An improved map of conserved regulatory sites for Saccharomyces cerevisiae

Impact of Fungal MAPK Pathway Targets on the Cell Wall

The fungal cell wall is an extracellular organelle that provides structure and protection to cells. The cell wall also influences the interactions of cells with each other and surfaces. The cell wall can be reorganized in response to changing environmental conditions and different types of stress. Signaling pathways control the remodeling of the cell wall through target proteins that are in many cases not well defined. The Mitogen Activated Protein Kinase pathway that controls filamentous growth in yeast (fMAPK) was required for normal growth in media containing the cell wall perturbing agent Calcofluor White (CFW). A mass spectrometry (MASS-SPEC) approach and analysis of expression profiling data identified cell wall proteins and modifying enzymes whose levels were influenced by the fMAPK pathway. These include Flo11p, Flo10p, Tip1p, Pry2p and the mannosyltransferase, Och1p. Cells lacking Flo11p or Och1p were sensitive to CFW. The identification of cell wall proteins controlled by a MAPK pathway may provide insights into how signaling pathways regulate the cell wall.

Detection of network motifs using three-way ANOVA

Motivation

Gene regulatory networks (GRN) can be determined via various experimental techniques, and also by computational methods, which infer networks from gene expression data. However, these techniques treat interactions separately such that interdependencies of interactions forming meaningful subnetworks are typically not considered.

Methods

For the investigation of network properties and for the classification of different (sub-)networks based on gene expression data, we consider biological network motifs consisting of three genes and up to three interactions, e.g. the cascade chain (CSC), feed-forward loop (FFL), and dense-overlapping regulon (DOR). We examine several conventional methods for the inference of network motifs, which typically consider each interaction individually. In addition, we propose a new method based on three-way ANOVA (ANalysis Of VAriance) (3WA) that analyzes entire subnetworks at once. To demonstrate the advantages of such a more holistic perspective, we compare the ability of 3WA and other methods to detect and categorize network motifs on large real and artificial datasets.

Results

We find that conventional methods perform much better on artificial data (AUC up to 80%), than on real E. coli expression datasets (AUC 50% corresponding to random guessing). To explain this observation, we examine several important properties that differ between datasets and analyze predicted motifs in detail. We find that in case of real networks our new 3WA method outperforms (AUC 70% in E. coli) previous methods by exploiting the interdependencies in the full motif structure. Because of important differences between current artificial datasets and real measurements, the construction and testing of motif detection methods should focus on real data.

Mining features for biomedical data using clustering tree ensembles

The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets suffer from lack of variance in the instance representation, or even worse, contain instances with identical features and different class labels. Indisputably, this directly affects the performance of machine learning algorithms, as well as the ability to interpret their results. In this article, we emphasize on the aforementioned problem and propose a target-informed feature induction method based on tree ensemble learning. The method brings more variance into the data representation, thereby potentially increasing predictive performance of a learner applied to the induced features. The contribution of this article is twofold. Firstly, a problem affecting the quality of biomedical data is highlighted, and secondly, a method to handle that problem is proposed. The efficiency of the presented approach is validated on multi-target prediction tasks. The obtained results indicate that the proposed approach is able to boost the discrimination between the data instances and increase the predictive performance.

Genetics of trans-regulatory variation in gene expression

Heritable variation in gene expression forms a crucial bridge between genomic variation and the biology of many traits. However, most expression quantitative trait loci (eQTLs) remain unidentified. We mapped eQTLs by transcriptome sequencing in 1012 yeast segregants. The resulting eQTLs accounted for over 70% of the heritability of mRNA levels, allowing comprehensive dissection of regulatory variation. Most genes had multiple eQTLs. Most expression variation arose from trans-acting eQTLs distant from their target genes. Nearly all trans-eQTLs clustered at 102 hotspot locations, some of which influenced the expression of thousands of genes. Fine-mapped hotspot regions were enriched for transcription factor genes. While most genes had a local eQTL, most of these had no detectable effects on the expression of other genes in trans. Hundreds of non-additive genetic interactions accounted for small fractions of expression variation. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.

An optimization framework for network annotation

Motivation

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

Results

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

Availability and implementation

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

The Long History of the Diverse Roles of Short ORFs: sPEPs in Fungi

Since the completion of the genome sequence of the model eukaryote Saccharomyces cerevisiae, there have been significant advancements in the field of genome annotation, in no small part due to the availability of datasets that make large-scale comparative analyses possible. As a result, since its completion there has been a significant change in annotated ORF size distribution in this first eukaryotic genome, especially in short ORFs (sORFs) predicted to encode polypeptides less than 150 amino acids in length. Due to their small size and the difficulties associated with their study, it is only relatively recently that these genomic features and the sORF-encoded peptides (sPEPs) they encode have become a focus of many researchers. Yet while this class of peptides may seem new and exciting, the study of this part of the proteome is nothing new in S. cerevisiae, a species where the biological importance of sPEPs has been elegantly illustrated over the past 30 years. Here the authors showcase a range of different sORFs found in S. cerevisiae and the diverse biological roles of their encoded sPEPs, and provide an insight into the sORFs found in other fungal species, particularly those pathogenic to humans.

Gcn4 Binding in Coding Regions Can Activate Internal and Canonical 5' Promoters in Yeast

Gcn4 is a yeast transcriptional activator induced by amino acid starvation. ChIP-seq analysis revealed 546 genomic sites occupied by Gcn4 in starved cells, representing ∼30% of Gcn4-binding motifs. Surprisingly, only ∼40% of the bound sites are in promoters, of which only ∼60% activate transcription, indicating extensive negative control over Gcn4 function. Most of the remaining ∼300 Gcn4-bound sites are within coding sequences (CDSs), with ∼75 representing the only bound sites near Gcn4-induced genes. Many such unconventional sites map between divergent antisense and sub-genic sense transcripts induced within CDSs adjacent to induced TBP peaks, consistent with Gcn4 activation of cryptic bidirectional internal promoters. Mutational analysis confirms that Gcn4 sites within CDSs can activate sub-genic and full-length transcripts from the same or adjacent genes, showing that functional Gcn4 binding is not confined to promoters. Our results show that internal promoters can be regulated by an activator that functions at conventional 5'-positioned promoters.

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

Histone deacetylase-mediated regulation of endolysosomal pH

The pH of the endolysosomal system is tightly regulated by a balance of proton pump and leak mechanisms that are critical for storage, recycling, turnover, and signaling functions in the cell. Dysregulation of endolysosomal pH has been linked to aging, amyloidogenesis, synaptic dysfunction, and various neurodegenerative disorders, including Alzheimer's disease. Therefore, understanding the mechanisms that regulate luminal pH may be key to identifying new targets for managing these disorders. Meta-analysis of yeast microarray databases revealed that nutrient-limiting conditions inhibited the histone deacetylase (HDAC) Rpd3 and thereby up-regulated transcription of the endosomal Na⁺/H⁺ exchanger Nhx1, resulting in vacuolar alkalinization. Consistent with these findings, Rpd3 inhibition by the HDAC inhibitor and antifungal drug trichostatin A induced Nhx1 expression and vacuolar alkalinization. Bioinformatics analysis of Drosophila and mouse databases revealed that caloric control of the Nhx1 orthologs DmNHE3 and NHE6, respectively, is also mediated by HDACs. We show that NHE6 is a target of the transcription factor cAMP-response element-binding protein (CREB), a known regulator of cellular responses to low-nutrient conditions, providing a molecular mechanism for nutrient- and HDAC-dependent regulation of endosomal pH. Of note, pharmacological targeting of the CREB pathway to increase NHE6 expression helped regulate endosomal pH and correct defective clearance of amyloid Aβ in an apoE4 astrocyte model of Alzheimer's disease. These observations from yeast, fly, mouse, and cell culture models point to an evolutionarily conserved mechanism for HDAC-mediated regulation of endosomal NHE expression. Our insights offer new therapeutic strategies for modulation of endolysosomal pH in fungal infection and human disease.

A comprehensive evaluation of module detection methods for gene expression data

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods.

Modules composed of groups of genes with similar expression profiles tend to be functionally related and co-regulated. Here, Saelens et al evaluate the performance of 42 computational methods and provide practical guidelines for module detection in gene expression data.

Roles of the Skn7 response regulator in stress resistance, cell wall integrity and GA biosynthesis in Ganoderma lucidum

The transcription factor Skn7 is a highly conserved fungal protein that participates in a variety of processes, including oxidative stress adaptation, fungicide sensitivity, cell wall biosynthesis, cell cycle, and sporulation. In this study, a homologous gene of Saccharomyces cerevisiae Skn7 was cloned from Ganoderma lucidum. RNA interference (RNAi) was used to study the functions of Skn7, and the two knockdown strains Skn7i-5 and Skn7i-7 were obtained in G. lucidum. The knockdown of GlSkn7 resulted in hypersensitivity to oxidative and cell wall stresses. The concentrations of chitin and β-1,3-glucan distinctly decreased in the GlSkn7 knockdown strains compared with those of the wild type (WT). In addition, the expression of cell wall biosynthesis related genes was also significantly down-regulated and the thickness of the cell wall also significantly reduced in the GlSkn7 knockdown strains. The intracellular reactive oxygen species (ROS) content and ganoderic acids biosynthesis increased significantly in the GlSkn7 knockdown strains. Interestingly, the level of intracellular ROS and the content of ganoderic acids decreased after N-acetyl-L-cysteine (NAC), an ROS scavenger, was added, indicating that GlSkn7 might regulate ganoderic acids biosynthesis via the intracellular ROS level. The transcript level of GlSkn7 were up-regulated in osmotic stress, heat stress and fungicide condition. At the same time, the content of ganoderic acids in the GlSkn7 knockdown strains also changed distinctly in these conditions. Overall, GlSkn7 is involved in stress resistance, cell wall integrity and ganoderic acid biosynthesis in G. lucidum.

High-resolution transcription maps reveal the widespread impact of roadblock termination in yeast

Transcription termination delimits transcription units but also plays important roles in limiting pervasive transcription. We have previously shown that transcription termination occurs when elongating RNA polymerase II (RNAPII) collides with the DNA-bound general transcription factor Reb1. We demonstrate here that many different DNA-binding proteins can induce termination by a similar roadblock (RB) mechanism. We generated high-resolution transcription maps by the direct detection of RNAPII upon nuclear depletion of two essential RB factors or when the canonical termination pathways for coding and non-coding RNAs are defective. We show that RB termination occurs genomewide and functions independently of (and redundantly with) the main transcription termination pathways. We provide evidence that transcriptional readthrough at canonical terminators is a significant source of pervasive transcription, which is controlled to a large extent by RB termination. Finally, we demonstrate the occurrence of RB termination around centromeres and tRNA genes, which we suggest shields these regions from RNAPII to preserve their functional integrity.

Condition-dependent co-regulation of genomic clusters of virulence factors in the grapevine trunk pathogen Neofusicoccum parvum

The ascomycete Neofusicoccum parvum, one of the causal agents of Botryosphaeria dieback, is a destructive wood-infecting fungus and a serious threat to grape production worldwide. The capability to colonize woody tissue, combined with the secretion of phytotoxic compounds, is thought to underlie its pathogenicity and virulence. Here, we describe the repertoire of virulence factors and their transcriptional dynamics as the fungus feeds on different substrates and colonizes the woody stem. We assembled and annotated a highly contiguous genome using single-molecule real-time DNA sequencing. Transcriptome profiling by RNA sequencing determined the genome-wide patterns of expression of virulence factors both in vitro (potato dextrose agar or medium amended with grape wood as substrate) and in planta. Pairwise statistical testing of differential expression, followed by co-expression network analysis, revealed that physically clustered genes coding for putative virulence functions were induced depending on the substrate or stage of plant infection. Co-expressed gene clusters were significantly enriched not only in genes associated with secondary metabolism, but also in those associated with cell wall degradation, suggesting that dynamic co-regulation of transcriptional networks contributes to multiple aspects of N. parvum virulence. In most of the co-expressed clusters, all genes shared at least a common motif in their promoter region, indicative of co-regulation by the same transcription factor. Co-expression analysis also identified chromatin regulators with correlated expression with inducible clusters of virulence factors, suggesting a complex, multi-layered regulation of the virulence repertoire of N. parvum.

From Genes to Networks: The Regulatory Circuitry Controlling Candida albicans Morphogenesis

Candida albicans is a commensal yeast of most healthy individuals, but also one of the most prevalent human fungal pathogens. During adaptation to the mammalian host, C. albicans encounters different niches where it is exposed to several types of stress, including oxidative, nitrosative (e.g., immune system), osmotic (e.g., kidney and oral cavity) stresses and pH variation (e.g., gastrointestinal (GI) tract and vagina). C. albicans has developed the capacity to respond to the environmental changes by modifying its morphology, which comprises the yeast-to-hypha transition, white-opaque switching, and chlamydospore formation. The yeast-to-hypha transition has been very well characterized and was shown to be modulated by several external stimuli that mimic the host environment. For instance, temperature above 37 ℃, serum, alkaline pH, and CO₂ concentration are all reported to enhance filamentation. The transition is characterized by the activation of an intricate regulatory network of signaling pathways, involving many transcription factors. The regulatory pathways that control either the stress response or morphogenesis are required for full virulence and promote survival of C. albicans in the host. Many of these transcriptional circuitries have been characterized, highlighting the complexity and the interconnections between the different pathways. Here, we present the major signaling pathways and the main transcription factors involved in the yeast-to-hypha transition. Furthermore, we describe the role of heat shock transcription factors in the morphogenetic transition, providing an edifying example of the complex cross talk between pathways involved in morphogenesis and stress response.

Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single Saccharomyces cerevisiae cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.

Genetically identical cells growing in the same environment can vary in their cellular state and behavior. Such heterogeneity may explain why some cells in an isogenic population can survive sudden severe environmental stress whereas other cells succumb. Cell-to-cell variation in gene expression has been linked to variable stress survival, but how and why transcript levels vary across the transcriptome in single cells is only beginning to emerge. Here, we used single-cell RNA sequencing (scRNA-seq) to measure cell-to-cell heterogeneity in the transcriptome of budding yeast (Saccharomyces cerevisiae). We find surprising patterns of variation across known sets of transcription factor targets, indicating that cells vary in their transcriptome profile both before and after stress exposure. scRNA-seq analysis combined with live-cell imaging of transcription factor activation dynamics revealed some cells in which the stress response was coordinately activated and other cells in which the traditional response was decoupled, suggesting unrecognized regulatory nuances that expand our understanding of stress response and survival.

Identification of Fkh1 and Fkh2 binding site variants associated with dynamically bound DNA elements including replication origins

Forkhead Box (Fox) DNA binding proteins control multiple genome activities, including transcription, replication, and repair. These activities are organized spatially and temporally in the nucleus, and Fox proteins Fkh1 and Fkh2 have emerged as regulators of long-range chromosomal interactions involved with these activities, such as the clustering of replication origins programmed for early initiation. Fkh1 and Fkh2 bind a subset of replication origins and are thought to dimerize to mediate long-range chromosomal contacts between these origins. The binding of Fkh1 and/or Fkh2 (Fkh1/2) to replication origins and the recombination enhancer (RE), which is involved in DNA repair required for mating-type switching, is cell cycle-regulated and thus appears to be more dynamic than Fkh1/2 binding at regulated target genes. Here we report the identification of Fkh1/2 binding sequence variants at replication origins and the RE compared with Fkh1/2 binding sequences found at target genes of the CLB2 group. These different binding sequences have previously been characterized as weak and strong, respectively, suggesting that the presence of weak sites contributes to more dynamic interactions at replication origins and RE, possibly facilitated by Fkh1/2 dimerization and cooperative interactions with accessory proteins. We discuss the wealth of regulatory potential imbued in these features of the DNA and its binding proteins.

Logo2PWM: a tool to convert sequence logo to position weight matrix

Background

position weight matrix (PWM) and sequence logo are the most widely used representations of transcription factor binding site (TFBS) in biological sequences. Sequence logo - a graphical representation of PWM, has been widely used in scientific publications and reports, due to its easiness of human perception, rich information, and simple format. Different from sequence logo, PWM works great as a precise and compact digitalized form, which can be easily used by a variety of motif analysis software. There are a few available tools to generate sequence logos from PWM; however, no tool does the reverse. Such tool to convert sequence logo back to PWM is needed to scan a TFBS represented in logo format in a publication where the PWM is not provided or hard to be acquired. A major difficulty in developing such tool to convert sequence logo to PWM is to deal with the diversity of sequence logo images.

Results

We propose logo2PWM for reconstructing PWM from a large variety of sequence logo images. Evaluation results on over one thousand logos from three sources of different logo format show that the correlation between the reconstructed PWMs and the original PWMs are constantly high, where median correlation is greater than 0.97.

Conclusion

Because of the high recognition accuracy, the easiness of usage, and, the availability of both web-based service and stand-alone application, we believe that logo2PWM can readily benefit the study of transcription by filling the gap between sequence logo and PWM.

The Gcn4 transcription factor reduces protein synthesis capacity and extends yeast lifespan

In Saccharomyces cerevisiae, deletion of large ribosomal subunit protein-encoding genes increases the replicative lifespan in a Gcn4-dependent manner. However, how Gcn4, a key transcriptional activator of amino acid biosynthesis genes, increases lifespan, is unknown. Here we show that Gcn4 acts as a repressor of protein synthesis. By analyzing the messenger RNA and protein abundance, ribosome occupancy and protein synthesis rate in various yeast strains, we demonstrate that Gcn4 is sufficient to reduce protein synthesis and increase yeast lifespan. Chromatin immunoprecipitation reveals Gcn4 binding not only at genes that are activated, but also at genes, some encoding ribosomal proteins, that are repressed upon Gcn4 overexpression. The promoters of repressed genes contain Rap1 binding motifs. Our data suggest that Gcn4 is a central regulator of protein synthesis under multiple perturbations, including ribosomal protein gene deletions, calorie restriction, and rapamycin treatment, and provide an explanation for its role in longevity and stress response.

The transcription factor Gcn4 is known to regulate yeast amino acid synthesis. Here, the authors show that Gcn4 also acts as a repressor of protein biosynthesis in a range of conditions that enhance yeast lifespan, such as ribosomal protein knockout, calorie restriction or mTOR inhibition.

Study of the Plasma Membrane Proteome Dynamics Reveals Novel Targets of the Nitrogen Regulation in Yeast

Yeast cells, to be able to grow on a wide variety of nitrogen sources, regulate the set of nitrogen transporters present at their plasma membrane. Such regulation relies on both transcriptional and post-translational events. Although microarray studies have identified most nitrogen-sensitive genes, nitrogen-induced post-translational regulation has only been studied for very few proteins among which the general amino acid permease Gap1. Adding a preferred nitrogen source to proline-grown cells triggers Gap1 endocytosis and vacuolar degradation in an Rsp5-Bul1/2-dependent manner. Here, we used a proteomic approach to follow the dynamics of the plasma membrane proteome after addition of a preferred nitrogen source. We identified new targets of the nitrogen regulation and four transporters of poor nitrogen sources-Put4, Opt2, Dal5, and Ptr2-that rapidly decrease in abundance. Although the kinetics is different for each transporter, we found that three of them-Put4, Dal5, and Ptr2-are endocytosed, like Gap1, in an Rsp5-dependent manner and degraded in the vacuole. Finally, we showed that Gap1 stabilization at the plasma membrane, through deletion of Bul proteins, regulates the abundance of Put4, Dal5 and Ptr2.

Reduced dosage of the chromosome axis factor Red1 selectively disrupts the meiotic recombination checkpoint in Saccharomyces cerevisiae

Meiotic chromosomes assemble characteristic "axial element" structures that are essential for fertility and provide the chromosomal context for meiotic recombination, synapsis and checkpoint signaling. Whether these meiotic processes are equally dependent on axial element integrity has remained unclear. Here, we investigated this question in S. cerevisiae using the putative condensin allele ycs4S. We show that the severe axial element assembly defects of this allele are explained by a linked mutation in the promoter of the major axial element gene RED1 that reduces Red1 protein levels to 20-25% of wild type. Intriguingly, the Red1 levels of ycs4S mutants support meiotic processes linked to axis integrity, including DNA double-strand break formation and deposition of the synapsis protein Zip1, at levels that permit 70% gamete survival. By contrast, the ability to elicit a meiotic checkpoint arrest is completely eliminated. This selective loss of checkpoint function is supported by a RED1 dosage series and is associated with the loss of most of the cytologically detectable Red1 from the axial element. Our results indicate separable roles for Red1 in building the structural axis of meiotic chromosomes and mounting a sustained recombination checkpoint response.

Measurement of In Vivo Protein Binding Affinities in a Signaling Network with Mass Spectrometry

Protein interaction networks play a key role in signal processing. Despite the progress in identifying the interactions, the quantification of their strengths lags behind. Here we present an approach to quantify the in vivo binding of proteins to their binding partners in signaling-transcriptional networks, by the pairwise genetic isolation of each interaction and by varying the concentration of the interacting components over time. The absolute quantification of the protein concentrations was performed with targeted mass spectrometry. The strengths of the interactions, as defined by the apparent dissociation constants, ranged from subnanomolar to micromolar values in the yeast galactose signaling network. The weak homodimerization of the Gal4 activator amplifies the signal elicited by glucose. Furthermore, combining the binding constants in a feedback loop correctly predicted cellular memory, a characteristic network behavior. Thus, this genetic-proteomic binding assay can be used to faithfully quantify how strongly proteins interact with proteins, DNA and metabolites.

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

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

A synthetic hybrid promoter for D-xylonate production at low pH in the tolerant yeast Candida glycerinogenes

The tolerant yeast Candida glycerinogenes, with high D-xylonate and low-pH tolerances, was used as the host for D-xylonate production at low pH in this study. A low-pH inducible promoter, pGUKd, was engineered using the core promoter of the glyceraldehyde-3-phosphate dehydrogenase gene (pGAP) combined with the upstream activating sequence of the promoter of the guanylate kinase gene (pGUK1) that had substituted pH-responsive TF binding sites. The recombinant cells that expressed GFP from the hybrid promoter pGUKd displayed dramatically increased fluorescence intensity at pH 2.5, thus verifying that pGUKd is a low-pH inducible promoter. The promoter pGUKd was then used to express the D-xylose dehydrogenase gene xylB, resulting in increased expression levels of xylB at low pH. The recombinant protein exhibited higher specific activities under lower pH conditions and produced 38 g/l D-xylonate at pH 2.5. This rate is much higher than that produced by fermentation at pH 5.5. These results suggest that the novel hybrid promoter pGUKd functions to direct the production of D-xylonate at low pH, and we provide a candidate genetic tool for the stress tolerant yeast C. glycerinogenes.

From Lipid Homeostasis to Differentiation: Old and New Functions of the Zinc Cluster Proteins Ecm22, Upc2, Sut1 and Sut2

Zinc cluster proteins are a large family of transcriptional regulators with a wide range of biological functions. The zinc cluster proteins Ecm22, Upc2, Sut1 and Sut2 have initially been identified as regulators of sterol import in the budding yeast Saccharomyces cerevisiae. These proteins also control adaptations to anaerobic growth, sterol biosynthesis as well as filamentation and mating. Orthologs of these zinc cluster proteins have been identified in several species of Candida. Upc2 plays a critical role in antifungal resistance in these important human fungal pathogens. Upc2 is therefore an interesting potential target for novel antifungals. In this review we discuss the functions, mode of actions and regulation of Ecm22, Upc2, Sut1 and Sut2 in budding yeast and Candida.

Form and function of topologically associating genomic domains in budding yeast

The genome of metazoan cells is organized into topologically associating domains (TADs) that have similar histone modifications, transcription level, and DNA replication timing. Although similar structures appear to be conserved in fission yeast, computational modeling and analysis of high-throughput chromosome conformation capture (Hi-C) data have been used to argue that the small, highly constrained budding yeast chromosomes could not have these structures. In contrast, herein we analyze Hi-C data for budding yeast and identify 200-kb scale TADs, whose boundaries are enriched for transcriptional activity. Furthermore, these boundaries separate regions of similarly timed replication origins connecting the long-known effect of genomic context on replication timing to genome architecture. To investigate the molecular basis of TAD formation, we performed Hi-C experiments on cells depleted for the Forkhead transcription factors, Fkh1 and Fkh2, previously associated with replication timing. Forkhead factors do not regulate TAD formation, but do promote longer-range genomic interactions and control interactions between origins near the centromere. Thus, our work defines spatial organization within the budding yeast nucleus, demonstrates the conserved role of genome architecture in regulating DNA replication, and identifies a molecular mechanism specifically regulating interactions between pericentric origins.

A global characterization of the translational and transcriptional programs induced by methionine restriction through ribosome profiling and RNA-seq

BACKGROUND

Among twenty amino acids, methionine has a special role as it is coded by the translation initiation codon and methionyl-tRNAi (Met-tRNAi) is required for the assembly of the translation initiation complex. Thus methionine may play a special role in global gene regulation. Methionine has also been known to play important roles in cell growth, development, cancer, and aging. In this work, we characterize the translational and transcriptional programs induced by methionine restriction (MetR) and investigate the potential mechanisms through which methionine regulates gene expression, using the budding yeast S. cerevisiae as the model organism.

RESULTS

Using ribosomal profiling and RNA-seq, we observed a broad spectrum of gene expression changes in response to MetR and identified hundreds of genes whose transcript level and/or translational efficiency changed significantly. These genes show clear functional themes, suggesting that cell slows down its growth and cell cycle progression and increases its stress resistance and maintenance in response to MetR. Interestingly, under MetR cell also decreases glycolysis and increases respiration, and increased respiration was linked to lifespan extension caused by caloric restriction. Analysis of genes whose translational efficiency changed significantly under MetR revealed different modes of translational regulation: 1) Ribosome loading patterns in the 5'UTR and coding regions of genes with increased translational efficiency suggested mechanisms both similar and different from that for the translational regulation of Gcn4 under general amino acid starvation condition; 2) Genes with decreased translational efficiency showed strong enrichment of lysine, glutamine, and glutamate codons, supporting the model that methionine can regulate translation by controlling tRNA thiolation.

CONCLUSIONS

MetR induced a broad spectrum of gene expression changes at both the transcriptional and translational levels, with clear functional themes indicative of the physiological state of the cell under MetR. Different modes of translational regulation were induced by MetR, including the regulation of the ribosome loading at 5'UTR and regulation by tRNA thiolation. Since MetR extends the lifespan of many species, the list of genes we identified in this study can be good candidates for studying the mechanisms of lifespan extension.

A thousand empirical adaptive landscapes and their navigability

The adaptive landscape is an iconic metaphor that pervades evolutionary biology. It was mostly applied in theoretical models until recent years, when empirical data began to allow partial landscape reconstructions. Here, we exhaustively analyse 1,137 complete landscapes from 129 eukaryotic species, each describing the binding affinity of a transcription factor to all possible short DNA sequences. We find that the navigability of these landscapes through single mutations is intermediate to that of additive and shuffled null models, suggesting that binding affinity-and thereby gene expression-is readily fine-tuned via mutations in transcription factor binding sites. The landscapes have few peaks that vary in their accessibility and in the number of sequences they contain. Binding sites in the mouse genome are enriched in sequences found in the peaks of especially navigable landscapes and the genetic diversity of binding sites in yeast increases with the number of sequences in a peak. Our findings suggest that landscape navigability may have contributed to the enormous success of transcriptional regulation as a source of evolutionary adaptations and innovations.

Learning the Structural Vocabulary of a Network

Networks have become instrumental in deciphering how information is processed and transferred within systems in almost every scientific field today. Nearly all network analyses, however, have relied on humans to devise structural features of networks believed to be most discriminative for an application. We present a framework for comparing and classifying networks without human-crafted features using deep learning. After training, autoencoders contain hidden units that encode a robust structural vocabulary for succinctly describing graphs. We use this feature vocabulary to tackle several network mining problems and find improved predictive performance versus many popular features used today. These problems include uncovering growth mechanisms driving the evolution of networks, predicting protein network fragility, and identifying environmental niches for metabolic networks. Deep learning offers a principled approach for mining complex networks and tackling graph-theoretic problems.

Molecular mechanisms that distinguish TFIID housekeeping from regulatable SAGA promoters

An important distinction is frequently made between constitutively expressed housekeeping genes versus regulated genes. Although generally characterized by different DNA elements, chromatin architecture and cofactors, it is not known to what degree promoter classes strictly follow regulatability rules and which molecular mechanisms dictate such differences. We show that SAGA‐dominated/TATA‐box promoters are more responsive to changes in the amount of activator, even compared to TFIID/TATA‐like promoters that depend on the same activator Hsf1. Regulatability is therefore an inherent property of promoter class. Further analyses show that SAGA/TATA‐box promoters are more dynamic because TATA‐binding protein recruitment through SAGA is susceptible to removal by Mot1. In addition, the nucleosome configuration upon activator depletion shifts on SAGA/TATA‐box promoters and seems less amenable to preinitiation complex formation. The results explain the fundamental difference between housekeeping and regulatable genes, revealing an additional facet of combinatorial control: an activator can elicit a different response dependent on core promoter class.

Evolution of transcriptional networks in yeast: alternative teams of transcriptional factors for different species

Background

The diversity in eukaryotic life reflects a diversity in regulatory pathways. Nocedal and Johnson argue that the rewiring of gene regulatory networks is a major force for the diversity of life, that changes in regulation can create new species.

Results

We have created a method (based on our new “ping-pong algorithm) for detecting more complicated rewirings, where several transcription factors can substitute for one or more transcription factors in the regulation of a family of co-regulated genes. An example is illustrative. A rewiring has been reported by Hogues et al. that RAP1 in Saccharomyces cerevisiae substitutes for TBF1/CBF1 in Candida albicans for ribosomal RP genes. There one transcription factor substitutes for another on some collection of genes. Such a substitution is referred to as a “rewiring”. We agree with this finding of rewiring as far as it goes but the situation is more complicated. Many transcription factors can regulate a gene and our algorithm finds that in this example a “team” (or collection) of three transcription factors including RAP1 substitutes for TBF1 for 19 genes. The switch occurs for a branch of the phylogenetic tree containing 10 species (including Saccharomyces cerevisiae), while the remaining 13 species (Candida albicans) are regulated by TBF1.

Conclusions

To gain insight into more general evolutionary mechanisms, we have created a mathematical algorithm that finds such general switching events and we prove that it converges. Of course any such computational discovery should be validated in the biological tests. For each branch of the phylogenetic tree and each gene module, our algorithm finds a sub-group of co-regulated genes and a team of transcription factors that substitutes for another team of transcription factors. In most cases the signal will be small but in some cases we find a strong signal of switching. We report our findings for 23 Ascomycota fungi species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3102-7) contains supplementary material, which is available to authorized users.

Transcription Factor-DNA Binding Motifs in Saccharomyces cerevisiae: Tools and Resources

The DNA binding specificity of transcription factors (TFs) is typically represented in the form of a position weight matrix (PWM), also known as a DNA motif. A PWM is a matrix that specifies, for each position in the DNA binding site of a TF, the "weight" or contribution of each possible nucleotide. DNA motifs can be derived from various types of TF-DNA binding data, from small collections of known TF binding sites to large data sets generated using high-throughput technologies. One drawback of this simple model of DNA binding specificity is that it makes the implicit assumption that individual base pairs within a TF binding site contribute independently to the TF-DNA binding affinity. Although this assumption does not always hold, PWM models have been shown to provide reasonable approximations to the DNA binding specificity, and they are still widely used in practice. DNA motifs are currently available for more than 150 Saccharomyces cerevisiae TFs. Here, we briefly describe how these models are built, we provide information on databases containing DNA motifs for S. cerevisiae TFs, and we introduce guidelines on how to interpret the motifs and use them in practice to generate hypotheses about transcriptional regulatory regions.

Mediator binding to UASs is broadly uncoupled from transcription and cooperative with TFIID recruitment to promoters

Mediator is a conserved, essential transcriptional coactivator complex, but its in vivo functions have remained unclear due to conflicting data regarding its genome-wide binding pattern obtained by genome-wide ChIP Here, we used ChEC-seq, a method orthogonal to ChIP, to generate a high-resolution map of Mediator binding to the yeast genome. We find that Mediator associates with upstream activating sequences (UASs) rather than the core promoter or gene body under all conditions tested. Mediator occupancy is surprisingly correlated with transcription levels at only a small fraction of genes. Using the same approach to map TFIID, we find that TFIID is associated with both TFIID- and SAGA-dependent genes and that TFIID and Mediator occupancy is cooperative. Our results clarify Mediator recruitment and binding to the genome, showing that Mediator binding to UASs is widespread, partially uncoupled from transcription, and mediated in part by TFIID.

Identification of Position-Specific Correlations between DNA-Binding Domains and Their Binding Sites. Application to the MerR Family of Transcription Factors

The large and increasing volume of genomic data analyzed by comparative methods provides information about transcription factors and their binding sites that, in turn, enables statistical analysis of correlations between factors and sites, uncovering mechanisms and evolution of specific protein-DNA recognition. Here we present an online tool, Prot-DNA-Korr, designed to identify and analyze crucial protein-DNA pairs of positions in a family of transcription factors. Correlations are identified by analysis of mutual information between columns of protein and DNA alignments. The algorithm reduces the effects of common phylogenetic history and of abundance of closely related proteins and binding sites. We apply it to five closely related subfamilies of the MerR family of bacterial transcription factors that regulate heavy metal resistance systems. We validate the approach using known 3D structures of MerR-family proteins in complexes with their cognate DNA binding sites and demonstrate that a significant fraction of correlated positions indeed form specific side-chain-to-base contacts. The joint distribution of amino acids and nucleotides hence may be used to predict changes of specificity for point mutations in transcription factors.

Engineering of synthetic, stress-responsive yeast promoters

Advances in synthetic biology and our understanding of the rules of promoter architecture have led to the development of diverse synthetic constitutive and inducible promoters in eukaryotes and prokaryotes. However, the design of promoters inducible by specific endogenous or environmental conditions is still rarely undertaken. In this study, we engineered and characterized a set of strong, synthetic promoters for budding yeast Saccharomyces cerevisiae that are inducible under acidic conditions (pH ≤ 3). Using available expression and transcription factor binding data, literature on transcriptional regulation, and known rules of promoter architecture we improved the low-pH performance of the YGP1 promoter by modifying transcription factor binding sites in its upstream activation sequence. The engineering strategy outlined for the YGP1 promoter was subsequently applied to create a response to low pH in the unrelated CCW14 promoter. We applied our best promoter variants to low-pH fermentations, enabling ten-fold increased production of lactic acid compared to titres obtained with the commonly used, native TEF1 promoter. Our findings outline and validate a general strategy to iteratively design and engineer synthetic yeast promoters inducible to environmental conditions or stresses of interest.

Comparative analysis of linker histone H1, MeCP2, and HMGD1 on nucleosome stability and target site accessibility

Chromatin architectural proteins (CAPs) bind the entry/exit DNA of nucleosomes and linker DNA to form higher order chromatin structures with distinct transcriptional outcomes. How CAPs mediate nucleosome dynamics is not well understood. We hypothesize that CAPs regulate DNA target site accessibility through alteration of the rate of spontaneous dissociation of DNA from nucleosomes. We investigated the effects of histone H1, high mobility group D1 (HMGD1), and methyl CpG binding protein 2 (MeCP2), on the biophysical properties of nucleosomes and chromatin. We show that MeCP2, like the repressive histone H1, traps the nucleosome in a more compact mononucleosome structure. Furthermore, histone H1 and MeCP2 hinder model transcription factor Gal4 from binding to its cognate DNA site within the nucleosomal DNA. These results demonstrate that MeCP2 behaves like a repressor even in the absence of methylation. Additionally, MeCP2 behaves similarly to histone H1 and HMGD1 in creating a higher-order chromatin structure, which is susceptible to chromatin remodeling by ISWI. Overall, we show that CAP binding results in unique changes to nucleosome structure and dynamics.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

Fluorescence microscopic analysis of antifungal effects of cold atmospheric pressure plasma in Saccharomyces cerevisiae

Cold atmospheric pressure plasma (CAP) has potential to be utilized as an alternative method for sterilization in food industries without thermal damage or toxic residues. In contrast to the bactericidal effects of CAP, information regarding the efficacy of CAP against eukaryotic microorganisms is very limited. Therefore, herein we investigated the effects of CAP on the budding yeast Saccharomyces cerevisiae, with a focus on the cellular response to CAP. The CAP treatment caused oxidative stress responses including the nuclear accumulation of the oxidative stress responsive transcription factor Yap1, mitochondrial fragmentation, and enhanced intracellular oxidation. Yeast cells also induced the expression of heat shock protein (HSP) genes and formation of Hsp104 aggregates when treated with CAP, suggesting that CAP denatures proteins. As phenomena unique to eukaryotic cells, the formation of cytoplasmic mRNP granules such as processing bodies and stress granules and changes in the intracellular localization of Ire1 were caused by the treatment with CAP, indicating that translational repression and endoplasmic reticulum (ER) stress were induced by the CAP treatment. These results suggest that the fungicidal effects of CAP are attributed to the multiple severe stresses.

CLIMP: Clustering Motifs via Maximal Cliques with Parallel Computing Design

A set of conserved binding sites recognized by a transcription factor is called a motif, which can be found by many applications of comparative genomics for identifying over-represented segments. Moreover, when numerous putative motifs are predicted from a collection of genome-wide data, their similarity data can be represented as a large graph, where these motifs are connected to one another. However, an efficient clustering algorithm is desired for clustering the motifs that belong to the same groups and separating the motifs that belong to different groups, or even deleting an amount of spurious ones. In this work, a new motif clustering algorithm, CLIMP, is proposed by using maximal cliques and sped up by parallelizing its program. When a synthetic motif dataset from the database JASPAR, a set of putative motifs from a phylogenetic foot-printing dataset, and a set of putative motifs from a ChIP dataset are used to compare the performances of CLIMP and two other high-performance algorithms, the results demonstrate that CLIMP mostly outperforms the two algorithms on the three datasets for motif clustering, so that it can be a useful complement of the clustering procedures in some genome-wide motif prediction pipelines. CLIMP is available at http://sqzhang.cn/climp.html.

Methods for causal inference from gene perturbation experiments and validation

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.

DNA signals at isoform promoters

Transcriptional heterogeneity is extensive in the genome, and most genes express variable transcript isoforms. However, whether variable transcript isoforms of one gene are regulated by common promoter elements remain to be elucidated. Here, we investigated whether isoform promoters of one gene have separated DNA signals for transcription and translation initiation. We found that TATA box and nucleosome-disfavored DNA sequences are prevalent in distinct transcript isoform promoters of one gene. These DNA signals are conserved among species. Transcript isoform has a RNA-determined unstructured region around its start site. We found that these DNA/RNA features facilitate isoform transcription and translation. These results suggest a DNA-encoded mechanism by which transcript isoform is generated.

The Role of Transcription Factors at Antisense-Expressing Gene Pairs in Yeast

Genes encoded close to one another on the chromosome are often coexpressed, by a mechanism and regulatory logic that remain poorly understood. We surveyed the yeast genome for tandem gene pairs oriented tail-to-head at which expression antisense to the upstream gene was conserved across species. The intergenic region at most such tandem pairs is a bidirectional promoter, shared by the downstream gene mRNA and the upstream antisense transcript. Genomic analyses of these intergenic loci revealed distinctive patterns of transcription factor regulation. Mutation of a given transcription factor verified its role as a regulator in trans of tandem gene pair loci, including the proximally initiating upstream antisense transcript and downstream mRNA and the distally initiating upstream mRNA. To investigate cis-regulatory activity at such a locus, we focused on the stress-induced NAD(P)H dehydratase YKL151C and its downstream neighbor, the metabolic enzyme GPM1. Previous work has implicated the region between these genes in regulation of GPM1 expression; our mutation experiments established its function in rich medium as a repressor in cis of the distally initiating YKL151C sense RNA, and an activator of the proximally initiating YKL151C antisense RNA. Wild-type expression of all three transcripts required the transcription factor Gcr2. Thus, at this locus, the intergenic region serves as a focal point of regulatory input, driving antisense expression and mediating the coordinated regulation of YKL151C and GPM1. Together, our findings implicate transcription factors in the joint control of neighboring genes specialized to opposing conditions and the antisense transcripts expressed between them.

PIF1-Interacting Transcription Factors and Their Binding Sequence Elements Determine the in Vivo Targeting Sites of PIF1

The bHLH transcription factor PHYTOCHROME INTERACTING FACTOR1 (PIF1) binds G-box elements in vitro and inhibits light-dependent germination in Arabidopsis thaliana A previous genome-wide analysis of PIF1 targeting indicated that PIF1 binds 748 sites in imbibed seeds, only 59% of which possess G-box elements. This suggests the G-box is not the sole determinant of PIF1 targeting. The targeting of PIF1 to specific sites could be stabilized by PIF1-interacting transcription factors (PTFs) that bind other nearby sequence elements. Here, we report PIF1 targeting sites are enriched with not only G-boxes but also with other hexameric sequence elements we named G-box coupling elements (GCEs). One of these GCEs possesses an ACGT core and serves as a binding site for group A bZIP transcription factors, including ABSCISIC ACID INSENSITIVE5 (ABI5), which inhibits seed germination in abscisic acid signaling. PIF1 interacts with ABI5 and other group A bZIP transcription factors and together they target a subset of PIF1 binding sites in vivo. In vitro single-molecule fluorescence imaging confirms that ABI5 facilitates PIF1 binding to DNA fragments possessing multiple G-boxes or the GCE alone. Thus, we show in vivo PIF1 targeting to specific binding sites is determined by its interaction with PTFs and their binding to GCEs.

Chromatin dynamics during DNA replication

Chromatin is composed of DNA and histones, which provide a unified platform for regulating DNA-related processes, mostly through their post-translational modification. During DNA replication, histone arrangement is perturbed, first to allow progression of DNA polymerase and then during repackaging of the replicated DNA. To study how DNA replication influences the pattern of histone modification, we followed the cell-cycle dynamics of 10 histone marks in budding yeast. We find that histones deposited on newly replicated DNA are modified at different rates: While some marks appear immediately upon replication (e.g., H4K16ac, H3K4me1), others increase with transcription-dependent delays (e.g., H3K4me3, H3K36me3). Notably, H3K9ac was deposited as a wave preceding the replication fork by ∼5–6 kb. This replication-guided H3K9ac was fully dependent on the acetyltransferase Rtt109, while expression-guided H3K9ac was deposited by Gcn5. Further, topoisomerase depletion intensified H3K9ac in front of the replication fork and in sites where RNA polymerase II was trapped, suggesting supercoiling stresses trigger H3K9 acetylation. Our results assign complementary roles for DNA replication and gene expression in defining the pattern of histone modification.

Predicting link directionality in gene regulation from gene expression profiles using volatility-constrained correlation

To uncover potential disease molecular pathways and signaling networks, we do not only need undirected maps but also we need to infer the directionality of functional or physical interactions between cellular components. A wide range of methods for identifying functional interactions between genes relies on correlations between experimental gene expression measurements to some extent. However, the standard Pearson or Spearman correlation-based approaches can only determine undirected correlations between cellular components. Here, we apply a volatility-constrained correlation method for gene expression profiles that offers a new metric to capture directionality of interactions between genes. To evaluate the predictions we used four datasets distributed by the DREAM5 network inference challenge including an in silico-constructed network and three organisms such as S. aureus, E. coli and S. cerevisiae. The predictions performed by our proposed method were compared to a gold standard of experimentally verified directionality of genetic regulatory links. Our findings show that our method successfully predicts the genetic interaction directionality with a success rate higher than 0.5 with high statistical significance.

A Modeling Framework for Generation of Positional and Temporal Simulations of Transcriptional Regulation

We present a modeling framework aimed at capturing both the positional and temporal behavior of transcriptional regulatory proteins in eukaryotic cells. There is growing evidence that transcriptional regulation is the complex behavior that emerges not solely from the individual components, but rather from their collective behavior, including competition and cooperation. Our framework describes individual regulatory components using generic action oriented descriptions of their biochemical interactions with a DNA sequence. All the possible actions are based on the current state of factors bound to the DNA. We developed a rule builder to automatically generate the complete set of biochemical interaction rules for any given DNA sequence. Off-the-shelf stochastic simulation engines can model the behavior of a system of rules and the resulting changes in the configuration of bound factors can be visualized. We compared our model to experimental data at well-studied loci in yeast, confirming that our model captures both the positional and temporal behavior of transcriptional regulation.

An Evolving Genetic Architecture Interacts with Hill-Robertson Interference to Determine the Benefit of Sex

Sex is ubiquitous in the natural world, but the nature of its benefits remains controversial. Previous studies have suggested that a major advantage of sex is its ability to eliminate interference between selection on linked mutations, a phenomenon known as Hill-Robertson interference. However, those studies may have missed both important advantages and important disadvantages of sexual reproduction because they did not allow the distributions of mutational effects and interactions (i.e., the genetic architecture) to evolve. Here we investigate how Hill-Robertson interference interacts with an evolving genetic architecture to affect the evolutionary origin and maintenance of sex by simulating evolution in populations of artificial gene networks. We observed a long-term advantage of sex-equilibrium mean fitness of sexual populations exceeded that of asexual populations-that did not depend on population size. We also observed a short-term advantage of sex-sexual modifier mutations readily invaded asexual populations-that increased with population size, as was observed in previous studies. We show that the long- and short-term advantages of sex were both determined by differences between sexual and asexual populations in the evolutionary dynamics of two properties of the genetic architecture: the deleterious mutation rate ([Formula: see text]) and recombination load ([Formula: see text]). These differences resulted from a combination of selection to minimize [Formula: see text] which is experienced only by sexuals, and Hill-Robertson interference experienced primarily by asexuals. In contrast to the previous studies, in which Hill-Robertson interference had only a direct impact on the fitness advantages of sex, the impact of Hill-Robertson interference in our simulations was mediated additionally by an indirect impact on the efficiency with which selection acted to reduce [Formula: see text].