NPJ Systems Biology and Applications最新文献

Glycyrrhiza uralensis, a key component of over 70% of traditional herbal medicines (Kampo) in Japan, exhibits diverse pharmacological effects, including immunoregulation, anti-tumor, and antioxidant properties. Despite over 300 identified compounds, the molecular mechanisms remain unclear due to the chemical diversity. Here, we performed a multiomics analysis incorporating untargeted hydrophilic metabolomics, lipidomics, and phosphoproteomics to elucidate the mechanisms distinguishing the G. uralensis extract (GU) from a single bioactive compound, isoliquiritigenin (ILG). Time-course analyses of lipopolysaccharide (LPS)-stimulated RAW264.7 cells under four conditions (control, LPS(+), LPS(+)/ILG(+), and LPS(+)/GU(+)) quantified 182 hydrophilic metabolites, 381 lipids, and 13,211 phosphopeptides. Both ILG(+) and GU(+) attenuated inflammatory signatures characterized by elevated glycolytic intermediates, succinate, citrulline, triacylglycerols, and cholesteryl esters. A multiset partial least squares technique identified sirtuin (SIRT) 1/2 phosphorylation and altered nicotinamide adenine dinucleotide metabolism specific to ILG(+). SIRT2 inhibition abolished ILG's suppression of interleukin-6 (IL-6). Furthermore, GU(+) uniquely increased γ-aminobutyric acid (GABA) and 4-guanidinobutyric acid via endogenous synthesis by glutamic acid decarboxylase. Exogenous GABA reduced IL-6 and IL-1β expression, and its co-administration with ILG enhanced anti-inflammatory effects. This study demonstrates that multiomics can elucidate the synergistic anti-inflammatory actions of G. uralensis, highlighting endogenous GABA production as a key contributor to ILG-mediated immunomodulation.

Genome-scale metabolic models (GEMs) are valuable tools for investigating healthy and disease states, but often lack the specificity to capture context-dependent metabolic adaptations. Tailoring GEMs using transcriptomic data is crucial for studying these context-specific variations by accurately identifying active metabolic reactions. This study introduces an algorithm called 'Localgini', which uses the Gini coefficient to quantify gene expression variability across samples, enabling precise identification of active reactions for context-specific models (CSMs). To evaluate Localgini, CSMs were generated using six different model extraction methods (MeMs) for NCI-60 cancer cell lines and human tissue datasets. Localgini-based CSMs better represent housekeeping functionalities and known metabolic pathways. Moreover, Localgini-generated active reaction sets require minimal support from the MeMs to build the CSMs. Localgini minimizes variability across CSMs built with different MeMs and the same gene expression data. Overall, by incorporating gene expression heterogeneity, Localgini provides an accurate method for constructing CSMs.

Although two-thirds of cancers arise from loss-of-function mutations in tumor suppressor genes, there are few approved targeted therapies linked to these alterations. Synthetic lethality offers a promising strategy to treat such cancers by targeting vulnerabilities unique to cancer cells with these mutations. To identify clinically relevant synthetic lethal interactions, we analyzed genome-wide CRISPR/Cas9 knock-out (KO) viability screens from the Cancer Dependency Map and evaluated their clinical relevance in patient tumors through mutual exclusivity, a pattern indicative of synthetic lethality. Indeed, we found significant enrichment of mutual exclusivity for interactions involving cancer driver genes compared to non-driver mutations. To identify therapeutic opportunities, we integrated drug sensitivity data to identify inhibitors that mimic the effects of CRISPR-mediated KO. This approach revealed potential drug repurposing opportunities, including BRD2 inhibitors for bladder cancers with ARID1A mutations and SIN3A-mutated cell lines showing sensitivity to nicotinamide phosphoribosyltransferase (NAMPT) inhibitors. However, we discovered that pharmacological inhibitors often fail to phenocopy KO of matched drug targets, with only a small fraction of drugs inducing similar effects. This discrepancy reveals fundamental differences between pharmacological and genetic perturbations, emphasizing the need for approaches that directly assess the interplay of loss-of-function mutations and drug activity in cancer models.

Predicting how gut microbial communities assemble and change requires models that capture the underlying mechanisms driving interspecies interactions, not just taxonomic correlations. We present SIMBA, a simulation-augmented graph neural network that integrates mechanistic insights from metabolic simulations with edge-aware graph transformers to predict microbial community composition. Using a high-fiber dietary cohort mapped to metabolic networks, we ran thousands of pairwise simulations to infer cross-feeding probabilities, pathway activity fingerprints, and microbe-microbe functional similarity. These signals instantiate a global microbe-metabolite-pathway graph for learning. A custom heterogeneous graph transformer incorporates scalar edge attributes into attention. It is trained through a multi-stage pipeline combining self-supervised learning, supervised pretraining on simulated graphs, and fine-tuning on experimental microbial abundance data. Each individual's microbiome is represented as a sample-specific instantiation of the shared mechanistic graph derived from metabolic simulations, where only the set of microbes detected in that individual varies. SIMBA learns from this mechanistic prior to predict microbial presence and relative abundance across individuals, enabling hypothesis-driven exploration of microbial ecosystems.

Big biological datasets, such as gene expression profiles, often contain redundant features that degrade model performance and limit generalization across independent datasets with complexities like class imbalance and hidden sub-clusters. To overcome challenges, we present 'FLASH', a novel feature selection method combining filtration and heuristic-based systematic elimination. FLASH generates random samples and computes p-values for each feature using multiple statistical tests (t-test, ANOVA, Wilcoxon Rank-Sum, Brunner-Munzel, Mann-Whitney). Features are scored by aggregating significant p-values across samples. The coefficient from the machine learning model with the highest accuracy on the filtered features is used to rank them. Recursive elimination with cross-validation systematically removes features while monitoring accuracy. The final subset is selected based on the highest performance during elimination, to achieve effective feature selection. We show that our method preserves predictive performance on independent datasets. Our comprehensive evaluation across diverse datasets showed that FLASH outperforms the compared feature selection methods dRFE, Mutual information, MRMR, ElasticNet, NeuralNet, Permutation test and SAGA within the scope of our tested datasets and evaluation settings. Additionally, features selected by FLASH demonstrated greater biological relevance, as evidenced by higher overlap with disease-associated genes from DisGeNET in an independent dataset.

Escalated doses of radiotherapy associate with improved local control and overall survival (OS) in intrahepatic cholangiocarcinoma (iCCA), but personalization remains limited because conventional size-based CT criteria correlate poorly with outcomes. We hypothesized that quantitative enhancement measurements would better predict clinical outcomes and guide individualized RT optimization. In a retrospective cohort of 154 patients, we analyzed pre- and post-RT CT scans using quantitative European Association for Study of Liver (qEASL) to derive viable tumor volumes, comparing enhancement-based metrics with size-based RECIST and linking them to outcomes via survival and mathematical modeling. Change in enhancement volume was strongly associated with OS after adjustment, outperforming RECIST, and a ≥ 33% reduction optimally distinguished responders. From modeling analyses, the patient-specific tumor growth rate parameter emerged as the dominant mechanistic predictor, achieving 80.5% classification accuracy. Importantly, CT-derived mathematical parameters from this framework may inform RT planning and dose adaptation, particularly for resistant tumors, by bridging imaging with mechanistic insight.

Drug repurposing (DR) has gained significant attention as a cost-effective strategy for identifying new therapeutic uses for existing drugs. Heterogeneous network-based methods are particularly promising because they exploit complex biological interactions. However, comprehensive benchmarking across multiple datasets is still needed to assess their reliability and generalizability. We systematically evaluate ten advanced heterogeneous network-based DR methods across eight datasets, including six publicly available and two newly introduced drug-disease datasets. The methods include (i) matrix factorization: NMF, NMF-PDR, NMF-DR, VDA-GKSBMF, (ii) matrix completion: BNNR, OMC, HGIMC, (iii) recommendation systems: IBCF, LIBMF, and (iv) a deep learning approach: DRDM. Performance is assessed using the area under the receiver operating characteristic (AUC) and precision-recall curve (AUPR). We also analyze the impact of data sparsity and compare findings with previous benchmarking studies. Our results reveal that OMC consistently achieves the highest AUC and AUPR across most datasets. BNNR, DRDM, HGIMC, VDA-GKSBMF, and NMF-PDR, also demonstrate competitive performance, with NMF-PDR outperforming other NMF-based approaches. We find that differences in cross-validation strategies substantially impact reported AUPR values, with previous studies overestimating performance by omitting many negative instances. This work provides a reliable benchmarking framework and new datasets, offering insights for future research in DR.

Stroke disrupts brain function beyond focal lesions, altering multiscale temporal dynamics essential for information processing. We investigated intrinsic neural timescales (INT) and other properties of long-range temporal correlations, using longitudinal fMRI data from 15 ischemic stroke patients across 6 months, and compared them to age-matched controls. Results show that stroke patients exhibited significantly prolonged INT in multiple cortical regions, reflecting slowed temporal dynamics and disrupted hierarchy. These dynamic changes persisted through recovery and were more pronounced in patients with poor outcomes, especially within cognitive control networks. Computational modeling suggested that stroke-induced INT prolongation driven by heightened neuronal excitability reflects a dynamic shift towards criticality. Our findings position long-range temporal correlations and INT as potential biomarkers for monitoring and predicting functional recovery. This framework provides a novel perspective on stroke-induced brain changes and suggests avenues for targeted neurorehabilitation using interventions aiming at restoring intrinsic temporal dynamics.

Cellular processes evolve dynamically across time and space. Single-cell and spatial omics technologies have provided high-resolution snapshots of gene expression, greatly expanding the capability to characterize cellular states. This review summarizes recent modeling strategies for time-series and spatiotemporal transcriptomic data, emphasizing links between dynamical systems, generative modeling, and biological insight. These approaches illustrate how computational tools can deepen our understanding of the dynamic nature of single cells.

This paper reviews the design and application of mammalian synthetic gene circuits for biopharmaceutical manufacturing. It discusses key design principles and outlines transcription factors, DNA-binding proteins, and RNA as input and regulatory modules, while also presenting computational modelling as a driver for circuit optimisation. The review highlights potential applications towards the production of next-generation biotherapeutics by providing examples on monoclonal antibody glycosylation control, CAR-T cell therapy safety, and gene therapy viral vector yields.