasts. We show that many of the events show a relative rate of duplicate gene loss before the first postpolyploidy speciation that is significantly higher than in later phases of their evolution. The relatively weak selective constraint experienced by the single-copy genes these losses produced leads us to suggest that most of the purely selectively neutral duplicate gene losses occur in the immediate postpolyploid period. Nearly all of the events show strong evidence of biases in the duplicate losses, consistent with them being allopolyploidies, with 2 distinct progenitors contributing to the modern species. We also find ongoing and extensive reciprocal gene losses (alternative losses of duplicated ancestral genes) between these genomes. With the exception of a handful of closely related taxa, all of these polyploid organisms are separated from each other by tens to thousands of reciprocal gene losses. As a result, it is very unlikely that viable diploid hybrid species could form between these taxa, since matings between such hybrids would tend to produce offspring lacking essential genes. It is, therefore, possible that the relatively high frequency of recurrent polyploidies in some lineages may be due to the ability of new polyploidies to bypass reciprocal gene loss barriers.

Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p

The methylation of histidine is a post-translational modification whose function is poorly understood. Methyltransferase histidine protein methyltransferase 1 (Hpm1p) monomethylates H243 in the ribosomal protein Rpl3p and represents the only known histidine methyltransferase in Saccharomyces cerevisiae. Interestingly, the hpm1 deletion strain is highly pleiotropic, with many extraribosomal phenotypes including improved growth rates in alternative carbon sources. Here, we investigate how the loss of histidine methyltransferase Hpm1p results in diverse phenotypes, through use of targeted mass spectrometry (MS), growth assays, quantitative proteomics, and differential crosslinking MS. We confirmed the localization and stoichiometry of the H243 methylation site, found unreported sensitivities of Δhpm1 yeast to nonribosomal stressors, and identified differentially abundant proteins upon hpm1 knockout with clear links to the coordination of sugar metabolism. We adapted the emerging technique of quantitative large-scale stable isotope labeling of amino acids in cell culture crosslinking MS for yeast, which resulted in the identification of 1267 unique in vivo lysine-lysine crosslinks. By reproducibly monitoring over 350 of these in WT and Δhpm1, we detected changes to protein structure or protein-protein interactions in the ribosome, membrane proteins, chromatin, and mitochondria. Importantly, these occurred independently of changes in protein abundance and could explain a number of phenotypes of Δhpm1, not addressed by expression analysis. Further to this, some phenotypes were predicted solely from changes in protein structure or interactions and could be validated by orthogonal techniques. Taken together, these studies reveal a broad role for Hpm1p in yeast and illustrate how crosslinking MS will be an essential tool for understanding complex phenotypes.

Profiling Yeast Deletion Strains Using Sample Multiplexing and Network-Based Analyses

The yeast, Saccharomyces cerevisiae, is a widely used model system for investigating conserved biological functions and pathways. Advancements in sample multiplexing have facilitated the study of the yeast proteome, yet many yeast proteins remain uncharacterized or only partially characterized. Yeast deletion strain collections are powerful resources for yeast proteome studies, uncovering the effects of gene function, genetic interactions, and cellular stresses. As complex biological systems cannot be understood by simply analyzing the individual components, a systems approach is often required in which a protein is represented as a component of large, interacting networks. Here, we evaluate the current state of yeast proteome analysis using isobaric tag-based sample multiplexing (TMTpro16) to profile the proteomes of 75 yeast deletion strains for which we measured the abundance of nearly 5000 proteins. Using statistical approaches, we highlighted covariance and regulation subnetworks and the enrichment of gene ontology classifications for covarying and coregulated proteins. This dataset presents a resource that is amenable to further data mining to study individual deletion strains, pathways, proteins, and/or interactions thereof while serving as a template for future network-based investigations using yeast deletion strain collections.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

Background

Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.

Results

In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.

Conclusions

We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

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

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

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

Bidirectional Genetic Control of Phenotypic Heterogeneity and Its Implication for Cancer Drug Resistance

Negative genetic regulators of phenotypic heterogeneity, or phenotypic capacitors/stabilizers, elevate population average fitness by limiting deviation from the optimal phenotype and increase the efficacy of natural selection by enhancing the phenotypic differences among genotypes. Stabilizers can presumably be switched off to release phenotypic heterogeneity in the face of extreme or fluctuating environments to ensure population survival. This task could, however, also be achieved by positive genetic regulators of phenotypic heterogeneity, or "phenotypic diversifiers," as shown by recently reported evidence that a bacterial divisome factor enhances antibiotic resistance. We hypothesized that such active creation of phenotypic heterogeneity by diversifiers, which is functionally independent of stabilizers, is more common than previously recognized. Using morphological phenotypic data from 4,718 single-gene knockout strains of Saccharomyces cerevisiae, we systematically identified 324 stabilizers and 160 diversifiers and constructed a bipartite network between these genes and the morphological traits they control. Further analyses showed that, compared with stabilizers, diversifiers tended to be weaker and more promiscuous (regulating more traits) regulators targeting traits unrelated to fitness. Moreover, there is a general division of labor between stabilizers and diversifiers. Finally, by incorporating NCI-60 human cancer cell line anticancer drug screening data, we found that human one-to-one orthologs of yeast diversifiers/stabilizers likely regulate the anticancer drug resistance of human cancer cell lines, suggesting that these orthologs are potential targets for auxiliary treatments. Our study therefore highlights stabilizers and diversifiers as the genetic regulators for the bidirectional control of phenotypic heterogeneity as well as their distinct evolutionary roles and functional independence.

QTL mapping: an innovative method for investigating the genetic determinism of yeast-bacteria interactions in wine

The two most commonly used wine microorganisms, Saccharomyces cerevisiae yeast and Oenococcus oeni bacteria, are responsible for completion of alcoholic and malolactic fermentation (MLF), respectively. For successful co-inoculation, S. cerevisiae and O. oeni must be able to complete fermentation; however, this relies on compatibility between yeast and bacterial strains. For the first time, quantitative trait loci (QTL) analysis was used to elucidate whether S. cerevisiae genetic makeup can play a role in the ability of O. oeni to complete MLF. Assessment of 67 progeny from a hybrid S. cerevisiae strain (SBxGN), co-inoculated with a single O. oeni strain, SB3, revealed a major QTL linked to MLF completion by O. oeni. This QTL encompassed a well-known translocation, XV-t-XVI, that results in increased SSU1 expression and is functionally linked with numerous phenotypes including lag phase duration and sulphite export and production. A reciprocal hemizygosity assay was performed to elucidate the effect of the gene SSU1 in the SBxGN background. Our results revealed a strong effect of SSU1 haploinsufficiency on O. oeni's ability to complete malolactic fermentation during co-inoculation and pave the way for the implementation of QTL mapping projects for deciphering the genetic bases of microbial interactions. KEY POINTS: • For the first time, QTL analysis has been used to study yeast-bacteria interactions. • A QTL encompassing a translocation, XV-t-XVI, was linked to MLF outcomes. • S. cerevisiae SSU1 haploinsufficiency positively impacted MLF by O. oeni.

Breeding sake yeast and identification of mutation patterns by synchrotron light irradiation

Sake yeast is one of the important factors that characterize the aroma and taste of sake. To obtain sake yeast strains with different metabolic capabilities from other strains, breeding of a sake yeast is an effective way. In this study, sake yeast strain Y5201 was mutagenized by synchrotron light irradiation to obtain the mutant strains showing different brewing characteristics from parental strain Y5201, and comparative genome analysis between strain Y5201 and mutant strains was performed to identify mutation points and patterns induced by synchrotron light irradiation. Screening with the drug-resistant and fermentation tests selected the nine mutants (C18, C19, C29, C50, C51, C52, C54, T25, and T49) from the mutagenized Y5201 cells. Principal component analysis results based on the analysis of the small-scale brewing test metabolites showed that the mutant strain C19 was different from other strains, which had higher productivity of ethyl caproate and isoamyl acetate than those of the Y5201. Comparative genome analysis revealed that mutants by synchrotron light irradiation had a higher diversity of single nucleotide substitutions and a higher frequency of Indel (insertion/deletion) in these DNA than ethyl methanesulfonate and UV irradiation. These results suggest that synchrotron light irradiation is an effective and unique mutagen for yeast breeding.

ING2 tumor suppressive protein translocates into mitochondria and is involved in cellular metabolism homeostasis

ING2 (Inhibitor of Growth 2) is a tumor suppressor gene that has been implicated in critical biological functions (cell-cycle regulation, replicative senescence, DNA repair and DNA replication), most of which are recognized hallmarks of tumorigenesis occurring in the cell nucleus. As its close homolog ING1 has been recently observed in the mitochondrial compartment, we hypothesized that ING2 could also translocate into the mitochondria and be involved in new biological functions. In the present study, we demonstrate that ING2 is imported in the inner mitochondrial fraction in a redox-sensitive manner in human cells and that this mechanism is modulated by 14-3-3η protein expression. Remarkably, ING2 is necessary to maintain mitochondrial ultrastructure integrity without interfering with mitochondrial networks or polarization. We observed an interaction between ING2 and mtDNA under basal conditions. This interaction appears to be mediated by TFAM, a critical regulator of mtDNA integrity. The loss of mitochondrial ING2 does not impair mtDNA repair, replication or transcription but leads to a decrease in mitochondrial ROS production, suggesting a detrimental impact on OXPHOS activity. We finally show using multiple models that ING2 is involved in mitochondrial respiration and that its loss confers a protection against mitochondrial respiratory chain inhibition in vitro. Consequently, we propose a new tumor suppressor role for ING2 protein in the mitochondria as a metabolic shift gatekeeper during tumorigenesis.

Pbp1, the yeast ortholog of human Ataxin-2, functions in the cell growth on non-fermentable carbon sources

Pbp1, the yeast ortholog of human Ataxin-2, was originally isolated as a poly(A) binding protein (Pab1)-binding protein. Pbp1 regulates the Pan2-Pan3 deadenylase complex, thereby modulating the mRNA stability and translation efficiency. However, the physiological significance of Pbp1 remains unclear since a yeast strain harboring PBP1 deletion grows similarly to wild-type strain on normal glucose-containing medium. In this study, we found that Pbp1 has a role in cell growth on the medium containing non-fermentable carbon sources. While the pbp1Δ mutant showed a similar growth compared to the wild-type cell on a normal glucose-containing medium, the pbp1Δ mutant showed a slower growth on the medium containing glycerol and lactate. Microarray analyses revealed that expressions of the genes involved in gluconeogenesis, such as PCK1 and FBP1, and of the genes involved in mitochondrial function, such as COX10 and COX11, were decreased in the pbp1Δ mutant. Pbp1 regulated the expressions of PCK1 and FBP1 via their promoters, while the expressions of COX10 and COX11 were regulated by Pbp1, not through their promoters. The decreased expressions of COX10 and COX11 in the pbp1Δ mutant were recovered by the loss of Dcp1 decapping enzyme or Xrn1 5’-3’exonuclease. Our results suggest that Pbp1 regulates the expressions of the genes involved in gluconeogenesis and mitochondrial function through multiple mechanisms.

Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Reciprocal hemizygosity analysis reveals that the Saccharomyces cerevisiae CGI121 gene affects lag time duration in synthetic grape must

It is standard practice to ferment white wines at low temperatures (10–18°C). However, low temperatures increase fermentation duration and risk of problem ferments, leading to significant costs. The lag duration at fermentation initiation is heavily impacted by temperature; therefore, identification of Saccharomyces cerevisiae genes influencing fermentation kinetics is of interest for winemaking. We selected 28 S. cerevisiae BY4743 single deletants, from a prior list of open reading frames (ORFs) mapped to quantitative trait loci (QTLs) on Chr. VII and XIII, influencing the duration of fermentative lag time. Five BY4743 deletants, Δapt1, Δcgi121, Δclb6, Δrps17a, and Δvma21, differed significantly in their fermentative lag duration compared to BY4743 in synthetic grape must (SGM) at 15 °C, over 72 h. Fermentation at 12.5°C for 528 h confirmed the longer lag times of BY4743 Δcgi121, Δrps17a, and Δvma21. These three candidates ORFs were deleted in S. cerevisiae RM11-1a and S288C to perform single reciprocal hemizygosity analysis (RHA). RHA hybrids and single deletants of RM11-1a and S288C were fermented at 12.5°C in SGM and lag time measurements confirmed that the S288C allele of CGI121 on Chr. XIII, encoding a component of the EKC/KEOPS complex, increased fermentative lag phase duration. Nucleotide sequences of RM11-1a and S288C CGI121 alleles differed by only one synonymous nucleotide, suggesting that intron splicing, codon bias, or positional effects might be responsible for the impact on lag phase duration. This research demonstrates a new role of CGI121 and highlights the applicability of QTL analysis for investigating complex phenotypic traits in yeast.

Specificity of RNA Folding and Its Association with Evolutionarily Adaptive mRNA Secondary Structures

The secondary structure is a fundamental feature of both noncoding and messenger RNAs. However, our understanding of the secondary structure of mRNA, especially that of the coding regions, remains elusive, likely due to translation and the lack of RNA-binding proteins that sustain the consensus structure, such as those that bind to noncoding RNA. Indeed, mRNA has recently been found to adopt diverse alternative structures, the overall functional significance of which remains untested. We hereby approached this problem by estimating the folding specificity, i.e., the probability that a fragment of RNA folds back to the same partner once refolded. We showed that the folding specificity of mRNA is lower than that of noncoding RNA and exhibits moderate evolutionary conservation. Notably, we found that specific rather than alternative folding is likely evolutionarily adaptive since specific folding is frequently associated with functionally important genes or sites within a gene. Additional analysis in combination with ribosome density suggests the ability to modulate ribosome movement as one potential functional advantage provided by specific folding. Our findings revealed a novel facet of the RNA structurome with important functional and evolutionary implications and indicated a potential method for distinguishing the mRNA secondary structures maintained by natural selection from molecular noise.

Transcriptional Regulatory Networks Involved in C3-C4 Alcohol Stress Response and Tolerance in Yeast

Alcohol toxicity significantly impacts the titer and productivity of industrially produced biofuels. To overcome this limitation, we must find and use strategies to improve stress tolerance in production strains. Previously, we developed a multiplex navigation of a global regulatory network (MINR) library that targeted 25 regulatory genes that are predicted to modify global regulation in yeast under different stress conditions. In this study, we expanded this concept to target the active sites of 47 transcriptional regulators using a saturation mutagenesis library. The 47 targeted regulators interact with more than half of all yeast genes. We then screened and selected for C3-C4 alcohol tolerance. We identified specific mutants that have resistance to isopropanol and isobutanol. Notably, the WAR1_K110N variant improved tolerance to both isopropanol and isobutanol. In addition, we investigated the mechanisms for improvement of isopropanol and isobutanol stress tolerance and found that genes related to glycolysis play a role in tolerance to isobutanol, while changes in ATP synthesis and mitochondrial respiration play a role in tolerance to both isobutanol and isopropanol. Overall, this work sheds light on basic mechanisms for isopropanol and isobutanol toxicity and demonstrates a promising strategy to improve tolerance to C3-C4 alcohols by perturbing the transcriptional regulatory network.

DIA-based systems biology approach unveils E3 ubiquitin ligase-dependent responses to a metabolic shift

The yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run, data-independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement, and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source-dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system readout pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation of targets of the GID E3 ligase. Moreover, this approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole-proteome-based readouts can serve as comprehensive systems-level assays in all cellular systems.

Predicting Essential Proteins by Integrating Network Topology, Subcellular Localization Information, Gene Expression Profile and GO Annotation Data

Essential proteins are indispensable for maintaining normal cellular functions. Identification of essential proteins from Protein-protein interaction (PPI) networks has become a hot topic in recent years. Traditionally biological experimental based approaches are time-consuming and expensive, although lots of computational based methods have been developed in the past years; however, the prediction accuracy is still unsatisfied. In this research, by introducing the protein sub-cellular localization information, we define a new measurement for characterizing the protein's subcellular localization essentiality, and a new data fusion based method is developed for identifying essential proteins, named TEGS, based on integrating network topology, gene expression profile, GO annotation information, and protein subcellular localization information. To demonstrate the efficiency of the proposed method TEGS, we evaluate its performance on two Saccharomyces cerevisiae datasets and compare with other seven state-of-the-art methods (DC, BC, NC, PeC, WDC, SON, and TEO) in terms of true predicted number, jackknife curve, and precision-recall curve. Simulation results show that the TEGS outperforms the other compared methods in identifying essential proteins. The source code of TEGS is freely available at https://github.com/wzhangwhu/TEGS.

RNA-binding and prion domains: the Yin and Yang of phase separation

Proteins and RNAs assemble in membrane-less organelles that organize intracellular spaces and regulate biochemical reactions. The ability of proteins and RNAs to form condensates is encoded in their sequences, yet it is unknown which domains drive the phase separation (PS) process and what are their specific roles. Here, we systematically investigated the human and yeast proteomes to find regions promoting condensation. Using advanced computational methods to predict the PS propensity of proteins, we designed a set of experiments to investigate the contributions of Prion-Like Domains (PrLDs) and RNA-binding domains (RBDs). We found that one PrLD is sufficient to drive PS, whereas multiple RBDs are needed to modulate the dynamics of the assemblies. In the case of stress granule protein Pub1 we show that the PrLD promotes sequestration of protein partners and the RBD confers liquid-like behaviour to the condensate. Our work sheds light on the fine interplay between RBDs and PrLD to regulate formation of membrane-less organelles, opening up the avenue for their manipulation.

Predicting essential genes of 41 prokaryotes by a semi-supervised method

Essential genes are vitally important to the survival and reproduction of organisms. Many machine learning methods have been widely employed to predict essential genes and have obtained satisfactory results. However, most of these methods are supervised methods and may not obtain the desired result when the labeled data are insufficient. In this paper, we proposed a learning with local and global consistency (LGC) method-based classifier, which was employed to predict the essential genes of 41 prokaryotes. LGC is a graph-based semi-supervised learning method that can construct a prediction model using finite label and constraint information. The performance of the proposed classifier was evaluated by employing intra-organism prediction and leave-one-species-out validation. The average AUC value of 41 organisms in intra-organisms prediction was 0.723 when the labeled sample ratio was 0.5. The results of this study indicate that the proposed method can achieve acceptable prediction performance with limited labeled data. Additionally, the results demonstrate that this method has good universality.

Systematic analysis of nuclear gene function in respiratory growth and expression of the mitochondrial genome in S. cerevisiae

The production of metabolic energy in form of ATP by oxidative phosphorylation depends on the coordinated action of hundreds of nuclear-encoded mitochondrial proteins and a handful of proteins encoded by the mitochondrial genome (mtDNA). We used the yeast Saccharomyces cerevisiae as a model system to systematically identify the genes contributing to this process. Integration of genome-wide high-throughput growth assays with previously published large data sets allowed us to define with high confidence a set of 254 nuclear genes that are indispensable for respiratory growth. Next, we induced loss of mtDNA in the yeast deletion collection by growth on ethidium bromide-containing medium and identified twelve genes that are essential for viability in the absence of mtDNA (i.e. petite-negative). Replenishment of mtDNA by cytoduction showed that respiratory-deficient phenotypes are highly variable in many yeast mutants. Using a mitochondrial genome carrying a selectable marker, ARG8^m, we screened for mutants that are specifically defective in maintenance of mtDNA and mitochondrial protein synthesis. We found that up to 176 nuclear genes are required for expression of mitochondria-encoded proteins during fermentative growth. Taken together, our data provide a comprehensive picture of the molecular processes that are required for respiratory metabolism in a simple eukaryotic cell.

Overexpression of the peroxin Pex34p suppresses impaired acetate utilization in yeast lacking the mitochondrial aspartate/glutamate carrier Agc1p

PEX34, encoding a peroxisomal protein implicated in regulating peroxisome numbers, was identified as a high copy suppressor, capable of bypassing impaired acetate utilization of agc1∆ yeast. However, improved growth of agc1∆ yeast on acetate is not mediated through peroxisome proliferation. Instead, stress to the endoplasmic reticulum and mitochondria from PEX34 overexpression appears to contribute to enhanced acetate utilization of agc1∆ yeast. The citrate/2-oxoglutarate carrier Yhm2p is required for PEX34 stimulated growth of agc1∆ yeast on acetate medium, suggesting that the suppressor effect is mediated through increased activity of a redox shuttle involving mitochondrial citrate export. Metabolomic analysis also revealed redirection of acetyl-coenzyme A (CoA) from synthetic reactions for amino acids in PEX34 overexpressing yeast. We propose a model in which increased formation of products from the glyoxylate shunt, together with enhanced utilization of acetyl-CoA, promotes the activity of an alternative mitochondrial redox shuttle, partially substituting for loss of yeast AGC1.

Mitochondrial unfolded protein-related responses across kingdoms: similar problems, different regulators

Mitochondria are key components of eukaryotic cells, so their proper functioning is monitored via different mitochondrial signalling responses. One of these mitochondria-to-nuclear 'retrograde' responses to maintain mitochondrial homeostasis is the mitochondrial unfolded protein response (UPR^mt), which can be activated by a variety of defects including blocking mitochondrial translation, respiration, protein import or transmembrane potential. Although UPR^mt was first reported in cultured mammalian cells, this signalling pathway has also been extensively studied in the nematode Caenorhabditis elegans. In yeast, there are no published studies focusing on UPR^mt in a strict sense, but other unfolded protein responses (UPR) that appear related to UPR^mt have been described, such as the UPR activated by protein mistargeting (UPR^am) and mitochondrial compromised protein import response (mitoCPR). In plants, very little is known about UPR^mt and only recently some of the regulators have been identified. In this paper, we summarise and compare the current knowledge of the UPR^mt and related responses across eukaryotic kingdoms: animals, fungi and plants. Our comparison suggests that each kingdom has evolved its own specific set of regulators, however, the functional categories represented among UPR^mt-related target genes appear to be largely overlapping. This indicates that the strategies for preserving proper mitochondrial functions are partially conserved, targeting mitochondrial chaperones, proteases, import components, dynamics and stress response, but likely also non-mitochondrial functions including growth regulators/hormone balance and amino acid metabolism. We also identify homologs of known UPR^mt regulators and responsive genes across kingdoms, which may be interesting targets for future research.

Targeted Perturb-seq enables genome-scale genetic screens in single cells

The transcriptome contains rich information on molecular, cellular and organismal phenotypes. However, experimental and statistical limitations constrain sensitivity and throughput of genetic screening with single-cell transcriptomics readout. To overcome these limitations, we introduce targeted Perturb-seq (TAP-seq), a sensitive, inexpensive and platform-independent method focusing single-cell RNA-seq coverage on genes of interest, thereby increasing the sensitivity and scale of genetic screens by orders of magnitude. TAP-seq permits routine analysis of thousands of CRISPR-mediated perturbations within a single experiment, detects weak effects and lowly expressed genes, and decreases sequencing requirements by up to 50-fold. We apply TAP-seq to generate perturbation-based enhancer-target gene maps for 1,778 enhancers within 2.5% of the human genome. We thereby show that enhancer-target association is jointly determined by three-dimensional contact frequency and epigenetic states, allowing accurate prediction of enhancer targets throughout the genome. In addition, we demonstrate that TAP-seq can identify cell subtypes with only 100 sequencing reads per cell.

Yeast as a Model to Understand Actin-Mediated Cellular Functions in Mammals-Illustrated with Four Actin Cytoskeleton Proteins

The budding yeast Saccharomyces cerevisiae has an actin cytoskeleton that comprises a set of protein components analogous to those found in the actin cytoskeletons of higher eukaryotes. Furthermore, the actin cytoskeletons of S. cerevisiae and of higher eukaryotes have some similar physiological roles. The genetic tractability of budding yeast and the availability of a stable haploid cell type facilitates the application of molecular genetic approaches to assign functions to the various actin cytoskeleton components. This has provided information that is in general complementary to that provided by studies of the equivalent proteins of higher eukaryotes and hence has enabled a more complete view of the role of these proteins. Several human functional homologues of yeast actin effectors are implicated in diseases. A better understanding of the molecular mechanisms underpinning the functions of these proteins is critical to develop improved therapeutic strategies. In this article we chose as examples four evolutionarily conserved proteins that associate with the actin cytoskeleton: (1) yeast Hof1p/mammalian PSTPIP1, (2) yeast Rvs167p/mammalian BIN1, (3) yeast eEF1A/eEF1A1 and eEF1A2 and (4) yeast Yih1p/mammalian IMPACT. We compare the knowledge on the functions of these actin cytoskeleton-associated proteins that has arisen from studies of their homologues in yeast with information that has been obtained from in vivo studies using live animals or in vitro studies using cultured animal cell lines.

Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

Background

Cellular aging is best studied in the budding yeast Saccharomyces cerevisiae. As an example of a pleiotropic trait, yeast lifespan is influenced by hundreds of interconnected genes. However, no quantitative methods are currently available to infer system-level changes in gene networks during cellular aging.

Results

We propose a parsimonious mathematical model of cellular aging based on stochastic gene interaction networks. This network model is made of only non-aging components: the strength of gene interactions declines with a constant mortality rate. Death of a cell occurs in the model when an essential node loses all of its interactions with other nodes, and is equivalent to the deletion of an essential gene. Stochasticity of gene interactions is modeled using a binomial distribution. We show that the exponential increase of mortality rate over time can emerge from this gene network model during the early stages of aging.We developed a maximal likelihood approach to estimate three lifespan-influencing network parameters from experimental lifespans: t₀, the initial virtual age of the network system; n, the average lifespan-influencing interactions per essential node; and R, the initial mortality rate. We applied this model to yeast mutants with known effects on replicative lifespans. We found that deletion of SIR2, FOB1, and HXK2 considerably altered the initial virtual age but not the average lifespan-influencing interactions per essential node, suggesting that these mutations mainly influence the reliability of gene interactions but not the overall configurations of gene networks.We applied this model to investigate replicative lifespans of yeast natural isolates. We estimated that the average number of lifespan-influencing interactions per essential node is 7.0 (6.1–8) and the average estimated initial virtual age is 45.4 (30.6–74) cell divisions in these isolates. We also found that t₀ could potentially mediate the observed Strehler-Mildvan correlation in yeast natural isolates.

Conclusions

Our theoretical model provides a parsimonious interpretation of experimental lifespan data from the perspective of gene networks. We hope that our work will stimulate more interest in developing network models to study aging as a pleiotropic trait.

Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae

Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the 10 genes surveyed and are inconsistent with normality. For example, all 10 distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene's expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.

Reverse genetic analysis of yeast YPR099C/MRPL51 reveals a critical role of both overlapping ORFs in respiratory growth and MRPL51 in mitochondrial DNA maintenance

The Saccharomyces cerevisiae genome contains 6572 ORFs, of which 680 ORFs are classified as dubious ORFs. A dubious ORF is a small, noncoding, nonconserved ORF that overlaps with another ORF of the complementary strand. Our study characterizes a dubious/nondubious ORF pair, YPR099C/MRPL51, and shows the transcript and protein level expression of YPR099C. Its subcellular localization was observed in the mitochondria. The overlapping ORF, MRPL51, encodes a mitochondrial ribosomal protein of large subunit. Deletion of any ORF from YPR099C/MRPL51 pair induces common phenotypes, i.e. loss of mtDNA, lack of mitochondrial fusion and lack of respiratory growth, due to the double deletion (ypr099cΔ/Δmrpl51Δ/Δ) caused by sequence overlap. Hence, we created the single deletions of each ORF of the YPR099C/MRPL51 pair by an alternative approach to distinguish their phenotypes and identify the specific functions. Both the ORFs were found essential for the functional mitochondria and respiratory growth, but MRPL51 showed its specific requirement in mtDNA stability. The mechanism of mtDNA maintenance by Mrpl51 is probably Mhr1 dependent that physically interacts with Mrpl51 and also regulates mtDNA repair. Overall, our study provides strong evidence for the protein level expression of a dubious ORF YPR099C and the bifunctional role of Mrpl51 in mtDNA maintenance.

Link clustering explains non-central and contextually essential genes in protein interaction networks

Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29–47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.

Candida albicans Pma1p Contributes to Growth, pH Homeostasis, and Hyphal Formation

Candida albicans occupies diverse ecological niches within the host and must tolerate a wide range of environmental pH. The plasma membrane H⁺-ATPase Pma1p is the major regulator of cytosolic pH in fungi. Pma1p extrudes protons from the cytosol to maintain neutral-to-alkaline pH and is a potential drug target due to its essentiality and fungal specificity. We characterized mutants in which one allele of PMA1 has been deleted and the other truncated by 18–38 amino acids. Increasing C-terminal truncation caused corresponding decreases in plasma membrane ATPase-specific activity and cytosolic pH. Pma1p is regulated by glucose: glucose rapidly activates the ATPase, causing a sharp increase in cytosolic pH. Increasing Pma1p truncation severely impaired this glucose response. Pma1p truncation also altered cation responses, disrupted vacuolar morphology and pH, and reduced filamentation competence. Early studies of cytosolic pH and filamentation have described a rapid, transient alkalinization of the cytosol preceding germ tube formation; Pma1p has been proposed as a regulator of this process. We find Pma1p plays a role in the establishment of cell polarity, and distribution of Pma1p is non-homogenous in emerging hyphae. These findings suggest a role of PMA1 in cytosolic alkalinization and in the specialized form of polarized growth that is filamentation.

A proteomic analysis of Caenorhabditis elegans mitochondria during bacterial infection

Mitochondria are involved in a variety of cellular metabolic processes and their functions are regulated by intrinsic and extrinsic stimuli. Recent studies have revealed functional diversity and importance of mitochondria in many cellular processes, including the innate immune response. This study evaluated the specific response and proteomic changes in host Caenorhabditis elegans mitochondria during Pseudomonas aeruginosa PAO1 infection. We performed an inclusive approach to determine the C. elegans mitochondria proteome. The protein fractions of mitochondria were analysed by tandem LC-MS/MS, 129 differentially regulated proteins were identified, indicating an involvement of various mitochondrial processes. The several known components of the oxidative phosphorylation (OXPHOS) machinery, the tricarboxylic acid (TCA) cycle, mitochondrial unfolded protein response (UPR^mt) and stable mitochondria-encoded proteins were found to be differentially expressed. Our results in-depth provide new horizons for mitochondria-associated protein functions and the classification of mitochondrial diseases during host-pathogen interaction.

A Survey on Computational Methods for Essential Proteins and Genes Prediction

Background:

Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research.

Objective:

The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.

IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.