Pub Date : 2024-07-29DOI: 10.1101/2024.07.28.605515
Lohani Esterhuizen, Nicholas Ampimah, Marna Yandeau-Nelson, Basil J. Nikolau, Erin E Sparks, Rajib Saha
Being the first plant to have its genome sequenced, Arabidopsis thaliana (Arabidopsis) is a well-established genetic model plant system. Studies on Arabidopsis have provided major insights into plants' physiological and biochemical nature. Methods that allow us to computationally study the metabolism of organisms include the use of genome-scale metabolic models (GEMs). Despite its popularity, currently no GEM maps the metabolic activity in the roots of Arabidopsis, which is the organ that faces and responds to stress conditions in the soil. We've developed a comprehensive GEM of the Arabidopsis root system - AraRoot. The final model includes 2,682 reactions, 2,748 metabolites, and 1,310 genes. Analyzing the metabolic pathways in the model identified 158 possible bottleneck genes that impact biomass production, most of which were found to be related to phosphorous-containing- and energy-related pathways. Further insights into tissue-specific metabolic reprogramming conclude that the cortex layer in the roots is likely responsible for root growth under prolonged exposure to high salt conditions, while the endodermis and epidermis are responsible for producing metabolites responsible for increased cell wall biosynthesis. The epidermis was found to have a very poor ability to regulate its metabolism during exposure to high salt concentrations. Overall, AraRoot is the first GEM that accurately captures the comprehensive biomass formation and stress responses of the tissues in the Arabidopsis root system.
{"title":"AraRoot - A Comprehensive Genome-Scale Metabolic Model for the Arabidopsis Root System.","authors":"Lohani Esterhuizen, Nicholas Ampimah, Marna Yandeau-Nelson, Basil J. Nikolau, Erin E Sparks, Rajib Saha","doi":"10.1101/2024.07.28.605515","DOIUrl":"https://doi.org/10.1101/2024.07.28.605515","url":null,"abstract":"Being the first plant to have its genome sequenced, Arabidopsis thaliana (Arabidopsis) is a well-established genetic model plant system. Studies on Arabidopsis have provided major insights into plants' physiological and biochemical nature. Methods that allow us to computationally study the metabolism of organisms include the use of genome-scale metabolic models (GEMs). Despite its popularity, currently no GEM maps the metabolic activity in the roots of Arabidopsis, which is the organ that faces and responds to stress conditions in the soil. We've developed a comprehensive GEM of the Arabidopsis root system - AraRoot. The final model includes 2,682 reactions, 2,748 metabolites, and 1,310 genes. Analyzing the metabolic pathways in the model identified 158 possible bottleneck genes that impact biomass production, most of which were found to be related to phosphorous-containing- and energy-related pathways. Further insights into tissue-specific metabolic reprogramming conclude that the cortex layer in the roots is likely responsible for root growth under prolonged exposure to high salt conditions, while the endodermis and epidermis are responsible for producing metabolites responsible for increased cell wall biosynthesis. The epidermis was found to have a very poor ability to regulate its metabolism during exposure to high salt concentrations. Overall, AraRoot is the first GEM that accurately captures the comprehensive biomass formation and stress responses of the tissues in the Arabidopsis root system.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-29DOI: 10.1101/2024.07.27.605454
Janusz Wiśniewski, Heng-Chang CHEN
We developed a k-mer-based pipeline, namely the Pathogen Origin Recognition Tool using Enriched K-mers (PORT-EK) to identify genomic regions enriched in the respective hosts after the comparison of metagenomes of isolates between two host species. Using it we identified thousands of k-mers enriched in US white-tailed deer and betacoronaviruses in bat reservoirs while comparing them with human isolates. We demonstrated different coverage landscapes of k-mers enriched in deer and bats and unraveled 148 mutations in enriched k-mers yielded from the comparison of viral metagenomes between bat and human isolates. We observed that the third position within a genetic codon is prone to mutations, resulting in a high frequency of synonymous mutations of amino acids harboring the same physicochemical properties as unaltered amino acids. Finally, we classified and predicted the likelihood of host species based on the enriched k-mer counts. Altogether, PORT-EK showcased its feasibility for identifying enriched viral genomic regions, illuminating the different intrinsic tropisms of coronavirus after host domestication.
{"title":"Identification of potential SARS-CoV-2 genetic markers resulting from host domestication.","authors":"Janusz Wiśniewski, Heng-Chang CHEN","doi":"10.1101/2024.07.27.605454","DOIUrl":"https://doi.org/10.1101/2024.07.27.605454","url":null,"abstract":"We developed a k-mer-based pipeline, namely the Pathogen Origin Recognition Tool using Enriched K-mers (PORT-EK) to identify genomic regions enriched in the respective hosts after the comparison of metagenomes of isolates between two host species. Using it we identified thousands of k-mers enriched in US white-tailed deer and betacoronaviruses in bat reservoirs while comparing them with human isolates. We demonstrated different coverage landscapes of k-mers enriched in deer and bats and unraveled 148 mutations in enriched k-mers yielded from the comparison of viral metagenomes between bat and human isolates. We observed that the third position within a genetic codon is prone to mutations, resulting in a high frequency of synonymous mutations of amino acids harboring the same physicochemical properties as unaltered amino acids. Finally, we classified and predicted the likelihood of host species based on the enriched k-mer counts. Altogether, PORT-EK showcased its feasibility for identifying enriched viral genomic regions, illuminating the different intrinsic tropisms of coronavirus after host domestication.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-28DOI: 10.1101/2024.07.26.605310
Marco Fondi, Christopher Riccardi, Francesca Di Patti, Francesca Coscione, Alessio Mengoni, Elena Perrin
Quorum sensing (QS) is a cell-to-cell communication system used by bacteria to act collectively. Often, bacteria possess more than one QS regulatory module that form complex regulatory networks. Presumably, these configurations have evolved through the integration of novel transcription factors into the native regulatory systems. The selective advantages provided by these alternative configurations on QS-related phenotypes is poorly predictable only based on their underlying network structure. Here we show that the acquisition of extra regulatory modules of QS has important consequences on the overall regulation of microbial growth dynamics by significantly reducing the variability in the final size of the population in Burkholderia. We mapped the distribution of horizontally transferred QS modules in extant bacterial genomes, finding that these tend to add up to already-present modules in the majority of cases, 63.32%. We then selected a strain harboring two intertwined QS modules and, using mathematical modelling, we predicted an intrinsic ability of the newly acquired module to buffer noise in growth dynamics. We experimentally validated this prediction choosing one strain possessing both systems, deleting one of the two and measuring key growth parameters and QS synthase expression. We extended such considerations on two other strains naturally implementing the two versions of the QS regulation studied herein. Finally, using transcriptomics, we show that the de-regulation of metabolism likely plays a key role in differentiating the two configurations. Our results shed light on the role of additional control over QS regulation and illuminate on the possible phenotypes that may arise after HGT events.
{"title":"The acquisition of additional control over quorum sensing regulation buffers noise in microbial growth dynamics","authors":"Marco Fondi, Christopher Riccardi, Francesca Di Patti, Francesca Coscione, Alessio Mengoni, Elena Perrin","doi":"10.1101/2024.07.26.605310","DOIUrl":"https://doi.org/10.1101/2024.07.26.605310","url":null,"abstract":"Quorum sensing (QS) is a cell-to-cell communication system used by bacteria to act collectively. Often, bacteria possess more than one QS regulatory module that form complex regulatory networks. Presumably, these configurations have evolved through the integration of novel transcription factors into the native regulatory systems. The selective advantages provided by these alternative configurations on QS-related phenotypes is poorly predictable only based on their underlying network structure. Here we show that the acquisition of extra regulatory modules of QS has important consequences on the overall regulation of microbial growth dynamics by significantly reducing the variability in the final size of the population in <em>Burkholderia</em>. We mapped the distribution of horizontally transferred QS modules in extant bacterial genomes, finding that these tend to add up to already-present modules in the majority of cases, 63.32%. We then selected a strain harboring two intertwined QS modules and, using mathematical modelling, we predicted an intrinsic ability of the newly acquired module to buffer noise in growth dynamics. We experimentally validated this prediction choosing one strain possessing both systems, deleting one of the two and measuring key growth parameters and QS synthase expression. We extended such considerations on two other strains naturally implementing the two versions of the QS regulation studied herein. Finally, using transcriptomics, we show that the de-regulation of metabolism likely plays a key role in differentiating the two configurations. Our results shed light on the role of additional control over QS regulation and illuminate on the possible phenotypes that may arise after HGT events.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cell cycle is crucial for maintaining normal cellular functions and preventing replication errors. FOXM1, a key transcription factor, plays a pivotal role in regulating cell cycle progression and is implicated in various physiological and pathological processes, including cancers like liver, prostate, breast, lung, and colon cancer. Despite previous research, our understanding of FOXM1 dynamics under different cell cycle perturbations and its connection to heterogeneous cell fate decisions remains limited. In this study, we investigated FOXM1 behavior in individual cells exposed to various perturbagens. We found that different drugs induce diverse responses due to heterogeneous FOXM1 dynamics at the single-cell level. Single-cell analysis identified six distinct cellular phenotypes: on-time cytokinesis, cytokinesis delay, cell cycle delay, G1 arrest, G2 arrest, and cell death, observed across different drug types and doses. Specifically, treatments with PLK1, CDK1, CDK1/2, and Aurora kinase inhibitors revealed varied FOXM1 dynamics leading to heterogeneous cellular outcomes. Our findings affirm that FOXM1 dynamics are pivotal in determining cellular outcomes, independent of the specific inhibitor employed. Our results gave insights into how FOXM1 dynamics contribute to cell cycle fate decisions, especially under different cell-cycle perturbations.
{"title":"Exploring the Single-Cell Dynamics of FOXM1 Under Cell Cycle Perturbations","authors":"Tooba Jawwad, Maliwan Kamkaew, Kriengkrai Phongkitkarun, Porncheera Chusorn, Somponnat Sampattavanich","doi":"10.1101/2024.07.27.605093","DOIUrl":"https://doi.org/10.1101/2024.07.27.605093","url":null,"abstract":"The cell cycle is crucial for maintaining normal cellular functions and preventing replication errors. FOXM1, a key transcription factor, plays a pivotal role in regulating cell cycle progression and is implicated in various physiological and pathological processes, including cancers like liver, prostate, breast, lung, and colon cancer. Despite previous research, our understanding of FOXM1 dynamics under different cell cycle perturbations and its connection to heterogeneous cell fate decisions remains limited. In this study, we investigated FOXM1 behavior in individual cells exposed to various perturbagens. We found that different drugs induce diverse responses due to heterogeneous FOXM1 dynamics at the single-cell level. Single-cell analysis identified six distinct cellular phenotypes: on-time cytokinesis, cytokinesis delay, cell cycle delay, G1 arrest, G2 arrest, and cell death, observed across different drug types and doses. Specifically, treatments with PLK1, CDK1, CDK1/2, and Aurora kinase inhibitors revealed varied FOXM1 dynamics leading to heterogeneous cellular outcomes. Our findings affirm that FOXM1 dynamics are pivotal in determining cellular outcomes, independent of the specific inhibitor employed. Our results gave insights into how FOXM1 dynamics contribute to cell cycle fate decisions, especially under different cell-cycle perturbations.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1101/2024.07.26.605307
Eli Metzner, Kaden M. Southard, Thomas M. Norman
Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.
{"title":"Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome","authors":"Eli Metzner, Kaden M. Southard, Thomas M. Norman","doi":"10.1101/2024.07.26.605307","DOIUrl":"https://doi.org/10.1101/2024.07.26.605307","url":null,"abstract":"Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that <em>ARID1A</em> and <em>SUZ12</em> knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1101/2024.07.25.605202
Stewart WC WC Masson, Rebecca C Simpson, Harry B Cutler, Patrick W Carlos, Oana C Marian, Meg Potter, Søren Madsen, Kristen C Cooke, Niamh R Craw, Oliver K Fuller, Dylan J Harney, Mark Larance, Gregory J Cooney, Grant Morahan, Erin R Shanahan, Christopher Hodgkins, Richard J Payne, Jacqueline Stöckli, David E James
Insulin resistance is heritable; however, the underlying genetic drivers remain elusive. In seeking these, we performed genetic mapping of insulin sensitivity in 670 chow-fed Diversity Outbred in Australia (DOz) mice and identified a genome-wide significant quantitative trait loci (QTL) within the chromosome 8 defensin gene cluster. Defensins are antimicrobial peptides secreted from Paneth cells into the intestinal lumen that can alter the abundance of beneficial and detrimental microbes. Proteomic analysis of the small intestine from Diversity Outbred founder strains revealed that alpha-defensin 26 positively correlated with whole-body insulin sensitivity, and founder strain genetic contributions to the insulin sensitivity QTL. To validate these findings, we synthesised the secreted form of alpha-defensin 26 and performed diet supplementation experiments in two mouse strains with distinct endogenous alpha-defensin 26 expression levels. In validation of our DOz data, the strain with lower endogenous expression (C57BL/6J) exhibited improved insulin sensitivity and reduced gut permeability following defensin supplementation. In contrast, the higher expressing strain (A/J) exhibited hypoinsulinemia, glucose intolerance and muscle wasting. Gut microbiome profiling in these mice revealed both global and strain-specific changes including some observed in DOz mice positive for the putative insulin sensitivity allele. Inspired by previous work linking glucose homeostasis to gut microbiome mediated changes in plasma bile acids, we investigated these as a potential mechanism. As with metabolic changes, A/J but not C57BL/6J mice exhibited differential plasma bile acid concentrations following defensin supplementation. These data highlight the importance of considering individual differences when designing metabolic therapeutics and paves the way for further studies investigating links between the host genetics and the microbiome.
{"title":"Genetic variance in the murine defensin locus modulates glucose homeostasis","authors":"Stewart WC WC Masson, Rebecca C Simpson, Harry B Cutler, Patrick W Carlos, Oana C Marian, Meg Potter, Søren Madsen, Kristen C Cooke, Niamh R Craw, Oliver K Fuller, Dylan J Harney, Mark Larance, Gregory J Cooney, Grant Morahan, Erin R Shanahan, Christopher Hodgkins, Richard J Payne, Jacqueline Stöckli, David E James","doi":"10.1101/2024.07.25.605202","DOIUrl":"https://doi.org/10.1101/2024.07.25.605202","url":null,"abstract":"Insulin resistance is heritable; however, the underlying genetic drivers remain elusive. In seeking these, we performed genetic mapping of insulin sensitivity in 670 chow-fed Diversity Outbred in Australia (DOz) mice and identified a genome-wide significant quantitative trait loci (QTL) within the chromosome 8 defensin gene cluster. Defensins are antimicrobial peptides secreted from Paneth cells into the intestinal lumen that can alter the abundance of beneficial and detrimental microbes. Proteomic analysis of the small intestine from Diversity Outbred founder strains revealed that alpha-defensin 26 positively correlated with whole-body insulin sensitivity, and founder strain genetic contributions to the insulin sensitivity QTL. To validate these findings, we synthesised the secreted form of alpha-defensin 26 and performed diet supplementation experiments in two mouse strains with distinct endogenous alpha-defensin 26 expression levels. In validation of our DOz data, the strain with lower endogenous expression (C57BL/6J) exhibited improved insulin sensitivity and reduced gut permeability following defensin supplementation. In contrast, the higher expressing strain (A/J) exhibited hypoinsulinemia, glucose intolerance and muscle wasting. Gut microbiome profiling in these mice revealed both global and strain-specific changes including some observed in DOz mice positive for the putative insulin sensitivity allele. Inspired by previous work linking glucose homeostasis to gut microbiome mediated changes in plasma bile acids, we investigated these as a potential mechanism. As with metabolic changes, A/J but not C57BL/6J mice exhibited differential plasma bile acid concentrations following defensin supplementation. These data highlight the importance of considering individual differences when designing metabolic therapeutics and paves the way for further studies investigating links between the host genetics and the microbiome.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1101/2024.07.24.605014
Thomas A Geddes, Rima Chauduri, Benjamin Parker, Pengyi Yang, James G Burchfield
Summary: Mass-spectrometry (MS) datasets present a unique set of challenges that make in-depth bioinformatics analysis non-trivial, with analysis requiring both expertise and time. Often these datasets have unique structures that need to be dealt with on an individual basis. Currently, tools providing a fast, interactive and guided way of exploring and analysing these data sets are not readily available. To this end, we have developed ThunderBolt: a highly interactive, point-and-click web-based application providing both bioinformaticians and biologists with a platform for i) searching and comparing multiple omics datasets, ii) fast data exploration and quality control, iii) interactive visualization, iv) pre-processing, v) statistical analysis and vi) functional and network enrichment analysis of large proteomics datasets using the Shiny framework. Availability: ThunderBolt is a shiny-application accessible at https://thunderbolt.sydney.edu.au/
{"title":"ThunderBolt: An interactive data sharing and analysis platform for large-omics experiments","authors":"Thomas A Geddes, Rima Chauduri, Benjamin Parker, Pengyi Yang, James G Burchfield","doi":"10.1101/2024.07.24.605014","DOIUrl":"https://doi.org/10.1101/2024.07.24.605014","url":null,"abstract":"Summary: Mass-spectrometry (MS) datasets present a unique set of challenges that make in-depth bioinformatics analysis non-trivial, with analysis requiring both expertise and time. Often these datasets have unique structures that need to be dealt with on an individual basis. Currently, tools providing a fast, interactive and guided way of exploring and analysing these data sets are not readily available. To this end, we have developed ThunderBolt: a highly interactive, point-and-click web-based application providing both bioinformaticians and biologists with a platform for i) searching and comparing multiple omics datasets, ii) fast data exploration and quality control, iii) interactive visualization, iv) pre-processing, v) statistical analysis and vi) functional and network enrichment analysis of large proteomics datasets using the Shiny framework.\u0000Availability: ThunderBolt is a shiny-application accessible at https://thunderbolt.sydney.edu.au/","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1101/2024.07.26.605281
Stephen Chapman, Theo Brunet, Arnaud Mourier, Bianca H Habermann
Mitochondria perform several essential functions in order to maintain cellular homeostasis and mitochondrial metabolism is inherently flexible to allow correct function in a wide range of tissues. Dysregulated mitochondrial metabolism can therefore affect different tissues in different ways which presents experimental challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is therefore useful in gaining in-depth insights into tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism used in research, there is currently no mouse specific mitochondrial metabolic model available. In this work, building upon the similarity between human and mouse mitochondrial metabolism, we have created mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is therefore able to model mouse and human mitochondrial metabolism. To demonstrate this feature, using an adapted E-Flux2 algorithm, we first integrated proteomic data extracted from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue. We then integrated transcriptomic data from in vitro differentiated human brown adipose cells and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research relating to cardiac disease and diabetes in both mouse and human contexts.
{"title":"MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism","authors":"Stephen Chapman, Theo Brunet, Arnaud Mourier, Bianca H Habermann","doi":"10.1101/2024.07.26.605281","DOIUrl":"https://doi.org/10.1101/2024.07.26.605281","url":null,"abstract":"Mitochondria perform several essential functions in order to maintain cellular homeostasis and mitochondrial metabolism is inherently flexible to allow correct function in a wide range of tissues. Dysregulated mitochondrial metabolism can therefore affect different tissues in different ways which presents experimental challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is therefore useful in gaining in-depth insights into tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism used in research, there is currently no mouse specific mitochondrial metabolic model available. In this work, building upon the similarity between human and mouse mitochondrial metabolism, we have created mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is therefore able to model mouse and human mitochondrial metabolism. To demonstrate this feature, using an adapted E-Flux2 algorithm, we first integrated proteomic data extracted from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue. We then integrated transcriptomic data from in vitro differentiated human brown adipose cells and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research relating to cardiac disease and diabetes in both mouse and human contexts.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1101/2024.07.25.605137
George S Biggs, Emma E Cawood, Aini Vuorinen, William J McCarthy, Harry Wilders, Ioannis G Riziotis, Antonie J van der Zouwen, Jonathan Pettinger, Luke Nightingale, Peiling Chen, Andrew J Powell, David House, Simon J Boulton, J Mark Skehel, Katrin Rittinger, Jacob T Bush
Identifying pharmacological probes for human proteins represents a key opportunity to accelerate the discovery of new therapeutics. High-content screening approaches to expand the ligandable proteome offer the potential to expedite the discovery of novel chemical probes to study protein function. Screening libraries of reactive fragments by chemoproteomics offers a compelling approach to ligand discovery, however, optimising sample throughput, proteomic depth, and data reproducibility remains a key challenge. We report a versatile, label-free quantification proteomics platform for competitive profiling of cysteine-reactive fragments against the native proteome. This high-throughput platform combines SP4 plate-based sample preparation with rapid chromatographic gradients. Data-independent acquisition performed on a Bruker timsTOF Pro 2 consistently identified ~23,000 cysteine sites per run, with a total of ~32,000 cysteine sites profiled in HEK293T and Jurkat lysate. Crucially, this depth in cysteinome coverage is met with high data completeness, enabling robust identification of liganded proteins. In this study, 80 reactive fragments were screened in two cell lines identifying >400 ligand-protein interactions. Hits were validated through concentration-response experiments and the platform was utilised for hit expansion and live cell experiments. This label-free platform represents a significant step forward in high-throughput proteomics to evaluate ligandability of cysteines across the human proteome.
{"title":"Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics","authors":"George S Biggs, Emma E Cawood, Aini Vuorinen, William J McCarthy, Harry Wilders, Ioannis G Riziotis, Antonie J van der Zouwen, Jonathan Pettinger, Luke Nightingale, Peiling Chen, Andrew J Powell, David House, Simon J Boulton, J Mark Skehel, Katrin Rittinger, Jacob T Bush","doi":"10.1101/2024.07.25.605137","DOIUrl":"https://doi.org/10.1101/2024.07.25.605137","url":null,"abstract":"Identifying pharmacological probes for human proteins represents a key opportunity to accelerate the discovery of new therapeutics. High-content screening approaches to expand the ligandable proteome offer the potential to expedite the discovery of novel chemical probes to study protein function. Screening libraries of reactive fragments by chemoproteomics offers a compelling approach to ligand discovery, however, optimising sample throughput, proteomic depth, and data reproducibility remains a key challenge. We report a versatile, label-free quantification proteomics platform for competitive profiling of cysteine-reactive fragments against the native proteome. This high-throughput platform combines SP4 plate-based sample preparation with rapid chromatographic gradients. Data-independent acquisition performed on a Bruker timsTOF Pro 2 consistently identified ~23,000 cysteine sites per run, with a total of ~32,000 cysteine sites profiled in HEK293T and Jurkat lysate. Crucially, this depth in cysteinome coverage is met with high data completeness, enabling robust identification of liganded proteins. In this study, 80 reactive fragments were screened in two cell lines identifying >400 ligand-protein interactions. Hits were validated through concentration-response experiments and the platform was utilised for hit expansion and live cell experiments. This label-free platform represents a significant step forward in high-throughput proteomics to evaluate ligandability of cysteines across the human proteome.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1101/2024.07.24.604987
Yuzhen Liu, Christopher D McGann, Mary Krebs, Thomas A Perkins, Rose Fields, Conor K Camplisson, Chris Hsu, Shayan Avanessian, Ashley F Tsue, Evan E Kania, David M Shechner, Brian J Beliveau, Devin K. Schweppe
The accuracy of crucial nuclear processes such as transcription, replication, and repair, depends on the local composition of chromatin and the regulatory proteins that reside there. Understanding these DNA-protein interactions at the level of specific genomic loci has remained challenging due to technical limitations. Here, we introduce a method termed "DNA O-MAP", which uses programmable peroxidase-conjugated oligonucleotide probes to biotinylate nearby proteins. We show that DNA O-MAP can be coupled with sample multiplexed quantitative proteomics and next-generation sequencing to quantify DNA-protein and DNA-DNA interactions at specific genomic loci.
转录、复制和修复等关键核过程的准确性取决于染色质的局部组成和其中的调控蛋白。由于技术上的限制，在特定基因组位点水平上理解这些 DNA 蛋白相互作用仍然具有挑战性。在这里，我们介绍了一种被称为 "DNA O-MAP "的方法，它使用可编程过氧化物酶连接的寡核苷酸探针对附近的蛋白质进行生物素化。我们的研究表明，DNA O-MAP可与样本多重定量蛋白质组学和下一代测序相结合，对特定基因组位点的DNA-蛋白质和DNA-DNA相互作用进行定量分析。
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