Pub Date : 2024-08-06DOI: 10.1101/2024.08.02.606335
Maddalena M Bolognesi, Lorenzo Dall’Olio, Amy Maerten, Simone Borghesi, Gastone Castellani, Giorgio Cattoretti
Hyperplexed in-situ targeted proteomics via antibody immunodetection (i.e. > 15 markers) is changing how we classify cells and tissues. Differently from other high-dimensional single-cell assays (flow cytometry, single cell RNA sequencing), the human eye is a necessary component in multiple procedural steps: image segmentation, signal thresholding, antibody validation and iconographic rendering. Established methods complement the human image evaluation, but may carry undisclosed biases in such a new context, therefore we re-evaluate all the steps in hyperplexed proteomics. We found that the human eye can discriminate less than 64 out of 256 gray levels and has limitations in discriminating luminance levels in conventional histology images. Furthermore, only images containing visible signals are selected and eye-guided digital thresholding separates signal from noise. BRAQUE, a hyperplexed proteomic tool, can extract, in a marker-agnostic fashion, granular information from markers which have a very low signal-to-noise ratio and therefore are not visualized by traditional visual rendering. By analyzing a public human lymph node dataset, we also found unpredicted staining results by validated antibodies, which highlight the need to upgrade the definition of antibody specificity in hyperplexed immunostaining. Spatially hyperplexed methods upgrade and supplant traditional image-based analysis of tissue immunostaining, beyond the human eye contribution.
{"title":"Seeing or believing in hyperplexed spatial proteomics via antibodies. New and old biases for an image-based technology","authors":"Maddalena M Bolognesi, Lorenzo Dall’Olio, Amy Maerten, Simone Borghesi, Gastone Castellani, Giorgio Cattoretti","doi":"10.1101/2024.08.02.606335","DOIUrl":"https://doi.org/10.1101/2024.08.02.606335","url":null,"abstract":"Hyperplexed in-situ targeted proteomics via antibody immunodetection (i.e. > 15 markers) is changing how we classify cells and tissues. Differently from other high-dimensional single-cell assays (flow cytometry, single cell RNA sequencing), the human eye is a necessary component in multiple procedural steps: image segmentation, signal thresholding, antibody validation and iconographic rendering. Established methods complement the human image evaluation, but may carry undisclosed biases in such a new context, therefore we re-evaluate all the steps in hyperplexed proteomics. We found that the human eye can discriminate less than 64 out of 256 gray levels and has limitations in discriminating luminance levels in conventional histology images. Furthermore, only images containing visible signals are selected and eye-guided digital thresholding separates signal from noise. BRAQUE, a hyperplexed proteomic tool, can extract, in a marker-agnostic fashion, granular information from markers which have a very low signal-to-noise ratio and therefore are not visualized by traditional visual rendering. By analyzing a public human lymph node dataset, we also found unpredicted staining results by validated antibodies, which highlight the need to upgrade the definition of antibody specificity in hyperplexed immunostaining. Spatially hyperplexed methods upgrade and supplant traditional image-based analysis of tissue immunostaining, beyond the human eye contribution.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945362","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 : 2024-08-05DOI: 10.1101/2024.08.01.606198
Ling Wei, Fred D Mast, John D Aitchison, Alexis Kaushansky
Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproetiomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.
{"title":"Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data","authors":"Ling Wei, Fred D Mast, John D Aitchison, Alexis Kaushansky","doi":"10.1101/2024.08.01.606198","DOIUrl":"https://doi.org/10.1101/2024.08.01.606198","url":null,"abstract":"Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproetiomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"169 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945360","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 : 2024-08-04DOI: 10.1101/2024.08.01.606107
Yao Li, Hui Xin, Zhexun Lian, Wei Zhang
Background: Estrogen significantly impacts women's health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce. Methods: An ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX+E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques. Results: The study successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension. Conclusion: This research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.
{"title":"Exploration of the Metabolomic Mechanisms of Postmenopausal Hypertension Induced by Low Estrogen State","authors":"Yao Li, Hui Xin, Zhexun Lian, Wei Zhang","doi":"10.1101/2024.08.01.606107","DOIUrl":"https://doi.org/10.1101/2024.08.01.606107","url":null,"abstract":"Background: Estrogen significantly impacts women's health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce.\u0000Methods: An ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX+E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques.\u0000Results: The study successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension.\u0000Conclusion: This research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945473","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 : 2024-08-01DOI: 10.1101/2024.07.31.605966
A. Samer Kadibalban, Axel Kunstner, Torsten Schroder, Christoph Kaleta, Julius Zauleck, Oliver Witt, Georgios Marinos
Type 2 diabetes presents a growing global health concern, with emerging evidence highlighting the pivotal role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in type 2 diabetes. Glucose levels, dietary intake, 16S sequences and metadata were estimated and collected for a cohort of 1,866 individuals. In addition, microbial community models, as well as ecological interactions were simulated for the gut microbiomes of the cohort participants. Our findings revealed a significant decrease in the fluxes of metabolites provided by the host to the microbiome through the diet in patients with type 2 diabetes, accompanied by an increase in within-community exchanges. Moreover, the diabetic microbial community shifts towards increased exploitative ecological interactions among its member species at the expense of collaborative interactions. The reduced butyrate flux from the community to the host and reduced tryptophan acquired by the microbiome from the host's diet further highlight the dysregulation in microbial-host interactions in diabetes. Additionally, microbiomes of type 2 diabetes patients exhibit enrichment in energy metabolism pathways, indicative of increased metabolic activity and antagonism. This study provides insights into the metabolic dynamics of the diabetic gut microbiome, shedding light on its increased autonomy and altered ecological interactions accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validations, such as butyrate, tryptophan, H2S, several nucleotides, amino acids, and B vitamins.
{"title":"Metabolic modelling reveals increased autonomy and antagonism in type 2 diabetic gut microbiota","authors":"A. Samer Kadibalban, Axel Kunstner, Torsten Schroder, Christoph Kaleta, Julius Zauleck, Oliver Witt, Georgios Marinos","doi":"10.1101/2024.07.31.605966","DOIUrl":"https://doi.org/10.1101/2024.07.31.605966","url":null,"abstract":"Type 2 diabetes presents a growing global health concern, with emerging evidence highlighting the pivotal role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in type 2 diabetes. Glucose levels, dietary intake, 16S sequences and metadata were estimated and collected for a cohort of 1,866 individuals. In addition, microbial community models, as well as ecological interactions were simulated for the gut microbiomes of the cohort participants. Our findings revealed a significant decrease in the fluxes of metabolites provided by the host to the microbiome through the diet in patients with type 2 diabetes, accompanied by an increase in within-community exchanges. Moreover, the diabetic microbial community shifts towards increased exploitative ecological interactions among its member species at the expense of collaborative interactions. The reduced butyrate flux from the community to the host and reduced tryptophan acquired by the microbiome from the host's diet further highlight the dysregulation in microbial-host interactions in diabetes. Additionally, microbiomes of type 2 diabetes patients exhibit enrichment in energy metabolism pathways, indicative of increased metabolic activity and antagonism. This study provides insights into the metabolic dynamics of the diabetic gut microbiome, shedding light on its increased autonomy and altered ecological interactions accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validations, such as butyrate, tryptophan, H2S, several nucleotides, amino acids, and B vitamins.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"187 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883904","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 : 2024-08-01DOI: 10.1101/2024.07.31.606029
Mackenzie Dalton, Emmanuel Asante-Asamani, James Greene
The dynamics of insulin and glucose are tightly regulated. The pancreatic islets of Langerhans contain both beta and alpha cells which produce insulin and glucagon, respectively. Insulin is the only hormone in the body that lowers blood glucose levels by acting like a key for glucose to enter cells. Without insulin, cells cannot utilize glucose, their primary source of energy. In contrast, glucagon functions as a hormone which elevates blood glucose levels by promoting the breakdown of glycogen in the liver. Maintaining blood glucose within a safe range is vital since both excessively high and low levels can be life-threatening (hyperglycemia and hypoglycemia, respectively), and these two hormones work together to achieve this balance. In this work we aim to underscore the significance of glucagon in the insulin-glucose regulatory system. We construct a three-compartment mechanistic model that includes insulin, glucose, and glucagon, which is then validated by fitting to publicly available from an intravenous glucose tolerance test (IVGTT). After model validation, we investigate how removing glucose feedback from insulin secretion, as seen in insulin-dependent diabetes, disrupts the regulation of glucose and glucagon. To do this, we simulate the model (a) when insulin secretion is reduced to mimic an insufficient dose of insulin, (b) when the peak of insulin action is delayed mimicking a dosing delay of insulin, and (c) when both occur simultaneously. Lastly, we test different half-lives of insulin to evaluate how an increased half-life of manufactured insulin may further disrupt the system. We find that when insulin secretion is decreased, glucagon still responds to high glucose levels by decreasing glucagon production. This suggests that in cases of type 2 diabetes, where glucagon secretion is elevated despite high levels of glucose, a lack of insulin response may not be the sole cause for glucagon dysfunction. We also find that delaying insulin secretion increases the risk of a hypoglycemic event through a suppression of glucagon production. Initially, the spike in glucose causes glucagon secretion to be reduced; this is then followed by the delay in insulin peak which then continues to suppress glucagon despite blood glucose levels falling, leading to a lack of response by glucagon and a subsequent hypoglycemic event. Furthermore, we find that a higher half-life of insulin causes it to remain longer in the blood stream, inhibiting glucagon's response to severely low glucose levels (glucose levels less than 3.9 mmol/L). This sheds light on why patients taking exogenous insulin, which has a longer half-life than endogenous insulin, may have difficulty recovering from hypoglycemic events. Hence, our model suggests that keeping the half-life of exogenous insulin below 10 minutes and administering it immediately after meals could help reduce the risk of hypoglycemic events in patients with type 1 or insulin dependent diabetes. Overall, we highligh
{"title":"Evaluating the Importance of Glucagon in the Insulin-Glucose Regulatory System: A Mechanistic Modeling Approach","authors":"Mackenzie Dalton, Emmanuel Asante-Asamani, James Greene","doi":"10.1101/2024.07.31.606029","DOIUrl":"https://doi.org/10.1101/2024.07.31.606029","url":null,"abstract":"The dynamics of insulin and glucose are tightly regulated. The pancreatic islets of Langerhans contain both beta and alpha cells which produce insulin and glucagon, respectively. Insulin is the only hormone in the body that lowers blood glucose levels by acting like a key for glucose to enter cells. Without insulin, cells cannot utilize glucose, their primary source of energy. In contrast, glucagon functions as a hormone which elevates blood glucose levels by promoting the breakdown of glycogen in the liver. Maintaining blood glucose within a safe range is vital since both excessively high and low levels can be life-threatening (hyperglycemia and hypoglycemia, respectively), and these two hormones work together to achieve this balance. In this work we aim to underscore the significance of glucagon in the insulin-glucose regulatory system. We construct a three-compartment mechanistic model that includes insulin, glucose, and glucagon, which is then validated by fitting to publicly available from an intravenous glucose tolerance test (IVGTT). After model validation, we investigate how removing glucose feedback from insulin secretion, as seen in insulin-dependent diabetes, disrupts the regulation of glucose and glucagon. To do this, we simulate the model (a) when insulin secretion is reduced to mimic an insufficient dose of insulin, (b) when the peak of insulin action is delayed mimicking a dosing delay of insulin, and (c) when both occur simultaneously. Lastly, we test different half-lives of insulin to evaluate how an increased half-life of manufactured insulin may further disrupt the system. We find that when insulin secretion is decreased, glucagon still responds to high glucose levels by decreasing glucagon production. This suggests that in cases of type 2 diabetes, where glucagon secretion is elevated despite high levels of glucose, a lack of insulin response may not be the sole cause for glucagon dysfunction. We also find that delaying insulin secretion increases the risk of a hypoglycemic event through a suppression of glucagon production. Initially, the spike in glucose causes glucagon secretion to be reduced; this is then followed by the delay in insulin peak which then continues to suppress glucagon despite blood glucose levels falling, leading to a lack of response by glucagon and a subsequent hypoglycemic event. Furthermore, we find that a higher half-life of insulin causes it to remain longer in the blood stream, inhibiting glucagon's response to severely low glucose levels (glucose levels less than 3.9 mmol/L). This sheds light on why patients taking exogenous insulin, which has a longer half-life than endogenous insulin, may have difficulty recovering from hypoglycemic events. Hence, our model suggests that keeping the half-life of exogenous insulin below 10 minutes and administering it immediately after meals could help reduce the risk of hypoglycemic events in patients with type 1 or insulin dependent diabetes. Overall, we highligh","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883905","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 : 2024-07-31DOI: 10.1101/2024.07.31.605988
Layla Hosseini-Gerami, Sara Masarone, Jordan Lane
This white paper details the research conducted by Ignota Labs using their advanced causal and explainable AI technology, SAFEPATH, to analyse the mechanisms of hepatotoxicity for two EGFR-TKI inhibitors, Erlotinib and Gefitinib, the latter having an as yet unknown mechanism of toxicity. The known mechanism of UGT1A1-mediated toxicity of Erlotinib was recovered, and a novel sphingolipid metabolism mechansim of toxicity of Gefitinib was hypothesised and subsequently experimentally validated. Crucially, we were also able to suggest the reason for the observed heterogeneous toxicity response to Gefitinib. This study exemplifies the potential of integrating AI tools with comprehensive datasets to improve drug safety and patient management.
{"title":"Using SAFEPATH to Uncover a Novel Mechanism of Hepatotoxicity for Gefitinib","authors":"Layla Hosseini-Gerami, Sara Masarone, Jordan Lane","doi":"10.1101/2024.07.31.605988","DOIUrl":"https://doi.org/10.1101/2024.07.31.605988","url":null,"abstract":"This white paper details the research conducted by Ignota Labs using their advanced causal and explainable AI technology, <strong><em>SAFEPATH</em></strong>, to analyse the mechanisms of hepatotoxicity for two EGFR-TKI inhibitors, Erlotinib and Gefitinib, the latter having an as yet unknown mechanism of toxicity. The known mechanism of UGT1A1-mediated toxicity of Erlotinib was recovered, and a novel sphingolipid metabolism mechansim of toxicity of Gefitinib was hypothesised and subsequently experimentally validated. Crucially, we were also able to suggest the reason for the observed heterogeneous toxicity response to Gefitinib. This study exemplifies the potential of integrating AI tools with comprehensive datasets to improve drug safety and patient management.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883906","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 : 2024-07-30DOI: 10.1101/2024.07.29.605545
Abraham A.J. Kerssemakers, Jayanth Krishnan, Kevin Rychel, Daniel Craig Zielinski, Bernhard Palsson, Suresh Sudarsan
The transcriptional regulatory network (TRN) in Yarrowia lipolytica coordinates its cellular processes, including the response to various stimuli. The TRN has been difficult to study due to its complex nature. In industrial-size fermenters, environments are often not homogenous, resulting in Yarrowia experiencing fluctuating conditions during a fermentation. Compared with homogenous laboratory conditions, these fluctuations result in altered cellular states and behaviours due to the action of the TRN. Here, a machine learning approach was deployed to modularize the transcriptome to enable meaningful description of its changing composition. To provide a sufficiently broad dataset, a wide range of relevant fermentation conditions (nutrient limitations, growth rates, pH values, oxygen availability and CO2 stresses) were run and samples obtained for RNA-Seq generation. We thus significantly increased the number of publicly available transcriptomic dataset on Y. lipolytica W29. In total, 23 independently modulated gene sets (termed iModulons) were identified of which 9 could be linked to corresponding regulons in S. cerevisiae. Strong responses were found in relation to oxygen limitation and elevated CO2 concentrations represented by (i) altered ribosomal protein synthesis, (ii) cell cycle disturbances, (iii) respiratory gene expression, and (iv) redox homeostasis. These results provide a fine-grained systems-level understanding of the Y. lipolytica TRN in response to industrially meaningful stresses, providing engineering targets to design more robust production strains. Moreover, this study provides a guide to perform similar work with poorly characterized single-cellular eukaryotic organisms.
{"title":"Deciphering the transcriptional regulatory network of Yarrowia lipolytica using machine learning","authors":"Abraham A.J. Kerssemakers, Jayanth Krishnan, Kevin Rychel, Daniel Craig Zielinski, Bernhard Palsson, Suresh Sudarsan","doi":"10.1101/2024.07.29.605545","DOIUrl":"https://doi.org/10.1101/2024.07.29.605545","url":null,"abstract":"The transcriptional regulatory network (TRN) in Yarrowia lipolytica coordinates its cellular processes, including the response to various stimuli. The TRN has been difficult to study due to its complex nature. In industrial-size fermenters, environments are often not homogenous, resulting in Yarrowia experiencing fluctuating conditions during a fermentation. Compared with homogenous laboratory conditions, these fluctuations result in altered cellular states and behaviours due to the action of the TRN. Here, a machine learning approach was deployed to modularize the transcriptome to enable meaningful description of its changing composition. To provide a sufficiently broad dataset, a wide range of relevant fermentation conditions\u0000(nutrient limitations, growth rates, pH values, oxygen availability and CO2 stresses) were run and samples obtained for RNA-Seq generation. We thus significantly increased the number of publicly available transcriptomic dataset on Y. lipolytica W29. In total, 23 independently modulated gene sets (termed iModulons) were identified of which 9 could be linked to corresponding regulons in S. cerevisiae. Strong responses were found in relation to oxygen limitation and elevated CO2 concentrations represented by (i) altered ribosomal protein synthesis, (ii) cell cycle disturbances, (iii) respiratory gene expression, and (iv) redox homeostasis. These results provide a fine-grained systems-level understanding of the Y. lipolytica TRN in response to industrially meaningful stresses, providing engineering targets to design more robust production strains. Moreover, this study provides a guide to perform similar work with poorly characterized single-cellular eukaryotic organisms.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"150 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866924","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 : 2024-07-30DOI: 10.1101/2024.07.29.605463
Zetao Jin, Xiaohua Lin, Daikun Ma, Richard G.J. Hodel, Liang Zhao, Chen Ren, Lei Duan, Chao Xu, Jun Wu, Binbin Liu
In contrast to the traditional Tree of Life (ToL) paradigm, the Web of Life (WoL) model provides a more nuanced and precise depiction of organismal phylogeny, particularly considering the prevalent incongruence observed among gene/species trees. The lack of a generalized pipeline for teasing apart potential evolutionary mechanisms-such as Incomplete Lineage Sorting (ILS), hybridization, introgression, polyploidization, and Whole-Genome Duplication-poses significant challenges to the delineation of the WoL. The pear genus Pyrus, characterized by extensive hybridization events, serves as an excellent model for investigating the WoL. This study introduces a novel Step-by-Step Exclusion (SSE) approach to deciphering the complexities inherent in the WoL. Our findings indicate: 1) ILS, rather than polyploidization, is identified as the primary driver behind the origin of Pyrus from the arid regions of the Himalayas-Central Asia; 2) the two subgenera of Pyrus have independent evolutionary trajectories, facilitated by the geographical barriers that arose via the uplift of the Tibetan Plateau and increased aridity in Central Asia; 3) ILS and hybridization have facilitated the diversification of Oriental pears, while hybridization alone has driven the reticulate evolution of Occidental pears; 4) the establishment of the Silk Road during the Han Dynasty acted as a conduit for genetic exchange between Occidental and Oriental pears. The novel SSE approach provides a universally applicable framework for investigating evolutionary mechanisms defining the WoL paradigm.
与传统的生命树（ToL）范式相比，生命网（WoL）模型对生物系统发育的描述更加细致和精确，特别是考虑到基因/物种树之间普遍存在的不一致性。由于缺乏一种通用的方法来区分潜在的进化机制，如不完全世系分选（ILS）、杂交、引入、多倍体化和全基因组复制等，这给 WoL 的划分带来了巨大的挑战。以广泛杂交事件为特征的梨属是研究 WoL 的绝佳模型。这项研究引入了一种新颖的分步排除法（SSE）来破解 WoL 固有的复杂性。我们的研究结果表明1）ILS，而非多倍体化，被认为是刺桐起源于喜马拉雅山-中亚干旱地区的主要驱动力；2）刺桐的两个亚属具有独立的进化轨迹，这得益于青藏高原隆起和中亚干旱加剧所造成的地理障碍；3）ILS 和杂交促进了东方梨的多样化，而单靠杂交则推动了西方梨的网状进化；4）汉代丝绸之路的建立为西方梨和东方梨之间的遗传交流提供了渠道。新颖的 SSE 方法为研究定义 WoL 范式的进化机制提供了一个普遍适用的框架。
{"title":"Unraveling the Web of Life: Incomplete lineage sorting and hybridization as primary mechanisms over polyploidization in the evolutionary dynamics of pear species","authors":"Zetao Jin, Xiaohua Lin, Daikun Ma, Richard G.J. Hodel, Liang Zhao, Chen Ren, Lei Duan, Chao Xu, Jun Wu, Binbin Liu","doi":"10.1101/2024.07.29.605463","DOIUrl":"https://doi.org/10.1101/2024.07.29.605463","url":null,"abstract":"In contrast to the traditional Tree of Life (ToL) paradigm, the Web of Life (WoL) model provides a more nuanced and precise depiction of organismal phylogeny, particularly considering the prevalent incongruence observed among gene/species trees. The lack of a generalized pipeline for teasing apart potential evolutionary mechanisms-such as Incomplete Lineage Sorting (ILS), hybridization, introgression, polyploidization, and Whole-Genome Duplication-poses significant challenges to the delineation of the WoL. The pear genus Pyrus, characterized by extensive hybridization events, serves as an excellent model for investigating the WoL. This study introduces a novel Step-by-Step Exclusion (SSE) approach to deciphering the complexities inherent in the WoL. Our findings indicate: 1) ILS, rather than polyploidization, is identified as the primary driver behind the origin of Pyrus from the arid regions of the Himalayas-Central Asia; 2) the two subgenera of Pyrus have independent evolutionary trajectories, facilitated by the geographical barriers that arose via the uplift of the Tibetan Plateau and increased aridity in Central Asia; 3) ILS and hybridization have facilitated the diversification of Oriental pears, while hybridization alone has driven the reticulate evolution of Occidental pears; 4) the establishment of the Silk Road during the Han Dynasty acted as a conduit for genetic exchange between Occidental and Oriental pears. The novel SSE approach provides a universally applicable framework for investigating evolutionary mechanisms defining the WoL paradigm.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866926","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 : 2024-07-30DOI: 10.1101/2024.07.29.605698
Andrew Ramirez, Brian T Orcutt-Jahns, Sean Pascoe, Armaan Abraham, Breanna Remigio, Nathaniel Thomas, Aaron Samuel Meyer
Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.
{"title":"Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2","authors":"Andrew Ramirez, Brian T Orcutt-Jahns, Sean Pascoe, Armaan Abraham, Breanna Remigio, Nathaniel Thomas, Aaron Samuel Meyer","doi":"10.1101/2024.07.29.605698","DOIUrl":"https://doi.org/10.1101/2024.07.29.605698","url":null,"abstract":"Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"207 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866925","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 : 2024-07-30DOI: 10.1101/2024.07.30.605767
Friday Gabriel Emunefe, Ifeanyichukwu Jeff Ugbene
Phylogenetic tree reconstruction relies on accurate estimation of evolutionary distances between sequences. However, the observed Hamming distance between sequences can be misleading due to saturation, where multiple substitutions at the same site obscure the true evolutionary history. The Jukes-Cantor correction method addresses this by accounting for multiple substitutions, providing a more accurate representation of evolutionary distance. This study investigates the application of the Jukes-Cantor correction to the Hamming distance of genetic sequences in a case study, highlighting its impact on phylogenetic tree reconstruction. Our results demonstrate that the Jukes-Cantor correction significantly improves the accuracy of phylogenetic inference, particularly for sequences with substantial evolutionary divergence. However, the model's reliance on simplifying assumptions, such as equal substitution rates and lack of base composition bias, limits its applicability to sequences with moderate levels of divergence. This study stands as a bedrock for further research into more complex models that can account for model violations and provide more accurate estimations of evolutionary distances for highly divergent sequences.
{"title":"Jukes-Cantor Correction for Phylogenetic Tree Reconstruction","authors":"Friday Gabriel Emunefe, Ifeanyichukwu Jeff Ugbene","doi":"10.1101/2024.07.30.605767","DOIUrl":"https://doi.org/10.1101/2024.07.30.605767","url":null,"abstract":"Phylogenetic tree reconstruction relies on accurate estimation of evolutionary distances between sequences. However, the observed Hamming distance between sequences can be misleading due to saturation, where multiple substitutions at the same site obscure the true evolutionary history. The Jukes-Cantor correction method addresses this by accounting for multiple substitutions, providing a more accurate representation of evolutionary distance. This study investigates the application of the Jukes-Cantor correction to the Hamming distance of genetic sequences in a case study, highlighting its impact on phylogenetic tree reconstruction. Our results demonstrate that the Jukes-Cantor correction significantly improves the accuracy of phylogenetic inference, particularly for sequences with substantial evolutionary divergence. However, the model's reliance on simplifying assumptions, such as equal substitution rates and lack of base composition bias, limits its applicability to sequences with moderate levels of divergence. This study stands as a bedrock for further research into more complex models that can account for model violations and provide more accurate estimations of evolutionary distances for highly divergent sequences.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"168 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866923","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：