Molecular Systems Biology最新文献_第7页

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

Derailed protein turnover in the aging mammalian brain. 哺乳动物大脑衰老过程中的蛋白质周转失调。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-02-01 Epub Date: 2024-01-05 DOI: 10.1038/s44320-023-00009-2

Nalini R Rao, Arun Upadhyay, Jeffrey N Savas

{"title":"Derailed protein turnover in the aging mammalian brain.","authors":"Nalini R Rao, Arun Upadhyay, Jeffrey N Savas","doi":"10.1038/s44320-023-00009-2","DOIUrl":"10.1038/s44320-023-00009-2","url":null,"abstract":"Efficient protein turnover is essential for cellular homeostasis and organ function. Loss of proteostasis is a hallmark of aging culminating in severe dysfunction of protein turnover. To investigate protein turnover dynamics as a function of age, we performed continuous in vivo metabolic stable isotope labeling in mice along the aging continuum. First, we discovered that the brain proteome uniquely undergoes dynamic turnover fluctuations during aging compared to heart and liver tissue. Second, trends in protein turnover in the brain proteome during aging showed sex-specific differences that were tightly tied to cellular compartments. Next, parallel analyses of the insoluble proteome revealed that several cellular compartments experience hampered turnover, in part due to misfolding. Finally, we found that age-associated fluctuations in proteasome activity were associated with the turnover of core proteolytic subunits, which was recapitulated by pharmacological suppression of proteasome activity. Taken together, our study provides a proteome-wide atlas of protein turnover across the aging continuum and reveals a link between the turnover of individual proteasome subunits and the age-associated decline in proteasome activity.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"120-139"},"PeriodicalIF":9.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10897147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139106318","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

Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA). 在健康和炎症组织中识别空间共现（ISCHIA）。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-02-01 Epub Date: 2024-01-15 DOI: 10.1038/s44320-023-00006-5

Atefeh Lafzi, Costanza Borrelli, Simona Baghai Sain, Karsten Bach, Jonas A Kretz, Kristina Handler, Daniel Regan-Komito, Xenia Ficht, Andreas Frei, Andreas Moor

{"title":"Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA).","authors":"Atefeh Lafzi, Costanza Borrelli, Simona Baghai Sain, Karsten Bach, Jonas A Kretz, Kristina Handler, Daniel Regan-Komito, Xenia Ficht, Andreas Frei, Andreas Moor","doi":"10.1038/s44320-023-00006-5","DOIUrl":"10.1038/s44320-023-00006-5","url":null,"abstract":"Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"98-119"},"PeriodicalIF":9.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10897385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139472182","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

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation. 利用 AlphaFold 系统发现蛋白质相互作用界面并进行实验验证。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-02-01 Epub Date: 2024-01-15 DOI: 10.1038/s44320-023-00005-6

Chop Yan Lee, Dalmira Hubrich, Julia K Varga, Christian Schäfer, Mareen Welzel, Eric Schumbera, Milena Djokic, Joelle M Strom, Jonas Schönfeld, Johanna L Geist, Feyza Polat, Toby J Gibson, Claudia Isabelle Keller Valsecchi, Manjeet Kumar, Ora Schueler-Furman, Katja Luck

{"title":"Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.","authors":"Chop Yan Lee, Dalmira Hubrich, Julia K Varga, Christian Schäfer, Mareen Welzel, Eric Schumbera, Milena Djokic, Joelle M Strom, Jonas Schönfeld, Johanna L Geist, Feyza Polat, Toby J Gibson, Claudia Isabelle Keller Valsecchi, Manjeet Kumar, Ora Schueler-Furman, Katja Luck","doi":"10.1038/s44320-023-00005-6","DOIUrl":"10.1038/s44320-023-00005-6","url":null,"abstract":"Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"75-97"},"PeriodicalIF":8.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139472172","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

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics. 通过磷酸蛋白组学阐明肉瘤细胞对激酶抑制剂的表型药物反应。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-01-01 Epub Date: 2023-12-18 DOI: 10.1038/s44320-023-00004-7

Chien-Yun Lee, Matthew The, Chen Meng, Florian P Bayer, Kerstin Putzker, Julian Müller, Johanna Streubel, Julia Woortman, Amirhossein Sakhteman, Moritz Resch, Annika Schneider, Stephanie Wilhelm, Bernhard Kuster

{"title":"Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics.","authors":"Chien-Yun Lee, Matthew The, Chen Meng, Florian P Bayer, Kerstin Putzker, Julian Müller, Johanna Streubel, Julia Woortman, Amirhossein Sakhteman, Moritz Resch, Annika Schneider, Stephanie Wilhelm, Bernhard Kuster","doi":"10.1038/s44320-023-00004-7","DOIUrl":"10.1038/s44320-023-00004-7","url":null,"abstract":"Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":"20 1","pages":"28-55"},"PeriodicalIF":9.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139098308","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