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2. Biclustering random matrix partitions with an application to classification of forensic body fluids

Author

Wu, Chieh-Hsi, Roeder, Amy D., and Nicholls, Geoff K.

Subjects
Statistics - Methodology, Statistics - Applications, 62F15 (Primary) 62P10 (Secondary)
Abstract

Classification of unlabeled data is usually achieved by supervised learning from labeled samples. Although there exist many sophisticated supervised machine learning methods that can predict the missing labels with a high level of accuracy, they often lack the required transparency in situations where it is important to provide interpretable results and meaningful measures of confidence. Body fluid classification of forensic casework data is the case in point. We develop a new Biclustering Dirichlet Process for Class-assignment with Random Matrices (BDP-CaRMa), with a three-level hierarchy of clustering, and a model-based approach to classification that adapts to block structure in the data matrix. As the class labels of some observations are missing, the number of rows in the data matrix for each class is unknown. BDP-CaRMa handles this and extends existing biclustering methods by simultaneously biclustering multiple matrices each having a randomly variable number of rows. We demonstrate our method by applying it to the motivating problem, which is the classification of body fluids based on mRNA profiles taken from crime scenes. The analyses of casework-like data show that our method is interpretable and produces well-calibrated posterior probabilities. Our model can be more generally applied to other types of data with a similar structure to the forensic data., Comment: 23 pages and 4 figures (paper); 22 pages and 6 figures (supplement); revision adds model comparisons

Published
2023

3. Valid belief updates for prequentially additive loss functions arising in Semi-Modular Inference

Author

Nicholls, Geoff K., Lee, Jeong Eun, Wu, Chieh-Hsi, and Carmona, Chris U.

Subjects
Statistics - Methodology, 62C10, 62C10 (Primary) 62F35, 65C05 (Secondary)
Abstract

Model-based Bayesian evidence combination leads to models with multiple parameteric modules. In this setting the effects of model misspecification in one of the modules may in some cases be ameliorated by cutting the flow of information from the misspecified module. Semi-Modular Inference (SMI) is a framework allowing partial cuts which modulate but do not completely cut the flow of information between modules. We show that SMI is part of a family of inference procedures which implement partial cuts. It has been shown that additive losses determine an optimal, valid and order-coherent belief update. The losses which arise in Cut models and SMI are not additive. However, like the prequential score function, they have a kind of prequential additivity which we define. We show that prequential additivity is sufficient to determine the optimal valid and order-coherent belief update and that this belief update coincides with the belief update in each of our SMI schemes., Comment: 39 pages including supplement, 6 figures

Published
2022

4. Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors

Author

Blake, Andrew, Buffa, Francesca, Domingo, Enric, Higgins, Geoffrey, Holmes, Christopher, Koelzer, Viktor, Leedham, Simon, Maughan, Timothy, McKenna, Gillies, Robineau, James, Tomlinson, Ian, Youdell, Michael, Quirke, Philip, Richman, Susan, Sebag-Montefiore, David, Seymour, Matthew, West, Nicholas, Dunne, Philip, Kennedy, Richard, Lawler, Mark, Redmond, Keara, Salto-Tellez, Manuel, Campbell, Peter, Chatzipli, Aikaterini, Hardy, Claire, McDermott, Ultan, Bach, Simon, Beggs, Andrew, Cazier, Jean-Baptiste, Middleton, Gary, Morton, Dion, Whalley, Celina, Brown, Louise, Kaplan, Richard, Murray, Graeme, Wilson, Richard, Adams, Richard, Sullivan, Richard, Samuel, Leslie, Harkin, Paul, Walker, Steven, Hill, Jim, Wu, Chieh-Hsi, Horgan, Dennis, Rathee, Sanjay, Gollins, Simon, Begum, Rubina, Quirke, Phil, Barberis, Alessandro, Hassanieh, Sylvana, Mahmood, Umair, Buffa, Francesca M., and Maughan, Timothy S.

Published
2024
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7. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil

Author

Faria, Nuno R, Mellan, Thomas A, Whittaker, Charles, Claro, Ingra M, Candido, Darlan da S, Mishra, Swapnil, Crispim, Myuki AE, Sales, Flavia CS, Hawryluk, Iwona, McCrone, John T, Hulswit, Ruben JG, Franco, Lucas AM, Ramundo, Mariana S, de Jesus, Jaqueline G, Andrade, Pamela S, Coletti, Thais M, Ferreira, Giulia M, Silva, Camila AM, Manuli, Erika R, Pereira, Rafael HM, Peixoto, Pedro S, Kraemer, Moritz UG, Gaburo, Nelson, Camilo, Cecilia da C, Hoeltgebaum, Henrique, Souza, William M, Rocha, Esmenia C, de Souza, Leandro M, de Pinho, Mariana C, Araujo, Leonardo JT, Malta, Frederico SV, de Lima, Aline B, Silva, Joice do P, Zauli, Danielle AG, Ferreira, Alessandro C de S, Schnekenberg, Ricardo P, Laydon, Daniel J, Walker, Patrick GT, Schlüter, Hannah M, Dos Santos, Ana LP, Vidal, Maria S, Del Caro, Valentina S, Filho, Rosinaldo MF, Dos Santos, Helem M, Aguiar, Renato S, Proença-Modena, José L, Nelson, Bruce, Hay, James A, Monod, Mélodie, Miscouridou, Xenia, Coupland, Helen, Sonabend, Raphael, Vollmer, Michaela, Gandy, Axel, Prete, Carlos A, Nascimento, Vitor H, Suchard, Marc A, Bowden, Thomas A, Pond, Sergei LK, Wu, Chieh-Hsi, Ratmann, Oliver, Ferguson, Neil M, Dye, Christopher, Loman, Nick J, Lemey, Philippe, Rambaut, Andrew, Fraiji, Nelson A, Carvalho, Maria do PSS, Pybus, Oliver G, Flaxman, Seth, Bhatt, Samir, and Sabino, Ester C

Subjects
Human Genome, Immunization, Pneumonia & Influenza, Lung, Prevention, Genetics, Vaccine Related, Biotechnology, Pneumonia, Emerging Infectious Diseases, Biodefense, Infectious Diseases, Infection, Good Health and Well Being, Angiotensin-Converting Enzyme 2, Brazil, COVID-19, Communicable Diseases, Emerging, Epidemiological Monitoring, Genome, Viral, Genomics, Humans, Models, Theoretical, Molecular Epidemiology, Mutation, Protein Binding, SARS-CoV-2, Spike Glycoprotein, Coronavirus, Viral Load, General Science & Technology
Abstract

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

Published
2021

8. Epidemiological data from the COVID-19 outbreak, real-time case information.

Author

Xu, Bo, Gutierrez, Bernardo, Mekaru, Sumiko, Sewalk, Kara, Goodwin, Lauren, Loskill, Alyssa, Cohn, Emily L, Hswen, Yulin, Hill, Sarah C, Cobo, Maria M, Zarebski, Alexander E, Li, Sabrina, Wu, Chieh-Hsi, Hulland, Erin, Morgan, Julia D, Wang, Lin, O'Brien, Katelynn, Scarpino, Samuel V, Brownstein, John S, Pybus, Oliver G, Pigott, David M, and Kraemer, Moritz UG

Subjects
Humans, Pneumonia, Viral, Coronavirus Infections, Public Health, Geography, China, Epidemics, Pandemics, Geographic Mapping, Betacoronavirus, Pneumonia, Viral
Abstract

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Published
2020

9. Supervised variable selection in randomised controlled trials prior to exploration of treatment effect heterogeneity: an example from severe malaria

Author

Wu, Chieh-Hsi and Holmes, Chris C.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Exploration of treatment effect heterogeneity (TEH) is an increasingly important aspect of modern statistical analysis for stratified medicine in randomised controlled trials (RCTs) as we start to gather more information on trial participants and wish to maximise the opportunities for learning from data. However, the analyst should refrain from including a large number of variables in a treatment interaction discovery stage. Because doing so can significantly dilute the power to detect any true outcome-predictive interactions between treatments and covariates. Current guidance is limited and mainly relies on the use of unsupervised learning methods, such as hierarchical clustering or principal components analysis, to reduce the dimension of the variable space prior to interaction tests. In this article we show that outcome-driven dimension reduction, i.e. supervised variable selection, can maintain power without inflating the type-I error or false-positive rate. We provide theoretical and applied results to support our approach. The applied results are obtained from illustrating our framework on the dataset from an RCT in severe malaria. We also pay particular attention to the internal risk model approach for TEH discovery, which we show is a particular case of our method and we point to improvements over current implementation.

Published
2019

10. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

Author

Bouckaert, Remco, Vaughan, Timothy G, Barido-Sottani, Joëlle, Duchêne, Sebastián, Fourment, Mathieu, Gavryushkina, Alexandra, Heled, Joseph, Jones, Graham, Kühnert, Denise, De Maio, Nicola, Matschiner, Michael, Mendes, Fábio K, Müller, Nicola F, Ogilvie, Huw A, du Plessis, Louis, Popinga, Alex, Rambaut, Andrew, Rasmussen, David, Siveroni, Igor, Suchard, Marc A, Wu, Chieh-Hsi, Xie, Dong, Zhang, Chi, Stadler, Tanja, and Drummond, Alexei J

Subjects
Animals, Humans, Monte Carlo Method, Bayes Theorem, Markov Chains, Computational Biology, Evolution, Molecular, Phylogeny, Models, Genetic, Computer Simulation, Software, Biological Evolution, Evolution, Molecular, Models, Genetic, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.

Published
2019

11. SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent

Author

De Maio, Nicola, Wu, Chieh-Hsi, and Wilson, Daniel J

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Exploiting pathogen genomes to reconstruct transmission represents a powerful tool in the fight against infectious disease. However, their interpretation rests on a number of simplifying assumptions that regularly ignore important complexities of real data, in particular within-host evolution and non-sampled patients. Here we propose a new approach to transmission inference called SCOTTI (Structured COalescent Transmission Tree Inference). This method is based on a statistical framework that models each host as a distinct population, and transmissions between hosts as migration events. Our computationally efficient implementation of this model enables the inference of host-to-host transmission while accommodating within-host evolution and non-sampled hosts. SCOTTI is distributed as an open source package for the phylogenetic software BEAST2. We show that SCOTTI can generally infer transmission events even in the presence of considerable within-host variation, can account for the uncertainty associated with the possible presence of non-sampled hosts, and can efficiently use data from multiple samples of the same host, although there is some reduction in accuracy when samples are collected very close to the infection time. We illustrate the features of our approach by investigating transmission from genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV) veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali neonatal unit. Transmission histories inferred with SCOTTI will be important in devising effective measures to prevent and halt transmission.

Published
2016
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14. Genomic and epidemiological characterisation of a dengue virus outbreak among blood donors in Brazil.

Author

Faria, Nuno, da Costa, Antonio, Lourenço, José, Loureiro, Paula, Lopes, Maria, Ribeiro, Roberto, Alencar, Cecilia, Kraemer, Moritz, Villabona-Arenas, Christian, Wu, Chieh-Hsi, Thézé, Julien, Khan, Kamran, Brent, Shannon, Romano, Camila, Delwart, Eric, Custer, Brian, Busch, Michael, Pybus, Oliver, and Sabino, Ester

Subjects
Blood Donors, Brazil, Dengue, Dengue Virus, Disease Outbreaks, Disease Transmission, Infectious, Genotype, Humans, Molecular Epidemiology, Phylogeny, RNA, Viral, Sequence Analysis, DNA, Serogroup
Abstract

Outbreaks caused by Dengue, Zika and Chikungunya viruses can spread rapidly in immunologically naïve populations. By analysing 92 newly generated viral genome sequences from blood donors and recipients, we assess the dynamics of dengue virus serotype 4 during the 2012 outbreak in Rio de Janeiro. Phylogenetic analysis indicates that the outbreak was caused by genotype II, although two isolates of genotype I were also detected for the first time in Rio de Janeiro. Evolutionary analysis and modelling estimates are congruent, indicating a reproduction number above 1 between January and June, and at least two thirds of infections being unnoticed. Modelling analysis suggests that viral transmission started in early January, which is consistent with multiple introductions, most likely from the northern states of Brazil, and with an increase in within-country air travel to Rio de Janeiro. The combination of genetic and epidemiological data from blood donor banks may be useful to anticipate epidemic spread of arboviruses.

Published
2017

15. Identifying lineage effects when controlling for population structure improves power in bacterial association studies

Author

Earle, Sarah G, Wu, Chieh-Hsi, Charlesworth, Jane, Stoesser, Nicole, Gordon, N Claire, Walker, Timothy M, Spencer, Chris C A, Iqbal, Zamin, Clifton, David A, Hopkins, Katie L, Woodford, Neil, Smith, E Grace, Ismail, Nazir, Llewelyn, Martin J, Peto, Tim E, Crook, Derrick W, McVean, Gil, Walker, A Sarah, and Wilson, Daniel J

Subjects
Quantitative Biology - Genomics
Abstract

Bacteria pose unique challenges for genome-wide association studies (GWAS) because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome. While methods developed for human studies can correct for strain structure, this risks considerable loss- of-power because genetic differences between strains often contribute substantial phenotypic variability. Here we propose a new method that captures lineage-level associations even when locus-specific associations cannot be fine-mapped. We demonstrate its ability to detect genes and genetic variants underlying resistance to 17 antimicrobials in 3144 isolates from four taxonomically diverse clonal and recombining bacteria: Mycobacterium tuberculosis, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae. Strong selection, recombination and penetrance confer high power to recover known antimicrobial resistance mechanisms, and reveal a candidate association between the outer membrane porin nmpC and cefazolin resistance in E. coli. Hence our method pinpoints locus-specific effects where possible, and boosts power by detecting lineage-level differences when fine-mapping is intractable.

Published
2015

16. New Routes to Phylogeography

Author

De Maio, Nicola, Wu, Chieh-Hsi, O'Reilly, Kathleen M, and Wilson, Daniel

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Phylogeographic methods aim to infer migration trends and the history of sampled lineages from genetic data. Applications of phylogeography are broad, and in the context of pathogens include the reconstruction of transmission histories and the origin and emergence of outbreaks. Phylogeographic inference based on bottom-up population genetics models is computationally expensive, and as a result faster alternatives based on the evolution of discrete traits have become popular. In this paper, we show that inference of migration rates and root locations based on discrete trait models is extremely unreliable and sensitive to biased sampling. To address this problem, we introduce BASTA (BAyesian STructured coalescent Approximation), a new approach implemented in BEAST2 that combines the accuracy of methods based on the structured coalescent with the computational efficiency required to handle more than just few populations. We illustrate the potentially severe implications of poor model choice for phylogeographic analyses by investigating the zoonotic transmission of Ebola virus. Whereas the structured coalescent analysis correctly infers that successive human Ebola outbreaks have been seeded by a large unsampled non-human reservoir population, the discrete trait analysis implausibly concludes that undetected human-to-human transmission has allowed the virus to persist over the past four decades. As genomics takes on an increasingly prominent role informing the control and prevention of infectious diseases, it will be vital that phylogeographic inference provides robust insights into transmission history.

Published
2015

21. Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil

Author

de Souza, William Marciel, Buss, Lewis Fletcher, Candido, Darlan da Silva, Carrera, Jean-Paul, Li, Sabrina, Zarebski, Alexander E., Pereira, Rafael Henrique Moraes, Prete, Jr, Carlos A., de Souza-Santos, Andreza Aruska, Parag, Kris V., Belotti, Maria Carolina T. D., Vincenti-Gonzalez, Maria F., Messina, Janey, da Silva Sales, Flavia Cristina, Andrade, Pamela dos Santos, Nascimento, Vítor Heloiz, Ghilardi, Fabio, Abade, Leandro, Gutierrez, Bernardo, Kraemer, Moritz U. G., Braga, Carlos K. V., Aguiar, Renato Santana, Alexander, Neal, Mayaud, Philippe, Brady, Oliver J., Marcilio, Izabel, Gouveia, Nelson, Li, Guangdi, Tami, Adriana, de Oliveira, Silvano Barbosa, Porto, Victor Bertollo Gomes, Ganem, Fabiana, de Almeida, Walquiria Aparecida Ferreira, Fantinato, Francieli Fontana Sutile Tardetti, Macário, Eduardo Marques, de Oliveira, Wanderson Kleber, Nogueira, Mauricio L., Pybus, Oliver G., Wu, Chieh-Hsi, Croda, Julio, Sabino, Ester C., and Faria, Nuno Rodrigues

Published
2020
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24. Emergence of the Asian lineage of Zika virus in Angola: an outbreak investigation

Author

Hill, Sarah C, Vasconcelos, Jocelyne, Neto, Zoraima, Jandondo, Domingos, Zé-Zé, Líbia, Aguiar, Renato Santana, Xavier, Joilson, Thézé, Julien, Mirandela, Marinela, Micolo Cândido, Ana Luísa, Vaz, Filipa, Sebastião, Cruz dos Santos, Wu, Chieh-Hsi, Kraemer, Moritz U G, Melo, Adriana, Schamber-Reis, Bruno L F, de Azevedo, Girlene S, Tanuri, Amilcar, Higa, Luiza M, Clemente, Carina, da Silva, Sara Pereira, da Silva Candido, Darlan, Claro, Ingra M, Quibuco, Domingos, Domingos, Cristóvão, Pocongo, Bárbara, Watts, Alexander G, Khan, Kamran, Alcantara, Luiz Carlos Junior, Sabino, Ester C, Lackritz, Eve, Pybus, Oliver G, Alves, Maria-João, Afonso, Joana, and Faria, Nuno R

Published
2019
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26. BEAST 2: a software platform for Bayesian evolutionary analysis.

Author

Bouckaert, Remco, Heled, Joseph, Kühnert, Denise, Vaughan, Tim, Wu, Chieh-Hsi, Xie, Dong, Suchard, Marc A, Rambaut, Andrew, and Drummond, Alexei J

Subjects
Bayes Theorem, Software, Programming Languages, Biological Evolution, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.

Published
2014

29. Bayesian selection of nucleotide substitution models and their site assignments.

Author

Wu, Chieh-Hsi, Suchard, Marc A, and Drummond, Alexei J

Subjects
Animals, Humans, Hepacivirus, Respiratory Syncytial Viruses, Cluster Analysis, Monte Carlo Method, Bayes Theorem, Markov Chains, Sequence Alignment, Evolution, Molecular, Phylogeny, Point Mutation, Genome, Viral, Algorithms, Models, Genetic, Computer Simulation, Software, Ebolavirus, across-site rate variation, Dirichlet process mixture model, Bayesian model selection, Evolution, Molecular, Genome, Viral, Models, Genetic, Biochemistry and Cell Biology, Evolutionary Biology, Genetics
Abstract

Probabilistic inference of a phylogenetic tree from molecular sequence data is predicated on a substitution model describing the relative rates of change between character states along the tree for each site in the multiple sequence alignment. Commonly, one assumes that the substitution model is homogeneous across sites within large partitions of the alignment, assigns these partitions a priori, and then fixes their underlying substitution model to the best-fitting model from a hierarchy of named models. Here, we introduce an automatic model selection and model averaging approach within a Bayesian framework that simultaneously estimates the number of partitions, the assignment of sites to partitions, the substitution model for each partition, and the uncertainty in these selections. This new approach is implemented as an add-on to the BEAST 2 software platform. We find that this approach dramatically improves the fit of the nucleotide substitution model compared with existing approaches, and we show, using a number of example data sets, that as many as nine partitions are required to explain the heterogeneity in nucleotide substitution process across sites in a single gene analysis. In some instances, this improved modeling of the substitution process can have a measurable effect on downstream inference, including the estimated phylogeny, relative divergence times, and effective population size histories.

Published
2013

33. Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2

Author

Yang, Lingyue, primary, Wang, Zengmiao, additional, Wang, Lin, additional, Vrancken, Bram, additional, Wang, Ruixue, additional, Wei, Yuanlong, additional, Rader, Benjamin, additional, Wu, Chieh-Hsi, additional, Chen, Yuyang, additional, Wu, Peiyi, additional, Li, Bingying, additional, Lin, Qiushi, additional, Dong, Lu, additional, Cui, Yujun, additional, Shi, Mang, additional, Brownstein, John S., additional, Stenseth, Nils Chr., additional, Yang, Ruifu, additional, and Tian, Huaiyu, additional

Published
2023
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34. Ascophyllum nodosumand Fucus vesiculosusameliorate restenosis via improving inflammation and regulating the PTEN/PI3K/AKT signaling pathway

Author

Zumbi, Crystal Ngofi, Pan, Chun-Hsu, Huang, Hui-Yu, and Wu, Chieh-Hsi

Abstract

Restenosis is a common complication following coronary angioplasty. The traditional use of seaweeds for health benefits has increasingly been explored, however few studies exist reporting its protective effects on the development of restenosis and gut dysbiosis. The aim of this study was to investigate the potential of seaweed extracts (SE) of Ascophyllum nodosumand Fucus vesiculosusin inhibiting intimal hyperplasia in a rat model of restenosis and its underlying mechanisms in macrophages and vascular smooth muscle cells (vSMCs). 16S rRNA sequencing was done to investigate the regulatory effect of SE on the gut microbiome of injured rats. As indicated by the results, SE significantly inhibited the progression of intimal hyperplasia in vivo, attenuated inflammation in macrophages and could inhibit the proliferation, dedifferentiation and migration of vSMCs. It was observed through immunoblotting assays that treatment with SE significantly upregulated PTEN expression in macrophages and inhibited the upregulation of PI3K and AKT expression in vSMCs. Meanwhile, according to the 16S rRNA gene sequencing analysis, supplementation with SE modulated gut microbiota composition in injured rats. In conclusion, SE could ameliorate intimal hyperplasia by inhibiting inflammation and vSMCs proliferation through the regulation of the PTEN/PI3K/AKT pathway and modulating the gut microbiome.

Published
2024
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42. Climate and land-use shape the spread of zoonotic yellow fever virus

Author

Hill, Sarah C., primary, Dellicour, Simon, additional, Claro, Ingra M., additional, Sequeira, Patricia C., additional, Adelino, Talita, additional, Thézé, Julien, additional, Wu, Chieh-Hsi, additional, Rebello Moreira, Filipe Romero, additional, Giovanetti, Marta, additional, Li, Sabrina L., additional, de Jesus, Jaqueline G., additional, Colón-González, Felipe J., additional, Chamberlain, Heather R., additional, Pannell, Oliver, additional, Tejedor-Garavito, Natalia, additional, de Bruycker-Nogueira, Fernanda, additional, Fabri, Allison A., additional, Angélica Mares-Guia, Maria, additional, Xavier, Joilson, additional, Zarebski, Alexander E., additional, Hamlet, Arran, additional, Mureb Sallum, Maria Anice, additional, da Costa, Antonio C., additional, Manuli, Erika R., additional, Levin, Anna S., additional, Mucci, Luís Filipe, additional, Tubaki, Rosa Maria, additional, de Menezes, Regiane Maria Tironi, additional, de Deus, Juliana Telles, additional, Spinola, Roberta, additional, Saad, Leila, additional, Kallas, Esper G., additional, Wint, G.R. William, additional, Peixoto, Pedro S., additional, de Souza Santos, Andreza Aruska, additional, Messina, Jane P., additional, Brady, Oliver J., additional, Tatem, Andrew J., additional, Suchard, Marc A., additional, Mendez-Rico, Jairo A., additional, Abreu, André, additional, Aguiar, Renato Santana, additional, Pybus, Oliver G., additional, Baele, Guy, additional, Lemey, Philippe, additional, Iani, Felipe, additional, Cunha, Mariana S., additional, de Filippis, Ana M. Bispo, additional, Sabino, Ester C., additional, and Faria, Nuno R., additional

Published
2022
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