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Development is regulated by coordinated changes in gene expression. Control of these changes in expression is largely governed by the binding of transcription factors to specific regulatory elements. However, the packaging of DNA into chromatin prevents the binding of many transcription factors. Pioneer factors overcome this barrier owing to unique properties that enable them to bind closed chromatin, promote accessibility and, in so doing, mediate binding of additional factors that activate gene expression. Because of these properties, pioneer factors act at the top of gene-regulatory networks and drive developmental transitions. Despite the ability to bind target motifs in closed chromatin, pioneer factors have cell type-specific chromatin occupancy and activity. Thus, developmental context clearly shapes pioneer-factor function. Here, we discuss this reciprocal interplay between pioneer factors and development: how pioneer factors control changes in cell fate and how cellular environment influences pioneerfactor binding and activity. [ABSTRACT FROM AUTHOR]

The aging process is a complex and multifaceted phenomenon affecting all living organisms. It involves a gradual deterioration of tissue and cellular function, leading to a higher risk of developing various age-related diseases (ARDs), including cancer, neurodegenerative, and cardiovascular diseases. The gene regulatory networks (GRNs) and their respective niches are crucial in determining the aging rate. Unveiling these GRNs holds promise for developing novel therapies and diagnostic tools to enhance healthspan and longevity. This review examines GRN modeling approaches in aging, encompassing differential equations, Boolean/fuzzy logic decision trees, Bayesian networks, mutual information, and regression clustering. These approaches provide nuanced insights into the intricate gene-protein interactions in aging, unveiling potential therapeutic targets and ARD biomarkers. Nevertheless, outstanding challenges persist, demanding more comprehensive datasets and advanced algorithms to comprehend and predict GRN behavior accurately. Despite these hurdles, identifying GRNs associated with aging bears immense potential and is poised to transform our comprehension of human health and aging. This review aspires to stimulate further research in aging, fostering the innovation of computational approaches for promoting healthspan and longevity. [ABSTRACT FROM AUTHOR]

The aging process is a complex and multifaceted phenomenon affecting all living organisms. It involves a gradual deterioration of tissue and cellular function, leading to a higher risk of developing various age-related diseases (ARDs), including cancer, neurodegenerative, and cardiovascular diseases. The gene regulatory networks (GRNs) and their respective niches are crucial in determining the aging rate. Unveiling these GRNs holds promise for developing novel therapies and diagnostic tools to enhance healthspan and longevity. This review examines GRN modeling approaches in aging, encompassing differential equations, Boolean/fuzzy logic decision trees, Bayesian networks, mutual information, and regression clustering. These approaches provide nuanced insights into the intricate gene-protein interactions in aging, unveiling potential therapeutic targets and ARD biomarkers. Nevertheless, outstanding challenges persist, demanding more comprehensive datasets and advanced algorithms to comprehend and predict GRN behavior accurately. Despite these hurdles, identifying GRNs associated with aging bears immense potential and is poised to transform our comprehension of human health and aging. This review aspires to stimulate further research in aging, fostering the innovation of computational approaches for promoting healthspan and longevity.

Abstract Background Siraitia grosvenorii (Swingle) C. Jeffrey, also known as Luohanguo or monk fruit, is a famous traditional Chinese medicine ingredient with important medicinal value and broad development prospects. Diploid S. grosvenorii has too many seeds, which will increase the utilization cost of active ingredients. Thus, studying the molecular mechanism of seed abortion in triploid S. grosvenorii, identifying the abortion-related genes, and regulating their expression will be a new direction to obtain seedless S. grosvenorii. Herein, we examined the submicroscopic structure of triploid S. grosvenorii seeds during abortion. Results Upon measuring the endogenous hormone content, we found that abscisic acid (ABA) and trans-zeatin (ZR) levels were significantly downregulated after days 15 and 20 of flowering. RNA sequencing of triploid seeds at different developmental stages was performed to identify key genes regulating abortion in triploid S. grosvenorii seeds. Multiple genes with differential expression between adjacent stages were identified; seven genes were differentially expressed across all stages. Weight gene co-expression network analysis revealed that the enhancement of monoterpene and terpene metabolic processes might lead to seed abortion by reducing the substrate flow to ABA and ZR. Conclusions These findings provide insights into the gene-regulatory network of seed abortion in triploid S. grosvenorii from different perspectives, thereby facilitating the innovation of the breeding technology of S. grosvenorii.

Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the “Sparse Identification of Nonlinear Dynamics” (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact.

Background: Siraitia grosvenorii (Swingle) C. Jeffrey, also known as Luohanguo or monk fruit, is a famous traditional Chinese medicine ingredient with important medicinal value and broad development prospects. Diploid S. grosvenorii has too many seeds, which will increase the utilization cost of active ingredients. Thus, studying the molecular mechanism of seed abortion in triploid S. grosvenorii, identifying the abortion-related genes, and regulating their expression will be a new direction to obtain seedless S. grosvenorii. Herein, we examined the submicroscopic structure of triploid S. grosvenorii seeds during abortion. Results: Upon measuring the endogenous hormone content, we found that abscisic acid (ABA) and trans-zeatin (ZR) levels were significantly downregulated after days 15 and 20 of flowering. RNA sequencing of triploid seeds at different developmental stages was performed to identify key genes regulating abortion in triploid S. grosvenorii seeds. Multiple genes with differential expression between adjacent stages were identified; seven genes were differentially expressed across all stages. Weight gene co-expression network analysis revealed that the enhancement of monoterpene and terpene metabolic processes might lead to seed abortion by reducing the substrate flow to ABA and ZR. Conclusions: These findings provide insights into the gene-regulatory network of seed abortion in triploid S. grosvenorii from different perspectives, thereby facilitating the innovation of the breeding technology of S. grosvenorii. [ABSTRACT FROM AUTHOR]

Abstract Background Diverse architectures of nervous systems (NSs) such as a plexus in cnidarians or a more centralized nervous system (CNS) in insects and vertebrates are present across Metazoa, but it is unclear what selection pressures drove evolution and diversification of NSs. One underlying aspect of this diversity lies in the cellular and molecular mechanisms driving neurogenesis, i.e. generation of neurons from neural precursor cells (NPCs). In cnidarians, vertebrates, and arthropods, homologs of SoxB and bHLH proneural genes control different steps of neurogenesis, suggesting that some neurogenic mechanisms may be conserved. However, data are lacking for spiralian taxa. Results To that end, we characterized NPCs and their daughters at different stages of neurogenesis in the spiralian annelid Capitella teleta. We assessed cellular division patterns in the neuroectoderm using static and pulse-chase labeling with thymidine analogs (EdU and BrdU), which enabled identification of NPCs that underwent multiple rounds of division. Actively-dividing brain NPCs were found to be apically-localized, whereas actively-dividing NPCs for the ventral nerve cord (VNC) were found apically, basally, and closer to the ventral midline. We used lineage tracing to characterize the changing boundary of the trunk neuroectoderm. Finally, to start to generate a genetic hierarchy, we performed double-fluorescent in-situ hybridization (FISH) and single-FISH plus EdU labeling for neurogenic gene homologs. In the brain and VNC, Ct-soxB1 and Ct-neurogenin were expressed in a large proportion of apically-localized, EdU+ NPCs. In contrast, Ct-ash1 was expressed in a small subset of apically-localized, EdU+ NPCs and subsurface, EdU− cells, but not in Ct-neuroD + or Ct-elav1 + cells, which also were subsurface. Conclusions Our data suggest a putative genetic hierarchy with Ct-soxB1 and Ct-neurogenin at the top, followed by Ct-ash1, then Ct-neuroD, and finally Ct-elav1. Comparison of our data with that from Platynereis dumerilii revealed expression of neurogenin homologs in proliferating NPCs in annelids, which appears different than the expression of vertebrate neurogenin homologs in cells that are exiting the cell cycle. Furthermore, differences between neurogenesis in the head versus trunk of C. teleta suggest that these two tissues may be independent developmental modules, possibly with differing evolutionary trajectories.

Background: Diverse architectures of nervous systems (NSs) such as a plexus in cnidarians or a more centralized nervous system (CNS) in insects and vertebrates are present across Metazoa, but it is unclear what selection pressures drove evolution and diversification of NSs. One underlying aspect of this diversity lies in the cellular and molecular mechanisms driving neurogenesis, i.e. generation of neurons from neural precursor cells (NPCs). In cnidarians, vertebrates, and arthropods, homologs of SoxB and bHLH proneural genes control different steps of neurogenesis, suggesting that some neurogenic mechanisms may be conserved. However, data are lacking for spiralian taxa. Results: To that end, we characterized NPCs and their daughters at different stages of neurogenesis in the spiralian annelid Capitella teleta. We assessed cellular division patterns in the neuroectoderm using static and pulse-chase labeling with thymidine analogs (EdU and BrdU), which enabled identification of NPCs that underwent multiple rounds of division. Actively-dividing brain NPCs were found to be apically-localized, whereas actively-dividing NPCs for the ventral nerve cord (VNC) were found apically, basally, and closer to the ventral midline. We used lineage tracing to characterize the changing boundary of the trunk neuroectoderm. Finally, to start to generate a genetic hierarchy, we performed double-fluorescent in-situ hybridization (FISH) and single-FISH plus EdU labeling for neurogenic gene homologs. In the brain and VNC, Ct-soxB1 and Ct-neurogenin were expressed in a large proportion of apically-localized, EdU+ NPCs. In contrast, Ct-ash1 was expressed in a small subset of apically-localized, EdU+ NPCs and subsurface, EdU− cells, but not in Ct-neuroD+ or Ct-elav1+ cells, which also were subsurface. Conclusions: Our data suggest a putative genetic hierarchy with Ct-soxB1 and Ct-neurogenin at the top, followed by Ct-ash1, then Ct-neuroD, and finally Ct-elav1. Comparison of our data with that from Platynereis dumerilii revealed expression of neurogenin homologs in proliferating NPCs in annelids, which appears different than the expression of vertebrate neurogenin homologs in cells that are exiting the cell cycle. Furthermore, differences between neurogenesis in the head versus trunk of C. teleta suggest that these two tissues may be independent developmental modules, possibly with differing evolutionary trajectories. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the local stability of a gene-regulatory network and immunotherapy for cancer modelled as nonlinear time-delay systems. A numerically generated kernel, using the sum-of-squares decomposition of multivariate polynomials, is used in the construction of an appropriate Lyapunov–Krasovskii functional for stability analysis of the networks around an equilibrium point. This analysis translates to verifying equivalent LMI conditions. A delay-independent asymptotic stability of a second-order model of a gene regulatory network, taking into consideration multiple commensurate delays, is established. In the case of cancer immunotherapy, a predator–prey type model is adopted to describe the dynamics with cancer cells and immune cells contributing to the predator–prey population, respectively. A delay-dependent asymptotic stability of the cancer-free equilibrium point is proved. Apart from the system and control point of view, in the case of gene-regulatory networks such stability analysis of dynamics aids mimicking gene networks synthetically using integrated circuits like neurochips learnt from biological neural networks, and in the case of cancer immunotherapy it helps determine the long-term outcome of therapy and thus aids oncologists in deciding upon the right approach.

B cell lineage acute lymphoblastic leukemia (B-ALL) is composed of diverse molecular subtypes, and while transcriptional and DNA methylation profiling has been extensively examined, the chromatin landscape is not well characterized for many subtypes. We therefore mapped chromatin accessibility using ATAC-seq in primary B-ALL cells from 156 patients spanning ten molecular subtypes and present this dataset as a resource. Differential chromatin accessibility and transcription factor (TF) footprint profiling were employed and identified B-ALL cell of origin, TF-target gene interactions enriched in B-ALL, and key TFs associated with accessible chromatin sites preferentially active in B-ALL. We further identified over 20% of accessible chromatin sites exhibiting strong subtype enrichment and candidate TFs that maintain subtype-specific chromatin architectures. Over 9,000 genetic variants were uncovered, contributing to variability in chromatin accessibility among patient samples. Our data suggest that distinct chromatin architectures are driven by diverse TFs and inherited genetic variants that promote unique gene-regulatory networks., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

Abstract The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Highlights • Tissue interactions and signaling involved in neural (plate) border (NB) formation in vertebrate embryos. • Key NB transcription factors and their roles in neural crest induction. • NB-GRN and the Hourglass Model of NC induction. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Classification into Brain-specific, Non-brain and Common miRNAs. • Higher evolutionary divergence in the human Brain-specific pre-miRNAs. • CNNC motif in the human Brain-specific pre-miRNAs favors DROSHA/DICER processing. • Efficient processing of Brain-specific pre-miRNAs leads to elevated production of mature miRNAs. • miRNA-driven gene regulatory network governing neuronal pathways is identified. Abstract The hallmark of human evolution encompasses the dramatic increase in brain size and complexity. The intricate interplays of micro-RNAs (miRNAs) and their target genes are indispensable in brain development. Sequence divergence in distinct structural regions of Brain-specific precursor miRNAs (pre-miRNAs) and its consequence in the production of corresponding mature miRNAs in human are unknown. To address these questions, first we classified miRNAs into three categories based on tissue expression: Brain-specific (expressed exclusively in brain), Non-brain (expressed in Non-brain tissues) and Common (expressed in all tissues) and compared the sequence divergence of different structural regions (basal segment, lower and upper stem, internal and terminal loop) of categorized pre-miRNAs across human, non-human primates and rodents. Our analysis revealed that unpaired regions of Brain-specific pre-miRNAs in human bear traces of relatively high rate of evolutionary divergence compared to those in other species. Cross-tissue expression analysis unveiled the higher expression of the Brain-specific miRNAs in human compared to other species. Intriguingly, in human brain, expression levels of these miRNAs superseded the levels of the ubiquitously expressed "Common-miRNAs". Further analysis revealed that presence of certain motif and nucleotide preference in the Brain-specific pre-miRNAs may favor DROSHA and DICER to ameliorate miRNA processing. The higher processing efficiency of human Brain-specific miRNAs was reflected as an elevated production of corresponding mature miRNAs in the human brain. Finally, re-construction of gene-regulatory network uncovers different pathways driven by Brain-specific miRNAs that may contribute to the development of brain in human. [ABSTRACT FROM AUTHOR]

Reproductive development in plants is controlled by complex and intricate gene-regulatory networks of transcription factors. These networks integrate the information from endogenous, hormonal and environmental regulatory pathways. Many of the key players have been identified in Arabidopsis and other flowering plant species, and their interactions and molecular modes of action are being elucidated. An emerging theme is that there is extensive crosstalk between different pathways, which can be accomplished at the molecular level by modulation of transcription factor activity or of their downstream targets. In this review, we aim to summarize current knowledge on transcription factors and epigenetic regulators that control basic developmental programs during inflorescence and flower morphogenesis in the model plant Arabidopsis thaliana . This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer. [ABSTRACT FROM AUTHOR]

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Dysregulated inflammation underlies various diseases. Specialized pro-resolving mediators (SPMs) like Resolvin D1 (RvD1) have been shown to resolve inflammation and halt disease progression. Macrophages, key immune cells that drive inflammation, respond to the presence of RvD1 by polarizing to an anti-inflammatory type (M 2 ). However, RvD1's mechanisms, roles, and utility are not fully understood. This paper introduces a gene-regulatory network (GRN) model that contains pathways for RvD1 and other SPMs and proinflammatory molecules like lipopolysaccharides. We couple this GRN model to a partial differential equation-agent-based hybrid model using a multiscale framework to simulate an acute inflammatory response with and without the presence of RvD1. We calibrate and validate the model using experimental data from two animal models. The model reproduces the dynamics of key immune components and the effects of RvD1 during acute inflammation. Our results suggest RvD1 can drive macrophage polarization through the G protein-coupled receptor 32 (GRP32) pathway. The presence of RvD1 leads to an earlier and increased M 2 polarization, reduced neutrophil recruitment, and faster apoptotic neutrophil clearance. These results support a body of literature that suggests that RvD1 is a promising candidate for promoting the resolution of acute inflammation. We conclude that once calibrated and validated on human data, the model can identify critical sources of uncertainty, which could be further elucidated in biological experiments and assessed for clinical use., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Diabetic kidney disease (DKD) is the most common cause of renal failure. Therapeutics development is hampered by our incomplete understanding of animal models on a cellular level. We show that ZSF1 rats recapitulate human DKD on a phenotypic and transcriptomic level. Tensor decomposition prioritizes proximal tubule (PT) and stroma as phenotype-relevant cell types exhibiting a continuous lineage relationship. As DKD features endothelial dysfunction, oxidative stress, and nitric oxide depletion, soluble guanylate cyclase (sGC) is a promising DKD drug target. sGC expression is specifically enriched in PT and stroma. In ZSF1 rats, pharmacological sGC activation confers considerable benefits over stimulation and is mechanistically related to improved oxidative stress regulation, resulting in enhanced downstream cGMP effects. Finally, we define sGC gene co-expression modules, which allow stratification of human kidney samples by DKD prevalence and disease-relevant measures such as kidney function, proteinuria, and fibrosis, underscoring the relevance of the sGC pathway to patients., Competing Interests: Declaration of interests A.F., J.V., I.M., K. Siudak, F.E., P.S., M.G., and M.P. are employees of Bayer AG. M.S.B. reports consultancy: Boehringer Ingelheim; editorial board membership: Journal of the American Society of Nephrology. K. Susztak reports research support: AstraZeneca, Bayer, Boehringer Ingelheim, Calico, Genentech, Gilead, GSK, Jnana, Lilly, Maze, Merck, Novartis, Novo Nordisk, Regeneron, Variant Bio, and Ventus; advisory board membership: Jnana Therapeutics and Pfizer; consultancy: AstraZeneca, Bayer, GSK, Jnana Therapeutics, Maze, Novo Nordisk, Pfizer, and Ventus; patents: Jag1- and Notch-based targeting of chronic kidney disease; editorial board membership: Cell Metabolism, eBioMedicine, Journal of the American Society of Nephrology, Journal of Clinical Investigation, Kidney International, and Med., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the "Sparse Identification of Nonlinear Dynamics" (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact., Competing Interests: The authors declare no competing interests., (© 2023 The Authors. Published by Elsevier Ltd.)

Contemporary evolution has the potential to significantly alter biotic responses to global change, including range expansion dynamics and biological invasions. Models predicting range dynamics often make highly simplifying assumptions about the genetic architecture underlying relevant traits. However, genetic architecture defines evolvability and higher-order evolutionary processes, which determine whether evolution will be able to keep up with environmental change or not. Therefore, we here study the impact of the genetic architecture of dispersal and local adaptation, two central traits of high relevance for range expansions, on the dynamics and predictability of invasion into an environmental gradient, such as temperature. In our theoretical model we assume that dispersal and local adaptation traits result from the products of two noninteracting gene-regulatory networks (GRNs). We compare our model to simpler quantitative genetics models and show that in the GRN model, range expansions are accelerating and less predictable. We further find that accelerating dynamics in the GRN model are primarily driven by an increase in the rate of local adaptation to novel habitats which results from greater sensitivity to mutation (decreased robustness) and increased gene expression. Our results highlight how processes at microscopic scales, here within genomes, can impact the predictions of large-scale, macroscopic phenomena, such as range expansions, by modulating the rate of evolution.

Background: In the pathogen P. aeruginosa, the formation of virulence factors is regulated via Quorum sensing signaling pathways. Due to the increasing number of strains that are resistant to antibiotics, there is a high interest to develop novel antiinfectives. In the combat of resistant bacteria, selective blockade of the bacterial cell-to-cell communication (Quorum sensing) has gained special interest as anti-virulence strategy. Here, we modeled the las, rhl, and pqs Quorum sensing systems by a multi-level logical approach to analyze how enzyme inhibitors and receptor antagonists effect the formation of autoinducers and virulence factors. Results: Our rule-based simulations fulfill the behavior expected from literature considering the external level of autoinducers. In the presence of PqsBCD inhibitors, the external HHQ and PQS levels are indeed clearly reduced. The magnitude of this effect strongly depends on the inhibition level. However, it seems that the pyocyanin pathway is incomplete. Conclusions: To match experimental observations we suggest a modified network topology in which PqsE and PqsR acts as receptors and an autoinducer as ligand that up-regulate pyocyanin in a concerted manner. While the PQS biosynthesis is more appropriate as target to inhibit the HHQ and PQS formation, blocking the receptor PqsR that regulates the biosynthesis reduces the pyocyanin level stronger. [ABSTRACT FROM AUTHOR]

Abstract: Deployment of the gene-regulatory network (GRN) responsible for skeletogenesis in the embryo of the sea urchin Strongylocentrotus purpuratus is restricted to the large micromere lineage by a double negative regulatory gate. The gate consists of a GRN subcircuit composed of the pmar1 and hesC genes, which encode repressors and are wired in tandem, plus a set of target regulatory genes under hesC control. The skeletogenic cell state is specified initially by micromere-specific expression of these regulatory genes, viz. alx1, ets1, tbrain and tel, plus the gene encoding the Notch ligand Delta. Here we use a recently developed high throughput methodology for experimental cis-regulatory analysis to elucidate the genomic regulatory system controlling alx1 expression in time and embryonic space. The results entirely confirm the double negative gate control system at the cis-regulatory level, including definition of the functional HesC target sites, and add the crucial new information that the drivers of alx1 expression are initially Ets1, and then Alx1 itself plus Ets1. Cis-regulatory analysis demonstrates that these inputs quantitatively account for the magnitude of alx1 expression. Furthermore, the Alx1 gene product not only performs an auto-regulatory role, promoting a fast rise in alx1 expression, but also, when at high levels, it behaves as an auto-repressor. A synthetic experiment indicates that this behavior is probably due to dimerization. In summary, the results we report provide the sequence level basis for control of alx1 spatial expression by the double negative gate GRN architecture, and explain the rising, then falling temporal expression profile of the alx1 gene in terms of its auto-regulatory genetic wiring. [Copyright &y& Elsevier]

Convergent gene pairs with head-to-head configurations are widespread in both eukaryotic and prokaryotic genomes and are speculated to be involved in gene regulation. Here we present a unique mechanism of gene regulation due to convergent transcription from the antagonistic prgX/prgQ operon in Enterococcus faecalis controlling conjugative transfer of the antibiotic resistance plasmid pCF10 from donor cells to recipient cells. Using mathematical modeling and experimentation, we demonstrate that convergent transcription in the prgX/prgQ operon endows the system with the properties of a robust genetic switch through premature termination of elongating transcripts due to collisions between RNA polymerases (RNAPs) transcribing from opposite directions and antisense regulation between complementary counter-transcripts. Evidence is provided for the presence of truncated RNAs resulting from convergent transcription from both the promoters that are capable of sense-antisense interactions. A mathematical model predicts that both RNAP collision and antisense regulation are essential for a robust bistable switch behavior in the control of conjugation initiation by prgX/prgQ operons. Moreover, given that convergent transcription is conserved across species, the mechanism of coupling RNAP collision and antisense interaction is likely to have a significant regulatory role in gene expression. [ABSTRACT FROM AUTHOR]

Polynomial-time data reduction is a classical approach to hard graph problems. Typically, particular small subgraphs are replaced by smaller gadgets. We generalize this approach to handle any small subgraph that has a small separator connecting it to the rest of the graph. The problem we study is the NP-hard Balanced Subgraph problem, which asks for a 2-coloring of a graph that minimizes the inconsistencies with given edge labels. It has applications in social networks, systems biology, and integrated circuit design. The data reduction scheme unifies and generalizes a number of previously known data reductions, and can be applied to a large number of graph problems where a coloring or a subset of the vertices is sought. To solve the instances that remain after reduction, we use a fixed-parameter algorithm based on iterative compression with a very effective heuristic speedup. Our implementation can solve biological real-world instances exactly for which previously only approximations were known. In addition, we present experimental results for financial networks and random networks. [ABSTRACT FROM AUTHOR]

Abstract: We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks’ graph-theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [A. Gupta, J.D. Varner, C.D. Maranas, Comput. Chem. Eng. 29 (2005) 565], we are able to identify trends in graph-theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise. [Copyright &y& Elsevier]

Cambium drives the lateral growth of stems and roots, contributing to diverse plant growth forms. The root crop is one of the outstanding examples of the cambium-driven growth. To understand its molecular basis, we used radish to generate a compendium of root-tissue- and stage-specific transcriptomes from two contrasting inbred lines during root growth. Expression patterns of key cambium regulators and hormone signaling components were validated. Clustering and gene ontology (GO) enrichment analyses of radish datasets followed by a comparative analysis against the newly established Arabidopsis early cambium data revealed evolutionary conserved stress-response transcription factors that may intimately control the cambium. Indeed, an in vivo network consisting of selected stress-response and cambium regulators indicated ERF-1 as a potential key checkpoint of cambial activities, explaining how cambium-driven growth is altered in response to environmental changes. The findings here provide valuable information about dynamic gene expression changes during cambium-driven root growth and have implications with regard to future engineering schemes, leading to better crop yields. • High-resolution spatiotemporal transcriptome data for radish storage roots • Conserved stress response networks are enriched in the root cambium • In vivo cambium regulatory networks are entwined with stress response factors • ERF-1 acts as a balancer between cambium-driven growth and stress sensing Hoang, Choe, Zheng et al. generate tissue-specific transcriptome data of radish storage roots and present a compendium of analyses to understand the cambium-driven root growth and yields. Their study identifies conserved gene-regulatory networks that integrate environmental sensing and growth and ERF-1 as a balancer of these two processes. [ABSTRACT FROM AUTHOR]

The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage "master" regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)-target interactions, microRNA (miRNA)-target interaction, and protein-protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host-pathogen interactions for comprehensive understanding of manipulated biological pathways., (© 2021. Springer Science+Business Media, LLC, part of Springer Nature.)

Boolean approaches and extensions thereof are becoming increasingly popular to model signaling and regulatory networks, including those controlling cell differentiation, pattern formation and embryonic development. Here, we describe a logical modeling framework relying on three steps: the delineation of a regulatory graph, the specification of multilevel components, and the encoding of Boolean rules specifying the behavior of model components depending on the levels or activities of their regulators. Referring to a non-deterministic, asynchronous updating scheme, we present several complementary methods and tools enabling the computation of stable activity patterns, the verification of the reachability of such patterns, as well as the generation of mean temporal evolution curves and the computation of the probabilities to reach distinct activity patterns. We apply this logical framework to the regulatory network controlling T lymphocyte specification. This process involves cross-regulations between specific T cell regulatory factors and factors driving alternative differentiation pathways, which remain accessible during the early steps of thymocyte development. Many transcription factors needed for T cell specification are required in other hematopoietic differentiation pathways and are combined in a fine-tuned, time-dependent fashion to achieve T cell commitment. Using the software GINsim, we integrated current knowledge into a dynamical model, which recapitulates the main developmental steps from early progenitors entering the thymus up to T cell commitment, as well as the impact of various documented environmental and genetic perturbations. Our model analysis further enabled the identification of several knowledge gaps. The model, software and whole analysis workflow are provided in computer-readable and executable form to ensure reproducibility and ease extensions., (© 2020 Elsevier Inc. All rights reserved.)

Plants, like other eukaryotes, have evolved complex mechanisms to coordinate gene expression during development, environmental response, and cellular homeostasis. Transcription factors (TFs), accompanied by basic cofactors and posttranscriptional regulators, are key players in gene-regulatory networks (GRNs). The coordinated control of gene activity is achieved by the interplay of these factors and by physical interactions between TFs and DNA. Here, we will briefly outline recent technological progress made to elucidate GRNs in plants. We will focus on techniques that allow us to characterize physical interactions in GRNs in plants and to analyze their regulatory consequences. Targeted manipulation allows us to test the relevance of specific gene-regulatory interactions. The combination of genome-wide experimental approaches with mathematical modeling allows us to get deeper insights into key-regulatory interactions and combinatorial control of important processes in plants.

Reproductive development in plants is controlled by complex and intricate gene-regulatory networks of transcription factors. These networks integrate the information from endogenous, hormonal and environmental regulatory pathways. Many of the key players have been identified in Arabidopsis and other flowering plant species, and their interactions and molecular modes of action are being elucidated. An emerging theme is that there is extensive crosstalk between different pathways, which can be accomplished at the molecular level by modulation of transcription factor activity or of their downstream targets. In this review, we aim to summarize current knowledge on transcription factors and epigenetic regulators that control basic developmental programs during inflorescence and flower morphogenesis in the model plant Arabidopsis thaliana. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Human testis determination is initiated by SRY (sex determining region on Y chromosome). Mutations in SRY cause gonadal dysgenesis with female somatic phenotype. Two subtle variants (V60L and I90M in the high-mobility group box) define inherited alleles shared by an XY sterile daughter and fertile father. Whereas specific DNA binding and bending are unaffected in a rat embryonic pre-Sertoli cell line, the variants exhibited selective defects in nucleocytoplasmic shuttling due to impaired nuclear import (V60L; mediated by Exportin-4) or export (I90M; mediated by chromosome region maintenance 1). Decreased shuttling limits nuclear accumulation of phosphorylated (activated) SRY, in turn reducing occupancy of DNA sites regulating Sertoli-cell differentiation [the testis-specific SRY-box 9 (Sox9) enhancer]. Despite distinct patterns of biochemical and cell-biological perturbations, V60L and I90M each attenuated Sox9 expression in transient transfection assays by twofold. Such attenuation was also observed in studies of V60A, a clinical variant associated with ovotestes and hence ambiguity between divergent cell fates. This shared twofold threshold is reminiscent of autosomal syndromes of transcription-factor haploinsufficiency, including XY sex reversal associated with mutations in SOX9. Our results demonstrate that nucleocytoplasmic shuttling of SRY is necessary for robust initiation of testicular development. Although also characteristic of ungulate orthologs, such shuttling is not conserved among rodents wherein impaired nuclear export of the high-mobility group box and import-dependent phosphorylation are compensated by a microsatellite-associated transcriptional activation domain. Human sex reversal due to subtle defects in the nucleocytoplasmic shuttling of SRY suggests that its transcriptional activity lies near the edge of developmental ambiguity.

Elucidation of pathogenicity mechanisms of the most important human-pathogenic fungi, Aspergillus fumigatus and Candida albicans, has gained great interest in the light of the steadily increasing number of cases of invasive fungal infections. A key feature of these infections is the interaction of the different fungal morphotypes with epithelial and immune effector cells in the human host. Because of the high level of complexity, it is necessary to describe and understand invasive fungal infection by taking a systems biological approach, i.e., by a comprehensive quantitative analysis of the non-linear and selective interactions of a large number of functionally diverse, and frequently multifunctional, sets of elements, e.g., genes, proteins, metabolites, which produce coherent and emergent behaviors in time and space. The recent advances in systems biology will now make it possible to uncover the structure and dynamics of molecular and cellular cause-effect relationships within these pathogenic interactions. We review current efforts to integrate omics and image-based data of host-pathogen interactions into network and spatio-temporal models. The modeling will help to elucidate pathogenicity mechanisms and to identify diagnostic biomarkers and potential drug targets for therapy and could thus pave the way for novel intervention strategies based on novel antifungal drugs and cell therapy.