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82 results on '"Will P"'

1. Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

Author

Anna Paola Carrieri, Niina Haiminen, Sean Maudsley-Barton, Laura-Jayne Gardiner, Barry Murphy, Andrew E. Mayes, Sarah Paterson, Sally Grimshaw, Martyn Winn, Cameron Shand, Panagiotis Hadjidoukas, Will P. M. Rowe, Stacy Hawkins, Ashley MacGuire-Flanagan, Jane Tazzioli, John G. Kenny, Laxmi Parida, Michael Hoptroff, and Edward O. Pyzer-Knapp

Subjects
Medicine, Science
Abstract

Abstract Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

Published
2021
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11. MicroCT data provide evidence correcting the previous misidentification of an Eocene amber beetle (Coleoptera, Cicindelidae) as an extant species.

Author

Schmidt, Joachim, Scholz, Stephan, Wiesner, Jürgen, and Will, Kipling

Subjects
Animals, Coleoptera, Amber, X-Ray Microtomography, Extinction, Psychological, Fossils
Abstract

The fossil record suggests some insect species have a marked longevity. The oldest fossils purported to represent extant insect species are from the Oligocene and Eocene. One of the most cited fossils is the extant tiger beetle Tetracha carolina (Coleoptera: Cicindelidae) that was identified over a century ago by Walther Horn in Eocene Baltic amber. We examined this and compared it to the previously described cincindelid Baltic amber fossil Palaeoiresina cassolai using X-ray microscopy and 3D imaging techniques. We conclude that Horns fossil tiger beetle specimen is conspecific with the Eocene P. cassolai and is a member of an extinct stem group lineage of Cicindelidae. Based on a review of all the tiger beetle fossils described from Cretaceous and Paleogene deposits, we found that the assignment of these fossil species to extant lineages is not supported. There are currently no synapomorphies known from fossils that can provide evidence for Cretaceous Manticorni or Megacephalini nor is there evidence for Eocene Iresina. We provide evidence that rejects the idea of a recent beetle species persisting since the Eocene period, which is crucial for using the currently known fossil Cicindelidae species to calibrate divergence dating of beetle phylogenies.

Published
2023

12. Multiomic interpretation of fungus-infected ant metabolomes during manipulated summit disease

Author

Will, I, Attardo, GM, and de Bekker, C

Subjects
Medical Biochemistry and Metabolomics, Biological Sciences, Biomedical and Clinical Sciences, Behavioral and Social Science, Neurosciences, 2.2 Factors relating to the physical environment, 2.1 Biological and endogenous factors, Aetiology, Infection, Animals, Ants, Chromatography, Liquid, Tandem Mass Spectrometry, Metabolome, Metabolomics
Abstract

Camponotus floridanus ants show altered behaviors followed by a fatal summiting phenotype when infected with manipulating Ophiocordyceps camponoti-floridani fungi. Host summiting as a strategy to increase transmission is also observed with parasite taxa beyond fungi, including aquatic and terrestrial helminths and baculoviruses. The drastic phenotypic changes can sometimes reflect significant molecular changes in gene expression and metabolite concentrations measured in manipulated hosts. Nevertheless, the underlying mechanisms still need to be fully characterized. To investigate the small molecules producing summiting behavior, we infected C. floridanus ants with O. camponoti-floridani and sampled their heads for LC-MS/MS when we observed the characteristic summiting phenotype. We link this metabolomic data with our previous genomic and transcriptomic data to propose mechanisms that underlie manipulated summiting behavior in "zombie ants." This "multiomic" evidence points toward the dysregulation of neurotransmitter levels and neuronal signaling. We propose that these processes are altered during infection and manipulation based on (1) differential expression of neurotransmitter synthesis and receptor genes, (2) altered abundance of metabolites and neurotransmitters (or their precursors) with known behavioral effects in ants and other insects, and (3) possible suppression of a connected immunity pathway. We additionally report signals for metabolic activity during manipulation related to primary metabolism, detoxification, and anti-stress protectants. Taken together, these findings suggest that host manipulation is likely a multi-faceted phenomenon, with key processes changing at multiple levels of molecular organization.

Published
2023

22. GOATOOLS: A Python library for Gene Ontology analyses.

Author

Klopfenstein, DV, Zhang, Liangsheng, Pedersen, Brent S, Ramírez, Fidel, Warwick Vesztrocy, Alex, Naldi, Aurélien, Mungall, Christopher J, Yunes, Jeffrey M, Botvinnik, Olga, Weigel, Mark, Dampier, Will, Dessimoz, Christophe, Flick, Patrick, and Tang, Haibao

Subjects
Animals, Mice, Alzheimer Disease, Disease Models, Animal, Gene Expression Profiling, Computational Biology, Gene Expression Regulation, Developmental, Algorithms, Software, Gene Ontology, Biomarkers, Biochemistry and Cell Biology, Other Physical Sciences
Abstract

The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology (GO) enrichment analysis may return hundreds of statistically significant GO results in a "flat" list, which can be challenging to summarize. It can also be difficult to keep pace with rapidly expanding biological knowledge, which often results in daily changes to any of the over 47,000 gene ontologies that describe biological knowledge. GOATOOLS, a Python-based library, makes it more efficient to stay current with the latest ontologies and annotations, perform gene ontology enrichment analyses to determine over- and under-represented terms, and organize results for greater clarity and easier interpretation using a novel GOATOOLS GO grouping method. We performed functional analyses on both stochastic simulation data and real data from a published RNA-seq study to compare the enrichment results from GOATOOLS to two other popular tools: DAVID and GOstats. GOATOOLS is freely available through GitHub: https://github.com/tanghaibao/goatools .

Published
2018

36. Platelet derived growth factor receptor β (PDGFRβ) is a host receptor for the human malaria parasite adhesin TRAP

Author

Steel, Ryan W. J., Vigdorovich, Vladimir, Dambrauskas, Nicholas, Wilder, Brandon K., Arredondo, Silvia A., Goswami, Debashree, Kumar, Sudhir, Carbonetti, Sara, Swearingen, Kristian E., Nguyen, Thao, Betz, Will, Camargo, Nelly, Fisher, Bridget S., Soden, Jo, Thomas, Helen, Freeth, Jim, Moritz, Robert L., Noah Sather, D., and Kappe, Stefan H. I.

Published
2021
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42. Chemoprotective antimalarials identified through quantitative high-throughput screening of Plasmodium blood and liver stage parasites

Author

Dorjsuren, Dorjbal, Eastman, Richard T., Wicht, Kathryn J., Jansen, Daniel, Talley, Daniel C., Sigmon, Benjamin A., Zakharov, Alexey V., Roncal, Norma, Girvin, Andrew T., Antonova-Koch, Yevgeniya, Will, Paul M., Shah, Pranav, Sun, Hongmao, Klumpp-Thomas, Carleen, Mok, Sachel, Yeo, Tomas, Meister, Stephan, Marugan, Juan Jose, Ross, Leila S., Xu, Xin, Maloney, David J., Jadhav, Ajit, Mott, Bryan T., Sciotti, Richard J., Winzeler, Elizabeth A., Waters, Norman C., Campbell, Robert F., Huang, Wenwei, Simeonov, Anton, and Fidock, David A.

Published
2021
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46. A physical sciences network characterization of non-tumorigenic and metastatic cells

Author

Agus, David B, Alexander, Jenolyn F, Arap, Wadih, Ashili, Shashanka, Aslan, Joseph E, Austin, Robert H, Backman, Vadim, Bethel, Kelly J, Bonneau, Richard, Chen, Wei-Chiang, Chen-Tanyolac, Chira, Choi, Nathan C, Curley, Steven A, Dallas, Matthew, Damania, Dhwanil, Davies, Paul CW, Decuzzi, Paolo, Dickinson, Laura, Estevez-Salmeron, Luis, Estrella, Veronica, Ferrari, Mauro, Fischbach, Claudia, Foo, Jasmine, Fraley, Stephanie I, Frantz, Christian, Fuhrmann, Alexander, Gascard, Philippe, Gatenby, Robert A, Geng, Yue, Gerecht, Sharon, Gillies, Robert J, Godin, Biana, Grady, William M, Greenfield, Alex, Hemphill, Courtney, Hempstead, Barbara L, Hielscher, Abigail, Hillis, W Daniel, Holland, Eric C, Ibrahim-Hashim, Arig, Jacks, Tyler, Johnson, Roger H, Joo, Ahyoung, Katz, Jonathan E, Kelbauskas, Laimonas, Kesselman, Carl, King, Michael R, Konstantopoulos, Konstantinos, Kraning-Rush, Casey M, Kuhn, Peter, Kung, Kevin, Kwee, Brian, Lakins, Johnathon N, Lambert, Guillaume, Liao, David, Licht, Jonathan D, Liphardt, Jan T, Liu, Liyu, Lloyd, Mark C, Lyubimova, Anna, Mallick, Parag, Marko, John, McCarty, Owen JT, Meldrum, Deirdre R, Michor, Franziska, Mumenthaler, Shannon M, Nandakumar, Vivek, O’Halloran, Thomas V, Oh, Steve, Pasqualini, Renata, Paszek, Matthew J, Philips, Kevin G, Poultney, Christopher S, Rana, Kuldeepsinh, Reinhart-King, Cynthia A, Ros, Robert, Semenza, Gregg L, Senechal, Patti, Shuler, Michael L, Srinivasan, Srimeenakshi, Staunton, Jack R, Stypula, Yolanda, Subramanian, Hariharan, Tlsty, Thea D, Tormoen, Garth W, Tseng, Yiider, van Oudenaarden, Alexander, Verbridge, Scott S, Wan, Jenny C, Weaver, Valerie M, Widom, Jonathan, Will, Christine, Wirtz, Denis, Wojtkowiak, Jonathan, and Wu, Pei-Hsun

Subjects
Biochemistry and Cell Biology, Biological Sciences, Cancer, Breast Cancer, Women's Health, 2.1 Biological and endogenous factors, Biomarkers, Tumor, Cell Line, Tumor, Cell Movement, Cell Size, Cell Survival, Computer Simulation, Gene Expression Regulation, Neoplastic, Humans, Models, Biological, Neoplasm Metastasis, Neoplasm Proteins, Physical Sciences - Oncology Centers Network
Abstract

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences-Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells' regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.

Published
2013

47. A physical sciences network characterization of non-tumorigenic and metastatic cells.

Author

Physical Sciences - Oncology Centers Network, Agus, David B, Alexander, Jenolyn F, Arap, Wadih, Ashili, Shashanka, Aslan, Joseph E, Austin, Robert H, Backman, Vadim, Bethel, Kelly J, Bonneau, Richard, Chen, Wei-Chiang, Chen-Tanyolac, Chira, Choi, Nathan C, Curley, Steven A, Dallas, Matthew, Damania, Dhwanil, Davies, Paul CW, Decuzzi, Paolo, Dickinson, Laura, Estevez-Salmeron, Luis, Estrella, Veronica, Ferrari, Mauro, Fischbach, Claudia, Foo, Jasmine, Fraley, Stephanie I, Frantz, Christian, Fuhrmann, Alexander, Gascard, Philippe, Gatenby, Robert A, Geng, Yue, Gerecht, Sharon, Gillies, Robert J, Godin, Biana, Grady, William M, Greenfield, Alex, Hemphill, Courtney, Hempstead, Barbara L, Hielscher, Abigail, Hillis, W Daniel, Holland, Eric C, Ibrahim-Hashim, Arig, Jacks, Tyler, Johnson, Roger H, Joo, Ahyoung, Katz, Jonathan E, Kelbauskas, Laimonas, Kesselman, Carl, King, Michael R, Konstantopoulos, Konstantinos, Kraning-Rush, Casey M, Kuhn, Peter, Kung, Kevin, Kwee, Brian, Lakins, Johnathon N, Lambert, Guillaume, Liao, David, Licht, Jonathan D, Liphardt, Jan T, Liu, Liyu, Lloyd, Mark C, Lyubimova, Anna, Mallick, Parag, Marko, John, McCarty, Owen JT, Meldrum, Deirdre R, Michor, Franziska, Mumenthaler, Shannon M, Nandakumar, Vivek, O'Halloran, Thomas V, Oh, Steve, Pasqualini, Renata, Paszek, Matthew J, Philips, Kevin G, Poultney, Christopher S, Rana, Kuldeepsinh, Reinhart-King, Cynthia A, Ros, Robert, Semenza, Gregg L, Senechal, Patti, Shuler, Michael L, Srinivasan, Srimeenakshi, Staunton, Jack R, Stypula, Yolanda, Subramanian, Hariharan, Tlsty, Thea D, Tormoen, Garth W, Tseng, Yiider, van Oudenaarden, Alexander, Verbridge, Scott S, Wan, Jenny C, Weaver, Valerie M, Widom, Jonathan, Will, Christine, Wirtz, Denis, Wojtkowiak, Jonathan, and Wu, Pei-Hsun

Subjects
Physical Sciences - Oncology Centers Network, Cell Line, Tumor, Humans, Neoplasm Metastasis, Neoplasm Proteins, Cell Movement, Cell Size, Cell Survival, Gene Expression Regulation, Neoplastic, Models, Biological, Computer Simulation, Biomarkers, Tumor, Cancer, Breast Cancer, Biotechnology, 2.1 Biological and endogenous factors
Abstract

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences-Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells' regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.

Published
2013

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