Endogenous retroviruses (ERVs) are remnants of ancestral viral infections. Feline leukemia virus (FeLV) is an exogenous and endogenous retrovirus in domestic cats. It is classified into several subgroups (A, B, C, D, E, and T) based on viral receptor interference properties or receptor usage. ERV-derived molecules benefit animals, conferring resistance to infectious diseases. However, the soluble protein encoded by the defective envelope (env) gene of endogenous FeLV (enFeLV) functions as a co-factor in FeLV subgroup T infections. Thus, whether the gene emerged to facilitate viral infection is unclear. Based on the properties of ERV-derived molecules, we hypothesized that the defective env genes possess antiviral activity that would be advantageous to the host because FeLV subgroup B (FeLV-B), a recombinant virus derived from enFeLV env, is restricted to viral transmission among domestic cats. When soluble truncated Env proteins from enFeLV were tested for their inhibitory effects against enFeLV and FeLV-B, they inhibited viral infection. Notably, this antiviral machinery was extended to infection with the Gibbon ape leukemia virus, Koala retrovirus-A, and Hervey pteropid gammaretrovirus. Although these viruses used feline phosphate transporter1 (fePit1) or fePit1 and phosphate transporter2 (fePit2) as receptors, the inhibitory mechanism involved competitive receptor binding in a fePit1-dependent manner. The shift of receptor usage may have occurred to avoid the inhibitory effect. Overall, these findings highlight the possible emergence of soluble truncated Env proteins from enFeLV as a restriction factor against retroviral infection, and might help in the control of retroviral spread for host immunity and antiviral defense.
{"title":"FeLIX is a restriction factor for mammalian retrovirus infection","authors":"Didik Pramono, Dai Takeuchi, Masato Katsuki, Loai AbuEed, Dimas Abdillah, Tohru Kimura, Junna Kawasaki, Ariko Miyake, Kazuo Nishigaki","doi":"10.1101/2023.11.14.567074","DOIUrl":"https://doi.org/10.1101/2023.11.14.567074","url":null,"abstract":"Endogenous retroviruses (ERVs) are remnants of ancestral viral infections. Feline leukemia virus (FeLV) is an exogenous and endogenous retrovirus in domestic cats. It is classified into several subgroups (A, B, C, D, E, and T) based on viral receptor interference properties or receptor usage. ERV-derived molecules benefit animals, conferring resistance to infectious diseases. However, the soluble protein encoded by the defective envelope (env) gene of endogenous FeLV (enFeLV) functions as a co-factor in FeLV subgroup T infections. Thus, whether the gene emerged to facilitate viral infection is unclear. Based on the properties of ERV-derived molecules, we hypothesized that the defective env genes possess antiviral activity that would be advantageous to the host because FeLV subgroup B (FeLV-B), a recombinant virus derived from enFeLV env, is restricted to viral transmission among domestic cats. When soluble truncated Env proteins from enFeLV were tested for their inhibitory effects against enFeLV and FeLV-B, they inhibited viral infection. Notably, this antiviral machinery was extended to infection with the Gibbon ape leukemia virus, Koala retrovirus-A, and Hervey pteropid gammaretrovirus. Although these viruses used feline phosphate transporter1 (fePit1) or fePit1 and phosphate transporter2 (fePit2) as receptors, the inhibitory mechanism involved competitive receptor binding in a fePit1-dependent manner. The shift of receptor usage may have occurred to avoid the inhibitory effect. Overall, these findings highlight the possible emergence of soluble truncated Env proteins from enFeLV as a restriction factor against retroviral infection, and might help in the control of retroviral spread for host immunity and antiviral defense.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"48 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992197","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 : 2023-11-14DOI: 10.1101/2023.11.10.566577
Dominique Gordy, Theresa Swayne, Gregory J Berry, Tiffany A. Thomas, Krystalyn E Hudson, Elizabeth F Stone
BACKGROUND: Platelet transfusions are increasing with advances in medical care. Based on FDA criteria, platelet units are assessed by in vitro measures; however, it is not known how platelet processing and storage duration affect function in vivo. To address this, we developed a novel platelet transfusion model that meets FDA criteria adapted to mice, and transfused fresh and stored platelets are detected in clots in vivo. STUDY DESIGN AND METHODS: Platelet units stored in mouse plasma were prepared using a modified platelet rich plasma collection protocol. Characteristics of fresh and stored units, including pH, cell count, in vitro measures of activity, including activation and aggregation, and post-transfusion recovery (PTR), were determined. Lastly, a tail transection assay was conducted using mice transfused with fresh or stored units, and transfused platelets were identified by confocal imaging. RESULTS: Platelet units had acceptable platelet and white cell counts and were negative for bacterial contamination. Fresh and 1-day stored units had acceptable pH; the platelets were activatable by thrombin and ADP, aggregable with thrombin, had acceptable PTR, and were present in vivo in clots of recipients after tail transection. In contrast, 2-day stored units had clinically unacceptable quality. DISCUSSION: We developed mouse platelets for transfusion analogous to human platelet units using a modified platelet rich plasma collection protocol with maximum storage of 1 day for an "old" unit. This provides a powerful tool to test how process modifications and storage conditions affect transfused platelet function in vivo.
{"title":"Characterization of a Novel Mouse Platelet Transfusion Model","authors":"Dominique Gordy, Theresa Swayne, Gregory J Berry, Tiffany A. Thomas, Krystalyn E Hudson, Elizabeth F Stone","doi":"10.1101/2023.11.10.566577","DOIUrl":"https://doi.org/10.1101/2023.11.10.566577","url":null,"abstract":"BACKGROUND: Platelet transfusions are increasing with advances in medical care. Based on FDA criteria, platelet units are assessed by in vitro measures; however, it is not known how platelet processing and storage duration affect function in vivo. To address this, we developed a novel platelet transfusion model that meets FDA criteria adapted to mice, and transfused fresh and stored platelets are detected in clots in vivo. STUDY DESIGN AND METHODS: Platelet units stored in mouse plasma were prepared using a modified platelet rich plasma collection protocol. Characteristics of fresh and stored units, including pH, cell count, in vitro measures of activity, including activation and aggregation, and post-transfusion recovery (PTR), were determined. Lastly, a tail transection assay was conducted using mice transfused with fresh or stored units, and transfused platelets were identified by confocal imaging. RESULTS: Platelet units had acceptable platelet and white cell counts and were negative for bacterial contamination. Fresh and 1-day stored units had acceptable pH; the platelets were activatable by thrombin and ADP, aggregable with thrombin, had acceptable PTR, and were present in vivo in clots of recipients after tail transection. In contrast, 2-day stored units had clinically unacceptable quality. DISCUSSION: We developed mouse platelets for transfusion analogous to human platelet units using a modified platelet rich plasma collection protocol with maximum storage of 1 day for an \"old\" unit. This provides a powerful tool to test how process modifications and storage conditions affect transfused platelet function in vivo.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"44 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992318","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 : 2023-11-14DOI: 10.1101/2023.11.10.566445
Ludivine Bertonnier-Brouty, Jonas Andersson, Tuomas Kaprio, Jaana Hagstrom, Sara Bsharat, Olof Asplund, Gad Hatem, Caj Haglund, Hanna Seppanen, Rashmi B Prasad, Isabella Artner
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with limited treatment options, illustrating an urgent need to identify new drugable targets in PDACs. Using the similarities between tumor development and normal embryonic development, which is accompanied by rapid cell expansion, we identified embryonic signalling pathways that were reinitiated during tumor formation and expansion. Here, we report that the transcription factors E2F1 and E2F8 are potential key regulators in PDAC. E2F1 and E2F8 RNA expression is mainly localized in proliferating cells in the developing pancreas and in malignant ductal cells in PDAC. Silencing of E2F1 and E2F8 in PANC-1 pancreatic tumor cells inhibited cell proliferation and impaired cell spreading and migration. Moreover, loss of E2F1 also affected cell viability and apoptosis with E2F expression in PDAC tissues correlating with expression of apoptosis and mitosis pathway genes, suggesting that E2F factors promote cell cycle regulation and tumorigenesis in PDAC cells. In conclusion, our findings show that E2F1 and E2F8 transcription factors regulate cell proliferation, survival, and migration during pancreatic carcinogenesis.
{"title":"E2F transcription factors promote tumorigenicity in pancreatic ductal adenocarcinoma","authors":"Ludivine Bertonnier-Brouty, Jonas Andersson, Tuomas Kaprio, Jaana Hagstrom, Sara Bsharat, Olof Asplund, Gad Hatem, Caj Haglund, Hanna Seppanen, Rashmi B Prasad, Isabella Artner","doi":"10.1101/2023.11.10.566445","DOIUrl":"https://doi.org/10.1101/2023.11.10.566445","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with limited treatment options, illustrating an urgent need to identify new drugable targets in PDACs. Using the similarities between tumor development and normal embryonic development, which is accompanied by rapid cell expansion, we identified embryonic signalling pathways that were reinitiated during tumor formation and expansion. Here, we report that the transcription factors E2F1 and E2F8 are potential key regulators in PDAC. E2F1 and E2F8 RNA expression is mainly localized in proliferating cells in the developing pancreas and in malignant ductal cells in PDAC. Silencing of E2F1 and E2F8 in PANC-1 pancreatic tumor cells inhibited cell proliferation and impaired cell spreading and migration. Moreover, loss of E2F1 also affected cell viability and apoptosis with E2F expression in PDAC tissues correlating with expression of apoptosis and mitosis pathway genes, suggesting that E2F factors promote cell cycle regulation and tumorigenesis in PDAC cells. In conclusion, our findings show that E2F1 and E2F8 transcription factors regulate cell proliferation, survival, and migration during pancreatic carcinogenesis.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993137","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 : 2023-11-13DOI: 10.1101/2023.11.09.566358
Uchechukwu C Ogbodo, Ojochenemi A Enejoh, Chinelo H Okonkwo, Pranavathiyani Gnanasekar, Pauline W Gachanja, Shamim Osata, Halimat C Atanda, Emmanuel A Iwuchukwu, Ikechukwu Achilonu, Olaitan I Awe
Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Four hit compounds namely spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.
{"title":"Computational Identification of Potential Inhibitors Targeting cdk1 in Colorectal Cancer","authors":"Uchechukwu C Ogbodo, Ojochenemi A Enejoh, Chinelo H Okonkwo, Pranavathiyani Gnanasekar, Pauline W Gachanja, Shamim Osata, Halimat C Atanda, Emmanuel A Iwuchukwu, Ikechukwu Achilonu, Olaitan I Awe","doi":"10.1101/2023.11.09.566358","DOIUrl":"https://doi.org/10.1101/2023.11.09.566358","url":null,"abstract":"Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Four hit compounds namely spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"35 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281456","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 : 2023-11-13DOI: 10.1101/2023.11.13.566882
Roberto Alonso-Matilla, Alice Lam, Teemu P Miettinen
Cytokinesis is the process where the mother cell's cytoplasm separates into daughter cells. This is driven by an actomyosin contractile ring that produces cortical contractility and drives cleavage furrow ingression, resulting in the formation of a thin intercellular bridge. While cytoskeletal reorganization during cytokinesis has been extensively studied, little is known about the spatiotemporal dynamics of the plasma membrane. Here, we image and model plasma membrane lipid and protein dynamics on the cell surface during leukemia cell cytokinesis. We reveal an extensive accumulation and folding of plasma membrane at the cleavage furrow and the intercellular bridge, accompanied by a depletion and unfolding of plasma membrane at the cell poles. These membrane dynamics are caused by two actomyosin-driven biophysical mechanisms: the radial constriction of the cleavage furrow causes local compression of the apparent cell surface area and accumulation of the plasma membrane at the furrow, while actomyosin cortical flows drag the plasma membrane towards the cell division plane as the furrow ingresses. The magnitude of these effects depends on the plasma membrane fluidity and cortex adhesion. Overall, our work reveals cell intrinsic mechanical regulation of plasma membrane accumulation at the cleavage furrow that generates localized membrane tension differences across the cytokinetic cell. This may locally alter endocytosis, exocytosis and mechanotransduction, while also serving as a self-protecting mechanism against cytokinesis failures that arise from high membrane tension at the intercellular bridge.
{"title":"Cell intrinsic mechanical regulation of plasma membrane accumulation in the cytokinetic furrow","authors":"Roberto Alonso-Matilla, Alice Lam, Teemu P Miettinen","doi":"10.1101/2023.11.13.566882","DOIUrl":"https://doi.org/10.1101/2023.11.13.566882","url":null,"abstract":"Cytokinesis is the process where the mother cell's cytoplasm separates into daughter cells. This is driven by an actomyosin contractile ring that produces cortical contractility and drives cleavage furrow ingression, resulting in the formation of a thin intercellular bridge. While cytoskeletal reorganization during cytokinesis has been extensively studied, little is known about the spatiotemporal dynamics of the plasma membrane. Here, we image and model plasma membrane lipid and protein dynamics on the cell surface during leukemia cell cytokinesis. We reveal an extensive accumulation and folding of plasma membrane at the cleavage furrow and the intercellular bridge, accompanied by a depletion and unfolding of plasma membrane at the cell poles. These membrane dynamics are caused by two actomyosin-driven biophysical mechanisms: the radial constriction of the cleavage furrow causes local compression of the apparent cell surface area and accumulation of the plasma membrane at the furrow, while actomyosin cortical flows drag the plasma membrane towards the cell division plane as the furrow ingresses. The magnitude of these effects depends on the plasma membrane fluidity and cortex adhesion. Overall, our work reveals cell intrinsic mechanical regulation of plasma membrane accumulation at the cleavage furrow that generates localized membrane tension differences across the cytokinetic cell. This may locally alter endocytosis, exocytosis and mechanotransduction, while also serving as a self-protecting mechanism against cytokinesis failures that arise from high membrane tension at the intercellular bridge.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"35 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281457","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 : 2023-11-13DOI: 10.1101/2023.11.09.566470
Timothy A. Daugird, Yu Shi, Katie L. Holland, Hosein Rostamian, Zhe Liu, Luke D. Lavis, Joseph Rodriguez, Brian D. Strahl, Wesley R. Legant
In the nucleus, biological processes are driven by proteins that diffuse through and bind to a meshwork of nucleic acid polymers. To better understand this interplay, we developed an imaging platform to simultaneously visualize single protein dynamics together with the local chromatin environment in live cells. Together with super-resolution imaging, new fluorescent probes, and biophysical modeling, we demonstrated that nucleosomes display differential diffusion and packing arrangements as chromatin density increases whereas the viscoelastic properties and accessibility of the interchromatin space remain constant. Perturbing nuclear functions impacted nucleosome diffusive properties in a manner that was dependent on local chromatin density and supportive of a model wherein transcription locally stabilizes nucleosomes while simultaneously allowing for the free exchange of nuclear proteins. Our results reveal that nuclear heterogeneity arises from both active and passive process and highlights the need to account for different organizational principals when modeling different chromatin environments.
{"title":"Correlative single molecule lattice light sheet imaging reveals the dynamic relationship between nucleosomes and the local chromatin environment","authors":"Timothy A. Daugird, Yu Shi, Katie L. Holland, Hosein Rostamian, Zhe Liu, Luke D. Lavis, Joseph Rodriguez, Brian D. Strahl, Wesley R. Legant","doi":"10.1101/2023.11.09.566470","DOIUrl":"https://doi.org/10.1101/2023.11.09.566470","url":null,"abstract":"In the nucleus, biological processes are driven by proteins that diffuse through and bind to a meshwork of nucleic acid polymers. To better understand this interplay, we developed an imaging platform to simultaneously visualize single protein dynamics together with the local chromatin environment in live cells. Together with super-resolution imaging, new fluorescent probes, and biophysical modeling, we demonstrated that nucleosomes display differential diffusion and packing arrangements as chromatin density increases whereas the viscoelastic properties and accessibility of the interchromatin space remain constant. Perturbing nuclear functions impacted nucleosome diffusive properties in a manner that was dependent on local chromatin density and supportive of a model wherein transcription locally stabilizes nucleosomes while simultaneously allowing for the free exchange of nuclear proteins. Our results reveal that nuclear heterogeneity arises from both active and passive process and highlights the need to account for different organizational principals when modeling different chromatin environments.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"36 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281609","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 : 2023-11-13DOI: 10.1101/2023.11.09.566395
Huey Lim Jang
Cyanobacterial algae blooms have proven to suppress diversity and abundance of other organisms while previous research shows the direct correlation between the growth of cyanobacteria and increasing global temperatures. Freshwater temperatures in Jeju island are most prone to climate change within the Korean peninsula, but research on Harmful Algae Blooms (HABs) in these environments has been scarcely conducted. The purpose of this study is to predict the cell numbers of the four HAB species in Jeju island's four water supply sources in 2050 and 2100. Using the water quality data across the last 24 years, Scikit-learn GBM was developed to predict cell numbers of HAB based on four variables determined through multiple linear regression: temperature, pH, EC, and DO. Meanwhile, XGBoost was designed to predict four different levels of HAB bloom warnings. Future freshwater temperature was obtained through the linear relationship model between air and freshwater temperature. The performances of the Scikit-learn GBM on the cell numbers of each species were as follows (measured by MAE and R2): Microcystis (132.313; 0.857), Anabaena (36.567; 0.035), Oscillatoria (24.213; 0.672), and Apahnizomenon (65.716; 0.506). This model predicted that Oscillatoria would increase by 31.04% until 2100 and the total cell number of the four algeas would increase 376,414/ml until 2050 and reach 393,873/ml in 2100 (247.088; 0.617). The XGboost model predicted a 17% increase in the 'Warning' level of the Algae Alert System until 2100. The increase in HABs will ultimately lead to agricultural setbacks throughout Jeju; algae blooms in dams will produce neurotoxins and hapatotoxins, limiting the usage of agricultural water. Immediate solutions are required to suppress the growth rate of algae cells brought by global climate change in Jeju freshwaters.
{"title":"Machine Learning Approaches Reveal Future Harmful Algae Blooms in Jeju, Korea","authors":"Huey Lim Jang","doi":"10.1101/2023.11.09.566395","DOIUrl":"https://doi.org/10.1101/2023.11.09.566395","url":null,"abstract":"Cyanobacterial algae blooms have proven to suppress diversity and abundance of other organisms while previous research shows the direct correlation between the growth of cyanobacteria and increasing global temperatures. Freshwater temperatures in Jeju island are most prone to climate change within the Korean peninsula, but research on Harmful Algae Blooms (HABs) in these environments has been scarcely conducted. The purpose of this study is to predict the cell numbers of the four HAB species in Jeju island's four water supply sources in 2050 and 2100. Using the water quality data across the last 24 years, Scikit-learn GBM was developed to predict cell numbers of HAB based on four variables determined through multiple linear regression: temperature, pH, EC, and DO. Meanwhile, XGBoost was designed to predict four different levels of HAB bloom warnings. Future freshwater temperature was obtained through the linear relationship model between air and freshwater temperature. The performances of the Scikit-learn GBM on the cell numbers of each species were as follows (measured by MAE and R2): Microcystis (132.313; 0.857), Anabaena (36.567; 0.035), Oscillatoria (24.213; 0.672), and Apahnizomenon (65.716; 0.506). This model predicted that Oscillatoria would increase by 31.04% until 2100 and the total cell number of the four algeas would increase 376,414/ml until 2050 and reach 393,873/ml in 2100 (247.088; 0.617). The XGboost model predicted a 17% increase in the 'Warning' level of the Algae Alert System until 2100. The increase in HABs will ultimately lead to agricultural setbacks throughout Jeju; algae blooms in dams will produce neurotoxins and hapatotoxins, limiting the usage of agricultural water. Immediate solutions are required to suppress the growth rate of algae cells brought by global climate change in Jeju freshwaters.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"35 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281620","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 : 2023-11-13DOI: 10.1101/2023.11.08.566196
Ben Griffin, Christine Ahrends, Fidel Alfaro-Almagro, Mark Woolrich, Stephen Smith, Diego Vidaurre
Beyond structural and time-averaged functional connectivity brain measures, the way brain activity dynamically unfolds can add important information when investigating individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, there are two potential sources of variation in the models' estimation which will in turn affect the predictions: first, in certain cases, the estimation variability due to different initialisations or choice of inference method; and second, the variability induced by the choice of the model hyperparameters that determine the complexity of the model. Rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. We propose stacking, a prediction-driven approach for model selection, to leverage this variability. Specifically, we combine predictions from multiple models of brain dynamics to generate predictions that are accurate and robust across multiple cognitive traits. We demonstrate the approach using the Hidden Markov Model, a probabilistic generative model of brain network dynamics. We show that stacking can significantly improve the prediction of subject-specific phenotypes, which is crucial for the clinical translation of findings.
{"title":"Stacking models of brain dynamics improves prediction of subject traits in fMRI","authors":"Ben Griffin, Christine Ahrends, Fidel Alfaro-Almagro, Mark Woolrich, Stephen Smith, Diego Vidaurre","doi":"10.1101/2023.11.08.566196","DOIUrl":"https://doi.org/10.1101/2023.11.08.566196","url":null,"abstract":"Beyond structural and time-averaged functional connectivity brain measures, the way brain activity dynamically unfolds can add important information when investigating individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, there are two potential sources of variation in the models' estimation which will in turn affect the predictions: first, in certain cases, the estimation variability due to different initialisations or choice of inference method; and second, the variability induced by the choice of the model hyperparameters that determine the complexity of the model. Rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. We propose stacking, a prediction-driven approach for model selection, to leverage this variability. Specifically, we combine predictions from multiple models of brain dynamics to generate predictions that are accurate and robust across multiple cognitive traits. We demonstrate the approach using the Hidden Markov Model, a probabilistic generative model of brain network dynamics. We show that stacking can significantly improve the prediction of subject-specific phenotypes, which is crucial for the clinical translation of findings.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"50 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281642","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 : 2023-11-13DOI: 10.1101/2023.11.09.566384
Christopher D. Klocke, Amy Moran, Andrew Adey, Shannon McWeeney, Guanming Wu
While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.
{"title":"Identification of Cellular Interactions in the Tumor Immune Microenvironment Underlying CD8 T Cell Exhaustion","authors":"Christopher D. Klocke, Amy Moran, Andrew Adey, Shannon McWeeney, Guanming Wu","doi":"10.1101/2023.11.09.566384","DOIUrl":"https://doi.org/10.1101/2023.11.09.566384","url":null,"abstract":"While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"37 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281737","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 : 2023-11-13DOI: 10.1101/2023.11.08.566216
Quentin PETITJEAN, Silene LARTIGUE, Melina COINTE, Nicolas RIS, vincent calcagno
Animal movement and behavior are critical to understanding ecological and evolutionary processes. Recent years have witnessed an increase in methodological and technological innovations in video-tracking solutions for phenotyping animal behavior. Although these advances enable the collection of high-resolution data describing the movement of multiple individuals, analyzing and interpreting them remains challenging due to their complexity, heterogeneity, and noisiness. Here, we introduce MoveR, an R package for importing, filtering, visualizing, and analyzing data from common video-tracking solutions. MoveR includes flexible tools for polishing data, removing tracking artifacts, subsetting and plotting individual paths, and computing different movement and behavior metrics.
{"title":"MoveR: an R package for easy processing and analysis of animal video-tracking data","authors":"Quentin PETITJEAN, Silene LARTIGUE, Melina COINTE, Nicolas RIS, vincent calcagno","doi":"10.1101/2023.11.08.566216","DOIUrl":"https://doi.org/10.1101/2023.11.08.566216","url":null,"abstract":"Animal movement and behavior are critical to understanding ecological and evolutionary processes. Recent years have witnessed an increase in methodological and technological innovations in video-tracking solutions for phenotyping animal behavior. Although these advances enable the collection of high-resolution data describing the movement of multiple individuals, analyzing and interpreting them remains challenging due to their complexity, heterogeneity, and noisiness. Here, we introduce MoveR, an R package for importing, filtering, visualizing, and analyzing data from common video-tracking solutions. MoveR includes flexible tools for polishing data, removing tracking artifacts, subsetting and plotting individual paths, and computing different movement and behavior metrics.","PeriodicalId":486943,"journal":{"name":"bioRxiv (Cold Spring Harbor Laboratory)","volume":"39 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281889","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}
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