Making sense of transcription networks

Gene Regulatory Networks: Improving Inferences with Transfer Learning

Deep transfer learning improves the inference of gene regulatory networks in human cells, reveals disease-associated genes, and identifies network-based druggable targets in human heart disease.

Multi-omic network analysis identified betacellulin as a novel target of omega-3 fatty acid attenuation of western diet-induced nonalcoholic steatohepatitis

Clinical and preclinical studies established that supplementing diets with ω3 polyunsaturated fatty acids (PUFA) can reduce hepatic dysfunction in nonalcoholic steatohepatitis (NASH) but molecular underpinnings of this action were elusive. Herein, we used multi-omic network analysis that unveiled critical molecular pathways involved in ω3 PUFA effects in a preclinical mouse model of western diet induced NASH. Since NASH is a precursor of liver cancer, we also performed meta-analysis of human liver cancer transcriptomes that uncovered betacellulin as a key EGFR-binding protein upregulated in liver cancer and downregulated by ω3 PUFAs in animals and humans with NASH. We then confirmed that betacellulin acts by promoting proliferation of quiescent hepatic stellate cells, inducing transforming growth factor-β2 and increasing collagen production. When used in combination with TLR2/4 agonists, betacellulin upregulated integrins in macrophages thereby potentiating inflammation and fibrosis. Taken together, our results suggest that suppression of betacellulin is one of the key mechanisms associated with anti-inflammatory and anti-fibrotic effects of ω3 PUFA on NASH.

A synthetic biology approach to engineering circuits in immune cells

A synthetic circuit in a biological system involves the designed assembly of genetic elements, biomolecules, or cells to create a defined function. These circuits are central in synthetic biology, enabling the reprogramming of cellular behavior and the engineering of cells with customized responses. In cancer therapeutics, engineering T cells with circuits have the potential to overcome the challenges of current approaches, for example, by allowing specific recognition and killing of cancer cells. Recent advances also facilitate engineering integrated circuits for the controlled release of therapeutic molecules at specified locations, for example, in a solid tumor. In this review, we discuss recent strategies and applications of synthetic receptor circuits aimed at enhancing immune cell functions for cancer immunotherapy. We begin by introducing the concept of circuits in networks at the molecular and cellular scales and provide an analysis of the development and implementation of several synthetic circuits in T cells that have the goal to overcome current challenges in cancer immunotherapy. These include specific targeting of cancer cells, increased T-cell proliferation, and persistence in the tumor microenvironment. By harnessing the power of synthetic biology, and the characteristics of certain circuit architectures, it is now possible to engineer a new generation of immune cells that recognize cancer cells, while minimizing off-target toxicities. We specifically discuss T-cell circuits for antigen density sensing. These circuits allow targeting of solid tumors that share antigens with normal tissues. Additionally, we explore designs for synthetic circuits that could control T-cell differentiation or T-cell fate as well as the concept of synthetic multicellular circuits that leverage cellular communication and division of labor to achieve improved therapeutic efficacy. As our understanding of cell biology expands and novel tools for genome, protein, and cell engineering are developed, we anticipate further innovative approaches to emerge in the design and engineering of circuits in immune cells.

The duplication of genomes and genetic networks and its potential for evolutionary adaptation and survival during environmental turmoil

The importance of whole-genome duplication (WGD) for evolution is controversial. Whereas some view WGD mainly as detrimental and an evolutionary dead end, there is growing evidence that polyploidization can help overcome environmental change, stressful conditions, or periods of extinction. However, despite much research, the mechanistic underpinnings of why and how polyploids might be able to outcompete or outlive nonpolyploids at times of environmental upheaval remain elusive, especially for autopolyploids, in which heterosis effects are limited. On the longer term, WGD might increase both mutational and environmental robustness due to redundancy and increased genetic variation, but on the short-or even immediate-term, selective advantages of WGDs are harder to explain. Here, by duplicating artificially generated Gene Regulatory Networks (GRNs), we show that duplicated GRNs-and thus duplicated genomes-show higher signal output variation than nonduplicated GRNs. This increased variation leads to niche expansion and can provide polyploid populations with substantial advantages to survive environmental turmoil. In contrast, under stable environments, GRNs might be maladaptive to changes, a phenomenon that is exacerbated in duplicated GRNs. We believe that these results provide insights into how genome duplication and (auto)polyploidy might help organisms to adapt quickly to novel conditions and to survive ecological uproar or even cataclysmic events.

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.

Evolution of a new form of haploid-specific gene regulation appearing in a limited clade of ascomycete yeast species

Over evolutionary timescales, the logic and pattern of cell-type specific gene expression can remain constant, yet the molecular mechanisms underlying such regulation can drift between alternative forms. Here, we document a new example of this principle in the regulation of the haploid-specific genes in a small clade of fungal species. For most ascomycete fungal species, transcription of these genes is repressed in the a/α cell type by a heterodimer of two homeodomain proteins, Mata1 and Matα2. We show that in the species Lachancea kluyveri, most of the haploid-specific genes are regulated in this way, but repression of one haploid-specific gene (GPA1) requires, in addition to Mata1 and Matα2, a third regulatory protein, Mcm1. Model building, based on x-ray crystal structures of the three proteins, rationalizes the requirement for all three proteins: no single pair of the proteins is optimally arranged, and we show that no single pair can bring about repression. This case study exemplifies the idea that the energy of DNA binding can be "shared out" in different ways and can result in different DNA-binding solutions across different genes-while maintaining the same overall pattern of gene expression.

Leukemia core transcriptional circuitry is a sparsely interconnected hierarchy stabilized by incoherent feed-forward loops

Lineage-defining transcription factors form densely interconnected circuits in chromatin occupancy assays, but the functional significance of these networks remains underexplored. We reconstructed the functional topology of a leukemia cell transcription network from the direct gene-regulatory programs of eight core transcriptional regulators established in pre-steady state assays coupling targeted protein degradation with nascent transcriptomics. The core regulators displayed narrow, largely non-overlapping direct transcriptional programs, forming a sparsely interconnected functional hierarchy stabilized by incoherent feed-forward loops. BET bromodomain and CDK7 inhibitors disrupted the core regulators' direct programs, acting as mixed agonists/antagonists. The network is predictive of dynamic gene expression behaviors in time-resolved assays and clinically relevant pathway activity in patient populations.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

Reinforcement amid genetic diversity in the Candida albicans biofilm regulatory network

Biofilms of the fungal pathogen Candida albicans include abundant long filaments called hyphae. These cells express hypha-associated genes, which specify diverse virulence functions including surface adhesins that ensure biofilm integrity. Biofilm formation, virulence, and hypha-associated gene expression all depend upon the transcription factor Efg1. This transcription factor has been characterized extensively in the C. albicans type strain SC5314 and derivatives, but only recently has its function been explored in other clinical isolates. Here we define a principal set of Efg1-responsive genes whose expression is significantly altered by an efg1Δ/Δ mutation across 17 clinical isolates. This principal gene set includes 68 direct Efg1 targets, whose 5' regions are bound by Efg1 in five clinical isolates, and 42 indirect Efg1 targets, whose 5' regions are not detectably bound by Efg1. Three direct Efg1 target genes encode transcription factors-BRG1, UME6, and WOR3 -whose increased expression in an efg1Δ/Δ mutant restores expression of multiple indirect and direct principal targets, as well as biofilm formation ability. Although BRG1 and UME6 are well known positive regulators of hypha-associated genes and biofilm formation, WOR3 is best known as an antagonist of Efg1 in the sexual mating pathway. We confirm the positive role of WOR3 in biofilm formation with the finding that a wor3Δ/Δ mutation impairs biofilm formation in vitro and in an in vivo biofilm model. Positive control of Efg1 direct target genes by other Efg1 direct target genes-BRG1, UME6, and WOR3 -may buffer principal Efg1-responsive gene expression against the impact of genetic variation in the C. albicans species.

In search of a Drosophila core cellular network with single-cell transcriptome data

Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.

Collaboration between Antagonistic Cell Type Regulators Governs Natural Variation in the Candida albicans Biofilm and Hyphal Gene Expression Network

Candida albicans is among the most significant human fungal pathogens. However, the vast majority of C. albicans studies have focused on a single clinical isolate and its marked derivatives. We investigated natural variation among clinical C. albicans isolates in gene regulatory control of biofilm formation, a process crucial to virulence. The transcription factor Efg1 is required for biofilm-associated gene expression and biofilm formation. Previously, we found extensive variation in Efg1-responsive gene expression among 5 diverse clinical isolates. However, chromatin immunoprecipitation sequencing analysis showed that Efg1 binding to genomic loci was uniform among the isolates. Functional dissection of strain differences identified three transcription factors, Brg1, Tec1, and Wor1, for which small changes in expression levels reshaped the Efg1 regulatory network. Brg1 and Tec1 are known biofilm activators, and their role in Efg1 network variation may be expected. However, Wor1 is a known repressor of EFG1 expression and an inhibitor of biofilm formation. In contrast, we found that a modest increase in WOR1 RNA levels, reflecting the expression differences between C. albicans strains, could augment biofilm formation and expression of biofilm-related genes. The analysis of natural variation here reveals a novel function for a well-characterized gene and illustrates that strain diversity offers a unique resource for elucidation of network interactions.

An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

The evolutionary rates of functionally related genes often covary. We present a gene coevolution network inferred from examining nearly 3 million orthologous gene pairs from 332 budding yeast species spanning ~400 million years of evolution. Network modules provide insight into cellular and genomic structure and function. Examination of the phenotypic impact of network perturbation using deletion mutant data from the baker's yeast Saccharomyces cerevisiae, which were obtained from previously published studies, suggests that fitness in diverse environments is affected by orthologous gene neighborhood and connectivity. Mapping the network onto the chromosomes of S. cerevisiae and Candida albicans revealed that coevolving orthologous genes are not physically clustered in either species; rather, they are often located on different chromosomes or far apart on the same chromosome. The coevolution network captures the hierarchy of cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as a linked ensemble of genes.

Evolution of the human pathogenic lifestyle in fungi

Fungal pathogens cause more than a billion human infections every year, resulting in more than 1.6 million deaths annually. Understanding the natural history and evolutionary ecology of fungi is helping us understand how disease-relevant traits have repeatedly evolved. Different types and mechanisms of genetic variation have contributed to the evolution of fungal pathogenicity and specific genetic differences distinguish pathogens from non-pathogens. Insights into the traits, genetic elements, and genetic and ecological mechanisms that contribute to the evolution of fungal pathogenicity are crucial for developing strategies to both predict emergence of fungal pathogens and develop drugs to combat them.

Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops

The regulatory relationships between genes and proteins in a cell form a gene regulatory network (GRN) that controls the cellular response to changes in the environment. A number of inference methods to reverse engineer the original GRN from large-scale expression data have recently been developed. However, the absence of ground-truth GRNs when evaluating the performance makes realistic simulations of GRNs necessary. One aspect of this is that local network motif analysis of real GRNs indicates that the feed-forward loop (FFL) is significantly enriched. To simulate this properly, we developed a novel motif-based preferential attachment algorithm, FFLatt, which outperformed the popular GeneNetWeaver network generation tool in reproducing the FFL motif occurrence observed in literature-based biological GRNs. It also preserves important topological properties such as scale-free topology, sparsity, and average in/out-degree per node. We conclude that FFLatt is well-suited as a network generation module for a benchmarking framework with the aim to provide fair and robust performance evaluation of GRN inference methods.

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.

Clustering of Sex-Biased Genes and Transposable Elements in the Genome of the Medaka Fish Oryzias latipes

Although genes with similar expression patterns are sometimes found in the same genomic regions, almost nothing is known about the relative organization in genomes of genes and transposable elements (TEs), which might influence each other at the regulatory level. In this study, we used transcriptomic data from male and female gonads of the Japanese medaka Oryzias latipes to define sexually biased genes and TEs and analyze their relative genomic localization. We identified 20,588 genes expressed in the adult gonads of O. latipes. Around 39% of these genes are differentially expressed between male and female gonads. We further analyzed the expression of TEs using the program SQuIRE and showed that more TE copies are overexpressed in testis than in ovaries (36% vs. 10%, respectively). We then developed a method to detect genomic regions enriched in testis- or ovary-biased genes. This revealed that sex-biased genes and TEs are not randomly distributed in the genome and a part of them form clusters with the same expression bias. We also found a correlation of expression between TE copies and their closest genes, which increases with decreasing intervening distance. Such a genomic organization suggests either that TEs hijack the regulatory sequences of neighboring sexual genes, allowing their expression in germ line cells and consequently new insertions to be transmitted to the next generation, or that TEs are involved in the regulation of sexual genes, and might therefore through their mobility participate in the rewiring of sex regulatory networks.

Genome-Wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a large-scale application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a bacterium. We applied DAP-seq to > 90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA-, and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike toward a desired phenotype.

Plant cell cultures as heterologous bio-factories for secondary metabolite production

Synthetic biology has been developing rapidly in the last decade and is attracting increasing attention from many plant biologists. The production of high-value plant-specific secondary metabolites is, however, limited mostly to microbes. This is potentially problematic because of incorrect post-translational modification of proteins and differences in protein micro-compartmentalization, substrate availability, chaperone availability, product toxicity, and cytochrome p450 reductase enzymes. Unlike other heterologous systems, plant cells may be a promising alternative for the production of high-value metabolites. Several commercial plant suspension cell cultures from different plant species have been used successfully to produce valuable metabolites in a safe, low cost, and environmentally friendly manner. However, few metabolites are currently being biosynthesized using plant platforms, with the exception of the natural pigment anthocyanin. Both Arabidopsis thaliana and Nicotiana tabacum cell cultures can be developed by multiple gene transformations and CRISPR-Cas9 genome editing. Given that the introduction of heterologous biosynthetic pathways into Arabidopsis and N. tabacum is not widely used, the biosynthesis of foreign metabolites is currently limited; however, therein lies great potential. Here, we discuss the exemplary use of plant cell cultures and prospects for using A. thaliana and N. tabacum cell cultures to produce valuable plant-specific metabolites.

A Time-Saving Strategy to Generate Double Maternal Mutants by an Oocyte-Specific Conditional Knockout System in Zebrafish

Simple Summary

Maternally supplied mRNAs and proteins, termed maternal factors, are produced by over 14,000 coding genes in zebrafish. They play exclusive roles in controlling the formation of oocytes and the development of early embryos. These maternal factors can also compensate for the loss of function of its corresponding zygotic gene products. Thus, eliminating both maternal and zygotic gene products is essential to elucidate the functions of more than half of zebrafish genes. However, it is always challenging to inactivate maternal factors, because traditional genetic methods are either technically demanding or time-consuming. Our recent work established a rapid conditional knockout method to generate maternal or maternal and zygotic mutants in one fish generation. Here, we further test the feasibility of this approach to knock out two maternal genes with functional redundancy simultaneously. As a proof of principle, we successfully generated double maternal mutant embryos for dvl2 and dvl3a genes in three months for the first time. The cell movement defects in mutant embryos obtained by this approach mimic the genuine mutant embryos generated after fifteen months of time-consuming screening following the previously reported mosaic strategy. Therefore, this method has the potential to speed up the functional study of paralogous maternal genes.

Abstract

Maternal products are those mRNAs and proteins deposited during oogenesis, which play critical roles in controlling oocyte formation, fertilization, and early embryonic development. However, loss-of-function studies for these maternal factors are still lacking, mainly because of the prolonged period of transgenerational screening and technical barriers that prevent the generation of maternal (M) and maternal and zygotic (MZ) mutant embryos. By the transgenic expression of multiple sgRNAs targeting a single gene of interest in the background of a transgenic line Tg(zpc:zcas9) with oocyte-specific cas9 expression, we have successfully obtained maternal or maternal–zygotic mutant for single genes in F1 embryos. In this work, we tandemly connected a maternal GFP marker and eight sgRNA expression units to target dvl2 and dvl3a simultaneously and introduced this construct to the genome of Tg(zpc:zcas9) by meganuclease I-Sce I. As expected, we confirmed the existence of Mdvl2;Mdvl3a embryos with strong defective convergence and extension movement during gastrulation among outcrossed GFP positive F1 offspring. The MZdvl2;MZdvl3a embryos were also obtained by crossing the mutant carrying mosaic F0 female with dvl2^+/−;dvl3a^−/− male fish. This proof-of-principle thus highlights the potential of this conditional knockout strategy to circumvent the current difficulty in the study of genes with multiple functionally redundant paralogs.

Transcription Factors in the Fungus Aspergillus nidulans: Markers of Genetic Innovation, Network Rewiring and Conflict between Genomics and Transcriptomics

Gene regulatory networks (GRNs) are shaped by the democratic/hierarchical relationships among transcription factors (TFs) and associated proteins, together with the cis-regulatory sequences (CRSs) bound by these TFs at target promoters. GRNs control all cellular processes, including metabolism, stress response, growth and development. Due to the ability to modify morphogenetic and developmental patterns, there is the consensus view that the reorganization of GRNs is a driving force of species evolution and differentiation. GRNs are rewired through events including the duplication of TF-coding genes, their divergent sequence evolution and the gain/loss/modification of CRSs. Fungi (mainly Saccharomycotina) have served as a reference kingdom for the study of GRN evolution. Here, I studied the genes predicted to encode TFs in the fungus Aspergillus nidulans (Pezizomycotina). The analysis of the expansion of different families of TFs suggests that the duplication of TFs impacts the species level, and that the expansion in Zn2Cys6 TFs is mainly due to dispersed duplication events. Comparison of genomic annotation and transcriptomic data suggest that a significant percentage of genes should be re-annotated, while many others remain silent. Finally, a new regulator of growth and development is identified and characterized. Overall, this study establishes a novel theoretical framework in synthetic biology, as the overexpression of silent TF forms would provide additional tools to assess how GRNs are rewired.

Recurrent Loss of abaA, a Master Regulator of Asexual Development in Filamentous Fungi, Correlates with Changes in Genomic and Morphological Traits

Gene regulatory networks (GRNs) drive developmental and cellular differentiation, and variation in their architectures gives rise to morphological diversity. Pioneering studies in Aspergillus fungi, coupled with subsequent work in other filamentous fungi, have shown that the GRN governed by the BrlA, AbaA, and WetA proteins controls the development of the asexual fruiting body or conidiophore. A specific aspect of conidiophore development is the production of phialides, conidiophore structures that are under the developmental control of AbaA and function to repetitively generate spores. Fungal genome sequencing has revealed that some filamentous fungi lack abaA, and also produce asexual structures that lack phialides, raising the hypothesis that abaA loss is functionally linked to diversity in asexual fruiting body morphology. To examine this hypothesis, we carried out an extensive search for the abaA gene across 241 genomes of species from the fungal subphylum Pezizomycotina. We found that abaA was independently lost in four lineages of Eurotiomycetes, including from all sequenced species within the order Onygenales, and that all four lineages that have lost abaA also lack the ability to form phialides. Genetic restoration of abaA from Aspergillus nidulans into Histoplasma capsulatum, a pathogenic species from the order Onygenales that lacks an endogenous copy of abaA, did not alter Histoplasma conidiation morphology but resulted in a marked increase in spore viability. We also discovered that species lacking abaA contain fewer AbaA binding motifs in the regulatory regions of orthologs of some AbaA target genes, suggesting that the asexual fruiting body GRN of organisms that have lost abaA has likely been rewired. Our results provide an illustration of how repeated losses of a key regulatory transcription factor have contributed to the diversity of an iconic fungal morphological trait.

Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality

Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the exact steady-state probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the exact topological features of their high-dimensional probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 probability landscapes, where each landscape resides over 10⁵–10⁶ microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.

Network motif-based analysis of regulatory patterns in paralogous gene pairs

Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as S. cerevisiae, and others. The availability of such networks provides an opportunity to compare gene regulatory processes at the whole genome level, and in particular, to assess similarity of regulatory interactions for homologous gene pairs either from the same or from different species. We present here a new technique for analyzing the regulatory interaction neighborhoods of paralogous gene pairs. Our central focus is the analysis of S. cerevisiae gene interaction graphs, which are of particular interest due to the ancestral whole-genome duplication (WGD) that allows to distinguish between paralogous transcription factors that are traceable to this duplication event and other paralogues. Similar analysis is also applied to E. coli and C. elegans networks. We compare paralogous gene pairs according to the presence and size of bi-fan arrays, classically associated in the literature with gene duplication, within other network motifs. We further extend this framework beyond transcription factor comparison to obtain topology-based similarity metrics based on the overlap of interaction neighborhoods applicable to most genes in a given organism. We observe that our network divergence metrics show considerably larger similarity between paralogues, especially those traceable to WGD. This is the case for both yeast and C. elegans, but not for E. coli regulatory network. While there is no obvious cross-species link between metrics, different classes of paralogues show notable differences in interaction overlap, with traceable duplications tending toward higher overlap compared to genes with shared protein families. Our findings indicate that divergence in paralogous interaction networks reflects a shared genetic origin, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.

Intraspecies Transcriptional Profiling Reveals Key Regulators of Candida albicans Pathogenic Traits

The human commensal and opportunistic fungal pathogen Candida albicans displays extensive genetic and phenotypic variation across clinical isolates. Here, we performed RNA sequencing on 21 well-characterized isolates to examine how genetic variation contributes to gene expression differences and to link these differences to phenotypic traits. C. albicans adapts primarily through clonal evolution, and yet hierarchical clustering of gene expression profiles in this set of isolates did not reproduce their phylogenetic relationship. Strikingly, strain-specific gene expression was prevalent in some strain backgrounds. Association of gene expression with phenotypic data by differential analysis, linear correlation, and assembly of gene networks connected both previously characterized and novel genes with 23 C. albicans traits. Construction of de novo gene modules produced a gene atlas incorporating 67% of C. albicans genes and revealed correlations between expression modules and important phenotypes such as systemic virulence. Furthermore, targeted investigation of two modules that have novel roles in growth and filamentation supported our bioinformatic predictions. Together, these studies reveal widespread transcriptional variation across C. albicans isolates and identify genetic and epigenetic links to phenotypic variation based on coexpression network analysis.IMPORTANCE Infectious fungal species are often treated uniformly despite clear evidence of genotypic and phenotypic heterogeneity being widespread across strains. Identifying the genetic basis for this phenotypic diversity is extremely challenging because of the tens or hundreds of thousands of variants that may distinguish two strains. Here, we use transcriptional profiling to determine differences in gene expression that can be linked to phenotypic variation among a set of 21 Candida albicans isolates. Analysis of this transcriptional data set uncovered clear trends in gene expression characteristics for this species and new genes and pathways that were associated with variation in pathogenic processes. Direct investigation confirmed functional predictions for a number of new regulators associated with growth and filamentation, demonstrating the utility of these approaches in linking genes to important phenotypes.

Evolution of the complex transcription network controlling biofilm formation in Candida species

We examine how a complex transcription network composed of seven 'master' regulators and hundreds of target genes evolved over a span of approximately 70 million years. The network controls biofilm formation in several Candida species, a group of fungi that are present in humans both as constituents of the microbiota and as opportunistic pathogens. Using a variety of approaches, we observed two major types of changes that have occurred in the biofilm network since the four extant species we examined last shared a common ancestor. Master regulator 'substitutions' occurred over relatively long evolutionary times, resulting in different species having overlapping but different sets of master regulators of biofilm formation. Second, massive changes in the connections between the master regulators and their target genes occurred over much shorter timescales. We believe this analysis is the first detailed, empirical description of how a complex transcription network has evolved.

The Arabidopsis zinc finger proteins SRG2 and SRG3 are positive regulators of plant immunity and are differentially regulated by nitric oxide

Nitric oxide (NO) regulates the deployment of a phalanx of immune responses, chief among which is the activation of a constellation of defence-related genes. However, the underlying molecular mechanisms remain largely unknown. The Arabidopsis thaliana zinc finger transcription factor (ZF-TF), S-nitrosothiol (SNO) Regulated 1 (SRG1), is a central target of NO bioactivity during plant immunity. Here we characterize the remaining members of the SRG gene family. Both SRG2 and, especially, SRG3 were positive regulators of salicylic acid-dependent plant immunity. Analysis of SRG single, double and triple mutants implied that SRG family members have additive functions in plant immunity and, surprisingly, are under reciprocal regulation. SRG2 and SRG3 localized to the nucleus and functioned as ethylene-responsive element binding factor-associated amphiphilic repression (EAR) domain-dependent transcriptional repressors: NO abolished this activity for SRG3 but not for SRG2. Consistently, loss of GSNOR function, resulting in increased (S)NO concentrations, fully suppressed the disease resistance phenotype established from SRG3 but not SRG2 overexpression. Remarkably, SRG3 but not SRG2 was S-nitrosylated in vitro and in vivo. Our findings suggest that the SRG family has separable functions in plant immunity, and, surprisingly, these ZF-TFs exhibit reciprocal regulation. It is remarkable that, through neofunctionalization, the SRG family has evolved to become differentially regulated by the key immune-related redox cue, NO.

Constructive Neutral Evolution 20 Years Later

Evolution has led to a great diversity that ranges from elegant simplicity to ornate complexity. Many complex features are often assumed to be more functional or adaptive than their simpler alternatives. However, in 1999, Arlin Stolzfus published a paper in the Journal of Molecular Evolution that outlined a framework in which complexity can arise through a series of non-adaptive steps. He called this framework Constructive Neutral Evolution (CNE). Despite its two-decade-old roots, many evolutionary biologists still appear to be unaware of this explanatory framework for the origins of complexity. In this perspective piece, we explain the theory of CNE and how it changes the order of events in narratives that describe the evolution of complexity. We also provide an extensive list of cellular features that may have become more complex through CNE. We end by discussing strategies to determine whether complexity arose through neutral or adaptive processes.

Transcriptional Circuits Regulating Developmental Processes in Candida albicans

Candida albicans is a commensal member of the human microbiota that colonizes multiple niches in the body including the skin, oral cavity, and gastrointestinal and genitourinary tracts of healthy individuals. It is also the most common human fungal pathogen isolated from patients in clinical settings. C. albicans can cause a number of superficial and invasive infections, especially in immunocompromised individuals. The ability of C. albicans to succeed as both a commensal and a pathogen, and to thrive in a wide range of environmental niches within the host, requires sophisticated transcriptional regulatory programs that can integrate and respond to host specific environmental signals. Identifying and characterizing the transcriptional regulatory networks that control important developmental processes in C. albicans will shed new light on the strategies used by C. albicans to colonize and infect its host. Here, we discuss the transcriptional regulatory circuits controlling three major developmental processes in C. albicans: biofilm formation, the white-opaque phenotypic switch, and the commensal-pathogen transition. Each of these three circuits are tightly knit and, through our analyses, we show that they are integrated together by extensive regulatory crosstalk between the core regulators that comprise each circuit.

Modeling the evolution of interconnected processes: It is the song and the singers: Tracking units of selection with interaction networks

Recently, Doolittle and Inkpen formulated a thought provoking theory, asserting that evolution by natural selection was responsible for the sideways evolution of two radically different kinds of selective units (also called Domains). The former entities, termed singers, correspond to the usual objects studied by evolutionary biologists (gene, genomes, individuals, species, etc.), whereas the later, termed songs, correspond to re-produced biological and ecosystemic functions, processes, information, and memes. Singers perform songs through selected patterns of interactions, meaning that a wealth of critical phenomena might receive novel evolutionary explanations. However, this theory did not provide an empirical approach to study evolution in such a broadened context. Here, we show that analyzing songs and singers, using patterns of interaction networks as a common ontology for both, offers a novel, actionable, inclusive and mathematical way to analyze not only the re-production but also the evolution and fitness of biological and ecosystemic interconnected processes.

Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.

Motifs enable communication efficiency and fault-tolerance in transcriptional networks

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets.

Evolution of Plant Metabolism: A (Bio)synthesis

Studying the evolution of metabolism is technically challenging. A new study combining in silico metabolic maps and phylogenomics allows reconstructing the diversification of plant metabolism across one billion years of evolution.

Protein-coding changes preceded cis-regulatory gains in a newly evolved transcription circuit

Changes in both the coding sequence of transcriptional regulators and in the cis-regulatory sequences recognized by these regulators have been implicated in the evolution of transcriptional circuits. However, little is known about how they evolved in concert. We describe an evolutionary pathway in fungi where a new transcriptional circuit (a-specific gene repression by the homeodomain protein Matα2) evolved by coding changes in this ancient regulator, followed millions of years later by cis-regulatory sequence changes in the genes of its future regulon. By analyzing a group of species that has acquired the coding changes but not the cis-regulatory sites, we show that the coding changes became necessary for the regulator's deeply conserved function, thereby poising the regulator to jump-start formation of the new circuit.

Sensitivities of Regulation Intensities in Feed-Forward Loops with Multistability

Gene regulatory networks depict the interactions among genes, proteins, and other components of the cell. These interactions are stochastic when large differences in reaction rates and small copy number of molecules are involved. Discrete Chemical Master Equation (dCME) provides a general framework for understanding the stochastic nature of these networks. Here we used the Accurate Chemical Master Equation method to directly compute the exact steady state probability landscape of the feed-forward loop motif (FFL). FFL is one of the most abundant gene regulatory networks motifs where the regulation is carried out from the top nodes to the bottom ones. We examine the behavior of stochastic FFLs under different conditions of various regulation intensities. Under the conditions with slow promoter binding, we show how FFL can exhibit different multistabilities in their landscapes. We also study the sensitivities of regulations of FFLs and introduce a new definition of stochastic sensitivity to characterize how FFLs respond in their probability distributions at the steady state to perturbations of system parameters. We show how change in gene expression under FFL regulations are sensitive to system parameters, including the state of multistability in FFLs.

When is a transcription factor a NAP?

Proteins that regulate transcription often also play an architectural role in the genome. Thus, it has been difficult to define with precision the distinctions between transcription factors and nucleoid-associated proteins (NAPs). Anachronistic descriptions of NAPs as 'histone-like' implied an organizational function in a bacterial chromatin-like complex. Definitions based on protein abundance, regulatory mechanisms, target gene number, or the features of their DNA-binding sites are insufficient as marks of distinction, and trying to distinguish transcription factors and NAPs based on their ranking within regulatory hierarchies or positions in gene-control networks is also unsatisfactory. The terms 'transcription factor' and 'NAP' are ad hoc operational definitions with each protein lying along a spectrum of structural and functional features extending from highly specific actors with few gene targets to those with a pervasive influence on the transcriptome. The Streptomyces BldC protein is used to illustrate these issues.

Unraveling How Candida albicans Forms Sexual Biofilms

Biofilms, structured and densely packed communities of microbial cells attached to surfaces, are considered to be the natural growth state for a vast majority of microorganisms. The ability to form biofilms is an important virulence factor for most pathogens, including the opportunistic human fungal pathogen Candida albicans. C. albicans is one of the most prevalent fungal species of the human microbiota that asymptomatically colonizes healthy individuals. However, C. albicans can also cause severe and life-threatening infections when host conditions permit (e.g., through alterations in the host immune system, pH, and resident microbiota). Like many other pathogens, this ability to cause infections depends, in part, on the ability to form biofilms. Once formed, C. albicans biofilms are often resistant to antifungal agents and the host immune response, and can act as reservoirs to maintain persistent infections as well as to seed new infections in a host. The majority of C. albicans clinical isolates are heterozygous (a/α) at the mating type-like (MTL) locus, which defines Candida mating types, and are capable of forming robust biofilms when cultured in vitro. These “conventional” biofilms, formed by MTL-heterozygous (a/α) cells, have been the primary focus of C. albicans biofilm research to date. Recent work in the field, however, has uncovered novel mechanisms through which biofilms are generated by C. albicans cells that are homozygous or hemizygous (a/a, a/Δ, α/α, or α/Δ) at the MTL locus. In these studies, the addition of pheromones of the opposite mating type can induce the formation of specialized “sexual” biofilms, either through the addition of synthetic peptide pheromones to the culture, or in response to co-culturing of cells of the opposite mating types. Although sexual biofilms are generally less robust than conventional biofilms, they could serve as a protective niche to support genetic exchange between mating-competent cells, and thus may represent an adaptive mechanism to increase population diversity in dynamic environments. Although conventional and sexual biofilms appear functionally distinct, both types of biofilms are structurally similar, containing yeast, pseudohyphal, and hyphal cells surrounded by an extracellular matrix. Despite their structural similarities, conventional and sexual biofilms appear to be governed by distinct transcriptional networks and signaling pathways, suggesting that they may be adapted for, and responsive to, distinct environmental conditions. Here we review sexual biofilms and compare and contrast them to conventional biofilms of C. albicans.

Unknown to Known: Advancing Knowledge of Coral Gene Function

Given the catastrophic changes befalling coral reefs, understanding coral gene function is essential to advance reef conservation. This has proved challenging due to the paucity of genomic data and genetic tools available for corals. Recently, CRISPR/Cas9 gene editing was applied to these species; however, a major bottleneck is the identification and prioritization of candidate genes for manipulation. This issue is exacerbated by the many unknown ('dark') coral genes that may play key roles in the stress response. We review the use of gene coexpression networks that incorporate both known and unknown genes to identify targets for reverse genetic analysis. This approach also provides a framework for the annotation of dark genes in established interaction networks to improve our fundamental knowledge of coral gene function.

Identification and Characterization of Aspergillus nidulans Mutants Impaired in Asexual Development under Phosphate Stress

The transcription factor BrlA plays a central role in the production of asexual spores (conidia) in the fungus Aspergillus nidulans. BrlA levels are controlled by signal transducers known collectively as UDAs. Furthermore, it governs the expression of CDP regulators, which control most of the morphological transitions leading to the production of conidia. In response to the emergence of fungal cells in the air, the main stimulus triggering conidiation, UDA mutants such as the flbB deletant fail to induce brlA expression. Nevertheless, ΔflbB colonies conidiate profusely when they are cultured on a medium containing high H₂PO₄⁻ concentrations, suggesting that the need for FlbB activity is bypassed. We used this phenotypic trait and an UV-mutagenesis procedure to isolate ΔflbB mutants unable to conidiate under these stress conditions. Transformation of mutant FLIP166 with a wild-type genomic library led to the identification of the putative transcription factor SocA as a multicopy suppressor of the FLIP (Fluffy, aconidial, In Phosphate) phenotype. Deregulation of socA altered both growth and developmental patterns. Sequencing of the FLIP166 genome enabled the identification and characterization of PmtC^P282L as the recessive mutant form responsible for the FLIP phenotype. Overall, results validate this strategy for identifying genes/mutations related to the control of conidiation.

Sex-specific evolution of relative leg size in Drosophila prolongata results from changes in the intersegmental coordination of tissue growth

Evolution of relative organ size is the most prolific source of morphological diversity, yet the underlying molecular mechanisms that modify growth control are largely unknown. Models where organ proportions have undergone recent evolutionary changes hold the greatest promise for understanding this process. Uniquely among Drosophila species, Drosophila prolongata displays a dramatic, male-specific increase in the size of its forelegs relative to other legs. By comparing leg development between males and females of D. prolongata and its closest relative Drosophila carrolli, we show that the exaggerated male forelegs are produced by a sex- and segment-specific increase in mitosis during the final larval instar. Intersegmental compensatory control, where smaller leg primordia grow at a faster rate, is observed in both species and sexes. However, the equilibrium growth rates that determine the final relative proportion between the first and second legs have shifted in male D. prolongata compared both to conspecific females and to D. carrolli. We suggest that the observed developmental changes that produce new adult proportions reflect an interplay between conserved growth coordination mechanisms and evolving organ-specific growth targets.

Microbial life cycles link global modularity in regulation to mosaic evolution

Microbes are exposed to changing environments, to which they can respond by adopting various lifestyles such as swimming, colony formation or dormancy. These lifestyles are often studied in isolation, thereby giving a fragmented view of the life cycle as a whole. Here, we study lifestyles in the context of this whole. We first use machine learning to reconstruct the expression changes underlying life cycle progression in the bacterium Bacillus subtilis, based on hundreds of previously acquired expression profiles. This yields a timeline that reveals the modular organization of the life cycle. By analysing over 380 Bacillales genomes, we then show that life cycle modularity gives rise to mosaic evolution in which life stages such as motility and sporulation are conserved and lost as discrete units. We postulate that this mosaic conservation pattern results from habitat changes that make these life stages obsolete or detrimental. Indeed, when evolving eight distinct Bacillales strains and species under laboratory conditions that favour colony growth, we observe rapid and parallel losses of the sporulation life stage across species, induced by mutations that affect the same global regulator. We conclude that a life cycle perspective is pivotal to understanding the causes and consequences of modularity in both regulation and evolution.

Division of labor in honey bees is associated with transcriptional regulatory plasticity in the brain

Studies in evolutionary and developmental biology show that relationships between transcription factors (TFs) and their target genes can be altered to result in novel regulatory relationships that generate phenotypic plasticity. We hypothesized that context-dependent shifts in the nervous system associated with behavior may also be linked to changes in TF-target relationships over physiological time scales. We tested this hypothesis using honey bee (Apis mellifera) division of labor as a model system by performing bioinformatic analyses of previously published brain transcriptomic profiles together with new RNAi and behavioral experiments. The bioinformatic analyses identified five TFs that exhibited strong signatures of regulatory plasticity as a function of division of labor. RNAi targeting of one of these TFs (broad complex) and a related TF that did not exhibit plasticity (fushi tarazu transcription factor 1) was administered in conjunction with automated analyses of foraging behavior in the field, laboratory assays of aggression and brood care behavior, and endocrine treatments. The results showed that changes in the regulatory relationships of these TFs were associated with behavioral state, social context and endocrine state. These findings provide the first empirical evidence that TF-target relationships in the brain are altered in conjunction with behavior and social context. They also suggest that one mechanism for this plasticity involves pleiotropic TFs high up in regulatory hierarchies producing behavior-specific transcriptional responses by activating different downstream TFs to induce discrete context-dependent transcriptional cascades. These findings provide new insights into the dynamic nature of the transcriptional regulatory architecture underlying behavior in the brain.

Transcriptional Regulation of Abscission Zones

Precise and timely regulation of organ separation from the parent plant (abscission) is consequential to improvement of crop productivity as it influences both the timing of harvest and fruit quality. Abscission is tightly associated with plant fitness as unwanted organs (petals, sepals, filaments) are shed after fertilization while seeds, fruits, and leaves are cast off as means of reproductive success or in response to abiotic/biotic stresses. Floral organ abscission in Arabidopsis has been a useful model to elucidate the molecular mechanisms that underlie the separation processes, and multiple abscission signals associated with the activation and downstream pathways have been uncovered. Concomitantly, large-scale analyses of omics studies in diverse abscission systems of various plants have added valuable insights into the abscission process. The results suggest that there are common molecular events linked to the biosynthesis of a new extracellular matrix as well as cell wall disassembly. Comparative analysis between Arabidopsis and soybean abscission systems has revealed shared and yet disparate regulatory modules that affect the separation processes. In this review, we discuss our current understanding of the transcriptional regulation of abscission in several different plants that has improved on the previously proposed four-phased model of organ separation.

Reversible Disruption of Specific Transcription Factor-DNA Interactions Using CRISPR/Cas9

The control of gene expression by transcription factor binding sites frequently determines phenotype. However, it is difficult to determine the function of single transcription factor binding sites within larger transcription networks. Here, we use deactivated Cas9 (dCas9) to disrupt binding to specific sites, a method we term CRISPRd. Since CRISPR guide RNAs are longer than transcription factor binding sites, flanking sequence can be used to target specific sites. Targeting dCas9 to an Oct4 site in the Nanog promoter displaced Oct4 from this site, reduced Nanog expression, and slowed division. In contrast, disrupting the Oct4 binding site adjacent to Pax6 upregulated Pax6 transcription and disrupting Nanog binding its own promoter upregulated its transcription. Thus, we can easily distinguish between activating and repressing binding sites and examine autoregulation. Finally, multiple guide RNA expression allows simultaneous inhibition of multiple binding sites, and conditionally destabilized dCas9 allows rapid reversibility.

Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors

We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one "giant" connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a "rich-gets-richer" mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors.

IndeCut evaluates performance of network motif discovery algorithms

Motivation

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs.

Results

In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.

Availability and implementation

The open source software package is available at https://github.com/megrawlab/IndeCut.

Contact

megrawm@science.oregonstate.edu or david.koslicki@math.oregonstate.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Rice Genome-Scale Network Integration Reveals Transcriptional Regulators of Grass Cell Wall Synthesis

Grasses have evolved distinct cell wall composition and patterning relative to dicotyledonous plants. However, despite the importance of this plant family, transcriptional regulation of its cell wall biosynthesis is poorly understood. To identify grass cell wall-associated transcription factors, we constructed the Rice Combined mutual Ranked Network (RCRN). The RCRN covers >90% of annotated rice (Oryza sativa) genes, is high quality, and includes most grass-specific cell wall genes, such as mixed-linkage glucan synthases and hydroxycinnamoyl acyltransferases. Comparing the RCRN and an equivalent Arabidopsis network suggests that grass orthologs of most genetically verified eudicot cell wall regulators also control this process in grasses, but some transcription factors vary significantly in network connectivity between these divergent species. Reverse genetics, yeast-one-hybrid, and protoplast-based assays reveal that OsMYB61a activates a grass-specific acyltransferase promoter, which confirms network predictions and supports grass-specific cell wall synthesis genes being incorporated into conserved regulatory circuits. In addition, 10 of 15 tested transcription factors, including six novel Wall-Associated regulators (WAP1, WACH1, WAHL1, WADH1, OsMYB13a, and OsMYB13b), alter abundance of cell wall-related transcripts when transiently expressed. The results highlight the quality of the RCRN for examining rice biology, provide insight into the evolution of cell wall regulation, and identify network nodes and edges that are possible leads for improving cell wall composition.

Diversification of DNA binding specificities enabled SREBP transcription regulators to expand the repertoire of cellular functions that they govern in fungi

The Sterol Regulatory Element Binding Proteins (SREBPs) are basic-helix-loop-helix transcription regulators that control the expression of sterol biosynthesis genes in higher eukaryotes and some fungi. Surprisingly, SREBPs do not regulate sterol biosynthesis in the ascomycete yeasts (Saccharomycotina) as this role was handed off to an unrelated transcription regulator in this clade. The SREBPs, nonetheless, expanded in fungi such as the ascomycete yeasts Candida spp., raising questions about their role and evolution in these organisms. Here we report that the fungal SREBPs diversified their DNA binding preferences concomitantly with an expansion in function. We establish that several branches of fungal SREBPs preferentially bind non-palindromic DNA sequences, in contrast to the palindromic DNA motifs recognized by most basic-helix-loop-helix proteins (including SREBPs) in higher eukaryotes. Reconstruction and biochemical characterization of the likely ancestor protein suggest that an intrinsic DNA binding promiscuity in the family was resolved by alternative mechanisms in different branches of fungal SREBPs. Furthermore, we show that two SREBPs in the human commensal yeast Candida albicans drive a transcriptional cascade that inhibits a morphological switch under anaerobic conditions. Preventing this morphological transition enhances C. albicans colonization of the mammalian intestine, the fungus' natural niche. Thus, our results illustrate how diversification in DNA binding preferences enabled the functional expansion of a family of eukaryotic transcription regulators.