Functional Networks Research Articles (Page 1)

Microtubule-associated serine/threonine-protein kinase 3 (MAST3) is a member of the MAST kinase family implicated in neuronal and immune pathways and is predicted to associate with cytoskeletal regulation. However, insights into its functional role in cytoskeletal organization remain unexplored. In this study, we performed a large-scale phosphoproteomic analysis of MAST3 using 562 datasets to delineate its functional network. We identified four predominant phosphosites, S134, S146, S792, and S793, based on the frequency of detection and differential regulation, with S134 and S146 localized within the Domain of Unknown Function domain, a noncatalytic region. These phosphosites exhibited distinct coregulatory profiles, suggesting regulation through noncatalytic domains. Coregulated phosphosites were enriched for cytoskeleton-associated functions, including actin filament organization, microtubule organization, and spindle assembly. Additionally, predicted downstream substrates such as KIF15, EPB41L1, CP110, and HNRNPU, and binary interactors including LMNA, CKAP4, and CAMSAP2, further support the involvement of MAST3 in cytoskeletal regulation. The convergence of these cytoskeletal partners across phosphosites, substrates, and interactors suggests that MAST3 may act as a key modulator of cytoskeletal organization through phosphorylation-dependent protein-protein interactions. Notably, frequent phosphorylation of S146 across cancer types points to a potential tumor-specific regulatory role. Together, these findings provide the first systems-level insight into the role of MAST3 in cytoskeletal regulation and disease relevance.

The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.

Systemic complications are common after acute brain injury (ABI) and may trigger coagulation cascades, systemic inflammation, as well as dysfunction of the cardiovascular, respiratory, and gastrointestinal systems, etc. The pathogenesis of these systemic manifestations is multifactorial but not yet fully elucidated. This paper introduces the novel term neurogenic organ dysfunction syndrome (NODS) to characterize systemic instability arising from internal and external perturbations of the neuronal center following ABI. Elucidating the central neurogenic mechanisms of NODS is critical for early detection and prevention of complications, thereby reducing mortality and improving patient outcomes following ABI. In this paper, we explore the potential central neurogenic mechanisms of NODS from the perspective of complex brain network theory, focusing on the structural network of the central autonomic system (CAS) that maintains systemic stability, and the functional network governed by the central stress system (CSS). The CAScan be divided into the cortical autonomic network, which involves higher cortical regions, and the subcortical autonomic network, which is relatively conserved, with its main connections located in deep brain structures. The CSS is a large-scale complex network characterized by hierarchy, hubs, and modularity, which together enable the competitive optimization of functional segregation and integration. Under physiological conditions, modules (mediating functional segregation) and hubs (functional integration) within the CSS dynamically trade-off with each other to maintain the overall homeostasis. However, this balance is disrupted following pathological insults or injury, resulting in weakened functional integrity of the CSS following ABI, impaired module activity, and disturbed hub integration. This paper also demonstrates the distinct pathological manifestations arising from disturbances at different levels of the homeostatic system. Finally, this study proposes potential clinical interventions, including analgesia and sedation, neuromodulation, and receptor regulation, for early interventions and potential treatment of NODS, aiming to improve patient outcomes.

Review of nonlinear activation functions in optical neural networks

Cerebellar vermis and somatosensory-motor cortex differentially contribute to sex differences in acute pain perception in rats.

In Multiple Sclerosis, inflammation and neurodegeneration disrupt structural and functional brain networks. While the association between structural connectivity and disability is rather clear, functional connectivity changes are not yet characterised as a physiological response to the disease, as functionally meaningful adaptation or as a deceptive response. We explored the topology of brain networks of 65 Multiple Sclerosis patients over up to seven years in comparison to 59 controls. Connectomes based on probabilistic tractography from diffusion weighted imaging and resting-state MRI, were analysed with graph theory. The hub disruption index estimated connectivity perturbation in relation to the network hierarchy. In controls, we observed a transient increase in functional hub connectivity in the 5th and 6th age decade as a response to a subtle diffuse loss of structural connectivity, before structural and functional connectomes show a pronounced loss of hub connectivity. In Multiple Sclerosis, structural hub disruption was present from the disease onset while the transient upregulation of functional hub connectivity in the middle age was lacking. Patients seem to transition directly into an exhausted hub connectivity configuration. However, we observed the transient functional reorganisation of hubs in the first years after disease onset. Multiple Sclerosis patients present a probable physiological response to structural connectivity loss very early in the disease, potentially leading to an accelerated hub overload with accelerated neurodegeneration. The onset of chronic progression in the 5th age decade might be partially driven by the absence of the physiological increased hub connectivity observed in healthy individuals.

Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne viral disease associated with high mortality. This study aimed to characterize serum proteomic signatures linked to adverse outcomes and to identify prognostic biomarkers with potential translational value for patient management. Methods Serum samples from 55 survivors, 32 non-survivors, and 10 healthy controls were analyzed by data-independent acquisition–based proteomics. Differential abundance analysis, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein–protein interaction (PPI) network analyses with Markov clustering were conducted to characterize disease-associated proteins. XGBoost and Random Forest machine learning models were applied to prioritize candidate biomarkers, and discriminative performance was evaluated by the receiver operating characteristic (ROC) curve. Spearman correlation analyses were further used to examine associations between candidate proteins, clinical laboratory indicators, and viral load. Results Non-survivors exhibited 642 differentially abundant proteins (DAPs) compared with survivors. Functional enrichment and PPI network analyses revealed a proteasome-centered module overrepresented in non-survivors. XGBoost and Random Forest consistently prioritized four candidate biomarkers (PSMD11, IL1RL1, PSMC4, and IFIH1) with areas under the ROC curve of 0.847, 0.847, 0.843, and 0.791, respectively. PSMD11 emerged as the strongest predictor of adverse outcome and showed strong correlations with markers of organ injury and dysfunction such as lactate dehydrogenase ( r = 0.77), thrombin time ( r = 0.76), aspartate aminotransferase ( r = 0.75), hydroxybutyrate dehydrogenase ( r = 0.74), viral load ( r = 0.63), and platelet count ( r = −0.57) (all p < 0.001). Conclusions This study identified a proteasome-centered signature associated with adverse outcomes in SFTS, with PSMD11 emerging as a key prognostic biomarker. Its strong correlations with viral load and multi-organ injury support potential utility for early risk stratification and prognostic prediction, while also providing mechanistic insights into disease progression and a foundation for future translational research and therapeutic development.

Resting-state electroencephalography (EEG) metrics are influenced by task instructions and momentary factors such as cognitive state, complicating their use as biomarkers. We assessed how strongly three classes of resting-state EEG metrics depend on time and state: functional connectivity (FC; amplitude-envelope correlation, AECc, and phase-lag index, PLI), signal complexity (inverted joint permutation entropy, JPEINV, and permutation entropy, PE) and network topology derived from minimum spanning trees (MST). Sixty-four-channel EEG was recorded in healthy adults during two sessions six weeks apart (n = 42) and during semi-resting-state epochs embedded in a P50-gating task (n = 24). Reliability for repeated-resting recordings and resting-state versus semi-resting-state comparisons was quantified with intraclass correlation coefficients (ICC) at sensor and source level. PE showed consistently good-to-excellent reliability (ICC > 0.75–0.90). FC metrics ranged from poor to excellent, and MST metrics from poor to good. Across analyses, theta and alpha bands outperformed delta and beta bands. Alpha and theta PE and alpha PLI were the most robust, whereas MST and AECc require caution, especially outside theta and alpha bands. Our results identify theta and alpha PE and alpha PLI as robust measures suitable for biomarker development, while urging caution for MST and AECc due to their limited stability across time and state.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-23662-z.

Background Osteoporosis (OP), as a systemic bone disorder, has a complex pathogenesis and faces significant challenges in clinical treatment. Oligomeric proanthocyanidin (OPC), a type of natural polyphenolic flavonoid compound, demonstrates outstanding therapeutic potential due to its excellent antioxidant and anti-inflammatory properties and good safety. The breakthrough advances in single-cell RNA sequencing (scRNA-seq) technology have provided a powerful research tool for elucidating the multitarget mechanisms of OPC in the treatment of OP. Methods This study first screened the active components of OPC leveraging the TCMSP database. The protein–protein interaction network of OPC target proteins was generated through the STRING database, and visual analysis was accomplished using the Cytoscape software. The ClusterProfiler R package and ClueGO plugin were employed for functional enrichment analysis and network visualization. At the same time, scRNA-seq data from the GEO database were integrated, and cell-type identification was attained using the Seurat tool. The differentiation trajectories of subtypes were inferred using Monocle and Slingshot software. The cell communication network was analyzed using CellChat. Results This study utilized scRNA-seq to identify C2 NR4A1 + MSCs with distinct metabolic features and differentiation potential in the bone microenvironment during the early stage of OP, namely, osteopenia. The natural component OPC can precisely target this subtype and exert therapeutic effects through two mechanisms: inhibiting the transcriptional activity of NR4A1 to suppress the expression of PTGS2 in MSCs and simultaneously activating the β-catenin-dependent NR4A1 – Runx2 signaling axis to promote osteogenesis and inhibit osteoclastogenesis. These findings establish a new therapeutic paradigm of “targeting cell subtypes–multipathway regulation,” providing an important basis for the development of novel anti-OP drugs. Conclusion Our research integrated multilevel approaches, including single-cell transcriptomics, network pharmacology, cellular experiments, and animal models, to systematically reveal the dual mechanism of OPC in treating OP. This discovery not only established C2 NR4A1 + MSCs as key mediators in the pathological process of OP but also clarified the molecular mechanism of multitarget synergy of natural active compounds in restoring bone homeostasis, providing a theoretical basis and practical guidance for the development of new OP therapies.

Hybrid integrated quantum photonics offers a scalable route to chip-based quantum networks by combining solid-state quantum dots (QDs) with low-loss and reconfigurable photonic circuits. However, limited integration scalability, spectral inhomogeneity of QD emissions and the challenge of achieving quantum interference between independent sources have impeded progress towards this goal. Here we demonstrate a hybrid lithium niobate photonic platform integrating arrays of QD-containing waveguides with 20 deterministic single-photon sources. Leveraging the piezoelectric properties of thin-film lithium niobate, we develop a circuit-compatible local strain-tuning technique that enables on-chip spectral tuning of individual QDs by up to 7.7 meV. This capability allows quantum interference with a visibility of 0.73 between two spatially separated waveguide-coupled QD single-photon sources, thereby establishing a functional on-chip quantum network. The large-scale integration of tunable and interconnected QD-based single-photon sources within low-loss lithium niobate circuits paves the way for realizing compact and scalable quantum networks on a photonic chip.

Background: Chemotherapy-related cognitive impairment (CRCI), commonly known as "chemobrain," frequently occurs during breast cancer treatment and has been linked to altered brain function. This resting-state functional magnetic resonance imaging study examined chemotherapy-related changes in functional brain activity, network connectivity, and associations with cognitive outcomes. Methods: Twenty-eight patients with breast cancer were assessed prechemotherapy (BB) and postchemotherapy (BBF), alongside 27 healthy controls of comparable age at baseline (BH) and follow-up (BHF). Mean fractional amplitude of low-frequency fluctuations (mfALFF) and mean regional homogeneity (mReHo) quantified functional brain activity. Graph theoretical analysis (GTA) assessed network topology; network-based statistics (NBS) evaluated interregional connectivity. Cognitive performance was evaluated through standardized assessments. Results: Postchemotherapy patients exhibited reduced anxiety and lower FACT-Cog scores. Voxel-wise analyses showed increased mfALFF in frontal regions and mReHo in superior temporal and inferior frontal gyri, alongside decreases in postcentral, lingual, and parahippocampal areas. Healthy controls showed increased activity in medial frontal and cingulate regions, with reductions in the temporal lobe and putamen. GTA revealed higher global efficiency and reduced modularity, path length, and network complexity in the BBF group compared with BHF. NBS showed weaker structural connectivity in motor and occipital regions prechemotherapy and decreased parietal and insular connectivity postchemotherapy. Multiple regression showed brain-behavior correlations: declines in FACT-Cog, Digit Symbol Substitution, and mood scores were linked to altered activity in frontal, parietal, cingulate, and occipital areas, while positive correlations suggested compensatory activation. Conclusions: Chemotherapy was associated with longitudinal alterations in brain activity, network organization, and connectivity in breast cancer survivors. Brain-behavior associations suggest disrupted neural networks may underlie CRCI.

Can network modelling of single-cell transcriptomic data identify cellular developmental trajectories of fallopian tube (FT) epithelium and reveal functional and pathological divergence from the endometrium? A bidirectional differentiation trajectory originating from a novel OVGP1+ progenitor population of FT epithelial cells was uncovered, and causal network modelling of whole-transcriptome activity in the FT and endometrium revealed functional divergence between their secretory epithelial cells, with implications for ectopic pregnancy candidate genes. The FT forms the in vivo peri-conceptual environment, which has a significant impact on programming offspring health. The FT epithelium establishes this environment; however, the epithelial cell types are poorly characterized in health and disease. Publicly available, benign FT single-cell RNA sequencing (scRNA-seq) samples from 13 women across three previous studies were combined. Endometrial scRNA-seq samples from 13 women from one study were used to demonstrate transcriptomic differences between the epithelia of the two tissues. Network models of transcriptomic action were constructed with hypergraphs. A meta-analysis of FT scRNA-seq samples was performed to identify epithelial populations. Differential gene expression assessed differences between FT and endometrial epithelial scRNA-seq data. Functional differences between secretory cells in the tissues were characterized using hypergraph models. To identify associations with ectopic pregnancy, expression quantitative trait loci (eQTLs) from a recent GWAS were mapped onto the network models. Epithelial cells (n = 14360) were clustered into eight secretory and ciliated epithelial populations in the meta-analysis of three scRNA-seq datasets. A novel OVGP1+ epithelial progenitor cell was also identified, and its bidirectional differentiation to mature secretory or mature ciliated populations was mapped by RNA velocity analysis. This progenitor exhibited a high velocity magnitude (12.47) and low confidence (0.69): a combination strongly indicative of multipotent progenitor status. Comparing FT epithelial cells with endometrial epithelial cells revealed 5.3-fold fewer shared genes between FT and endometrial glandular secretory cells than between FT and endometrial ciliated cells, suggesting functional divergence of secretory cells along the reproductive tract. Hypergraphs were used to identify highly coordinated regions of the transcriptome robustly associated with functional gene networks. In the FT secretory cells, these networks were enriched for lipid-related (false discovery rate (FDR) < 0.002) and immune-related (FDR < 0.00007) pathways. We mapped eQTLs from a GWAS meta-analysis of 7070 women with ectopic pregnancy over a range of significance (P = 1.68 × 10-21-5.8 × 10-4) to the hypergraphs of FT and endometrium. Of the 22 genes present in the hypergraphs, 13 of these clustered as highly coordinated genes. This demonstrated the functional importance of MUC1 in the FT and endometrium (GWAS Study P = 5.32 × 10-9) and identified additional genes (SLC7A2, CLDN1, GLS, PEX6, PLXNA4, NR2F1, CLGN, PGGHG, and ANKRD36) implicated in ectopic pregnancy and eutopic pregnancy. The sample size of reproductive age women was limited in previous studies, and though causal network modelling was used and previous mechanistic data supports candidate gene involvement, no in vitro or in vivo validation of candidate was performed. These findings consolidate the existing single-cell transcriptomic datasets of the FT to provide a comprehensive understanding of epithelial populations and define functionally distinct secretory cells that contribute to the peri-conceptual environment of the FT. This study further implicates the role of MUC1 and secretory cells in ectopic pregnancy and suggests future targets for investigating embryo implantation in the FT and endometrium. No funding was received for this study. The authors do not disclose any competing interests. N/A.

KRAS mutations are implicated in approximately 23% of all human malignancies, with particularly high prevalence in pancreatic ductal adenocarcinoma (PDAC) (~92%), colorectal cancer (CRC) (~49%), and non-small cell lung cancer (NSCLC) (~35%). The recent approval of the KRASG12C-specific inhibitors for NSCLC represents a pivotal advancement in KRAS-targeted therapy. Nevertheless, the emergence of intrinsic and acquired resistance to KRAS-targeted therapies poses a significant clinical obstacle to targeting KRAS, which necessitates a deeper understanding of the resistance mechanisms. Recent progress in proteomic studies has enabled comprehensive profiling of the proteomic alterations driven by KRAS mutations, offering valuable insights into the disrupted KRAS interactome, aberrant signaling pathways and dysregulated cellular processes contributing to tumorigenesis. This review discusses current knowledge on proteomic alterations associated with oncogenic KRAS mutations, with particular focus on allele-specific proteome signatures and the roles of post-translational modifications (PTMs) of KRAS in modulating the functional networks. Furthermore, we highlight recent therapeutic advances targeting KRAS variants and discuss emerging resistance mechanisms from a proteomics-informed perspective.

BackgroundAudiovisual integration deficits are frequent in patients with Alzheimer's disease (AD). In addition, patients with AD have altered functional brain networks, such as those supporting auditory and visual processing. However, the mechanisms driving this association remain unclear.ObjectiveTo investigate whether dynamic functional network disruptions underlie audiovisual integration and cognitive deficits in AD.MethodsSeventy-nine participants (41 AD, 38 controls) completed audiovisual stimuli tasks. A multilayer modularity algorithm was utilized to assess the resting-state fMRI-based brain dynamics of the primary sensory and higher-order functional networks. Mediation analysis was conducted to test our hypothesis.ResultsAD patients showed delayed response time and reduced peak benefit of audiovisual integration. Dynamic switching rates of primary sensory and higher-order networks were significantly increased in AD, particularly in the dynamic integration between the default mode network (DMN) and visual network (VN). The peak benefit of audiovisual integration negatively correlated with DMN-VN dynamic integration and positively with Mini-Mental State Examination, Montreal Cognitive Assessment, and Auditory Verbal Learning Test delayed scores. Notably, excessive integration between the DMN and VN mediated the relationship between audiovisual integration deficits and cognitive impairment in patients with AD.ConclusionsThese findings suggest that audiovisual integration impairment may disturb the dynamic integration between the DMN and VN, contributing to cognitive impairment in AD. The neural mechanisms underlying audiovisual integration deficit and cognitive decline might help with early diagnosis and intervention for AD.

SCN1A rs3812718 polymorphism modulates structural and functional brain networks in TLE: A multimodal imaging-genomics study.

Stronger pre-surgical functional connectivity networks are associated with improved surgical outcome in temporal lobe epilepsy.

Functional reorganization and systemic toxicity induced by microplastics and climate-relevant stressors in Tribolium castaneum: Are we simulating tomorrow's toxic reality?

Deciphering the neural signatures of auditory hallucinations in early-onset schizophrenia: A topological brain network analysis.

Constructing a bridge between functioning of oscillatory neuronal networks and quantum-like cognition along with quantum-inspired computation and AI.

Redundancy reduction penalty term of loss function in deep neural network