11 results on '"Sean M Ryan"'
Search Results
2. The molecular characterization of antibody binding to a superantigen-like protein from a commensal microbe
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Alexander E. Yarawsky, Sean M Ryan, Christoph Drees, Jeffrey J. Bunker, Andrew B. Herr, Marta T. Borowska, Erin J. Adams, Mayuri Viswanathan, and Albert Bendelac
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B-cell receptor ,Immunoglobulin Variable Region ,Mutagenesis (molecular biology technique) ,Receptors, Antigen, B-Cell ,Mice ,Immune system ,Superantigen ,Animals ,Humans ,Microbiome ,B-Lymphocytes ,Clostridiales ,Multidisciplinary ,Binding Sites ,Superantigens ,biology ,Chemistry ,Antibodies, Monoclonal ,Biological Sciences ,In vitro ,Cell biology ,Mice, Inbred C57BL ,biology.protein ,Antibody ,Immunoglobulin Heavy Chains ,Binding domain - Abstract
Microorganisms have coevolved diverse mechanisms to impair host defenses. A major one, superantigens, can result in devastating effects on the immune system. While all known superantigens induce vast immune cell proliferation and come from opportunistic pathogens, recently, proteins with similar broad specificity to antibody variable (V) domain families were identified in a commensal microbiota. These proteins, identified in the human commensal Ruminococcus gnavus, are called immunoglobulin-binding protein (Ibp) A and B and have been shown to activate B cells in vitro expressing either human VH3 or murine VH5/6/7. Here, we provide molecular and functional studies revealing the basis of this Ibp/immunoglobulin (Ig) interaction. The crystal structure and biochemical assays of a truncated IbpA construct in complex with mouse VH5 antigen-binding fragment (Fab) shows a binding of Ig heavy chain framework residues to the Ibp Domain D and the C-terminal heavy chain binding domain (HCBD). We used targeted mutagenesis of contact residues and affinity measurements and performed studies of the Fab-IbpA complex to determine the stoichiometry between Ibp and VH domains, suggesting Ibp may serve to cluster full-length IgA antibodies in vivo. Furthermore, in vitro stimulation experiments indicate that binding of the Ibp HCBD alone is sufficient to activate responsive murine B cell receptors. The presence of these proteins in a commensal microbe suggest that binding a broad repertoire of immunoglobulins, particularly in the gut/microbiome environment, may provide an important function in the maintenance of host/microbiome homeostasis contrasting with the pathogenic role of structurally homologous superantigens expressed by pathogens.
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- 2021
3. Spironolactone affects cardiovascular and craniofacial development in zebrafish embryos (Danio rerio)
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Hannah J, Petty, Jacob E, Barrett, Erin G, Kosmowski, Dandre S, Amos, Sean M, Ryan, Lucas D, Jones, and Christopher S, Lassiter
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Pharmacology ,Health, Toxicology and Mutagenesis ,Animals ,Embryonic Development ,General Medicine ,Spironolactone ,Diuretics ,Toxicology ,Zebrafish - Abstract
Spironolactone, a potassium-sparing diuretic and aldosterone antagonist, is a mineralocorticoid hormone commonly prescribed to patients suffering from heart failure, hirsutism, dermatological afflictions, and hypertension. Interestingly, relatively little work has been done on the development of vertebrate embryos after exposure to this compound. Here, we treat zebrafish embryos with spironolactone at 10
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- 2022
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4. A connectome and analysis of the adult Drosophila central brain
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Temour Tokhi, Tom Dolafi, Nneoma Okeoma, Tanya Wolff, Philip M Hubbard, Kazunori Shinomiya, Madelaine K Robertson, Gerald M. Rubin, Gregory S.X.E. Jefferis, Christopher J Knecht, Laramie Leavitt, Alia Suleiman, Satoko Takemura, Christopher Ordish, Jody Clements, Ian A. Meinertzhagen, Alexander Shakeel Bates, Takashi Kawase, Samantha Finley, Nicholas Padilla, Jackie Swift, C. Shan Xu, Stuart Berg, Tyler Paterson, Ashley L Scott, Erika Neace, Shirley Lauchie, Sean M Ryan, Emily M Joyce, Shin-ya Takemura, Tim Blakely, Michael A Cook, Christopher Patrick, Bryon Eubanks, Audrey Francis, Robert Svirskas, William T. Katz, Eric T. Trautman, Caroline Mooney, Ting Zhao, Nicole A Kirk, Megan Sammons, Brandon S Canino, Reed A. George, Louis K. Scheffer, Jolanta A. Borycz, Jon Thomson Rymer, Natasha Cheatham, Dagmar Kainmueller, Gary B. Huang, Khaled Khairy, Nicole Neubarth, Elliott E Phillips, John A. Bogovic, Neha Rampally, Larry Lindsey, Viren Jain, David G. Ackerman, Jane Anne Horne, Kelli Fairbanks, Lowell Umayam, Jens Goldammer, Emily M Phillips, Donald J. Olbris, Feng Li, Emily A Manley, Philipp Schlegel, Hideo Otsuna, Marta Costa, Stephen M. Plaza, Omotara Ogundeyi, Samantha Ballinger, Charli Maldonado, Kelsey Smith, Gary Patrick Hopkins, Vivek Jayaraman, Emily Tenshaw, Julie Kovalyak, Peter H. Li, Tansy Yang, Masayoshi Ito, Miatta Ndama, Claire Smith, Michał Januszewski, Alanna Lohff, SungJin Kim, Anne K Scott, Kei Ito, Iris Talebi, Jeremy Maitlin-Shepard, Nora Forknall, Marisa Dreher, Harald F. Hess, Sari McLin, Patricia K. Rivlin, Dennis A Bailey, Kenneth J. Hayworth, Octave Duclos, Caitlin Ribeiro, John J. Walsh, Zhiyuan Lu, Dorota Tarnogorska, Ruchi Parekh, Aya Shinomiya, Stephan Saalfeld, Margaret A Sobeski, Natalie L Smith, Chelsea X Alvarado, Scheffer, Louis K [0000-0002-3289-6564], Xu, C Shan [0000-0002-8564-7836], Januszewski, Michal [0000-0002-3480-2744], Lu, Zhiyuan [0000-0002-4128-9774], Takemura, Shin-ya [0000-0003-2400-6426], Huang, Gary B [0000-0002-9606-3510], Shinomiya, Kazunori [0000-0003-0262-6421], Maitlin-Shepard, Jeremy [0000-0001-8453-7961], Hubbard, Philip M [0000-0002-6746-5035], Katz, William T [0000-0002-9417-6212], Ackerman, David [0000-0003-0172-6594], Blakely, Tim [0000-0003-0995-5471], Bogovic, John [0000-0002-4829-9457], Kainmueller, Dagmar [0000-0002-9830-2415], Khairy, Khaled A [0000-0002-9274-5928], Li, Peter H [0000-0001-6193-4454], Trautman, Eric T [0000-0001-8588-0569], Bates, Alexander S [0000-0002-1195-0445], Goldammer, Jens [0000-0002-5623-8339], Wolff, Tanya [0000-0002-8681-1749], Svirskas, Robert [0000-0001-8374-6008], Schlegel, Philipp [0000-0002-5633-1314], Knecht, Christopher J [0000-0002-5663-5967], Alvarado, Chelsea X [0000-0002-5973-7512], Bailey, Dennis A [0000-0002-4675-8373], Borycz, Jolanta A [0000-0002-4402-9230], Canino, Brandon S [0000-0002-8454-865X], Cook, Michael [0000-0002-7892-6845], Dreher, Marisa [0000-0002-0041-9229], Eubanks, Bryon [0000-0002-9288-2009], Fairbanks, Kelli [0000-0002-6601-4830], Finley, Samantha [0000-0002-8086-206X], Forknall, Nora [0000-0003-2139-7599], Francis, Audrey [0000-0003-1974-7174], Joyce, Emily M [0000-0001-5794-6321], Kovalyak, Julie [0000-0001-7864-7734], Lauchie, Shirley A [0000-0001-8223-9522], Lohff, Alanna [0000-0002-1242-1836], McLin, Sari [0000-0002-9120-1136], Patrick, Christopher M [0000-0001-8830-1892], Phillips, Elliott E [0000-0002-4918-2058], Phillips, Emily M [0000-0001-7615-301X], Robertson, Madelaine K [0000-0002-1764-0245], Rymer, Jon Thomson [0000-0002-4271-6774], Ryan, Sean M [0000-0002-8879-6108], Sammons, Megan [0000-0003-4516-5928], Shinomiya, Aya [0000-0002-6358-9567], Smith, Natalie L [0000-0002-8271-9873], Swift, Jackie [0000-0003-1321-8183], Takemura, Satoko [0000-0002-2863-0050], Talebi, Iris [0000-0002-0173-8053], Tarnogorska, Dorota [0000-0002-7063-6165], Walsh, John J [0000-0002-7176-4708], Yang, Tansy [0000-0003-1131-0410], Horne, Jane Anne [0000-0001-9673-2692], Parekh, Ruchi [0000-0002-8060-2807], Jayaraman, Vivek [0000-0003-3680-7378], Costa, Marta [0000-0001-5948-3092], Jefferis, Gregory SXE [0000-0002-0587-9355], Ito, Kei [0000-0002-7274-5533], Saalfeld, Stephan [0000-0002-4106-1761], Rubin, Gerald M [0000-0001-8762-8703], Hess, Harald F [0000-0003-3000-1533], Plaza, Stephen M [0000-0001-7425-8555], Apollo - University of Cambridge Repository, Takemura, Shin-Ya [0000-0003-2400-6426], and Jefferis, Gregory Sxe [0000-0002-0587-9355]
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Male ,Computer science ,computational biology ,0302 clinical medicine ,Drosophila Proteins ,Research article ,Biology (General) ,Neurons ,Cognitive science ,0303 health sciences ,biology ,D. melanogaster ,General Neuroscience ,connectome ,Brain ,systems biology ,graph properties ,General Medicine ,Human brain ,Drosophila melanogaster ,medicine.anatomical_structure ,Connectome ,Medicine ,Drosophila ,Female ,synapse detecton ,Insight ,Function and Dysfunction of the Nervous System ,cell types ,Research Article ,Computational and Systems Biology ,brain regions ,Connectomes ,QH301-705.5 ,Ubiquitin-Protein Ligases ,Science ,connectome reconstuction methods ,Small mammal ,Central region ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,medicine ,Animals ,030304 developmental biology ,General Immunology and Microbiology ,biology.organism_classification ,synapse detection ,Synapses ,030217 neurology & neurosurgery ,Neuroscience - Abstract
The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain., eLife digest Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path.
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- 2020
5. Author response: A connectome and analysis of the adult Drosophila central brain
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Dennis A Bailey, Kenneth J. Hayworth, Aya Shinomiya, Madelaine K Robertson, Tim Blakely, C. Shan Xu, Temour Tokhi, Jon Thomson Rymer, Nicole Neubarth, Zhiyuan Lu, Dorota Tarnogorska, Shirley Lauchie, Sean M Ryan, Nneoma Okeoma, Erika Neace, Khaled Khairy, Emily M Phillips, Margaret A Sobeski, Bryon Eubanks, Christopher Patrick, Marisa Dreher, Natalie L Smith, Philipp Schlegel, John A. Bogovic, David G. Ackerman, Jane Anne Horne, Tom Dolafi, Gary B. Huang, Kelli Fairbanks, Claire Smith, Michał Januszewski, Octave Duclos, Satoko Takemura, Christopher Ordish, Chelsea X Alvarado, Jody Clements, Viren Jain, Samantha Finley, John J. Walsh, Nicole A Kirk, Kelsey Smith, Omotara Ogundeyi, Takashi Kawase, Reed A. George, Tyler Paterson, Laramie Leavitt, Kazunori Shinomiya, SungJin Kim, Christopher J Knecht, Nicholas Padilla, Anne K Scott, Tansy Yang, Ashley L Scott, Hideo Otsuna, Jeremy Maitlin-Shepard, Marta Costa, Nora Forknall, Stuart Berg, Alia Suleiman, Harald F. Hess, Audrey Francis, Donald J. Olbris, Caroline Mooney, Emily M Joyce, Eric T. Trautman, Gerald M. Rubin, Jackie Swift, Philip M Hubbard, Ting Zhao, Brandon S Canino, Gary Patrick Hopkins, Kei Ito, Jolanta A. Borycz, Shin-ya Takemura, Masayoshi Ito, Stephen M. Plaza, Ian A. Meinertzhagen, Louis K. Scheffer, Dagmar Kainmueller, Larry Lindsey, Miatta Ndama, Elliott E Phillips, Lowell Umayam, Jens Goldammer, Vivek Jayaraman, Emily Tenshaw, Gregory S.X.E. Jefferis, Alexander Shakeel Bates, William T. Katz, Sari McLin, Neha Rampally, Emily A Manley, Patricia K. Rivlin, Charli Maldonado, Peter H. Li, Samantha Ballinger, Tanya Wolff, Megan Sammons, Julie Kovalyak, Stephan Saalfeld, Alanna Lohff, Natasha Cheatham, Iris Talebi, Michael A Cook, Robert Svirskas, Feng Li, Caitlin Ribeiro, and Ruchi Parekh
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biology ,Connectome ,Drosophila (subgenus) ,biology.organism_classification ,Neuroscience - Published
- 2020
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6. A Connectome of the Adult Drosophila Central Brain
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Audrey Francis, Ting Zhao, Feng Li, Megan Sammons, Madelaine K Robertson, SungJin Kim, Tyler Paterson, Philipp Schlegel, Chelsea X Alvarado, Viren Jain, Brandon S Canino, Omotara Ogundeyi, Nora Forknall, Dagmar Kainmueller, Tansy Yang, Natasha Cheatham, Neha Rampally, Caitlin Ribeiro, Kimothy L. Smith, Emily M Phillips, Ruchi Parekh, Jackie Swift, Donald J. Olbris, Takashi Kawase, Jon Thomson Rymer, Zhiyuan Lu, Nicholas Padilla, Christopher Ordish, Dorota Tarnogorska, Nicole Neubarth, Aya Shinomiya, Miatta Ndama, Samantha Finley, Stuart Berg, Erika Neace, Bryon Eubanks, John A. Bogovic, David G. Ackerman, Robert Svirskas, Sari McLin, Emily A Manley, Jane Anne Horne, Michael A Cook, Samantha Ballinger, Michał Januszewski, Jeremy Maitin-Shepard, Caroline Mooney, Nicole A Kirk, Shin-ya Takemura, Iris Talebi, Temour Tokhi, Kei K. Ito, Khaled Khairy, Stephen M. Plaza, Julie Kovalyak, Patricia K. Rivlin, Emily M Joyce, Kelli Fairbanks, Philip M Hubbard, Charli Maldonado, Nneoma Okeoma, Hideo Otsuna, Laurence F. Lindsey, Tim Blakely, Gerald M. Rubin, Alanna Lohff, William T. Katz, Anne K Scott, Mutsumi Ito, Peter H. Li, Ian A. Meinertzhagen, Natalie L Smith, Gary B. Huang, Dennis A Bailey, Reed A. George, Kenneth J. Hayworth, Tom Dolafi, Marisa Dreher, Tanya Wolff, Kazunori Shinomiya, Harald F. Hess, E.T. Troutman, Christopher J Knecht, Gary Patrick Hopkins, Alia Suleiman, Vivek Jayaraman, Emily Tenshaw, Octave Duclos, John J. Walsh, Stephan Saalfeld, Louis K. Scheffer, Elliott E Phillips, Lowell Umayam, Jens Goldammer, Sobeski, Jody Clements, Ashley L Scott, Shirley Lauchie, Sean M Ryan, Christopher Patrick, Jolanta A. Borycz, Claire Smith, C.S. Xu, and Laramie Leavitt
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Cell type ,Computer science ,Cell ,Machine learning ,computer.software_genre ,Synapse ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Biological neural network ,030304 developmental biology ,Structure (mathematical logic) ,0303 health sciences ,biology ,business.industry ,Motor control ,biology.organism_classification ,Associative learning ,medicine.anatomical_structure ,Mushroom bodies ,Identity (object-oriented programming) ,Connectome ,Artificial intelligence ,Drosophila melanogaster ,Function and Dysfunction of the Nervous System ,business ,computer ,030217 neurology & neurosurgery - Abstract
The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.
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- 2020
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7. Phenolic Compounds and Metals in Some Edible Annonaceae Fruits
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Maria Rosa, de Moraes, Sean M, Ryan, Helena Teixeira, Godoy, Andrew L, Thomas, José Guilherme S, Maia, Kristy M, Richards, Kevin, Tran, and Robert E, Smith
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Phenols ,Plant Extracts ,Tandem Mass Spectrometry ,Fruit ,Annonaceae ,Quercetin ,Antioxidants - Abstract
The concentrations of 3,4-dihydroxybenzoic acid, caffeic acid, catechin, chlorogenic acid, epicatechin, gallic acid, p-coumaric acid, quercetin, rutin, ferulic acid, and the major metals in graviola (Annona muricata), atemoya (A. squamosa x A. cherimola), fruta do conde (A. squamosa), biribá (Rollinia mucosa), and the North American pawpaw (Asimina triloba) were determined by UPLC-ESI (-)-MS/MS. It enabled the identification and quantification of phenolic compounds. Catechin was only found in atemoya, at a concentration of 38.6 μg/g-dw. Only 3,4-dihydroxybenzoic acid was found in the fruit pulps of all five fruits analyzed. Atemoya stands out for not only having catechin but also for having much more epicatechin (239 μg/g-dw) than the other fruits. At the same time, graviola had more p-coumaric acid (62.6 μg/g-dw), and the North American pawpaw had more chlorogenic acid (48.1 μg/g-dw) than the other fruits. Metals were determined by ICP equipped with axially viewed plasma. All five fruit pulps had relatively high levels of potassium, with concentrations ranging from 7640 to 15,000 μg/g-dw, with pawpaw being the lowest and atemoya being the highest. The concentrations of other metals ranged from Ca 547 to 1110, Na 14.3 to 123, P 1210 to 1690, Mg 472 to 980, Mn 1.86 to 5.27, and Zn 5.55 to 7.32 μg/g-dw. All five fruits in the Annonaceae family that were analyzed in this study have several phenolic compounds in them and were good sources of potassium, calcium, phosphorus, and magnesium.
- Published
- 2019
8. Cross-linkers both drive and brake cytoskeletal remodeling and furrowing in cytokinesis
- Author
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François Nédélec, Carlos Patino Descovich, Amy Shaub Maddox, Daniel B. Cortes, Paul S. Maddox, Li Zhang, Sean M Ryan, Jazmine Nash, Nedelec, Francois [0000-0002-8141-5288], and Apollo - University of Cambridge Repository
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0301 basic medicine ,Scaffold protein ,Contraction (grammar) ,Biology ,Microtubules ,Protein filament ,Contractility ,03 medical and health sciences ,Myosin ,Animals ,Cytoskeleton ,Caenorhabditis elegans ,Caenorhabditis elegans Proteins ,Molecular Biology ,Cytokinesis ,Myosin Type II ,Cell Biology ,Actomyosin ,biology.organism_classification ,Actins ,Cell biology ,body regions ,030104 developmental biology - Abstract
Cell shape changes such as cytokinesis are driven by the actomyosin contractile cytoskeleton. The molecular rearrangements that bring about contractility in nonmuscle cells are currently debated. Specifically, both filament sliding by myosin motors, as well as cytoskeletal cross-linking by myosins and nonmotor cross-linkers, are thought to promote contractility. Here we examined how the abundance of motor and nonmotor cross-linkers affects the speed of cytokinetic furrowing. We built a minimal model to simulate contractile dynamics in the Caenorhabditis elegans zygote cytokinetic ring. This model predicted that intermediate levels of nonmotor cross-linkers are ideal for contractility; in vivo, intermediate levels of the scaffold protein anillin allowed maximal contraction speed. Our model also demonstrated a nonlinear relationship between the abundance of motor ensembles and contraction speed. In vivo, thorough depletion of nonmuscle myosin II delayed furrow initiation, slowed F-actin alignment, and reduced maximum contraction speed, but partial depletion allowed faster-than-expected kinetics. Thus, cytokinetic ring closure is promoted by moderate levels of both motor and nonmotor cross-linkers but attenuated by an over-abundance of motor and nonmotor cross-linkers. Together, our findings extend the growing appreciation for the roles of cross-linkers in cytokinesis and reveal that they not only drive but also brake cytoskeletal remodeling.
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- 2017
9. Crosslinkers both drive and brake cytoskeletal remodeling and furrowing in cytokinesis
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Carlos Patino Descovich, Jazmine Nash, François Nédélec, Li Zhang, Sean M Ryan, Daniel B. Cortes, Amy Shaub Maddox, and Paul S. Maddox
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Scaffold protein ,Contractility ,Protein filament ,Contraction (grammar) ,Zygote ,Myosin ,technology, industry, and agriculture ,macromolecular substances ,Biology ,Cytoskeleton ,Cytokinesis ,Cell biology - Abstract
Cytokinesis and other cell shape changes are driven by the actomyosin contractile cytoskeleton. The molecular rearrangements that bring about contractility in non-muscle cells are currently debated. Specifically, both filament sliding by myosin motors, as well as cytoskeletal crosslinking by myosins and non-motor crosslinkers, are thought to promote contractility. Here, we examined how the abundance of motor and non-motor crosslinkers controls the speed of cytokinetic furrowing. We built a minimal model to simulate the contractile dynamics of the C. elegans zygote cytokinetic ring. This model predicted that intermediate levels of non-motor crosslinkers would allow maximal contraction speed, which we found to be the case for the scaffold protein anillin, in vivo. Our model also demonstrated a non-linear relationship between the abundance of motor ensembles and contraction speed. In vivo, thorough depletion of non-muscle myosin II delayed furrow initiation, slowed F-actin alignment, and reduced maximum contraction speed, but partial depletion allowed faster-than-expected kinetics. Thus, both motor and non-motor crosslinkers promote cytokinetic ring closure when present at low levels, but act as a brake when present at higher levels. Together, our findings extend the growing appreciation for the roles of crosslinkers, but reveal that they not only drive but also brake cytoskeletal remodeling.
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- 2017
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10. Analyzing Food Samples—Inorganic Chemicals
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Sean M. Ryan
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Materials science ,Parts-per notation ,Analytical chemistry ,Atomic emission spectroscopy ,chemistry.chemical_element ,law.invention ,Mercury (element) ,Ashing ,chemistry ,law ,Sample preparation ,Muffle furnace ,Inductively coupled plasma ,Atomic absorption spectroscopy - Abstract
In this chapter, several different sample preparation techniques and analytical methods are described in order to analyze 16 elements determined in the Total Diet Study (TDS). The concentrations of these elements found in TDS samples vary from parts per billion to parts per hundred (percent). A variety of sample preparation techniques are described consisting of the digestion of these samples by various mixtures of acids or the direct oxidation of organic matter via ashing of these samples in a muffle furnace. A variety of instruments are used for the determination of the elements of interest including inductively coupled plasma atomic emission spectrophotometers, atomic absorption spectrophotometers (heated graphite furnace and hydride generation techniques), ultraviolet-visible spectrometers, and a direct reading mercury analyzer. These topics are all briefly addressed in this chapter.
- Published
- 2013
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11. Determining the effect of calcium cations on acrylamide formation in cooked wheat products using a model system
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Sean M. Ryan and Robert A. Levine
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chemistry.chemical_classification ,Acrylamide ,Chemistry ,Food Handling ,Sodium ,Inorganic chemistry ,Wheat flour ,PH reduction ,food and beverages ,chemistry.chemical_element ,General Chemistry ,Calcium ,Hydrogen-Ion Concentration ,Calcium chlorate ,Models, Biological ,Divalent ,chemistry.chemical_compound ,Cations ,Food science ,Gas chromatography–mass spectrometry ,General Agricultural and Biological Sciences ,Triticum - Abstract
A model system was used to cook wheat flour and water dough pieces in sealed pressure tubes under controlled pH conditions and with various additives in the recipe water to determine acrylamide (AA) formation and elimination. The potential effectiveness of calcium as CaCl2 or CaCO3 salts to reduce the formation of AA in wheat based food products was assessed. Since the divalent Ca2+ was capable of inducing significant pH reduction in the dough, and pH lowering is known to reduce AA formation, it was necessary in some cases to adjust the pH before cooking or use a pH matched control. For comparison, the effect of NaCl on AA formation was also determined. It was found that AA reduction up to 36% was obtained by adding CaCl2 to the recipe water at a 0.04 M concentration, compared to 23% for 0.04 M NaCl, and there was no reduction when CaCO3 was added to simulate a calcium enriched flour.
- Published
- 2009
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