Mapping yeast transcriptional networks

Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

Study of Impacts of Two Types of Cellular Aging on the Yeast Bud Morphogenesis

Understanding the mechanisms of cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short lifespan and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. In this study, by analyzing experimental data it was shown that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional chemical-mechanical model was developed and used to suggest and test hypothesized mechanisms of bud morphogenesis during aging. Experimentally calibrated simulations showed that tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip guided by the polarized Cdc42 signal during the early stage of budding. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage, as observed in experiments, through a reduction on the new cell surface material insertion or an expansion of the polarization site. Thus model simulations suggest the maintenance of new cell surface material insertion or chemical signal polarization could be weakened due to cellular aging in yeast and other cell types.

Resonant learning in scale-free networks

Large networks of interconnected components, such as genes or machines, can coordinate complex behavioral dynamics. One outstanding question has been to identify the design principles that allow such networks to learn new behaviors. Here, we use Boolean networks as prototypes to demonstrate how periodic activation of network hubs provides a network-level advantage in evolutionary learning. Surprisingly, we find that a network can simultaneously learn distinct target functions upon distinct hub oscillations. We term this emergent property resonant learning, as the new selected dynamical behaviors depend on the choice of the period of the hub oscillations. Furthermore, this procedure accelerates the learning of new behaviors by an order of magnitude faster than without oscillations. While it is well-established that modular network architecture can be selected through evolutionary learning to produce different network behaviors, forced hub oscillations emerge as an alternative evolutionary learning strategy for which network modularity is not necessarily required.

Genetic-interaction screens uncover novel biological roles and regulators of transcription factors in fission yeast

In Schizosaccharomyces pombe, systematic analyses of single transcription factor deletion or overexpression strains have made substantial advances in determining the biological roles and target genes of transcription factors, yet these characteristics are still relatively unknown for over a quarter of them. Moreover, the comprehensive list of proteins that regulate transcription factors remains incomplete. To further characterize Schizosaccharomyces pombe transcription factors, we performed synthetic sick/lethality and synthetic dosage lethality screens by synthetic genetic array. Examination of 2,672 transcription factor double deletion strains revealed a sick/lethality interaction frequency of 1.72%. Phenotypic analysis of these sick/lethality strains revealed potential cell cycle roles for several poorly characterized transcription factors, including SPBC56F2.05, SPCC320.03, and SPAC3C7.04. In addition, we examined synthetic dosage lethality interactions between 14 transcription factors and a miniarray of 279 deletion strains, observing a synthetic dosage lethality frequency of 4.99%, which consisted of known and novel transcription factor regulators. The miniarray contained deletions of genes that encode primarily posttranslational-modifying enzymes to identify putative upstream regulators of the transcription factor query strains. We discovered that ubiquitin ligase Ubr1 and its E2/E3-interacting protein, Mub1, degrade the glucose-responsive transcriptional repressor Scr1. Loss of ubr1⁺ or mub1⁺ increased Scr1 protein expression, which resulted in enhanced repression of flocculation through Scr1. The synthetic dosage lethality screen also captured interactions between Scr1 and 2 of its known repressors, Sds23 and Amk2, each affecting flocculation through Scr1 by influencing its nuclear localization. Our study demonstrates that sick/lethality and synthetic dosage lethality screens can be effective in uncovering novel functions and regulators of Schizosaccharomyces pombe transcription factors.

Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks

Gene regulatory networks ensure that important genes are expressed at precise levels. When gene expression is sufficiently perturbed, it can lead to disease. To understand how gene expression disruptions percolate through a network, we must first map connections between regulatory genes and their downstream targets. However, we lack comprehensive knowledge of the upstream regulators of most genes. Here, we developed an approach for systematic discovery of upstream regulators of critical immune factors-IL2RA, IL-2 and CTLA4-in primary human T cells. Then, we mapped the network of the target genes of these regulators and putative cis-regulatory elements using CRISPR perturbations, RNA-seq and ATAC-seq. These regulators form densely interconnected networks with extensive feedback loops. Furthermore, this network is enriched for immune-associated disease variants and genes. These results provide insight into how immune-associated disease genes are regulated in T cells and broader principles about the structure of human gene regulatory networks.

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.

Using Synthetic Genetic Interactions in Candida glabrata as a Novel Method to Detect Genes with Roles in Antifungal Drug Resistance

Synthetic genetic interaction analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the interactions between genes, categorize into biological pathways, and characterize genes of unknown function. It has been extensively employed in model organisms for fundamental, systems-level assessment of the interactions between genes. However, more recently, genetic interaction mapping has been applied to fungal pathogens and has been instrumental for the study of clinically important infectious organisms. This protocol herein explains in the detail the methodology and analysis that can be employed to develop interaction maps in microbial pathogens. Such techniques can aid in bridging our understanding of complex genetic networks, with applications to diverse microbial pathogens to further our understanding of virulence, the use of antimicrobial therapies, and host-pathogen interactions.

Genetic interaction between RLM1 and F-box motif encoding gene SAF1 contributes to stress response in Saccharomyces cerevisiae

BACKGROUND

Stress response is mediated by the transcription of stress-responsive genes. The F-box motif protein Saf1p is involved in SCF-E3 ligase mediated degradation of the adenine deaminase, Aah1p upon nutrient stress. The four transcription regulators, BUR6, MED6, SPT10, SUA7, are listed for SAF1 in the genome database of Saccharomyces cerevisiae. Here in this study, we carried out an in-silico analysis of gene expression and transcription factor databases to understand the regulation of SAF1 expression during stress for hypothesis and experimental analysis.

RESULT

An analysis of the GEO profile database indicated an increase in SAF1 expression when cells were treated with stress agents such as Clioquinol, Pterostilbene, Gentamicin, Hypoxia, Genotoxic, desiccation, and heat. The increase in expression of SAF1 during stress conditions correlated positively with the expression of RLM1, encoding the Rlm1p transcription factor. The expression of AAH1 encoding Aah1p, a Saf1p substrate for ubiquitination, appeared to be negatively correlated with the expression of RLM1 as revealed by an analysis of the Yeastract expression database. Based on analysis of expression profile and regulatory association of SAF1 and RLM1, we hypothesized that inactivation of both the genes together may contribute to stress tolerance. The experimental analysis of cellular growth response of cells lacking both SAF1 and RLM1 to selected stress agents such as cell wall and osmo-stressors, by spot assay indicated stress tolerance phenotype similar to parental strain however sensitivity to genotoxic and microtubule depolymerizing stress agents.

CONCLUSIONS

Based on in-silico and experimental data we suggest that SAF1 and RLM1 both interact genetically in differential response to genotoxic and general stressors.

Mutational sources of trans-regulatory variation affecting gene expression in Saccharomyces cerevisiae

Heritable variation in a gene’s expression arises from mutations impacting cis- and trans-acting components of its regulatory network. Here, we investigate how trans-regulatory mutations are distributed within the genome and within a gene regulatory network by identifying and characterizing 69 mutations with trans-regulatory effects on expression of the same focal gene in Saccharomyces cerevisiae. Relative to 1766 mutations without effects on expression of this focal gene, we found that these trans-regulatory mutations were enriched in coding sequences of transcription factors previously predicted to regulate expression of the focal gene. However, over 90% of the trans-regulatory mutations identified mapped to other types of genes involved in diverse biological processes including chromatin state, metabolism, and signal transduction. These data show how genetic changes in diverse types of genes can impact a gene’s expression in trans, revealing properties of trans-regulatory mutations that provide the raw material for trans-regulatory variation segregating within natural populations.

Trade-offs Among Resilience, Robustness, Stability, and Performance and How We Might Study Them

Biological systems are likely to be constrained by trade-offs among robustness, resilience, and performance. A better understanding of these trade-offs is important for basic biology, as well as applications where biological systems can be designed for different goals. We focus on redundancy and plasticity as mechanisms governing some types of trade-offs, but mention others as well. Whether trade-offs are due to resource constraints or "design" constraints (i.e., structure of nodes and links within a network) will also affect the types of trade-offs that are important. Identifying common themes across scales of biological organization will require that researchers use similar approaches to quantifying robustness, resilience, and performance, using units that can be compared across systems.

The cell cycle and differentiation as integrated processes: Cyclins and CDKs reciprocally regulate Sox and Notch to balance stem cell maintenance

Development and maintenance of diverse organ systems require context-specific regulation of stem cell behaviour. We hypothesize that this is achieved via reciprocal regulation between the cell cycle machinery and differentiation factors. This idea is supported by the parallel evolutionary emergence of differentiation pathways, cell cycle components and complex multicellularity. In addition, the activities of different cell cycle phases have been found to bias cells towards stem cell maintenance or differentiation. Finally, several direct mechanistic links between these two processes have been established. Here, we focus on interactions between cyclin-CDK complexes and differentiation regulators of the Notch pathway and Sox family of transcription factors within the context of pluripotent and neural stem cells. Thus, this hypothesis formalizes the links between these two processes as an integrated network. Since such factors are common to all stem cells, better understanding their interconnections will help to explain their behaviour in health and disease.

A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

Epigenome engineering: new technologies for precision medicine

Chromatin adopts different configurations that are regulated by reversible covalent modifications, referred to as epigenetic marks. Epigenetic inhibitors have been approved for clinical use to restore epigenetic aberrations that result in silencing of tumor-suppressor genes, oncogene addictions, and enhancement of immune responses. However, these drugs suffer from major limitations, such as a lack of locus selectivity and potential toxicities. Technological advances have opened a new era of precision molecular medicine to reprogram cellular physiology. The locus-specificity of CRISPR/dCas9/12a to manipulate the epigenome is rapidly becoming a highly promising strategy for personalized medicine. This review focuses on new state-of-the-art epigenome editing approaches to modify the epigenome of neoplasms and other disease models towards a more 'normal-like state', having characteristics of normal tissue counterparts. We highlight biomolecular engineering methodologies to assemble, regulate, and deliver multiple epigenetic effectors that maximize the longevity of the therapeutic effect, and we discuss limitations of the platforms such as targeting efficiency and intracellular delivery for future clinical applications.

Autoregulation of yeast ribosomal proteins discovered by efficient search for feedback regulation

Post-transcriptional autoregulation of gene expression is common in bacteria but many fewer examples are known in eukaryotes. We used the yeast collection of genes fused to GFP as a rapid screen for examples of feedback regulation in ribosomal proteins by overexpressing a non-regulatable version of a gene and observing the effects on the expression of the GFP-fused version. We tested 95 ribosomal protein genes and found a wide continuum of effects, with 30% showing at least a 3-fold reduction in expression. Two genes, RPS22B and RPL1B, showed over a 10-fold repression. In both cases the cis-regulatory segment resides in the 5’ UTR of the gene as shown by placing that segment of the mRNA upstream of GFP alone and demonstrating it is sufficient to cause repression of GFP when the protein is over-expressed. Further analyses showed that the intron in the 5’ UTR of RPS22B is required for regulation, presumably because the protein inhibits splicing that is necessary for translation. The 5’ UTR of RPL1B contains a sequence and structure motif that is conserved in the binding sites of Rpl1 orthologs from bacteria to mammals, and mutations within the motif eliminate repression.

Here, the authors screen for feedback regulation of ribosomal proteins by overexpressing a non- regulatable version of a gene and observing its effects on the expression of the GFP-fused version. They find that 30% show at least a 3-fold reduction in expression and two genes show a 10-fold reduction with the regulatory site being in the 5’ untranslated region of the gene.

A Bright IDEA

Transcription factors (TFs) control the rate of mRNA production. Technological advances have made the task of measuring mRNA levels for all genes straightforward, but identifying causal relationships between TFs and their target genes remains an unsolved problem in biology. In their recent study, McIsaac and colleagues (Hackett et al, 2020) apply a method for inducing the overexpression of a TF and studying the dynamics with which all transcripts respond. Using time series analysis, they are able to resolve direct effects of TFs from secondary effects. This new experimental and analytical approach provides an efficient means of defining gene regulatory relationships for all TFs.

Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest.

Conventional methods cannot reveal transient transcription factors (TFs) and targets interactions. Here, Alvarez et al. capture both stable and transient TF-target interactions by time-series ChIP-seq and/or DamID-seq in a cell-based TF perturbation system and show NLP7 as a master TF to initiate a rapid nitrogen-response cascade.

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity, and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.

Gene expression levels in yeast are predicted using a massive dataset on promoters with random sequences.

Diverse DNA Sequence Motifs Activate Meiotic Recombination Hotspots Through a Common Chromatin Remodeling Pathway

In meiosis, multiple different DNA sequence motifs help to position homologous recombination at hotspots in the genome. How do the seemingly disparate cis-acting regulatory modules each promote locally the activity of the basal recombination machinery? We defined molecular mechanisms of action for five different hotspot-activating DNA motifs (M26, CCAAT, Oligo-C, 4095, 4156) located independently at the same site within the ade6 locus of the fission yeast Schizosaccharomyces pombe Each motif promoted meiotic recombination (i.e., is active) within this context, and this activity required the respective binding proteins (transcription factors Atf1, Pcr1, Php2, Php3, Php5, Rst2). High-resolution analyses of chromatin structure by nucleosome scanning assays revealed that each motif triggers the displacement of nucleosomes surrounding the hotspot motif in meiosis. This chromatin remodeling required the respective sequence-specific binding proteins, was constitutive for two motifs, and was enhanced meiotically for three others. Hotspot activity of each motif strongly required the ATP-dependent chromatin remodeling enzyme Snf22 (Snf2/Swi2), with lesser dependence on Gcn5, Mst2, and Hrp3. These findings support a model in which most meiotic recombination hotspots are positioned by the binding of transcription factors to their respective DNA sites. The functional redundancy of multiple, sequence-specific protein-DNA complexes converges upon shared chromatin remodeling pathways that help provide the basal recombination machinery (Spo11/Rec12 complex) access to its DNA substrates within chromatin.

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.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

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.

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.

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

To build transcription regulatory networks, transcription factor binding must be analyzed in cells grown under different conditions because their responses and targets differ depending on environmental conditions. We performed whole-genome analysis of the DNA binding of five Saccharomyces cerevisiae transcription factors involved in lipid metabolism, Ino2, Ino4, Hap1, Oaf1, and Pip2, in response to four different environmental conditions in chemostat cultures, which allowed us to keep the specific growth rate constant. Chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) enabled the detection of binding events at a high resolution. We discovered a large number of unidentified targets and thus expanded functions for each transcription factor (e.g., glutamate biosynthesis as a target of Oaf1 and Pip2). Moreover, condition-dependent binding of transcription factors in response to cell metabolic state (e.g., differential binding of Ino2 between fermentative and respiratory metabolic conditions) was clearly suggested. Combining the new binding data with previously published data from transcription factor deletion studies revealed the high complexity of the transcriptional regulatory network for lipid metabolism in yeast, which involves the combinatorial and complementary regulation by multiple transcription factors. We anticipate that our work will provide insights into transcription factor binding dynamics that will prove useful for the understanding of transcription regulatory networks. IMPORTANCE Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism.

Genome-wide determinants of sequence-specific DNA binding of general regulatory factors

General regulatory factors (GRFs), such as Reb1, Abf1, Rap1, Mcm1, and Cbf1, positionally organize yeast chromatin through interactions with a core consensus DNA sequence. It is assumed that sequence recognition via direct base readout suffices for specificity and that spurious nonfunctional sites are rendered inaccessible by chromatin. We tested these assumptions through genome-wide mapping of GRFs in vivo and in purified biochemical systems at near–base pair (bp) resolution using several ChIP-exo–based assays. We find that computationally predicted DNA shape features (e.g., minor groove width, helix twist, base roll, and propeller twist) that are not defined by a unique consensus sequence are embedded in the nonunique portions of GRF motifs and contribute critically to sequence-specific binding. This dual source specificity occurs at GRF sites in promoter regions where chromatin organization starts. Outside of promoter regions, strong consensus sites lack the shape component and consequently lack an intrinsic ability to bind cognate GRFs, without regard to influences from chromatin. However, sites having a weak consensus and low intrinsic affinity do exist in these regions but are rendered inaccessible in a chromatin environment. Thus, GRF site-specificity is achieved through integration of favorable DNA sequence and shape readouts in promoter regions and by chromatin-based exclusion from fortuitous weak sites within gene bodies. This study further revealed a severe G/C nucleotide cross-linking selectivity inherent in all formaldehyde-based ChIP assays, which includes ChIP-seq. However, for most tested proteins, G/C selectivity did not appreciably affect binding site detection, although it does place limits on the quantitativeness of occupancy levels.

Ixr1 Regulates Ribosomal Gene Transcription and Yeast Response to Cisplatin

Ixr1 is a Saccharomyces cerevisiae HMGB protein that regulates the hypoxic regulon and also controls the expression of other genes involved in the oxidative stress response or re-adaptation of catabolic and anabolic fluxes when oxygen is limiting. Ixr1 also binds with high affinity to cisplatin-DNA adducts and modulates DNA repair. The influence of Ixr1 on transcription in the absence or presence of cisplatin has been analyzed in this work. Ixr1 regulates other transcriptional factors that respond to nutrient availability or extracellular and intracellular stress stimuli, some controlled by the TOR pathway and PKA signaling. Ixr1 controls transcription of ribosomal RNAs and genes encoding ribosomal proteins or involved in ribosome assembly. qPCR, ChIP, and 18S and 25S rRNAs measurement have confirmed this function. Ixr1 binds directly to several promoters of genes related to rRNA transcription and ribosome biogenesis. Cisplatin treatment mimics the effect of IXR1 deletion on rRNA and ribosomal gene transcription, and prevents Ixr1 binding to specific promoters related to these processes.

How transcription circuits explore alternative architectures while maintaining overall circuit output

This review by Dalal and Johnson focuses on the evolutionary rewiring of transcription regulators and the conservation of patterns of gene expression. They describe how preservation of gene expression patterns in the wake of extensive rewiring is a general feature of transcription circuit evolution.

Transcription regulators bind to cis-regulatory sequences and thereby control the expression of target genes. While transcription regulators and the target genes that they regulate are often deeply conserved across species, the connections between the two change extensively over evolutionary timescales. In this review, we discuss case studies where, despite this extensive evolutionary rewiring, the resulting patterns of gene expression are preserved. We also discuss in silico models that reach the same general conclusions and provide additional insights into how this process occurs. Together, these approaches make a strong case that the preservation of gene expression patterns in the wake of extensive rewiring is a general feature of transcription circuit evolution.

Functional Profiling of Transcription Factor Genes in Neurospora crassa

Regulation of gene expression by DNA-binding transcription factors is essential for proper control of growth and development in all organisms. In this study, we annotate and characterize growth and developmental phenotypes for transcription factor genes in the model filamentous fungus Neurospora crassa. We identified 312 transcription factor genes, corresponding to 3.2% of the protein coding genes in the genome. The largest class was the fungal-specific Zn₂Cys₆ (C6) binuclear cluster, with 135 members, followed by the highly conserved C2H2 zinc finger group, with 61 genes. Viable knockout mutants were produced for 273 genes, and complete growth and developmental phenotypic data are available for 242 strains, with 64% possessing at least one defect. The most prominent defect observed was in growth of basal hyphae (43% of mutants analyzed), followed by asexual sporulation (38%), and the various stages of sexual development (19%). Two growth or developmental defects were observed for 21% of the mutants, while 8% were defective in all three major phenotypes tested. Analysis of available mRNA expression data for a time course of sexual development revealed mutants with sexual phenotypes that correlate with transcription factor transcript abundance in wild type. Inspection of this data also implicated cryptic roles in sexual development for several cotranscribed transcription factor genes that do not produce a phenotype when mutated.

The Upsides and Downsides of Organelle Interconnectivity

Interconnectivity and feedback control are hallmarks of biological systems. This includes communication between organelles, which allows them to function and adapt to changing cellular environments. While the specific mechanisms for all communications remain opaque, unraveling the wiring of organelle networks is critical to understand how biological systems are built and why they might collapse, as occurs in aging. A comprehensive understanding of all the routes involved in inter-organelle communication is still lacking, but important themes are beginning to emerge, primarily in budding yeast. These routes are reviewed here in the context of sub-system proteostasis and complex adaptive systems theory.

Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Role of Ectopic Gene Conversion in the Evolution of a Candida krusei Pleiotropic Drug Resistance Transporter Family

Gene duplications enable the evolution of novel gene function, but strong positive selection is required to preserve advantageous mutations in a population. This is because frequent ectopic gene conversions (EGCs) between highly similar, tandem-duplicated, sequences, can rapidly remove fate-determining mutations by replacing them with the neighboring parent gene sequences. Unfortunately, the high sequence similarities between tandem-duplicated genes severely hamper empirical studies of this important evolutionary process, because deciphering their correct sequences is challenging. In this study, we employed the eukaryotic model organism Saccharomyces cerevisiae to clone and functionally characterize all 30 alleles of an important pair of tandem-duplicated multidrug efflux pump genes, ABC1 and ABC11, from seven strains of the diploid pathogenic yeast Candida krusei Discovery and functional characterization of their closest ancestor, C. krusei ABC12, helped elucidate the evolutionary history of the entire gene family. Our data support the proposal that the pleiotropic drug resistance (PDR) transporters Abc1p and Abc11p have evolved by concerted evolution for ∼134 MY. While >90% of their sequences remained identical, very strong purifying selection protected six short DNA patches encoding just 18 core amino acid (aa) differences in particular trans membrane span (TMS) regions causing two distinct efflux pump functions. A proline-kink change at the bottom of Abc11p TMS3 was possibly fate determining. Our data also enabled the first empirical estimates for key parameters of eukaryotic gene evolution, they provided rare examples of intron loss, and PDR transporter phylogeny confirmed that C. krusei belongs to a novel, yet unnamed, third major Saccharomycotina lineage.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.

Combinatorial control of plant gene expression

Combinatorial gene regulation provides a mechanism by which relatively small numbers of transcription factors can control the expression of a much larger number of genes with finely tuned temporal and spatial patterns. This is achieved by transcription factors assembling into complexes in a combinatorial fashion, exponentially increasing the number of genes that they can target. Such an arrangement also increases the specificity and affinity for the cis-regulatory sequences required for accurate target gene expression. Superimposed on this transcription factor combinatorial arrangement is the increasing realization that histone modification marks expand the regulatory information, which is interpreted by histone readers and writers that are part of the regulatory apparatus. Here, we review the progress in these areas from the perspective of plant combinatorial gene regulation, providing examples of different regulatory solutions and comparing them to other metazoans. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Identification and Characterization of a cis-Regulatory Element for Zygotic Gene Expression in Chlamydomonas reinhardtii

Upon fertilization Chlamydomonas reinhardtii zygotes undergo a program of differentiation into a diploid zygospore that is accompanied by transcription of hundreds of zygote-specific genes. We identified a distinct sequence motif we term a zygotic response element (ZYRE) that is highly enriched in promoter regions of C reinhardtii early zygotic genes. A luciferase reporter assay was used to show that native ZYRE motifs within the promoter of zygotic gene ZYS3 or intron of zygotic gene DMT4 are necessary for zygotic induction. A synthetic luciferase reporter with a minimal promoter was used to show that ZYRE motifs introduced upstream are sufficient to confer zygotic upregulation, and that ZYRE-controlled zygotic transcription is dependent on the homeodomain transcription factor GSP1. We predict that ZYRE motifs will correspond to binding sites for the homeodomain proteins GSP1-GSM1 that heterodimerize and activate zygotic gene expression in early zygotes.

Intracellular Action of a Secreted Peptide Required for Fungal Virulence

Quorum sensing (QS) is a bacterial communication mechanism in which secreted signaling molecules impact population function and gene expression. QS-like phenomena have been reported in eukaryotes with largely unknown contributing molecules, functions, and mechanisms. We identify Qsp1, a secreted peptide, as a central signaling molecule that regulates virulence in the fungal pathogen Cryptococcus neoformans. QSP1 is a direct target of three transcription factors required for virulence, and qsp1Δ mutants exhibit attenuated infection, slowed tissue accumulation, and greater control by primary macrophages. Qsp1 mediates autoregulatory signaling that modulates secreted protease activity and promotes cell wall function at high cell densities. Peptide production requires release from a secreted precursor, proQsp1, by a cell-associated protease, Pqp1. Qsp1 sensing requires an oligopeptide transporter, Opt1, and remarkably, cytoplasmic expression of mature Qsp1 complements multiple phenotypes of qsp1Δ. Thus, C. neoformans produces an autoregulatory peptide that matures extracellularly but functions intracellularly to regulate virulence.

Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae

In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103, encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae.

Transcription factors regulate the activity of various biological processes through binding to specific DNA sequences. Therefore, the determination of binding positions is important for the understanding of the regulatory effects of transcription factors. In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to regulate several biological processes, while its genome-wide targets remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. We show that the binding of Cst6p to its target promoters is condition dependent and explain the mechanism for the retarded growth of the CST6 deletion mutant on ethanol. Furthermore, we demonstrate that Cst6p is a new member of a stress-responsive transcriptional regulatory network. These results provide deeper understanding of the function of the dynamic transcriptional regulatory network in S. cerevisiae.

Investigating the Combinatory Effects of Biological Networks on Gene Co-expression

Co-expressed genes often share similar functions, and gene co-expression networks have been widely used in studying the functionality of gene modules. Previous analysis indicated that genes are more likely to be co-expressed if they are either regulated by the same transcription factors, forming protein complexes or sharing similar topological properties in protein-protein interaction networks. Here, we reconstructed transcriptional regulatory and protein-protein networks for Saccharomyces cerevisiae using well-established databases, and we evaluated their co-expression activities using publically available gene expression data. Based on our network-dependent analysis, we found that genes that were co-regulated in the transcription regulatory networks and shared similar neighbors in the protein-protein networks were more likely to be co-expressed. Moreover, their biological functions were closely related.

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.

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

Systematic Determination of Transcription Factor DNA-Binding Specificities in Yeast

Understanding how genes are regulated, decoding their "regulome", is one of the main challenges of the post-genomic era. Here, we describe the in vitro method we used to associate cis-regulatory sites with cognate trans-regulators by characterizing the DNA-binding specificity of the vast majority of yeast transcription factors using Protein Binding Microarrays. This approach can be implemented to any given organism.

Utilising established SDL-screening methods as a tool for the functional genomic characterisation of model and non-model organisms

The trend for large-scale genetic and phenotypic screens has revealed a wealth of information on biological systems. A major challenge is understanding how genes function and putative roles in networks. The majority of current gene knowledge is garnered from studies utilising the model yeast Saccharomyces cerevisiae. We demonstrate that synthetic dosage lethal genetic array methodologies can be used to study genetic networks in other yeasts, namely the fungal pathogen Candida glabrata, which has limited forward genetic tools, due to the lack of 'natural' mating. We performed two SDL screens in S. cerevisiae, overexpressing the transcriptional regulator UME6 as bait in the first screen and its C. glabrata ortholog CAGL0F05357g in the second. Analysis revealed that SDL maps share 204 common interactors, with 10 genetic interactions unique to C. glabrata indicating a level of genetic rewiring, indicative of linking genotype to phenotype in fungal pathogens. This was further validated by incorporating our results into the global genetic landscape map of the cell from Costanzo et al. to identify common and novel gene attributes. This data demonstrated the utility large data sets and more robust analysis made possible by interrogating exogenous genes in the context of the eukaryotic global genetic landscape.

Characterization of a cAMP responsive transcription factor, Cmr (Rv1675c), in TB complex mycobacteria reveals overlap with the DosR (DevR) dormancy regulon

Mycobacterium tuberculosis (Mtb) Cmr (Rv1675c) is a CRP/FNR family transcription factor known to be responsive to cAMP levels and during macrophage infections. However, Cmr's DNA binding properties, cellular targets and overall role in tuberculosis (TB) complex bacteria have not been characterized. In this study, we used experimental and computational approaches to characterize Cmr's DNA binding properties and identify a putative regulon. Cmr binds a 16-bp palindromic site that includes four highly conserved nucleotides that are required for DNA binding. A total of 368 binding sites, distributed in clusters among ∼200 binding regions throughout the Mycobacterium bovis BCG genome, were identified using ChIP-seq. One of the most enriched Cmr binding sites was located upstream of the cmr promoter, and we demonstrated that expression of cmr is autoregulated. cAMP affected Cmr binding at a subset of DNA loci in vivo and in vitro, including multiple sites adjacent to members of the DosR (DevR) dormancy regulon. Our findings of cooperative binding of Cmr to these DNA regions and the regulation by Cmr of the DosR-regulated virulence gene Rv2623 demonstrate the complexity of Cmr-mediated gene regulation and suggest a role for Cmr in the biology of persistent TB infection.

High-Resolution Global Analysis of the Influences of Bas1 and Ino4 Transcription Factors on Meiotic DNA Break Distributions in Saccharomyces cerevisiae

Meiotic recombination initiates with DNA double-strand breaks (DSBs) made by Spo11. In Saccharomyces cerevisiae, many DSBs occur in "hotspots" coinciding with nucleosome-depleted gene promoters. Transcription factors (TFs) stimulate DSB formation in some hotspots, but TF roles are complex and variable between locations. Until now, available data for TF effects on global DSB patterns were of low spatial resolution and confined to a single TF. Here, we examine at high resolution the contributions of two TFs to genome-wide DSB distributions: Bas1, which was known to regulate DSB activity at some loci, and Ino4, for which some binding sites were known to be within strong DSB hotspots. We examined fine-scale DSB distributions in TF mutant strains by deep sequencing oligonucleotides that remain covalently bound to Spo11 as a byproduct of DSB formation, mapped Bas1 and Ino4 binding sites in meiotic cells, evaluated chromatin structure around DSB hotspots, and measured changes in global messenger RNA levels. Our findings show that binding of these TFs has essentially no predictive power for DSB hotspot activity and definitively support the hypothesis that TF control of DSB numbers is context dependent and frequently indirect. TFs often affected the fine-scale distributions of DSBs within hotspots, and when seen, these effects paralleled effects on local chromatin structure. In contrast, changes in DSB frequencies in hotspots did not correlate with quantitative measures of chromatin accessibility, histone H3 lysine 4 trimethylation, or transcript levels. We also ruled out hotspot competition as a major source of indirect TF effects on DSB distributions. Thus, counter to prevailing models, roles of these TFs on DSB hotspot strength cannot be simply explained via chromatin "openness," histone modification, or compensatory interactions between adjacent hotspots.

"Hit-and-Run" leaves its mark: catalyst transcription factors and chromatin modification

Understanding how transcription factor (TF) binding is related to gene regulation is a moving target. We recently uncovered genome‐wide evidence for a “Hit‐and‐Run” model of transcription. In this model, a master TF “hits” a target promoter to initiate a rapid response to a signal. As the “hit” is transient, the model invokes recruitment of partner TFs to sustain transcription over time. Following the “run”, the master TF “hits” other targets to propagate the response genome‐wide. As such, a TF may act as a “catalyst” to mount a broad and acute response in cells that first sense the signal, while the recruited TF partners promote long‐term adaptive behavior in the whole organism. This “Hit‐and‐Run” model likely has broad relevance, as TF perturbation studies across eukaryotes show small overlaps between TF‐regulated and TF‐bound genes, implicating transient TF‐target binding. Here, we explore this “Hit‐and‐Run” model to suggest molecular mechanisms and its biological relevance.