Molecular Systems Biology最新文献_第8页

Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease. 基础 MET 磷酸化是肝病中肝细胞失调的一个指标。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-12 DOI: 10.1038/s44320-023-00007-4

Sebastian Burbano de Lara, Svenja Kemmer, Ina Biermayer, Svenja Feiler, Artyom Vlasov, Lorenza A D'Alessandro, Barbara Helm, Christina Mölders, Yannik Dieter, Ahmed Ghallab, Jan G Hengstler, Christiane Körner, Madlen Matz-Soja, Christina Götz, Georg Damm, Katrin Hoffmann, Daniel Seehofer, Thomas Berg, Marcel Schilling, Jens Timmer, Ursula Klingmüller

{"title":"Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.","authors":"Sebastian Burbano de Lara, Svenja Kemmer, Ina Biermayer, Svenja Feiler, Artyom Vlasov, Lorenza A D'Alessandro, Barbara Helm, Christina Mölders, Yannik Dieter, Ahmed Ghallab, Jan G Hengstler, Christiane Körner, Madlen Matz-Soja, Christina Götz, Georg Damm, Katrin Hoffmann, Daniel Seehofer, Thomas Berg, Marcel Schilling, Jens Timmer, Ursula Klingmüller","doi":"10.1038/s44320-023-00007-4","DOIUrl":"10.1038/s44320-023-00007-4","url":null,"abstract":"Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"187-216"},"PeriodicalIF":9.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139432683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X. 无处不在的 GC 含量特征是 DDX3X 多模式 mRNA 调控的基础。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-25 DOI: 10.1038/s44320-024-00013-0

Ziad Jowhar, Albert Xu, Srivats Venkataramanan, Francesco Dossena, Mariah L Hoye, Debra L Silver, Stephen N Floor, Lorenzo Calviello

{"title":"A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X.","authors":"Ziad Jowhar, Albert Xu, Srivats Venkataramanan, Francesco Dossena, Mariah L Hoye, Debra L Silver, Stephen N Floor, Lorenzo Calviello","doi":"10.1038/s44320-024-00013-0","DOIUrl":"10.1038/s44320-024-00013-0","url":null,"abstract":"The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"276-290"},"PeriodicalIF":8.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Canalizing cell fate by transcriptional repression. 通过转录抑制改变细胞命运

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-02-01 DOI: 10.1038/s44320-024-00014-z

Bryce Lim, Katrin Domsch, Moritz Mall, Ingrid Lohmann

{"title":"Canalizing cell fate by transcriptional repression.","authors":"Bryce Lim, Katrin Domsch, Moritz Mall, Ingrid Lohmann","doi":"10.1038/s44320-024-00014-z","DOIUrl":"10.1038/s44320-024-00014-z","url":null,"abstract":"Precision in the establishment and maintenance of cellular identities is crucial for the development of multicellular organisms and requires tight regulation of gene expression. While extensive research has focused on understanding cell type-specific gene activation, the complex mechanisms underlying the transcriptional repression of alternative fates are not fully understood. Here, we provide an overview of the repressive mechanisms involved in cell fate regulation. We discuss the molecular machinery responsible for suppressing alternative fates and highlight the crucial role of sequence-specific transcription factors (TFs) in this process. Depletion of these TFs can result in unwanted gene expression and increased cellular plasticity. We suggest that these TFs recruit cell type-specific repressive complexes to their cis-regulatory elements, enabling them to modulate chromatin accessibility in a context-dependent manner. This modulation effectively suppresses master regulators of alternative fate programs and their downstream targets. The modularity and dynamic behavior of these repressive complexes enables a limited number of repressors to canalize and maintain major and minor cell fate decisions at different stages of development.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"144-161"},"PeriodicalIF":9.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139672247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion. 肌母细胞分化和融合过程中连续单细胞状态转换的机器学习推断。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-18 DOI: 10.1038/s44320-024-00010-3

Amit Shakarchy, Giulia Zarfati, Adi Hazak, Reut Mealem, Karina Huk, Tamar Ziv, Ori Avinoam, Assaf Zaritsky

{"title":"Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.","authors":"Amit Shakarchy, Giulia Zarfati, Adi Hazak, Reut Mealem, Karina Huk, Tamar Ziv, Ori Avinoam, Assaf Zaritsky","doi":"10.1038/s44320-024-00010-3","DOIUrl":"10.1038/s44320-024-00010-3","url":null,"abstract":"Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"217-241"},"PeriodicalIF":9.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139491766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources. 机器学习识别细菌在不同碳源上生长的关键代谢反应。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-30 DOI: 10.1038/s44320-024-00017-w

Hyunjae Woo, Youngshin Kim, Dohyeon Kim, Sung Ho Yoon

{"title":"Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources.","authors":"Hyunjae Woo, Youngshin Kim, Dohyeon Kim, Sung Ho Yoon","doi":"10.1038/s44320-024-00017-w","DOIUrl":"10.1038/s44320-024-00017-w","url":null,"abstract":"Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"170-186"},"PeriodicalIF":9.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139642553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Linking patient-specific basal MET phosphorylation levels to liver health. 将特定患者的基础 MET 磷酸化水平与肝脏健康联系起来。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-02-16 DOI: 10.1038/s44320-024-00023-y

Fabian Fröhlich

引用次数: 0

The population context is a driver of the heterogeneous response of epithelial cells to interferons. 种群背景是上皮细胞对干扰素作出不同反应的驱动因素。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-25 DOI: 10.1038/s44320-024-00011-2

Camila Metz-Zumaran, Zina M Uckeley, Patricio Doldan, Francesco Muraca, Yagmur Keser, Pascal Lukas, Benno Kuropka, Leonie Küchenhoff, Soheil Rastgou Talemi, Thomas Höfer, Christian Freund, Elisabetta Ada Cavalcanti-Adam, Frederik Graw, Megan Stanifer, Steeve Boulant

{"title":"The population context is a driver of the heterogeneous response of epithelial cells to interferons.","authors":"Camila Metz-Zumaran, Zina M Uckeley, Patricio Doldan, Francesco Muraca, Yagmur Keser, Pascal Lukas, Benno Kuropka, Leonie Küchenhoff, Soheil Rastgou Talemi, Thomas Höfer, Christian Freund, Elisabetta Ada Cavalcanti-Adam, Frederik Graw, Megan Stanifer, Steeve Boulant","doi":"10.1038/s44320-024-00011-2","DOIUrl":"10.1038/s44320-024-00011-2","url":null,"abstract":"Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"242-275"},"PeriodicalIF":9.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Deep learning for protein structure prediction and design-progress and applications. 用于蛋白质结构预测和设计的深度学习--进展与应用。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 Epub Date: 2024-01-30 DOI: 10.1038/s44320-024-00016-x

Jürgen Jänes, Pedro Beltrao

引用次数: 0

Author Correction: Bacterial expression of a designed single-chain IL-10 prevents severe lung inflammation. 作者更正：细菌表达设计的单链 IL-10 可预防严重的肺部炎症。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-03-01 DOI: 10.1038/s44320-023-00008-3

Ariadna Montero-Blay, Javier Delgado Blanco, Irene Rodriguez-Arce, Claire Lastrucci, Carlos Piñero-Lambea, Maria Lluch-Senar, Luis Serrano

引用次数: 0

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT). 利用优化传输（PILOT）从单细胞基因组学和病理组学数据中检测专利级距离。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-02-01 Epub Date: 2023-12-19 DOI: 10.1038/s44320-023-00003-8

Mehdi Joodaki, Mina Shaigan, Victor Parra, Roman D Bülow, Christoph Kuppe, David L Hölscher, Mingbo Cheng, James S Nagai, Michaël Goedertier, Nassim Bouteldja, Vladimir Tesar, Jonathan Barratt, Ian Sd Roberts, Rosanna Coppo, Rafael Kramann, Peter Boor, Ivan G Costa

{"title":"Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).","authors":"Mehdi Joodaki, Mina Shaigan, Victor Parra, Roman D Bülow, Christoph Kuppe, David L Hölscher, Mingbo Cheng, James S Nagai, Michaël Goedertier, Nassim Bouteldja, Vladimir Tesar, Jonathan Barratt, Ian Sd Roberts, Rosanna Coppo, Rafael Kramann, Peter Boor, Ivan G Costa","doi":"10.1038/s44320-023-00003-8","DOIUrl":"10.1038/s44320-023-00003-8","url":null,"abstract":"Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"57-74"},"PeriodicalIF":9.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139098306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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