Pub Date : 2023-10-01Epub Date: 2023-08-28DOI: 10.15252/msb.202211427
Haiyue Liu, Roberto Arsiè, Daniel Schwabe, Marcel Schilling, Igor Minia, Jonathan Alles, Anastasiya Boltengagen, Christine Kocks, Martin Falcke, Nir Friedman, Markus Landthaler, Nikolaus Rajewsky
RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.
在真核细胞中,RNA丰度是通过调节RNA产生、加工和降解的动力学速率而受到严格调节的。迄今为止,人们对动态过程中随时间变化的动力学速率知之甚少。在这里,我们提出了SLAM - Drop - seq,一种结合了甲醇固定细胞中RNA代谢标记和修饰核苷酸烷基化与液滴测序的方法,以检测单细胞中新合成的和预先存在的mrna。作为第一个应用,我们对7280个HEK293细胞进行测序,并使用新型包装Eskrate计算细胞周期内的基因特异性动力学速率。在我们鉴定的377个强循环基因中,只有一小部分仅由动态转录或降解调节(分别为6%和4%)。相比之下,绝大多数(89%)在细胞周期中表现出动态调节的转录和降解率。因此,我们的研究表明,暂时调节的mRNA降解是大多数循环基因正确表达的基础。SLAM - Drop - seq与Eskrate结合,是了解连续生物过程中单细胞基因表达动态的潜在mRNA动力学的有力方法。
{"title":"SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle.","authors":"Haiyue Liu, Roberto Arsiè, Daniel Schwabe, Marcel Schilling, Igor Minia, Jonathan Alles, Anastasiya Boltengagen, Christine Kocks, Martin Falcke, Nir Friedman, Markus Landthaler, Nikolaus Rajewsky","doi":"10.15252/msb.202211427","DOIUrl":"10.15252/msb.202211427","url":null,"abstract":"<p><p>RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10568207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42650591","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}
Pub Date : 2023-09-12Epub Date: 2023-08-21DOI: 10.15252/msb.202211503
Marvin Thielert, Ericka Cm Itang, Constantin Ammar, Florian A Rosenberger, Isabell Bludau, Lisa Schweizer, Thierry M Nordmann, Patricia Skowronek, Maria Wahle, Wen-Feng Zeng, Xie-Xuan Zhou, Andreas-David Brunner, Sabrina Richter, Mitchell P Levesque, Fabian J Theis, Martin Steger, Matthias Mann
Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.
{"title":"Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.","authors":"Marvin Thielert, Ericka Cm Itang, Constantin Ammar, Florian A Rosenberger, Isabell Bludau, Lisa Schweizer, Thierry M Nordmann, Patricia Skowronek, Maria Wahle, Wen-Feng Zeng, Xie-Xuan Zhou, Andreas-David Brunner, Sabrina Richter, Mitchell P Levesque, Fabian J Theis, Martin Steger, Matthias Mann","doi":"10.15252/msb.202211503","DOIUrl":"10.15252/msb.202211503","url":null,"abstract":"<p><p>Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10236507","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}
Pub Date : 2023-09-12Epub Date: 2023-07-24DOI: 10.15252/msb.202311525
Sander Wuyts, Renato Alves, Maria Zimmermann-Kogadeeva, Suguru Nishijima, Sonja Blasche, Marja Driessen, Philipp E Geyer, Rajna Hercog, Ece Kartal, Lisa Maier, Johannes B Müller, Sarela Garcia Santamarina, Thomas Sebastian B Schmidt, Daniel C Sevin, Anja Telzerow, Peter V Treit, Tobias Wenzel, Athanasios Typas, Kiran R Patil, Matthias Mann, Michael Kuhn, Peer Bork
Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.
{"title":"Consistency across multi-omics layers in a drug-perturbed gut microbial community.","authors":"Sander Wuyts, Renato Alves, Maria Zimmermann-Kogadeeva, Suguru Nishijima, Sonja Blasche, Marja Driessen, Philipp E Geyer, Rajna Hercog, Ece Kartal, Lisa Maier, Johannes B Müller, Sarela Garcia Santamarina, Thomas Sebastian B Schmidt, Daniel C Sevin, Anja Telzerow, Peter V Treit, Tobias Wenzel, Athanasios Typas, Kiran R Patil, Matthias Mann, Michael Kuhn, Peer Bork","doi":"10.15252/msb.202311525","DOIUrl":"10.15252/msb.202311525","url":null,"abstract":"<p><p>Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10240795","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}
Pub Date : 2023-09-12Epub Date: 2023-08-07DOI: 10.15252/msb.202311613
Javier Ruiz, Miguel de Celis, Juan Diaz-Colunga, Jean Cc Vila, Belen Benitez-Dominguez, Javier Vicente, Antonio Santos, Alvaro Sanchez, Ignacio Belda
Predictively linking taxonomic composition and quantitative ecosystem functions is a major aspiration in microbial ecology, which must be resolved if we wish to engineer microbial consortia. Here, we have addressed this open question for an ecological function of major biotechnological relevance: alcoholic fermentation in wine yeast communities. By exhaustively phenotyping an extensive collection of naturally occurring wine yeast strains, we find that most ecologically and industrially relevant traits exhibit phylogenetic signal, allowing functional traits in wine yeast communities to be predicted from taxonomy. Furthermore, we demonstrate that the quantitative contributions of individual wine yeast strains to the function of complex communities followed simple quantitative rules. These regularities can be integrated to quantitatively predict the function of newly assembled consortia. Besides addressing theoretical questions in functional ecology, our results and methodologies can provide a blueprint for rationally managing microbial processes of biotechnological relevance.
{"title":"Predictability of the community-function landscape in wine yeast ecosystems.","authors":"Javier Ruiz, Miguel de Celis, Juan Diaz-Colunga, Jean Cc Vila, Belen Benitez-Dominguez, Javier Vicente, Antonio Santos, Alvaro Sanchez, Ignacio Belda","doi":"10.15252/msb.202311613","DOIUrl":"10.15252/msb.202311613","url":null,"abstract":"<p><p>Predictively linking taxonomic composition and quantitative ecosystem functions is a major aspiration in microbial ecology, which must be resolved if we wish to engineer microbial consortia. Here, we have addressed this open question for an ecological function of major biotechnological relevance: alcoholic fermentation in wine yeast communities. By exhaustively phenotyping an extensive collection of naturally occurring wine yeast strains, we find that most ecologically and industrially relevant traits exhibit phylogenetic signal, allowing functional traits in wine yeast communities to be predicted from taxonomy. Furthermore, we demonstrate that the quantitative contributions of individual wine yeast strains to the function of complex communities followed simple quantitative rules. These regularities can be integrated to quantitatively predict the function of newly assembled consortia. Besides addressing theoretical questions in functional ecology, our results and methodologies can provide a blueprint for rationally managing microbial processes of biotechnological relevance.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10291995","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}
Lara Urban, Albert Perlas, Olga Francino, Joan Martí-Carreras, Brenda A Muga, Jenniffer W Mwangi, Laura Boykin Okalebo, Jo-Ann L Stanton, Amanda Black, Nick Waipara, Claudia Fontsere, David Eccles, Harika Urel, Tim Reska, Hernán E Morales, Marc Palmada-Flores, Tomas Marques-Bonet, Mrinalini Watsa, Zane Libke, Gideon Erkenswick, Cock van Oosterhout
The ongoing degradation of natural systems and other environmental changes has put our society at a crossroad with respect to our future relationship with our planet. While the concept of One Health describes how human health is inextricably linked with environmental health, many of these complex interdependencies are still not well-understood. Here, we describe how the advent of real-time genomic analyses can benefit One Health and how it can enable timely, in-depth ecosystem health assessments. We introduce nanopore sequencing as the only disruptive technology that currently allows for real-time genomic analyses and that is already being used worldwide to improve the accessibility and versatility of genomic sequencing. We showcase real-time genomic studies on zoonotic disease, food security, environmental microbiome, emerging pathogens, and their antimicrobial resistances, and on environmental health itself - from genomic resource creation for wildlife conservation to the monitoring of biodiversity, invasive species, and wildlife trafficking. We stress why equitable access to real-time genomics in the context of One Health will be paramount and discuss related practical, legal, and ethical limitations.
{"title":"Real-time genomics for One Health.","authors":"Lara Urban, Albert Perlas, Olga Francino, Joan Martí-Carreras, Brenda A Muga, Jenniffer W Mwangi, Laura Boykin Okalebo, Jo-Ann L Stanton, Amanda Black, Nick Waipara, Claudia Fontsere, David Eccles, Harika Urel, Tim Reska, Hernán E Morales, Marc Palmada-Flores, Tomas Marques-Bonet, Mrinalini Watsa, Zane Libke, Gideon Erkenswick, Cock van Oosterhout","doi":"10.15252/msb.202311686","DOIUrl":"https://doi.org/10.15252/msb.202311686","url":null,"abstract":"<p><p>The ongoing degradation of natural systems and other environmental changes has put our society at a crossroad with respect to our future relationship with our planet. While the concept of One Health describes how human health is inextricably linked with environmental health, many of these complex interdependencies are still not well-understood. Here, we describe how the advent of real-time genomic analyses can benefit One Health and how it can enable timely, in-depth ecosystem health assessments. We introduce nanopore sequencing as the only disruptive technology that currently allows for real-time genomic analyses and that is already being used worldwide to improve the accessibility and versatility of genomic sequencing. We showcase real-time genomic studies on zoonotic disease, food security, environmental microbiome, emerging pathogens, and their antimicrobial resistances, and on environmental health itself - from genomic resource creation for wildlife conservation to the monitoring of biodiversity, invasive species, and wildlife trafficking. We stress why equitable access to real-time genomics in the context of One Health will be paramount and discuss related practical, legal, and ethical limitations.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963827","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}
Pub Date : 2023-08-08Epub Date: 2023-05-26DOI: 10.15252/msb.202211407
Eyal Simonovsky, Moran Sharon, Maya Ziv, Omry Mauer, Idan Hekselman, Juman Jubran, Ekaterina Vinogradov, Chanan M Argov, Omer Basha, Lior Kerber, Yuval Yogev, Ayellet V Segrè, Hae Kyung Im, Ohad Birk, Lior Rokach, Esti Yeger-Lotem
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.
{"title":"Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.","authors":"Eyal Simonovsky, Moran Sharon, Maya Ziv, Omry Mauer, Idan Hekselman, Juman Jubran, Ekaterina Vinogradov, Chanan M Argov, Omer Basha, Lior Kerber, Yuval Yogev, Ayellet V Segrè, Hae Kyung Im, Ohad Birk, Lior Rokach, Esti Yeger-Lotem","doi":"10.15252/msb.202211407","DOIUrl":"10.15252/msb.202211407","url":null,"abstract":"<p><p>How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed \"Tissue Risk Assessment of Causality by Expression\" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10318151","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}
Kyle Mohler, Jack M Moen, Svetlana Rogulina, Jesse Rinehart
Over the past two decades, synthetic biological systems have revolutionized the study of cellular physiology. The ability to site-specifically incorporate biologically relevant non-standard amino acids using orthogonal translation systems (OTSs) has proven particularly useful, providing unparalleled access to cellular mechanisms modulated by post-translational modifications, such as protein phosphorylation. However, despite significant advances in OTS design and function, the systems-level biology of OTS development and utilization remains underexplored. In this study, we employ a phosphoserine OTS (pSerOTS) as a model to systematically investigate global interactions between OTS components and the cellular environment, aiming to improve OTS performance. Based on this analysis, we design OTS variants to enhance orthogonality by minimizing host process interactions and reducing stress response activation. Our findings advance understanding of system-wide OTS:host interactions, enabling informed design practices that circumvent deleterious interactions with host physiology while improving OTS performance and stability. Furthermore, our study emphasizes the importance of establishing a pipeline for systematically profiling OTS:host interactions to enhance orthogonality and mitigate mechanisms underlying OTS-mediated host toxicity.
{"title":"System-wide optimization of an orthogonal translation system with enhanced biological tolerance.","authors":"Kyle Mohler, Jack M Moen, Svetlana Rogulina, Jesse Rinehart","doi":"10.15252/msb.202110591","DOIUrl":"https://doi.org/10.15252/msb.202110591","url":null,"abstract":"<p><p>Over the past two decades, synthetic biological systems have revolutionized the study of cellular physiology. The ability to site-specifically incorporate biologically relevant non-standard amino acids using orthogonal translation systems (OTSs) has proven particularly useful, providing unparalleled access to cellular mechanisms modulated by post-translational modifications, such as protein phosphorylation. However, despite significant advances in OTS design and function, the systems-level biology of OTS development and utilization remains underexplored. In this study, we employ a phosphoserine OTS (pSerOTS) as a model to systematically investigate global interactions between OTS components and the cellular environment, aiming to improve OTS performance. Based on this analysis, we design OTS variants to enhance orthogonality by minimizing host process interactions and reducing stress response activation. Our findings advance understanding of system-wide OTS:host interactions, enabling informed design practices that circumvent deleterious interactions with host physiology while improving OTS performance and stability. Furthermore, our study emphasizes the importance of establishing a pipeline for systematically profiling OTS:host interactions to enhance orthogonality and mitigate mechanisms underlying OTS-mediated host toxicity.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9964398","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}
Matthias Weith, Jan Großbach, Mathieu Clement-Ziza, Ludovic Gillet, María Rodríguez-López, Samuel Marguerat, Christopher T Workman, Paola Picotti, Jürg Bähler, Ruedi Aebersold, Andreas Beyer
The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.
{"title":"Genetic effects on molecular network states explain complex traits.","authors":"Matthias Weith, Jan Großbach, Mathieu Clement-Ziza, Ludovic Gillet, María Rodríguez-López, Samuel Marguerat, Christopher T Workman, Paola Picotti, Jürg Bähler, Ruedi Aebersold, Andreas Beyer","doi":"10.15252/msb.202211493","DOIUrl":"https://doi.org/10.15252/msb.202211493","url":null,"abstract":"<p><p>The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10318644","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}
Pub Date : 2023-08-08Epub Date: 2023-06-13DOI: 10.15252/msb.202211474
Benjamin J Livesey, Joseph A Marsh
The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.
{"title":"Updated benchmarking of variant effect predictors using deep mutational scanning.","authors":"Benjamin J Livesey, Joseph A Marsh","doi":"10.15252/msb.202211474","DOIUrl":"10.15252/msb.202211474","url":null,"abstract":"<p><p>The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960586","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}
Lucas van Duin, Robert Krautz, Sarah Rennie, Robin Andersson
Many genes are co-expressed and form genomic domains of coordinated gene activity. However, the regulatory determinants of domain co-activity remain unclear. Here, we leverage human individual variation in gene expression to characterize the co-regulatory processes underlying domain co-activity and systematically quantify their effect sizes. We employ transcriptional decomposition to extract from RNA expression data an expression component related to co-activity revealed by genomic positioning. This strategy reveals close to 1,500 co-activity domains, covering most expressed genes, of which the large majority are invariable across individuals. Focusing specifically on domains with high variability in co-activity reveals that contained genes have a higher sharing of eQTLs, a higher variability in enhancer interactions, and an enrichment of binding by variably expressed transcription factors, compared to genes within non-variable domains. Through careful quantification of the relative contributions of regulatory processes underlying co-activity, we find transcription factor expression levels to be the main determinant of gene co-activity. Our results indicate that distal trans effects contribute more than local genetic variation to individual variation in co-activity domains.
{"title":"Transcription factor expression is the main determinant of variability in gene co-activity.","authors":"Lucas van Duin, Robert Krautz, Sarah Rennie, Robin Andersson","doi":"10.15252/msb.202211392","DOIUrl":"https://doi.org/10.15252/msb.202211392","url":null,"abstract":"<p><p>Many genes are co-expressed and form genomic domains of coordinated gene activity. However, the regulatory determinants of domain co-activity remain unclear. Here, we leverage human individual variation in gene expression to characterize the co-regulatory processes underlying domain co-activity and systematically quantify their effect sizes. We employ transcriptional decomposition to extract from RNA expression data an expression component related to co-activity revealed by genomic positioning. This strategy reveals close to 1,500 co-activity domains, covering most expressed genes, of which the large majority are invariable across individuals. Focusing specifically on domains with high variability in co-activity reveals that contained genes have a higher sharing of eQTLs, a higher variability in enhancer interactions, and an enrichment of binding by variably expressed transcription factors, compared to genes within non-variable domains. Through careful quantification of the relative contributions of regulatory processes underlying co-activity, we find transcription factor expression levels to be the main determinant of gene co-activity. Our results indicate that distal trans effects contribute more than local genetic variation to individual variation in co-activity domains.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":null,"pages":null},"PeriodicalIF":9.9,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9789760","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}