NPJ Systems Biology and Applications最新文献

{"title":"Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease.","authors":"Brendan K Ball, Jee Hyun Park, Alexander M Bergendorf, Elizabeth A Proctor, Douglas K Brubaker","doi":"10.1038/s41540-025-00539-5","DOIUrl":"https://doi.org/10.1038/s41540-025-00539-5","url":null,"abstract":"<p><p>Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. We next assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Finally, we validated our findings using single cell RNA-sequencing blood data of AD and healthy individuals and found erythroid cells contained the most gene expression signatures to the T2D PC. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"58"},"PeriodicalIF":3.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep exploration of logical models of cell differentiation in human preimplantation embryos.","authors":"Mathieu Bolteau, Célia Messaoudi, Laurent David, Jérémie Bourdon, Carito Guziolowski","doi":"10.1038/s41540-025-00537-7","DOIUrl":"10.1038/s41540-025-00537-7","url":null,"abstract":"<p><p>The advent of single-cell transcriptomics (scRNA-seq) has provided unprecedented access to specific cell type signatures, including during transient developmental stages. One key expectation is to be able to model gene regulatory networks (GRNs) from the cell-type scRNA-seq signatures. However, most computed GRNs are static models and lack the ability to predict the effects of genetic or environmental perturbations. Here, we focus on the maturation process of the trophectoderm (TE), the outer layer of cells of human embryos, which is critical for their ability to attach to the endometrium. Addressing this challenge required overcoming two major limitations: (i) handling the search space generated by the high dimensionality of single-cell data, (ii) the lack of feasible perturbation data for certain biological systems, which limits validation or generation of dynamic models. To address these challenges, we created SCIBORG, a computational package designed to infer Boolean networks (BNs) of gene regulation by integrating single-cell transcriptomic data with prior knowledge networks. SCIBORG uses logic programming to manage the combinatorial explosion. It learns two distinct BN families for each of the two developmental stages studied (TE and mature TE) by identifying specific gene regulatory mechanisms. The comparison between these two BN families reveals different pathways, identifying potential key genes critical for trophectoderm maturation. In silico validation through cell classification into studied stages reveals balanced precision 67% - 73% for inferred BN families. We demonstrate that SCIBORG is a powerful tool that integrates the diversity between gene expression profiles of cells at two different stages of development in the construction of Boolean models.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"57"},"PeriodicalIF":3.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144160678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bringing evolutionary cancer therapy to the clinic: a systems approach.","authors":"Arina Soboleva, Irene Grossmann, Anne-Marie C Dingemans, Jafar Rezaei, Kateřina Staňková","doi":"10.1038/s41540-025-00528-8","DOIUrl":"10.1038/s41540-025-00528-8","url":null,"abstract":"<p><p>Evolutionary cancer therapy (ECT) delays or forestalls the progression of metastatic cancer by adjusting treatment based on individual patient and disease characteristics. Clinical implementation of ECT can improve patient outcomes but faces technical and cultural challenges. To address those, we propose a systems approach incorporating systems modeling, problem structuring, and stakeholder engagement. This approach identifies and addresses barriers to implementation, ensuring the feasibility of ECT in clinical practice and enabling better metastatic cancer care.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"56"},"PeriodicalIF":3.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144160677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida.","authors":"Juan D Tibocha-Bonilla, Vishant Gandhi, Chloe Lieng, Oriane Moyne, Rodrigo Santibáñez-Palominos, Karsten Zengler","doi":"10.1038/s41540-025-00521-1","DOIUrl":"10.1038/s41540-025-00521-1","url":null,"abstract":"<p><p>The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"55"},"PeriodicalIF":3.5,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin.","authors":"Chase Christenson, Chengyue Wu, David A Hormuth, Jingfei Ma, Clinton Yam, Gaiane M Rauch, Thomas E Yankeelov","doi":"10.1038/s41540-025-00531-z","DOIUrl":"10.1038/s41540-025-00531-z","url":null,"abstract":"<p><p>Despite advances triple negative breast cancer treatment, ~50% of patients will not achieve a pathological complete response prior to surgery with standard of care neoadjuvant therapy (NAT). We hypothesize that personalized regimens for NAT could significantly improve patient outcomes, which we address with a patient-specific digital twin framework. This framework is established by calibrating a biology-based model to longitudinal magnetic resonance images with approximate Bayesian computation. We then apply optimal control theory to either (1) reduce the final tumor cell number with equivalent dose, or (2) reduce the total dose of NAT with equivalent response. For (1), the personalized regimens (n = 50) achieved a median (range) reduction in the final tumor cell number of 17.62% (0.00-37.36%). For (2), the personalized regimens achieved a median reduction in dose delivered of 12.62% (0.00-56.55%) when compared to the standard-of-care regimen, while providing statistically equivalent tumor control.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"53"},"PeriodicalIF":3.5,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating treatment sensitivity in synthetic and in vitro tumors using a random differential equation model.","authors":"Natalie Meacham, Erica M Rutter","doi":"10.1038/s41540-025-00530-0","DOIUrl":"10.1038/s41540-025-00530-0","url":null,"abstract":"<p><p>Resistance to treatment, which comes from the heterogeneity of cell types within tumors, is a leading cause of poor treatment outcomes in cancer patients. Previous mathematical work modeling cancer over time has neither emphasized the relationship between cell heterogeneity and treatment resistance nor depicted heterogeneity with sufficient nuance. To respond to the need to depict a wide range of resistance levels, we develop a random differential equation model of tumor growth. Random differential equations are differential equations in which the parameters are random variables. In the inverse problem, we aim to recover the sensitivity to treatment as a probability mass function. This allows us to observe what proportions of cells exist at different sensitivity levels. After validating the method with synthetic data, we apply it to monoclonal and mixture cell population data of isogenic Ba/F3 murine cell lines to uncover each tumor's levels of sensitivity to treatment as a probability mass function.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"54"},"PeriodicalIF":3.5,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergence of multiple collective motility modes in a physical model of cell chains.","authors":"Ying Zhang, Effie E Bastounis, Calina Copos","doi":"10.1038/s41540-025-00529-7","DOIUrl":"10.1038/s41540-025-00529-7","url":null,"abstract":"<p><p>Collective cell migration is central to processes like development and cancer metastasis. While mechanisms of collective motility are increasingly understood, their classification remains incomplete. Here, we study the migration of small cell chains, namely cohesive pairs. Experiments with Dictyostelium discoideum (Dd) revealed two motility modes: the individual contributor (IC) mode, where each cell generates its own traction dipole, and the supracellular (S) mode, characterized by a single dipole. Dd pairs favored the IC mode, while Madin-Darby canine kidney (MDCK) doublets predominantly used the S mode. A 2D biophysical model recapitulated many experimental observations; the IC mode emerged naturally in ameboid Dd doublets when both cells exerted similar traction stresses, while the S mode dominated with stronger leaders. Contrary to amebas, MDCK-like cell chains showed a bias towards the IC mode when increasing cell-cell adhesion. Extending the model to longer chains, we show its potential for understanding emergent migration patterns across cell types and scales.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"52"},"PeriodicalIF":3.5,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COmmunity and Single Microbe Optimisation System (COSMOS).","authors":"Lavanya Raajaraam, Karthik Raman","doi":"10.1038/s41540-025-00534-w","DOIUrl":"10.1038/s41540-025-00534-w","url":null,"abstract":"<p><p>Bioprocessing utilises microbial monocultures and communities to convert renewable resources into valuable products. While monocultures offer simplicity, communities provide metabolic diversity and cooperative biosynthesis. To systematically evaluate these systems, we developed COmmunity and Single Microbe Optimisation System (COSMOS), a dynamic computational framework that simulates and compares monocultures and co-cultures to determine optimal microbial systems tailored to a specific environment. COSMOS revealed key factors shaping biosynthetic performance, such as environmental conditions, microbial interactions, and carbon sources. Notably, it predicted the Shewanella oneidensis-Klebsiella pneumoniae co-culture as the most efficient producer of 1,3-propanediol under anaerobic conditions, aligning closely with experimental data, including optimal carbon source concentrations and inoculum ratios. Additional findings highlight the resilience of microbial communities in nutrient-limited processes and emphasise the role of computational tools in balancing productivity with operational simplicity. Overall, this study advances the rational design of microbial systems, paving the way for sustainable bioprocesses and circular bio-economies.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"51"},"PeriodicalIF":3.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation.","authors":"Srijith Sasikumar, S Pavan Kumar, Nirav Pravinbhai Bhatt, Himanshu Sinha","doi":"10.1038/s41540-025-00503-3","DOIUrl":"10.1038/s41540-025-00503-3","url":null,"abstract":"<p><p>Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. However, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. Using gene expression data from a yeast allele replacement panel grown during sporulation, we constructed co-expression networks and SNP-specific GEMs. Analysis of co-expression networks revealed that during sporulation, SNP-SNP interactions impact the connectivity of metabolic regulators involved in glycolysis, steroid and histidine biosynthesis, and amino acid metabolism. Further, genome-scale differential flux analysis identified reactions within six major metabolic pathways associated with sporulation efficiency variation. Notably, autophagy was predicted to act as a pentose pathway-dependent compensatory mechanism supplying critical precursors like nucleotides and amino acids, enhancing sporulation. Our study highlights how transcription factor polymorphisms interact to shape metabolic pathways in yeast, offering insights into genetic variants associated with metabolic traits in genome-wide association studies.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"50"},"PeriodicalIF":3.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEgene identifies links between super enhancers and gene expression across cell types.","authors":"Norio Shinkai, Ken Asada, Hidenori Machino, Ken Takasawa, Satoshi Takahashi, Nobuji Kouno, Masaaki Komatsu, Ryuji Hamamoto, Syuzo Kaneko","doi":"10.1038/s41540-025-00533-x","DOIUrl":"10.1038/s41540-025-00533-x","url":null,"abstract":"<p><p>Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through histone mark chromatin immunoprecipitation followed by sequencing data using existing analytical tools. However, conventional super-enhancer detection methodologies often do not accurately reflect actual gene expression levels, and the large volume of identified super-enhancers complicates comprehensive analysis. To address these limitations, we developed the super-enhancer to gene links (SE-to-gene Links) analysis, a platform named \"SEgene\" which incorporates the peak-to-gene links approach-a statistical method designed to reveal correlations between genes and peak regions ( https://github.com/hamamoto-lab/SEgene ). This platform enables a targeted evaluation of super-enhancer regions in relation to gene expression, facilitating the identification of super-enhancers that are functionally linked to transcriptional activity. Here, we demonstrate the application of SE-to-gene Links analysis to public datasets, confirming its efficacy in accurately detecting super-enhancers and identifying functionally associated genes. Additionally, SE-to-gene Links analysis identified ERBB2 as a significant gene of interest in the lung adenocarcinoma dataset from the National Cancer Center Japan cohort, suggesting a potential impact across multiple patient samples. Thus, the SE-to-gene Links analysis provides an analytical tool for evaluating super-enhancers as potential therapeutic targets, supporting the identification of clinically significant super-enhancer regions and their functionally associated genes.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"49"},"PeriodicalIF":3.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}