Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture

LowTempGAL: a highly responsive low temperature-inducible GAL system in Saccharomyces cerevisiae

Temperature is an important control factor for biologics biomanufacturing in precision fermentation. Here, we explored a highly responsive low temperature-inducible genetic system (LowTempGAL) in the model yeast Saccharomyces cerevisiae. Two temperature biosensors, a heat-inducible degron and a heat-inducible protein aggregation domain, were used to regulate the GAL activator Gal4p, rendering the leaky LowTempGAL systems. Boolean-type induction was achieved by implementing a second-layer control through low-temperature-mediated repression on GAL repressor gene GAL80, but suffered delayed response to low-temperature triggers and a weak response at 30°C. Application potentials were validated for protein and small molecule production. Proteomics analysis suggested that residual Gal80p and Gal4p insufficiency caused suboptimal induction. 'Turbo' mechanisms were engineered through incorporating a basal Gal4p expression and a galactose-independent Gal80p-supressing Gal3p mutant (Gal3Cp). Varying Gal3Cp configurations, we deployed the LowTempGAL systems capable for a rapid stringent high-level induction upon the shift from a high temperature (37-33°C) to a low temperature (≤30°C). Overall, we present a synthetic biology procedure that leverages 'leaky' biosensors to deploy highly responsive Boolean-type genetic circuits. The key lies in optimisation of the intricate layout of the multi-factor system. The LowTempGAL systems may be applicable in non-conventional yeast platforms for precision biomanufacturing.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Genetic and bioinformatic analyses reveal transcriptional networks underlying dual genomic coordination of mitochondrial biogenesis

Mitochondrial genome encodes handful genes of respiratory chain complexes, whereas all the remaining mitochondrial proteins are encoded on the nuclear genome. However, the mechanisms coordinating these two genomes to control mitochondrial biogenesis remain largely unknown. To identify transcription circuits involved in these processes, we performed a candidate RNAi screen in developing eyes that had reduced mitochondrial DNA contents. We reasoned that impaired mitochondrial biogenesis would synergistically interact with mtDNA deficiency in disrupting tissue development. Over 638 transcription factors annotated in the fly genome, we identified 77 transcription factors that may be involved in mitochondrial genome maintenance and gene expression. Additional genetic and genomic analyses revealed that a novel transcription factor, CG1603, and its upstream factor YL-1 are essential for mitochondrial biogenesis. We constructed a regulator network among positive hits using the published CHIP-seq data. The network analysis revealed extensive connections, and complex hierarchical organization underlying the transcription regulation of mitochondrial biogenesis.

TroR is the primary regulator of the iron homeostasis transcription network in the halophilic archaeon Haloferax volcanii

Maintaining the intracellular iron concentration within the homeostatic range is vital to meet cellular metabolic needs and reduce oxidative stress. Previous research revealed that the haloarchaeon Halobacterium salinarum encodes four diphtheria toxin repressor (DtxR) family transcription factors (TFs) that together regulate the iron response through an interconnected transcriptional regulatory network (TRN). However, the conservation of the TRN and the metal specificity of DtxR TFs remained poorly understood. Here we identified and characterized the TRN of Haloferax volcanii for comparison. Genetic analysis demonstrated that Hfx. volcanii relies on three DtxR transcriptional regulators (Idr, SirR, and TroR), with TroR as the primary regulator of iron homeostasis. Bioinformatics and molecular approaches revealed that TroR binds a conserved cis-regulatory motif located ∼100 nt upstream of the start codon of iron-related target genes. Transcriptomics analysis demonstrated that, under conditions of iron sufficiency, TroR repressed iron uptake and induced iron storage mechanisms. TroR repressed the expression of one other DtxR TF, Idr. This reduced DtxR TRN complexity relative to that of Hbt. salinarum appeared correlated with natural variations in iron availability. Based on these data, we hypothesize that variable environmental conditions such as iron availability appear to select for increasing TRN complexity.

Transcription factor expression levels and environmental signals constrain transcription factor innovation

Evolutionary innovation of transcription factors frequently drives phenotypic diversification and adaptation to environmental change. Transcription factors can gain or lose connections to target genes, resulting in novel regulatory responses and phenotypes. However the frequency of functional adaptation varies between different regulators, even when they are closely related. To identify factors influencing propensity for innovation, we utilise a Pseudomonas fluorescens SBW25 strain rendered incapable of flagellar mediated motility in soft-agar plates via deletion of the flagellar master regulator (fleQ ). This bacterium can evolve to rescue flagellar motility via gene regulatory network rewiring of an alternative transcription factor to rescue activity of FleQ. Previously, we have identified two members (out of 22) of the RpoN-dependent enhancer binding protein (RpoN-EBP) family of transcription factors (NtrC and PFLU1132) that are capable of innovating in this way. These two transcription factors rescue motility repeatably and reliably in a strict hierarchy – with NtrC the only route in a ∆fleQ background, and PFLU1132 the only route in a ∆fleQ ∆ntrC background. However, why other members in the same transcription factor family have not been observed to rescue flagellar activity is unclear. Previous work shows that protein homology cannot explain this pattern within the protein family (RpoN-EBPs), and mutations in strains that rescued motility suggested high levels of transcription factor expression and activation drive innovation. We predict that mutations that increase expression of the transcription factor are vital to unlock evolutionary potential for innovation. Here, we construct titratable expression mutant lines for 11 of the RpoN-EBPs in P. fluorescens . We show that in five additional RpoN-EBPs (FleR, HbcR, GcsR, DctD, AauR and PFLU2209), high expression levels result in different mutations conferring motility rescue, suggesting alternative rewiring pathways. Our results indicate that expression levels (and not protein homology) of RpoN-EBPs are a key constraining factor in determining evolutionary potential for innovation. This suggests that transcription factors that can achieve high expression through few mutational changes, or transcription factors that are active in the selective environment, are more likely to innovate and contribute to adaptive gene regulatory network evolution.

Transcriptome-wide analysis reveals core transcriptional regulators associated with culm development and variation in Dendrocalamus sinicus, the strongest woody bamboo in the world

Transcription factors (TFs) play indispensable roles in plant development and stress responses. As the largest woody bamboo species in the world, Dendrocalamus sinicus is endemic to Yunnan Province, China, and possesses two natural variants characterized by culm shape, namely straight or bent culms. Understanding the transcriptional regulation network of D. sinicus provides a unique opportunity to clarify the growth and development characteristics of woody bamboos. In this study, 10,236 TF transcripts belonging to 57 families were identified from transcriptome data of two variants at different developmental stages, from which we constructed a transcriptional regulatory network and unigene-coding protein-TFs interactive network of culm development for this attractive species. Gene function enrichment analysis revealed that hormone signaling and MAPK signaling pathways were two most enriched pathways in TF-regulated network. Based on PPI analysis, 50 genes interacting with nine TFs were screened as the core regulation components related to culm development. Of them, 18 synergistic genes of seven TFs, including nuclear cap-binding protein subunit 1, transcription factor GTE9-like, and ATP-dependent DNA helicase DDX11 isoform X1, involved in culm-shape variation. Most of these genes would interact with MYB, C3H, and ARF transcription factors. Six members with two each from ARF, C3H, and MYB transcription factor families and six key interacting genes (IAA3, IAA19, leucine-tRNA ligase, nuclear cap-binding protein subunit 1, elongation factor 2, and coiled-coil domain-containing protein 94) cooperate with these transcription factors were differentially expressed at development stage of young culms, and were validated by quantitative PCR. Our results represent a crucial step towards understanding the regulatory mechanisms of TFs involved in culm development and variation of D. sinicus.

Transcription factor network analysis identifies REST/NRSF as an intrinsic regulator of CNS regeneration in mice

The inability of neurons to regenerate long axons within the CNS is a major impediment to improving outcome after spinal cord injury, stroke, and other CNS insults. Recent advances have uncovered an intrinsic program that involves coordinate regulation by multiple transcription factors that can be manipulated to enhance growth in the peripheral nervous system. Here, we use a systems genomics approach to characterize regulatory relationships of regeneration-associated transcription factors, identifying RE1-Silencing Transcription Factor (REST; Neuron-Restrictive Silencer Factor, NRSF) as a predicted upstream suppressor of a pro-regenerative gene program associated with axon regeneration in the CNS. We validate our predictions using multiple paradigms, showing that mature mice bearing cell type-specific deletions of REST or expressing dominant-negative mutant REST show improved regeneration of the corticospinal tract and optic nerve after spinal cord injury and optic nerve crush, which is accompanied by upregulation of regeneration-associated genes in cortical motor neurons and retinal ganglion cells, respectively. These analyses identify a role for REST as an upstream suppressor of the intrinsic regenerative program in the CNS and demonstrate the utility of a systems biology approach involving integrative genomics and bio-informatics to prioritize hypotheses relevant to CNS repair.

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Functional network motifs defined through integration of protein-protein and genetic interactions

Cells are enticingly complex systems. The identification of feedback regulation is critically important for understanding this complexity. Network motifs defined as small graphlets that occur more frequently than expected by chance have revolutionized our understanding of feedback circuits in cellular networks. However, with their definition solely based on statistical over-representation, network motifs often lack biological context, which limits their usefulness. Here, we define functional network motifs (FNMs) through the systematic integration of genetic interaction data that directly inform on functional relationships between genes and encoded proteins. Occurring two orders of magnitude less frequently than conventional network motifs, we found FNMs significantly enriched in genes known to be functionally related. Moreover, our comprehensive analyses of FNMs in yeast showed that they are powerful at capturing both known and putative novel regulatory interactions, thus suggesting a promising strategy towards the systematic identification of feedback regulation in biological networks. Many FNMs appeared as excellent candidates for the prioritization of follow-up biochemical characterization, which is a recurring bottleneck in the targeting of complex diseases. More generally, our work highlights a fruitful avenue for integrating and harnessing genomic network data.

Mechanisms behind context-dependent role of glucocorticoids in breast cancer progression

Glucocorticoids (GCs), mostly dexamethasone (dex), are routinely administered as adjuvant therapy to manage side effects in breast cancer. However, recently, it has been revealed that dex triggers different effects and correlates with opposite outcomes depending on the breast cancer molecular subtype. This has raised new concerns regarding the generalized use of GC and suggested that the context-dependent effects of GCs can be taken into potential consideration during treatment design. Based on this, attention has recently been drawn to the role of the glucocorticoid receptor (GR) in development and progression of breast cancer. Therefore, in this comprehensive review, we aimed to summarize the different mechanisms behind different context-dependent GC actions in breast cancer by applying a multilevel examination, starting from the association of variants of the GR-encoding gene to expression at the mRNA and protein level of the receptor, and its interactions with other factors influencing GC action in breast cancer. The role of GCs in chemosensitivity and chemoresistance observed during breast cancer therapy is discussed. In addition, experiences using GC targeting therapeutic options (already used and investigated in preclinical and clinical trials), such as classic GC dexamethasone, selective glucocorticoid receptor agonists and modulators, the GC antagonist mifepristone, and GR coregulators, are also summarized. Evidence presented can aid a better understanding of the biology of context-dependent GC action that can lead to further advances in the personalized therapy of breast cancer by the evaluation of GR along with the conventional estrogen receptor (ER) and progesterone receptor (PR) in the routine diagnostic procedure.

Differences in evolutionary accessibility determine which equally effective regulatory motif evolves to generate pulses

Transcriptional regulatory networks (TRNs) are enriched for certain "motifs." Motif usage is commonly interpreted in adaptationist terms, i.e., that the optimal motif evolves. But certain motifs can also evolve more easily than others. Here, we computationally evolved TRNs to produce a pulse of an effector protein. Two well-known motifs, type 1 incoherent feed-forward loops (I1FFLs) and negative feedback loops (NFBLs), evolved as the primary solutions. The relative rates at which these two motifs evolve depend on selection conditions, but under all conditions, either motif achieves similar performance. I1FFLs generally evolve more often than NFBLs. Selection for a tall pulse favors NFBLs, while selection for a fast response favors I1FFLs. I1FFLs are more evolutionarily accessible early on, before the effector protein evolves high expression; when NFBLs subsequently evolve, they tend to do so from a conjugated I1FFL-NFBL genotype. In the empirical S. cerevisiae TRN, output genes of NFBLs had higher expression levels than those of I1FFLs. These results suggest that evolutionary accessibility, and not relative functionality, shapes which motifs evolve in TRNs, and does so as a function of the expression levels of particular genes.

DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays

Motivation

Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping.

Results

Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization.

Availability and implementation

DECODE source code and pre-processing scripts are available at decode.gersteinlab.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bow-tie architecture of gene regulatory networks in species of varying complexity

The gene regulatory network (GRN) architecture plays a key role in explaining the biological differences between species. We aim to understand species differences in terms of some universally present dynamical properties of their gene regulatory systems. A network architectural feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of nodes, the in and the out layers. Though a bow-tie architecture has been observed in many networks, its existence has not been extensively investigated in GRNs of species of widely varying biological complexity. We analyse publicly available GRNs of several well-studied species from prokaryotes to unicellular eukaryotes to multicellular organisms. In their GRNs, we find the existence of a bow-tie architecture with a distinct largest strongly connected core layer. We show that the bow-tie architecture is a characteristic feature of GRNs. We observe an increasing trend in the relative core size with species complexity. Using studied relationships of the core size with dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged differently with biological complexity, based on the observed differences of the GRN bow-tie architectures.

Scaling of intrinsic noise in an autocratic reaction network

Biochemical reactions in living cells often produce stochastic trajectories due to the fluctuations of the finite number of the macromolecular species present inside the cell. A significant number of computational and theoretical studies have previously investigated stochasticity in small regulatory networks to understand its origin and regulation. At the systems level regulatory networks have been determined to be hierarchical resembling social networks. In order to determine the stochasticity in networks with hierarchical architecture, here we computationally investigated intrinsic noise in an autocratic reaction network in which only the upstream regulators regulate the downstream regulators. We studied the effects of the qualitative and quantitative nature of regulatory interactions on the stochasticity in the network. We established an unconventional scaling of noise with average abundance in which the noise passes through a minimum indicating that the network can be noisy both in the low and high abundance regimes. We determined that the bursty kinetics of the trajectories are responsible for such scaling. The scaling of noise remains intact for a mixed network that includes democratic subnetworks within the autocratic network.

Dissecting molecular network structures using a network subgraph approach

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

How directed is a directed network?

The trophic levels of nodes in directed networks can reveal their functional properties. Moreover, the trophic coherence of a network, defined in terms of trophic levels, is related to properties such as cycle structure, stability and percolation. The standard definition of trophic levels, however, borrowed from ecology, suffers from drawbacks such as requiring basal nodes, which limit its applicability. Here we propose simple improved definitions of trophic levels and coherence that can be computed on any directed network. We demonstrate how the method can identify node function in examples including ecosystems, supply chain networks, gene expression and global language networks. We also explore how trophic levels and coherence relate to other topological properties, such as non-normality and cycle structure, and show that our method reveals the extent to which the edges in a directed network are aligned in a global direction.

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

Background

Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure.

Results

Here, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs.

Conclusions

The LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.

Qualitative and quantitative nature of mutual interactions dictate chemical noise in a democratic reaction network

The functions of a living cell rely on a complex network of biochemical reactions that allow it to respond against various internal and external cues. The outcomes of these chemical reactions are often stochastic due to intrinsic and extrinsic noise leading to population heterogeneity. The majority of calculations of stochasticity in reaction networks have focused on small regulatory networks addressing the role of timescales, feedback regulations, and network topology in propagation of noise. Here we computationally investigated chemical noise in a network with democratic architecture where each node is regulated by all other nodes in the network. We studied the effects of the qualitative and quantitative nature of mutual interactions on the propagation of both intrinsic and extrinsic noise in the network. We show that an increased number of inhibitory signals lead to ultrasensitive switching of average and that leads to sharp transition of intrinsic noise. The intrinsic noise exhibits a biphasic power-law scaling with the average, and the scaling coefficients strongly correlate with the strength of inhibitory signal. The noise strength critically depends on the strength of the interactions, where negative interactions attenuate both intrinsic and extrinsic noise.

The Architecture of the Human RNA-Binding Protein Regulatory Network

RNA-binding proteins (RBPs) are key players of post-transcriptional regulation of gene expression, relying on competitive and cooperative interactions to fine-tune their action. Several studies have described individual interactions of RBPs with RBP mRNAs. Here we present a systematic network investigation of fifty thousand interactions between RBPs and the UTRs of RBP mRNAs. We identified two structural features in this network. RBP clusters are groups of densely interconnected RBPs co-binding their targets, suggesting a tight control of cooperative and competitive behaviors. RBP chains are hierarchical structures connecting RBP clusters and driven by evolutionarily ancient RBPs. These features suggest that RBP chains may coordinate the different cell programs controlled by RBP clusters. Under this model, the regulatory signal flows through chains from one cluster to another, implementing elaborate regulatory plans. This work thus suggests RBP-RBP interactions as a backbone driving post-transcriptional regulation of gene expression to control RBPs action on their targets.

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The RBP-RBP network is a robust and efficient hierarchical structure

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The RBP-RBP network is formed by RBP clusters and chains

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RBP-RBP interactions cluster to cooperate and compete on common target mRNAs

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RBP chains are master regulatory units of the cell led by ancient RBPs

Complex genetic and epigenetic regulation deviates gene expression from a unifying global transcriptional program

Environmental or genetic perturbations lead to gene expression changes. While most analyses of these changes emphasize the presence of qualitative differences on just a few genes, we now know that changes are widespread. This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell. However, given the sophistication of eukaryotic regulation, we expect to have a complex architecture of specific control affecting this program. Here, we examine this architecture. Using data of Saccharomyces cerevisiae expression in different nutrient conditions, we first propose a five-sector genome partition, which integrates earlier models of resource allocation, as a framework to examine the deviations from the global control. In this scheme, we recognize invariant genes, whose regulation is dominated by physiology, specific genes, which substantially depart from it, and two additional classes that contain the frequently assumed growth-dependent genes. Whereas the invariant class shows a considerable absence of specific regulation, the rest is enriched by regulation at the level of transcription factors (TFs) and epigenetic modulators. We nevertheless find markedly different strategies in how these classes deviate. On the one hand, there are TFs that act in a unique way between partition constituents, and on the other, the action of chromatin modifiers is significantly diverse. The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale.

How can we understand expression changes observed as a result of environmental or genetic perturbations? This issue has been conventionally answered by examining small groups of genes whose expression becomes qualitatively altered after these perturbations. But this approach is too simplistic, as we now know that extensive variation is typically observed. To explain this variation, recent works proposed a model in which genome-wide changes are explained by the action of a general program of transcription. Our manuscript reasons that given the complexity of eukaryotic transcriptional control, a unifying program of regulation cannot be achievable. Instead, we propose within an integrated framework of resource allocation that a rich structure of deviations from it exists and that by characterizing these deviations we can fully appreciate large-scale expression change.

Rewiring of transcriptional networks as a major event leading to the diversity of asexual multicellularity in fungi

Complex multicellularity (CM) is characterized by the generation of three-dimensional structures that follow a genetically controlled program. CM emerged at least five times in evolution, one of them in fungi. There are two types of CM programs in fungi, leading, respectively, to the formation of sexual or asexual spores. Asexual spores foment the spread of mycoses, as they are the main vehicle for dispersion. In spite of this key dependence, there is great morphological diversity of asexual multicellular structures in fungi. To advance the understanding of the mechanisms that control initiation and progression of asexual CM and how they can lead to such a remarkable morphological diversification, we studied 503 fungal proteomes, representing all phyla and subphyla, and most known classes. Conservation analyses of 33 regulators of asexual development suggest stepwise emergence of transcription factors. While velvet proteins constitute one of the most ancient systems, the central regulator BrlA emerged late in evolution (with the class Eurotiomycetes). Some factors, such as MoConX4, seem to be species-specific. These observations suggest that the emergence and evolution of transcriptional regulators rewire transcriptional networks. This process could reach the species level, resulting in a vast diversity of morphologies.

Hierarchical cooperation of transcription factors from integration analysis of DNA sequences, ChIP-Seq and ChIA-PET data

Background

Chromosomal architecture, which is constituted by chromatin loops, plays an important role in cellular functions. Gene expression and cell identity can be regulated by the chromatin loop, which is formed by proximal or distal enhancers and promoters in linear DNA (1D). Enhancers and promoters are fundamental non-coding elements enriched with transcription factors (TFs) to form chromatin loops. However, the specific cooperation of TFs involved in forming chromatin loops is not fully understood.

Results

Here, we proposed a method for investigating the cooperation of TFs in four cell lines by the integrative analysis of DNA sequences, ChIP-Seq and ChIA-PET data. Results demonstrate that the interaction of enhancers and promoters is a hierarchical and dynamic complex process with cooperative interactions of different TFs synergistically regulating gene expression and chromatin structure. The TF cooperation involved in maintaining and regulating the chromatin loop of cells can be regulated by epigenetic factors, such as other TFs and DNA methylation.

Conclusions

Such cooperation among TFs provides the potential features that can affect chromatin’s 3D architecture in cells. The regulation of chromatin 3D organization and gene expression is a complex process associated with the hierarchical and dynamic prosperities of TFs.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5535-2) contains supplementary material, which is available to authorized users.

The fitness cost and benefit of phase-separated protein deposits

Phase separation of soluble proteins into insoluble deposits is associated with numerous diseases. However, protein deposits can also function as membrane-less compartments for many cellular processes. What are the fitness costs and benefits of forming such deposits in different conditions? Using a model protein that phase-separates into deposits, we distinguish and quantify the fitness contribution due to the loss or gain of protein function and deposit formation in yeast. The environmental condition and the cellular demand for the protein function emerge as key determinants of fitness. Protein deposit formation can influence cell-to-cell variation in free protein abundance between individuals of a cell population (i.e., gene expression noise). This results in variable manifestation of protein function and a continuous range of phenotypes in a cell population, favoring survival of some individuals in certain environments. Thus, protein deposit formation by phase separation might be a mechanism to sense protein concentration in cells and to generate phenotypic variability. The selectable phenotypic variability, previously described for prions, could be a general property of proteins that can form phase-separated assemblies and may influence cell fitness.

Computational Strategies for Niche-Dependent Cell Conversion to Assist Stem Cell Therapy

The field of regenerative medicine has blossomed in recent decades. However, the ultimate goal of tissue regeneration - replacing damaged or aged cells with healthy functioning cells - still faces a number of challenges. In particular, better understanding of the role of the cellular niche in shaping stem cell phenotype and conversion would aid in improving current protocols for stem cell therapies. In this regard, the implementation of novel computational approaches that consider the niche effect on stem cells would be valuable. Here we discuss current problems in stem cell transplantation and rejuvenation, and we propose computational strategies to control niche-dependent cell conversion to overcome them.

Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis

The genetic regulatory network (GRN) plays a key role in controlling the response of the cell to changes in the environment. Although the structure of GRNs has been the subject of many studies, their large scale structure in the light of feedbacks from the metabolic network (MN) has received relatively little attention. Here we study the causal structure of the GRNs, namely the chain of influence of one component on the other, taking into account feedback from the MN. First we consider the GRNs of E. coli and B. subtilis without feedback from MN and illustrate their causal structure. Next we augment the GRNs with feedback from their respective MNs by including (a) links from genes coding for enzymes to metabolites produced or consumed in reactions catalyzed by those enzymes and (b) links from metabolites to genes coding for transcription factors whose transcriptional activity the metabolites alter by binding to them. We find that the inclusion of feedback from MN into GRN significantly affects its causal structure, in particular the number of levels and relative positions of nodes in the hierarchy, and the number and size of the strongly connected components (SCCs). We then study the functional significance of the SCCs. For this we identify condition specific feedbacks from the MN into the GRN by retaining only those enzymes that are essential for growth in specific environmental conditions simulated via the technique of flux balance analysis (FBA). We find that the SCCs of the GRN augmented by these feedbacks can be ascribed specific functional roles in the organism. Our algorithmic approach thus reveals relatively autonomous subsystems with specific functionality, or regulatory modules in the organism. This automated approach could be useful in identifying biologically relevant modules in other organisms for which network data is available, but whose biology is less well studied.

DREB2 (dehydration-responsive element-binding protein 2) type transcription factor in sorghum (Sorghum bicolor): genome-wide identification, characterization and expression profiles under cadmium and salt stresses

Biotic and abiotic stresses negatively affect fitness, biomass production, and crop yield in plants. The dehydration-responsive element-binding proteins (DREB) are important transcription factors (TFs), and are induced by abiotic and biotic stresses. In this study, genome-wide identification, in silico sequence, and phylogenetic analyses and expression analyses of DREB2 genes under cadmium (Cd) and salt (NaCl) stresses in sorghum (Sorghum bicolor, Sb) were performed. Six putative SbDREB2 genes were identified in sorghum genome and all contained AP2 domain (PF00847). Nucleotide diversities in SbDREB2 genes were calculated as π: 0.53 and θ: 0.39, respectively. While exon numbers of them were either one or two, length of SbDREB2 proteins ranged from 238 to 388 amino acid residues. Fifty-six cis-acting regulatory elements, which are tissue specific, light, hormone, and stress responsive, were identified in the promotor regions of SbDREB2 genes. Analyses on digital expression data indicated that SbDREB2A and SbDREB2B are more expressed genes than other SbDREB genes in sorghum. Under Cd and NaCl stresses, expressions of SbDREB2 genes were induced at different levels. All SbDREB2 genes in root were up-regulated under salt stress. In case of Cd stress, SbDREB2D gene was particularly up-regulated in leaves and roots. Co-expression analyses revealed four of TFs in co-expression network, indicating that they have roles in transcriptional cascade. Furthermore, five miRNA target regions were identified for four SbDREB2 genes, indicating their roles in post-transcriptional regulation. The predicted 3D structure of SbDREB2 proteins showed some structural divergences and structure overlap between rice and sorghum varied at between 26.58 and 50%. Finally, obtained data could be used in breeding of stress-tolerant plants, particularly genetically engineered DREB2 expressing plants. Findings in this study would also contribute to the understanding of DREB2 genes in plants, especially in sorghum.

Layers of regulation - Insights into the role of transcription factors controlling mucilage production in the Arabidopsis seed coat

A polysaccharide-rich mucilage is released from the seed coat epidermis of numerous plant species and has been intensively studied in the model plant Arabidopsis. This has led to the identification of a large number of genes involved in the synthesis, secretion and modification of cell wall polysaccharides such as pectin, hemicellulose and cellulose being identified. These genes include a small network of transcription factors (TFs) and transcriptional co-regulators, that not only regulate mucilage production, but epidermal cell differentiation and in some cases flavonoid biosynthesis in the internal endothelial layer of the seed coat. Here we focus on the function of these regulators and propose a simplified model where they are assigned to a hierarchical gene network with three regulatory levels (tiers) as a means of assisting in the interpretation of the complexity. We discuss limitations of current methodologies and highlight some of the problems associated with defining the function of TFs, particularly those that perform different functions in adjacent layers of the seed coat. We suggest approaches that should provide a more accurate picture of the function of transcription factors involved with mucilage production and release.

Similar bowtie structures and distinct largest strong components are identified in the transcriptional regulatory networks of Arabidopsis thaliana during photomorphogenesis and heat shock

Photomorphogenesis and heat shock are critical biological processes of plants. A recent research constructed the transcriptional regulatory networks (TRNs) of Arabidopsis thaliana during these processes using DNase-seq. In this study, by strong decomposition, we revealed that each of these TRNs can be represented as a similar bowtie structure with only one non-trivial and distinct strong component. We further identified distinct patterns of variation of a few light-related genes in these bowtie structures during photomorphogenesis. These results suggest that bowtie structure may be a common property of TRNs of plants, and distinct variation patterns of genes in bowtie structures of TRNs during biological processes may reflect distinct functions. Overall, our study provides an insight into the molecular mechanisms underlying photomorphogenesis and heat shock, and emphasizes the necessity to investigate the strong connectivity structures while studying TRNs.

Reduced Neuronal Transcription of Escargot, the Drosophila Gene Encoding a Snail-Type Transcription Factor, Promotes Longevity

In recent years, several genes involved in complex neuron specification networks have been shown to control life span. However, information on these genes is scattered, and studies to discover new neuronal genes and gene cascades contributing to life span control are needed, especially because of the recognized role of the nervous system in governing homeostasis, aging, and longevity. Previously, we demonstrated that several genes that encode RNA polymerase II transcription factors and that are involved in the development of the nervous system affect life span in Drosophila melanogaster. Among other genes, escargot (esg) was demonstrated to be causally associated with an increase in the life span of male flies. Here, we present new data on the role of esg in life span control. We show that esg affects the life spans of both mated and unmated males and females to varying degrees. By analyzing the survival and locomotion of the esg mutants, we demonstrate that esg is involved in the control of aging. We show that increased longevity is caused by decreased esg transcription. In particular, we demonstrate that esg knockdown in the nervous system increased life span, directly establishing the involvement of the neuronal esg function in life span control. Our data invite attention to the mechanisms regulating the esg transcription rate, which is changed by insertions of DNA fragments of different sizes downstream of the structural part of the gene, indicating the direction of further research. Our data agree with the previously made suggestion that alterations in gene expression during development might affect adult lifespan, due to epigenetic patterns inherited in cell lineages or predetermined during the development of the structural and functional properties of the nervous system.

Proteotranscriptomic Measurements of E6-Associated Protein (E6AP) Targets in DU145 Prostate Cancer Cells

Prostate cancer is a common cause of cancer-related death in men. E6AP (E6-Associated Protein), an E3 ubiquitin ligase and a transcription cofactor, is elevated in a subset of prostate cancer patients. Genetic manipulations of E6AP in prostate cancer cells expose a role of E6AP in promoting growth and survival of prostate cancer cells in vitro and in vivo However, the effect of E6AP on prostate cancer cells is broad and it cannot be explained fully by previously identified tumor suppressor targets of E6AP, promyelocytic leukemia protein and p27. To explore additional players that are regulated downstream of E6AP, we combined a transcriptomic and proteomic approach. We identified and quantified 16,130 transcripts and 7,209 proteins in castration resistant prostate cancer cell line, DU145. A total of 2,763 transcripts and 308 proteins were significantly altered on knockdown of E6AP. Pathway analyses supported the known phenotypic effects of E6AP knockdown in prostate cancer cells and in parallel exposed novel potential links of E6AP with cancer metabolism, DNA damage repair and immune response. Changes in expression of the top candidates were confirmed using real-time polymerase chain reaction. Of these, clusterin, a stress-induced chaperone protein, commonly deregulated in prostate cancer, was pursued further. Knockdown of E6AP resulted in increased clusterin transcript and protein levels in vitro and in vivo Concomitant knockdown of E6AP and clusterin supported the contribution of clusterin to the phenotype induced by E6AP. Overall, results from this study provide insight into the potential biological pathways controlled by E6AP in prostate cancer cells and identifies clusterin as a novel target of E6AP.

Dosage-Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome

DNA copy number variation is associated with many high phenotypic heterogeneity disorders. We systematically examined the impact of Drosophila melanogaster deletions on gene expression profiles to ask whether increased expression variability owing to reduced gene dose might underlie this phenotypic heterogeneity. Indeed, we found that one-dose genes have higher gene expression variability relative to two-dose genes. We then asked whether this increase in variability could be explained by intrinsic noise within cells due to stochastic biochemical events, or whether expression variability is due to extrinsic noise arising from more complex interactions. Our modeling showed that intrinsic gene expression noise averages at the organism level and thus cannot explain increased variation in one-dose gene expression. Interestingly, expression variability was related to the magnitude of expression compensation, suggesting that regulation, induced by gene dose reduction, is noisy. In a remarkable exception to this rule, the single X chromosome of males showed reduced expression variability, even compared with two-dose genes. Analysis of sex-transformed flies indicates that X expression variability is independent of the male differentiation program. Instead, we uncovered a correlation between occupancy of the chromatin-modifying protein encoded by males absent on the first (mof) and expression variability, linking noise suppression to the specialized X chromosome dosage compensation system. MOF occupancy on autosomes in both sexes also lowered transcriptional noise. Our results demonstrate that gene dose reduction can lead to heterogeneous responses, which are often noisy. This has implications for understanding gene network regulatory interactions and phenotypic heterogeneity. Additionally, chromatin modification appears to play a role in dampening transcriptional noise.

Early transcriptome responses of the bovine midcycle corpus luteum to prostaglandin F2α includes cytokine signaling

In ruminants, prostaglandin F2alpha (PGF2α)-mediated luteolysis is essential prior to estrous cycle resumption, and is a target for improving fertility. To deduce early PGF2α-provoked changes in the corpus luteum a short time-course (0.5-4 h) was performed on cows at midcycle. A microarray-determined transcriptome was established and examined by bioinformatic pathway analysis. Classic PGF2α effects were evident by changes in early response genes (FOS, JUN, ATF3) and prediction of active pathways (PKC, MAPK). Several cytokine transcripts were elevated and NF-κB and STAT activation were predicted by pathway analysis. Self-organizing map analysis grouped differentially expressed transcripts into ten mRNA expression patterns indicative of temporal signaling cascades. Comparison with two analogous datasets revealed a conserved group of 124 transcripts similarly altered by PGF2α treatment, which both, directly and indirectly, indicated cytokine activation. Elevated levels of cytokine transcripts after PGF2α and predicted activation of cytokine pathways implicate inflammatory reactions early in PGF2α-mediated luteolysis.

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast

Background

Kinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted.

Results

We assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package (https://cran.r-project.org/web/packages/VertexSort/index.html).

Conclusions

The KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes in general.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0418-0) contains supplementary material, which is available to authorized users.

An Asymmetrically Balanced Organization of Kinases versus Phosphatases across Eukaryotes Determines Their Distinct Impacts

Protein phosphorylation underlies cellular response pathways across eukaryotes and is governed by the opposing actions of phosphorylating kinases and de-phosphorylating phosphatases. While kinases and phosphatases have been extensively studied, their organization and the mechanisms by which they balance each other are not well understood. To address these questions we performed quantitative analyses of large-scale 'omics' datasets from yeast, fly, plant, mouse and human. We uncovered an asymmetric balance of a previously-hidden scale: Each organism contained many different kinase genes, and these were balanced by a small set of highly abundant phosphatase proteins. Kinases were much more responsive to perturbations at the gene and protein levels. In addition, kinases had diverse scales of phenotypic impact when manipulated. Phosphatases, in contrast, were stable, highly robust and flatly organized, with rather uniform impact downstream. We validated aspects of this organization experimentally in nematode, and supported additional aspects by theoretic analysis of the dynamics of protein phosphorylation. Our analyses explain the empirical bias in the protein phosphorylation field toward characterization and therapeutic targeting of kinases at the expense of phosphatases. We show quantitatively and broadly that this is not only a historical bias, but stems from wide-ranging differences in their organization and impact. The asymmetric balance between these opposing regulators of protein phosphorylation is also common to opposing regulators of two other post-translational modification systems, suggesting its fundamental value.

Protein phosphorylation is a reversible modification that underlies cellular responses to stimuli across organisms. Historically, the study of protein phosphorylation concentrated on the role of kinases, which introduce the phosphate, at the expense of phosphatases, which remove it. Many kinases have been associated with specific phenotypes and considered attractive drug targets, while phosphatases remained far less characterized. It has been unclear whether this discrepancy is due to historical biases or reflects real systemic differences between these enzymes. By analyzing large-scale ‘omics’ datasets across genes, transcripts, proteins, interactions, and organisms, we uncovered an asymmetric architecture of kinases versus phosphatases that balances between them, determines their distinct impact patterns, and affects their therapeutic potential. This architecture is conserved from yeast to human and is partially shared by two other protein modification systems, suggesting it is a general feature of these systems.

Using gene networks in EvoDevo analyses

An intricate web of regulatory relationships between DNA, RNA, proteins and metabolites regulates how organisms achieve form and function. Genome sequencing combined with computational methods has allowed us to look at diverse readouts and generate a comprehensive framework for how molecules generate morphological phenotypes. RNAseq has evolved and proved useful for identifying links between transcription factor activity and transcript abundance, and for the generation of transcriptomes in non-model species through de novo assembly. Gene coexpression networks, combined with differential correlation analysis, offer the most versatile gene interaction exploratory tools, using gene expression patterns to determine potential associations and modularity of gene interactions and the differential associations between networks. Networks incorporating different types of biological data can help reduce the complexity of the entirety of biological information into discrete and interpretable pieces of data that can be the starting point for hypothesis construction and testing.

Transcriptomic Analysis of the Activity of a Novel Polymyxin against Staphylococcus aureus

S. aureus is currently one of the most pervasive multidrug-resistant pathogens and commonly causes nosocomial infections. Clinicians are faced with a dwindling armamentarium to treat infections caused by S. aureus, as resistance develops to current antibiotics. This accentuates the urgent need for antimicrobial drug discovery. In the present study, we characterized the global gene expression profile of S. aureus treated with FADDI-019, a novel synthetic polymyxin analogue. In contrast to the concentration-dependent killing and rapid regrowth in Gram-negative bacteria treated with polymyxin B and colistin, FADDI-019 killed S. aureus progressively without regrowth at 24 h. Notably, FADDI-019 activated several vancomycin resistance genes and significantly downregulated the expression of a number of virulence determinants and enterotoxin genes. A synergistic combination with sulfamethoxazole was predicted by pathway analysis and demonstrated experimentally. This is the first study revealing the transcriptomics of S. aureus treated with a novel synthetic polymyxin analog.

Polymyxin B and colistin are exclusively active against Gram-negative pathogens and have been used in the clinic as a last-line therapy. In this study, we investigated the antimicrobial activity of a novel polymyxin, FADDI-019, against Staphylococcus aureus. MIC and time-kill assays were employed to measure the activity of FADDI-019 against S. aureus ATCC 700699. Cell morphology was examined with scanning electron microscopy (SEM), and cell membrane polarity was measured using flow cytometry. Transcriptome changes caused by FADDI-019 treatment were investigated using transcriptome sequencing (RNA-Seq). Pathway analysis was conducted to examine the mechanism of the antibacterial activity of FADDI-019 and to rationally design a synergistic combination. Polymyxin B and colistin were not active against S. aureus strains with MICs of >128 mg/liter; however, FADDI-019 had a MIC of 16 mg/liter. Time-kill assays revealed that no S. aureus regrowth was observed after 24 h at 2× to 4× MIC of FADDI-019. Scanning electron microscopy (SEM) and flow cytometry results indicated that FADDI-019 treatment had no effect on cell morphology but caused membrane depolarization. The vancomycin resistance genes vraRS, as well as the VraRS regulon, were activated by FADDI-019. Virulence determinants controlled by SaeRS and the expression of enterotoxin genes yent2, sei, sem, and seo were significantly downregulated by FADDI-019. Pathway analysis of transcriptomic data was predictive of a synergistic combination comprising FADDI-019 and sulfamethoxazole. Our study is the first to examine the mechanism of the killing of a novel polymyxin against S. aureus. We also show the potential of transcriptomic and pathway analysis as tools to design synergistic antibiotic combinations.

IMPORTANCES. aureus is currently one of the most pervasive multidrug-resistant pathogens and commonly causes nosocomial infections. Clinicians are faced with a dwindling armamentarium to treat infections caused by S. aureus, as resistance develops to current antibiotics. This accentuates the urgent need for antimicrobial drug discovery. In the present study, we characterized the global gene expression profile of S. aureus treated with FADDI-019, a novel synthetic polymyxin analogue. In contrast to the concentration-dependent killing and rapid regrowth in Gram-negative bacteria treated with polymyxin B and colistin, FADDI-019 killed S. aureus progressively without regrowth at 24 h. Notably, FADDI-019 activated several vancomycin resistance genes and significantly downregulated the expression of a number of virulence determinants and enterotoxin genes. A synergistic combination with sulfamethoxazole was predicted by pathway analysis and demonstrated experimentally. This is the first study revealing the transcriptomics of S. aureus treated with a novel synthetic polymyxin analog.

Integrative analysis of cancer genes in a functional interactome

The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional interactome. Specifically, we show that drug targets tend to lie upstream of somatic mutations and disease susceptibility germline variants. In addition, we introduce a new approach to measuring hierarchy between drug targets, somatic mutants, and disease susceptibility genes by utilizing directionality and path length information. Overall, our results provide new insight into the intrinsic relationships between these node classes that broaden our understanding of cancer. In addition, our results align with current knowledge on the therapeutic actionability of GWAS and somatic mutant nodes, while demonstrating relationships between node classes from a global network perspective.

A Network of Paralogous Stress Response Transcription Factors in the Human Pathogen Candida glabrata

The yeast Candida glabrata has become the second cause of systemic candidemia in humans. However, relatively few genome-wide studies have been conducted in this organism and our knowledge of its transcriptional regulatory network is quite limited. In the present work, we combined genome-wide chromatin immunoprecipitation (ChIP-seq), transcriptome analyses, and DNA binding motif predictions to describe the regulatory interactions of the seven Yap (Yeast AP1) transcription factors of C. glabrata. We described a transcriptional network containing 255 regulatory interactions and 309 potential target genes. We predicted with high confidence the preferred DNA binding sites for 5 of the 7 CgYaps and showed a strong conservation of the Yap DNA binding properties between S. cerevisiae and C. glabrata. We provided reliable functional annotation for 3 of the 7 Yaps and identified for Yap1 and Yap5 a core regulon which is conserved in S. cerevisiae, C. glabrata, and C. albicans. We uncovered new roles for CgYap7 in the regulation of iron-sulfur cluster biogenesis, for CgYap1 in the regulation of heme biosynthesis and for CgYap5 in the repression of GRX4 in response to iron starvation. These transcription factors define an interconnected transcriptional network at the cross-roads between redox homeostasis, oxygen consumption, and iron metabolism.

Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures

Constraints placed upon the phenotypes of organisms result from their interactions with the environment. Over evolutionary time scales, these constraints feed back onto smaller molecular subnetworks comprising the organism. The evolution of biological networks is studied by considering a network of a few nodes embedded in a larger context. Taking into account this fact that any network under study is actually embedded in a larger context, we define network architecture, not on the basis of physical interactions alone, but rather as a specification of the manner in which constraints are placed upon the states of its nodes. We show that such network architectures possessing cycles in their topology, in contrast to those that do not, may be subjected to unsatisfiable constraints. This may be a significant factor leading to selection biased against those network architectures where such inconsistent constraints are more likely to arise. We proceed to quantify the likelihood of inconsistency arising as a function of network architecture finding that, in the absence of sampling bias over the space of possible constraints and for a given network size, networks with a larger number of cycles are more likely to have unsatisfiable constraints placed upon them. Our results identify a constraint that, at least in isolation, would contribute to a bias in the evolutionary process towards more hierarchical -modular versus completely connected network architectures. Together, these results highlight the context dependence of the functionality of biological networks.

Affinity and competition for TBP are molecular determinants of gene expression noise

Cell-to-cell variation in gene expression levels (noise) generates phenotypic diversity and is an important phenomenon in evolution, development and disease. TATA-box binding protein (TBP) is an essential factor that is required at virtually every eukaryotic promoter to initiate transcription. While the presence of a TATA-box motif in the promoter has been strongly linked with noise, the molecular mechanism driving this relationship is less well understood. Through an integrated analysis of multiple large-scale data sets, computer simulation and experimental validation in yeast, we provide molecular insights into how noise arises as an emergent property of variable binding affinity of TBP for different promoter sequences, competition between interaction partners to bind the same surface on TBP (to either promote or disrupt transcription initiation) and variable residence times of TBP complexes at a promoter. These determinants may be fine-tuned under different conditions and during evolution to modulate eukaryotic gene expression noise.

Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants

RNA Polymerase II (Pol II) regulatory cascades involving transcription factors (TFs) and their targets orchestrate the genetic circuitry of every eukaryotic organism. In order to understand how these cascades function, they can be dissected into small genetic networks, each containing just a few Pol II transcribed genes, that generate specific signal-processing outcomes. Small RNA regulatory circuits involve direct regulation of a small RNA by a TF and/or direct regulation of a TF by a small RNA and have been shown to play unique roles in many organisms. Here, we will focus on small RNA regulatory circuits containing Pol II transcribed microRNAs (miRNAs). While the role of miRNA-containing regulatory circuits as modular building blocks for the function of complex networks has long been on the forefront of studies in the animal kingdom, plant studies are poised to take a lead role in this area because of their advantages in probing transcriptional and posttranscriptional control of Pol II genes. The relative simplicity of tissue- and cell-type organization, miRNA targeting, and genomic structure make the Arabidopsis thaliana plant model uniquely amenable for small RNA regulatory circuit studies in a multicellular organism. In this Review, we cover analysis, tools, and validation methods for probing the component interactions in miRNA-containing regulatory circuits. We then review the important roles that plant miRNAs are playing in these circuits and summarize methods for the identification of small genetic circuits that strongly influence plant function. We conclude by noting areas of opportunity where new plant studies are imminently needed.

Multi-hierarchical profiling: an emerging and quantitative approach to characterizing diverse biological networks

Multi-hierarchical profiling may offer valuable insights into the structural stability and functional direction of biological networks in cellular development, pathological process and disease variation. Owing to the emergence of several new techniques, such as bioinformatics for omics data, structural biology and structural bioinformatics, the pace of network hierarchical research has accelerated a lot in recent years. Here, we discuss and compare the techniques available for quantifying multilevel hierarchies, with a focus on their features, capabilities and drawbacks when used for different applications. Then, we classify these methods into three types: topological spatial-scales, multilevel hierarchical control and feature ordering. We observe that challenges and limitations do exist in functional hierarchical identification. And, we also provide useful suggestions on how to analyze the dynamic data of complex network studies.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation

Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that noncognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here, we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative nonequilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and predicts increased noise in gene expression as a trade-off for improved specificity. Using information theory, we quantify this trade-off to find when optimal proofreading architectures are favored over their equilibrium counterparts. Such architectures exhibit significant super-Poisson noise at low expression in steady state.

Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering

Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.

Construction and analysis of dynamic transcription factor regulatory networks in the progression of glioma

The combinatorial cross-regulation of transcription factors (TFs) plays an important role in cellular identity and function; however, the dynamic regulation of TFs during glioma progression remains largely unknown. Here, we used the genome-wide expression of TFs to construct an extensive human TF network comprising interactions among 513 TFs and to analyse the dynamics of the TF-TF network during glioma progression. We found that the TF regulatory networks share a common architecture and that the topological structures are conserved. Strikingly, despite the conservation of the network architecture, TF regulatory networks are highly grade specific, and TF circuitry motifs are dynamically rewired during glioma progression. In addition, the most frequently observed structure in the grade-specific TF networks was the feedforward loop (FFL). We described applications that show how investigating the behaviour of FFLs in glioblastoma can reveal FFLs (such as RARG-NR1I2-CDX2) that are associated with prognosis. We constructed comprehensive TF-TF networks and systematically analysed the circuitry, dynamics, and topological principles of the networks during glioma progression, which will further enhance our understanding of the functions of TFs in glioma.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

A gene regulatory network (GRN) represents how some genes encoding regulatory molecules such as transcription factors or microRNAs regulate the expression of other genes. Researchers commonly study GRNs involved in a specific biological process with the aim of identifying a few important regulatory genes. In higher organisms such as humans, a regulatory gene regulates multiple target genes and correspondingly any gene is regulated by multiple regulatory genes. Due to such multiplicity of interactions, a GRN usually resembles a tangled hairball wherein it is difficult to identify few most influential regulatory genes. In this study, we show that network analysis algorithms such as K-core, pagerank and betweenness centrality are useful for identifying a few important or core regulatory genes in a GRN, and the K-core algorithm is also useful for organizing regulatory genes in a hierarchical layered structure where the most influential genes in a GRN are found within the innermost layer or core. These few core regulatory genes determine to a large extent the expression status of the remaining genes in the network. We illustrate a pragmatic application of this technique to GRNs reconstructed from genome-wide gene expression measurements in the MCF-7 human breast cancer cell line.