Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations

BACKGROUND

Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms.

RESULTS

In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse.

CONCLUSIONS

Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.

Neuroprotectants attenuate hypobaric hypoxia-induced brain injuries in cynomolgus monkeys

Hypobaric hypoxia (HH) exposure can cause serious brain injury as well as life-threatening cerebral edema in severe cases. Previous studies on the mechanisms of HH-induced brain injury have been conducted primarily using non-primate animal models that are genetically distant to humans, thus hindering the development of disease treatment. Here, we report that cynomolgus monkeys (Macacafascicularis) exposed to acute HH developed human-like HH syndrome involving severe brain injury and abnormal behavior. Transcriptome profiling of white blood cells and brain tissue from monkeys exposed to increasing altitude revealed the central role of the HIF-1 and other novel signaling pathways, such as the vitamin D receptor (VDR) signaling pathway, in co-regulating HH-induced inflammation processes. We also observed profound transcriptomic alterations in brains after exposure to acute HH, including the activation of angiogenesis and impairment of aerobic respiration and protein folding processes, which likely underlie the pathological effects of HH-induced brain injury. Administration of progesterone (PROG) and steroid neuroprotectant 5α-androst-3β,5,6β-triol (TRIOL) significantly attenuated brain injuries and rescued the transcriptomic changes induced by acute HH. Functional investigation of the affected genes suggested that these two neuroprotectants protect the brain by targeting different pathways, with PROG enhancing erythropoiesis and TRIOL suppressing glutamate-induced excitotoxicity. Thus, this study advances our understanding of the pathology induced by acute HH and provides potential compounds for the development of neuroprotectant drugs for therapeutic treatment.

The Multifaceted Role of HSF1 in Tumorigenesis

Heat Shock Factor 1 (HSF1), the master transcriptional regulator of the heat shock response (HSR), was first cloned more than 30 years ago. Most early research interrogating the role that HSF1 plays in biology focused on its cytoprotective functions, as a factor that promotes the survival of organisms by protecting against the proteotoxicity associated with neurodegeneration and other pathological conditions. However, recent studies have revealed a deleterious role of HSF1, as a factor that is co-opted by cancer cells to promote their own survival to the detriment of the organism. In cancer, HSF1 operates in a multifaceted manner to promote oncogenic transformation, proliferation, metastatic dissemination, and anti-cancer drug resistance. Here we review our current understanding of HSF1 activation and function in malignant progression and discuss the potential for HSF1 inhibition as a novel anticancer strategy. Collectively, this ever-growing body of work points to a prominent role of HSF1 in nearly every aspect of carcinogenesis.

Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach

Background

Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only partly understood.

Results

We assembled regulator binding information from serveral sources to construct a generic human and mouse gene regulatory network. Advancing our “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter.

Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/KoenigLabNM/MIPRIP.

Conclusion

MIPRIP 2.0 identified common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.

A biphasic role of non-canonical Wnt16 signaling during early anterior-posterior patterning and morphogenesis of the sea urchin embryo

A Wnt signaling network governs early anterior-posterior (AP) specification and patterning of the deuterostome sea urchin embryo. We have previously shown that non-canonical Fzl1/2/7 signaling antagonizes the progressive posterior-to-anterior downregulation of the anterior neuroectoderm (ANE) gene regulatory network (GRN) by canonical Wnt/β-catenin and non-canonical Wnt1/Wnt8-Fzl5/8-JNK signaling. This study focuses on the non-canonical function of the Wnt16 ligand during early AP specification and patterning. Maternally supplied wnt16 is expressed ubiquitously during cleavage and zygotic wnt16 expression is concentrated in the endoderm/mesoderm beginning at mid-blastula stage. Wnt16 antagonizes the ANE restriction mechanism and this activity depends on a functional Fzl1/2/7 receptor. Our results also show that zygotic wnt16 expression depends on both Fzl5/8 and Wnt/β-catenin signaling. Furthermore, Wnt16 is necessary for the activation and/or maintenance of key regulatory endoderm/mesoderm genes and is essential for gastrulation. Together, our data show that Wnt16 has two functions during early AP specification and patterning: (1) an initial role activating the Fzl1/2/7 pathway that antagonizes the ANE restriction mechanism; and (2) a subsequent function in activating key endoderm GRN factors and the morphogenetic movements of gastrulation.

Identification of candidate genes and miRNAs for sensitizing resistant colorectal cancer cells to oxaliplatin and irinotecan

Drug resistance to irinotecan and oxaliplatin, two widely used chemotherapeutic, has become a common problem in cancerous patients. Despite numerous valuable studies, distinct molecular mechanisms involved in the acquisition of resistance to these anti-cancer drugs have remained a challenge. In this study, we studied the possible resistance mechanisms to irinotecan and oxaliplatin in three CRC cell lines (HCT116, HT29, and LoVo) via integration of microarray data with gene regulatory networks. After determination of hub genes, corresponding miRNAs were predicted using several databases and used in construction and subsequent analysis of miRNA-gene networks. Following to preparation of chemo-resistance CRC cells, a standard real-time PCR was conducted for validation of in silico findings. Topological and functional enrichment analyses of the resulted networks introduced several previously reported drug-resistance genes as well as novel biomarkers as hub genes which seem to be crucial in resistance of colon cancer cells to irinotecan and oxaliplatin. Furthermore, results of the functional annotation revealed the essential role of different signaling pathways like metabolic pathways in drug resistance of CRC cell lines to these drugs. A part of in silico findings was also validated in vitro using oxaliplatin-resistant cell lines. While FOXC1 and NFIC were upregulated in cell lines which were resistant to oxaliplatin, silencing FOXC1 decreased the resistance of SW480 cell line to oxaliplatin. In conclusion, our comparative in silico and in vitro study introduces several novel genes and miRNAs as the resistance-mediators which can be used for sensitizing resistant CRC cells to oxaliplatin and irinotecan.

Gene networks in cancer are biased by aneuploidies and sample impurities

Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.

A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps

Inferring gene regulatory networks (GRNs) is vital to understand the complex cellular processes and reveal the regulatory mechanisms among genes. Although various methods have been developed, more accurate algorithms which can control the sparseness of GRNs still need to be developed. In this work, we model GRNs by fuzzy cognitive maps (FCMs), and a node in an FCM means a gene. Then, a new sparse and decomposed particle swarm optimization, termed as SDPSO_FCM-GRN, is proposed to train FCMs, which employs the least absolute shrinkage and selection operator (Lasso) to control the network sparseness with a decomposed strategy. In the experiments, the performance of SDPSO_FCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that SDPSO_FCM-GRN can well control the sparseness of GRNs, and infer directed GRNs with high accuracy and efficiency.

Variable levels of drift in tunicate cardiopharyngeal gene regulatory elements

BACKGROUND

Mutations in gene regulatory networks often lead to genetic divergence without impacting gene expression or developmental patterning. The rules governing this process of developmental systems drift, including the variable impact of selective constraints on different nodes in a gene regulatory network, remain poorly delineated.

RESULTS

Here we examine developmental systems drift within the cardiopharyngeal gene regulatory networks of two tunicate species, Corella inflata and Ciona robusta. Cross-species analysis of regulatory elements suggests that trans-regulatory architecture is largely conserved between these highly divergent species. In contrast, cis-regulatory elements within this network exhibit distinct levels of conservation. In particular, while most of the regulatory elements we analyzed showed extensive rearrangements of functional binding sites, the enhancer for the cardiopharyngeal transcription factor FoxF is remarkably well-conserved. Even minor alterations in spacing between binding sites lead to loss of FoxF enhancer function, suggesting that bound trans-factors form position-dependent complexes.

CONCLUSIONS

Our findings reveal heterogeneous levels of divergence across cardiopharyngeal cis-regulatory elements. These distinct levels of divergence presumably reflect constraints that are not clearly associated with gene function or position within the regulatory network. Thus, levels of cis-regulatory divergence or drift appear to be governed by distinct structural constraints that will be difficult to predict based on network architecture.

Homeostasis in a feed forward loop gene regulatory motif

The internal state of a cell is affected by inputs from the extra-cellular environment such as external temperature. If some output, such as the concentration of a target protein, remains approximately constant as inputs vary, the system exhibits homeostasis. Special sub-networks called motifs are unusually common in gene regulatory networks (GRNs), suggesting that they may have a significant biological function. Potentially, one such function is homeostasis. In support of this hypothesis, we show that the feed-forward loop GRN produces homeostasis. Here the inputs are subsumed into a single parameter that affects only the first node in the motif, and the output is the concentration of a target protein. The analysis uses the notion of infinitesimal homeostasis, which occurs when the input-output map has a critical point (zero derivative). In model equations such points can be located using implicit differentiation. If the second derivative of the input-output map also vanishes, the critical point is a chair: the output rises roughly linearly, then flattens out (the homeostasis region or plateau), and then starts to rise again. Chair points are a common cause of homeostasis. In more complicated equations or networks, numerical exploration would have to augment analysis. Thus, in terms of finding chairs, this paper presents a proof of concept. We apply this method to a standard family of differential equations modeling the feed-forward loop GRN, and deduce that chair points occur. This function determines the production of a particular mRNA and the resulting chair points are found analytically. The same method can potentially be used to find homeostasis regions in other GRNs. In the discussion and conclusion section, we also discuss why homeostasis in the motif may persist even when the rest of the network is taken into account.

Development and use of a Cytoscape app for GRNCOP2

BACKGROUND AND OBJECTIVE

Gene regulatory networks (GRNs) are essential for understanding most molecular processes. In this context, the so-called model-free approaches have an advantage modeling the complex topologies behind these dynamic molecular networks, since most GRNs are difficult to map correctly by any other mathematical model. Abstract model-free approaches, also known as rule-based extraction methods, offer valuable benefits when performing data-driven analysis; such as requiring the least amount of data and simplifying the inference of large models at a faster analysis speed. In particular, GRNCOP2 is a combinatorial optimization method with an adaptive criterion for the discretization of gene expression data and high performance, in contrast to other rule-based extraction methods for discovering GRNs. However, the analysis of the large relational structures of the networks inferred by GRNCOP2 requires the support of effective tools for interactive network visualization and topological analysis of the extracted associations. This need motivated the possibility of integrating GRNCOP2 in the Cytoscape ecosystem in order to benefit from Cytoscapes core functionality, as well as all the other apps in its ecosystem.

METHODS

In this paper, we introduce the implementation of a GRNCOP2 Cytoscape app. This incorporation to Cytoscape platform includes new functionality for GRN visualizations, dynamic user-interaction and integration with other apps for topological analysis of the networks.

RESULTS

In order to demonstrate the usefulness of integrating GRNCOP2 in Cytoscape, the new app was used to tackle a novel use case for GRNCOP2: the analysis of crosstalk between pathways. In this regard, datasets associated with Alzheimer's disease (AD) were analyzed using GRNCOP2 app and other apps of the Cytoscape ecosystem by performing a topological analysis of the AD progression and its synchronization with the Ubiquitin Mediated Proteolysis pathway. Finally, the biological relevance of the findings achieved by this new app were evaluated by searching for evidence in the literature.

CONCLUSIONS

The proposed crosstalk analysis with the new GRNCOP2 app focused on assessing the phase of the Alzheimer's disease progression where the coordination with the Ubiquitin Mediated Proteolysis pathway increase, and identifying the genes that explain the signalling between these cellular processes. Both questions were explored by topological contrastive analysis of the GRNs generated for the GRNCOP2 app, where several facilities of Cytoscape were exploited. The topological patterns inferred by this new App have been consistent with biological evidence reported in the scientic literature, illustrating the effectiveness of using this new GRNCOP2 App in pathway analysis.

AVAILABILITY

The GRNCOP2 App is freely available at the official Cytoscape app store: http://apps.cytoscape.org/apps/grncop2.

Transcriptomic analysis of probable asymptomatic and symptomatic alzheimer brains

Individuals with intact cognition and neuropathology consistent with Alzheimer's disease (AD) are referred to as asymptomatic AD (AsymAD). These individuals are highly likely to develop AD, yet transcriptomic changes in the brain which might reveal mechanisms for their AD vulnerability are currently unknown. Entorhinal cortex, frontal cortex, temporal cortex and cerebellum tissue from 27 control, 33 AsymAD and 52 AD human brains were microarray expression profiled. Differential expression analysis identified a significant increase of transcriptomic activity in the frontal cortex of AsymAD subjects, suggesting fundamental changes in AD may initially begin within the frontal cortex region prior to AD diagnosis. Co-expression analysis identified an overactivation of the brain "glutamate-glutamine cycle", and disturbances in the brain energy pathways in both AsymAD and AD subjects, while the connectivity of key hub genes in this network indicates a shift from an already increased cell proliferation in AsymAD subjects to stress response and removal of amyloidogenic proteins in AD subjects. This study provides new insight into the earliest biological changes occurring in the brain prior to the manifestation of clinical AD symptoms and provides new potential therapeutic targets for early disease intervention.

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.

Large scale control and programming of gene expression using CRISPR

The control of gene expression in cells and organisms allows to unveil gene to function relationships and to reprogram biological responses. Several systems, such as Zinc fingers, TALE (Transcription activator-like effectors), and siRNAs (small-interfering RNAs), have been exploited to achieve this. However, recent advances in Clustered Regularly Interspaced Short Palindromic Repeats and Cas9 (CRISPR-Cas9) have overshadowed them due to high specificity, compatibility with many different organisms, and design flexibility. In this review we summarize state-of-the art for CRISPR-Cas9 technology for large scale gene perturbation studies, including single gene and multiple genes knock-out, knock-down, knock-up libraries, and their associated screening assays. We feature in particular the combination of these methods with single-cell transcriptomics approaches. Finally, we highlight the application of CRISPR-Cas9 systems in building synthetic circuits that can be interfaced with gene networks to control cellular states.

Boolean gene regulatory network model of centromere function in Saccharomyces cerevisiae

Centromeres, a highly conserved locus of eukaryotic chromosomes, have critical function for genome stability and integrity. Because their centromeric DNA sequences are necessary and sufficient for kinetochore recruitment and DNA segregation, point centromeres of Saccharomyces cerevisiae chromosomes provide an attractive system for the study of the regulation of centromere function. Using the mathematical model of Boolean gene regulatory networks, the gene regulatory dynamics of centromere region of S. cerevisiae (budding yeast), which is actively involved in the cell-cycle, has been examined. A gene regulatory network containing the relevant centromere genes of the model organism from biological databases was established and all possible cellular phenotypes subjected to a synchronous gene regulation and attracted to several basins. Gene expression in the largest attractor was compared with the biological data by obtaining changes in the cell-cycle. We show that the model for centromere function recovers a single cyclic attractor. The trajectory flow diagram plotted over all initial conditions of the system also shows good correspondence with the cell-cycle phases. Although other upstream signals are possibly involved in the regulation of centromere genes, proposed interactions with selected cell-cycle genes were sufficient to recover whole cell-cycle process. To truly clarify these proposed regulatory interactions of candidate genes for centromere function, profiling and analyzing their expression levels over time with expanded nodes/edges are required. Moreover, a previously modeled gene knock-down mechanism applied to the network and robustness versus knock-down was interpreted based on the obtained consequences.

Unsupervised GRN Ensemble

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data

A challenging problem in systems biology is the reconstruction of gene regulatory networks from postgenomic data. A variety of reverse engineering methods from machine learning and computational statistics have been proposed in the literature. However, deciding on the best method to adopt for a particular application or data set might be a confusing task. The present chapter provides a broad overview of state-of-the-art methods with an emphasis on conceptual understanding rather than a deluge of mathematical details, and the pros and cons of the various approaches are discussed. Guidance on practical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.

Inference of gene regulatory networks with multi-objective cellular genetic algorithm

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.

Dynamics of gene regulatory networks and their dependence on network topology and quantitative parameters - the case of phage λ

BACKGROUND

Gene regulatory networks can be modelled in various ways depending on the level of detail required and biological questions addressed. One of the earliest formalisms used for modeling is a Boolean network, although these models cannot describe most temporal aspects of a biological system. Differential equation models have also been used to model gene regulatory networks, but these frameworks tend to be too detailed for large models and many quantitative parameters might not be deducible in practice. Hybrid models bridge the gap between these two model classes - these are useful when concentration changes are important while the information about precise concentrations and binding site affinities is partial.

RESULTS

In this paper we study the stable behaviours of phage λ via a hybrid system based model. We identify wild type and mutant behaviours that arise for various orderings of binding site affinities. We propose experiments for detecting these behaviours: we suggest several ways of altering binding affinities with either mutations or genome rearrangements to achieve modified behaviours. The feasibility of these experiments is assessed. The interplay between the qualitative aspects of a network, e.g. network topology, and quantitative parameters, e.g. growth and degradation rates of proteins, is demonstrated. We also provide a software for exploring all feasible states of a hybrid system model and identifying all attractors.

CONCLUSIONS

The behaviours of phage λ are determined mainly by the topology of this network and by the mutual order of binding affinities. Exact affinities and growth and degradation rates of proteins fine tune the system. We show that only two stable behaviours are possible for phage λ if the main constraints of λ switch are preserved - these behaviours correspond to lysis and lysogeny. We identify several variants of both lysis and lysogeny - one wild type and one modified behaviour for each. We elucidate the necessary constraints for binding site affinities to achieve both wild type lysis and lysogeny. Our software is applicable to a wide range of biological models described as a hybrid system.

Prokaryotic Information Games: How and When to Take up and Secrete DNA

Besides transduction via bacteriophages natural transformation and bacterial conjugation are the most important mechanisms driving bacterial evolution and horizontal gene spread. Conjugation systems have evolved in eubacteria and archaea. In Gram-positive and Gram-negative bacteria, cell-to-cell DNA transport is typically facilitated by a type IV secretion system (T4SS). T4SSs also mediate uptake of free DNA in Helicobacter pylori, while most transformable bacteria use a type II secretion/type IV pilus system. In this chapter, we focus on how and when bacteria "decide" that such a DNA transport apparatus is to be expressed and assembled in a cell that becomes competent. Development of DNA uptake competence and DNA transfer competence is driven by a variety of stimuli and often involves intricate regulatory networks leading to dramatic changes in gene expression patterns and bacterial physiology. In both cases, genetically homogeneous populations generate a distinct subpopulation that is competent for DNA uptake or DNA transfer or might uniformly switch into competent state. Phenotypic conversion from one state to the other can rely on bistable genetic networks that are activated stochastically with the integration of external signaling molecules. In addition, we discuss principles of DNA uptake processes in naturally transformable bacteria and intend to understand the exceptional use of a T4SS for DNA import in the gastric pathogen H. pylori. Realizing the events that trigger developmental transformation into competence within a bacterial population will eventually help to create novel and effective therapies against the transmission of antibiotic resistances among pathogens.

Quantitative analysis of the ThrbCRM1-centered gene regulatory network

Enhancer activity is determined by both the activity and occupancy of transcription factors as well as the specific sequences they bind. Experimental investigation of this dynamic requires the ability to manipulate components of the system, ideally in as close to an in vivo context as possible. Here we use electroporation of plasmid reporters to define critical parameters of a specific cis-regulatory element, ThrbCRM1, during retinal development. ThrbCRM1 is associated with cone photoreceptor genesis and activated in a subset of developing retinal cells that co-express the Otx2 and Onecut1 (OC1) transcription factors. Variation of reporter plasmid concentration was used to generate dose response curves and revealed an effect of binding site availability on the number and strength of cells with reporter activity. Critical sequence elements of the ThrbCRM1 element were defined using both mutagenesis and misexpression of the Otx2 and OC1 transcription factors in the developing retina. Additionally, these experiments suggest that the ThrbCRM1 element is co-regulated by Otx2 and OC1 even under conditions of sub-optimal binding of OC1.

Whole mount in situ hybridization techniques for analysis of the spatial distribution of mRNAs in sea urchin embryos and early larvae

A critical process in embryonic development is the activation and spatial localization of mRNAs to specific cells and territories of the embryo. Revealing the spatial distribution of mRNAs and how it changes during development is a vital piece of information that aids in understanding the signaling and regulatory genes driving specific gene regulatory networks. In the laboratory, a cost-efficient, reliable method to determine the spatial distribution of mRNAs in embryos is in situ hybridization. This sensitive and straightforward method employs exogenous antisense RNA probes to find specific and complementary sequences in fixed embryos. Antigenic moieties conjugated to the ribonucleotides incorporated in the probe cross-react with antibodies, and numerous staining methods can be subsequently employed to reveal the spatial distribution of the targeted mRNA. The quality of the data produced by this method is equivalent to the experience of the researcher, and thus a thorough understanding of the numerous steps comprising this method is important for obtaining high quality data. Here we compile and summarize several protocols that have been employed chiefly on five sea urchin species in numerous laboratories around the world. Whereas the protocols can vary for the different species, the overarching steps are similar and can be readily mastered. When properly and carefully undertaken, in situ hybridization is a powerful tool providing unambiguous data for which there currently is no comparable substitute and will continue to be an important method in the era of big data and beyond.

In silico simulations of occurrence of transcription factor binding sites in bacterial genomes

Background

Interactions between transcription factors and their specific binding sites are a key component of regulation of gene expression. Until recently, it was generally assumed that most bacterial transcription factor binding sites are located at or near promoters. However, several recent works utilizing high-throughput technology to detect transcription factor binding sites in bacterial genomes found a large number of binding sites in unexpected locations, particularly inside genes, as opposed to known or expected promoter regions. While some of these intragenic binding sites likely have regulatory functions, an alternative scenario is that many of these binding sites arise by chance in the absence of selective constraints. The latter possibility was supported by in silico simulations for σ⁵⁴ binding sites in Salmonella.

Results

In this work, we extend these simulations to more than forty transcription factors from E. coli and other bacteria. The results suggest that binding sites for all analyzed transcription factors are likely to arise throughout the genome by random genetic drift and many transcription factor binding sites found in genomes may not have specific regulatory functions. In addition, when comparing observed and expected patterns of occurrence of binding sites in genomes, we observed distinct differences among different transcription factors.

Conclusions

We speculate that transcription factor binding sites randomly occurring throughout the genome could be beneficial in promoting emergence of new regulatory interactions and thus facilitating evolution of gene regulatory networks.

Electronic supplementary material

The online version of this article (10.1186/s12862-019-1381-8) contains supplementary material, which is available to authorized users.

Approximate kernel reconstruction for time-varying networks

Background

Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular networks are typically "re-wired" over time in response to cellular development and environmental changes. In our previous work, we formulated the inference of time-varying or dynamic networks as a tracking problem, where the target state is the ensemble of edges in the network. We used the Kalman filter to track the network topology over time. Unfortunately, the output of the Kalman filter does not reflect known properties of molecular networks, such as sparsity.

Results

To address the problem of inferring sparse time-varying networks from a set of under-sampled measurements, we propose the Approximate Kernel RecONstruction (AKRON) Kalman filter. AKRON supersedes the Lasso regularization by starting from the Lasso-Kalman inferred network and judiciously searching the space for a sparser solution. We derive theoretical bounds for the optimality of AKRON. We evaluate our approach against the Lasso-Kalman filter on synthetic data. The results show that not only does AKRON-Kalman provide better reconstruction errors, but it is also better at identifying if edges exist within a network. Furthermore, we perform a real-world benchmark on the lifecycle (embryonic, larval, pupal, and adult stages) of the Drosophila Melanogaster.

Conclusions

We show that the networks inferred by the AKRON-Kalman filter are sparse and can detect more known gene-to-gene interactions for the Drosophila melanogaster than the Lasso-Kalman filter. Finally, all of the code reported in this contribution will be publicly available.

Artificial Gene Regulatory Networks-A Review

In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.