Search Results (120)

Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling of TnP-treated larvae following tail fin amputation revealed 558 differentially expressed genes (DEGs), categorized into four functional networks: (1) drug-metabolizing enzymes (cyp3a65, cyp1a) and transporters (SLC/ABC families), where TnP alters xenobiotic processing through Phase I/II modulation; (2) cellular trafficking and immune regulation, with upregulated myosin genes (myhb/mylz3) enhancing wound repair and tlr5-cdc42 signaling fine-tuning inflammation; (3) proteolytic cascades (c6ast4, prss1) coupled to autophagy (ulk1a, atg2a) and metabolic rewiring (g6pca.1-tg axis); and (4) melanogenesis-circadian networks (pmela/dct-fbxl3l) linked to ubiquitin-mediated protein turnover. Key findings highlight TnP’s unique coordination of rapid (protease activation) and sustained (metabolic adaptation) responses, enabled by short network path lengths (1.6–2.1 edges). Hub genes, such as nr1i2 (pxr), ppara, and bcl6aa/b, mediate crosstalk between these systems, while potential risks—including muscle hypercontractility (myhb overexpression) or cardiovascular effects (ace2-ppp3ccb)—underscore the need for targeted delivery. The zebrafish model validated TnP-conserved mechanisms with human relevance, particularly in drug metabolism and tissue repair. TnP’s ability to synchronize extracellular matrix remodeling, immune resolution, and metabolic homeostasis supports its development for the treatment of fibrosis, metabolic disorders, and inflammatory conditions. Conclusions: Future work should focus on optimizing tissue-specific delivery and assessing genetic variability to advance clinical translation. This system-level analysis positions TnP as a model example for next-generation multi-pathway therapeutics. Full article

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

The worldwide agriculture industry is facing increasing problems due to rapid population increase and increasingly unfavorable weather patterns. In order to reach the projected food production targets, which are essential for guaranteeing global food security, innovative and sustainable agricultural methods must be adopted. Conventional approaches, including traditional breeding procedures, often cannot handle the complex and simultaneous effects of biotic pressures such as pest infestations, disease attacks, and nutritional imbalances, as well as abiotic stresses including heat, salt, drought, and heavy metal toxicity. Applying phytohormonal approaches, particularly those involving hormonal crosstalk, presents a viable way to increase crop resilience in this context. Abscisic acid (ABA), gibberellins (GAs), auxin, cytokinins, salicylic acid (SA), jasmonic acid (JA), ethylene, and GA are among the plant hormones that control plant stress responses. In order to precisely respond to a range of environmental stimuli, these hormones allow plants to control gene expression, signal transduction, and physiological adaptation through intricate networks of antagonistic and constructive interactions. This review focuses on how the principal hormonal signaling pathways (in particular, ABA-ET, ABA-JA, JA-SA, and ABA-auxin) intricately interact and how they affect the plant stress response. For example, ABA-driven drought tolerance controls immunological responses and stomatal behavior through antagonistic interactions with ET and SA, while using SnRK2 kinases to activate genes that react to stress. Similarly, the transcription factor MYC2 is an essential node in ABA–JA crosstalk and mediates the integration of defense and drought signals. Plants’ complex hormonal crosstalk networks are an example of a precisely calibrated regulatory system that strikes a balance between growth and abiotic stress adaptation. ABA, JA, SA, ethylene, auxin, cytokinin, GA, and BR are examples of central nodes that interact dynamically and context-specifically to modify signal transduction, rewire gene expression, and change physiological outcomes. To engineer stress-resilient crops in the face of shifting environmental challenges, a systems-level view of these pathways is provided by a combination of enrichment analyses and STRING-based interaction mapping. These hormonal interactions are directly related to the United Nations Sustainable Development Goals (SDGs), particularly SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action). This review emphasizes the potential of biotechnologies to use hormone signaling to improve agricultural performance and sustainability by uncovering the molecular foundations of hormonal crosstalk. Increasing our understanding of these pathways presents a strategic opportunity to increase crop resilience, reduce environmental degradation, and secure food systems in the face of increasing climate unpredictability. Full article

Background: Antibiotics at sub-inhibitory concentrations can rewire bacterial regulatory networks, impacting virulence. Objective: The way that exposure to selected antibiotics (ciprofloxacin, amikacin, azithromycin, ceftazidime, and meropenem) below their minimum inhibitory concentration (sub-MIC) modulates the physiology of Pseudomonas aeruginosa is examined in this study using growth-phase-resolved analysis. Methods: Standard P. aeruginosa strain cultures were exposed to ¼ and ½ MIC to determine the growth kinetics under antibiotic stress. The study measured protease and pyocyanin production and the expression level of important quorum sensing and virulence genes (lasI/R, rhlI/R, pqsR/A, and phzA) at different growth phases. Results: Meropenem produced the most noticeable growth suppression at ½ MIC. Sub-MIC antibiotics did not completely stop growth, but caused distinct, dose-dependent changes. Azithromycin eliminated protease activity in all phases and had a biphasic effect on pyocyanin. Ciprofloxacin consistently inhibited both pyocyanin and protease in all phases. The effects of amikacin varied by phase and dose, while β-lactams markedly increased pyocyanin production during the log phase. In contrast to the plateau phase, when expression was often downregulated or unchanged, most quorum-sensing- and virulence-associated genes showed significant upregulation during the death phase under sub-MIC exposure. Conclusions: These findings indicate that sub-MIC antibiotics act as biochemical signal modulators, preserving stress-adapted sub-populations that, in late growth phases, activate quorum sensing and stress tolerance pathways. Full article

Ginseng rusty root symptoms (GRSs) compromise the yield and quality of Panax ginseng. While transcriptomic analyses have demonstrated extensive remodeling of stress signaling networks, the post-transcriptional defense circuitry remains obscure. We profiled alternative splicing (AS) in three phloem tissues, the healthy phloem (AG), the non-reddened phloem neighboring lesions (BG), and the reddened lesion core (CG), to delineate AS reprogramming during GRS progression. The frequency of AS was sharply elevated in CG, with intron retention predominating. Extensive gains and losses of splice events indicate large-scale rewiring of the splice network. Overlapping differentially alternative spliced genes (DAGs) identified in both CG vs AG and CG vs BG contrasts were significantly enriched for RNA–spliceosome assembly and stress–response pathways, revealing a conserved post-transcriptional response associated with lesion formation. Integrative analysis of differentially expressed genes uncovered 671 loci under dual regulation; functional classification categorized these genes in receptor-like kinase signaling and chromatin-remodeling modules, underscoring the synergy between AS and transcriptional control. Moreover, the B subgenome disproportionately contributed stress-responsive transcripts in diseased tissue, suggesting an adaptive, subgenome-biased strategy. These findings demonstrate that dynamic AS remodeling and subgenome expression bias jointly orchestrate ginseng defense against GRS and provide a framework for breeding disease-resilient crops. Full article

The increasing global demand for energy and the urgent need to mitigate climate change have driven the search for sustainable alternatives to fossil fuels, with biofuels emerging as a promising solution. However, the low yields and inefficiencies in biofuel production processes necessitate advanced strategies to enhance their commercial viability. Metabolic engineering has become a pivotal tool in optimizing microbial pathways to improve biofuel production, addressing these challenges through innovative genetic and synthetic biology approaches. This review highlights the role of metabolic engineering in enhancing biofuel production by focusing on microbial engineering for lignocellulosic biomass utilization, strategies to overcome inhibitor effects, and pathway optimization for biofuels like n-butanol and iso-butanol. It also explores the production of advanced biofuels from fatty acid and isoprenoid pathways, emphasizing the use of model organisms such as Escherichia coli and Saccharomyces cerevisiae. Key insights include the application of CRISPR/Cas9 and multiplex automated genome engineering for precise genetic modifications, as well as metabolic flux analysis to optimize pathway efficiency. Additionally, the review discusses synthetic biology methodologies to rewire metabolic networks and improve biofuel yields, providing a comprehensive overview of current advancements and their implications for industrial-scale production. Full article

Activating mutations in the epidermal growth factor receptor (EGFR) are key oncogenic drivers across multiple cancers, yet the structural mechanisms by which these mutations promote persistent receptor activation remain elusive. Here, we investigate how three clinically relevant mutations—T790M, L858R, and the T790M_L858R double mutant—reshape EGFR’s conformational ensemble and regulatory network architecture. Using multiscale molecular simulations and kinetic modeling, we show that these mutations, particularly in combination, enhance flexibility in the αC-helix and A-loop, favoring activation-competent states. Markov state modeling reveals a shift in equilibrium toward active macrostates and accelerated transitions between metastable conformations. To resolve the underlying coordination mechanism, we apply neural relational inference to reconstruct time-dependent interaction networks, uncovering the mutation-induced rewiring of allosteric pathways linking distant regulatory regions. This coupling of conformational redistribution with network remodeling provides a mechanistic rationale for sustained EGFR activation and suggests new opportunities for targeting dynamically organized allosteric circuits in therapeutic design. Full article

Long noncoding RNAs (lncRNAs), non-protein-coding transcripts exceeding 200 nucleotides, are critical regulators of gene expression through chromatin remodeling, transcriptional modulation, and post-transcriptional modifications. While ionizing radiation (IR) induces cellular damage through direct DNA breaks, reactive oxygen species (ROS)-mediated oxidative stress, and bystander effects, the functional involvement of lncRNAs in the radiation response remains incompletely characterized. Here, through genome-wide CRISPR activation (CRISPRa) screening in non-small cell lung cancer (NSCLC) cells, we identified LOC401312 as a novel radiosensitizing lncRNA, the stable overexpression of which significantly enhanced IR sensitivity. Transcriptomic profiling revealed that LOC401312 transcriptionally upregulates carbamoyl-phosphate synthase 1 (CPS1), a mitochondrial enzyme involved in pyrimidine biosynthesis. Notably, CPS1 overexpression recapitulated the radiosensitization phenotype observed with LOC401312 activation. Mechanistic investigations revealed that CPS1 suppresses the phosphorylation of ATM kinase (Ser1981) protein, which is a key mediator of DNA damage checkpoint activation. This study established the LOC401312–CPS1–ATM axis as a previously unrecognized regulatory network governing radiation sensitivity, highlighting the potential of lncRNA-directed metabolic rewiring to impair DNA repair fidelity. Our findings not only expand the functional landscape of lncRNAs in DNA damage response but also provide a therapeutic rationale for targeting the LOC401312–CPS1 axis to improve radiotherapy efficacy in NSCLC. Full article

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain poorly understood. In this study, we employ microsecond molecular dynamics simulations, mutational scanning, and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K19 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues that function as universal hotspots in KRAS recognition. KRAS residues I36, Y40, M67, and H95 consistently emerge as critical contributors to binding stability. Binding free energy computations show that, despite similar binding modes, K27 relies heavily on electrostatic contributions from major binding hotspots while K55 exploits a dense hydrophobic cluster enhancing its effector-mimetic signature. The allosteric binders K13 and K19, by contrast, stabilize a KRAS-specific pocket in the α3–loop–α4 motif, introducing new hinges and bottlenecks that rewire the communication architecture of KRAS without full immobilization. Network-based analysis reveals a strikingly consistent theme: despite their distinct mechanisms of recognition, all systems engage a unifying allosteric architecture that spans multiple functional motifs. This architecture is not only preserved across complexes but also mirrors the intrinsic communication framework of KRAS itself, where specific residues function as central hubs transmitting conformational changes across the protein. By integrating dynamic profiling, energetic mapping, and network modeling, our study provides a multi-scale mechanistic roadmap for targeting KRAS, revealing how engineered proteins can exploit both conserved motifs and isoform-specific features to enable precision modulation of KRAS signaling in oncogenic contexts. Full article

KRAS mutations are major drivers of human cancers, yet how distinct mutations rewire protein interactions and metabolic pathways to promote tumorigenesis remains poorly understood. To address this, we systematically mapped the protein interaction networks of wild-type KRAS and three high-frequency oncogenic mutants (G12C, G12D, and G12V) using TurboID proximity labeling coupled with quantitative proteomics. Bioinformatic analysis revealed mutant-specific binding partners and metabolic pathway alterations, including significant enrichment in insulin signaling, reactive oxygen species regulation, and glucose/lipid metabolism. These changes collectively drive tumor proliferation and immune evasion. Comparative analysis identified shared interactome shifts across all mutants: reduced binding to LZTR1, an adaptor for KRAS degradation, and enhanced recruitment of LAMTOR1, a regulator of mTORC1-mediated growth signaling. Our multi-dimensional profiling establishes the first comprehensive map of KRAS-mutant interactomes and links specific mutations to metabolic reprogramming. These findings provide mechanistic insights into KRAS-driven malignancy and highlight LZTR1 and LAMTOR1 as potential therapeutic targets. The study further lays a foundation for developing mutation-specific strategies to counteract KRAS oncogenic signaling. Full article

In this paper, we investigate the classical Axelrod model of cultural dissemination under an adaptive network framework. Unlike the original model, we place agents on a complex network, where they cut connections with any agent that does not share at least one cultural trait. This rewiring process alters the network topology, and key parameters—such as the number of traits, the neighborhood search range, and the degree-based preferential attachment exponent—also influence the distribution of cultural traits. Unlike conventional Axelrod models, our approach introduces a dynamic network structure where the rewiring mechanism allows agents to actively modify their social connections based on cultural similarity. This adaptation leads to network fragmentation or consolidation depending on the interaction among model parameters, offering a framework to study cultural homogeneity and diversity. The results show that, while long-range reconnections can promote more homogeneous clusters in certain conditions, variations in the local search radius and preferential attachment can lead to rich and sometimes counterintuitive dynamics. Extensive simulations demonstrate that this adaptive mechanism can either increase or decrease cultural diversity, depending on the interplay of network structure and cultural dissemination parameters. These findings have practical implications for understanding opinion dynamics and cultural polarization in social networks, particularly in digital environments where rewiring mechanisms are analogous to recommendation systems or user-driven connection adjustments. Full article

Graphs are an effective data structure for characterizing ubiquitous connections as well as evolving behaviors that emerge in inter-wined systems. Limited by the stereotype of node-to-node connections, learning node representations is often confined in a graph diffusion process where local information has been excessively aggregated, as the random walk of graph neural networks (GNN) explores far-reaching neighborhoods layer-by-layer. In this regard, tremendous efforts have been made to alleviate feature over-smoothing issues such that current backbones can lend themselves to be used in a deep network architecture. However, compared to designing a new GNN, less attention has been paid to underlying topology by graph re-wiring, which mitigates not only flaws of the random walk but also the over-smoothing risk incurred by reducing unnecessary diffusion in deep layers. Inspired by the notion of non-local mean techniques in the area of image processing, we propose a non-local information exchange mechanism by establishing an express connection to the distant node, instead of propagating information along the (possibly very long) original pathway node-after-node. Since the process of seeking express connections throughout a graph can be computationally expensive in real-world applications, we propose a re-wiring framework (coined the express messenger wrapper) to progressively incorporate express links in a non-local manner, which allows us to capture multi-scale features without using a very deep model; our approach is thus free of the over-smoothing challenge. We integrate our express messenger wrapper with existing GNN backbones (either using graph convolution or tokenized transformer) and achieve a new record on the Roman-empire dataset as well as in terms of SOTA performance on both homophilous and heterophilous datasets. Full article

The Arabidopsis transcription factor WUSCHEL-related homeobox 14 (AtWOX14) plays versatile roles in plant growth and development. However, its biochemical specificity of DNA binding, its genome-wide regulatory targets, and how these are affected by DNA methylation remain uncharacterized. To clarify the biochemistry underlying the regulatory function of AtWOX14, using the recently developed 5mC-incorporation strategy, this study performed SELEX and DAP-seq for AtWOX14 both in the presence and absence of cytosine methylation, systematically curated 65 motif models and identified 51,039 genomic binding sites for AtWOX14, and examined how 5mC affects DNA binding of AtWOX14 through bioinformatic analyses. Overall, 5mC represses the DNA binding of AtWOX14 monomers but facilitates the binding of its dimers, and the methylation effect on a cytosine’s affinity to AtWOX14 is position-dependent. Notably, we found that the most preferred homodimeric configuration of AtWOX14 has changed from ER1 to ER0 upon methylation. This change has the potential to rewire the regulatory network downstream of AtWOX14, as suggested by the GO analyses and the strength changes in the DAP-seq peaks upon methylation. Therefore, this work comprehensively illustrates the specificity and targets of AtWOX14 and reports a previously unrecognized effect of DNA methylation on transcription factor binding. Full article

Background: Transposable elements (TEs) and noncoding sequences are major components of the genome, yet their functional contributions to long noncoding RNAs (lncRNAs) are not well understood. Although many lncRNAs originating from TEs (TE-lncRNAs) have been identified across various organisms, their characteristics and regulatory roles, particularly in insects, remain largely unexplored. This study integrated multi-omics data to investigate TE-lncRNAs in D. melanogaster, focusing on the influence of transposons across different omics levels. Results: We identified 16,118 transposons overlapping with lncRNA sequences that constitute 2119 TE-lncRNAs (40.4% of all lncRNAs) using 256 public RNA-seq samples and 15 lncRNA-seq samples of Drosophila S2 cells treated with heavy metals. Of these, 67.2% of TE-lncRNAs contain more than one TE. The LTR/Gypsy family was the most common transposon insertion. Transposons preferred to insert into promoters, transcription starting sites, and intronic regions, especially in chromosome ends. Compared with lncRNAs, TE-lncRNAs showed longer lengths, a lower conservation, and lower levels but a higher specificity of expression. Multi-omics data analysis revealed positive correlations between transposon insertions and chromatin openness at the pre-transcriptional level. Notably, a total of 516 TE-lncRNAs provided transcriptional factor binding sites through transposon insertions. The regulatory network of a key transcription factor was rewired by transposons, potentially recruiting other transcription factors to exert regulatory functions under heavy metal stress. Additionally, 99 TE-lncRNAs were associated with m⁶A methylation modification sites, and 115 TE-lncRNAs potentially provided candidate small open reading frames through transposon insertions. Conclusions: Our data analysis demonstrated that TEs contribute to the regulation of lncRNAs. TEs not only promote the transcriptional regulation of lncRNAs, but also facilitate their post-transcriptional and epigenetic regulation. Full article

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology. Full article