TFRank: network-based prioritization of regulatory associations underlying transcriptional responses

A Hierarchical Transcriptional Regulatory Network Required for Long-Term Thermal Stress Tolerance in an Industrial Saccharomyces cerevisiae Strain

Yeast cells suffer from continuous and long-term thermal stress during high-temperature ethanol fermentation. Understanding the mechanism of yeast thermotolerance is important not only for studying microbial stress biology in basic research but also for developing thermotolerant strains for industrial application. Here, we compared the effects of 23 transcription factor (TF) deletions on high-temperature ethanol fermentation and cell survival after heat shock treatment and identified three core TFs, Sin3p, Srb2p and Mig1p, that are involved in regulating the response to long-term thermotolerance. Further analyses of comparative transcriptome profiling of the core TF deletions and transcription regulatory associations revealed a hierarchical transcriptional regulatory network centered on these three TFs. This global transcriptional regulatory network provided a better understanding of the regulatory mechanism behind long-term thermal stress tolerance as well as potential targets for transcriptome engineering to improve the performance of high-temperature ethanol fermentation by an industrial Saccharomyces cerevisiae strain.

Stress tolerance enhancement via SPT15 base editing in Saccharomyces cerevisiae

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Proteomic analysis of the S. cerevisiae response to the anticancer ruthenium complex KP1019

Like platinum-based chemotherapeutics, the anticancer ruthenium complex indazolium trans-[tetrachlorobis(1H-indazole)ruthenate(iii)], or KP1019, damages DNA, induces apoptosis, and causes tumor regression in animal models. Unlike platinum-based drugs, KP1019 showed no dose-limiting toxicity in a phase I clinical trial. Despite these advances, the mechanism(s) and target(s) of KP1019 remain unclear. For example, the drug may damage DNA directly or by causing oxidative stress. Likewise, KP1019 binds cytosolic proteins, suggesting DNA is not the sole target. Here we use the budding yeast Saccharomyces cerevisiae as a model in a proteomic study of the cellular response to KP1019. Mapping protein level changes onto metabolic pathways revealed patterns consistent with elevated synthesis and/or cycling of the antioxidant glutathione, suggesting KP1019 induces oxidative stress. This result was supported by increased fluorescence of the redox-sensitive dye DCFH-DA and increased KP1019 sensitivity of yeast lacking Yap1, a master regulator of the oxidative stress response. In addition to oxidative and DNA stress, bioinformatic analysis revealed drug-dependent increases in proteins involved ribosome biogenesis, translation, and protein (re)folding. Consistent with proteotoxic effects, KP1019 increased expression of a heat-shock element (HSE) lacZ reporter. KP1019 pre-treatment also sensitized yeast to oxaliplatin, paralleling prior research showing that cancer cell lines with elevated levels of translation machinery are hypersensitive to oxaliplatin. Combined, these data suggest that one of KP1019's many targets may be protein metabolism, which opens up intriguing possibilities for combination therapy.

Constructing temporal regulatory cascades in the context of development and cell differentiation

Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that affect the expression of broader sets of genes, ending up in the formation of different tissues and organ parts. The identification of stage-specific master regulators and the mechanism by which they activate each other is a key to understanding and controlling differentiation, particularly in the fields of tissue regeneration and organoid engineering. Here we present a workflow that combines a comprehensive general regulatory network based on binding site predictions with user-provided temporal gene expression data, to generate a a temporally connected series of stage-specific regulatory networks, which we call a temporal regulatory cascade (TRC). A TRC identifies those regulators that are unique for each time point, resulting in a cascade that shows the emergence of these regulators and regulatory interactions across time. The model was implemented in the form of a user-friendly, visual web-tool, that requires no expert knowledge in programming or statistics, making it directly usable for life scientists. In addition to generating TRCs the tool links multiple interactive visual workflows, in which a user can track and investigate further different regulators, target genes, and interactions, directing the tool along the way into biologically sensible results based on the given dataset. We applied the TRC model on two different expression datasets, one based on experiments conducted on human induced pluripotent stem cells (hiPSCs) undergoing differentiation into mature cardiomyocytes and the other based on the differentiation of H1-derived human neuronal precursor cells. The model was successful in identifying previously known and new potential key regulators, in addition to the particular time points with which these regulators are associated, in cardiac and neural development.

REGGAE: a novel approach for the identification of key transcriptional regulators

Motivation

Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes.

Results

Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant effect on the expression of a given set of genes, e.g. genes that are differentially expressed between two sample groups. REGGAE uses a Kolmogorov-Smirnov-like test statistic that implicitly combines associations between regulators and their target genes with an enrichment approach to prioritize the influence of transcriptional regulators. We evaluated our method in two different application scenarios, which demonstrate that REGGAE is well suited for uncovering the influence of transcriptional regulators and is a valuable tool for the elucidation of complex regulatory mechanisms.

Availability and implementation

REGGAE is freely available at https://regulatortrail.bioinf.uni-sb.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genomewide mechanisms of chronological longevity by dietary restriction in budding yeast

Dietary restriction is arguably the most promising nonpharmacological intervention to extend human life and health span. Yet, only few genetic regulators mediating the cellular response to dietary restriction are known, and the question remains which other regulatory factors are involved. Here, we measured at the genomewide level the chronological lifespan of Saccharomyces cerevisiae gene deletion strains under two nitrogen source regimens, glutamine (nonrestricted) and γ‐aminobutyric acid (restricted). We identified 473 mutants with diminished or enhanced extension of lifespan. Functional analysis of such dietary restriction genes revealed novel processes underlying longevity by the nitrogen source quality, which also allowed us to generate a prioritized catalogue of transcription factors orchestrating the dietary restriction response. Importantly, deletions of transcription factors Msn2, Msn4, Snf6, Tec1, and Ste12 resulted in diminished lifespan extension and defects in cell cycle arrest upon nutrient starvation, suggesting that regulation of the cell cycle is a major mechanism of chronological longevity. We further show that STE12 overexpression is enough to extend lifespan, linking the pheromone/invasive growth pathway with cell survivorship. Our global picture of the genetic players of longevity by dietary restriction highlights intricate regulatory cross‐talks in aging cells.

RegulatorTrail: a web service for the identification of key transcriptional regulators

Transcriptional regulators such as transcription factors and chromatin modifiers play a central role in most biological processes. Alterations in their activities have been observed in many diseases, e.g. cancer. Hence, it is of utmost importance to evaluate and assess the effects of transcriptional regulators on natural and pathogenic processes. Here, we present RegulatorTrail, a web service that provides rich functionality for the identification and prioritization of key transcriptional regulators that have a strong impact on, e.g. pathological processes. RegulatorTrail offers eight methods that use regulator binding information in combination with transcriptomic or epigenomic data to infer the most influential regulators. Our web service not only provides an intuitive web interface, but also a well-documented RESTful API that allows for a straightforward integration into third-party workflows. The presented case studies highlight the capabilities of our web service and demonstrate its potential for the identification of influential regulators: we successfully identified regulators that might explain the increased malignancy in metastatic melanoma compared to primary tumors, as well as important regulators in macrophages. RegulatorTrail is freely accessible at: https://regulatortrail.bioinf.uni-sb.de/.

Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis

Transcriptional regulation is one of the key steps in the control of gene expression, with huge impact on the survival, adaptation, and fitness of all organisms. However, it is becoming increasingly clear that transcriptional regulation is far more complex than initially foreseen. In model organisms such as the yeast Saccharomyces cerevisiae evidence has been piling up showing that the expression of each gene can be controlled by several transcription factors, in the close dependency of the environmental conditions. Furthermore, transcription factors work in intricate networks, being themselves regulated at the transcriptional, post-transcriptional, and post-translational levels, working in cooperation or antagonism in the promoters of their target genes.In this chapter, a step-by-step guide using the YEASTRACT database is provided, for the prediction and ranking of the transcription factors required for the regulation of the expression a single gene and of a genome-wide response. These analyses are illustrated with the regulation of the PDR18 gene and of the transcriptome-wide changes induced upon exposure to the herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D), respectively. The newest potentialities of this information system are explored, and the various results obtained in the dependency of the querying criteria are discussed in terms of the knowledge gathered on the biological responses considered as case studies.

MicroRNA Expression Profiling in Clear Cell Renal Cell Carcinoma: Identification and Functional Validation of Key miRNAs

OBJECTIVE

This study aims to profile dysregulated microRNA (miRNA) expression in clear cell renal cell carcinoma (ccRCC) and to identify key regulatory miRNAs in ccRCC.

METHODS AND RESULTS

miRNA expression profiles in nine pairs of ccRCC tumor samples at three different stages and the adjacent, non-tumorous tissues were investigated using miRNA arrays. Eleven miRNAs were identified to be commonly dysregulated, including three up-regulated (miR-487a, miR-491-3p and miR-452) and eight down-regulated (miR-125b, miR-142-3p, miR-199a-5p, miR-22, miR-299-3p, miR-29a, miR-429, and miR-532-5p) in tumor tissues as compared with adjacent normal tissues. The 11 miRNAs and their predicted target genes were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and three key miRNAs (miR-199a-5p, miR-22 and miR-429) were identified by microRNA-gene network analysis. Dysregulation of the three key miRNAs were further validated in another cohort of 15 ccRCC samples, and the human kidney carcinoma cell line 786-O, as compared with five normal kidney samples. Further investigation showed that over-expression of miR-199a-5p significantly inhibited the invasion ability of 786-O cells. Luciferase reporter assays indicated that miR-199a-5p regulated expression of TGFBR1 and JunB by directly interacting with their 3' untranslated regions. Transfection of miR-199a-5p successfully suppressed expression of TGFBR1 and JunB in the human embryonic kidney 293T cells, further confirming the direct regulation of miR-199a-5p on these two genes.

CONCLUSIONS

This study identified 11 commonly dysregulated miRNAs in ccRCC, three of which (miR-199a-5p, miR-22 and miR-429) may represent key miRNAs involved in the pathogenesis of ccRCC. Further studies suggested that miR-199a-5p plays an important role in inhibition of cell invasion of ccRCC cells by suppressing expression of TGFBR1 and JunB.

Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation

Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases. Several computational algorithms have recently been developed for this purpose by using transcriptome and network data. However, it remains largely unclear which algorithm performs better under a specific condition. Such knowledge is important for both appropriate application and future enhancement of these algorithms. Here, we systematically evaluated seven main algorithms (TED, TDD, TFactS, RIF1, RIF2, dCSA_t2t, and dCSA_r2t), using both simulated and real datasets. In our simulation evaluation, we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators. We found that all these algorithms could effectively discern signals arising from regulatory network differences, indicating the validity of our simulation schema. Among the seven tested algorithms, TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered. When applied to two independent lung cancer datasets, both TED and TFactS replicated a substantial fraction of their respective differential regulators. Since TED and TFactS rely on two distinct features of transcriptome data, namely differential co-expression and differential expression, both may be applied as mutual references during practical application.

Signaling hypergraphs

Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight a representation of signaling pathways that is underutilized: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.

The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

Reconciling differential gene expression data with molecular interaction networks

MOTIVATION

Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease.

RESULTS

To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone.

AVAILABILITY

C++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/∼murali/software/biorithm-docs.

Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores

Disease gene prioritization aims to suggest potential implications of genes in disease susceptibility. Often accomplished in a guilt-by-association scheme, promising candidates are sorted according to their relatedness to known disease genes. Network-based methods have been successfully exploiting this concept by capturing the interaction of genes or proteins into a score. Nonetheless, most current approaches yield at least some of the following limitations: (1) networks comprise only curated physical interactions leading to poor genome coverage and density, and bias toward a particular source; (2) scores focus on adjacencies (direct links) or the most direct paths (shortest paths) within a constrained neighborhood around the disease genes, ignoring potentially informative indirect paths; (3) global clustering is widely applied to partition the network in an unsupervised manner, attributing little importance to prior knowledge; (4) confidence weights and their contribution to edge differentiation and ranking reliability are often disregarded. We hypothesize that network-based prioritization related to local clustering on graphs and considering full topology of weighted gene association networks integrating heterogeneous sources should overcome the above challenges. We term such a strategy Interactogeneous. We conducted cross-validation tests to assess the impact of network sources, alternative path inclusion and confidence weights on the prioritization of putative genes for 29 diseases. Heat diffusion ranking proved the best prioritization method overall, increasing the gap to neighborhood and shortest paths scores mostly on single source networks. Heterogeneous associations consistently delivered superior performance over single source data across the majority of methods. Results on the contribution of confidence weights were inconclusive. Finally, the best Interactogeneous strategy, heat diffusion ranking and associations from the STRING database, was used to prioritize genes for Parkinson’s disease. This method effectively recovered known genes and uncovered interesting candidates which could be linked to pathogenic mechanisms of the disease.

Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks

Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots.