Identifying transcription factor functions and targets by phenotypic activation

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.

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.

SIR telomere silencing depends on nuclear envelope lipids and modulates sensitivity to a lysolipid

The nuclear envelope (NE) is important in maintaining genome organization. The role of lipids in communication between the NE and telomere regulation was investigated, including how changes in lipid composition impact gene expression and overall nuclear architecture. Yeast was treated with the non-metabolizable lysophosphatidylcholine analog edelfosine, known to accumulate at the perinuclear ER. Edelfosine induced NE deformation and disrupted telomere clustering but not anchoring. Additionally, the association of Sir4 at telomeres decreased. RNA-seq analysis showed altered expression of Sir-dependent genes located at sub-telomeric (0-10 kb) regions, consistent with Sir4 dispersion. Transcriptomic analysis revealed that two lipid metabolic circuits were activated in response to edelfosine, one mediated by the membrane sensing transcription factors, Spt23/Mga2, and the other by a transcriptional repressor, Opi1. Activation of these transcriptional programs resulted in higher levels of unsaturated fatty acids and the formation of nuclear lipid droplets. Interestingly, cells lacking Sir proteins displayed resistance to unsaturated-fatty acids and edelfosine, and this phenotype was connected to Rap1.

Transcriptomic changes in single yeast cells under various stress conditions

BACKGROUND

The stress response of Saccharomyces cerevisiae has been extensively studied in the past decade. However, with the advent of recent technology in single-cell transcriptome profiling, there is a new opportunity to expand and further understanding of the yeast stress response with greater resolution on a system level. To understand transcriptomic changes in baker's yeast S. cerevisiae cells under stress conditions, we sequenced 117 yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method.

RESULTS

We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes.

CONCLUSION

Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods.

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

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

Systematic 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.

Transcriptional neighborhoods regulate transcript isoform lengths and expression levels

Sequence features of genes and their flanking regulatory regions are determinants of RNA transcript isoform expression and have been used as context-independent plug-and-play modules in synthetic biology. However, genetic context-including the adjacent transcriptional environment-also influences transcript isoform expression levels and boundaries. We used synthetic yeast strains with stochastically repositioned genes to systematically disentangle the effects of sequence and context. Profiling 120 million full-length transcript molecules across 612 genomic perturbations, we observed sequence-independent alterations to gene expression levels and transcript isoform boundaries that were influenced by neighboring transcription. We identified features of transcriptional context that could predict these alterations and used these features to engineer a synthetic circuit where transcript length was controlled by neighboring transcription. This demonstrates how positional context can be leveraged in synthetic genome engineering.

WRKY transcription factors and ethylene signaling modify root growth during the shade-avoidance response

Shade-intolerant plants rapidly elongate their stems, branches, and leaf stalks to compete with neighboring vegetation, maximizing sunlight capture for photosynthesis. This rapid growth adaptation, known as the shade-avoidance response (SAR), comes at a cost: reduced biomass, crop yield, and root growth. Significant progress has been made on the mechanistic understanding of hypocotyl elongation during SAR; however, the molecular interpretation of root growth repression is not well understood. Here, we explore the mechanisms by which SAR induced by low red:far-red light restricts primary and lateral root (LR) growth. By analyzing the whole-genome transcriptome, we identified a core set of shade-induced genes in roots of Arabidopsis (Arabidopsis thaliana) and tomato (Solanum lycopersicum) seedlings grown in the shade. Abiotic and biotic stressors also induce many of these shade-induced genes and are predominantly regulated by WRKY transcription factors. Correspondingly, a majority of WRKY genes were among the shade-induced genes. Functional analysis using transgenics of these shade-induced WRKYs revealed that their role is essentially to restrict primary root and LR growth in the shade; captivatingly, they did not affect hypocotyl elongation. Similarly, we also found that ethylene hormone signaling is necessary for limiting root growth in the shade. We propose that during SAR, shade-induced WRKY26, 45, and 75, and ethylene reprogram gene expression in the root to restrict its growth and development.

Zap1 is required for Candida glabrata response to fluconazole

The increasing prevalence of fluconazole-resistant clinical isolates of Candida spp. strongly hinders the widespread use of the drug. To tackle this problem, great efforts have been made to fully understand the fungal response to fluconazole. In this work, we show that the role of Zap1 in Candida glabrata goes beyond regulating yeast adaptation to zinc deficiency. In line with our previous observation that deletion of ZAP1 makes yeast cells more sensitive to fluconazole, we found that the mutant CgΔzap1 accumulates higher levels of the drug, which correlates well with its lower levels of ergosterol. Surprisingly, Zap1 is a negative regulator of the drug efflux transporter gene CDR1 and of its regulator, PDR1. The apparent paradox of drug accumulation in cells where genes encoding transporters relevant for drug extrusion are being overexpressed led us to postulate that their activity could be impaired. In agreement, Zap1-depleted cells present, in addition to decreased ergosterol levels, an altered composition of membrane phospholipids, which together should impact membrane function and impair the detoxification of fluconazole. Overall, our study brings to light Zap1 as an important hub in Candida glabrata response to fluconazole.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

Functional analysis of a Phytophthora host-translocated effector using the yeast model system

Background

Phytophthora plant pathogens secrete effector proteins that are translocated into host plant cells during infection and collectively contribute to pathogenicity. A subset of these host-translocated effectors can be identified by the amino acid motif RXLR (arginine, any amino acid, leucine, arginine). Bioinformatics analysis has identified hundreds of putative RXLR effector genes in Phytophthora genomes, but the specific molecular function of most remains unknown.

Methods

Here we describe initial studies to investigate the use of Saccharomyces cerevisiae as a eukaryotic model to explore the function of Phytophthora RXLR effector proteins.

Results and Conclusions

Expression of individual RXLR effectors in yeast inhibited growth, consistent with perturbation of a highly conserved cellular process. Transcriptome analysis of yeast cells expressing the poorly characterized P. sojae RXLR effector Avh110 identified nearly a dozen yeast genes whose expression levels were altered greater than two-fold compared to control cells. All five of the most down-regulated yeast genes are normally induced under low phosphate conditions via the PHO4 transcription factor, indicating that PsAvh110 perturbs the yeast regulatory network essential for phosphate homeostasis and suggesting likely PsAvh110 targets during P. sojae infection of its soybean host.

Regulation of sphingolipid synthesis by the G1/S transcription factor Swi4

SBF (Swi4/Swi6 Binding Factor) complex is a crucial regulator of G1/S transition in Saccharomyces cerevisiae. Here, we show that SBF complex is required for myriocin resistance, an inhibitor of sphingolipid synthesis. This phenotype was not shared with MBF complex mutants nor with deletion of the Swi4p downstream targets, CLN1/CLN2. Based on data mining results, we selected putative Swi4p targets related to sphingolipid metabolism and studied their gene transcription as well as metabolite levels during progression of the cell cycle. Genes which encode key enzymes for the synthesis of long chain bases (LCBs) and ceramides were periodically transcribed during the mitotic cell cycle, having a peak at G1/S, and required SWI4 for full transcription at this stage. In addition, HPLC-MS/MS data indicated that swi4Δ cells have decreased levels of sphingolipids during progression of the cell cycle, particularly, dihydrosphingosine (DHS), C24-phytoceramides and C24-inositolphosphoryl ceramide (IPC) while it had increased levels of mannosylinositol phosphorylceramide (MIPC). Furthermore, we demonstrated that both inhibition of de novo sphingolipid synthesis by myriocin or SWI4 deletion caused partial arrest at the G2/M phase. Importantly, our lipidomic data demonstrated that the sphingolipid profile of WT cells treated with myriocin resembled that of swi4Δ cells, with lower levels of DHS, IPC and higher levels of MIPC. Taken together, these results show that SBF complex plays an essential role in the regulation of sphingolipid homeostasis, which reflects in the correct progression through the G2/M phase of the cell cycle.

A genome-scale yeast library with inducible expression of individual genes

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

Molecular biology interventions for activity improvement and production of industrial enzymes

Metagenomics and directed evolution technology have brought a revolution in search of novel enzymes from extreme environment and improvement of existing enzymes and tuning them towards certain desired properties. Using advanced tools of molecular biology i.e. next generation sequencing, site directed mutagenesis, fusion protein, surface display, etc. now researchers can engineer enzymes for improved activity, stability, and substrate specificity to meet the industrial demand. Although many enzymatic processes have been developed up to industrial scale, still there is a need to overcome limitations of maintaining activity during the catalytic process. In this article recent developments in enzymes industrial applications and advancements in metabolic engineering approaches to improve enzymes efficacy and production are reviewed.

Learning causal networks using inducible transcription factors and transcriptome-wide time series

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

A Candida parapsilosis Overexpression Collection Reveals Genes Required for Pathogenesis

Relative to the vast data regarding the virulence mechanisms of Candida albicans, there is limited knowledge on the emerging opportunistic human pathogen Candida parapsilosis. The aim of this study was to generate and characterize an overexpression mutant collection to identify and explore virulence factors in C. parapsilosis. With the obtained mutants, we investigated stress tolerance, morphology switch, biofilm formation, phagocytosis, and in vivo virulence in Galleria mellonella larvae and mouse models. In order to evaluate the results, we compared the data from the C. parapsilosis overexpression collection analysis to the results derived from previous deletion mutant library characterizations. Of the 37 overexpression C. parapsilosis mutants, we identified eight with altered phenotypes compared to the controls. This work is the first report to identify CPAR2_107240, CPAR2_108840, CPAR2_302400, CPAR2_406400, and CPAR2_602820 as contributors to C. parapsilosis virulence by regulating functions associated with host-pathogen interactions and biofilm formation. Our findings also confirmed the role of CPAR2_109520, CPAR2_200040, and CPAR2_500180 in pathogenesis. This study was the first attempt to use an overexpression strategy to systematically assess gene function in C. parapsilosis, and our results demonstrate that this approach is effective for such investigations.

The Identification of Genetic Determinants of Methanol Tolerance in Yeast Suggests Differences in Methanol and Ethanol Toxicity Mechanisms and Candidates for Improved Methanol Tolerance Engineering

Methanol is a promising feedstock for metabolically competent yeast strains-based biorefineries. However, methanol toxicity can limit the productivity of these bioprocesses. Therefore, the identification of genes whose expression is required for maximum methanol tolerance is important for mechanistic insights and rational genomic manipulation to obtain more robust methylotrophic yeast strains. The present chemogenomic analysis was performed with this objective based on the screening of the Euroscarf Saccharomyces cerevisiae haploid deletion mutant collection to search for susceptibility phenotypes in YPD medium supplemented with 8% (v/v) methanol, at 35 °C, compared with an equivalent ethanol concentration (5.5% (v/v)). Around 400 methanol tolerance determinants were identified, 81 showing a marked phenotype. The clustering of the identified tolerance genes indicates an enrichment of functional categories in the methanol dataset not enriched in the ethanol dataset, such as chromatin remodeling, DNA repair and fatty acid biosynthesis. Several genes involved in DNA repair (eight RAD genes), identified as specific for methanol toxicity, were previously reported as tolerance determinants for formaldehyde, a methanol detoxification pathway intermediate. This study provides new valuable information on genes and potential regulatory networks involved in overcoming methanol toxicity. This knowledge is an important starting point for the improvement of methanol tolerance in yeasts capable of catabolizing and copying with methanol concentrations present in promising bioeconomy feedstocks, including industrial residues.

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

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

Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae

The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks. It appears that in-degree distribution, as well as motif enrichment evolve as the yeast transcriptional network is being completed. Overall, our analyses challenged some results previously published and confirmed others. These analyses further pointed towards the paucity of experimental evidence to support theories and, more generally, towards the partial knowledge of the complete network.

Assessment of the ptxD gene as a growth and selective marker in Trichoderma atroviride using Pccg6, a novel constitutive promoter

Background

Trichoderma species are among the most effective cell factories to produce recombinant proteins, whose productivity relies on the molecular toolkit and promoters available for the expression of the target protein. Although inducible promoter systems have been developed for producing recombinant proteins in Trichoderma, constitutive promoters are often a desirable alternative. Constitutive promoters are simple to use, do not require external stimuli or chemical inducers to be activated, and lead to purer enzyme preparations. Moreover, most of the promoters for homologous and heterologous expression reported in Trichoderma have been commonly evaluated by directly assessing production of industrial enzymes, requiring optimization of laborious protocols.

Results

Here we report the identification of Pccg6, a novel Trichoderma atroviride constitutive promoter, that has similar transcriptional strength as that of the commonly used pki1 promoter. Pccg6 displayed conserved arrangements of transcription factor binding sites between promoter sequences of Trichoderma ccg6 orthologues genes, potentially involved in their regulatory properties. The predicted ccg6-encoded protein potentially belongs to the SPE1/SPI1 protein family and shares high identity with CCG6 orthologue sequences from other fungal species including Trichoderma reesei, Trichoderma virens, Trichoderma asperellum, and to a lesser extent to that of Neurospora crassa. We also report the use of the Pccg6 promoter to drive the expression of PTXD, a phosphite oxidoreductase of bacterial origin, which allowed T. atroviride to utilize phosphite as a sole source of phosphorus. We propose ptxD as a growth reporter gene that allows real-time comparison of the functionality of different promoters by monitoring growth of Trichoderma transgenic lines and enzymatic activity of PTXD. Finally, we show that constitutive expression of ptxD provided T. atroviride a competitive advantage to outgrow bacterial contaminants when supplied with phosphite as a sole source of phosphorus.

Conclusions

A new constitutive promoter, ccg6, for expression of homologous and heterologous proteins has been identified and tested in T. atroviride to express PTXD, which resulted in an effective and visible phenotype to evaluate transcriptional activity of sequence promoters. Use of PTXD as a growth marker holds great potential for assessing activity of other promoters and for biotechnological applications as a contamination control system.

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

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

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

Transcription Factors Indirectly Regulate Genes through Nuclear Colocalization

Various types of data, including genomic sequences, transcription factor (TF) knockout data, TF-DNA interaction and expression profiles, have been used to decipher TF regulatory mechanisms. However, most of the genes affected by knockout of a particular TF are not bound by that factor. Here, I showed that this interesting result can be partially explained by considering the nuclear positioning of TF knockout affected genes and TF bound genes. I found that a statistically significant number of TF knockout affected genes show nuclear colocalization with genes bound by the corresponding TF. Although these TF knockout affected genes are not directly bound by the corresponding TF; the TF tend to be in the same cellular component with the TFs that directly bind these genes. TF knockout affected genes show co-expression and tend to be involved in the same biological process with the spatially adjacent genes that are bound by the corresponding TF. These results demonstrate that TFs can regulate genes through nuclear colocalization without direct DNA binding, complementing the conventional view that TFs directly bind DNA to regulate genes. My findings will have implications in understanding TF regulatory mechanisms.

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

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

A resource of variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure-based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

Systematic gene overexpression in Candida albicans identifies a regulator of early adaptation to the mammalian gut

Candida albicans is part of the human gastrointestinal (GI) microbiota. To better understand how C. albicans efficiently establishes GI colonisation, we competitively challenged growth of 572 signature-tagged strains (~10% genome coverage), each conditionally overexpressing a single gene, in the murine gut. We identified CRZ2, a transcription factor whose overexpression and deletion respectively increased and decreased early GI colonisation. Using clues from genome-wide expression and gene-set enrichment analyses, we found that the optimal activity of Crz2p occurs under hypoxia at 37°C, as evidenced by both phenotypic and transcriptomic analyses following CRZ2 genetic perturbation. Consistent with early colonisation of the GI tract, we show that CRZ2 overexpression confers resistance to acidic pH and bile salts, suggesting an adaptation to the upper sections of the gut. Genome-wide location analyses revealed that Crz2p directly modulates the expression of many mannosyltransferase- and cell-wall protein-encoding genes, suggesting a link with cell-wall function. We show that CRZ2 overexpression alters cell-wall phosphomannan abundance and increases sensitivity to tunicamycin, suggesting a role in protein glycosylation. Our study reflects the powerful use of gene overexpression as a complementary approach to gene deletion to identify relevant biological pathways involved in C. albicans interaction with the host environment.

Modulating Transcriptional Regulation of Plant Biomass Degrading Enzyme Networks for Rational Design of Industrial Fungal Strains

Filamentous fungi are the most important microorganisms for the industrial production of plant polysaccharide degrading enzymes due to their unique ability to secrete these proteins efficiently. These carbohydrate active enzymes (CAZymes) are utilized industrially for the hydrolysis of plant biomass for the subsequent production of biofuels and high-value biochemicals. The expression of the genes encoding plant biomass degrading enzymes is tightly controlled. Naturally, large amounts of CAZymes are produced and secreted only in the presence of the plant polysaccharide they specifically act on. The signal to produce is conveyed via so-called inducer molecules which are di- or mono-saccharides (or derivatives thereof) released from the specific plant polysaccharides. The presence of the inducer results in the activation of a substrate-specific transcription factor (TF), which is required not only for the controlled expression of the genes encoding the CAZymes, but often also for the regulation of the expression of the genes encoding sugar transporters and catabolic pathway enzymes needed to utilize the released monosaccharide. Over the years, several substrate-specific TFs involved in the degradation of cellulose, hemicellulose, pectin, starch and inulin have been identified in several fungal species and systems biology approaches have made it possible to uncover the enzyme networks controlled by these TFs. The requirement for specific inducers for TF activation and subsequently the expression of particular enzyme networks determines the choice of feedstock to produce enzyme cocktails for industrial use. It also results in batch-to-batch variation in the composition and amounts of enzymes due to variations in sugar composition and polysaccharide decorations of the feedstock which hampers the use of cheap feedstocks for constant quality of enzyme cocktails. It is therefore of industrial interest to produce specific enzyme cocktails constitutively and independently of inducers. In this review, we focus on the methods to modulate TF activities for inducer-independent production of CAZymes and highlight various approaches that are used to construct strains displaying constitutive expression of plant biomass degrading enzyme networks. These approaches and combinations thereof are also used to construct strains displaying increased expression of CAZymes under inducing conditions, and make it possible to design strains in which different enzyme mixtures are simultaneously produced independently of the carbon source.

A prior-based integrative framework for functional transcriptional regulatory network inference

Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.

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

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

Gene dosage effects in yeast support broader roles for the LOG1, HAM1 and DUT1 genes in detoxification of nucleotide analogues

Purine and pyrimidine analogues have important uses in chemotherapies against cancer, and a better understanding of the mechanisms that cause resistance to these drugs is therefore of importance in cancer treatment. In the yeast Saccharomyces cerevisiae, overexpression of the HAM1 gene encoding inosine triphosphate pyrophosphatase confers resistance to both the purine analogue 6-N-hydroxylaminopurine (HAP) and the pyrimidine analogue 5-fluorouracil (5-FU) (Carlsson et al., 2013, PLoS One 8, e52094). To find out more about the mechanisms of resistance to nucleotide analogues, and possible interdependencies between purine and pyrimidine analogue resistance mechanisms, we screened a plasmid library in yeast for genes that confer HAP resistance when overexpressed. We cloned four such genes: ADE4, DUT1, APT2, and ATR1. We further looked for genetic interactions between these genes and genes previously found to confer resistance to 5-FU. We found that HMS1, LOG1 (YJL055W), HAM1, and ATR1 confer resistance to both 5-FU and HAP, whereas ADE4, DUT1 and APT2 are specific for HAP resistance, and CPA1 and CPA2 specific for 5-FU resistance. Possible mechanisms for 5-FU and HAP detoxification are discussed based on the observed genetic interactions. Based on the effect of LOG1 against both 5-FU and HAP toxicity, we propose that the original function of the LOG (LONELY GUY) family of proteins likely was to degrade non-canonical nucleotides, and that their role in cytokinin production is a later development in some organisms.

Inducer-independent production of pectinases in Aspergillus niger by overexpression of the D-galacturonic acid-responsive transcription factor gaaR

The transcription factor GaaR is needed for the expression of genes required for pectin degradation and transport and catabolism of the main degradation product, D-galacturonic acid (GA) in Aspergillus niger. In this study, we used the strong constitutive gpdA promoter of Aspergillus nidulans to overexpress gaaR in A. niger. Overexpression of gaaR resulted in an increased transcription of the genes encoding pectinases, (putative) GA transporters, and catabolic pathway enzymes even under non-inducing conditions, i.e., in the absence of GA. Exoproteome analysis of a strain overexpressing gaaR showed that this strain secretes highly elevated levels of pectinases when grown in fructose. The genes encoding exo-polygalacturonases were found to be subjected to CreA-mediated carbon catabolite repression, even in the presence of fructose. Deletion of creA in the strain overexpressing gaaR resulted in a further increase in pectinase production in fructose. We showed that GaaR localizes mainly in the nucleus regardless of the presence of an inducer, and that overexpression of gaaR leads to an increased concentration of GaaR in the nucleus.

Functional Analysis of Kinases and Transcription Factors in Saccharomyces cerevisiae Using an Integrated Overexpression Library

Kinases and transcription factors (TFs) are key modulators of important signaling pathways and their activities underlie the proper function of many basic cellular processes such as cell division, differentiation, and development. Changes in kinase and TF dosage are often associated with disease, yet a systematic assessment of the cellular phenotypes caused by the combined perturbation of kinases and TFs has not been undertaken. We used a reverse-genetics approach to study the phenotypic consequences of kinase and TF overexpression (OE) in the budding yeast, Saccharomyces cerevisiae. We constructed a collection of strains expressing stably integrated inducible alleles of kinases and TFs and used a variety of assays to characterize the phenotypes caused by TF and kinase OE. We used the Synthetic Genetic Array (SGA) method to examine dosage-dependent genetic interactions (GIs) between 239 gain-of-function (OE) alleles of TFs and six loss-of-function (LOF) and seven OE kinase alleles, the former identifying Synthetic Dosage Lethal (SDL) interactions and the latter testing a GI we call Double Dosage Lethality (DDL). We identified and confirmed 94 GIs between 65 OE alleles of TFs and 9 kinase alleles. Follow-up experiments validated regulatory relationships between genetically interacting pairs (Cdc28–Stb1 and Pho85–Pdr1), suggesting that GI studies involving OE alleles of regulatory proteins will be a rich source of new functional information.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

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

Inference of gene regulation functions from dynamic transcriptome data

To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a ‘gene regulation function’ (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

DOI:http://dx.doi.org/10.7554/eLife.12188.001

Living cells rely on networks of genes to control their behavior, including how they grow, develop and respond to stress. Genes encode instructions needed to make proteins and other molecules, and much of the control is exerted at the first stage of protein production, known as transcription. During this process, a gene is copied to make molecules known as transcripts that may later be used as templates to make proteins.

Many genes encode proteins that act to regulate transcription. Therefore, an individual gene may receive inputs from other genes, and these inputs affect how much transcript the gene produces, which can be considered as the gene’s output. While these inputs and outputs can often be wired together to form a network, it is less clear exactly how all the different inputs at a gene interact to determine its output. These interactions are known as “gene regulation functions”, and knowing them would be an important step towards understanding gene networks, which would help us to predict how cells will behave in different situations.

Gene regulation functions are difficult to measure directly, so researchers would like to find other ways to assess them indirectly. A recently developed experimental technique called “dynamic transcriptome analysis” seemed promising as it measures both the inputs and outputs of all genes in a cell over time. Hillenbrand et al. used this technique to infer gene regulation functions with one or two inputs in yeast cells.

Comparing these estimates with experimental data from previous studies showed that these inferred gene regulation functions could successfully predict the output of a gene based on its inputs. Hillenbrand et al. then used these estimates to search and model a well-known genetic network that is thought to be part of the molecular clockwork that controls the timing of events that cause a cell to divide.

Currently, the approach used by Hillenbrand et al. treats gene regulation functions like “black boxes”. This means that, while an output can be predicted if the inputs are known, it cannot reveal all of the detailed mechanisms behind it. Gaining insights into the inner workings of these black boxes will require taking more data into account, such as how abundant the proteins that regulate transcription are, where they are located within cells or whether they are active or not. Therefore, the next challenge is to incorporate these kinds of data to gain a fuller picture of how gene networks operate within cells.

DOI:http://dx.doi.org/10.7554/eLife.12188.002

Parallel reverse genetic screening in mutant human cells using transcriptomics

Reverse genetic screens have driven gene annotation and target discovery in model organisms. However, many disease‐relevant genotypes and phenotypes cannot be studied in lower organisms. It is therefore essential to overcome technical hurdles associated with large‐scale reverse genetics in human cells. Here, we establish a reverse genetic approach based on highly robust and sensitive multiplexed RNA sequencing of mutant human cells. We conduct 10 parallel screens using a collection of engineered haploid isogenic cell lines with knockouts covering tyrosine kinases and identify known and unexpected effects on signaling pathways. Our study provides proof of concept for a scalable approach to link genotype to phenotype in human cells, which has broad applications. In particular, it clears the way for systematic phenotyping of still poorly characterized human genes and for systematic study of uncharacterized genomic features associated with human disease.

Construction and evaluation of yeast expression networks by database-guided predictions

DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.

Conserved and Diverged Functions of the Calcineurin-Activated Prz1 Transcription Factor in Fission Yeast

Gene regulation in response to intracellular calcium is mediated by the calcineurin-activated transcription factor Prz1 in the fission yeast Schizosaccharomyces pombe Genome-wide studies of the Crz1 and CrzA fungal orthologs have uncovered numerous target genes involved in conserved and species-specific cellular processes. In contrast, very few target genes of Prz1 have been published. This article identifies an extensive list of genes using transcriptome and ChIP-chip analyses under inducing conditions of Prz1, including CaCl2 and tunicamycin treatment, as well as a ∆pmr1 genetic background. We identified 165 upregulated putative target genes of Prz1 in which the majority contained a calcium-dependent response element in their promoters, similar to that of the Saccharomyces cerevisiae ortholog Crz1 These genes were functionally enriched for Crz1-conserved processes such as cell-wall biosynthesis. Overexpression of prz1(+)increased resistance to the cell-wall degradation enzyme zymolyase, likely from upregulation of theO-mannosyltransferase encoding gene omh1(+) Loss of omh1(+)abrogates this phenotype. We uncovered a novel inhibitory role in flocculation for Prz1. Loss of prz1(+)resulted in constitutive flocculation and upregulation of genes encoding the flocculins Gsf2 and Pfl3, as well as the transcription factor Cbf12. The constitutive flocculation of the ∆prz1 strain was abrogated by the loss of gsf2(+) or cbf12(+) This study reveals that Prz1 functions as a positive and negative transcriptional regulator of genes involved in cell-wall biosynthesis and flocculation, respectively. Moreover, comparison of target genes between Crz1/CrzA and Prz1 indicate some conservation in DNA-binding specificity, but also substantial rewiring of the calcineurin-mediated transcriptional regulatory network.

A suite of Gateway® compatible ternary expression vectors for functional analysis in Zymoseptoria tritici

Gene overexpression is a widely used functional genomics approach in fungal biology. However, to date it has not been established in Zymoseptoria tritici which is an important pathogen of wheat (Triticum species). Here we report a suite of Gateway® recombination compatible ternary expression vectors for Agrobacterium tumefaciens mediated transformation of Z. tritici. The suite of 32 vectors is based on a combination of four resistance markers for positive selection against glufosinate ammonium, geneticin, hygromycin and sulfonylurea; three constitutive Z. tritici promoters (pZtATUB, pZtGAPDH and pZtTEF) and a nitrogen responsive promoter (pZtNIA1) for controlled expression of the open reading frames. Half of the vectors facilitate expression of proteins tagged with C-terminal EGFP. All 32 vectors allow high frequency targeting of the overexpression cassette into the Ku70 locus and complement the Ku70 gene when transformed into a Z. tritici ku70 null strain, thus circumventing additional phenotypes that can arise from random integration. This suite of ternary expression vectors will be a useful tool for functional analysis through gene overexpression in Z. tritici.

The propagation of perturbations in rewired bacterial gene networks

What happens to gene expression when you add new links to a gene regulatory network? To answer this question, we profile 85 network rewirings in E. coli. Here we report that concerted patterns of differential expression propagate from reconnected hub genes. The rewirings link promoter regions to different transcription factor and σ-factor genes, resulting in perturbations that span four orders of magnitude, changing up to ∼ 70% of the transcriptome. Importantly, factor connectivity and promoter activity both associate with perturbation size. Perturbations from related rewirings have more similar transcription profiles and a statistical analysis reveals ∼ 20 underlying states of the system, associating particular gene groups with rewiring constructs. We examine two large clusters (ribosomal and flagellar genes) in detail. These represent alternative global outcomes from different rewirings because of antagonism between these major cell states. This data set of systematically related perturbations enables reverse engineering and discovery of underlying network interactions.

Reconstruction of novel transcription factor regulons through inference of their binding sites

Background

In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present, identification of TF regulons is mostly done through comparative genomics approaches. Such methods could miss organism-specific regulatory interactions and often require expensive and time-consuming experimental techniques to generate the underlying data.

Results

In this work, we present an efficient algorithm that aims to identify a given transcription factor’s regulon through inference of its unknown binding sites, based on the discovery of its binding motif. The proposed approach relies on computational methods that utilize gene expression data sets and knockout fitness data sets which are available or may be straightforwardly obtained for many organisms. We computationally constructed the profiles of putative regulons for the TFs LexA, PurR and Fur in E. coli K12 and identified their binding motifs. Comparisons with an experimentally-verified database showed high recovery rates of the known regulon members, and indicated good predictions for the newly found genes with high biological significance. The proposed approach is also applicable to novel organisms for predicting unknown regulons of the transcriptional regulators. Results for the hypothetical protein Dde0289 in D. alaskensis include the discovery of a Fis-type TF binding motif.

Conclusions

The proposed motif-based regulon inference approach can discover the organism-specific regulatory interactions on a single genome, which may be missed by current comparative genomics techniques due to their limitations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0685-y) contains supplementary material, which is available to authorized users.

Comparative analysis of gene regulatory networks: from network reconstruction to evolution

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.

Constructing minimal models for complex system dynamics

One of the strengths of statistical physics is the ability to reduce macroscopic observations into microscopic models, offering a mechanistic description of a system's dynamics. This paradigm, rooted in Boltzmann's gas theory, has found applications from magnetic phenomena to subcellular processes and epidemic spreading. Yet, each of these advances were the result of decades of meticulous model building and validation, which are impossible to replicate in most complex biological, social or technological systems that lack accurate microscopic models. Here we develop a method to infer the microscopic dynamics of a complex system from observations of its response to external perturbations, allowing us to construct the most general class of nonlinear pairwise dynamics that are guaranteed to recover the observed behaviour. The result, which we test against both numerical and empirical data, is an effective dynamic model that can predict the system's behaviour and provide crucial insights into its inner workings.

Addressing false discoveries in network inference

MOTIVATION

Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles.

RESULTS

We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson's paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation.

CONCLUSIONS

CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well.

AVAILABILITY AND IMPLEMENTATION

Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe.

CONTACT

robert.kueffner@helmholtz-muenchen.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Targeted changes of the cell wall proteome influence Candida albicans ability to form single- and multi-strain biofilms

Biofilm formation is an important virulence trait of the pathogenic yeast Candida albicans. We have combined gene overexpression, strain barcoding and microarray profiling to screen a library of 531 C. albicans conditional overexpression strains (∼10% of the genome) for genes affecting biofilm development in mixed-population experiments. The overexpression of 16 genes increased strain occupancy within a multi-strain biofilm, whereas overexpression of 4 genes decreased it. The set of 16 genes was significantly enriched for those encoding predicted glycosylphosphatidylinositol (GPI)-modified proteins, namely Ihd1/Pga36, Phr2, Pga15, Pga19, Pga22, Pga32, Pga37, Pga42 and Pga59; eight of which have been classified as pathogen-specific. Validation experiments using either individually- or competitively-grown overexpression strains revealed that the contribution of these genes to biofilm formation was variable and stage-specific. Deeper functional analysis of PGA59 and PGA22 at a single-cell resolution using atomic force microscopy showed that overexpression of either gene increased C. albicans ability to adhere to an abiotic substrate. However, unlike PGA59, PGA22 overexpression led to cell cluster formation that resulted in increased sensitivity to shear forces and decreased ability to form a single-strain biofilm. Within the multi-strain environment provided by the PGA22-non overexpressing cells, PGA22-overexpressing cells were protected from shear forces and fitter for biofilm development. Ultrastructural analysis, genome-wide transcript profiling and phenotypic analyses in a heterologous context suggested that PGA22 affects cell adherence through alteration of cell wall structure and/or function. Taken together, our findings reveal that several novel predicted GPI-modified proteins contribute to the cooperative behaviour between biofilm cells and are important participants during C. albicans biofilm formation. Moreover, they illustrate the power of using signature tagging in conjunction with gene overexpression for the identification of novel genes involved in processes pertaining to C. albicans virulence.

Candida albicans is the most prevalent human fungal pathogen. Its ability to cause disease relies, in part, on the formation of biofilms, a protective structure of highly adherent cells tolerant to antifungal agents and the host immune response. The biofilm is considered as a persistent root of infection, disseminating infectious cells to other locations. In this study, we performed large-scale phenotypic analyses aimed at identifying genes whose overexpression affects biofilm development in C. albicans. Our screen relied on a collection of 531 C. albicans strains, each conditionally overexpressing one given gene and carrying one specific molecular tag allowing the quantification of strain abundance in mixed-population experiments. Our results strikingly revealed the enrichment of strains overproducing poorly-characterized surface proteins called Pgas (Putative GPI-Anchored proteins), within a 531-strain-containing biofilm model. We show that these PGA genes differentially contribute to single-strain and multi-strain biofilm formation and are involved in specific stages of the biofilm developmental process. Taken together, our results reveal the importance of C. albicans cell surface proteins during biofilm formation and reflect the powerful use of strain barcoding in combination with gene overexpression to identify genes and/or pathways involved in processes pertaining to virulence of pathogenic microbes.