Philipp Trepte, Christopher Secker, Julien Olivet, Jeremy Blavier, Simona Kostova, Sibusiso B Maseko, Igor Minia, Eduardo Silva Ramos, Patricia Cassonnet, Sabrina Golusik, Martina Zenkner, Stephanie Beetz, Mara J Liebich, Nadine Scharek, Anja Schütz, Marcel Sperling, Michael Lisurek, Yang Wang, Kerstin Spirohn, Tong Hao, Michael A Calderwood, David E Hill, Markus Landthaler, Soon Gang Choi, Jean-Claude Twizere, Marc Vidal, Erich E Wanker
{"title":"AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.","authors":"Philipp Trepte, Christopher Secker, Julien Olivet, Jeremy Blavier, Simona Kostova, Sibusiso B Maseko, Igor Minia, Eduardo Silva Ramos, Patricia Cassonnet, Sabrina Golusik, Martina Zenkner, Stephanie Beetz, Mara J Liebich, Nadine Scharek, Anja Schütz, Marcel Sperling, Michael Lisurek, Yang Wang, Kerstin Spirohn, Tong Hao, Michael A Calderwood, David E Hill, Markus Landthaler, Soon Gang Choi, Jean-Claude Twizere, Marc Vidal, Erich E Wanker","doi":"10.1038/s44320-024-00019-8","DOIUrl":"10.1038/s44320-024-00019-8","url":null,"abstract":"<p><p>Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"428-457"},"PeriodicalIF":8.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10987651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140101971","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}
Christian Sommerauer, Carlos J Gallardo-Dodd, Christina Savva, Linnea Hases, Madeleine Birgersson, Rajitha Indukuri, Joanne X Shen, Pablo Carravilla, Keyi Geng, Jonas Nørskov Søndergaard, Clàudia Ferrer-Aumatell, Grégoire Mercier, Erdinc Sezgin, Marion Korach-André, Carl Petersson, Hannes Hagström, Volker M Lauschke, Amena Archer, Cecilia Williams, Claudia Kutter
{"title":"Estrogen receptor activation remodels TEAD1 gene expression to alleviate hepatic steatosis.","authors":"Christian Sommerauer, Carlos J Gallardo-Dodd, Christina Savva, Linnea Hases, Madeleine Birgersson, Rajitha Indukuri, Joanne X Shen, Pablo Carravilla, Keyi Geng, Jonas Nørskov Søndergaard, Clàudia Ferrer-Aumatell, Grégoire Mercier, Erdinc Sezgin, Marion Korach-André, Carl Petersson, Hannes Hagström, Volker M Lauschke, Amena Archer, Cecilia Williams, Claudia Kutter","doi":"10.1038/s44320-024-00024-x","DOIUrl":"10.1038/s44320-024-00024-x","url":null,"abstract":"<p><p>Sex-based differences in obesity-related hepatic malignancies suggest the protective roles of estrogen. Using a preclinical model, we dissected estrogen receptor (ER) isoform-driven molecular responses in high-fat diet (HFD)-induced liver diseases of male and female mice treated with or without an estrogen agonist by integrating liver multi-omics data. We found that selective ER activation recovers HFD-induced molecular and physiological liver phenotypes. HFD and systemic ER activation altered core liver pathways, beyond lipid metabolism, that are consistent between mice and primates. By including patient cohort data, we uncovered that ER-regulated enhancers govern central regulatory and metabolic genes with clinical significance in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, including the transcription factor TEAD1. TEAD1 expression increased in MASLD patients, and its downregulation by short interfering RNA reduced intracellular lipid content. Subsequent TEAD small molecule inhibition improved steatosis in primary human hepatocyte spheroids by suppressing lipogenic pathways. Thus, TEAD1 emerged as a new therapeutic candidate whose inhibition ameliorates hepatic steatosis.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"374-402"},"PeriodicalIF":9.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10987545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140065564","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}
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":"<p><p>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.</p>","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}
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":"<p><p>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.</p>","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}
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":"<p><p>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.</p>","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}
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":"<p><p>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.</p>","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}
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":"<p><p>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.</p>","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}
{"title":"Linking patient-specific basal MET phosphorylation levels to liver health.","authors":"Fabian Fröhlich","doi":"10.1038/s44320-024-00023-y","DOIUrl":"10.1038/s44320-024-00023-y","url":null,"abstract":"","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"141-143"},"PeriodicalIF":9.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139747041","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}
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":"<p><p>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.</p>","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}
{"title":"Deep learning for protein structure prediction and design-progress and applications.","authors":"Jürgen Jänes, Pedro Beltrao","doi":"10.1038/s44320-024-00016-x","DOIUrl":"10.1038/s44320-024-00016-x","url":null,"abstract":"<p><p>Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"162-169"},"PeriodicalIF":8.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139642552","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}