Modularity and evolutionary constraints in a baculovirus gene regulatory network.

The genome of Anticarsia gemmatalis multiple nucleopolyhedrovirus isolate 2D (AgMNPV-2D), which is the most extensively used virus pesticide in the world, was completely sequenced and shown to have 132 239 bp (G+C content 44.5 mol%) and to be capable of encoding 152 non-overlapping open reading frames (ORFs). Three ORFs were unique to AgMNPV-2D, one of which (ag31) had similarity to eukaryotic poly(ADP-ribose) polymerases. The lack of chiA and v-cath may explain some of the success and growth of the AgMNPV biological control programme, as it may explain the high recovery of polyhedra sequestered inside dead larvae in the field, which are collected and used for further application as biological pesticides in soybean fields. The genome organization was similar to that of the Choristoneura fumiferana defective MNPV (CfDefNPV). Most of the variation between the two genomes took place near highly repetitive regions, which were also closely associated with bro-coding regions. The separation of the NPVs into groups I and II was supported by: (i) a phenogram of the complete genomes of 28 baculovirus and Heliothis zea virus 1, (ii) the most parsimonious reconstruction of gene content along the phenograms and (iii) comparisons of genomic features. Moreover, these data also reinforced the notion that group I of the NPVs can be split further into the AgMNPV lineage (AgMNPV, CfDefNPV, Epiphyas postvittana NPV, Orgyia pseudotsugata MNPV and C. fumiferana MNPV), sharing eight defining genes, and the Autographa californica MNPV (AcMNPV) lineage (AcMNPV, Rachiplusia ou NPV and Bombyx mori NPV), sharing nine defining genes.

Revealing gene regulation-based neural network computing in bacteria

BackgroundThe emergent dynamics of complex gene regulatory networks govern various cellular processes. However, understanding these dynamics is challenging due to the difficulty of parameterizing the computational models for these networks, especially as the network size increases. Here, we introduce a simulation library, Gene Regulatory Interaction Network Simulator (GRiNS), to address these challenges.ResultsGRiNS integrates popular parameter-agnostic simulation frameworks, RACIPE and Boolean Ising formalism, into a single Python library capable of leveraging GPU acceleration for efficient and scalable simulations. GRiNS extends the ordinary differential equations (ODE) based RACIPE framework with a more modular design, allowing users to choose parameters, initial conditions, and time-series outputs for greater customisability and accuracy in simulations. For large networks, where ODE-based simulation formalisms do not scale well, GRiNS implements Boolean Ising formalism, providing a simplified, coarse-grained alternative, significantly reducing the computational cost while capturing key dynamical behaviours of large regulatory networks.ConclusionGRiNS enables parameter-agnostic modeling of gene regulatory networks to study their dynamic and steady-state behaviors in a scalable and efficient manner. The documentation and installation instructions for GRiNS can be found at https://moltenecdysone09.github.io/GRiNS/.

KRAS mutations are a major cause of drug resistance to molecular-targeted therapies. Aberrant epidermal growth factor receptor (EGFR) signaling may cause dysregulation of microRNA (miRNA) and gene regulatory networks, which leads to cancer initiation and progression. To address the functional relevance of miRNAs in mutant KRAS cancers, we transfected exogenous KRASG12V into human embryonic kidney 293 and MRC5 cells with wild-type KRAS and BRAF genes, and we comprehensively profiled the dysregulated miRNAs. The result showed that mature miRNA oligonucleotide (miR)-4689, one of the significantly down-regulated miRNAs in KRASG12V overexpressed cells, was found to exhibit a potent growth-inhibitory and proapoptotic effect both in vitro and in vivo. miR-4689 expression was significantly down-regulated in cancer tissues compared to normal mucosa, and it was particularly decreased in mutant KRAS CRC tissues. miR-4689 directly targets v-ki-ras2 kirsten rat sarcoma viral oncogene homolog (KRAS) and v-akt murine thymoma viral oncogene homolog 1(AKT1), key components of two major branches in EGFR pathway, suggesting KRAS overdrives this signaling pathway through inhibition of miR-4689. Overall, this study provided additional evidence that mutant KRAS functions as a broad regulator of the EGFR signaling cascade by inhibiting miR-4689, which negatively regulates both RAS/mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/AKT pathways. These activities indicated that miR-4689 may be a promising therapeutic agent in mutant KRAS CRC.

MicroRNAs have been discovered in the noncoding nuclear genome. They inhibit partly in a nonspecific manner the transcription of numerous genes, and the corresponding “inhibitory noise” prevents the weakest positive interactions of the genetic regulatory networks to be actually efficient, hence microRNAs control the number of attractors of these networks, e.g., by frequently forcing them to have only one or two possible behaviors for fulfilling a precise cell function (if we identify a network attractor with a precise differentiated cell state). More specifically, microRNAs have a great influence on the chromatin clock, which ensures the controlled mode of updating genetic regulatory networks. We analyze this influence as well as their impact on important functions like controlling the cell cycle, improving the defenses of a host against pathogens like viruses, and maintaining the homeostasis of energy metabolism. In the last case, we show the role of two types of microRNAs, both involved in the control of the mitochondrial genome: (1) nuclear microRNAs, called mitoMirs, inhibiting mitochondrial genes and (2) putative mitochondrial microRNAs located in the noncoding part of the mitochondrial genome that inhibit tRNAs function. We show the complex involvement of microRNAs in the ubiquitous p53 regulatory function of cell cycle control, then their global role in cell respiration homeostasis, in carcinogenesis, and finally we discuss the influence of microRNAs on the increase of robustness of genetic networks during evolution.

In this paper, we investigate the robust asymptotic stability problem of genetic regulatory networks with time-varying delays and polytopic parameter uncertainties. Both cases of differentiable and nondifferentiable time-delays are considered, and the convex polytopic description is utilized to characterize the genetic network model uncertainties. By using a Lyapunov functional approach and linear matrix inequality (LMI) techniques, the stability criteria for the uncertain delayed genetic networks are established in the form of LMIs, which can be readily verified by using standard numerical software. An important feature of the results reported here is that all the stability conditions are dependent on the upper and lower bounds of the delays, which is made possible by using up-to-date techniques for achieving delay dependence. Another feature of the results lies in that a novel Lyapunov functional dependent on the uncertain parameters is utilized, which renders the results to be potentially less conservative than those obtained via a fixed Lyapunov functional for the entire uncertainty domain. A genetic network example is employed to illustrate the applicability and usefulness of the developed theoretical results.

The Gene Regulatory Networks (GRNs) ensure the stability of cellular states, preserving specific phenotypes and functions throughout the differentiation process. However, current tools still need improvement to effectively integrate multi-omics data and infer GRNs for particular cell types. We introduce CLMOGRI, a multi-omics TF-gene regulatory network inference framework based on heterogeneous networks and contrastive learning, designed to integrate multi-omics data for GRN inference. Through random walk techniques, CLMOGRI embeds multi-omics data into a unified feature space and extracts similar features between nodes. It then measures node similarity and predicts node relationships by contrastive learning. Finally, it includes a regulatory network interpreter to identify critical nodes and modules in GRNs, offering an analytical method for understanding complex interactions within biological systems. CLMOGRI surpasses existing baseline methods in terms of Area Under the Precision-Recall Curve (AUPR) and F-Score metrics, indicating its efficacy in capturing multi-omics information for GRN inference. It also reveals vital nodes and modules within the gene regulatory network, improving the interpretability of CLMOGRI and the utility of GRNs.

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 104 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data (Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

Comparing to neural networks, genetic regulatory networks (GRNs) have two variables, mRNA and proteins, interacting in a very complex way. This special structure of GRNs promises better adaptive performance. In this chapter, cooperative controllers inspired by GRNs are proposed. First, the robust H ∞ state estimation problem is investigated for a class of discrete-time stochastic GRNs with probabilistic measurement delays. Then, a bio-inspired cooperative controller via evolution of GRN is presented to make the performance index of each stage stable over the whole study horizon and the overall performance index close to the desired value against vibrations simultaneously.

BackgroundMaize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome.ResultsWe collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31.ConclusionsOur findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.

Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

Inferring gene regulatory network from single-cell transcriptomic data by integrating multiple prior networks

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA-binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analyzing the selective constraints driving evolution of miRNA and transcription factor (TF)–binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA- and TF-binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared with subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA- and TF-binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired, for example, clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions, and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our "just-in-time" analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to "prune" the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF "N-specificity" index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs-CRF4, SNZ, CDF1, HHO5/6, and PHL1-validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO3- uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal "transcriptional logic" for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.