Comprehensive reanalysis of transcription factor knockout expression data in Saccharomyces cerevisiae reveals many new targets

Transcriptomic and proteomic effects of gene deletion are not evolutionarily conserved

Although the textbook definition of gene function is the effect for which the gene was selected and/or by which it is maintained, gene function is commonly inferred from the phenotypic effects of deleting the gene. Because some of the deletion effects are byproducts of other effects, they may not reflect the gene's selected-effect function. To evaluate the degree to which the phenotypic effects of gene deletion inform gene function, we compare the transcriptomic and proteomic effects of systematic gene deletions in budding yeast (Saccharomyces cerevisiae) with those effects in fission yeast (Schizosaccharomyces pombe). Despite evidence for functional conservation of orthologous genes, their deletions result in no more sharing of transcriptomic or proteomic effects than that from deleting nonorthologous genes. Because the wild-type mRNA and protein levels of orthologous genes are significantly correlated between the two yeasts and because transcriptomic effects of deleting the same gene strongly overlap between studies in the same S. cerevisiae strain by different laboratories, our observation cannot be explained by rapid evolution or large measurement error of gene expression. Analysis of transcriptomic and proteomic effects of gene deletions in multiple S. cerevisiae strains by the same laboratory reveals a high sensitivity of these effects to the genetic background, explaining why these effects are not evolutionarily conserved. Together, our results suggest that most transcriptomic and proteomic effects of gene deletion do not inform selected-effect function. This finding has important implications for assessing and/or understanding gene function, pleiotropy, and biological complexity.

A high copy suppressor screen identifies factors enhancing the allotopic production of subunit II of cytochrome c oxidase

Allotopic expression refers to the artificial relocation of an organellar gene to the nucleus. Subunit 2 (Cox2) of cytochrome c oxidase, a subunit with 2 transmembrane domains (TMS1 and TMS2) residing in the inner mitochondrial membrane with a Nout-Cout topology, is typically encoded in the mitochondrial cox2 gene. In the yeast Saccharomyces cerevisiae, the cox2 gene can be allotopically expressed in the nucleus, yielding a functional protein that restores respiratory growth to a Δcox2 null mutant. In addition to a mitochondrial targeting sequence followed by its natural 15-residue leader peptide, the cytosol synthesized Cox2 precursor must carry one or several amino acid substitutions that decrease the mean hydrophobicity of TMS1 and facilitate its import into the matrix by the TIM23 translocase. Here, using a yeast strain that contains a COX2W56R gene construct inserted in a nuclear chromosome, we searched for genes whose overexpression could facilitate import into mitochondria of the Cox2W56R precursor and increase respiratory growth of the corresponding mutant strain. A COX2W56R expressing strain was transformed with a multicopy plasmid genomic library, and transformants exhibiting enhanced respiratory growth on nonfermentable carbon sources were selected. We identified 3 genes whose overexpression facilitates the internalization of the Cox2W56R subunit into mitochondria, namely: TYE7, RAS2, and COX12. TYE7 encodes a transcriptional factor, RAS2, a GTP-binding protein, and COX12, a non-core subunit of cytochrome c oxidase. We discuss potential mechanisms by which the TYE7, RAS2, and COX12 gene products could facilitate the import and assembly of the Cox2W56R subunit produced allotopically.

WENDY: Covariance dynamics based gene regulatory network inference

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.

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.

Spontaneous Attenuation of Alcoholic Fermentation via the Dysfunction of Cyc8p in Saccharomyces cerevisiae

A cell population characterized by the release of glucose repression and known as [GAR+] emerges spontaneously in the yeast Saccharomyces cerevisiae. This study revealed that the [GAR+] variants exhibit retarded alcoholic fermentation when glucose is the sole carbon source. To identify the key to the altered glucose response, the gene expression profile of [GAR+] cells was examined. Based on RNA-seq data, the [GAR+] status was linked to impaired function of the Cyc8p-Tup1p complex. Loss of Cyc8p led to a decrease in the initial rate of alcoholic fermentation under glucose-rich conditions via the inactivation of pyruvate decarboxylase, an enzyme unique to alcoholic fermentation. These results suggest that Cyc8p can become inactive to attenuate alcoholic fermentation. These findings may contribute to the elucidation of the mechanism of non-genetic heterogeneity in yeast alcoholic fermentation.

Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO)

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 co-expression 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 co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, 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 sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.

Towards universal synthetic heterotrophy using a metabolic coordinator

Engineering the utilization of non-native substrates, or synthetic heterotrophy, in proven industrial microbes such as Saccharomyces cerevisiae represents an opportunity to valorize plentiful and renewable sources of carbon and energy as inputs to bioprocesses. We previously demonstrated that activation of the galactose (GAL) regulon, a regulatory structure used by this yeast to coordinate substrate utilization with biomass formation during growth on galactose, during growth on the non-native substrate xylose results in a vastly altered gene expression profile and faster growth compared with constitutive overexpression of the same heterologous catabolic pathway. However, this effort involved the creation of a xylose-inducible variant of Gal3p (Gal3pSyn4.1), the sensor protein of the GAL regulon, preventing this semi-synthetic regulon approach from being easily adapted to additional non-native substrates. Here, we report the construction of a variant Gal3pMC (metabolic coordinator) that exhibits robust GAL regulon activation in the presence of structurally diverse substrates and recapitulates the dynamics of the native system. Multiple molecular modeling studies suggest that Gal3pMC occupies conformational states corresponding to galactose-bound Gal3p in an inducer-independent manner. Using Gal3pMC to test a regulon approach to the assimilation of the non-native lignocellulosic sugars xylose, arabinose, and cellobiose yields higher growth rates and final cell densities when compared with a constitutive overexpression of the same set of catabolic genes. The subsequent demonstration of rapid and complete co-utilization of all three non-native substrates suggests that Gal3pMC-mediated dynamic global gene expression changes by GAL regulon activation may be universally beneficial for engineering synthetic heterotrophy.

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.

SIR telomere silencing depends on nuclear envelope lipids and modulates sensitivity to a lysolipid

The nuclear envelope (NE) is important in maintaining genome organization. The role of lipids in communication between the NE and telomere regulation was investigated, including how changes in lipid composition impact gene expression and overall nuclear architecture. Yeast was treated with the non-metabolizable lysophosphatidylcholine analog edelfosine, known to accumulate at the perinuclear ER. Edelfosine induced NE deformation and disrupted telomere clustering but not anchoring. Additionally, the association of Sir4 at telomeres decreased. RNA-seq analysis showed altered expression of Sir-dependent genes located at sub-telomeric (0-10 kb) regions, consistent with Sir4 dispersion. Transcriptomic analysis revealed that two lipid metabolic circuits were activated in response to edelfosine, one mediated by the membrane sensing transcription factors, Spt23/Mga2, and the other by a transcriptional repressor, Opi1. Activation of these transcriptional programs resulted in higher levels of unsaturated fatty acids and the formation of nuclear lipid droplets. Interestingly, cells lacking Sir proteins displayed resistance to unsaturated-fatty acids and edelfosine, and this phenotype was connected to Rap1.

Chromatin regulator Ahc1p co-regulates nitrogen metabolism via interactions with multiple transcription factors in Saccharomyces cerevisiae

Chromatin regulation is an important gene expression/regulation system, but little is known about how it affects nitrogen metabolism in Saccharomyces cerevisiae. A previous study demonstrated the regulatory role of the chromatin regulator Ahc1p on multiple key genes of nitrogen metabolism in S. cerevisiae, but the regulatory mechanism remains unknown. In this study, multiple key nitrogen metabolism genes directly regulated by Ahc1p were identified, and the transcription factors interacting with Ahc1p were analyzed. It was ultimately found that Ahc1p may regulate some key nitrogen metabolism genes in two ways. First, Ahc1p acts as a co-factor and is recruited with transcription factors such as Rtg3p or Gcr1p to facilitate transcription complex binding to target gene core promoters and promote transcription initiation. Second, Ahc1p binds at enhancers to promote the transcription of target genes in concert with transcription factors. This study furthers the understanding of the regulatory network of nitrogen metabolism in S. cerevisiae from an epigenetic perspective.

NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks

The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF-Regulatory Network Reconstruction using EXpression and Collaborative Filtering-a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data.

Mining transcriptomic data to identify Saccharomyces cerevisiae signatures related to improved and repressed ethanol production under fermentation

Saccharomyces cerevisiae is known for its outstanding ability to produce ethanol in industry. Underlying the dynamics of gene expression in S. cerevisiae in response to fermentation could provide informative results, required for the establishment of any ethanol production improvement program. Thus, representing a new approach, this study was conducted to identify the discriminative genes between improved and repressed ethanol production as well as clarifying the molecular responses to this process through mining the transcriptomic data. The significant differential expression probe sets were extracted from available microarray datasets related to yeast fermentation performance. To identify the most effective probe sets contributing to discriminate ethanol content, 11 machine learning algorithms from RapidMiner were employed. Further analysis including pathway enrichment and regulatory analysis were performed on discriminative probe sets. Besides, the decision tree models were constructed, the performance of each model was evaluated and the roots were identified. Based on the results, 171 probe sets were identified by at least 5 attribute weighting algorithms (AWAs) and 17 roots were recognized with 100% performance Some of the top ranked presets were found to be involved in carbohydrate metabolism, oxidative phosphorylation, and ethanol fermentation. Principal component analysis (PCA) and heatmap clustering validated the top-ranked selective probe sets. In addition, the top-ranked genes were validated based on GSE78759 and GSE5185 dataset. From all discriminative probe sets, OLI1 and CYC3 were identified as the roots with the best performance, demonstrated by the most weighting algorithms and linked to top two significant enriched pathways including porphyrin biosynthesis and oxidative phosphorylation. ADH5 and PDA1 were also recognized as differential top-ranked genes that contribute to ethanol production. According to the regulatory clustering analysis, Tup1 has a significant effect on the top-ranked target genes CYC3 and ADH5 genes. This study provides a basic understanding of the S. cerevisiae cell molecular mechanism and responses to two different medium conditions (Mg²⁺ and Cu²⁺) during the fermentation process.

Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases

Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes.

To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae.

Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼ 8800 protein kinase/phosphatase-transcription factor interactions and ∼ 6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.

Adaptive Response of Saccharomyces Hosts to Totiviridae L-A dsRNA Viruses Is Achieved through Intrinsically Balanced Action of Targeted Transcription Factors

Totiviridae L-A virus is a widespread yeast dsRNA virus. The persistence of the L-A virus alone appears to be symptomless, but the concomitant presence of a satellite M virus provides a killer trait for the host cell. The presence of L-A dsRNA is common in laboratory, industrial, and wild yeasts, but little is known about the impact of the L-A virus on the host’s gene expression. In this work, based on high-throughput RNA sequencing data analysis, the impact of the L-A virus on whole-genome expression in three different Saccharomyces paradoxus and S. cerevisiae host strains was analyzed. In the presence of the L-A virus, moderate alterations in gene expression were detected, with the least impact on respiration-deficient cells. Remarkably, the transcriptional adaptation of essential genes was limited to genes involved in ribosome biogenesis. Transcriptional responses to L-A maintenance were, nevertheless, similar to those induced upon stress or nutrient availability. Based on these data, we further dissected yeast transcriptional regulators that, in turn, modulate the cellular L-A dsRNA levels. Our findings point to totivirus-driven fine-tuning of the transcriptional landscape in yeasts and uncover signaling pathways employed by dsRNA viruses to establish the stable, yet allegedly profitless, viral infection of fungi.

Set2 histone methyltransferase regulates transcription coupled-nucleotide excision repair in yeast

Helix-distorting DNA lesions, including ultraviolet (UV) light-induced damage, are repaired by the global genomic-nucleotide excision repair (GG-NER) and transcription coupled-nucleotide excision repair (TC-NER) pathways. Previous studies have shown that histone post-translational modifications (PTMs) such as histone acetylation and methylation can promote GG-NER in chromatin. Whether histone PTMs also regulate the repair of DNA lesions by the TC-NER pathway in transcribed DNA is unknown. Here, we report that histone H3 K36 methylation (H3K36me) by the Set2 histone methyltransferase in yeast regulates TC-NER. Mutations in Set2 or H3K36 result in UV sensitivity that is epistatic with Rad26, the primary TC-NER factor in yeast, and cause a defect in the repair of UV damage across the yeast genome. We further show that mutations in Set2 or H3K36 in a GG-NER deficient strain (i.e., rad16Δ) partially rescue its UV sensitivity. Our data indicate that deletion of SET2 rescues UV sensitivity in a GG-NER deficient strain by activating cryptic antisense transcription, so that the non-transcribed strand (NTS) of yeast genes is repaired by TC-NER. These findings indicate that Set2 methylation of H3K36 establishes transcriptional asymmetry in repair by promoting canonical TC-NER of the transcribed strand (TS) and suppressing cryptic TC-NER of the NTS.

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

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

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in "transport", which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

Mechanisms of Metabolic Adaptation in Wine Yeasts: Role of Gln3 Transcription Factor

Wine strains of Saccharomyces cerevisiae have to adapt their metabolism to the changing conditions during their biotechnological use, from the aerobic growth in sucrose-rich molasses for biomass propagation to the anaerobic fermentation of monosaccharides of grape juice during winemaking. Yeast have molecular mechanisms that favor the use of preferred carbon and nitrogen sources to achieve such adaptation. By using specific inhibitors, it was determined that commercial strains offer a wide variety of glucose repression profiles. Transcription factor Gln3 has been involved in glucose and nitrogen repression. Deletion of GLN3 in two commercial wine strains produced different mutant phenotypes and only one of them displayed higher glucose repression and was unable to grow using a respiratory carbon source. Therefore, the role of this transcription factor contributes to the variety of phenotypic behaviors seen in wine strains. This variability is also reflected in the impact of GLN3 deletion in fermentation, although the mutants are always more tolerant to inhibition of the nutrient signaling complex TORC1 by rapamycin, both in laboratory medium and in grape juice fermentation. Therefore, most aspects of nitrogen catabolite repression controlled by TORC1 are conserved in winemaking conditions.

Plastic rewiring of Sef1 transcriptional networks and the potential of non-functional transcription factor binding in facilitating adaptive evolution

Prior and extensive plastic rewiring of a transcriptional network, followed by a functional switch of the conserved transcriptional regulator, can shape the evolution of a new network with diverged functions. The presence of three distinct iron regulatory systems in fungi that use orthologous transcriptional regulators suggests that these systems evolved in that manner. Orthologs of the transcriptional activator Sef1 are believed to be central to how iron regulatory systems developed in fungi, involving gene gain, plastic network rewiring, and switches in regulatory function. We show that, in the protoploid yeast Lachancea kluyveri, plastic rewiring of the L. kluyveri Sef1 (Lk-Sef1) network, together with a functional switch, enabled Lk-Sef1 to regulate TCA cycle genes, unlike Candida albicans Sef1 that mainly regulates iron-uptake genes. Moreover, we observed pervasive nonfunctional binding of Sef1 to its target genes. Enhancing Lk-Sef1 activity resuscitated the corresponding transcriptional network, providing immediate adaptive benefits in changing environments. Our study not only sheds light on the evolution of Sef1-centered transcriptional networks but also shows the adaptive potential of nonfunctional transcription factor binding for evolving phenotypic novelty and diversity.

Inactivating histone deacetylase HDA promotes longevity by mobilizing trehalose metabolism

Histone acetylations are important epigenetic markers for transcriptional activation in response to metabolic changes and various stresses. Using the high-throughput SEquencing-Based Yeast replicative Lifespan screen method and the yeast knockout collection, we demonstrate that the HDA complex, a class-II histone deacetylase (HDAC), regulates aging through its target of acetylated H3K18 at storage carbohydrate genes. We find that, in addition to longer lifespan, disruption of HDA results in resistance to DNA damage and osmotic stresses. We show that these effects are due to increased promoter H3K18 acetylation and transcriptional activation in the trehalose metabolic pathway in the absence of HDA. Furthermore, we determine that the longevity effect of HDA is independent of the Cyc8-Tup1 repressor complex known to interact with HDA and coordinate transcriptional repression. Silencing the HDA homologs in C. elegans and Drosophila increases their lifespan and delays aging-associated physical declines in adult flies. Hence, we demonstrate that this HDAC controls an evolutionarily conserved longevity pathway.

Regulation of the conserved 3'-5' exoribonuclease EXOSC10/Rrp6 during cell division, development and cancer

The conserved 3'-5' exoribonuclease EXOSC10/Rrp6 processes and degrades RNA, regulates gene expression and participates in DNA double-strand break repair and control of telomere maintenance via degradation of the telomerase RNA component. EXOSC10/Rrp6 is part of the multimeric nuclear RNA exosome and interacts with numerous proteins. Previous clinical, genetic, biochemical and genomic studies revealed the protein's essential functions in cell division and differentiation, its RNA substrates and its relevance to autoimmune disorders and oncology. However, little is known about the regulatory mechanisms that control the transcription, translation and stability of EXOSC10/Rrp6 during cell growth, development and disease and how these mechanisms evolved from yeast to human. Herein, we provide an overview of the RNA- and protein expression profiles of EXOSC10/Rrp6 during cell division, development and nutritional stress, and we summarize interaction networks and post-translational modifications across species. Additionally, we discuss how known and predicted protein interactions and post-translational modifications influence the stability of EXOSC10/Rrp6. Finally, we explore the idea that different EXOSC10/Rrp6 alleles, which potentially alter cellular protein levels or affect protein function, might influence human development and disease progression. In this review we interpret information from the literature together with genomic data from knowledgebases to inspire future work on the regulation of this essential protein's stability in normal and malignant cells.

The Identification of Genetic Determinants of Methanol Tolerance in Yeast Suggests Differences in Methanol and Ethanol Toxicity Mechanisms and Candidates for Improved Methanol Tolerance Engineering

Methanol is a promising feedstock for metabolically competent yeast strains-based biorefineries. However, methanol toxicity can limit the productivity of these bioprocesses. Therefore, the identification of genes whose expression is required for maximum methanol tolerance is important for mechanistic insights and rational genomic manipulation to obtain more robust methylotrophic yeast strains. The present chemogenomic analysis was performed with this objective based on the screening of the Euroscarf Saccharomyces cerevisiae haploid deletion mutant collection to search for susceptibility phenotypes in YPD medium supplemented with 8% (v/v) methanol, at 35 °C, compared with an equivalent ethanol concentration (5.5% (v/v)). Around 400 methanol tolerance determinants were identified, 81 showing a marked phenotype. The clustering of the identified tolerance genes indicates an enrichment of functional categories in the methanol dataset not enriched in the ethanol dataset, such as chromatin remodeling, DNA repair and fatty acid biosynthesis. Several genes involved in DNA repair (eight RAD genes), identified as specific for methanol toxicity, were previously reported as tolerance determinants for formaldehyde, a methanol detoxification pathway intermediate. This study provides new valuable information on genes and potential regulatory networks involved in overcoming methanol toxicity. This knowledge is an important starting point for the improvement of methanol tolerance in yeasts capable of catabolizing and copying with methanol concentrations present in promising bioeconomy feedstocks, including industrial residues.

Analysis of Hub Genes Involved in Distinction Between Aged and Fetal Bone Marrow Mesenchymal Stem Cells by Robust Rank Aggregation and Multiple Functional Annotation Methods

Stem cells from fetal tissue protect against aging and possess greater proliferative capacity than their adult counterparts. These cells can more readily expand in vitro and senesce later in culture. However, the underlying molecular mechanisms for these differences are still not fully understood. In this study, we used a robust rank aggregation (RRA) method to discover robust differentially expressed genes (DEGs) between fetal bone marrow mesenchymal stem cells (fMSCs) and aged adult bone marrow mesenchymal stem cells (aMSCs). Multiple methods, including gene set enrichment analysis (GSEA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for functional annotation of the robust DEGs, and the results were visualized using the R software. The hub genes and other genes with which they interacted directly were detected by protein–protein interaction (PPI) network analysis. Correlation of gene expression was measured by Pearson correlation coefficient. A total of 388 up-regulated and 289 down-regulated DEGs were identified between aMSCs and fMSCs. We found that the down-regulated genes were mainly involved in the cell cycle, telomerase activity, and stem cell proliferation. The up-regulated DEGs were associated with cell adhesion molecules, extracellular matrix (ECM)–receptor interactions, and the immune response. We screened out four hub genes, MYC, KIF20A, HLA-DRA, and HLA-DPA1, through PPI-network analysis. The MYC gene was negatively correlated with TXNIP, an age-related gene, and KIF20A was extensively involved in the cell cycle. The results suggested that MSCs derived from the bone marrow of an elderly donor present a pro-inflammatory phenotype compared with that of fMSCs, and the HLA-DRA and HLA-DPA1 genes are related to the immune response. These findings provide new insights into the differences between aMSCs and fMSCs and may suggest novel strategies for ex vivo expansion and application of adult MSCs.

Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae

The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks. It appears that in-degree distribution, as well as motif enrichment evolve as the yeast transcriptional network is being completed. Overall, our analyses challenged some results previously published and confirmed others. These analyses further pointed towards the paucity of experimental evidence to support theories and, more generally, towards the partial knowledge of the complete network.

A programmable fate decision landscape underlies single-cell aging in yeast

Chromatin instability and mitochondrial decline are conserved processes that contribute to cellular aging. Although both processes have been explored individually in the context of their distinct signaling pathways, the mechanism that determines which process dominates during aging of individual cells is unknown. We show that interactions between the chromatin silencing and mitochondrial pathways lead to an epigenetic landscape of yeast replicative aging with multiple equilibrium states that represent different types of terminal states of aging. The structure of the landscape drives single-cell differentiation toward one of these states during aging, whereby the fate is determined quite early and is insensitive to intracellular noise. Guided by a quantitative model of the aging landscape, we genetically engineered a long-lived equilibrium state characterized by an extended life span.

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

Background

Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure.

Results

Here, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs.

Conclusions

The LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

BACKGROUND

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task.

RESULTS

We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4).

CONCLUSION

Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast

BACKGROUND

Current technologies for understanding the transcriptional reprogramming in cells include the transcription factor (TF) chromatin immunoprecipitation (ChIP) experiments and the TF knockout experiments. The ChIP experiments show the binding targets of TFs against which the antibody directs while the knockout techniques find the regulatory gene targets of the knocked-out TFs. However, it was shown that these two complementary results contain few common targets. Researchers have used the concept of TF functional redundancy to explain the low overlap between these two techniques. But the detailed molecular mechanisms behind TF functional redundancy remain unknown. Without knowing the possible molecular mechanisms, it is hard for biologists to fully unravel the cause of TF functional redundancy.

RESULTS

To mine out the molecular mechanisms, a novel algorithm to extract TF regulatory modules that help explain the observed TF functional redundancy effect was devised and proposed in this research. The method first searched for candidate TF sets from the TF binding data. Then based on these candidate sets the method utilized the modified Steiner Tree construction algorithm to construct the possible TF regulatory modules from protein-protein interaction data and finally filtered out the noise-induced results by using confidence tests. The mined-out regulatory modules were shown to correlate to the concept of functional redundancy and provided testable hypotheses of the molecular mechanisms behind functional redundancy. And the biological significance of the mined-out results was demonstrated in three different biological aspects: ontology enrichment, protein interaction prevalence and expression coherence. About 23.5% of the mined-out TF regulatory modules were literature-verified. Finally, the biological applicability of the proposed method was shown in one detailed example of a verified TF regulatory module for pheromone response and filamentous growth in yeast.

CONCLUSION

In this research, a novel method that mined out the potential TF regulatory modules which elucidate the functional redundancy observed among TFs is proposed. The extracted TF regulatory modules not only correlate the molecular mechanisms to the observed functional redundancy among TFs, but also show biological significance in inferring TF functional binding target genes. The results provide testable hypotheses for biologists to further design subsequent research and experiments.

A Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data

This paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L₁-based penalties. Moreover, the ALM allows the resultant non-smooth L₁-based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.

Transcription Factors Indirectly Regulate Genes through Nuclear Colocalization

Various types of data, including genomic sequences, transcription factor (TF) knockout data, TF-DNA interaction and expression profiles, have been used to decipher TF regulatory mechanisms. However, most of the genes affected by knockout of a particular TF are not bound by that factor. Here, I showed that this interesting result can be partially explained by considering the nuclear positioning of TF knockout affected genes and TF bound genes. I found that a statistically significant number of TF knockout affected genes show nuclear colocalization with genes bound by the corresponding TF. Although these TF knockout affected genes are not directly bound by the corresponding TF; the TF tend to be in the same cellular component with the TFs that directly bind these genes. TF knockout affected genes show co-expression and tend to be involved in the same biological process with the spatially adjacent genes that are bound by the corresponding TF. These results demonstrate that TFs can regulate genes through nuclear colocalization without direct DNA binding, complementing the conventional view that TFs directly bind DNA to regulate genes. My findings will have implications in understanding TF regulatory mechanisms.

Connectivity mapping of angiotensin-PPAR interactions involved in the amelioration of non-alcoholic steatohepatitis by Telmisartan

Nonalcoholic fatty liver disease (NAFLD) is a global health problem that is associated with various metabolic disorders. Telmisartan is a potential treatment for NAFLD due to its ability to improve insulin sensitivity and decrease hepatic fat accumulation via modulation of PPARγ, and to suppress hepatic fibrosis by blocking angiotensin II receptors. However, the underlying mechanisms of action of telmisartan have yet to be fully elucidated. In the present study, diabetic nonalcoholic steatohepatitis (NASH) mice (STAM mice) received daily administrations of telmisartan for 6 weeks to assess the improvements in NASH. Hepatic transcriptome analyses revealed that the amelioration of NASH likely occurred through the regulation of inflammatory- and fibrosis-related gene responses. An integrated network analysis including transcriptional and non-transcriptional genes regulated by telmisartan showed that the NAFLD pathway is interconnected with the dysregulated RAS-PPAR-NFκB pathways. The downstream targets of PPARα, PPARδ, and RELA in this network significantly overlapped with telmisartan-induced differentially expressed genes (DEGs), which were verified in palmitate-treated Hepa1c1c7 cell line. This transcriptome approach accompanied with cell-based molecular analyses provided the opportunity to understand the fundamental molecular mechanisms underpinning the therapeutic effects of telmisartan, and will contribute to the establishment of a novel pharmacological treatment for NASH patients.

Pentose Metabolism in Saccharomyces cerevisiae: The Need to Engineer Global Regulatory Systems

Extending the host substrate range of industrially relevant microbes, such as Saccharomyces cerevisiae, has been a highly-active area of research since the conception of metabolic engineering. Yet, rational strategies that enable non-native substrate utilization in this yeast without the need for combinatorial and/or evolutionary techniques are underdeveloped. Herein, this review focuses on pentose metabolism in S. cerevisiae as a case study to highlight the challenges in this field. In the last three decades, work has focused on expressing exogenous pentose metabolizing enzymes as well as endogenous enzymes for effective pentose assimilation, growth, and biofuel production. The engineering strategies that are employed for pentose assimilation in this yeast are reviewed, and compared with metabolism and regulation of native sugar, galactose. In the case of galactose metabolism, multiple signals regulate and aid growth in the presence of the sugar. However, for pentoses that are non-native, it is unclear if similar growth and regulatory signals are activated. Such a comparative analysis aids in identifying missing links in xylose and arabinose utilization. While research on pentose metabolism have mostly concentrated on pathway level optimization, recent transcriptomics analyses highlight the need to consider more global regulatory, structural, and signaling components.

The glycerophosphocholine acyltransferase Gpc1 is part of a phosphatidylcholine (PC)-remodeling pathway that alters PC species in yeast

Phospholipase B-mediated hydrolysis of phosphatidylcholine (PC) results in the formation of free fatty acids and glycerophosphocholine (GPC) in the yeast Saccharomyces cerevisiae GPC can be reacylated by the glycerophosphocholine acyltransferase Gpc1, which produces lysophosphatidylcholine (LPC), and LPC can be converted to PC by the lysophospholipid acyltransferase Ale1. Here, we further characterized the regulation and function of this distinct PC deacylation/reacylation pathway in yeast. Through in vitro and in vivo experiments, we show that Gpc1 and Ale1 are the major cellular GPC and LPC acyltransferases, respectively. Importantly, we report that Gpc1 activity affects the PC species profile. Loss of Gpc1 decreased the levels of monounsaturated PC species and increased those of diunsaturated PC species, whereas Gpc1 overexpression had the opposite effects. Of note, Gpc1 loss did not significantly affect phosphatidylethanolamine, phosphatidylinositol, and phosphatidylserine profiles. Our results indicate that Gpc1 is involved in postsynthetic PC remodeling that produces more saturated PC species. qRT-PCR analyses revealed that GPC1 mRNA abundance is regulated coordinately with PC biosynthetic pathways. Inositol availability, which regulates several phospholipid biosynthetic genes, down-regulated GPC1 expression at the mRNA and protein levels and, as expected, decreased levels of monounsaturated PC species. Finally, loss of GPC1 decreased stationary phase viability in inositol-free medium. These results indicate that Gpc1 is part of a postsynthetic PC deacylation/reacylation remodeling pathway (PC-DRP) that alters the PC species profile, is regulated in coordination with other major lipid biosynthetic pathways, and affects yeast growth.

Identifying novel genetic determinants for oxidative stress tolerance in Candida glabrata via adaptive laboratory evolution

Candida glabrata (C glabrata) is an important yeast of industrial and medical significance. Resistance to oxidative stress is an important trait affecting its robustness as a production host or virulence as a pathogenic agent, but current understanding of resistance mechanisms is still limited in this fungus. In this study, we rapidly evolved C glabrata population to adapt to oxidative challenge (from 80mM to 350mM of H₂ O₂ ) through short-term adaptive laboratory evolution. Adaptive mutants were isolated from evolved populations and subjected to phenotypic and omics analyses to identify potential mechanisms of tolerance to H₂ O₂ . Phenotypic characterizations revealed faster detoxification of H₂ O₂ and ability to initiate growth at a higher concentration of the oxidant in the isolated adaptive mutants compared with the wild type. Genome resequencing and genome-wide transcriptome analysis revealed multiple genetic determinants (eg, CAGL0E01243g, CAGL0F06831g, and CAGL0C00385g) that potentially contribute to enhanced H₂ O₂ resistance. Subsequent experimental verification confirmed that CgCth2 (CAGL0E01243g) and CgMga2 (CAGL0F06831g) are important in C glabrata tolerance to oxidative stress. Transcriptome profiling of adaptive mutants and bioinformatic analysis suggest that NADPH regeneration, modulation of membrane composition, cell wall remodeling, and/or global regulatory changes are involved in C glabrata tolerance to H₂ O₂ .

A prior-based integrative framework for functional transcriptional regulatory network inference

Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.

An optimization framework for network annotation

Motivation

A chief goal of systems biology is the reconstruction of large-scale executable models of cellular processes of interest. While accurate continuous models are still beyond reach, a powerful alternative is to learn a logical model of the processes under study, which predicts the logical state of any node of the model as a Boolean function of its incoming nodes. Key to learning such models is the functional annotation of the underlying physical interactions with activation/repression (sign) effects. Such annotations are pretty common for a few well-studied biological pathways.

Results

Here we present a novel optimization framework for large-scale sign annotation that employs different plausible models of signaling and combines them in a rigorous manner. We apply our framework to two large-scale knockout datasets in yeast and evaluate its different components as well as the combined model to predict signs of different subsets of physical interactions. Overall, we obtain an accurate predictor that outperforms previous work by a considerable margin.

Availability and implementation

The code is publicly available at https://github.com/spatkar94/NetworkAnnotation.git.

A semi-synthetic regulon enables rapid growth of yeast on xylose

Nutrient assimilation is the first step that allows biological systems to proliferate and produce value-added products. Yet, implementation of heterologous catabolic pathways has so far relied on constitutive gene expression without consideration for global regulatory systems that may enhance nutrient assimilation and cell growth. In contrast, natural systems prefer nutrient-responsive gene regulation (called regulons) that control multiple cellular functions necessary for cell survival and growth. Here, in Saccharomyces cerevisiae, by partially- and fully uncoupling galactose (GAL)-responsive regulation and metabolism, we demonstrate the significant growth benefits conferred by the GAL regulon. Next, by adapting the various aspects of the GAL regulon for a non-native nutrient, xylose, we build a semi-synthetic regulon that exhibits higher growth rate, better nutrient consumption, and improved growth fitness compared to the traditional and ubiquitous constitutive expression strategy. This work provides an elegant paradigm to integrate non-native nutrient catabolism with native, global cellular responses to support fast growth.

Efficient assimilation of nutrients is essential for the production of value-added products in microbial fermentation. Here the authors design a semi-synthetic xylose regulon to improve growth characteristics of Saccharomyces cerevisiae on this non-native sugar.

RegulatorDB: a resource for the analysis of yeast transcriptional regulation

Database URL

http://wyrickbioinfo2.smb.wsu.edu/RegulatorDB.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

A Chemogenomic Screen Reveals Novel Snf1p/AMPK Independent Regulators of Acetyl-CoA Carboxylase

Acetyl-CoA carboxylase (Acc1p) is a key enzyme in fatty acid biosynthesis and is essential for cell viability. To discover new regulators of its activity, we screened a Saccharomyces cerevisiae deletion library for increased sensitivity to soraphen A, a potent Acc1p inhibitor. The hits identified in the screen (118 hits) were filtered using a chemical-phenotype map to exclude those associated with pleiotropic drug resistance. This enabled the identification of 82 ORFs that are genetic interactors of Acc1p. The main functional clusters represented by these hits were “transcriptional regulation”, “protein post-translational modifications” and “lipid metabolism”. Further investigation of the “transcriptional regulation” cluster revealed that soraphen A sensitivity is poorly correlated with ACC1 transcript levels. We also studied the three top unknown ORFs that affected soraphen A sensitivity: SOR1 (YDL129W), SOR2 (YIL092W) and SOR3 (YJR039W). Since the C18/C16 ratio of lipid acyl lengths reflects Acc1p activity levels, we evaluated this ratio in the three mutants. Deletion of SOR2 and SOR3 led to reduced acyl lengths, suggesting that Acc1p is indeed down-regulated in these strains. Also, these mutants showed no differences in Snf1p/AMPK activation status and deletion of SNF1 in these backgrounds did not revert soraphen A sensitivity completely. Furthermore, plasmid maintenance was reduced in sor2Δ strain and this trait was shared with 18 other soraphen A sensitive hits. In summary, our screen uncovered novel Acc1p Snf1p/AMPK-independent regulators.

Quantitative CRISPR interference screens in yeast identify chemical-genetic interactions and new rules for guide RNA design

BACKGROUND

Genome-scale CRISPR interference (CRISPRi) has been used in human cell lines; however, the features of effective guide RNAs (gRNAs) in different organisms have not been well characterized. Here, we define rules that determine gRNA effectiveness for transcriptional repression in Saccharomyces cerevisiae.

RESULTS

We create an inducible single plasmid CRISPRi system for gene repression in yeast, and use it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identifies previously described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments using gRNA libraries allows us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility are clearly more effective. We also find that the best region to target gRNAs is between the transcription start site (TSS) and 200 bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, the specificity of truncated gRNAs (18 nt of complementarity to the target) is not clearly superior to full-length gRNAs (20 nt of complementarity), as truncated gRNAs are generally less potent against both mismatched and perfectly matched targets.

CONCLUSIONS

Our results establish a powerful functional and chemical genomics screening method and provide guidelines for designing effective gRNAs, which consider chromatin state and position relative to the target gene TSS. These findings will enable effective library design and genome-wide programmable gene repression in many genetic backgrounds.

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

Reduced Histone Expression or a Defect in Chromatin Assembly Induces Respiration

Regulation of mitochondrial biogenesis and respiration is a complex process that involves several signaling pathways and transcription factors as well as communication between the nuclear and mitochondrial genomes. Here we show that decreased expression of histones or a defect in nucleosome assembly in the yeast Saccharomyces cerevisiae results in increased mitochondrial DNA (mtDNA) copy numbers, oxygen consumption, ATP synthesis, and expression of genes encoding enzymes of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). The metabolic shift from fermentation to respiration induced by altered chromatin structure is associated with the induction of the retrograde (RTG) pathway and requires the activity of the Hap2/3/4/5p complex as well as the transport and metabolism of pyruvate in mitochondria. Together, our data indicate that altered chromatin structure relieves glucose repression of mitochondrial respiration by inducing transcription of the TCA cycle and OXPHOS genes carried by both nuclear and mitochondrial DNA.

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

cDREM: inferring dynamic combinatorial gene regulation

Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.