Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

Two Zn2Cys6-type transcription factors respond to aromatic compounds and regulate the expression of laccases in the white-rot fungus Trametes hirsuta

White-rot fungi differentially express laccases when they encounter aromatic compounds. However, the underlying mechanisms are still being explored. Here, proteomics analysis revealed that in addition to increased laccase activity, proteins involved in sphingolipid metabolism and toluene degradation as well as some cytochrome P450s (CYP450s) were differentially expressed and significantly enriched during 48 h of o-toluidine exposure, in Trametes hirsuta AH28-2. Two Zn2Cys6-type transcription factors (TFs), TH8421 and TH4300, were upregulated. Bioinformatics docking and isothermal titration calorimetry assays showed that each of them could bind directly to o-toluidine and another aromatic monomer, guaiacol. Binding to aromatic compounds promoted the formation of TH8421/TH4300 heterodimers. TH8421 and TH4300 silencing in T. hirsuta AH28-2 led to decreased transcriptional levels and activities of LacA and LacB upon o-toluidine and guaiacol exposure. EMSA and ChIP-qPCR analysis further showed that TH8421 and TH4300 bound directly with the promoter regions of lacA and lacB containing CGG or CCG motifs. Furthermore, the two TFs were involved in direct and positive regulation of the transcription of some CYP450s. Together, TH8421 and TH4300, two key regulators found in T. hirsuta AH28-2, function as heterodimers to simultaneously trigger the expression of downstream laccases and intracellular enzymes. Monomeric aromatic compounds act as ligands to promote heterodimer formation and enhance the transcriptional activities of the two TFs.IMPORTANCEWhite-rot fungi differentially express laccase isoenzymes when exposed to aromatic compounds. Clarification of the molecular mechanisms underlying differential laccase expression is essential to elucidate how white-rot fungi respond to the environment. Our study shows that two Zn2Cys6-type transcription factors form heterodimers, interact with the promoters of laccase genes, and positively regulate laccase transcription in Trametes hirsuta AH28-2. Aromatic monomer addition induces faster heterodimer formation and rate of activity. These findings not only identify two new transcription factors involved in fungal laccase transcription but also deepen our understanding of the mechanisms underlying the response to aromatics exposure in white-rot fungi.

ChEC-seq2: an improved chromatin endogenous cleavage sequencing method and bioinformatic analysis pipeline for mapping in vivo protein-DNA interactions

Defining the in vivo DNA binding specificity of transcription factors (TFs) has relied nearly exclusively on chromatin immunoprecipitation (ChIP). While ChIP reveals TF binding patterns, its resolution is low. Higher resolution methods employing nucleases such as ChIP-exo, chromatin endogenous cleavage (ChEC-seq) and CUT&RUN resolve both TF occupancy and binding site protection. ChEC-seq, in which an endogenous TF is fused to micrococcal nuclease, requires neither fixation nor antibodies. However, the specificity of DNA cleavage during ChEC has been suggested to be lower than the specificity of the peaks identified by ChIP or ChIP-exo, perhaps reflecting non-specific binding of transcription factors to DNA. We have simplified the ChEC-seq protocol to minimize nuclease digestion while increasing the yield of cleaved DNA. ChEC-seq2 cleavage patterns were highly reproducible between replicates and with published ChEC-seq data. Combined with DoubleChEC, a new bioinformatic pipeline that removes non-specific cleavage sites, ChEC-seq2 identified high-confidence cleavage sites for three different yeast TFs that are strongly enriched for their known binding sites and adjacent to known target genes.

New biomarkers underlying acetic acid tolerance in the probiotic yeast Saccharomyces cerevisiae var. boulardii

Evolutionary engineering experiments, in combination with omics technologies, revealed genetic markers underpinning the molecular mechanisms behind acetic acid stress tolerance in the probiotic yeast Saccharomyces cerevisiae var. boulardii. Here, compared to the ancestral Ent strain, evolved yeast strains could quickly adapt to high acetic acid levels (7 g/L) and displayed a shorter lag phase of growth. Bioinformatic-aided whole-genome sequencing identified genetic changes associated with enhanced strain robustness to acetic acid: a duplicated sequence in the essential endocytotic PAN1 gene, mutations in a cell wall mannoprotein (dan4Thr192del), a lipid and fatty acid transcription factor (oaf1Ser57Pro) and a thiamine biosynthetic enzyme (thi13Thr332Ala). Induction of PAN1 and its associated endocytic complex SLA1 and END3 genes was observed following acetic acid treatment in the evolved-resistant strain when compared to the ancestral strain. Genome-wide transcriptomic analysis of the evolved Ent acid-resistant strain (Ent ev16) also revealed a dramatic rewiring of gene expression among genes associated with cellular transport, metabolism, oxidative stress response, biosynthesis/organization of the cell wall, and cell membrane. Some evolved strains also displayed better growth at high acetic acid concentrations and exhibited adaptive metabolic profiles with altered levels of secreted ethanol (4.0-6.4% decrease), glycerol (31.4-78.5% increase), and acetic acid (53.0-60.3% increase) when compared to the ancestral strain. Overall, duplication/mutations and transcriptional alterations are key mechanisms driving improved acetic acid tolerance in probiotic strains. We successfully used adaptive evolutionary engineering to rapidly and effectively elucidate the molecular mechanisms behind important industrial traits to obtain robust probiotic yeast strains for myriad biotechnological applications. KEY POINTS: •Acetic acid adaptation of evolutionary engineered robust probiotic yeast S. boulardii •Enterol ev16 with altered genetic and transcriptomic profiles survives in up to 7 g/L acetic acid •Improved acetic acid tolerance of S. boulardii ev16 with mutated PAN1, DAN4, OAF1, and THI13 genes.

Key enzymes involved in the utilization of fatty acids by Saccharomyces cerevisiae: a review

Saccharomyces cerevisiae is a eukaryotic organism with a clear genetic background and mature gene operating system; in addition, it exhibits environmental tolerance. Therefore, S. cerevisiae is one of the most commonly used organisms for the synthesis of biological chemicals. The investigation of fatty acid catabolism in S. cerevisiae is crucial for the synthesis and accumulation of fatty acids and their derivatives, with β-oxidation being the predominant pathway responsible for fatty acid metabolism in this organism, occurring primarily within peroxisomes. The latest research has revealed distinct variations in β-oxidation among different fatty acids, primarily attributed to substrate preferences and disparities in the metabolic regulation of key enzymes involved in the S. cerevisiae fatty acid metabolic pathway. The synthesis of lipids, on the other hand, represents another crucial metabolic pathway for fatty acids. The present paper provides a comprehensive review of recent research on the key factors influencing the efficiency of fatty acid utilization, encompassing β-oxidation and lipid synthesis pathways. Additionally, we discuss various approaches for modifying β-oxidation to enhance the synthesis of fatty acids and their derivatives in S. cerevisiae, aiming to offer theoretical support and serve as a valuable reference for future studies.

The leucine zipper domain of the transcriptional repressor Opi1 underlies a signal transduction mechanism regulating lipid synthesis

In Saccharomyces cerevisiae, the transcriptional repressor Opi1 regulates the expression of genes involved in phospholipid synthesis responding to the abundance of the phospholipid precursor phosphatidic acid at the endoplasmic reticulum. We report here the identification of the conserved leucine zipper (LZ) domain of Opi1 as a hot spot for gain of function mutations and the characterization of the strongest variant identified, Opi1N150D. LZ modeling posits asparagine 150 embedded on the hydrophobic surface of the zipper and specifying dynamic parallel homodimerization by allowing electrostatic bonding across the hydrophobic dimerization interface. Opi1 variants carrying any of the other three ionic residues at amino acid 150 were also repressing. Genetic analyses showed that Opi1N150D variant is dominant, and its phenotype is attenuated when loss of function mutations identified in the other two conserved domains are present in cis. We build on the notion that membrane binding facilitates LZ dimerization to antagonize an intramolecular interaction of the zipper necessary for repression. Dissecting Opi1 protein in three polypeptides containing each conserved region, we performed in vitro analyses to explore interdomain interactions. An Opi11-190 probe interacted with Opi1291-404, the C terminus that bears the activator interacting domain (AID). LZ or AID loss of function mutations attenuated the interaction of the probes but was unaffected by the N150D mutation. We propose a model for Opi1 signal transduction whereby synergy between membrane-binding events and LZ dimerization antagonizes intramolecular LZ-AID interaction and transcriptional repression.

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

ChEC-seq2: an improved Chromatin Endogenous Cleavage sequencing method and bioinformatic analysis pipeline for mapping in vivo protein-DNA interactions

Defining the in vivo DNA binding specificity of transcription factors (TFs) has relied nearly exclusively on chromatin immunoprecipitation (ChIP). While ChIP reveals TF binding patterns, its resolution is low. Higher resolution methods employing nucleases such as ChIP-exo, chromatin endogenous cleavage (ChEC-seq) and CUT&RUN resolve both TF occupancy and binding site protection. ChEC-seq, in which an endogenous TF is fused to micrococcal nuclease, requires neither fixation nor antibodies. However, the specificity of DNA cleavage during ChEC has been suggested to be lower than the specificity of the peaks identified by ChIP or ChIP-exo, perhaps reflecting non-specific binding of transcription factors to DNA. We have simplified the ChEC-seq protocol to minimize nuclease digestion while increasing the yield of cleaved DNA. ChEC-seq2 cleavage patterns were highly reproducible between replicates and with published ChEC-seq data. Combined with DoubleChEC, a new bioinformatic pipeline that removes non-specific cleavage sites, ChEC-seq2 identified high-confidence cleavage sites for three different yeast TFs that are strongly enriched for their known binding sites and adjacent to known target genes.

Transcriptional regulation of ergosterol biosynthesis genes in response to iron deficiency

Iron participates as an essential cofactor in the biosynthesis of critical cellular components, including DNA, proteins and lipids. The ergosterol biosynthetic pathway, which is an important target of antifungal treatments, depends on iron in four enzymatic steps. Our results in the model yeast Saccharomyces cerevisiae show that the expression of ergosterol biosynthesis (ERG) genes is tightly modulated by iron availability probably through the iron-dependent variation of sterol and heme levels. Whereas the transcription factors Upc2 and Ecm22 are responsible for the activation of ERG genes upon iron deficiency, the heme-dependent factor Hap1 triggers their Tup1-mediated transcriptional repression. The combined regulation by both activating and repressing regulatory factors allows for the fine-tuning of ERG transcript levels along the progress of iron deficiency, avoiding the accumulation of toxic sterol intermediates and enabling efficient adaptation to rapidly changing conditions. The lack of these regulatory factors leads to changes in the yeast sterol profile upon iron-deficient conditions. Both environmental iron availability and specific regulatory factors should be considered in ergosterol antifungal treatments.

Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

Rational gRNA design based on transcription factor binding data

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor (TF) binding data to obtain a better understanding of the interplay between TF binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9–VPR toward binding sites of Gcr1–Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9–VPR was targeted next to a TF binding motif, whereas a downregulation or no change was observed when dCas9 was bound on a TF motif. This suggests a steric competition between dCas9 and the specific TF. Integrating TF binding data, therefore, proved to be useful for designing guide RNAs for CRISPR interference or CRISPR activation applications.

Trans-acting genetic variation affects the expression of adjacent genes

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve "vertical" cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via "horizontal" mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that also show coexpression in response to genetic and environmental perturbations, suggesting shared mechanisms. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects among adjacent genes. Paired effects of trans-eQTLs can occur at neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

Motivation

The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now.

Results

We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2.

Availability and implementation

Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573.

Supplementary information

Supplementary data are available at Bioinformatics online.

Transcription factors and ABC transporters: from pleiotropic drug resistance to cellular signaling in yeast

Budding yeast Saccharomyces cerevisiae survives in microenvironments utilizing networks of regulators and ATP-binding cassette (ABC) transporters to circumvent toxins and a variety of drugs. Our understanding of transcriptional regulation of ABC transporters in yeast is mainly derived from the study of multidrug resistance protein networks. Over the past two decades, this research has not only expanded the role of transcriptional regulators in pleiotropic drug resistance (PDR) but evolved to include the role that regulators play in cellular signaling and environmental adaptation. Inspection of the gene networks of the transcriptional regulators and characterization of the ABC transporters has clarified that they also contribute to environmental adaptation by controlling plasma membrane composition, toxic-metal sequestration, and oxidative stress adaptation. Additionally, ABC transporters and their regulators appear to be involved in cellular signaling for adaptation of S. cerevisiae populations to nutrient availability. In this review, we summarize the current understanding of the S. cerevisiae transcriptional regulatory networks and highlight recent work in other notable fungal organisms, underlining the expansion of the study of these gene networks across the kingdom fungi.

Regulation of Ergosterol Biosynthesis in Saccharomyces cerevisiae

Ergosterol is an essential component of fungal cell membranes that determines the fluidity, permeability and activity of membrane-associated proteins. Ergosterol biosynthesis is a complex and highly energy-consuming pathway that involves the participation of many enzymes. Deficiencies in sterol biosynthesis cause pleiotropic defects that limit cellular proliferation and adaptation to stress. Thereby, fungal ergosterol levels are tightly controlled by the bioavailability of particular metabolites (e.g., sterols, oxygen and iron) and environmental conditions. The regulation of ergosterol synthesis is achieved by overlapping mechanisms that include transcriptional expression, feedback inhibition of enzymes and changes in their subcellular localization. In the budding yeast Saccharomyces cerevisiae, the sterol regulatory element (SRE)-binding proteins Upc2 and Ecm22, the heme-binding protein Hap1 and the repressor factors Rox1 and Mot3 coordinate ergosterol biosynthesis (ERG) gene expression. Here, we summarize the sterol biosynthesis, transport and detoxification systems of S. cerevisiae, as well as its adaptive response to sterol depletion, low oxygen, hyperosmotic stress and iron deficiency. Because of the large number of ERG genes and the crosstalk between different environmental signals and pathways, many aspects of ergosterol regulation are still unknown. The study of sterol metabolism and its regulation is highly relevant due to its wide applications in antifungal treatments, as well as in food and pharmaceutical industries.

Motifs enable communication efficiency and fault-tolerance in transcriptional networks

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.

Yeast systems biology in understanding principles of physiology underlying complex human diseases

Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.

DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.

Differences in the DNA sequence of individual genomes contribute to differences in many traits, such as appearance, physiology, and the risk for common diseases. An important group of these DNA variants influences how individual genes across the genome are turned on or off. In this paper, we describe a strategy for identifying such “trans-acting” variants in different strains of baker’s yeast. We used this strategy to reveal three single DNA base changes that each influences the expression of dozens of genes. These three DNA variants were very different from each other. Two of them changed the protein sequence, one in a transcription factor and the other in a sugar sensor. The third changed the expression of an enzyme, a change that in turn caused other genes to alter their expression. One variant existed in only a few yeast isolates, while the other two existed in many isolates collected from around the world. This diversity of DNA variants that influence the expression of many other genes illustrates how difficult it is to predict which DNA variants in an individual’s genome will have effects on the organism.

Big data in yeast systems biology

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

A bioinformatic pipeline to analyze ChIP-exo datasets

The decrease of sequencing cost in the recent years has made genome-wide studies of transcription factor (TF) binding through chromatin immunoprecipitation methods like ChIP-seq and chromatin immunoprecipitation with lambda exonuclease (ChIP-exo) more accessible to a broader group of users. Especially with ChIP-exo, it is now possible to map TF binding sites in more detail and with less noise than previously possible. These improvements came at the cost of making the analysis of the data more challenging, which is further complicated by the fact that to this date no complete pipeline is publicly available. Here we present a workflow developed specifically for ChIP-exo data and demonstrate its capabilities for data analysis. The pipeline, which is completely publicly available on GitHub, includes all necessary analytical steps to obtain a high confidence list of TF targets starting from raw sequencing reads. During the pipeline development, we emphasized the inclusion of different quality control measurements and we show how to use these so users can have confidence in their obtained results.

A Thi2p Regulatory Network Controls the Post-glucose Effect of Xylose Utilization in Saccharomyces cerevisiae

The complete and efficient utilization of both glucose and xylose is necessary for the economically viable production of biofuels and chemicals using lignocellulosic feedstocks. Although recently obtained recombinant Saccharomyces cerevisiae strains metabolize xylose well when xylose is the sole carbon source in the medium (henceforth referred to as "X stage"), their xylose consumption rate is significantly reduced during the xylose-only consumption phase of glucose-xylose co-fermentation ("GX stage"). This post-glucose effect seriously decreases overall fermentation efficiency. We showed in previous work that THI2 deletion can alleviate this post-glucose effect, but the underlying mechanisms were ill-defined. In the present study, we profiled the transcriptome of a thi2Δ strain growing at the GX stage. Thi2p in GX stage cells regulates genes involved in the cell cycle, stress tolerance, and cell viability. Importantly, the regulation of Thi2p differs from a previous regulatory network that functions when glucose is the sole carbon source, which suggests that the function of Thi2p depends on the carbon source. Modeling research seeking to optimize metabolic engineering via TFs should account for this important carbon source difference. Building on our initial study, we confirmed that several identified factors did indeed increase fermentation efficiency. Specifically, overexpressing STT4, RGI2, and TFC3 increases specific xylose utilization rate of the strain by 36.9, 29.7, 42.8%, respectively, in the GX stage of anaerobic fermentation. Our study thus illustrates a promising strategy for the rational engineering of yeast for lignocellulosic ethanol production.

Predictive models of eukaryotic transcriptional regulation reveals changes in transcription factor roles and promoter usage between metabolic conditions

Transcription factors (TF) are central to transcriptional regulation, but they are often studied in relative isolation and without close control of the metabolic state of the cell. Here, we describe genome-wide binding (by ChIP-exo) of 15 yeast TFs in four chemostat conditions that cover a range of metabolic states. We integrate this data with transcriptomics and six additional recently mapped TFs to identify predictive models describing how TFs control gene expression in different metabolic conditions. Contributions by TFs to gene regulation are predicted to be mostly activating, additive and well approximated by assuming linear effects from TF binding signal. Notably, using TF binding peaks from peak finding algorithms gave distinctly worse predictions than simply summing the low-noise and high-resolution TF ChIP-exo reads on promoters. Finally, we discover indications of a novel functional role for three TFs; Gcn4, Ert1 and Sut1 during nitrogen limited aerobic fermentation. In only this condition, the three TFs have correlated binding to a large number of genes (enriched for glycolytic and translation processes) and a negative correlation to target gene transcript levels.

Yeast Systems Biology: Model Organism and Cell Factory

For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.