The hardwiring of development: organization and function of genomic regulatory systems

Genome-wide profiling of angiogenic cis-regulatory elements unravels cis-regulatory SNPs for vascular abnormality

Angiogenesis is extensively involved in embryonic development and requires complex regulation networks, whose defects can cause a variety of vascular abnormalities. Cis-regulatory elements control gene expression at all developmental stages, but they have not been studied or profiled in angiogenesis yet. In this study, we exploited public DNase-seq and RNA-seq datasets from a VEGFA-stimulated in vitro angiogenic model, and carried out an integrated analysis of the transcriptome and chromatin accessibility across the entire process. Totally, we generated a bank of 47,125 angiogenic cis-regulatory elements with promoter (marker by H3K4me3) and/or enhancer (marker by H3K27ac) activities. Motif enrichment analysis revealed that these angiogenic cis-regulatory elements interacted preferentially with ETS family TFs. With this tool, we performed an association study using our WES data of TAPVC and identified rs199530718 as a cis-regulatory SNP associated with disease risk. Altogether, this study generated a genome-wide bank of angiogenic cis-regulatory elements and illustrated its utility in identifying novel cis-regulatory SNPs for TAPVC, expanding new horizons of angiogenesis as well as vascular abnormality genetics.

Characterization of transcriptional enhancers in the chicken genome using CRISPR-mediated activation

DNA regulatory elements intricately control when, where, and how genes are activated. Therefore, understanding the function of these elements could unveil the complexity of the genetic regulation network. Genome-wide significant variants are predominantly found in non-coding regions of DNA, so comprehending the predicted functional regulatory elements is crucial for understanding the biological context of these genomic markers, which can be incorporated into breeding programs. The emergence of CRISPR technology has provided a powerful tool for studying non-coding regulatory elements in genomes. In this study, we leveraged epigenetic data from the Functional Annotation of Animal Genomes project to identify promoter and putative enhancer regions associated with three genes (HBBA, IRF7, and PPARG) in the chicken genome. To identify the enhancer regions, we designed guide RNAs targeting the promoter and candidate enhancer regions and utilized CRISPR activation (CRISPRa) with dCas9-p300 and dCas9-VPR as transcriptional activators in chicken DF-1 cells. By comparing the expression levels of target genes between the promoter activation and the co-activation of the promoter and putative enhancers, we were able to identify functional enhancers that exhibited augmented upregulation. In conclusion, our findings demonstrate the remarkable efficiency of CRISPRa in precisely manipulating the expression of endogenous genes by targeting regulatory elements in the chicken genome, highlighting its potential for functional validation of non-coding regions.

Using Synthetic DNA Libraries to Investigate Chromatin and Gene Regulation

Despite the recent explosion in genome-wide studies in chromatin and gene regulation, we are still far from extracting a set of genetic rules that can predict the function of the regulatory genome. One major reason for this deficiency is that gene regulation is a multi-layered process that involves an enormous variable space, which cannot be fully explored using native genomes. This problem can be partially solved by introducing synthetic DNA libraries into cells, a method that can test the regulatory roles of thousands to millions of sequences with limited variables. Here, we review recent applications of this method to study transcription factor (TF) binding, nucleosome positioning, and transcriptional activity. We discuss the design principles, experimental procedures, and major findings from these studies and compare the pros and cons of different approaches.

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.

Widespread cis- and trans-regulatory evolution underlies the origin, diversification, and loss of a sexually dimorphic fruit fly pigmentation trait

Changes in gene expression are a prominent feature of morphological evolution. These changes occur to hierarchical gene regulatory networks (GRNs) of transcription factor genes that regulate the expression of trait-building differentiation genes. While changes in the expression of differentiation genes are essential to phenotypic evolution, they can be caused by mutations within cis-regulatory elements (CREs) that drive their expression (cis-evolution) or within genes for CRE-interacting transcription factors (trans-evolution). Locating these mutations remains a challenge, especially when experiments are limited to one species that possesses the ancestral or derived phenotype. We investigated CREs that control the expression of the differentiation genes tan and yellow, the expression of which evolved during the gain, modification, and loss of dimorphic pigmentation among Sophophora fruit flies. We show these CREs to be necessary components of a pigmentation GRN, as deletion from Drosophila melanogaster (derived dimorphic phenotype) resulted in lost expression and lost male-specific pigmentation. We evaluated the ability of orthologous CRE sequences to drive reporter gene expression in species with modified (Drosophila auraria), secondarily lost (Drosophila ananassae), and ancestrally absent (Drosophila willistoni) pigmentation. We show that the transgene host frequently determines CRE activity, implicating trans-evolution as a significant factor for this trait's diversity. We validated the gain of dimorphic Bab transcription factor expression as a trans-change contributing to the dimorphic trait. Our findings suggest an amenability to change for the landscape of trans-regulators and begs for an explanation as to why this is so common compared to the evolution of differentiation gene CREs.

Diverse logics and grammar encode notochord enhancers

The notochord is a defining feature of all chordates. The transcription factors Zic and ETS regulate enhancer activity within the notochord. We conduct high-throughput screens of genomic elements within developing Ciona embryos to understand how Zic and ETS sites encode notochord activity. Our screen discovers an enhancer located near Lama, a gene critical for notochord development. Reversing the orientation of an ETS site within this enhancer abolishes expression, indicating that enhancer grammar is critical for notochord activity. Similarly organized clusters of Zic and ETS sites occur within mouse and human Lama1 introns. Within a Brachyury (Bra) enhancer, FoxA and Bra, in combination with Zic and ETS binding sites, are necessary and sufficient for notochord expression. This binding site logic also occurs within other Ciona and vertebrate Bra enhancers. Collectively, this study uncovers the importance of grammar within notochord enhancers and discovers signatures of enhancer logic and grammar conserved across chordates.

Genomic structural variation: A complex but important driver of human evolution

Structural variants (SVs)-including duplications, deletions, and inversions of DNA-can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single-nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well-documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single-nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever-expanding SV compendium propelled by biotechnology advancements.

Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease

Introduction

The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genesinvolved in biological aging in AD.

Methods

We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers.

Results

WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers.

Conclusion

Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD.

Highlights

Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes.Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging.Among the hub genes of the module, CX3CR1 was downregulated in AD.The CX3CR1 expression level was associated with cognitive performance and brain atrophy.

Targets for Renal Carcinoma Growth Control Identified by Screening FOXD1 Cell Proliferation Pathways

Simple Summary

FOXD1 regulates the proliferation of clear cell renal cell carcinoma (ccRCC) cells, and ccRCC cells in which FOXD1 has been inactivated do not form tumors efficiently in an animal model. Reproducing growth inhibition in tumor cells by inhibiting FOXD1 pathways presents a possible therapeutic approach for ccRCC and other cancers. We have established an analysis strategy to identify FOXD1-regulated target pathways that may be therapeutically tractable, and compounds that modulate these pathways were selected for testing. Targets in three pathways were identified: FOXM1, PME1, and TMEM167A, which were inhibited by compounds FDI-6, AMZ-30, and silibinin, respectively. The effects of these compounds on the growth of tumor cells from patients cultured in a novel 3D tumor-replica culture environment revealed that FDI-6 and silibinin had strong growth inhibitory effects. This investigation informs new therapeutic targets to control ccRCC tumor growth, and provides a strategy to compare the responsiveness of individual patient tumor replicas to growth-inhibitory compounds.

Abstract

Clinical association studies suggest that FOXD1 is a determinant of patient outcome in clear cell renal cell carcinoma (ccRCC), and laboratory investigations have defined a role for this transcription factor in controlling the growth of tumors through regulation of the G2/M cell cycle transition. We hypothesized that the identification of pathways downstream of FOXD1 may define candidates for pharmacological modulation to suppress the G2/M transition in ccRCC. We developed an analysis pipeline that utilizes RNA sequencing, transcription factor binding site analysis, and phenotype validation to identify candidate effectors downstream from FOXD1. Compounds that modulate candidate pathways were tested for their ability to cause growth delay at G2/M. Three targets were identified: FOXM1, PME1, and TMEM167A, which were targeted by compounds FDI-6, AMZ-30, and silibinin, respectively. A 3D ccRCC tumor replica model was used to investigate the effects of these compounds on the growth of primary cells from five patients. While silibinin reduced 3D growth in a subset of tumor replicas, FDI-6 reduced growth in all. This study identifies tractable pathways to target G2/M transition and inhibit ccRCC growth, demonstrates the applicability of these strategies across patient tumor replicas, and provides a platform for individualized patient testing of compounds that inhibit tumor growth.

Multi-layered transcriptional control of cranial neural crest development

The neural crest (NC) is an emblematic population of embryonic stem-like cells with remarkable migratory ability. These distinctive attributes have inspired the curiosity of developmental biologists for over 150 years, however only recently the regulatory mechanisms controlling the complex features of the NC have started to become elucidated at genomic scales. Regulatory control of NC development is achieved through combinatorial transcription factor binding and recruitment of associated transcriptional complexes to distal cis-regulatory elements. Together, they regulate when, where and to what extent transcriptional programmes are actively deployed, ultimately shaping ontogenetic processes. Here, we discuss how transcriptional networks control NC ontogeny, with a special emphasis on the molecular mechanisms underlying specification of the cephalic NC. We also cover emerging properties of transcriptional regulation revealed in diverse developmental systems, such as the role of three-dimensional conformation of chromatin, and how they are involved in the regulation of NC ontogeny. Finally, we highlight how advances in deciphering the NC transcriptional network have afforded new insights into the molecular basis of human diseases.

REDfly: An Integrated Knowledgebase for Insect Regulatory Genomics

Simple Summary

Understanding how genes are regulated is a vital area of current biological research and a crucial adjunct to ongoing efforts to sequence entire genomes. Knowing the DNA sequences responsible for gene regulation—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—is important for many areas of research including interpretation and validation of data developed by large-scale genomics projects, providing training data for machine-learning CRM-discovery methods, genome annotation, modeling gene-regulatory networks, studying the evolution of gene regulation, and numerous aspects of the basic biology of transcriptional regulation. Knowledge of insect CRMs is also an important step in developing biotechnology methods for control of insect disease vectors and for eliminating pathogen transmission. The REDfly (Regulatory Element Database for Fly) database integrates all of the available insect cis-regulatory information from multiple sources to provide a comprehensive collection of known regulatory elements. In this paper, we describe REDfly’s basic contents and data model, emphasizing recently added features, and provide illustrated walk-throughs of some common search scenarios.

Abstract

We provide here an updated description of the REDfly (Regulatory Element Database for Fly) database of transcriptional regulatory elements, a unique resource that provides regulatory annotation for the genome of Drosophila and other insects. The genomic sequences regulating insect gene expression—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—are not currently curated by any other major database resources. However, knowledge of such sequences is important, as CRMs play critical roles with respect to disease as well as normal development, phenotypic variation, and evolution. Characterized CRMs also provide useful tools for both basic and applied research, including developing methods for insect control. REDfly, which is the most detailed existing platform for metazoan regulatory-element annotation, includes over 40,000 experimentally verified CRMs and TFBSs along with their DNA sequences, their associated genes, and the expression patterns they direct. Here, we briefly describe REDfly’s contents and data model, with an emphasis on the new features implemented since 2020. We then provide an illustrated walk-through of several common REDfly search use cases.

Gene co-expression networks contributing to reproductive development in Holstein-Friesian bull calves

Enhanced early life nutrition stimulates the functionality of the hypothalamic-pituitary-testicular (HPT) biochemical signalling axis, resulting in precocious reproductive development in bull calves. Additionally, there is evidence that peptides and hormones produced within adipose tissue depots are also central in mediating the effect of metabolic status with reproductive development. The objective of this study was to undertake gene co-expression analyses on transcriptional data of the HPT and adipose tissues derived from bull calves fed contrasting planes of nutrition up to 18 weeks of life. The relationship between networks of co-expressed genes in each tissue dataset with calf phenotypic data was also assessed using a Pearson correlation analysis. Phenotypic data were related to metabolic status (systemic concentrations of insulin, leptin, adiponectin and IGF-1) reproductive development (systemic concentrations of testosterone, FSH and LH) and markers of testicular development (seminiferous tubule diameter, seminiferous tubule lumen score, spermatogenic cells and Sertoli cells). In the hypothalamus, gene co-expression networks involved in biochemical signalling processes related to gonadotropin-releasing hormone (GnRH) secretion were positively associated (P < 0.05) with systemic concentrations of IGF-1 and insulin. Similarly, a network of gene transcripts involved in GnRH signalling in the anterior pituitary was positively associated (P < 0.05) with systemic concentrations of LH. In the testes and adipose tissues, networks of co-expressed genes implicated in cholesterol and fatty acid biosynthesis were positively associated (P < 0.05) with lumen score, Sertoli cell number, and stage of spermatogenesis. Additionally, gene co-expression networks significantly associated (P < 0.05) with both metabolic and reproductive trait data were found to be enriched (P < 0.05) for biological pathways related to energy production, cellular growth and proliferation, GnRH signalling and cholesterol biosynthesis across multiple tissues examined. Results from this study highlight networks of co-expressed genes directly associated with markers of enhanced metabolic status and subsequent earlier reproductive development. Furthermore, genes involved in biological processes mentioned above may hold potential for informing genomic selection breeding programmes for the selection of calves capable of displaying earlier reproductive development as a consequence of enhanced dietary intake.

DrGA: cancer driver gene analysis in a simpler manner

Background

To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient.

Results

This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism.

Conclusions

DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA.

The role of the epidermis enhancer element in positive and negative transcriptional regulation of ebony in Drosophila melanogaster

The spatiotemporal regulation of gene expression is essential to ensure robust phenotypic outcomes. Pigmentation patterns in Drosophila are determined by pigments biosynthesized in the developing epidermis and the cis-regulatory elements of the genes involved in this process are well-characterized. Here, we report that the known primary epidermal enhancer is dispensable for the transcriptional activation of ebony (involved in light-colored pigment synthesis) in the developing epidermis of Drosophila melanogaster. The evidence was obtained by introducing an approximately 1 kbp deletion at the primary epidermal enhancer by genome editing. The effect of the primary epidermal enhancer deletion on pigmentation and on the endogenous expression pattern of a mCherry-fused ebony allele was examined in the abdomen. The expression levels of the mCherry-fused ebony in the primary epidermal enhancer-deleted strains were slightly higher than that of the control strain, indicating that the sequences outside the primary epidermal enhancer have an ability to drive an expression of this gene in the epidermis. Interestingly, the primary epidermal enhancer deletion resulted in a derepression of this gene in the dorsal midline of the abdominal tergites, where dark pigmentation is present in the wild-type individuals. This indicated that the primary epidermal enhancer fragment contains a silencer. Furthermore, the endogenous expression pattern of ebony in the 2 additional strains with partially deleted primary epidermal enhancer revealed that the silencer resides within a 351-bp fragment in the 5' portion of the primary epidermal enhancer. These results demonstrated that deletion assays combined with reporter assays are highly effective in detecting the presence of positively and negatively regulating sequences within and outside the focal cis-regulatory elements.

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Background

When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated.

Results

This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally.

Conclusions

oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08072-5.

Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

Gene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development-representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes-is one of the most challenging arenas for GRN prediction. In this work, we show that successful GRN predictions for a developmental network from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic Net. We test our GRN prediction methodology using two gene expression datasets for the purple sea urchin, Stronglyocentrotus purpuratus, and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results find a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 81.58%). We also generate novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. Published ChIPseq data and spatial co-expression analysis further support a subset of the top novel predictions. We conclude that GRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

[Identification of potential hub genes of Alzheimer's disease by weighted gene co-expression network analysis]

OBJECTIVE

To investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.

METHODS

We downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.

RESULTS

Ten gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).

CONCLUSION

The brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.

A deep learning model to identify gene expression level using cobinding transcription factor signals

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm

A detailed comprehension of transcriptional regulation is critical to understanding the genetic control of development and disease across many different organisms. To more fully investigate the complex molecular interactions controlling the precise expression of genes, many groups have constructed mathematical models to complement their experimental approaches. A critical step in such studies is choosing the most appropriate parameter estimation algorithm to enable detailed analysis of the parameters that contribute to the models. In this study, we develop a novel set of evolutionary algorithms that use a pseudo-random Sobol Set to construct the initial population and incorporate parameter sensitivities into the adaptation of mutation rates, using local, global, and hybrid strategies. Comparison of the performance of these new algorithms to a number of current state-of-the-art global parameter estimation algorithms on a range of continuous test functions, as well as synthetic biological data representing models of gene regulatory systems, reveals improved performance of the new algorithms in terms of runtime, error and reproducibility. In addition, by analyzing the ability of these algorithms to fit datasets of varying quality, we provide the experimentalist with a guide to how the algorithms perform across a range of noisy data. These results demonstrate the improved performance of the new set of parameter estimation algorithms and facilitate meaningful integration of model parameters and predictions in our understanding of the molecular mechanisms of gene regulation.

Novel interferon-sensitive genes unveiled by correlation-driven gene selection and systems biology

Interferons (IFNs) are key cytokines involved in alerting the immune system to viral infection. After IFN stimulation, cellular transcriptional profile critically changes, leading to the expression of several IFN stimulated genes (ISGs) that exert a wide variety of antiviral activities. Despite many ISGs have been already identified, a comprehensive network of coding and non-coding genes with a central role in IFN-response still needs to be elucidated. We performed a global RNA-Seq transcriptome profile of the HCV permissive human hepatoma cell line Huh7.5 and its parental cell line Huh7, upon IFN treatment, to define a network of genes whose coordinated modulation plays a central role in IFN-response. Our study adds molecular actors, coding and non-coding genes, to the complex molecular network underlying IFN-response and shows how systems biology approaches, such as correlation networks, network's topology and gene ontology analyses can be leveraged to this aim.

Beyond Casual Resemblances: Rigorous Frameworks for Comparing Regeneration Across Species

The majority of animal phyla have species that can regenerate. Comparing regeneration across animals can reconstruct the molecular and cellular evolutionary history of this process. Recent studies have revealed some similarity in regeneration mechanisms, but rigorous comparative methods are needed to assess whether these resemblances are ancestral pathways (homology) or are the result of convergent evolution (homoplasy). This review aims to provide a framework for comparing regeneration across animals, focusing on gene regulatory networks (GRNs), which are substrates for assessing process homology. The homology of the wound-induced activation of Wnt signaling and of adult stem cells are discussed as examples of ongoing studies of regeneration that enable comparisons in a GRN framework. Expanding the study of regeneration GRNs in currently studied species and broadening taxonomic sampling for these approaches will identify processes that are unifying principles of regeneration biology across animals. These insights are important both for evolutionary studies of regeneration and for human regenerative medicine. Expected final online publication date for the Annual Review of Cell and Developmental Biology, Volume 37 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Enhancer grammar in development, evolution, and disease: dependencies and interplay

Each language has standard books describing that language's grammatical rules. Biologists have searched for similar, albeit more complex, principles relating enhancer sequence to gene expression. Here, we review the literature on enhancer grammar. We introduce dependency grammar, a model where enhancers encode information based on dependencies between enhancer features shaped by mechanistic, evolutionary, and biological constraints. Classifying enhancers based on the types of dependencies may identify unifying principles relating enhancer sequence to gene expression. Such rules would allow us to read the instructions for development within genomes and pinpoint causal enhancer variants underlying disease and evolutionary changes.

A developmental perspective of homology and evolutionary novelty

The development and evolution of multicellular body plans is complex. Many distinct organs and body parts must be reproduced at each generation, and those that are traceable over long time scales are considered homologous. Among the most pressing and least understood phenomena in evolutionary biology is the mode by which new homologs, or "novelties" are introduced to the body plan and whether the developmental changes associated with such evolution deserve special treatment. In this chapter, we address the concepts of homology and evolutionary novelty through the lens of development. We present a series of case studies, within insects and vertebrates, from which we propose a developmental model of multicellular organ identity. With this model in hand, we make predictions regarding the developmental evolution of body plans and highlight the need for more integrative analysis of developing systems.

Deciphering and modelling the TGF-β signalling interplays specifying the dorsal-ventral axis of the sea urchin embryo

During sea urchin development, secretion of Nodal and BMP2/4 ligands and their antagonists Lefty and Chordin from a ventral organiser region specifies the ventral and dorsal territories. This process relies on a complex interplay between the Nodal and BMP pathways through numerous regulatory circuits. To decipher the interplay between these pathways, we used a combination of treatments with recombinant Nodal and BMP2/4 proteins and a computational modelling approach. We assembled a logical model focusing on cell responses to signalling inputs along the dorsal-ventral axis, which was extended to cover ligand diffusion and enable multicellular simulations. Our model simulations accurately recapitulate gene expression in wild-type embryos, accounting for the specification of ventral ectoderm, ciliary band and dorsal ectoderm. Our model simulations further recapitulate various morphant phenotypes, reveal a dominance of the BMP pathway over the Nodal pathway and stress the crucial impact of the rate of Smad activation in dorsal-ventral patterning. These results emphasise the key role of the mutual antagonism between the Nodal and BMP2/4 pathways in driving early dorsal-ventral patterning of the sea urchin embryo.

How haematopoiesis research became a fertile ground for regulatory network biology as pioneered by Eric Davidson

PURPOSE OF REVIEW

This historical perspective reviews how work of Eric H. Davidson was a catalyst and exemplar for explaining haematopoietic cell fate determination through gene regulation.

RECENT FINDINGS

Researchers studying blood and immune cells pioneered many of the early mechanistic investigations of mammalian gene regulatory processes. These efforts included the characterization of complex gene regulatory sequences exemplified by the globin and T-cell/B-cell receptor gene loci, as well as the identification of many key regulatory transcription factors through the fine mapping of chromosome translocation breakpoints in leukaemia patients. As the repertoire of known regulators expanded, assembly into gene regulatory network models became increasingly important, not only to account for the truism that regulatory genes do not function in isolation but also to devise new ways of extracting biologically meaningful insights from even more complex information. Here we explore how Eric H. Davidson's pioneering studies of gene regulatory network control in nonvertebrate model organisms have had an important and lasting impact on research into blood and immune cell development.

SUMMARY

The intellectual framework developed by Davidson continues to contribute to haematopoietic research, and his insistence on demonstrating logic and causality still challenges the frontier of research today.

New insights on the function of plant acyl carrier proteins from comparative and evolutionary analysis

Acyl carrier proteins (ACPs) play a central role in both plastidial and mitochondrial Type II fatty acid synthesis in plant cells. However, a large proportion of plant ACPs remain functionally uncharacterized, and their evolutionary history remains elusive. In present study, 97 putative ACPs were identified from ten angiosperm species examined. Based on phylogenetic analysis, ACP genes were grouped into plastidial (cpACP: ACP1/2/3/4/5) and mitochondrial (mtACP: mtACP1/mtACP2/mtACP3) ACPs. Protein sequence (motifs and length), tertiary structure, and gene structure (exon number, average intron length, and intron phase) were highly conserved in different ACP subclades. The differentiation of ACPs into distinct types occurred 85-98 and 45-57 million years ago. A limited proportion of ACP genes experience tandem or segmental duplication, corresponding to two rounds of whole genome duplication. Ka/Ks ratios revealed that duplicated ACP genes underwent a purifying selection. Regarding expression patterns, most ACPs were expressed constitutively and tissue-specifically. Notably, the average expression levels of ACP1, mtACP3, and mtACP1 were positively correlated with those of ACP3, ACP4, and mtACP2, respectively. Analysis of cis-elements showed that seven motifs (CACTFTPPCA1, DOFCOREZM, GT1CONSENSUS, CAATBOX1, ARR1AT, POLLEN1LELAT52, and GATABOX) related to tissue-specific, ABA, and light-mediated gene regulation were ubiquitous in all ACPs investigated, which shed new light on the regulation patterns of these central enzymatic partners of the FAS system. This study presents a thorough overview of angiosperm ACP gene families and provides informative clues for the functional characterization of plant ACPs in the future.

Tissue-specific patterns of regulatory changes underlying gene expression differences among Ficedula flycatchers and their naturally occurring F1 hybrids

Changes in interacting cis- and trans-regulatory elements are important candidates for Dobzhansky-Muller hybrid incompatibilities and may contribute to hybrid dysfunction by giving rise to misexpression in hybrids. To gain insight into the molecular mechanisms and determinants of gene expression evolution in natural populations, we analyzed the transcriptome from multiple tissues of two recently diverged Ficedula flycatcher species and their naturally occurring F₁ hybrids. Differential gene expression analysis revealed that the extent of differentiation between species and the set of differentially expressed genes varied across tissues. Common to all tissues, a higher proportion of Z-linked genes than autosomal genes showed differential expression, providing evidence for a fast-Z effect. We further found clear signatures of hybrid misexpression in brain, heart, kidney, and liver. However, while testis showed the highest divergence of gene expression among tissues, it showed no clear signature of misexpression in F₁ hybrids, even though these hybrids were found to be sterile. It is therefore unlikely that incompatibilities between cis-trans regulatory changes explain the observed sterility. Instead, we found evidence that cis-regulatory changes play a significant role in the evolution of gene expression in testis, which illustrates the tissue-specific nature of cis-regulatory evolution bypassing constraints associated with pleiotropic effects of genes.

Combinatorial interactions of the LEC1 transcription factor specify diverse developmental programs during soybean seed development

The LEAFY COTYLEDON1 (LEC1) transcription factor is a central regulator of seed development, because it controls diverse biological programs during seed development, such as embryo morphogenesis, photosynthesis, and seed maturation. To understand how LEC1 regulates different gene sets during development, we explored the possibility that LEC1 acts in combination with other transcription factors. We identified and compared genes that are directly transcriptionally regulated by ABA-RESPONSIVE ELEMENT BINDING PROTEIN3 (AREB3), BASIC LEUCINE ZIPPER67 (bZIP67), and ABA INSENSITIVE3 (ABI3) with those regulated by LEC1. We showed that LEC1 operates with specific sets of transcription factors to regulate different gene sets and, therefore, distinct developmental processes. Thus, LEC1 controls diverse processes through its combinatorial interactions with other transcription factors. DNA binding sites for the transcription factors are closely clustered in genomic regions upstream of target genes, defining cis-regulatory modules that are enriched for DNA sequence motifs that resemble sequences known to be bound by these transcription factors. Moreover, cis-regulatory modules for genes regulated by distinct transcription factor combinations are enriched for different sets of DNA motifs. Expression assays with embryo cells indicate that the enriched DNA motifs are functional cis elements that regulate transcription. Together, the results suggest that combinatorial interactions between LEC1 and other transcription factors are mediated by cis-regulatory modules containing clustered cis elements and by physical interactions that are documented to occur between the transcription factors.

Understanding Multicellularity: The Functional Organization of the Intercellular Space

The aim of this paper is to provide a theoretical framework to understand how multicellular systems realize functionally integrated physiological entities by organizing their intercellular space. From a perspective centered on physiology and integration, biological systems are often characterized as organized in such a way that they realize metabolic self-production and self-maintenance. The existence and activity of their components rely on the network they realize and on the continuous management of the exchange of matter and energy with their environment. One of the virtues of the organismic approach focused on organization is that it can provide an understanding of how biological systems are functionally integrated into coherent wholes. Organismic frameworks have been primarily developed by focusing on unicellular life. Multicellularity, however, presents additional challenges to our understanding of biological systems, related to how cells are capable to live together in higher-order entities, in such a way that some of their features and behaviors are constrained and controlled by the system they realize. Whereas most accounts of multicellularity focus on cell differentiation and increase in size as the main elements to understand biological systems at this level of organization, we argue that these factors are insufficient to provide an understanding of how cells are physically and functionally integrated in a coherent system. In this paper, we provide a new theoretical framework to understand multicellularity, capable to overcome these issues. Our thesis is that one of the fundamental theoretical principles to understand multicellularity, which is missing or underdeveloped in current accounts, is the functional organization of the intercellular space. In our view, the capability to be organized in space plays a central role in this context, as it enables (and allows to exploit all the implications of) cell differentiation and increase in size, and even specialized functions such as immunity. We argue that the extracellular matrix plays a crucial active role in this respect, as an evolutionary ancient and specific (non-cellular) control subsystem that contributes as a key actor to the functional specification of the multicellular space and to modulate cell fate and behavior. We also analyze how multicellular systems exert control upon internal movement and communication. Finally, we show how the organization of space is involved in some of the failures of multicellular organization, such as aging and cancer.

GATA3-Controlled Nucleosome Eviction Drives MYC Enhancer Activity in T-cell Development and Leukemia

Long-range enhancers govern the temporal and spatial control of gene expression; however, the mechanisms that regulate enhancer activity during normal and malignant development remain poorly understood. Here, we demonstrate a role for aberrant chromatin accessibility in the regulation of MYC expression in T-cell lymphoblastic leukemia (T-ALL). Central to this process, the NOTCH1-MYC enhancer (N-Me), a long-range T cell-specific MYC enhancer, shows dynamic changes in chromatin accessibility during T-cell specification and maturation and an aberrant high degree of chromatin accessibility in mouse and human T-ALL cells. Mechanistically, we demonstrate that GATA3-driven nucleosome eviction dynamically modulates N-Me enhancer activity and is strictly required for NOTCH1-induced T-ALL initiation and maintenance. These results directly implicate aberrant regulation of chromatin accessibility at oncogenic enhancers as a mechanism of leukemic transformation. SIGNIFICANCE: MYC is a major effector of NOTCH1 oncogenic programs in T-ALL. Here, we show a major role for GATA3-mediated enhancer nucleosome eviction as a driver of MYC expression and leukemic transformation. These results support the role of aberrant chromatin accessibility and consequent oncogenic MYC enhancer activation in NOTCH1-induced T-ALL.This article is highlighted in the In This Issue feature, p. 1631.

What are the roles of retinoids, other morphogens, and Hox genes in setting up the vertebrate body axis?

This article is concerned with the roles of retinoids and other known anterior-posterior morphogens in setting up the embryonic vertebrate anterior-posterior axis. The discussion is restricted to the very earliest events in setting up the anterior-posterior axis (from blastula to tailbud stages in Xenopus embryos). In these earliest developmental stages, morphogen concentration gradients are not relevant for setting up this axis. It emerges that at these stages, the core patterning mechanism is timing: BMP-anti BMP mediated time space translation that regulates Hox temporal and spatial collinearities and Hox-Hox auto- and cross- regulation. The known anterior-posterior morphogens and signaling pathways--retinoids, FGF's, Cdx, Wnts, Gdf11 and others--interact with this core mechanism at and after space-time defined "decision points," leading to the separation of distinct axial domains. There are also other roles for signaling pathways. Besides the Hox regulated hindbrain/trunk part of the axis, there is a rostral part (including the anterior part of the head and the extreme anterior domain [EAD]) that appears to be regulated by additional mechanisms. Key aspects of anterior-posterior axial patterning, including: the nature of different phases in early patterning and in the whole process; the specificities of Hox action and of intercellular signaling; and the mechanisms of Hox temporal and spatial collinearities, are discussed in relation to the facts and hypotheses proposed above.

Cloudy with a Chance of Insights: Context Dependent Gene Regulation and Implications for Evolutionary Studies

Research in various fields of evolutionary biology has shown that divergence in gene expression is a key driver for phenotypic evolution. An exceptional contribution of cis-regulatory divergence has been found to contribute to morphological diversification. In the light of these findings, the analysis of genome-wide expression data has become one of the central tools to link genotype and phenotype information on a more mechanistic level. However, in many studies, especially if general conclusions are drawn from such data, a key feature of gene regulation is often neglected. With our article, we want to raise awareness that gene regulation and thus gene expression is highly context dependent. Genes show tissue- and stage-specific expression. We argue that the regulatory context must be considered in comparative expression studies.

Dynamic repression by BCL6 controls the genome-wide liver response to fasting and steatosis

Transcription is tightly regulated to maintain energy homeostasis during periods of feeding or fasting, but the molecular factors that control these alternating gene programs are incompletely understood. Here, we find that the B cell lymphoma 6 (BCL6) repressor is enriched in the fed state and converges genome-wide with PPARα to potently suppress the induction of fasting transcription. Deletion of hepatocyte Bcl6 enhances lipid catabolism and ameliorates high-fat-diet-induced steatosis. In Ppara-null mice, hepatocyte Bcl6 ablation restores enhancer activity at PPARα-dependent genes and overcomes defective fasting-induced fatty acid oxidation and lipid accumulation. Together, these findings identify BCL6 as a negative regulator of oxidative metabolism and reveal that alternating recruitment of repressive and activating transcription factors to shared cis-regulatory regions dictates hepatic lipid handling.

Gene co-expression networks contributing to the expression of compensatory growth in metabolically active tissues in cattle

Compensatory growth (CG) is an accelerated growth phenomenon which occurs in animals upon re-alimentation following a period of dietary restriction. The objective of this study was to perform gene co-expression analysis on metabolic tissues of animals undergoing CG, in order to elucidate the molecular control governing this phenomenon. Thirty Holstein Friesian bulls were fed a restricted diet for 125 days, after which they received feed ad libitum. Following 55 days of ad libitum feeding all animals were slaughtered. RNAseq and gene co-expression analyses were performed on tissue samples collected at slaughter including liver, rumen papillae and jejunum epithelium tissues. A period of CG resulted in 15 networks of co-expressed genes. One network of genes, involved in proteasome core complex, signal transduction and protein synthesis was found to be similar across liver and jejunum tissue datasets (r = 0.68, P = 0.04). Results from this study also showed that a large portion of co-expressed genes had not previously been implicated in the expression of CG, thus this study identifies novel genes involved in controlling CG across tissues, with hub genes holding potential for use as biomarkers for the selection of animals with a greater propensity to display CG.

Insight into the combinatorial transcriptional regulation on α-amylase gene in animal groups with different dietary nutrient content

Gene expression is generally regulated by multiple transcription factors (TFs). Despite previous findings of individual TFs regulating pancreatic α-amylase gene expression, the combinatorial transcriptional regulation is not fully understood. To gain insight into multiple TF regulation for pancreatic α-amylase gene, we employed a function conservation approach to predict interacting TFs regulating pancreatic α-amylase gene for 3 dietary animal groups. To this end, we have identified 77, 25, and 118 interacting TFs for herbivore, omnivore, and carnivore, respectively. Computational modeling of TF regulatory networks demonstrated that known pancreas-specific TFs (e.g. GR, NFAT, and PR) may play important roles in recruiting non pancreas-specific TFs to the TF-TF interaction networks, offering specificity and flexibility for controlling pancreatic α-amylase gene expression in different dietary animal groups. The findings from this study indicate that combinatorial transcriptional regulation could be a critical component controlling pancreatic α-amylase gene expression.

Redundant and Cryptic Enhancer Activities of the Drosophila yellow Gene

Cis-regulatory sequences known as enhancers play a key role in regulating gene expression. Evolutionary changes in these DNA sequences contribute to phenotypic evolution. The Drosophila yellow gene, which is required for pigmentation, has emerged as a model system for understanding how cis-regulatory sequences evolve, providing some of the most detailed insights available into how activities of orthologous enhancers have diverged between species. Here, we examine the evolution of yellow cis-regulatory sequences on a broader scale, by comparing the distribution and function of yellow enhancer activities throughout the 5' intergenic and intronic sequences of Drosophila melanogaster, D. pseudoobscura, and D. willistoni We find that cis-regulatory sequences driving expression in a particular tissue are not as modular as previously described, but rather have many redundant and cryptic enhancer activities distributed throughout the regions surveyed. Interestingly, cryptic enhancer activities of sequences from one species often drove patterns of expression observed in other species, suggesting that the frequent evolutionary changes in yellow expression observed among Drosophila species may be facilitated by gaining and losing repression of preexisting cis-regulatory sequences.

Evolutionary Potential of Cis-Regulatory Mutations to Cause Rapid Changes in Transcription Factor Binding

Transcriptional regulation is crucial for all biological processes and well investigated at the molecular level for a wide range of organisms. However, it is quite unclear how innovations, such as the activity of a novel regulatory element, evolve. In the case of transcription factor (TF) binding, both a novel TF and a novel-binding site would need to evolve concertedly. Since promiscuous functions have recently been identified as important intermediate steps in creating novel specific functions in many areas such as enzyme evolution and protein-protein interactions, we ask here how promiscuous binding of TFs to TF-binding sites (TFBSs) affects the robustness and evolvability of this tightly regulated system. Specifically, we investigate the binding behavior of several hundred TFs from different species at unprecedented breadth. Our results illustrate multiple aspects of TF-binding interactions, ranging from correlations between the strength of the interaction bond and specificity, to preferences regarding TFBS nucleotide composition in relation to both domains and binding specificity. We identified a subset of high A/T binding motifs. Motifs in this subset had many functionally neutral one-error mutants, and were bound by multiple different binding domains. Our results indicate that, especially for some TF-TFBS associations, low binding specificity confers high degrees of evolvability, that is that few mutations facilitate rapid changes in transcriptional regulation, in particular for large and old TF families. In this study we identify binding motifs exhibiting behavior indicating high evolutionary potential for innovations in transcriptional regulation.

Multi-study inference of regulatory networks for more accurate models of gene regulation

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.

Encounters across networks: Windows into principles of genomic regulation

Gene regulatory networks account for the ability of the genome to program development in complex multi-cellular organisms. Such networks are based on principles of gene regulation by combinations of transcription factors that bind to specific cis-regulatory DNA sites to activate transcription. These cis-regulatory regions mediate logic processing at each network node, enabling progressive increases in organismal complexity with development. Gene regulatory network explanations of development have been shown to account for patterning and cell type diversification in fly and sea urchin embryonic systems, where networks are characterized by fast coupling between transcriptional inputs and changes in target gene transcription rates, and crucial cis-regulatory elements are concentrated relatively close to the protein coding sequences of the target genes, thus facilitating their identification. Stem cell-based development in post-embryonic mammalian systems also depends on gene networks, but differs from the fly and sea urchin systems. First, the number of regulatory elements per gene and the distances between regulatory elements and the genes they control are considerably larger, forcing searches via genome-wide transcription factor binding surveys rather than functional assays. Second, the intrinsic timing of network state transitions can be slowed considerably by the need to undo stem-cell chromatin configurations, which presumably add stability to stem-cell states but retard responses to transcription factor changes during differentiation. The dispersed, partially redundant cis-regulatory systems controlling gene expression and the slow state transition kinetics in these systems already reveal new insights and opportunities to extend understanding of the repertoire of gene networks and regulatory system logic.

Transcriptional Regulation in Prostate Cancer

Prostate cancer development involves corruption of the normal prostate transcriptional network, following deregulated expression or mutation of key transcription factors. Here, we provide an overview of the transcription factors that are important in normal prostate homeostasis (NKX3-1, p63, androgen receptor [AR]), primary prostate cancer (ETS family members, c-MYC), castration-resistant prostate cancer (AR, FOXA1), and AR-independent castration-resistant neuroendocrine prostate cancer (RB1, p53, N-MYC). We use functional (in vitro and in vivo) as well as clinical data to discuss evidence that unveils their roles in the initiation and progression of prostate cancer, with an emphasis on results of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq).

Mechanistic philosophies of development: Theodor Boveri and Eric H. Davidson

Theodor Boveri's (1862-1915) and Eric Davidson's (1937-2015) achievements represent thoroughly two quite distant time frames in the history of the mechanistic approaches to development, that Jane Maienschein (2014) has characterized respectively as the era of the "experimental embryo" and of the "computed embryo". Nonetheless, Davidson's special bond to Boveri is meant to emphasize the genealogical continuity of an embryological tradition of mechanistic philosophy that, differently from molecular biology, is committed to an explanation of the hereditary transmission of organization. Davidson's genealogical claim is reconsidered through a contextualized analysis of the function of machine-like models and of the role of experiment in the making of their respective mechanistic philosophies. This analysis may help to shed light on resilience and change in the understanding of a mechanistic approach to development.

Augmentation of a wound response element accompanies the origin of a Hox-regulated Drosophila abdominal pigmentation trait

A challenge for evolutionary research is to uncover how new morphological traits evolve the coordinated spatial and temporal expression patterns of genes that govern their formation during development. Detailed studies are often limited to characterizing how one or a few genes contributed to a trait's emergence, and thus our knowledge of how entire GRNs evolve their coordinated expression of each gene remains unresolved. The melanic color patterns decorating the male abdominal tergites of Drosophila (D.) melanogaster evolved in part by novel expression patterns for genes acting at the terminus of a pigment metabolic pathway, driven by cis-regulatory elements (CREs) with distinct mechanisms of Hox regulation. Here, we examined the expression and evolutionary histories of two important enzymes in this pathway, encoded by the pale and Ddc genes. We found that while both genes exhibit dynamic patterns of expression, a robust pattern of Ddc expression specifically evolved in the lineage of fruit flies with pronounced melanic abdomens. Derived Ddc expression requires the activity of a CRE previously shown to activate expression in response to epidermal wounding. We show that a binding site for the Grainy head transcription factor that promotes the ancestral wound healing function of this CRE is also required for abdominal activity. Together with previous findings in this system, our work shows how the GRN for a novel trait emerged by assembling unique yet similarly functioning CREs from heterogeneous starting points.

Rethinking Causation in Cancer with Evolutionary Developmental Biology

Despite the productivity of basic cancer research, cancer continues to be a health burden to society because this research has not yielded corresponding clinical applications. Many proposed solutions to this dilemma have revolved around implementing organizational and policy changes related to cancer research. Here I argue for a different solution: a new conceptualization of causation in cancer. Neither the standard molecular biomarker approaches nor evolutionary biology approaches to cancer fully capture its complex causal dynamics, even when considered jointly. These approaches map on to Ernst Mayr's proximate-ultimate distinction, which is an inadequate conceptualization of causation in biological systems and makes it difficult to connect developmental and evolutionary viewpoints. I propose looking to evolutionary developmental biology (EvoDevo) to overcome the distinction and integrate the proximate and ultimate causal frameworks. I use the concepts of modularity and evolvability to show how an EvoDevo perspective can be manifested in cancer translational research. This perspective on causation in cancer is better suited for integrating the complexity of current empirical results and can facilitate novel developments in the investigation and clinical treatment of cancer.

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

Computing with biological switches and clocks

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

The nuclear transportation routes of membrane-bound transcription factors

Membrane-bound transcription factors (MTFs) are transcription factors (TFs) that are anchored in membranes in a dormant state. Activated by external or internal stimuli, MTFs are released from parent membranes and are transported to the nucleus. Existing research indicates that some plasma membrane (PM)-bound proteins and some endoplasmic reticulum (ER) membrane-bound proteins have the ability to enter the nucleus. Upon specific signal recognition cues, some PM-bound TFs undergo proteolytic cleavage to liberate the intracellular fragments that enter the nucleus to control gene transcription. However, lipid-anchored PM-bound proteins enter the nucleus in their full length for depalmitoylation. In addition, some PM-bound TFs exist as full-length proteins in cell nucleus via trafficking to the Golgi and the ER, where membrane-releasing mechanisms rely on endocytosis. In contrast, the ER membrane-bound TFs relocate to the nucleus directly or by trafficking to the Golgi. In both of these pathways, only the fragments of the ER membrane-bound TFs transit to the nucleus. Several different nuclear trafficking modes of MTFs are summarized in this review, providing an effective supplement to the mechanisms of signal transduction and gene regulation. Moreover, targeting intracellular movement pathways of disease-associated MTFs may significantly improve the survival of patients.

Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design

Investigating how genes jointly affect complex human diseases is important, yet challenging. The network approach (e.g., weighted gene co-expression network analysis (WGCNA)) is a powerful tool. However, genomic data usually contain substantial batch effects, which could mask true genomic signals. Paired design is a powerful tool that can reduce batch effects. However, it is currently unclear how to appropriately apply WGCNA to genomic data from paired design. In this paper, we modified the current WGCNA pipeline to analyse high-throughput genomic data from paired design. We illustrated the modified WGCNA pipeline by analysing the miRNA dataset provided by Shiah et al. (2014), which contains forty oral squamous cell carcinoma (OSCC) specimens and their matched non-tumourous epithelial counterparts. OSCC is the sixth most common cancer worldwide. The modified WGCNA pipeline identified two sets of novel miRNAs associated with OSCC, in addition to the existing miRNAs reported by Shiah et al. (2014). Thus, this work will be of great interest to readers of various scientific disciplines, in particular, genetic and genomic scientists as well as medical scientists working on cancer.

On the developmental self-regulatory dynamics and evolution of individuated multicellular organisms

Changes in gene expression are thought to regulate the cell differentiation process intrinsically through complex epigenetic mechanisms. In fundamental terms, however, this assumed regulation refers only to the intricate propagation of changes in gene expression or else leads to non-explanatory regresses. The developmental self-regulatory dynamics and evolution of individuated multicellular organisms also lack a unified and falsifiable description. To fill this gap, I computationally analyzed publicly available high-throughput data of histone H3 post-translational modifications and mRNA abundance for different Homo sapiens, Mus musculus, and Drosophila melanogaster cell-type/developmental-period samples. My analysis of genomic regions adjacent to transcription start sites generated a profile from pairwise partial correlations between histone modifications controlling for the respective mRNA levels for each cell-type/developmental-period dataset. I found that these profiles, while explicitly uncorrelated with the respective transcriptional "identities" by construction, associate strongly with cell differentiation states. This association is not expected if cell differentiation is, in effect, regulated by epigenetic mechanisms. Based on these results, I propose a general, falsifiable theory of individuated multicellularity, which relies on the synergistic coupling across the extracellular space of two explicitly uncorrelated "self-organizing" systems constraining histone modification states at the same sites. This theory describes how the simplest multicellular individual-understood as an intrinsic, higher-order constraint-emerges from proliferating undifferentiated cells, and could explain the intrinsic regulation of gene transcriptional changes for cell differentiation and the evolution of individuated multicellular organisms.