An improved map of conserved regulatory sites for Saccharomyces cerevisiae

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.

Multilevel Regulation of Membrane Proteins in Response to Metal and Metalloid Stress: A Lesson from Yeast

In the face of flourishing industrialization and global trade, heavy metal and metalloid contamination of the environment is a growing concern throughout the world. The widespread presence of highly toxic compounds of arsenic, antimony, and cadmium in nature poses a particular threat to human health. Prolonged exposure to these toxins has been associated with severe human diseases, including cancer, diabetes, and neurodegenerative disorders. These toxins are known to induce analogous cellular stresses, such as DNA damage, disturbance of redox homeostasis, and proteotoxicity. To overcome these threats and improve or devise treatment methods, it is crucial to understand the mechanisms of cellular detoxification in metal and metalloid stress. Membrane proteins are key cellular components involved in the uptake, vacuolar/lysosomal sequestration, and efflux of these compounds; thus, deciphering the multilevel regulation of these proteins is of the utmost importance. In this review, we summarize data on the mechanisms of arsenic, antimony, and cadmium detoxification in the context of membrane proteome. We used yeast Saccharomyces cerevisiae as a eukaryotic model to elucidate the complex mechanisms of the production, regulation, and degradation of selected membrane transporters under metal(loid)-induced stress conditions. Additionally, we present data on orthologues membrane proteins involved in metal(loid)-associated diseases in humans.

SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction

Summary

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.

Availability and implementation

Data and code are available from https://github.com/iiiime/SPREd.

Characterizing Different Modes of Interplay Between Rap1 and H3 Using Inducible H3-depletion Yeast

Histones and transcription factors (TFs) are two important DNA-binding proteins that interact, compete, and together regulate transcriptional processes in response to diverse internal and external stimuli. Condition-specific depletion of histones in Saccharomyces cerevisiae using a galactose-inducible H3 promoter provides a suitable framework for examining transcriptional alteration resulting from reduced nucleosome content. However, the effect on DNA binding activities of TFs is yet to be fully explored. In this work, we combine ChIP-seq of H3 with RNA-seq to elucidate the genome-scale relationships between H3 occupancy patterns and transcriptional dynamics before and after global H3 depletion. ChIP-seq of Rap1 is also conducted in the H3-depletion and control treatments, to investigate the interplay between this master regulator TF and nucleosomal H3, and to explore the impact on diverse transcriptional responses of different groups of target genes and functions. Ultimately, we propose a working model and testable hypotheses regarding the impact of global and local H3 depletion on transcriptional changes. We also demonstrate different potential modes of interaction between Rap1 and H3, which sheds light on the potential multifunctional regulatory capabilities of Rap1 and potentially other pioneer factors.

Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae

Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralog TDH2. TDH2 is upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. TDH1, a second and more distantly related paralog of TDH3, has diverged in its regulation and is upregulated by another mechanism. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.

SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd" is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.

The architecture of binding cooperativity between densely bound transcription factors

The binding of transcription factors (TFs) along genomes is restricted to a subset of sites containing their preferred motifs. TF-binding specificity is often attributed to the co-binding of interacting TFs; however, apart from specific examples, this model remains untested. Here, we define dependencies among budding yeast TFs that localize to overlapping promoters by profiling the genome-wide consequences of co-depleting multiple TFs. We describe unidirectional interactions, revealing Msn2 as a central factor allowing TF binding at its target promoters. By contrast, no case of mutual cooperation was observed. Particularly, Msn2 retained binding at its preferred promoters upon co-depletion of fourteen similarly bound TFs. Overall, the consequences of TF co-depletions were moderate, limited to a subset of promoters, and failed to explain the role of regions outside the DNA-binding domain in directing TF-binding preferences. Our results call for re-evaluating the role of cooperative interactions in directing TF-binding preferences.

RNAseq analysis of mutants in coding and non-coding transcription of phosphate genes in the yeast Saccharomyces cerevisiae

In the yeast Saccharomyces cerevisiae phosphate starvation induces the expression of PHO genes, including PHO84, encoding an high-affinity phosphate transporter, and SPL2, encoding a regulatory protein. PHO84 is down-regulated by antisense transcription. Here, using strand-specific RNAseq the effect is studied of mutations related to sense and antisense transcription of phosphate genes. Replacement of the transcriptional terminator of PHO84 by that of CYC1 resulted, unexpectedly, in an increased antisense transcription and a strongly reduced sense transcription of PHO84 and a strongly reduced SPL2 expression. The expression of unrelated genes was altered as well. The data suggest that antisense transcription of PHO84 and not the Pho84 transporter affects the expression of SPL2. Deletion of the two putative binding sites for Ume6 in the SPL2 promoter or deletion of UME6 differently affected SPL2 expression, suggesting that Ume6 regulates SPL2 by a mechanism different from a simple binding to the putative Ume6 binding sites.

Overexpression of genes by stress-responsive promoters increases protein secretion in Saccharomyces cerevisiae

Recombinant proteins produced by cell factories are now widely used in various fields. Many efforts have been made to improve the secretion capacity of cell factories to meet the increasing demand for recombinant proteins. Recombinant protein production usually causes cell stress in the endoplasmic reticulum (ER). The overexpression of key genes possibly removes limitations in protein secretion. However, inappropriate gene expression may have negative effects. There is a need for dynamic control of genes adapted to cellular status. In this study, we constructed and characterized synthetic promoters that were inducible under ER stress conditions in Saccharomyces cerevisiae. The unfolded protein response element UPRE2, responding to stress with a wide dynamic range, was assembled with various promoter core regions, resulting in UPR-responsive promoters. Synthetic responsive promoters regulated gene expression by responding to stress level, which reflected the cellular status. The engineered strain using synthetic responsive promoters P_{4UPRE2 - TDH3} and P_{4UPRE2 - TEF1} for co-expression of ERO1 and SLY1 had 95% higher α-amylase production compared with the strain using the native promoters P_TDH3 and P_TEF1. This work showed that UPR-responsive promoters were useful in the metabolic engineering of yeast strains for tuning genes to support efficient protein production.

RNAPII Degradation Factor Def1 Is Required for Development, Stress Response, and Full Virulence of Magnaporthe oryzae

The RNA polymerase II degradation factor Degradation Factor 1 (Def1) is important for DNA damage repair and plays various roles in eukaryotes; however, the biological role in plant pathogenic fungi is still unknown. In this study, we investigated the role of Def1 during the development and infection of the rice blast fungus Magnaporthe oryzae. The deletion mutant of Def1 displayed slower mycelial growth, less conidial production, and abnormal conidial morphology. The appressoria of Δdef1 was impaired in the penetration into host cells, mainly due to blocking in the utilization of conidial storages, such as glycogen and lipid droplets. The invasive growth of the Δdef1 mutant was also retarded and accompanied with the accumulation of reactive oxygen species (ROS) inside the host cells. Furthermore, compared with the wild type, Δdef1 was more sensitive to multiple stresses, such as oxidative stress, high osmotic pressure, and alkaline/acidic pH. Interestingly, we found that Def1 was modified by O-GlcNAcylation at Ser232, which was required for the stability of Def1 and its function in pathogenicity. Taken together, the O-GlcNAc modified Def1 is required for hyphae growth, conidiation, pathogenicity, and stress response in M. oryzae. This study reveals a novel regulatory mechanism of O-GlcNAc-mediated Def1 in plant pathogenic fungi.

An in Silico Approach to Identifying TF Binding Sites: Analysis of the Regulatory Regions of BUSCO Genes from Fungal Species in the Ceratocystidaceae Family

Transcriptional regulation controls gene expression through regulatory promoter regions that contain conserved sequence motifs. These motifs, also known as regulatory elements, are critically important to expression, which is driving research efforts to identify and characterize them. Yeasts have been the focus of such studies in fungi, including in several in silico approaches. This study aimed to determine whether in silico approaches could be used to identify motifs in the Ceratocystidaceae family, and if present, to evaluate whether these correspond to known transcription factors. This study targeted the 1000 base-pair region upstream of the start codon of 20 single-copy genes from the BUSCO dataset for motif discovery. Using the MEME and Tomtom analysis tools, conserved motifs at the family level were identified. The results show that such in silico approaches could identify known regulatory motifs in the Ceratocystidaceae and other unrelated species. This study provides support to ongoing efforts to use in silico analyses for motif discovery.

Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks

Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on ElasticNet and OMP (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks. We evaluate these methods on four Microarray datasets from the DREAM5 benchmark, using both internal and external metrics. The results obtained by both methods are comparable to those obtained by state-of-the-art tools, but are fast to train and exhibit high levels of sparsity, which make them easier to interpret. Moreover we applied these methods to three complex datasets containing RNA-seq informations from different mammalian tissues/cell-types. Lastly, we applied our methodology to compare a normal vs. a disease condition (Alzheimer), which allowed us to detect differential expression of genes’ sub-networks between these two biological conditions. Globally, the gene networks obtained exhibit a sparse and modular structure, with inner communities of genes presenting statistically significant over/under-expression on specific cell types, as well as significant enrichment for some anatomical GO terms, suggesting that such communities may also drive important functional roles.

NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration

MOTIVATION

Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks.

RESULTS

We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data.

AVAILABILITY AND IMPLEMENTATION

All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae

Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralogs TDH1 and TDH2. TDH1 and TDH2 are upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.

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.

Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module

Regulatory module mining methods divide genes into multiple gene subgroups and explore potential biological mechanisms from omics data. By transforming gene expression profile data into gene co-expression network, we transform the task of gene module detection into the problem of finding community structure in the graph, and introduce the latest network representation learning method-graph neural network to optimize this problem. In order to systematically evaluate whether the algorithm allows overlap to affect such problems, we make two variants of the output of the algorithm, Deepgmd_cluster and Deepgmd. The difference between them is whether overlap is allowed. By comparing the known modules and the modules generated by the algorithm, we can evaluate the quality of the algorithm. We use this method to compare our algorithm with some current mainstream methods. The results show that our method has greater advantages. In the end, we analyze some typical modules from the modules found by the algorithm for visualization, and use the GO database and KEGG database to perform enrichment analysis and pathway analysis on these modules.

Global early replication disrupts gene expression and chromatin conformation in a single cell cycle

Background

The early embryonic divisions of many organisms, including fish, flies, and frogs, are characterized by a very rapid S-phase caused by high rates of replication initiation. In somatic cells, S-phase is much longer due to both a reduction in the total number of initiation events and the imposition of a temporal order of origin activation. The physiological importance of changes in the rate and timing of replication initiation in S-phase remains unclear.

Results

Here we assess the importance of the temporal control of replication initiation using a conditional system in budding yeast to drive the early replication of the majority of origins in a single cell cycle. We show that global early replication disrupts the expression of over a quarter of all genes. By deleting individual origins, we show that delaying replication is sufficient to restore normal gene expression, directly implicating origin firing control in this regulation. Global early replication disrupts nucleosome positioning and transcription factor binding during S-phase, suggesting that the rate of S-phase is important to regulate the chromatin landscape.

Conclusions

Together, these data provide new insight into the role of the temporal control of origin firing during S-phase for coordinating replication, gene expression, and chromatin establishment as occurs in the early embryo.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02788-7.

Distinct chromosomal “niches” in the genome of Saccharomyces cerevisiae provide the background for genomic innovation and shape the fate of gene duplicates

Nearly one third of Saccharomyces cerevisiae protein coding sequences correspond to duplicate genes, equally split between small-scale duplicates (SSD) and whole-genome duplicates (WGD). While duplicate genes have distinct properties compared to singletons, to date, there has been no systematic analysis of their positional preferences. In this work, we show that SSD and WGD genes are organized in distinct gene clusters that occupy different genomic regions, with SSD being more peripheral and WGD more centrally positioned close to centromeric chromatin. Duplicate gene clusters differ from the rest of the genome in terms of gene size and spacing, gene expression variability and regulatory complexity, properties that are also shared by singleton genes residing within them. Singletons within duplicate gene clusters have longer promoters, more complex structure and a higher number of protein–protein interactions. Particular chromatin architectures appear to be important for gene evolution, as we find SSD gene-pair co-expression to be strongly associated with the similarity of nucleosome positioning patterns. We propose that specific regions of the yeast genome provide a favourable environment for the generation and maintenance of small-scale gene duplicates, segregating them from WGD-enriched genomic domains. Our findings provide a valuable framework linking genomic innovation with positional genomic preferences.

Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.

H1.0 C Terminal Domain Is Integral for Altering Transcription Factor Binding within Nucleosomes

The linker histone H1 is a highly prevalent protein that compacts chromatin and regulates DNA accessibility and transcription. However, the mechanisms behind H1 regulation of transcription factor (TF) binding within nucleosomes are not well understood. Using in vitro fluorescence assays, we positioned fluorophores throughout human H1 and the nucleosome, then monitored the distance changes between H1 and the histone octamer, H1 and nucleosomal DNA, or nucleosomal DNA and the histone octamer to monitor the H1 movement during TF binding. We found that H1 remains bound to the nucleosome dyad, while the C terminal domain (CTD) releases the linker DNA during nucleosome partial unwrapping and TF binding. In addition, mutational studies revealed that a small 16 amino acid region at the beginning of the H1 CTD is largely responsible for altering nucleosome wrapping and regulating TF binding within nucleosomes. We then investigated physiologically relevant post-translational modifications (PTMs) in human H1 by preparing fully synthetic H1 using convergent hybrid phase native chemical ligation. Both individual PTMs and combinations of phosphorylation and citrullination of H1 had no detectable influence on nucleosome binding and nucleosome wrapping, and had only a minor impact on H1 regulation of TF occupancy within nucleosomes. This suggests that these H1 PTMs function by other mechanisms. Our results highlight the importance of the H1 CTD, in particular, the first 16 amino acids, in regulating nucleosome linker DNA dynamics and TF binding within the nucleosome.

Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks

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Findings from genome-wide ChIP studies on budding yeast Forkheads are interpreted.

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Power, challenges and limitation of ChIP studies are presented by target gene analysis.

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Forkheads regulate metabolic targets through which cell division may be coordinated.

Transcription factors are regulators of the cell’s genomic landscape. By switching single genes or entire molecular pathways on or off, transcription factors modulate the precise timing of their activation. The Forkhead (Fkh) transcription factors are evolutionarily conserved to regulate organismal physiology and cell division. In addition to molecular biology and biochemical efforts, genome-wide studies have been conducted to characterize the genomic landscape potentially regulated by Forkheads in eukaryotes. Here, we discuss and interpret findings reported in six genome-wide Chromatin ImmunoPrecipitation (ChIP) studies, with a particular focus on ChIP-chip and ChIP-exo. We highlight their power and challenges to address Forkhead-mediated regulation of the cellular landscape in budding yeast. Expression changes of the targets identified in the binding assays are investigated by taking expression data for Forkhead deletion and overexpression into account. Forkheads are revealed as regulators of the metabolic network through which cell cycle dynamics may be temporally coordinated further, in addition to their well-known role as regulators of the gene cluster responsible for cell division.

Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE

Transcriptional regulatory networks specify context-specific patterns of genes and play a central role in how species evolve and adapt. Inferring genome-scale regulatory networks in non-model species is the first step for examining patterns of conservation and divergence of regulatory networks. Transcriptomic data obtained under varying environmental stimuli in multiple species are becoming increasingly available, which can be used to infer regulatory networks. However, inference and analysis of multiple gene regulatory networks in a phylogenetic setting remains challenging. We developed an algorithm, Multi-species Regulatory neTwork LEarning (MRTLE), to facilitate such studies of regulatory network evolution. MRTLE is a probabilistic graphical model-based algorithm that uses phylogenetic structure, transcriptomic data for multiple species, and sequence-specific motifs in each species to simultaneously infer genome-scale regulatory networks across multiple species. We applied MRTLE to study regulatory network evolution across six ascomycete yeasts using transcriptomic measurements collected across different stress conditions. MRTLE networks recapitulated experimentally derived interactions in the model organism S. cerevisiae as well as non-model species, and it was more beneficial for network inference than methods that do not use phylogenetic information. We examined the regulatory networks across species and found that regulators associated with significant expression and network changes are involved in stress-related processes. MTRLE and its associated downstream analysis provide a scalable and principled framework to examine evolutionary dynamics of transcriptional regulatory networks across multiple species in a large phylogeny.

A pathogenic role for histone H3 copper reductase activity in a yeast model of Friedreich's ataxia

Disruptions to iron-sulfur (Fe-S) clusters, essential cofactors for a broad range of proteins, cause widespread cellular defects resulting in human disease. A source of damage to Fe-S clusters is cuprous (Cu1+) ions. Since histone H3 enzymatically produces Cu1+ for copper-dependent functions, we asked whether this activity could become detrimental to Fe-S clusters. Here, we report that histone H3–mediated Cu1+ toxicity is a major determinant of cellular functional pool of Fe-S clusters. Inadequate Fe-S cluster supply, due to diminished assembly as occurs in Friedreich’s ataxia or defective distribution, causes severe metabolic and growth defects in Saccharomyces cerevisiae. Decreasing Cu1+ abundance, through attenuation of histone cupric reductase activity or depletion of total cellular copper, restored Fe-S cluster–dependent metabolism and growth. Our findings reveal an interplay between chromatin and mitochondria in Fe-S cluster homeostasis and a potential pathogenic role for histone enzyme activity and Cu1+ in diseases with Fe-S cluster dysfunction.

Cell-Cycle-Dependent Chromatin Dynamics at Replication Origins

Origins of DNA replication are specified by the ordered recruitment of replication factors in a cell-cycle–dependent manner. The assembly of the pre-replicative complex in G1 and the pre-initiation complex prior to activation in S phase are well characterized; however, the interplay between the assembly of these complexes and the local chromatin environment is less well understood. To investigate the dynamic changes in chromatin organization at and surrounding replication origins, we used micrococcal nuclease (MNase) to generate genome-wide chromatin occupancy profiles of nucleosomes, transcription factors, and replication proteins through consecutive cell cycles in Saccharomyces cerevisiae. During each G1 phase of two consecutive cell cycles, we observed the downstream repositioning of the origin-proximal +1 nucleosome and an increase in protected DNA fragments spanning the ARS consensus sequence (ACS) indicative of pre-RC assembly. We also found that the strongest correlation between chromatin occupancy at the ACS and origin efficiency occurred in early S phase, consistent with the rate-limiting formation of the Cdc45–Mcm2-7–GINS (CMG) complex being a determinant of origin activity. Finally, we observed nucleosome disruption and disorganization emanating from replication origins and traveling with the elongating replication forks across the genome in S phase, likely reflecting the disassembly and assembly of chromatin ahead of and behind the replication fork, respectively. These results provide insights into cell-cycle–regulated chromatin dynamics and how they relate to the regulation of origin activity.

A systematic genome-wide account of binding sites for the model transcription factor Gcn4

Sequence-specific DNA-binding transcription factors are central to gene regulation. They are often associated with consensus binding sites that predict far more genomic sites than are bound in vivo. One explanation is that most sites are blocked by nucleosomes, such that only sites in nucleosome-depleted regulatory regions are bound. We compared the binding of the yeast transcription factor Gcn4 in vivo using published ChIP-seq data (546 sites) and in vitro, using a modified SELEX method (“G-SELEX”), which utilizes short genomic DNA fragments to quantify binding at all sites. We confirm that Gcn4 binds strongly to an AP-1-like sequence (TGACTCA) and weakly to half-sites. However, Gcn4 binds only some of the 1078 exact matches to this sequence, even in vitro. We show that there are only 166 copies of the high-affinity RTGACTCAY site (exact match) in the yeast genome, all occupied in vivo, largely independently of whether they are located in nucleosome-depleted or nucleosomal regions. Generally, RTGACTCAR/YTGACTCAY sites are bound much more weakly and YTGACTCAR sites are unbound, with biological implications for determining induction levels. We conclude that, to a first approximation, Gcn4 binding can be predicted using the high-affinity site, without reference to chromatin structure. We propose that transcription factor binding sites should be defined more precisely using quantitative data, allowing more accurate genome-wide prediction of binding sites and greater insight into gene regulation.

Reb1, Cbf1, and Pho4 bias histone sliding and deposition away from their binding sites

In transcriptionally active genes, nucleosome positions in promoters are regulated by nucleosome displacing factors (NDFs) and chromatin remodeling enzymes. Depletion of NDFs or the RSC chromatin remodeler shrinks or abolishes the nucleosome depleted regions (NDRs) in promoters, which can suppress gene activation and result in cryptic transcription. Despite their vital cellular functions, how the action of chromatin remodelers may be directly affected by site-specific binding factors like NDFs is poorly understood. Here we demonstrate that two NDFs, Reb1 and Cbf1, can direct both Chd1 and RSC chromatin remodeling enzymes in vitro, stimulating repositioning of the histone core away from their binding sites. Interestingly, although the Pho4 transcription factor had a much weaker effect on nucleosome positioning, both NDFs and Pho4 were able to similarly redirect positioning of hexasomes. In chaperone-mediated nucleosome assembly assays, Reb1 but not Pho4 showed an ability to block deposition of the histone H3/H4 tetramer, but Reb1 did not block addition of the H2A/H2B dimer to hexasomes. Our in vitro results show that NDFs bias the action of remodelers to increase the length of the free DNA in the vicinity of their binding sites. These results suggest that NDFs could directly affect NDR architecture through chromatin remodelers.

Isolation and analysis of a sake yeast mutant with phenylalanine accumulation

Sake is a traditional Japanese alcoholic beverage brewed by the yeast Saccharomyces cerevisiae. Since the consumption and connoisseurship of sake has spread around the world, the development of new sake yeast strains to meet the demand for unique sakes has been promoted. Phenylalanine is an essential amino acid that is used to produce proteins and important signaling molecules involved in feelings of pleasure. In addition, phenylalanine is a precursor of 2-phenylethanol, a high-value aromatic alcohol with a rose-like flavor. As such, adjusting the quantitative balance between phenylalanine and 2-phenylethanol may introduce value-added qualities to sake. Here, we isolated a sake yeast mutant (strain K9-F39) with phenylalanine accumulation and found a missense mutation on the ARO80 gene encoding the His309Gln variant of the transcriptional activator Aro80p involved in the biosynthesis of 2-phenylethanol from phenylalanine. We speculated that mutation of ARO80 would decrease transcriptional activity and suppress the phenylalanine catabolism, resulting in an increase of intracellular phenylalanine. Indeed, sake brewed with strain K9-F39 contained 60% increase in phenylalanine, but only 10% less 2-phenylethanol than sake brewed with the parent strain. Use of the ARO80 mutant in sake brewing may be promising for the production of distinctive new sake varieties.

DEF1: Much more than an RNA polymerase degradation factor

Degradation Factor 1 was discovered 20 years ago as a yeast protein copurifying with Rad26, a helicase involved in transcription-coupled DNA repair. It was subsequently shown to control the ubiquitylation and destruction of the large subunit of DNA damage-arrested RNA Polymerase II. Since that time, much has been learned about Def1's role in polymerase destruction and new functions of the protein have been revealed. We now understand that Def1 is involved in more than just RNA polymerase II regulation. Most of its known functions are associated with maintaining chromosome and genomic integrity, but other exciting activities outside this realm have been suggested. Here we review this fascinating protein, describe its regulation and present a hypothesis that Def1 is a central coordinator of ubiquitin signaling pathways in cells.

Post-replicative nick translation occurs on the lagging strand during prolonged depletion of DNA ligase I in Saccharomyces cerevisiae

During lagging-strand synthesis, strand-displacement synthesis by DNA polymerase delta (Pol ∂), coupled to nucleolytic cleavage of DNA flap structures, produces a nick-translation reaction that replaces the DNA at the 5′ end of the preceding Okazaki fragment. Previous work following depletion of DNA ligase I in Saccharomyces cerevisae suggests that DNA-bound proteins, principally nucleosomes and the transcription factors Abf1/Rap1/Reb1, pose a barrier to Pol ∂ synthesis and thereby limit the extent of nick translation in vivo. However, the extended ligase depletion required for these experiments could lead to ongoing, non-physiological nick translation. Here, we investigate nick translation by analyzing Okazaki fragments purified after transient nuclear depletion of DNA ligase I in synchronized or asynchronous Saccharomyces cerevisiae cultures. We observe that, even with a short ligase depletion, Okazaki fragment termini are enriched around nucleosomes and Abf1/Reb1/Rap1-binding sites. However, protracted ligase depletion leads to a global change in the location of these termini, moving them toward nucleosome dyads from a more upstream location and further enriching termini at Abf1/Reb1/Rap1-binding sites. In addition, we observe an under-representation of DNA derived from DNA polymerase alpha—the polymerase that initiates Okazaki fragment synthesis—around the sites of Okazaki termini obtained from very brief ligase depletion. Our data suggest that, while nucleosomes and transcription factors do limit strand-displacement synthesis by Pol ∂ in vivo, post-replicative nick translation can occur at unligated Okazaki fragment termini such that previous analyses represent an overestimate of the extent of nick translation occurring during normal lagging-strand synthesis.

Spatiotemporal control of pathway sensors and cross-pathway feedback regulate a differentiation MAPK pathway in yeast

Mitogen-activated protein kinase (MAPK) pathways control cell differentiation and the response to stress. In Saccharomyces cerevisiae, the MAPK pathway that controls filamentous growth (fMAPK) shares components with the pathway that regulates the response to osmotic stress (HOG). Here, we show that the two pathways exhibit different patterns of activity throughout the cell cycle. The different patterns resulted from different expression profiles of genes encoding mucin sensors that regulate the pathways. Cross-pathway regulation from the fMAPK pathway stimulated the HOG pathway, presumably to modulate fMAPK pathway activity. We also show that the shared tetraspan protein Sho1p, which has a dynamic localization pattern throughout the cell cycle, induced the fMAPK pathway at the mother-bud neck. A Sho1p-interacting protein, Hof1p, which also localizes to the mother-bud neck and regulates cytokinesis, also regulated the fMAPK pathway. Therefore, spatial and temporal regulation of pathway sensors, and cross-pathway regulation, control a MAPK pathway that regulates cell differentiation in yeast.

RoboCOP: jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data

Chromatin is a tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and occupancy levels of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. In contrast, epigenomic accessibility data like MNase-seq, DNase-seq, and ATAC-seq provide insight into the chromatin landscape of all factors bound along the genome, but with little insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin accessibility data with nucleotide sequence to jointly compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors. We apply RoboCOP to MNase-seq and ATAC-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome, and show that our model makes better predictions than existing methods. We also compute a chromatin occupancy profile of the yeast genome under cadmium stress, revealing chromatin dynamics associated with transcriptional regulation.

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges

Computational and mathematical models are key to obtain a system-level understanding of biological processes, but their limitations have to be clearly defined to allow their proper application and interpretation. Crowdsourced benchmarks in the form of challenges provide an unbiased assessment of methods, and for the past decade, the Dialogue for Reverse Engineering Assessment and Methods (DREAM) organized more than 15 systems biology challenges. From transcription factor binding to dynamical network models, from signaling networks to gene regulation, from whole-cell models to cell-lineage reconstruction, and from single-cell positioning in a tissue to drug combinations and cell survival, the breadth is broad. To celebrate the 5-year anniversary of Cell Systems, we review the genesis of these systems biology challenges and discuss how interlocking the forward- and reverse-modeling paradigms allows to push the rim of systems biology. This approach will persist for systems levels approaches in biology and medicine.

Cell-to-cell heterogeneity of phosphate gene expression in yeast is controlled by alternative transcription, 14-3-3 and Spl2

Dependent on phosphate availability the yeast Saccharomyces cerevisiae expresses either low or high affinity phosphate transporters. In the presence of phosphate yeast cells still express low levels of the high affinity phosphate transporter Pho84. The regulator Spl2 is expressed in approximately 90% of the cells, and is not expressed in the remaining cells. Here we report that deletion of RRP6, encoding an exonuclease degrading non-coding RNA, or BMH1, encoding the major 14-3-3 isoform, resulted in less cells expressing SPL2 and in increased levels of RNA transcribed from sequences upstream of the SPL2 coding region. SPL2 stimulates its own expression and that of PHO84 ensuing a positive feedback. Upon deletion of the region responsible for upstream SPL2 transcription almost all cells express SPL2. These results indicate that the cell-to-cell variation in PHO84 and SPL2 expression is dependent on a specific part of the SPL2 promoter and is controlled by Bmh1 and Spl2.

Linking the dynamics of chromatin occupancy and transcription with predictive models

Though the sequence of the genome within each eukaryotic cell is essentially fixed, it exists within a complex and changing chromatin state. This state is determined, in part, by the dynamic binding of proteins to the DNA. These proteins—including histones, transcription factors (TFs), and polymerases—interact with one another, the genome, and other molecules to allow the chromatin to adopt one of exceedingly many possible configurations. Understanding how changing chromatin configurations associate with transcription remains a fundamental research problem. We sought to characterize at high spatiotemporal resolution the dynamic interplay between transcription and chromatin in response to cadmium stress. Whereas gene regulatory responses to environmental stress in yeast have been studied, how the chromatin state changes and how those changes connect to gene regulation remain unexplored. By combining MNase-seq and RNA-seq data, we found chromatin signatures of transcriptional activation and repression involving both nucleosomal and TF-sized DNA-binding factors. Using these signatures, we identified associations between chromatin dynamics and transcriptional regulation, not only for known cadmium response genes, but across the entire genome, including antisense transcripts. Those associations allowed us to develop generalizable models that predict dynamic transcriptional responses on the basis of dynamic chromatin signatures.

A high-resolution protein architecture of the budding yeast genome

The genome-wide architecture of chromatin-associated proteins that maintains chromosome integrity and gene regulation is not well defined. Here we use chromatin immunoprecipitation, exonuclease digestion and DNA sequencing (ChIP-exo/seq)1,2 to define this architecture in Saccharomyces cerevisiae. We identify 21 meta-assemblages consisting of roughly 400 different proteins that are related to DNA replication, centromeres, subtelomeres, transposons and transcription by RNA polymerase (Pol) I, II and III. Replication proteins engulf a nucleosome, centromeres lack a nucleosome, and repressive proteins encompass three nucleosomes at subtelomeric X-elements. We find that most promoters associated with Pol II evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprise a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bind the transcription-initiation factor TFIID to form a preinitiation complex. Positioned insulators protect core promoters from upstream events. A small fraction of promoters evolved an architecture for inducibility, whereby sequence-specific transcription factors (ssTFs) create a nucleosome-depleted region (NDR) that is distinct from an NFR. We describe structural interactions among ssTFs, their cognate cofactors and the genome. These interactions include the nucleosomal and transcriptional regulators RPD3-L, SAGA, NuA4, Tup1, Mediator and SWI-SNF. Surprisingly, we do not detect interactions between ssTFs and TFIID, suggesting that such interactions do not stably occur. Our model for gene induction involves ssTFs, cofactors and general factors such as TBP and TFIIB, but not TFIID. By contrast, constitutive transcription involves TFIID but not ssTFs engaged with their cofactors. From this, we define a highly integrated network of gene regulation by ssTFs.

ModularBoost: an efficient network inference algorithm based on module decomposition

Background

Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods.

Results

ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms.

Conclusions

As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

Forkhead transcription factor Fkh1: insights into functional regulatory domains crucial for recruitment of Sin3 histone deacetylase complex

Transcription factors are inextricably linked with histone deacetylases leading to compact chromatin. The Forkhead transcription factor Fkh1 is mainly a negative transcriptional regulator which affects cell cycle control, silencing of mating-type cassettes and induction of pseudohyphal growth in the yeast Saccharomyces cerevisiae. Markedly, Fkh1 impinges chromatin architecture by recruiting large regulatory complexes. Implication of Fkh1 with transcriptional corepressor complexes remains largely unexplored. In this work we show that Fkh1 directly recruits corepressors Sin3 and Tup1 (but not Cyc8), providing evidence for its influence on epigenetic regulation. We also identified the specific domain of Fkh1 mediating Sin3 recruitment and substantiated that amino acids 51–125 of Fkh1 bind PAH2 of Sin3. Importantly, this part of Fkh1 overlaps with its Forkhead-associated domain (FHA). To analyse this domain in more detail, selected amino acids were replaced by alanine, revealing that hydrophobic amino acids L74 and I78 are important for Fkh1-Sin3 binding. In addition, we could prove Fkh1 recruitment to promoters of cell cycle genes CLB2 and SWI5. Notably, Sin3 is also recruited to these promoters but only in the presence of functional Fkh1. Our results disclose that recruitment of Sin3 to Fkh1 requires precisely positioned Fkh1/Sin3 binding sites which provide an extended view on the genetic control of cell cycle genes CLB2 and SWI5 and the mechanism of transcriptional repression by modulation of chromatin architecture at the G2/M transition.

Effects of Non-Thermal Plasma on Yeast Saccharomyces cerevisiae

Cold plasmas generated by various electrical discharges can affect cell physiology or induce cell damage that may often result in the loss of viability. Many cold plasma-based technologies have emerged in recent years that are aimed at manipulating the cells within various environments or tissues. These include inactivation of microorganisms for the purpose of sterilization, food processing, induction of seeds germination, but also the treatment of cells in the therapy. Mechanisms that underlie the plasma-cell interactions are, however, still poorly understood. Dissection of cellular pathways or structures affected by plasma using simple eukaryotic models is therefore desirable. Yeast Saccharomyces cerevisiae is a traditional model organism with unprecedented impact on our knowledge of processes in eukaryotic cells. As such, it had been also employed in studies of plasma-cell interactions. This review focuses on the effects of cold plasma on yeast cells.

Accumulation of cis- and trans-regulatory variations is associated with phenotypic divergence of a complex trait between yeast species

Gene regulatory variations accumulate during evolution and alter gene expression. While the importance of expression variation in phenotypic evolution is well established, the molecular basis remains largely unknown. Here, we examine two closely related yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus, which show phenotypical differences in morphology and cell cycle progression when grown in the same environment. By profiling the cell cycle transcriptome and binding of key transcription factors (TFs) in the two species and their hybrid, we show that changes in expression levels and dynamics of oscillating genes are dominated by upstream trans-variations. We find that multiple cell cycle regulators show both cis- and trans-regulatory variations, which alters their expression in favor of the different cell cycle phenotypes. Moreover, we show that variations in the cell cycle TFs, Fkh1, and Fkh2 affect both the expression of target genes, and the binding specificity of an interacting TF, Ace2. Our study reveals how multiple variations accumulate and propagate through the gene regulatory network, alter TFs binding, contributing to phenotypic changes in cell cycle progression.

Thermodynamic modeling of genome-wide nucleosome depleted regions in yeast

Nucleosome positioning in the genome is essential for the regulation of many nuclear processes. We currently have limited capability to predict nucleosome positioning in vivo, especially the locations and sizes of nucleosome depleted regions (NDRs). Here, we present a thermodynamic model that incorporates the intrinsic affinity of histones, competitive binding of sequence-specific factors, and nucleosome remodeling to predict nucleosome positioning in budding yeast. The model shows that the intrinsic affinity of histones, at near-saturating histone concentration, is not sufficient in generating NDRs in the genome. However, the binding of a few factors, especially RSC towards GC-rich and poly(A/T) sequences, allows us to predict ~ 66% of genome-wide NDRs. The model also shows that nucleosome remodeling activity is required to predict the correct NDR sizes. The validity of the model was further supported by the agreement between the predicted and the measured nucleosome positioning upon factor deletion or on exogenous sequences introduced into yeast. Overall, our model quantitatively evaluated the impact of different genetic components on NDR formation and illustrated the vital roles of sequence-specific factors and nucleosome remodeling in this process.

Nucleosome is the basic unit of chromatin, containing 147 base-pairs of DNA wrapped around a histone core. The positioning of nucleosomes, i.e., which parts of DNA are inside nucleosome and which parts are nucleosome-free, is highly regulated. In particular, regulatory sequences tend to be exposed in nucleosome-depleted regions (NDRs), and such exposure is crucial for a variety of processes including DNA replication, repair, and gene expression. Here, we used a thermodynamics model to predict nucleosome positioning on the yeast genome. The model shows that the intrinsic sequence preference of histones is not sufficient in generating NDRs. In contrast, binding of a few transcription factors, especially RSC, is largely responsible for NDR formation. Nucleosome remodeling activity is also required in the model to recapitulate the NDR sizes. This model contributes to our understanding of the mechanisms that regulate nucleosome positioning. It can also be used to predict nucleosome positioning in mutant yeast or on novel DNA sequences.

Transcription shapes genome-wide histone acetylation patterns

Histone acetylation is a ubiquitous hallmark of transcription, but whether the link between histone acetylation and transcription is causal or consequential has not been addressed. Using immunoblot and chromatin immunoprecipitation-sequencing in S. cerevisiae, here we show that the majority of histone acetylation is dependent on transcription. This dependency is partially explained by the requirement of RNA polymerase II (RNAPII) for the interaction of H4 histone acetyltransferases (HATs) with gene bodies. Our data also confirms the targeting of HATs by transcription activators, but interestingly, promoter-bound HATs are unable to acetylate histones in the absence of transcription. Indeed, HAT occupancy alone poorly predicts histone acetylation genome-wide, suggesting that HAT activity is regulated post-recruitment. Consistent with this, we show that histone acetylation increases at nucleosomes predicted to stall RNAPII, supporting the hypothesis that this modification is dependent on nucleosome disruption during transcription. Collectively, these data show that histone acetylation is a consequence of RNAPII promoting both the recruitment and activity of histone acetyltransferases.

Speed-Specificity Trade-Offs in the Transcription Factors Search for Their Genomic Binding Sites

Transcription factors (TFs) regulate gene expression by binding DNA sequences recognized by their DNA-binding domains (DBDs). DBD-recognized motifs are short and highly abundant in genomes. The ability of TFs to bind a specific subset of motif-containing sites, and to do so rapidly upon activation, is fundamental for gene expression in all eukaryotes. Despite extensive interest, our understanding of the TF-target search process is fragmented; although binding specificity and detection speed are two facets of this same process, trade-offs between them are rarely addressed. In this opinion article, we discuss potential speed-specificity trade-offs in the context of existing models. We further discuss the recently described 'distributed specificity' paradigm, suggesting that intrinsically disordered regions (IDRs) promote specificity while reducing the TF-target search time.

Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

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.

Dynamic transcriptional response of Saccharomyces cerevisiae cells to copper

Copper is a crucial trace element for all living systems and any deficiency in copper homeostasis leads to the development of severe diseases in humans. The observation of extensive evolutionary conservation in copper homeostatic systems between human and Saccharomyces cerevisiae made this organism a suitable model organism for elucidating molecular mechanisms of copper transport and homeostasis. In this study, the dynamic transcriptional response of both the reference strain and homozygous deletion mutant strain of CCC2, which encodes a Cu²⁺-transporting P-type ATPase, were investigated following the introduction of copper impulse to reach a copper concentration which was shown to improve the respiration capacity of CCC2 deletion mutants. The analysis of data by using different clustering algorithms revealed significantly affected processes and pathways in response to a switch from copper deficient environment to elevated copper levels. Sulfur compound, methionine and cysteine biosynthetic processes were identified as significantly affected processes for the first time in this study. Stress response, cellular response to DNA damage, iron ion homeostasis, ubiquitin dependent proteolysis, autophagy and regulation of macroautophagy, DNA repair and replication, as well as organization of mitochondrial respiratory chain complex IV, mitochondrial organization and translation were identified as significantly affected processes in only CCC2 deleted strain. The integration of the transcriptomic data with regulome revealed the differences in the extensive re-wiring of dynamic transcriptional organization and regulation in these strains.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data

Background

Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.

Results

This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations.

Conclusions

Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

Transcriptomic Insights into the Effect of Melatonin in Saccharomyces cerevisiae in the Presence and Absence of Oxidative Stress

Melatonin is a ubiquitous indolamine that plays important roles in various aspects of biological processes in mammals. In Saccharomyces cerevisiae, melatonin has been reported to exhibit antioxidant properties and to modulate the expression of some genes involved in endogenous defense systems. The aim of this study was to elucidate the role of supplemented melatonin at the transcriptional level in S. cerevisiae in the presence and absence of oxidative stress. This was achieved by exposing yeast cells pretreated with different melatonin concentrations to hydrogen peroxide and assessing the entry of melatonin into the cell and the yeast response at the transcriptional level (by microarray and qPCR analyses) and the physiological level (by analyzing changes in the lipid composition and mitochondrial activity). We found that exogenous melatonin crossed cellular membranes at nanomolar concentrations and modulated the expression of many genes, mainly downregulating the expression of mitochondrial genes in the absence of oxidative stress, triggering a hypoxia-like response, and upregulating them under stress, mainly the cytochrome complex and electron transport chain. Other categories that were enriched by the effect of melatonin were related to transport, antioxidant activity, signaling, and carbohydrate and lipid metabolism. The overall results suggest that melatonin is able to reprogram the cellular machinery to achieve tolerance to oxidative stress.

Genome-wide off-rates reveal how DNA binding dynamics shape transcription factor function

Protein-DNA interactions are dynamic, and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here, we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide range, varying between 4.2 and 33 min. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.