NPJ Systems Biology and Applications最新文献_第6页

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches. 通过自动工具和共识方法重建的微生物群落代谢模型的比较分析。

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-23 DOI: 10.1038/s41540-024-00384-y

Yunli Eric Hsieh, Kshitij Tandon, Heroen Verbruggen, Zoran Nikoloski

{"title":"Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches.","authors":"Yunli Eric Hsieh, Kshitij Tandon, Heroen Verbruggen, Zoran Nikoloski","doi":"10.1038/s41540-024-00384-y","DOIUrl":"10.1038/s41540-024-00384-y","url":null,"abstract":"Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11116368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141088139","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}

引用次数: 0

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. TGFβ诱导乳腺癌上皮细胞向间质转化的综合分子相互作用图。

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-17 DOI: 10.1038/s41540-024-00378-w

Sai Bhavani Gottumukkala, Trivadi Sundaram Ganesan, Anbumathi Palanisamy

{"title":"Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer.","authors":"Sai Bhavani Gottumukkala, Trivadi Sundaram Ganesan, Anbumathi Palanisamy","doi":"10.1038/s41540-024-00378-w","DOIUrl":"10.1038/s41540-024-00378-w","url":null,"abstract":"Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11101644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958661","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}

引用次数: 0

A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework 发现神经母细胞瘤生物标记物的多组学方法：基于网络的框架

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-17 DOI: 10.1038/s41540-024-00371-3

Rahma Hussein, Ahmed M. Abou-Shanab, Eman Badr

引用次数: 0

A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework. 发现神经母细胞瘤生物标记物的多组学方法：基于网络的框架。

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-17 DOI: 10.1038/s41540-024-00371-3

Rahma Hussein, Ahmed M Abou-Shanab, Eman Badr

{"title":"A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework.","authors":"Rahma Hussein, Ahmed M Abou-Shanab, Eman Badr","doi":"10.1038/s41540-024-00371-3","DOIUrl":"https://doi.org/10.1038/s41540-024-00371-3","url":null,"abstract":"Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958660","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}

引用次数: 0

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer TGFβ 诱导乳腺癌上皮细胞向间质转化的综合分子相互作用图谱

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-17 DOI: 10.1038/s41540-024-00378-w

Sai Bhavani Gottumukkala, T. S. Ganesan, A. Palanisamy

引用次数: 0

Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies. 模拟 BRAFV600E-MEK-ERK 信号动态对垂直抑制治疗策略的响应。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-15 DOI: 10.1038/s41540-024-00379-9

Alice De Carli, Yury Kapelyukh, Jochen Kursawe, Mark A J Chaplain, C Roland Wolf, Sara Hamis

{"title":"Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies.","authors":"Alice De Carli, Yury Kapelyukh, Jochen Kursawe, Mark A J Chaplain, C Roland Wolf, Sara Hamis","doi":"10.1038/s41540-024-00379-9","DOIUrl":"10.1038/s41540-024-00379-9","url":null,"abstract":"In vertical inhibition treatment strategies, multiple components of an intracellular pathway are simultaneously inhibited. Vertical inhibition of the BRAFV600E-MEK-ERK signalling pathway is a standard of care for treating BRAFV600E-mutated melanoma where two targeted cancer drugs, a BRAFV600E-inhibitor, and a MEK inhibitor, are administered in combination. Targeted therapies have been linked to early onsets of drug resistance, and thus treatment strategies of higher complexities and lower doses have been proposed as alternatives to current clinical strategies. However, finding optimal complex, low-dose treatment strategies is a challenge, as it is possible to design more treatment strategies than are feasibly testable in experimental settings. To quantitatively address this challenge, we develop a mathematical model of BRAFV600E-MEK-ERK signalling dynamics in response to combinations of the BRAFV600E-inhibitor dabrafenib (DBF), the MEK inhibitor trametinib (TMT), and the ERK-inhibitor SCH772984 (SCH). From a model of the BRAFV600E-MEK-ERK pathway, and a set of molecular-level drug-protein interactions, we extract a system of chemical reactions that is parameterised by in vitro data and converted to a system of ordinary differential equations (ODEs) using the law of mass action. The ODEs are solved numerically to produce simulations of how pathway-component concentrations change over time in response to different treatment strategies, i.e., inhibitor combinations and doses. The model can thus be used to limit the search space for effective treatment strategies that target the BRAFV600E-MEK-ERK pathway and warrant further experimental investigation. The results demonstrate that DBF and DBF-TMT-SCH therapies show marked sensitivity to BRAFV600E concentrations in silico, whilst TMT and SCH monotherapies do not.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945565","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}

引用次数: 0

Alzheimer's disease rewires gene coexpression networks coupling different brain regions. 阿尔茨海默病重构了连接不同脑区的基因共表达网络。

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-09 DOI: 10.1038/s41540-024-00376-y

Sanga Mitra, Kailash Bp, Srivatsan C R, Naga Venkata Saikumar, Philge Philip, Manikandan Narayanan

{"title":"Alzheimer's disease rewires gene coexpression networks coupling different brain regions.","authors":"Sanga Mitra, Kailash Bp, Srivatsan C R, Naga Venkata Saikumar, Philge Philip, Manikandan Narayanan","doi":"10.1038/s41540-024-00376-y","DOIUrl":"10.1038/s41540-024-00376-y","url":null,"abstract":"Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11082197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899024","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}

引用次数: 0

Reverse engineering morphogenesis through Bayesian optimization of physics-based models. 通过对基于物理的模型进行贝叶斯优化，实现形态发生的逆向工程。

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-07 DOI: 10.1038/s41540-024-00375-z

Nilay Kumar, Mayesha Sahir Mim, Alexander Dowling, Jeremiah J Zartman

{"title":"Reverse engineering morphogenesis through Bayesian optimization of physics-based models.","authors":"Nilay Kumar, Mayesha Sahir Mim, Alexander Dowling, Jeremiah J Zartman","doi":"10.1038/s41540-024-00375-z","DOIUrl":"10.1038/s41540-024-00375-z","url":null,"abstract":"Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11076624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140876954","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}

引用次数: 0

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy. DeepARV：利用集合深度学习预测抗逆转录病毒疗法的临床相关药物相互作用。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-06 DOI: 10.1038/s41540-024-00374-0

Thao Pham, Mohamed Ghafoor, Sandra Grañana-Castillo, Catia Marzolini, Sara Gibbons, Saye Khoo, Justin Chiong, Dennis Wang, Marco Siccardi

{"title":"DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.","authors":"Thao Pham, Mohamed Ghafoor, Sandra Grañana-Castillo, Catia Marzolini, Sara Gibbons, Saye Khoo, Justin Chiong, Dennis Wang, Marco Siccardi","doi":"10.1038/s41540-024-00374-0","DOIUrl":"10.1038/s41540-024-00374-0","url":null,"abstract":"Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11074332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140850663","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}

引用次数: 0

Decoding the principle of cell-fate determination for its reverse control. 解码细胞命运决定原理，实现逆向控制。

IF 4 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-05-06 DOI: 10.1038/s41540-024-00372-2

Jonghoon Lee, Namhee Kim, Kwang-Hyun Cho

{"title":"Decoding the principle of cell-fate determination for its reverse control.","authors":"Jonghoon Lee, Namhee Kim, Kwang-Hyun Cho","doi":"10.1038/s41540-024-00372-2","DOIUrl":"10.1038/s41540-024-00372-2","url":null,"abstract":"Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11074314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859866","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}

引用次数: 0