Genetic reconstruction of a functional transcriptional regulatory network

WENDY: Covariance dynamics based gene regulatory network inference

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

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome

BACKGROUND

Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae.

RESULTS

Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences.

CONCLUSIONS

Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.

Position-dependent function of human sequence-specific transcription factors

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

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.

Cdc73 is a major regulator of apoptosis-inducing factor 1 expression in Saccharomyces cerevisiae via H3K36 methylation

Apoptosis-inducing factor 1 (AIF1) overexpression is intimately linked to the sensitivity of yeast cells towards hydrogen peroxide or acetic acid. Therefore, studying the mechanism of AIF1 regulation in the cell would provide a significant understanding of the factors guiding yeast apoptosis. In this report, we show the time-dependent induction of AIF1 under hydrogen peroxide stress. Additionally, we find that AIF1 expression in response to hydrogen peroxide is mediated by two transcription factors, Yap5 (DNA binding) and Cdc73 (non-DNA binding). Furthermore, substituting the H3K36 residue with another amino acid significantly abrogates AIF1 expression. However, substituting the lysine (K) in H3K4 or H3K79 with alanine (A) does not affect AIF1 expression level under hydrogen peroxide stress. Altogether, reduced AIF1 expression in cdc73Δ is plausibly due to reduced H3K36me3 levels in the cells.

Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data

Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin 'peaks'. These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.

Binding profiles for 954 Drosophila and C. elegans transcription factors reveal tissue specific regulatory relationships

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the modERN (model organism Encyclopedia of Regulatory Networks) consortium that systematically assayed TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). We describe key features of these datasets, comprising 604 TFs identifying 3.6M sites in the fly and 350 TFs identifying 0.9 M sites in the worm. Applying a machine learning model to these data identifies sets of TFs with a prominent role in promoting target gene expression in specific cell types. TF binding data are available through the ENCODE Data Coordinating Center and at https://epic.gs.washington.edu/modERNresource, which provides access to processed and summary data, as well as widgets to probe cell type-specific TF-target relationships. These data are a rich resource that should fuel investigations into TF function during development.

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally determined from experiments, augmented with dynamical network computations involving endpoint objective functions, mutual information, change-point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method, based on the strategies described above. The cybernetic-inspired method is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

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.

Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors

Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

Surprising connections between DNA binding and function for the near-complete set of yeast transcription factors

DNA sequence-specific transcription factors (TFs) modulate transcription and chromatin architecture, acting from regulatory sites in enhancers and promoters of eukaryotic genes. How TFs locate their DNA targets and how multiple TFs cooperate to regulate individual genes is still unclear. Most yeast TFs are thought to regulate transcription via binding to upstream activating sequences, situated within a few hundred base pairs upstream of the regulated gene. While this model has been validated for individual TFs and specific genes, it has not been tested in a systematic way with the large set of yeast TFs. Here, we have integrated information on the binding and expression targets for the near-complete set of yeast TFs. While we found many instances of functional TF binding sites in upstream regulatory regions, we found many more instances that do not fit this model. In many cases, rapid TF depletion affects gene expression where there is no detectable binding of that TF to the upstream region of the affected gene. In addition, for most TFs, only a small fraction of bound TFs regulates the nearby gene, showing that TF binding does not automatically correspond to regulation of the linked gene. Finally, we found that only a small percentage of TFs are exclusively strong activators or repressors with most TFs having dual function. Overall, our comprehensive mapping of TF binding and regulatory targets have both confirmed known TF relationships and revealed surprising properties of TF function.

BERTwalk for integrating gene networks to predict gene- to pathway-level properties

Motivation

Graph representation learning is a fundamental problem in the field of data science with applications to integrative analysis of biological networks. Previous work in this domain was mostly limited to shallow representation techniques. A recent deep representation technique, BIONIC, has achieved state-of-the-art results in a variety of tasks but used arbitrarily defined components.

Results

Here, we present BERTwalk, an unsupervised learning scheme that combines the BERT masked language model with a network propagation regularization for graph representation learning. The transformation from networks to texts allows our method to naturally integrate different networks and provide features that inform not only nodes or edges but also pathway-level properties. We show that our BERTwalk model outperforms BIONIC, as well as four other recent methods, on two comprehensive benchmarks in yeast and human. We further show that our model can be utilized to infer functional pathways and their effects.

Availability and implementation

Code and data are available at https://github.com/raminass/BERTwalk.

Contact

roded@tauex.tau.ac.il.

Development of Terminator-Promoter Bifunctional Elements for Application in Saccharomyces cerevisiae Pathway Engineering

The construction of a genetic circuit requires the substitution and redesign of different promoters and terminators. The assembly efficiency of exogenous pathways will also decrease significantly when the number of regulatory elements and genes is increased. We speculated that a novel bifunctional element with promoter and terminator functions could be created via the fusion of a termination signal with a promoter sequence. In this study, the elements from a Saccharomyces cerevisiae promoter and terminator were employed to design a synthetic bifunctional element. The promoter strength of the synthetic element is apparently regulated through a spacer sequence and an upstream activating sequence (UAS) with a ~5-fold increase, and the terminator strength could be finely regulated by the efficiency element, with a ~5-fold increase. Furthermore, the use of a TATA box-like sequence resulted in the adequate execution of both functions of the TATA box and the efficiency element. By regulating the TATA box-like sequence, UAS, and spacer sequence, the strengths of the promoter-like and terminator-like bifunctional elements were optimally fine-tuned with ~8-fold and ~7-fold increases, respectively. The application of bifunctional elements in the lycopene biosynthetic pathway showed an improved pathway assembly efficiency and higher lycopene yield. The designed bifunctional elements effectively simplified pathway construction and can serve as a useful toolbox for yeast synthetic biology.

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present an elegant modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding entirely novel strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally-determined from experiments, augmented with dynamical network computations involving end point objective functions, mutual information, change point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method (CIM), utilizing the strategies described above. The CIM is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this state-of-the-art framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

STATEMENT OF SIGNIFICANCE

Cellular processes like cell cycle are overly complex, involving multiple players interacting at multiple levels, and explicit modeling of such systems is challenging. The availability of longitudinal RNA measurements provides an opportunity to "reverse-engineer" for novel regulatory models. We develop a novel framework, inspired using goal-oriented cybernetic model, to implicitly model transcriptional regulation by constraining the system using inferred temporal goals. A preliminary causal network based on information-theory is used as a starting point, and our framework is used to distill the network to temporally-based networks containing essential molecular players. The strength of this approach is its ability to dynamically model the RNA temporal measurements. The approach developed paves the way for inferring regulatory processes in many complex cellular processes.

Specific Activation of Yamanaka Factors via HSF1 Signaling in the Early Stage of Zebrafish Optic Nerve Regeneration

In contrast to the case in mammals, the fish optic nerve can spontaneously regenerate and visual function can be fully restored 3-4 months after optic nerve injury (ONI). However, the regenerative mechanism behind this has remained unknown. This long process is reminiscent of the normal development of the visual system from immature neural cells to mature neurons. Here, we focused on the expression of three Yamanaka factors (Oct4, Sox2, and Klf4: OSK), which are well-known inducers of induced pluripotent stem (iPS) cells in the zebrafish retina after ONI. mRNA expression of OSK was rapidly induced in the retinal ganglion cells (RGCs) 1-3 h after ONI. Heat shock factor 1 (HSF1) mRNA was most rapidly induced in the RGCs at 0.5 h. The activation of OSK mRNA was completely suppressed by the intraocular injection of HSF1 morpholino prior to ONI. Furthermore, the chromatin immunoprecipitation assay showed the enrichment of OSK genomic DNA bound to HSF1. The present study clearly showed that the rapid activation of Yamanaka factors in the zebrafish retina was regulated by HSF1, and this sequential activation of HSF1 and OSK might provide a key to unlocking the regenerative mechanism of injured RGCs in fish.

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.

The Trisubstituted Isoxazole MMV688766 Exerts Broad-Spectrum Activity against Drug-Resistant Fungal Pathogens through Inhibition of Lipid Homeostasis

Candida species are among the most prevalent causes of systemic fungal infection, posing a growing threat to public health. While Candida albicans is the most common etiological agent of systemic candidiasis, the frequency of infections caused by non-albicans Candida species is rising. Among these is Candida auris, which has emerged as a particular concern. Since its initial discovery in 2009, it has been identified worldwide and exhibits resistance to all three principal antifungal classes. Here, we endeavored to identify compounds with novel bioactivity against C. auris from the Medicines for Malaria Venture's Pathogen Box library. Of the five hits identified, the trisubstituted isoxazole MMV688766 emerged as the only compound displaying potent fungicidal activity against C. auris, as well as other evolutionarily divergent fungal pathogens. Chemogenomic profiling, as well as subsequent metabolomic and phenotypic analyses, revealed that MMV688766 disrupts cellular lipid homeostasis, driving a decrease in levels of early sphingolipid intermediates and fatty acids and a concomitant increase in lysophospholipids. Experimental evolution to further probe MMV688766's mode of action in the model fungus Saccharomyces cerevisiae revealed that loss of function of the transcriptional regulator HAL9 confers resistance to MMV688766, in part through the upregulation of the lipid-binding chaperone HSP12, a response that appears to assist in tolerating MMV688766-induced stress. The novel mode of action we have uncovered for MMV688766 against drug-resistant fungal pathogens highlights the broad utility of targeting lipid homeostasis to disrupt fungal growth and how screening structurally-diverse chemical libraries can provide new insights into resistance-conferring stress responses of fungi. IMPORTANCE As widespread antimicrobial resistance threatens to propel the world into a postantibiotic era, there is a pressing need to identify mechanistically distinct antimicrobial agents. This is of particular concern when considering the limited arsenal of drugs available to treat fungal infections, coupled with the emergence of highly drug-resistant fungal pathogens, including Candida auris. In this work, we demonstrate that existing libraries of drug-like chemical matter can be rich resources for antifungal molecular scaffolds. We discovered that the small molecule MMV688766, from the Pathogen Box library, displays previously undescribed broad-spectrum fungicidal activity through perturbation of lipid homeostasis. Characterization of the mode of action of MMV688766 provided new insight into the protective mechanisms fungi use to cope with the disruption of lipid homeostasis. Our findings highlight that elucidating the genetic circuitry required to survive in the presence of cellular stress offers powerful insights into the biological pathways that govern this important phenotype.

Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence

Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites' regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.

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

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

Single-cell RNA-seq reveals early heterogeneity during aging in yeast

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single-cell resolution through transcriptomic sequencing. We optimized a single-cell RNA sequencing (scRNA-seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell-to-cell transcriptional variability during aging. The single-cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation-reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level.

On the impermanence of species: The collapse of genetic incompatibilities in hybridizing populations

Species pairs often become genetically incompatible during divergence, which is an important source of reproductive isolation. An idealized picture is often painted where incompatibility alleles accumulate and fix between diverging species. However, recent studies have shown both that incompatibilities can collapse with ongoing hybridization, and that incompatibility loci can be polymorphic within species. This paper suggests some general rules for the behavior of incompatibilities under hybridization. In particular, we argue that redundancy of genetic pathways can strongly affect the dynamics of intrinsic incompatibilities. Since fitness in genetically redundant systems is unaffected by introducing a few foreign alleles, higher redundancy decreases the stability of incompatibilities during hybridization, but also increases tolerance of incompatibility polymorphism within species. We use simulations and theories to show that this principle leads to two types of collapse: in redundant systems, exemplified by classical Dobzhansky-Muller incompatibilities, collapse is continuous and approaches a quasi-neutral polymorphism between broadly sympatric species, often as a result of isolation-by-distance. In nonredundant systems, exemplified by co-evolution among genetic elements, incompatibilities are often stable, but can collapse abruptly with spatial traveling waves. As both types are common, the proposed principle may be useful in understanding the abundance of genetic incompatibilities in natural populations.

BIONIC: biological network integration using convolutions

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

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

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

Generating specificity in genome regulation through transcription factor sensitivity to chromatin

Cell type-specific gene expression relies on transcription factors (TFs) binding DNA sequence motifs embedded in chromatin. Understanding how motifs are accessed in chromatin is crucial to comprehend differential transcriptional responses and the phenotypic impact of sequence variation. Chromatin obstacles to TF binding range from DNA methylation to restriction of DNA access by nucleosomes depending on their position, composition and modification. In vivo and in vitro approaches now enable the study of TF binding in chromatin at unprecedented resolution. Emerging insights suggest that TFs vary in their ability to navigate chromatin states. However, it remains challenging to link binding and transcriptional outcomes to molecular characteristics of TFs or the local chromatin substrate. Here, we discuss our current understanding of how TFs access DNA in chromatin and novel techniques and directions towards a better understanding of this critical step in genome regulation.

Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases

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

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

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

A novel approach GRNTSTE to reconstruct gene regulatory interactions applied to a case study for rat pineal rhythm gene

Accurate inference and prediction of gene regulatory network are very important for understanding dynamic cellular processes. The large-scale time series genomics data are helpful to reveal the molecular dynamics and dynamic biological processes of complex biological systems. Firstly, we collected the time series data of the rat pineal gland tissue in the natural state according to a fixed sampling rate, and performed whole-genome sequencing. The large-scale time-series sequencing data set of rat pineal gland was constructed, which includes 480 time points, the time interval between adjacent time points is 3 min, and the sampling period is 24 h. Then, we proposed a new method of constructing gene expression regulatory network, named the gene regulatory network based on time series data and entropy transfer (GRNTSTE) method. The method is based on transfer entropy and large-scale time-series gene expression data to infer the causal regulatory relationship between genes in a data-driven mode. The comparative experiments prove that GRNTSTE has better performance than dynamical gene network inference with ensemble of trees (dynGENIE3) and SCRIBE, and has similar performance to TENET. Meanwhile, we proved that the performance of GRNTSTE is slightly lower than that of SINCERITIES method and better than other gene regulatory network construction methods in BEELINE framework, which is based on the BEELINE data set. Finally, the rat pineal rhythm gene expression regulatory network was constructed by us based on the GRNTSTE method, which provides an important reference for the study of the pineal rhythm mechanism, and is of great significance to the study of the pineal rhythm mechanism.

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.

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.

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

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

Quantitative Dissection of the Proximal Ciona brachyury Enhancer

A major goal in biology is to understand the rules by which cis-regulatory sequences control spatially and temporally precise expression patterns. Here we present a systematic dissection of the proximal enhancer for the notochord-specific transcription factor brachyury in the ascidian chordate Ciona. The study uses a quantitative image-based reporter assay that incorporates a dual-reporter strategy to control for variable electroporation efficiency. We identified and mutated multiple predicted transcription factor binding sites of interest based on statistical matches to the JASPAR binding motif database. Most sites (Zic, Ets, FoxA, RBPJ) were selected based on prior knowledge of cell fate specification in both the primary and secondary notochord. We also mutated predicted Brachyury sites to investigate potential autoregulation as well as Fos/Jun (AP1) sites that had very strong matches to JASPAR. Our goal was to quantitatively define the relative importance of these different sites, to explore the importance of predicted high-affinity versus low-affinity motifs, and to attempt to design mutant enhancers that were specifically expressed in only the primary or secondary notochord lineages. We found that the mutation of all predicted high-affinity sites for Zic, FoxA or Ets led to quantifiably distinct effects. The FoxA construct caused a severe loss of reporter expression whereas the Ets construct had little effect. A strong Ets phenotype was only seen when much lower-scoring binding sites were also mutated. This supports the enhancer suboptimization hypothesis proposed by Farley and Levine but suggests that it may only apply to some but not all transcription factor families. We quantified reporter expression separately in the two notochord lineages with the expectation that Ets mutations and RBPJ mutations would have distinct effects given that primary notochord is induced by Ets-mediated FGF signaling whereas secondary notochord is induced by RBPJ/Su(H)-mediated Notch/Delta signaling. We found, however, that ETS mutations affected primary and secondary notochord expression relatively equally and that RBPJ mutations were only moderately more severe in their effect on secondary versus primary notochord. Our results point to the promise of quantitative reporter assays for understanding cis-regulatory logic but also highlight the challenge of arbitrary statistical thresholds for predicting potentially important sites.

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.

YTLR: Extracting yeast transcription factor-gene associations from the literature using automated literature readers

Cells adapt to environmental stresses mainly via transcription reprogramming. Correct transcription control is mediated by the interactions between transcription factors (TF) and their target genes. These TF-gene associations can be probed by chromatin immunoprecipitation techniques and knockout experiments, revealing TF binding (TFB) and regulatory (TFR) evidence, respectively. Nevertheless, most evidence is still fragmentary in the literature and requires tremendous human resources to curate. We developed the first pipeline called YTLR (Yeast Transcription-regulation Literature Reader) to automate TF-gene relation extraction from the literature. YTLR first identifies articles with TFB and TFR information. Then TF-gene binding pairs are extracted from the TFB articles, and TF-gene regulatory associations are recognized from the TFR papers. On gathered test sets, YTLR achieves an AUC value of 98.8% in identifying articles with TFB evidence and AUC = 83.4% in extracting the detailed TF-gene binding pairs. And similarly, YTLR also obtains an AUC value of 98.2% in identifying TFR articles and AUC = 80.4% in extracting the detailed TF-gene regulatory associations. Furthermore, YTLR outperforms previous methods in both tasks. To facilitate researchers in extracting TF-gene transcriptional relations from large-scale queried articles, an automated and easy-to-use software tool based on the YTLR pipeline is constructed. In summary, YTLR aims to provide easier literature pre-screening for curators and help researchers gather yeast TF-gene transcriptional relation conclusions from articles in a high-throughput fashion. The YTLR pipeline software tool can be downloaded at https://github.com/cobisLab/YTLR/.

The Rpd3-Complex Regulates Expression of Multiple Cell Surface Recycling Factors in Yeast

Intracellular trafficking pathways control residency and bioactivity of integral membrane proteins at the cell surface. Upon internalisation, surface cargo proteins can be delivered back to the plasma membrane via endosomal recycling pathways. Recycling is thought to be controlled at the metabolic and transcriptional level, but such mechanisms are not fully understood. In yeast, recycling of surface proteins can be triggered by cargo deubiquitination and a series of molecular factors have been implicated in this trafficking. In this study, we follow up on the observation that many subunits of the Rpd3 lysine deacetylase complex are required for recycling. We validate ten Rpd3-complex subunits in recycling using two distinct assays and developed tools to quantify both. Fluorescently labelled Rpd3 localises to the nucleus and complements recycling defects, which we hypothesised were mediated by modulated expression of Rpd3 target gene(s). Bioinformatics implicated 32 candidates that function downstream of Rpd3, which were over-expressed and assessed for capacity to suppress recycling defects of rpd3∆ cells. This effort yielded three hits: Sit4, Dit1 and Ldb7, which were validated with a lipid dye recycling assay. Additionally, the essential phosphatidylinositol-4-kinase Pik1 was shown to have a role in recycling. We propose recycling is governed by Rpd3 at the transcriptional level via multiple downstream target genes.

Possible Role for Allelic Variation in Yeast MED15 in Ecological Adaptation

The propensity for Saccharomyces cerevisiae yeast to ferment sugars into ethanol and CO₂ has long been useful in the production of a wide range of food and drink. In the production of alcoholic beverages, the yeast strain selected for fermentation is crucial because not all strains are equally proficient in tolerating fermentation stresses. One potential mechanism by which domesticated yeast may have adapted to fermentation stresses is through changes in the expression of stress response genes. MED15 is a general transcriptional regulator and RNA Pol II Mediator complex subunit which modulates the expression of many metabolic and stress response genes. In this study, we explore the role of MED15 in alcoholic fermentation. In addition, we ask whether MED15 alleles from wine, sake or palm wine yeast improve fermentation activity and grape juice fermentation stress responses. And last, we investigate to what extent any differences in activity are due to allelic differences in the lengths of three polyglutamine tracts in MED15. We find that strains lacking MED15 are deficient in fermentation and fermentation stress responses and that MED15 alleles from alcoholic beverage yeast strains can improve both the fermentation capacity and the response to ethanol stresses when transplanted into a standard laboratory strain. Finally, we find that polyglutamine tract length in the Med15 protein is one determinant in the efficiency of the alcoholic fermentation process. These data lead to a working model in which polyglutamine tract length and other types of variability within transcriptional hubs like the Mediator subunit, Med15, may contribute to a reservoir of transcriptional profiles that may provide a fitness benefit in the face of environmental fluctuations.

Predict long-range enhancer regulation based on protein-protein interactions between transcription factors

Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.

HSF1 induces RNA polymerase II synthesis of ribosomal RNA in S. cerevisiae during nitrogen deprivation

The resource intensive process of accurate ribosome synthesis is essential for cell viability in all organisms. Ribosome synthesis regulation centers on RNA polymerase I (pol I) transcription of a 35S rRNA precursor that is processed into the mature 18S, 5.8S and 25S rRNAs. During nutrient deprivation or stress, pol I synthesis of rRNA is dramatically reduced. Conversely, chronic stress such as mitochondrial dysfunction induces RNA polymerase II (pol II) to transcribe functional rRNA using an evolutionarily conserved cryptic pol II rDNA promoter suggesting a universal phenomenon. However, this polymerase switches and its role in regulation of rRNA synthesis remain unclear. In this paper, we demonstrate that extended nitrogen deprivation induces the polymerase switch via components of the environmental stress response. We further show that the switch is repressed by Sch9 and activated by the stress kinase Rim15. Like stress-induced genes, the switch requires not only pol II transcription machinery, including the mediator, but also requires the HDAC, Rpd3 and stress transcription factor Hsf1. The current work shows that the constitutive allele, Hsf1^PO4* displays elevated levels of induction in non-stress conditions while binding to a conserved site in the pol II rDNA promoter upstream of the pol I promoter. Whether the polymerase switch serves to provide rRNA when pol I transcription is inhibited or fine-tunes pol I initiation via RNA interactions is yet to be determined. Identifying the underlying mechanism for this evolutionary conserved phenomenon will help understand the mechanism of pol II rRNA synthesis and its role in stress adaptation.

The Yeast eIF2 Kinase Gcn2 Facilitates H2O2-Mediated Feedback Inhibition of Both Protein Synthesis and Endoplasmic Reticulum Oxidative Folding during Recombinant Protein Production

Recombinant protein production is a known source of oxidative stress. However, knowledge of which reactive oxygen species are involved or the specific growth phase in which stress occurs remains lacking. Using modern, hypersensitive genetic H₂O₂-specific probes, microcultivation, and continuous measurements in batch culture, we observed H₂O₂ accumulation during and following the diauxic shift in engineered Saccharomyces cerevisiae, correlating with peak α-amylase production. In agreement with previous studies supporting a role of the translation initiation factor kinase Gcn2 in the response to H₂O₂, we find that Gcn2-dependent phosphorylation of eIF2α increases alongside translational attenuation in strains engineered to produce large amounts of α-amylase. Gcn2 removal significantly improved α-amylase production in two previously optimized high-producing strains but not in the wild type. Gcn2 deficiency furthermore reduced intracellular H₂O₂ levels and the Hac1 splicing ratio, while expression of antioxidants and the endoplasmic reticulum (ER) disulfide isomerase PDI1 increased. These results suggest protein synthesis and ER oxidative folding are coupled and subject to feedback inhibition by H₂O₂.

IMPORTANCE Recombinant protein production is a multibillion dollar industry. Optimizing the productivity of host cells is, therefore, of great interest. In several hosts, oxidants are produced as an unwanted side product of recombinant protein production. The buildup of oxidants can result in intracellular stress responses that could compromise the productivity of the host cell. Here, we document a novel protein synthesis inhibitory mechanism that is activated by the buildup of a specific oxidant (H₂O₂) in the cytosol of yeast cells upon the production of recombinant proteins. At the center of this inhibitory mechanism lies the protein kinase Gcn2. By removing Gcn2, we observed a doubling of recombinant protein productivity in addition to reduced H₂O₂ levels in the cytosol. In this study, we want to raise awareness of this inhibitory mechanism in eukaryotic cells to further improve protein production and contribute to the development of novel protein-based therapeutic strategies.

Stress tolerance enhancement via SPT15 base editing in Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae is widely used in traditional brewing and modern fermentation industries to produce biofuels, chemicals and other bioproducts, but challenged by various harsh industrial conditions, such as hyperosmotic, thermal and ethanol stresses. Thus, its stress tolerance enhancement has been attracting broad interests. Recently, CRISPR/Cas-based genome editing technology offers unprecedented tools to explore genetic modifications and performance improvement of S. cerevisiae.

RESULTS

Here, we presented that the Target-AID (activation-induced cytidine deaminase) base editor of enabling C-to-T substitutions could be harnessed to generate in situ nucleotide changes on the S. cerevisiae genome, thereby introducing protein point mutations in cells. The general transcription factor gene SPT15 was targeted, and total 36 mutants with diversified stress tolerances were obtained. Among them, the 18 tolerant mutants against hyperosmotic, thermal and ethanol stresses showed more than 1.5-fold increases of fermentation capacities. These mutations were mainly enriched at the N-terminal region and the convex surface of the saddle-shaped structure of Spt15. Comparative transcriptome analysis of three most stress-tolerant (A140G, P169A and R238K) and two most stress-sensitive (S118L and L214V) mutants revealed common and distinctive impacted global transcription reprogramming and transcriptional regulatory hubs in response to stresses, and these five amino acid changes had different effects on the interactions of Spt15 with DNA and other proteins in the RNA Polymerase II transcription machinery according to protein structure alignment analysis.

CONCLUSIONS

Taken together, our results demonstrated that the Target-AID base editor provided a powerful tool for targeted in situ mutagenesis in S. cerevisiae and more potential targets of Spt15 residues for enhancing yeast stress tolerance.

Transcription at a Distance in the Budding Yeast Saccharomyces cerevisiae

Proper transcriptional regulation depends on the collaboration of multiple layers of control simultaneously. Cells tightly balance cellular resources and integrate various signaling inputs to maintain homeostasis during growth, development and stressors, among other signals. Many eukaryotes, including the budding yeast Saccharomyces cerevisiae, exhibit a non-random distribution of functionally related genes throughout their genomes. This arrangement coordinates the transcription of genes that are found in clusters, and can occur over long distances. In this work, we review the current literature pertaining to gene regulation at a distance in budding yeast.

Systematic Analysis of Targets of Pumilio-Mediated mRNA Decay Reveals that PUM1 Repression by DNA Damage Activates Translesion Synthesis

RNA-binding proteins (RBPs) play a pivotal role in gene expression by modulating the stability of transcripts. However, the identification of degradation target mRNAs of RBPs remains difficult. By the combined analysis of transcriptome-wide mRNA stabilities and the binding of mRNAs to human Pumilio 1 (PUM1), we identify 48 mRNAs that both bind to PUM1 and exhibit PUM1-dependent degradation. Analysis of changes in the abundance of PUM1 and its degradation target mRNAs in RNA-seq data indicate that DNA-damaging agents negatively regulate PUM1-mediated mRNA decay. Cells exposed to cisplatin have reduced PUM1 abundance and increased PCNA and UBE2A mRNAs encoding proteins involved in DNA damage tolerance by translesion synthesis (TLS). Cells overexpressing PUM1 exhibit impaired DNA synthesis and TLS and increased sensitivity to the cytotoxic effect of cisplatin. Thus, our method identifies target mRNAs of PUM1-mediated decay and reveals that cells respond to DNA damage by inhibiting PUM1-mediated mRNA decay to activate TLS.

Distinct roles for RSC and SWI/SNF chromatin remodelers in genomic excision repair

Nucleosomes are a significant barrier to the repair of UV damage because they impede damage recognition by nucleotide excision repair (NER). The RSC and SWI/SNF chromatin remodelers function in cells to promote DNA access by moving or evicting nucleosomes, and both have been linked to NER in yeast. Here, we report genome-wide repair maps of UV-induced cyclobutane pyrimidine dimers (CPDs) in yeast cells lacking RSC or SWI/SNF activity. Our data indicate that SWI/SNF is not generally required for NER but instead promotes repair of CPD lesions at specific yeast genes. In contrast, mutation or depletion of RSC subunits causes a general defect in NER across the yeast genome. Our data indicate that RSC is required for repair not only in nucleosomal DNA but also in neighboring linker DNA and nucleosome-free regions (NFRs). Although depletion of the RSC catalytic subunit also affects base excision repair (BER) of N-methylpurine (NMP) lesions, RSC activity is less important for BER in linker DNA and NFRs. Furthermore, our data indicate that RSC plays a direct role in transcription-coupled NER (TC-NER) of transcribed DNA. These findings help to define the specific genomic and chromatin contexts in which each chromatin remodeler functions in DNA repair, and indicate that RSC plays a unique function in facilitating repair by both NER subpathways.

Epstein-Barr virus inactivates the transcriptome and disrupts the chromatin architecture of its host cell in the first phase of lytic reactivation

Epstein-Barr virus (EBV), a herpes virus also termed HHV 4 and the first identified human tumor virus, establishes a stable, long-term latent infection in human B cells, its preferred host. Upon induction of EBV's lytic phase, the latently infected cells turn into a virus factory, a process that is governed by EBV. In the lytic, productive phase, all herpes viruses ensure the efficient induction of all lytic viral genes to produce progeny, but certain of these genes also repress the ensuing antiviral responses of the virally infected host cells, regulate their apoptotic death or control the cellular transcriptome. We now find that EBV causes previously unknown massive and global alterations in the chromatin of its host cell upon induction of the viral lytic phase and prior to the onset of viral DNA replication. The viral initiator protein of the lytic cycle, BZLF1, binds to >105 binding sites with different sequence motifs in cellular chromatin in a concentration dependent manner implementing a binary molar switch probably to prevent noise-induced erroneous induction of EBV's lytic phase. Concomitant with DNA binding of BZLF1, silent chromatin opens locally as shown by ATAC-seq experiments, while previously wide-open cellular chromatin becomes inaccessible on a global scale within hours. While viral transcripts increase drastically, the induction of the lytic phase results in a massive reduction of cellular transcripts and a loss of chromatin-chromatin interactions of cellular promoters with their distal regulatory elements as shown in Capture-C experiments. Our data document that EBV's lytic cycle induces discrete early processes that disrupt the architecture of host cellular chromatin and repress the cellular epigenome and transcriptome likely supporting the efficient de novo synthesis of this herpes virus.

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.

Cprp-An Unusual, Repetitive Protein Which Impacts Pleuromutilin Biosynthesis in the Basidiomycete Clitopilus passeckerianus

Interrogation of an EST database for Clitopilus passeckerianus identified a putative homolog to the unusual stress response gene from yeast; ddr48, as being upregulated under pleuromutilin production conditions. Silencing of this gene, named cprp, produced a population of transformants which demonstrated significantly reduced pleuromutilin production. Attempts to complement a Saccharomyces cerevisiae ddr48 mutant strain (strain Y16748) with cprp were hampered by the lack of a clearly identifiable mutant phenotype, but interestingly, overexpression of either ddr48 or cprp in S. cerevisiae Y16748 led to a conspicuous and comparable reduction in growth rate. This observation, combined with the known role of DDR48 proteins from a range of fungal species in nutrient starvation and stress responses, raises the possibility that this family of proteins plays a role in triggering oligotrophic growth. Localization studies via the production of a Cprp:GFP fusion protein in C. passeckerianus showed clear localization adjacent to the hyphal septa and, to a lesser extent, cell walls, which is consistent with the identification of DDR48 as a cell wall-associated protein in various yeast species. To our knowledge this is the first study demonstrating that a DDR48-like protein plays a role in the regulation of a secondary metabolite, and represents the first DDR48-like protein from a basidiomycete. Potential homologs can be identified across much of the Dikarya, suggesting that this unusual protein may play a central role in regulating both primary and secondary metabolism in fungi.

Emergence of co-expression in gene regulatory networks

Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.