Mapping the Mechanobiome: Novel mathematically-derived 3D visualization of the cellular mechanoresponsive system for interactive publication
by Cecilia C. Johnson
A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Master of Arts.
Baltimore, Maryland March, 2019
© 2019 Cecilia Johnson All Rights Reserved Abstract
Mechanical forces, ubiquitous in biological settings, are major determinants of cell fate; they should not be considered a detail applicable to specialized circumstances but rather a vital component of cell biology. To sense, respond, and generate both intracellular and extracellular mechanical forces, cells contain a highly integrated and dynamic network of macromolecules throughout the cell. The Robinson Lab at the Johns Hopkins School of
Medicine developed the term “mechanobiome,” to describe and categorize that network of macromolecules. At the interface of cell biology, physics, and engineering, the concept of the mechanobiome provides researchers a systems-level understanding of the extensive contributions of physical force and mechanical cell properties on cell morphology, differentiation, physiology, and disease. Although numerous diseases, including cancer, cardiovascular disease, and chronic obstructive pulmonary disease, develop from abnormal cell mechanics, the mechanobiome is rarely explored as a novel source of therapeutic targets.
Increased understanding of the mechanobiome will enhance understanding of normal biological machinery and ultimately lead to new pathways for targeting disease.
To address the lack of comprehensive, accurate visualizations of the mechanobiome, two novel theoretical 3D models of the mechanobiome were developed: one at the cellular level and one at the nanoscale level. By integrating published data on components of the mechanobiome, such as crystal structures, macromolecule concentrations, and polymer dissociation constants, a proportionately accurate visualization of the cell’s mechanical system was produced. A platform was prototyped to present these novel 3D visuals as interactives on an accessible web-based educational resource, “Mapping the
Mechanobiome.” The resource also provides review-style descriptions of fundamental
ii concepts in mechanobiome research with accompanying visual media generated from the mathematically-derived models.
This resource will contribute to discussions on the forefront of mechanobiome research, provide a comprehensive understanding for new researchers in the field, and advance research efforts by highlighting the significance of fundamental mechanical properties. The novel, mathematically-derived models have the potential to reveal aspects of the mechanobiome not previously considered due to the lack of accurate visualization of the full working system. This resource provides a platform to further enhance our understanding of the role of mechanics in health and disease.
Cecilia C. Johnson, Author
Douglas N. Robinson, Ph.D., Preceptor
Professor, Departments of Cell Biology, Pharmacology and Molecular Sciences, Medicine,
Oncology, and Chemical and Biomolecular Engineering
Johns Hopkins University School of Medicine
Corinne Sandone, M.A., C.M.I., Thesis Advisor
Professor and Director, Department of Art as Applied to Medicine
Johns Hopkins University School of Medicine
iii Acknowledgements
I extend endless gratitude to Cory Sandone, my department advisor. Thank you for your wisdom, guidance, and support, and for never losing faith in me or my project. Your encouragement has kept me sane, and your insight taught me how to be a better communicator.
To Douglas Robinson and Priyanka Kothari: thank you for helping me navigate through such a complex and all-encompassing topic. Thank you for understanding the breadth of my project and helping to keep everything ambitious, but manageable. Finally, thank you for dedicating your time to providing me feedback, resources, and assistance with math.
I would like to thank David Rini, Jennifer Fairman, Veronica Falconieri, Graham
Johnson, Li Yao, and Sandra Gabelli for answering technical questions throughout my thesis and providing resources whenever I hit roadblocks. Additional thank you to the above names plus Tim Phelps, Gary Lees, Juan Garcia, Lydia Gregg, Norman Barker, Ian Suk,
Anne Altemus, Donald Bliss, Mike Linkinhoker and Sarah Poynton, for teaching me the lessons and skills necessary to complete this project. I am not the same artist I was before I started at Johns Hopkins, and that is thanks to your guidance. So long as I am an artist, scientist, or communicator, I will carry your lessons with me.
Thank you to Dacia Balch and Carol Pfeffer for consistently checking in on me and my classmate’s sanity and mental health, for many pats on the back, and for that foot massage that one time.
To my loving family, thank you for supporting my every move and for always believing in me. You are always there for me, from my most anxious to my most excited
iv moments. Extra thank you to my brothers for spoiling me with computer equipment I needed to work on my thesis at home. Un-acknowledgement to Emily and Will, who stole my bedroom when I left for school.
Additional appreciation must be given to Gina Martucci and Gregory Freideman, whose friendship and love define such a large part of who I am. Enough thanks can never be given to you and your endless amounts of patience. Special thanks are extended to Gregory’s cooking skills, for without them, I would have survived only on chocolates and coffee.
A huge thank you to my classmates, including those graduating in 2020, for your encouragement, laughter, support, silliness, dinners, Mario party breaks, late night studio companionship, love, and mug cakes.
Finally, thank you to The Vesalius Trust for Visual Communication in the Health
Sciences for their generous support of this project.
v Table of Contents
Abstract ...... Error! Bookmark not defined. Acknowledgements ...... iv Introduction ...... 1 Content Background ...... 1 The “Ome” in Mechanobiome ...... 1 Building Blocks of the Mechanobiome ...... 2 Actin ...... 2 Actin Regulatory Proteins ...... 3 Crosslinkers and Stabilizing Proteins ...... 4 Myosin-II ...... 5 Microtubules ...... 7 Intermediate Filaments ...... 7 Physiological Relevance of the Mechanobiome ...... 8 Implications in Health and Medicine ...... 10 Model Organism Dictyostelium discoideum ...... 11 Visualization Considerations...... 13 Difficulties of Visualizing “Omes” ...... 13 Existing Visuals of the Mechanobiome...... 17 Learning Theories ...... 19 Audience ...... 20 Materials and Methods ...... 21 Defining the Need ...... 21 Project Development ...... 23 Literature Review ...... 26 Interactive Development ...... 29 Production Workflow ...... 31 Interactive 3D Models ...... 31 Cortex Block Model ...... 31 Conversion Rate for Calculations ...... 32 Building Actin Filaments ...... 33 Building the Plasma Membrane ...... 37
vi Building Myosin-II Bipolar Thick Filaments ...... 38 Building Crosslinkers: Alpha-actinin and Filamin ...... 44 Building Anchoring Protein: Cortexillin-I ...... 48 Populating Cortex ...... 50 Cortical Actin Filament Concentration Determination ...... 50 Cortical Crosslinker Concentration Determination ...... 51 Cortical Myosin-II Concentration Determination ...... 52 Binding Affinities Calculation ...... 54 Whole Cell Model ...... 59 Building the Whole Cell Model Shape ...... 59 Building the Actin Cortex of the Whole Cell Model ...... 61 Building Cytoplasmic Actin ...... 71 Building the Nucleus and Microtubules...... 73 Building the Plasma Membrane ...... 76 Interactive Capabilities ...... 81 Information Architecture ...... 89 Wireframes ...... 94 Color Palette Development ...... 95 Colorblind Accessibility ...... 96 Software and Equipment Overview ...... 97 Results ...... 99 Novel Mathematically-Derived 3D Models ...... 99 Website Prototype ...... 104 Button Style ...... 113 Visualizations Derived from the Novel 3D Models ...... 114 Introduction Animation for Web Resource ...... 120 User Testing ...... 122 Access to Assets Resulting from this Thesis ...... 123 Discussion and Conclusion ...... 124 Novel Visualizations of the Mechanobiome ...... 124 Mathematically-Derived Models ...... 125 A Visual Resource to Teach Mechanobiome Concepts ...... 126 Future Directions ...... 127
vii Conclusion ...... 128 Appendix ...... 129 Appendix A: Plasma Membrane Style Development ...... 129 Appendix B: Early Information Architecture ...... 131 Appendix C: Early Wireframes ...... 135 Appendix D: Text Accompanying the Interactive 3D Models ...... 136 Appendix E: Introduction Animation Script and Storyboards ...... 143 Script ...... 143 Storyboards ...... 143 Glossary of Mechanobiome terms ...... 146 References ...... 149 Vita ...... 154
viii List of Tables
Table 1. Examples of terms used in the literature review...... 27 Table 2. Known values of components within the actin cortex...... 32 Table 3. Values showing approximation crosslinkers bound to one or two filaments ...... 55 Table 4. Number of filament-crosslinker combinations ...... 56 Table 5. Software and Equipment used in the creation of 2D and 3D assets ...... 98
ix List of Figures
Figure 1. A comparison of F-actin and G-actin ...... 3 Figure 2. Structures of crosslinking proteins ...... 5 Figure 3. Examples of actions of crosslinking proteins ...... 5 Figure 4. Myosin-II structure ...... 6 Figure 5. Myosin-II assemblies ...... 7 Figure 6. Various life stages of Dictyostelium ...... 12 Figure 7. KEGG map of the human metabolome ...... 14 Figure 8. Screenshots of the DNAInteractive ...... 15 Figure 9. Screenshots of the Human Proteome Map ...... 16 Figure 10. Screenshot of Visual Guide to Human Cells from the Allen Cell Explorer ...... 17 Figure 11. Ancestor Charts from the EMBL-EBI ...... 28 Figure 12. Close up of the ancestor chart for the cortical actin cytoskeleton ...... 29 Figure 13. Separated actin monomers within PyMOL ...... 33 Figure 14. Cinema 4D interphase using ePMV ...... 34 Figure 15. Actin filament cloner object values ...... 35 Figure 16. Actin filament displaying the correct rotation ...... 36 Figure 17. High polygon actin model compared to the low polygon actin ...... 36 Figure 18. Actin size compared to membrane size ...... 37 Figure 19. Plasma membrane and actin cortex volume showing a 1:20 depth ratio...... 38 Figure 20. File 1M8Q within PyMOL ...... 40 Figure 21. PPDB file 1D7M ...... 40 Figure 22. De novo coiled-coil tail built for myosin-II within Cinema 4D...... 41 Figure 23. Options for the attachment of the myosin head to the coiled-coil tail ...... 42 Figure 24. Myosin-II Bipolar Thick Filament final structure within Cinema 4D ...... 43 Figure 25. Organization of layers and cloners within Cinema 4D for myosin-II BTF ...... 43 Figure 26. Alpha-actinin PDB files in PyMOL ...... 45 Figure 27. Various options for alpha-actinin binding to actin filaments ...... 45 Figure 28. A) High-resolution and B) low-resolution models of alpha-actinin ...... 46 Figure 29. Comparison of filamin models ...... 47 Figure 30. A) High-resolution and B) low-resolution models of filamin ...... 47 Figure 31. Electron microscope analysis and predicted structure of cortexillin-I molecules used as reference for building the cortexillin-I 3D model ...... 48 Figure 32. Final 3D structure of cortexillin-I ...... 49 Figure 33. Cortex with 36 actin filaments and additional actin monomers distributed to the volume of the cube...... 51 Figure 34. Actin cortex cube reflecting calculated concentrations of actin filaments, crosslinkers, and anchoring proteins ...... 52 Figure 35. Actin cortex reflecting calculated concentrations of actin filaments, crosslinkers, anchoring proteins, and myosin-II BTFs...... 53
x Figure 36. Examples of the 2FC models ...... 57 Figure 37. Examples of the 2F2C models ...... 57 Figure 38. Examples of the 2F3C models ...... 57 Figure 39. Actin cortex styles ...... 58 Figure 40. Scanning Electron Micrograph of wild-type interphase Dictyostelium ...... 60 Figure 41. Cinema 4D interface: Dictyostelium SEM reference to model cell shape...... 60 Figure 42. Cortex texture tests using Volume Builder and Volume Mesher ...... 61 Figure 43. Example of one early test cloning filaments to the cell shape model ...... 63 Figure 44. Example of the low-polygon actin cortex ...... 64 Figure 45. Cell shape model in ZBrush before ZRemesher ...... 66 Figure 46. Two ZRemeshed cell shape models ...... 66 Figure 47. Cloned cortex blocks to cell shape models of varying point counts ...... 67 Figure 48. Low polygon myosin-II BTFs...... 68 Figure 49. Final cortex model: lighting, materials, and textures ...... 68 Figure 50. Boole set up to achieve a cortex cross-section ...... 69 Figure 51. Boole object settings to generate a cortex cross-section...... 70 Figure 52. Final actin cortex cross-section: colors, textures, and lighting...... 70 Figure 53. Resulting cytoplasmic actin density ...... 73 Figure 54. A comparison of the nucleus ...... 73 Figure 55. Immunofluorescence staining of a Dictyostelium cell...... 74 Figure 56. Cinema 4D settings used to generate the microtubules...... 75 Figure 57. Resulting model of microtubules and the MTOC ...... 75 Figure 58. SEM of a Dictyostelium cell plasma membrane...... 76 Figure 59. Iterations of the plasma membrane model within ZBrush...... 77 Figure 60. Editing the plasma membrane to reduce intersections with the cortex...... 79 Figure 61. Display property settings to see the inner geometry of an object...... 80 Figure 62. MaskByFeature: Border setting within ZBrush ...... 80 Figure 63. Modeling the plasma membrane cross section edge, ...... 81 Figure 64. Settings used to export animations from Cinema 4D...... 82 Figure 65. Settings used to import FBX animations into Blender...... 83 Figure 66. Early test of the whole cell interactive, exported to the web...... 84 Figure 67. Early iteration of interactive functionality: Membrane Half button...... 84 Figure 68. Early iteration of interactive functionality: Cortex Half button...... 85 Figure 69. Interactive functionality: early iterations of the cortex block...... 85 Figure 70. Verge 3D Puzzles ...... 86 Figure 71. Puzzles generating functionality: Play/Pause Animation button...... 86 Figure 72. Puzzles generating functionality: Zoom In/Out button...... 87 Figure 73. Final IA ...... 89 Figure 74. IA of the 3D Viewer ...... 90 Figure 75. IA of the Loading Screen, Introduction, and Key Concepts...... 91 Figure 76. IA of the News and Repository...... 92
xi Figure 77. IA of Appendix and About...... 93 Figure 78. Final website wireframes...... 94 Figure 79. Developed web interface palettes...... 95 Figure 80. Cortex colorblindness simulation results ...... 96 Figure 81. Whole cell colorblindness simulation results ...... 97 Figure 82. Whole cell model...... 99 Figure 83. Whole cell model transparent membrane...... 100 Figure 84. Whole cell model cross section ...... 101 Figure 85. Whole cell model additional visibility options ...... 102 Figure 86. Final cortex model ...... 103 Figure 87. Mapping the Mechanobiome interface ...... 105 Figure 88. Icon styles for the web prototype...... 113 Figure 89. Illustration summarizing mechanobiome components ...... 114 Figure 90. Stills from cortex block turntable animation ...... 116 Figure 91. Stills from didactic animation: a migrating cell ...... 118 Figure 92. Stills from didactic animation: recruitment of contractile machinery ...... 119 Figure 93 Introduction animation ...... 120 Figure 94. Membrane texture style and mapping...... 141 Figure 95. Plasma membrane materials tested...... 142 Figure 96. Early IA...... 144 Figure 97. Left half of the information architecture...... 145 Figure 98. Right half of the information architecture ...... 146 Figure 99. Early website wireframes ...... 147 Figure 100. Introduction animation storyboards ...... 157
xii Introduction
Content Background
The “Ome” in Mechanobiome
In scientific research, the suffix “ome” connotes totality; thus, the genome is the totality of an organism’s genetic material, the transcriptome the RNA transcripts, and the proteome the proteins. In the same way, the mechanobiome is the totality of macromolecules that sense, respond to, and generate mechanical forces. “Omes” are powerful tools in research, providing a systems-level approach to studying the interconnectivity of constituent parts. Cells never experience a single mechanical force at a time; multiple mechanical stimuli, internal and external to the cell, control physiological changes. Homeostatic imbalances in both mechanical and biochemical stimuli initiate physiological changes, yet even then, stimuli may activate genes and proteins that perform cooperative, null, or antagonistic actions to one another.
It is difficult to predict physiological changes when only studying a single mechanical stimulus or a single component of the mechanobiome. Mapping the mechanobiome allows for comprehensive understanding of biological systems’ reaction to forces, and how these forces or mechanical environments affect cellular development, physiology, and human disease. The mechanobiome is a term developed by the Robinson Laboratory at the Johns
Hopkins School of Medicine, but the concept was first proposed in 2007 at the
Massachusetts Institute of Technology, using the term “mechanome” (Lang, 2007). This project will use the term mechanobiome exclusively, although the concepts also apply to the mechanome.
1 Building Blocks of the Mechanobiome
The mechanobiome is involved in a diverse range of biological processes including cell shape control, cytokinesis, cell metabolism, transcription, movement, and more, from the cellular to tissue to organ levels. Mechanical forces are capable of directing cell behavior through a series of steps involving sensing mechanical force, propagating force, and translating force into biochemical signals. This allows cells to adapt to their dynamic physical environments by remodeling their cytoskeleton, activating signaling transduction pathways, and altering gene expression. Thus, the mechanobiome includes all the macromolecules involved in the sensation of force (mechanosensation), in the transmission of force from the cell’s surroundings to the proteins inside the cell (mechanotransduction), and in the generation of force within the cell.
The cytoskeleton is a major component of the mechanobiome which coordinates the activity of hundreds of thousands of proteins to (1) spatially organize the content of the cell,
(2) generate internal forces to allow the cell to move and change shape, and (3) connect the cell (physically and chemically) to its external environment (Fletcher et. al, 2010). The cytoskeleton is constantly reorganizing and can be thought of as a highly dynamic and adaptive skeleton of the cell. Cell division, differentiation, migration, morphogenesis, and stem cell fate determination are all possible due to the dynamic nature of the cytoskeleton
(Dickinson et al., 2012; Guilak et al., 2009; Kim et al., 2015; Lee et al., 2010; Ngo et al.,
2016).
Actin
Actin is the most abundant protein in the cytoskeleton, and the most abundant protein in most eukaryotic cells. It participates in more protein-protein interactions than any
2 other known protein (Dominguez et. al, 2011). Globular actin (G-actin) has the ability to polymerize into filaments (F-actin), generating forces that alter cell shape change and, in combination with molecular motors, organizes organelles and vesicles within the cell
(Figure 1). F-actin is particularly concentrated within the actin cortex, which lies just beneath the cell membrane. The actin cortex drives mechanical changes at the surface of cells. Fluctuations in the tension of the actin cortex directly affects cell surface tension, which drives changes in cell shape. Gradients of surface tension drive processes such as cell movement and cytokinesis.
Figure 1. A comparison of F-actin and G-actin
Actin Regulatory Proteins
Several classes of proteins interact with actin to regulate f-actin organization, polymerization, and movement. Mechanical forces can affect the activity of these regulatory proteins, which in turn affects the mechanical actin network properties, such as flexibility and adaptability. Cell viscoelastic properties are associated with significant changes in cell cytoskeletal structure, as regulated by the actions of the following classes of proteins on the actin network (Hu et al., 2018).
Nucleation-promoting factors initiate f-actin formation, polymerases promote the polymerization of g-actin into f-actin, and capping proteins terminate f-actin polymerization.
! If any one of these regulatory proteins is altered, the result is significant impact on the mechanical properties of cell. For example, the nucleator complex Arp2/3 is required for phagocytosis, helps establish cell polarity, and helps regulate asymmetric cytokinesis (He et. al, 2017). In the case of capping proteins, when depleted, cortical thickness increases and cortical tension decreases, suggesting that f-actin length directly impacts tension (Chugh et al., 2017). Depletion of depolymerases is correlated with excessive cortical actin accumulation, which causes cytokinesis failure (Ishikawa-Ankerhold et al., 2010; Severson et al., 2002).
Crosslinkers and Stabilizing Proteins
Crosslinkers and stabilizing proteins organize f-actin into network meshes, and anchoring proteins physically link the actin network to the cell membrane. Without crosslinkers or anchoring proteins, f-actin cannot tether to each other or to the membrane, so the actin network is unable to contract or move the plasma membrane or sense when a stress is applied to the cell. Crosslinkers include proteins such as alpha-actinin, filamin, and cortexillin-I (Figure 2). Alpha-actinin and cortexillin-I have the ability to link f-actin into both highly organized parallel bundles and random meshworks (Falzone et al., 2012) (Figure
3). Crosslinkers play important roles in the contraction of f-actin. This can be demonstrated through the increased crosslinking that slows cleavage furrow ingression during cytokinesis
(Reichl et al., 2008). Finally, anchoring proteins tether the actin cortex to the inner surface of the cell membrane. Cortexillin-I also acts as an anchoring protein, as it has both actin and lipid binding domains. Without anchoring proteins, blebs, the separations of the membrane from the cytoskeleton, form in cells. Blebs can be controlled or disastrous to cells; they are
4 the main feature of cells undergoing apoptosis (controlled-cell death), but they also play roles in locomotion and division (Goudarzi et al., 2019).
A
B
Figure 2. Structures of crosslinking proteins A) filamin and B) alpha-actinin. The structure of cortexillin-I is not shown because there is no published crystallization data for that protein.
C
Figure 3. Examples of actions of crosslinking proteins A) alpha-actinin, B) filamin, and C) cortexillin-I. Structures are not to scale.
Myosin-II
Myosin-II is considered the active force generator that drives contraction in all animal cells (Robinson et al., 2012). Myosin-II comes from a family of myosins (motor proteins) which cover a large range of cellular function, including actin polymerization
! (myosin-I) and vesicle trafficking (myosins-V and VI). All myosins contain a common motor domain that allow the myosin to perform mechanical work (Sellers, 2000). The basic motile phase of myosin involves swinging of a lever arm forward, translocating the myosin ~10 nm relative to the actin filament (Murphy et al., 2001; Spudich, 2001). Although the mechanism is essentially the same, each type of myosin reacts to mechanical force uniquely, allowing each to perform specific tasks, such as contraction of the actin network.
Myosin-II is formed from two heavy chains; each chain contains a globular head, lever arm and coiled-coil tail (Figure 4). Myosin-II associates into dimers and tetramers, but the functional contractile unit of myosin-II is the bipolar thick filament (BTF) (Figure 5)
(Spudich, 2001). BTF size ranges from as few as eight to as many as 400 monomers, depending on the organism and tissue type (Pollard, 1982; Pepe et al., 1979; Howard, 1997;
Niederman et al., 1975; Verkhovsky et al., 1993).
2019 Cecilia Johnson
Figure 4. Myosin-II structure
!
Figure 5. Myosin-II assemblies: monomer, dimer, tetramer and bipolar thick filament (BTF)
Microtubules
Microtubules are another building block of the cytoskeleton, stiffer and more elongated than actin filaments, with tracks spanning the length of a cell. Microtubules play a critical role in cell polarization, build the mitotic spindle during mitosis, form radial arrays during cell division, and function as tracks for intracellular movement (Duncan and
Wakefield, 2011; Harada et al., 1996; Siegrist and Doe, 2007). Microtubules assemble stably and disassemble rapidly, which helps cells separate chromosomes during mitosis through rapid reorganization (Lodish et al., 2000). Thus, microtubules play a large role in intracellular forces and the organization of intracellular materials.
Intermediate Filaments
Intermediate filaments are an additional important component of the mammalian cytoskeleton, although they are absent from model organisms such as Dictyostelium discoideum, the model for this thesis (Page 11).
! Alone, the above proteins have little influence on cellular behavior and identity, but considered together, proteins within the mechanobiome and their interactions are major determinants of cell fate.
Physiological Relevance of the Mechanobiome
The mechanobiome has broad implications in physiology on the molecular, cellular, organ, organ-systems, and organismal levels.
On a molecular level, the mechanobiome is responsible for generating forces within the cell via the actin cytoskeleton. As described previously, fluctuations in nucleators, capping proteins, crosslinkers, myosin-II BTFs, and anchoring proteins affect the length and network mesh of f-actin, which in turn affects cell mechanical properties. Deformation of actin networks provides cells with mechanosensation abilities, which leads to mechanotransduction and potentially the activation of signal transduction pathways. These can activate transcription or translation, which alter the behavior of cells.
On a cellular level, the mechanobiome helps to guide cell differentiation, cell division, migration, morphogenesis, and stem cell fate determination. In fact, mechanical stimuli are thought to be one of the earliest inputs that cells experience, vital for cell differentiation and continued regulation of cell morphology (Mohan et al 2015). This sensation of mechanical forces, not just chemical forces, guides cells to assume discinct shapes to fulfill their specialized roles, such as red blood cells or neurons, whose structures are vital to their success. The mechanobiome of these cells not only sense the initial stimuli for their differentiation but continue to control cell shape throughout the cell’s lifespan and modify cell behavior in response to additional physical stimuli. The mechanobiome is what holds red blood cells in their functional disc shape and what helps reshape neural networks
8 in response to learning. In addition, the mechanobiome is essential in cell migration and movement. Gradients in the cortex composition or organization can result in cellular contractions or deformation; during cell migration, gradients of tension in the cortex usually favor higher tensions towards the rear of the cell, which powers cell body retraction
(Chabaud et al., 2015; Vicente-Manzanares et al., 2009). These also result in tension flow throughout the cell, which aid in generating the forces that drive cells forward (Bergert et al.,
2015; Lämmermann et al., 2008; Liu et al., 2015; Ruprecht et al., 2015).
On a tissue level, uneven distribution of cortex tension can provide polarized ends of cells or bodies of cells. For instance, in polarized epithelia, apical cortex contractions can be instrumental to tissue morphogenesis (reviewed in Coravos et al., 2017; Levayer and Lecuit,
2012). Overall tissue contractions result from pulsations in cortex contractions (Martin et al.,
2009; Munjal et al., 2015; Solon et al., 2009), which may be caused by the pulsations facilitating cell rearrangement (Curran et al., 2017).
From the organ to organism levels, the mechanobiome is involved in an expansive list of physiological systems, including the musculoskeletal, respiratory, cardiovascular, lymphatic, and integumentary systems. Changes in cellular mechanical properties are instrumental for, but not limited to, blood flow regulation, wound healing, bone remodeling, brain morphology, tissue regeneration, and embryogenesis. For example, in the sensation of hearing, varying amplitudes of vibrations in the inner ear are sensed by proteins within the mechanobiome. Those mechanical forces are transmitted into electrical impulses through modulating tension felt by transduction channels (Cory, 1903; Gillespie et al, 2004). Bone requires mechanical stimulation to maintain density. This requirement is best demonstrated through long-duration astronauts, who return to Earth with lower bone density due to the lack of gravitational resistance (Orwoll et. al, 2013).
9 Implications in Health and Medicine
A wide variety of diseases including cancer, chronic obstructive pulmonary disease
(COPD), cardiovascular disease, osteoporosis, and malaria are due to an underlying mechanical failure. Consider the red blood cell described earlier: when children become infected with malaria, the mechanical properties of their red blood cells shift. Infected red blood cells lose their normal discoid shape, experience increased membrane rigidity, and increase adherence to the lining of small blood vessels, which causes obstruction to tissue perfusion. Severe malaria also reduces the deformability of uninfected red blood cells, additionally compromising blood flow through tissues. Malarial progression eventually causes destruction of red blood cells, and the host experiences anemia (Miller 2014). Loss of normal mechanical properties of the blood cell causes morbidity and, in many cases, mortality.
In cancer, abnormal expression of proteins in the mechanobiome alters cancer cell contractility and deformability. This altered mechanical state allows cancerous cells to release and disseminate from the primary tumor, in the process known as metastasis. Metastasis is the deadliest aspect of cancer and is estimated to be responsible for 90% of cancer related deaths (Chaffer 2011). Recent research shows that increasing the activity of mechanoresponsive proteins in the mechanobiome alters the mechanical properties of cells, reducing their ability to polarize and invade other microenvironments, thereby halting cancer cell dissemination and invasion (Thomas and Robinson, 2017; Surcel et al 2017). This research is especially relevant for cancers such as pancreatic duodenal adenocarcinoma
(PDAC), which currently has a survival rate of approximately 6% and no viable treatment.
Preliminary studies have demonstrated that it is possible to treat metastasis in PDAC by targeting the regulation of proteins within the mechanobiome. The concept behind this
10 research has the potential to treat a multitude of cancer types. Overexpressed levels of critical proteins within the mechanobiome are known to exist in breast, cervical, esophageal, colorectal, gastric, lung, liver, and glioblastoma cancers. Thus, understanding more about the mechanobiome may provide insight into a wide variety of metastatic diseases.
Cardiovascular disease currently affects an estimated 84 million people and causes
2,200 deaths per day in the US. Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the US. There are more than 3 million cases of osteoporosis in the
US each year, with significant impact on the risk of bone fracture. The etiology of all the above diseases include abnormal expression of mechanobiome properties. Understanding the mechanobiome will enhance our understanding of biological machinery, and ultimately lead to new pathways for targeting disease.
Model Organism Dictyostelium discoideum
Dictyostelium discoideum, a social amoeba, is a model organism commonly used to study cytokinesis, cell motility, chemotaxis, phagocytosis, endocytic vesicle traffic, cell adhesion, pattern formation, caspase-independent cell death, and more. Dictyostelium is also increasing as a model for investigating human diseases and has been a vital component in studying potential drug treatments, such as cis-platin, an anticancer agent (Li et al., 2000), lithium for bipolar disorder (Williams et al., 1999) and biphosphates for osteoporosis (Grove et al.
2000).
Dictyostelium has numerous features that make it well suited for studying the actin cytoskeleton. Although it is a unicellular organism, during periods of starvation, Dictyostelium cells will aggregate by chemotaxis and form a multicellular state, with further stages involving differentiation (Figure 6). This allows researchers to study within a single cell the biological
11 processes typical of a multicellular organism (Bozzaro 2013). The stability of a unicellular organism, rather than a mammalian cell culture, allows for higher reproducibility of experimental results and easier integration of many regulatory networks. In addition to its use in studying normal biological processes, Dictyostelium has been useful as a model for medical and pharmacological research. Many of the proteins in Dictyostelium are evolutionarily conserved or have similar mammalian homologs. Dictyostelium harbors at least 33 homologs of human disease genes within its fully sequenced 34-Mb genome (Eichinger 2005). As a single celled organism, Dictyostelium provides a rapid means of identifying which gene a drug targets and the drug’s mechanism of action. Lastly, Dictyostelium cells are handled more easily and are less costly than mammalian cell cultures, making them the preferred model organism for many research labs.
Figure 6. Various life stages of Dictyostelium, from earliest aggregates at the leftmost side of the image, to multicellular, differentiated fruiting bodies on the right. Image reproduced from Dormann and Weijer 2006, from the Nature Publishing Group. The Robinson Lab at the Johns Hopkins University School of Medicine uses
Dictyostelium to study fundamental principles of normal biological processes and applies those principles to a variety of disease states. In conjunction with the Robinson Lab, Dictyostelium cells were selected as the model for this project, due to the aforementioned use of
12 Dictyostelium as the model organism for studying processes related to the mechanobiome.
Actin isoforms in humans are named for the location in the body; many are specific to cardiac, skeletal, or smooth muscle tissues, or are found in the cytoplasm only (McHugh
1991, Vandekerckhove 1978). Dictyostelium has fewer isoforms of proteins than mammalian cells, which simplifies concepts and allows for more streamlined education. Furthermore, using Dictyostelium allows proteins to be modeled at a broader level; concepts learned from the Dictyostelium cells can be applied to more complex cell states. Dictyostelium provides a starting point for a complex body of macromolecules and may be a basis for future studies in additional cell types.
Visualization Considerations
Difficulties of Visualizing “Omes”
As omes are system-wide studies, involving hundreds to millions of interconnecting players, they are difficult to encapsulate in a single image. As a result, visuals of omes that attempt to characterize the entirety of the system are often either too complex or too simplified. When too complex, visuals may provide little understanding or teaching value.
Many illustrations which attempt to include the whole story in one image become highly schematic and disorienting (Figure 7). Conversely, in more simplified illustrations, select displays of information may not tell the whole story. Players in omes can be antagonistic or cooperative, and their actions can have little impact on the behavior of systems unless combined with other events. Many visuals of omes hyper-focus on one aspect of the story, often for use in academic publication, describing a specific discovery. While these are effective for publication, in isolation, they leave the state of visuals of omes scattered and incomplete. A repository of focused images from publications can provide a more complete
13 understanding of the various complexities of omes. An interactive resource can provide additional functionality, allowing user navigation through the complexity of omes without being overwhelmed with unorganized information.
Figure 7. KEGG map of the human metabolome, which displays known metabolic pathways. Note that while it includes all the known components, it isn’t an effective teaching tool for understanding the metabolic pathways. Image reproduced from Completing the Metabolome, Elkin et al., 2012. Introducing interactivity can be an effective way to organize the information, allowing viewers to selectively explore pathways. Several interactive modules for “omes” currently exist, including DNAInteractive (dnai.org), an educational resource from the Cold
Spring Harbor Library, which provides an interactive exploration of concepts surrounding the genome (Figure 8). It includes a timeline of the history of genomes, games to practice reading the DNA code, lessons on manipulating the genome, investigations into the techniques of forensic analysis, applications of the genome to healthcare, and more. It successfully organizes a complex topic into small sections which allow for nonlinear learning to gradually build understanding. The DNAInteractive even includes a news section and a blog, which functions to keep the interactive resource, built in 2002, relevant.
14 DNAInteractive is targeted towards grades 9-12, including features such as the “Gene Boy,” a wordplay on GameBoy, and towards teachers, as it includes lesson plans and teacher guides.
Figure 8. Screenshots of the DNAInteractive, displaying a background on the genome, and "Gene Boy," a game for finding and analyzing genes. Text not intended to be read.
The Human Proteome Map (HPM), built in 2014, is an interactive designed for the scientific community, which allows navigation of peptides sequences from the human proteome project (Figure 9). Containing 17,294 genes, 20,057 proteins, and 293,700 peptide sequences, the HPM aims to reorganize “the label-free quantitative proteomic data set in a simple graphical view.” (Kim et al., 2014). While it may be an effective tool for researchers to see levels of protein expression in different organs and cells, it is not an effective teaching tool for explanation of the proteome. The functionality of the website is limited for one purpose, and it is unclear if the website is updated as new proteomic data emerges.
15
Figure 9. Screenshots of the Human Proteome Map, an interactive resource for exploring the proteomic data set into “simple graphical view.” Text not intended to be read.
The Allen Institute for Cell Science hosts a website titled the Allen Cell Explorer, a data portal that hosts a database of 3D imaged human stem cells. The Allen Cell Explorer hosts interactives for predictive cell models, observations of cells, and an Interactive Visual
Guide to Human Cells, which allows viewers to zoom into and rotate cell models segmented from real data for each phase of mitosis (Figure 10). The Allen Cell Explorer is a powerful visual resource of cells; however, its teaching aspects are limited. The 3D models within the
Interactive Visual Guide to Human Cells indicate general location only, and the models do not reflect the specificity of known structures; for example, organelles are represented as amorphous shapes. In addition, the models are based on human stem cells, not on a common model in mechanobiome research. Human stem cell characteristics are highly dependent on how the stem cell lines are cultured (Robert, 2004).
!
Figure 10. Screenshot of Visual Guide to Human Cells from the Allen Cell Explorer. The human stem cell shown highlights the actin filaments during interphase. Given that actin filaments are one of the most abundant proteins in the cytoplasm, this model appears to underrepresent the number and concentration of actin filaments. Text not intended to be read.
Existing Visuals of the Mechanobiome
No visuals, let alone interactives, currently exist of the mechanome or mechanobiome. Instead, those interested in learning about the mechanobiome may find imagery for “cell mechanics,” “mechanosensation,” “mechanotransduction,” and for a variety of processes that the mechanobiome is involved in; however, no easily accessible visuals exist which bring together all the concepts of the mechanobiome. Unless one already has a significant working knowledge of the mechanobiome, it would be difficult to know what visuals to search for. Another avenue for understanding mechanobiome concepts could be textbooks, such as the Introduction to Cell Mechanics and Mechanobiology. Textbooks may bring together similar concepts involved in the mechanobiome, however, they quickly become outdated in fast growing fields, are expensive and inaccessible to the general public, and cannot display the necessary 3D relationships of the mechanobiome without
17 supplemental digital PDFs or webpages. This leaves the state of visualization of the mechanobiome incomplete, scattered, and outdated. A significant challenge in understanding the mechanobiome is this lack of organization of effective visuals.
In addition to the lack of an accessible mechanobiome resource, many existing visuals of the underlying components of the mechanobiome are ineffective in visualizing complex spatial relationships. For example, accurate visuals of the cytoskeleton are limited; most current illustrations of the cytoskeleton show actin as too large in size, low in concentration, or static. Spatial and temporal relationships within the cytoskeleton are difficult to address in two dimensional relationships, thus 3D models or animations are the most appropriate, effective venue for complete understanding. “The Inner Life of the Cell” is an 8.5-minute 3D animation explaining processes that occur in the cell, including a brief explanation of cytoskeletal structure. This groundbreaking animation was released in 2006 after a 14-month production period; much of the biological knowledge available at that time has evolved. While still an effective introduction to the inner workings of cells, its teaching aspect for the multitude of complex topics covered in the mechanobiome is limited. Due to the mechanobiome being a relatively new concept in use by highly specialized laboratories, it is understandable that very few visualizations exist, whether diagrammatic or didactic 3D.
However, more established concepts vital to understanding the mechanobiome, such as force sensation and cellular responses to external mechanical stress, also currently lack didactic 3D visualizations or animations. Understanding the complex 3D relationships of proteins during various cellular mechanics events may help researchers gain insight into the full picture of activity on a cellular and molecular level.
18 Learning Theories
Learning theories are vital to early stages of the design process, when considering organization of large bodies of information for an educational resource. Various learning theories were researched, examined, and considered in early planning stages of this project’s web-based mechanobiome resource and repository. Aspects of various learning theories inspired the design and organization of the project, to optimize the educational aspects of a mechanobiome resource.
The primary learning theory used was the adult learning theory of andragogy by
Malcolm Knowles. This theory proposes that adult learning is directed by independent exploration and involvement. For this web resource, it is important that users explore the concepts independently and at their own pace. The theory of andragogy provides that there is a need to explain the reasons specific things are being taught; thus, in the web resource, the relevancy of components of the mechanobiome are emphasized. Sections are included to tie molecular and cellular mechanisms to normal physiology and disease processes so that users can understand the broader implications of the mechanobiome. In addition, the andragogy theory states instruction should consider the wide range of different backgrounds of learners, allowing for different levels or types of previous experience. Therefore, in the web resource, introductory concepts are included to provide a base-level understanding, and a glossary of mechanobiome terms is provided for users who may come from non-cell mechanics backgrounds.
A secondary learning theory important for the organization of the web resource was the Reigeluth Elaboration Theory. At its core, this theory states that content should be organized from simple to complex, while providing a meaningful context in which subsequent ideas can be integrated. This learning theory values a holistic sequence of
19 instruction, to provide meaning and motivation. In addition, it allows learners to make scope and sequence decisions on their own during the learning process. This theory was implemented into the web resource design process through the order and organization of sections. The first section is the 3D interactive, which provides the simplest and most straightforward visual understanding of the mechanobiome. From there, users may be directed to more complex learning ideas in the other sections.
Audience
The primary audience for this web resource and repository is the cellular mechanics community at large. Increased understanding of the mechanobiome concepts will allow researchers to think collectively about the mechanical properties affecting normal physiology. Further, this resource will encourage further research by characterizing the mechanobiome as a viable option for targeting mechanical properties of diseases. Within the cellular mechanics community, the repository will support integration of new discoveries from multiple labs. The secondary audience will be graduate students learning the foundations of cell mechanics, specifically the concept of the mechanobiome. This repository integrates and aggregates disperse information in a visual, well-organized resource, currently lacking for these audiences. Additionally, as mechanobiology involves the fields of biology, chemistry, physics, mathematics, and engineering, this web resource will be designed to accommodate users of diverse backgrounds who may not share a common language, despite a common interest in cell mechanics.
20 Materials and Methods
Defining the Need
Basic needs finding for the content of the project was conducted through informal discussions with individuals of the Robinson lab at Johns Hopkins. Sample topics discussed and questions asked included:
1. Where do people get “stuck” when you explain your research, including other
researchers, students, and/or non-scientists?
2. Is there a basic science concept that you find you often have to explain to individuals
prior to describing your own research? Are there fundamental, big-picture concepts
for which most people lack understanding, for example, data collection or
cytoskeletal components?
3. Is there a conceptual or data-heavy aspect of your research that your colleagues
would benefit understanding through visualization?
After discussions with Robinson and four graduate students from the Robinson lab, feedback was reviewed. Feedback included a wide range of responses, but the largest concerns included the following:
(1) Cytoskeletal mechanics are comprised of more components than people may
initially assume. Individuals tend to underestimate how many players are involved, how
densely packed the cells are, and how organelles and proteins within the cell need to
move with respect to the densely packed cytoskeleton. Individuals struggle piecing
together all the components of cytoskeletal mechanics into one cohesive story.
21 A related concern was the lack of comprehension regarding what was being observed when a learner views cell mechanics on the subcellular scale. What would one’s world view look like? How can we make our visualizations more grounded in physics?
(2) Misconceptions arise regarding cell mechanics within cancer metastasis and how
the physical properties of the cell relate to cancerous properties. Common confusion
exists regarding the idea that cells must be softer or more deformable to be cancerous.
Individuals recognize that cancer will often be discovered as a hard nodule within the
tissue and question how cancerous tissue can be stiffer, yet the cells more deformable.
This confusion arises because the stiffness of cancer cells is less important to cancer cell
survivability than how adaptive the cells are to varying microenvironments. Cancer cells
must be keen at sensing and homogenizing to their environment, even if it differs greatly
from the original environment of the cell.
A related concern is comprehending the difference between adaptable and stabilizing functions within the cell machinery. Misconceptions exist regarding the ability to target the cytoskeleton in metastasis research, despite current treatments being developed in this area.
Concern with rigid “linear learning” was expressed, therefore, flexible learning styles were encouraged for any possible visual solutions. After the greatest needs were researched, potential visual solutions for each need were discussed and revised. Relative positive and negatives of each project and solution were discussed, i.e. is enough information available on the topic, is the scope of the project reasonable, and does the proposed solution address the found need?
22 Project Development
Based on the results from needs finding, the following two areas of focus were proposed:
(1) What do you see if you are actually on the cellular scale?
• What does the cell’s worldview look like? What would the inner workings of the cell look like if visualizations were based in physics? • How does the cell relate to and deal with its environment? To changing or dynamic environments? • What physical pressures is the cell encountering? Is the flexibility/adaptive potential related to squeezing through tight environments and reforming its shape? How are physical pressures related to cytokinesis, cell migration, and transcription?
To address these questions, a 3D interactive environment was proposed, with the possibility of an immersive environment, such as through a virtual reality (VR) headset or platform such as Google Cardboard. An individual would be able to rotate to see a 360° view of the internal or external cell environment and click on various components of the cell to obtain additional information, including the name and function of structures, relevant information, and short didactic animations about the structure’s actions and interactions.
While this idea would help move away from linear logic, concerns arose that this project would be less accessible to the public for learning and would be severely limited in teaching ability between how much could be shown on varying scales (i.e. molecular vs. cellular level of understanding).
(2) What does “flexibility” really mean in the context of metastasis?
• How can we demonstrate that flexibility is more than stretchiness? • What is the best way to explain the difference between hard breast cancer nodules and more adaptive cells? • What properties are different in a cancer cell compared to a normal cell that allow it to be more adaptable? • What properties of the cell affect its adaptability?
23 For this topic, an interactive of the anatomy of a metastatic cancer cell was proposed.
Users would be able to click on structures within the cancerous cell to learn about each structure’s role in the cell’s adaptive potential. This interactive would address the machinery executing the formation of the cancer cell, elevated in expression, that promotes a dynamic system, and would teach about the cancerous cell’s “toolset” to become more adaptive.
Navigation would allow users to click through stages of the cancer cell to review normal, pre-cancerous, and metastatic cell properties. The ability to put the cells through different
“stress tests” was also proposed to demonstrate how cells adapt and how the cytoskeleton rearranges in response to the stresses at the various states of metastasis. Concerns arose regarding what environment the interactive cell model would be placed in without implying that (a) the cancer cell environment was inconsequential (b) the environment was static (c) this behavior was only isolated to one type of environment. Furthermore, this proposal removes the cell from its context, potentially diminishing the understanding of the broader concepts. In addition, there may not be enough information for the differences between the cell stages.
Midway in the needs finding and project definition process, Priyanka Kothari, a graduate student of the Robinson Laboratory, joined the discussion and continued to participate as a content advisor.
After further discussion about each topic and additional research, the project direction was amended to include aspects from both initial proposals. The content was expanded from focusing on metastasis to focusing on the normal physiological machinery of the cell. This provides a better basis for understanding metastasis and opens up the
24 possibility to relate the biological machinery to other diseases such as COPD and cardiovascular disease. Components from the first concept (what is seen on the cellular scale) were included to show the physiology of the cell machinery in a mathematically and physically grounded model, so that viewers could understand the density and interconnectedness of cellular components. To achieve the goals of 3D interactivity and nonlinear learning, it was decided that interactive models would be used to teach the concepts of cell mechanics via a model of the components of the mechanobiome. A module on a website, rather than through an immersive system, would be accessible to a more expansive audience.
An additional component included in the project proposal was the idea of a repository of visuals within the 3D interactive. Cell mechanics is a growing field, and to be a useful tool, it is vital that the information and visuals within the resource stay relevant and up to date. As additional discoveries are made in cell mechanics and in how researchers may target mechanics in disease processes, it would be important that those discoveries are highlighted and implemented into the visual repository. In addition, after a research and literature search, it was determined that the state of visuals relating to cell mechanics was scattered. Therefore, a repository was proposed to include a growing database of visuals describing cellular mechanics. This would function to keep the project relevant and would also serve as a useful resource for those outside of the field to understand overarching concepts. Additional visuals could be added to the repository via crowdsourcing by researchers in the field of cell mechanics. Or, a review process could be established to determine which visuals would be accepted to the repository. Incentive to add visuals may include a means to further publicize their research and contribute to the available knowledge
25 of the field. The process can be further developed, but it was important to consider this functionality in the design.
To summarize, it was determined that the visual solution would be:
1. A collection of fundamental principles of the mechanobiome, hosted on a website for public access
2. An expandable repository of visuals relating to the mechanobiome and cellular mechanics
3. An interactive of novel 3D visualizations of the cellular mechanic machinery, the mechanobiome
The entire website would be designed, and one segment would be completed as a prototype.
The material in this segment would depict concepts to be included in a review article being prepared by Kothari.
Literature Review
Following needs finding, an extensive literature review to evaluate the current state of visuals was conducted on the following topics:
1. The mechanobiome
2. Existing visual repositories
3. Existing 3D interactives of the cell
A list of words and phrases were developed to perform the literature review. The list is provided in alphabetical order; it is not all inclusive (Table 1).
26 Adaptive vs. stabilizing Actin Actin meshwork machinery
Cell contractility Cell cortex Cell environment sensing
Cell infrastructure Cell machinery Cell mechanics
Cell physiology Cell shape control Cellular forces
Chemical and mechanical Contractility kits Cytokinesis signaling
External and internal cellular Cytoskeleton Differentiation forces
Mechanical properties of Feedback mechanisms Mechanical stress cells
Mechanoaccumulation Mechanobiome Mechanome
Mechanoresponsiveness Mechanosensation Mechanotransduction
Microtubules Myosin Non-muscle myosin
Physically grounded Visual repository 3D cytoskeleton interactive movement Table 1. Examples of terms used in the literature review.
Ancestor Charts from the EMBL-EBI were a valuable resource for the initial literature review. For example, the ancestor chart for “cortical actin cytoskeleton” displays what the cortical actin cytoskeleton is and its context in simple graphic format (Figure 11).
The chart also provides alternate and related terms which can be used in the literature search. Each of the terms within QuickGo are clickable and provide links to additional information, synonyms, new ancestor charts, and child terms (terms that are direct descendants of the original term) (Figure 12).
27
Figure 11. Ancestor Charts from the EMBL-EBI. This ancestor chart is for the cortical actin cytoskeleton. It displays what the cortical actin cytoskeleton is and what it is a part of within a simple graphic format. Text not intended to be read.
28
Figure 12. Close up of the ancestor chart for the cortical actin cytoskeleton. Although the phrase “actin cortex” was used in the initial search, this chart provides alternate phrases “cortical actin cytoskeleton,” “actin cytoskeleton,” “cortical cytoskeleton,” and “cell cortex.” Each of these terms within QuickGo are clickable and provide links to additional information, synonyms, new ancestor charts, and child terms (terms that are direct descendants of the original term).
Materials related to the above concepts were reviewed in preparation for the design of the web resource. The hierarchy of information for the web resource was developed after this research into peer-reviewed research articles and further discussions with the project preceptor, Douglas Robinson and content advisor, Priyanka Kothari.
Interactive Development
Early focus was placed on the development of the interactive 3D models teaching the components of the mechanobiome. Discussions with Robinson and Kothari were conducted weekly throughout the iterative design process to develop the 3D models.
Despite a focus on teaching cell response to mechanical stress, the interactive was designed
29 to display a cell at mechanical equilibrium. This was done to keep the cell model as an introduction to mechanobiome components without introducing additional concepts, such as cell shape deformation in response to stress, too early. The cell model was chosen to be modeled after a Dictyostelium cell, due to its common use in studying cell mechanics, its similarity to mammalian cell in terms of the mechanobiome proteins, and its simplicity in number of protein isoforms, as discussed in the Introduction.
Given the complexity of the mechanobiome, it was not possible within the scope of the project to fully develop 3D models for every known protein. Furthermore, including all known components may be visually confusing for audiences. In addition, every macromolecule involved in the mechanobiome is not known, and many of the proteins known to be involved do not have characterized structures. Therefore, discussion focused on (1) which components of the mechanobiome will be shown, (2) what proteins would be the best examples of each component, and (3) how many examples of each component are appropriate. The components included within the interactive are:
1. The plasma membrane 2. The actin cortex a. Actin (g-actin and f-actin) b. Myosin-II (monomer, dimer, tetramer, and bipolar thick filament) c. Crosslinkers (filamin and alpha-actinin) d. Anchoring proteins (cortexillin-I) 3. Cytoplasmic actin 4. Microtubules 5. The nucleus
Additional components relate to the mechanobiome but are not the main players. It was discussed that there may be a few examples within the interactive to show that these organelles, including mitochondria and ribosomes, exist in Dictyostelium cells. Dictyostelium do not have intermediate filaments, so they are not included in the mechanobiome model, although they would exist within mammalian cell models.
30 Production Workflow
Interactive 3D Models
The interactive component was split into two models: the cortex block and the complete cell model. Components of both models were built using one of two methods: 1) starting in the molecular visualization software PyMOL, using files from the Protein
Database (PDB) or 2) de novo within the 3D modeling and animation software, Cinema 4D.
Cortex Block Model
The actin cytoskeleton is composed of myosin-II motors, crosslinkers, anchoring proteins, and actin filaments, which are physically linked to the plasma membrane by anchoring proteins (Luo 2013). Known values are indicated in Table 2. The following sections describe how each of the above components of the actin cortex were built.
31
Average length Average diameter (depth) Concentration
Membrane - 4-10 nm - Actin Filament 94 ± 57 nm 5.4 nm 90 µM Myosin-II - - 3.4 µM Monomer 186 nm 5.6 nm - Dimer 200 nm 13 nm - Cortex - 200 nm - Crosslinkers - - - Cortexillin 22.6 ±1.0 nm - 1 µM Alpha-actinin 35 nm - 1 µM Filamin 160-190nm - 1 µM Table 2. Known values of components within the actin cortex. These values were taken into consideration in the following sections for building the 3D models within the interactive.
Conversion Rate for Calculations
While Cinema 4D has arbitrary values, it was vital to the accuracy of this project that the ratio of values between objects within the cell and cortex remain constant. Therefore, a conversion rate between Cinema 4D values and known values was created for future calculations. One actin monomer of 2.8 nm in diameter measured .28 m within Cinema 4D.
Thus, the conversion rate from Cinema 4D to accurate cellular scale is 1m:10nm.
32 Building Actin Filaments
In non-muscle cells, actin filaments undergo self-assembly and disassembly, in which monomeric globular actin (G-actin), polymerizes to form two-stranded, helical filaments (F- actin) (Carlier, 1991), as depicted in Figure 1.
Dictyostelium actin can be found in PDB file 3CIP, Complex of Dictyostelium discoideum
Actin with Gelsolin (Baek et al., 2008), but due to the high conservation of actin structures between organisms, the actin within PDB file 1M8Q, Molecular Models of Averaged Rigor
Crossbridges from Tomograms of Insect Flight Muscles (Chen et al., 2002), was used, because this latter file includes the interactions between myosin-II heads and the actin filament. 1M8Q was imported into PyMOL, and the actin chains were separated (Figure 13).
Chains were imported into Cinema 4D using embedded Python Molecular Viewer (ePMV), an open-source molecular modeling plug-in (Figure 14).
Figure 13. Separated actin monomers within PyMOL. Each color represents one actin monomer. Text not intended to be read.
33
Figure 14. Cinema 4D interphase using ePMV. Three actin monomers are shown with overlapping low-resolution course molecular surface (CMS) and high-resolution molecular surface (MSMS). The MSMS was obtained by using the command #molname = “4A7F” within PMV-Python scripts/commands. Text not intended to be read.
The following considerations were taken while building the actin filaments:
1. F-actin filaments are composed of two intertwined strands that are staggered by half a monomer, i.e., ~2.8 nm
2. One pseudorepeat of actin filaments is 36 nm and contains 13 actin subunits.
3. The average length of an actin filament is approximately 94 nm. (Reichl et al., 2008).
To determine the average number of actin monomers within an actin filament the following was calculated:
94 nm per filament / 36 nm per pseudorepeat = 2.6 pseudorepeats per actin filament on average. Thus, 2.6 pseudorepeats x 13 actin monomers per pseudorepeat = 34 actin monomers per filament on average.
34 To create the filament, the G-actin dimer was placed within mograph cloner in
Cinema 4D. The cloner was set to linear mode, to repeat the actin dimers in a linear fashion.
The number of repeats was set to 17, which is half of the 34 actin monomer per filament average calculated, due to the cloner containing two actin monomers (Figure 15). To calculate the dimer transformation along the Z axis, the following calculations were made:
Given that the intertwined strands are staggered by half a monomer, 2.8 nm, and there are two monomers within the cloner, 2.8 nm * 2 = 5.6 nm. Using the conversion rate from earlier, 5.6 nm / 10 nm/m = 0.56 m.
Thus, the transformation along the Z plane was set to 0.56 m. Finally, the rotation around the B axis was set to 21° (360° / 17 dimers = 21° rotation per dimer) (Figure 15).
The correct rotation of the actin filament was verified by counting the number of actin monomers in one pseudorepeat, a total of 13 in both strands (Figure 16).
Figure 15. Actin filament cloner object values.
35
Figure 16. Actin filament displaying the correct rotation. One pseudorepeat, as shown by the bracket, contains a total of 13 subunits.
Two actin models were generated (Figure 17). One is a high polygon model for close-up use and for 2D images of the actin filament. The second has a much lower polygon count for use in the online interactive model. The number of polygons can be controlled via the resolution when importing models from PyMOL; however, for this model, additional polygon reduction was performed in ZBrush via mesh generation tool, DynaMesh.
Figure 17. High polygon actin model compared to the low polygon actin.
36 Building the Plasma Membrane
The plasma membrane is a lipid bilayer measuring approximately 4-10 nm thick
(Hine, 2005). The membrane for the cortex block was built using a simple cube primitive in
Cinema 4D. The thickness of the cube primitive was determined by aligning the cube to an actin filament and choosing a Y value comparable to the filament’s thickness, which are approximately in a 1:1 relationship (Figure 18). The values within Cinema 4D are arbitrary, but the size relationship between the various components in the mechanobiome are vital.
Thus, relative sizes are based off the thickness of a single actin filament, giving the conversion rate of 1 m = 10 nm. All future values given may be converted into Cinema 4D values by this conversion rate.
Figure 18. Actin size compared to membrane size. Note the size relationship between the plasma membrane (top, white) (PDB) and an actin filament (bottom, green and blue), which are approximately in a 1:1 ratio. This relationship was maintained when modeling the membrane depth.
The thickness of the actin cortex is approximately 200 nm. (Clark et al., 2013). A primitive cube was placed beneath the plasma membrane to represent the volume of the actin cortex, which was used in future steps. The cube depth was set to a value 20 times the thickness of the plasma membrane to maintain a ratio of 10 nm : 200 nm (Figure 19).
37
Figure 19. Plasma membrane and actin cortex volume showing a 1:20 depth ratio.
Building Myosin-II Bipolar Thick Filaments
Myosin-II bipolar thick filaments (BTF) are assemblies of myosin-II that function in actin filament contraction. Myosin-II is a protein composed of two heavy chains, two regulatory light chains, and two essential light chains. The heavy chain contains the globular head, which binds actin and ATP, and a long α-helix tail. The α-helix tail dimerizes with another myosin-II α-helix tail, forming a coiled-coil tail. The resulting protein is the
“functional myosin-II monomer”.
BTF are highly organized bundles of myosin, formed from sequential association of myosin-II monomers, assembled by three association steps. Myosin-II monomers associate to form parallel dimers. Dimers form antiparallel tetramers by binding their tails in opposite directions; the tails represent the bare-zone of the final BTF. Finally, dimers associate laterally to form the antiparallel tetramer. Summarized below, where M represents myosin-II monomer, D is parallel dimer, and T is antiparallel tetramer:
M + M = D D + D = T nD + T = BTF (Mahajan and Pardee, 1996).
38 The following measurements were considered when building the BTF:
The rod portion of Dictyostelium myosin-II measures approximately 186 nm in length and 5.7 nm in width (Kuczmarski et al., 1987; Pasternak et al., 1989). Tail portions of BTF exhibit helical twists, with a helical pitch of approximately 14 nm.
The following calculation was made to determine the number of full twists in a myosin-II filament:
186 nm / 14 nm = 13.26 ≈ 13 full twists
Myosin-II dimers average 13 nm in width (compared to 5.7 nm width of monomer).
Dimer tails align with 15-20 nm of tail extending past each other; thus, alignment is staggered by approximately 10% (Hodge et al., 1992; Kuczmarski et al., 1987; Pasternak et al., 1989).
At no point do Dictyostelium BTF elongate in length as dimers are added. As subsequent dimers are added, the BTF thickens. The number of dimers in a BTF are highly variable depending on the pH, concentration of divalent cations, and ionic strength
(Mahajan and Pardee, 1996).
To build the myosin-II head, PDB file 1M8Q was used, as it contains the interactions between myosin-II, the myosin-II regulatory light chain, the myosin-II essential light chain, and actin (Chen et al., 2002). 1M8Q was imported into PyMOL (Figure 20), and the chains were separated, exported, and imported into Cinema 4D. Myosin-II is missing the rod portion in most PDB files, so it had to be reconstructed. Two techniques were attempted to create the rod portion of myosin-II.
39 A B
Figure 20. File 1M8Q within PyMOL. A) The file without any organization. Each color represents a different chain. B) Organization of the chains. Green chains represent the actin filament. Orange, blue, purple, and red chains represent myosin-II heads attached to the actin filament. Note that myosin-II is missing the rod portion. In the first technique, a short coiled-coil was downloaded from PDB 1D7M, Coiled- coil Dimerization Domain from Cortexillin-I (Burkhard et al., 2000). In PyMOL, the coil was duplicated and aligned end-to-end until it reached approximately 186 nm in length
(Figure 21). The final structure was imported into Cinema 4D. Ultimately, this technique was not chosen for the 3D model for numerous reasons: (1) the duplicated coils were separate objects in Cinema 4D and merging the objects produced overlapping geometry, (2) the coils were inconsistent in thickness and gaps occasionally appeared between duplicated coils, (3) when attempting to bend the coils, they stretched and deformed.
A
B
Figure 21. PPDB file 1D7M A) Unedited within PyMOL. B) Stacked end-to-end to create an elongated coiled-coil tail for myosin-II.
40 The second technique involved building the coiled-coil de novo. In Cinema 4D, the coiled tail was built using a circle spline, a helix spline, and the sweep modifier. Since the diameter of a myosin-II monomer is 5.7 nm, and the rod domain contains two myosin-II rods, the radius of the circle spline was estimated to be ¼ of 5.7 nm. The helix height was set to 186 nm. To get a full 13 twists, the end angle was set to 0° and the start angle to 13 *
360°, or 4680°. The resulting geometry (Figure 22) was aligned with the myosin-II head domains.
Figure 22. De novo coiled-coil tail built for myosin-II within Cinema 4D.
The PDB file chosen has myosin-II in the rigor state, so the alignment of the coiled- coil tail to the myosin heads was based off the pre-stroke position (Figure 23). For easy manipulation of the coiled-coil tail and the myosin head, a spline was drawn at approximately the same length of the coiled-coil tail. The tail was aligned to the spline using a spline wrap deformer, with mode set to “keep length” to ensure that the tail does not grow in length to fill the entire spline. An align-to-spline tag was placed on the myosin-II heads and set to position 0% along the same spline that the coiled-coil tail was wrapped. This allows the tail to be easily manipulated and the heads to follow where the tail moves.
41 A B
Figure 23. Options for the attachment of the myosin head to the coiled-coil tail. Red and orange structures represent an actin filament. A) Comparison of the myosin-II tail length to actin filament length. The coiled-coil tail is approximately twice the length of an average actin filament (gray bar displays average actin length, 98nm). B) Correct alignment of the coiled-coil tail to the myosin head in the pre-stroke position.
The myosin-II dimer was built using mograph cloner in Cinema 4D. The count was set to 2, and the clone was rotated 180° around the P axis. The clone was transformed in the
X axis so that the monomers did not overlap and in the Z axis to account for the 15-20 nm offset of monomers in dimerization. Next, that cloner was placed into another cloner to build one half of the BTF. The count was set to 10, and a randomizer was placed on the mograph cloner for the position and rotation of the dimers. Finally, that cloner was duplicated and rotated 180° about its B axis to form the antiparallel strands of the BTF
(Figures 24, 25). The cortex includes monomers, dimers, and full BTFs.
42
Figure 24. Myosin-II Bipolar Thick Filament final structure within Cinema 4D.
Figure 25. Organization of layers and cloners within Cinema 4D to achieve the final myosin-II BTF
43 Building Crosslinkers: Alpha-actinin and Filamin
Alpha-actinin and filamin are two strongly mechanoresponsive actin-binding crosslinkers found in Dictyostelium and mammalian cells. The structure of alpha-actinin in
Dictyostelium has not been significantly researched, so the models of alpha-actinin were based off mammalian structures. Alpha-actinin is an antiparallel homodimer with an actin-binding domain, a central domain (rod domain), and a calmodulin-like domain with two pairs of EF hand motifs (Ribeiro et al., 2014). Alpha-actinin was built using PDB file 1SJJ, the Cryo-EM
Structure of Chicken Gizzard Smooth Muscle Alpha-actinin (Liu et al., 2004). PDB file
3LUE, the Model of Alpha-Actinin CH1 Bound to F-Actin (Galkin et al., 2010), shows how the actin binding domains of human alpha-actinin bind to actin filaments. Both 1SJJ and
3LUE were imported into PyMOL. File 1SJJ was aligned to the actin binding heads of 3LUE to properly align the full alpha-actinin structure with the actin filament (Figure 26). The resulting model was verified with published images depicting interactions between alpha- actinin and actin filaments (Figure 27). The chains were imported separately into Cinema
4D to generate low-resolution and high-resolution models (Figure 28).
44 A B
C D
Figure 26. Alpha-actinin PDB files in PyMOL. A) 1SJJ: the structure of alpha-actinin. B) 3LUE: alpha-actinin heads (magenta) attached to an actin filament (cyan). C,D) The result of aligning ISJJ to 3LUE. This depicts full alpha-actinin molecules bound to an actin filament.
Figure 27. Various options for alpha-actinin binding to actin filaments. This image was used to verify the correct alignment of resulting alpha-actinin-actin filament complex. Image reproduced from Liu, Taylor, and Taylor 2004, from the Journal of Molecular Biology. Text not intended to be read.
45 A
B
Figure 28. A) High-resolution and B) low-resolution models of alpha-actinin.
The molecular structure of filamin, unlike alpha-actinin, has been described for
Dictyostelium. Dictyostelium filamin (ddFLN) has two anti-parallel chains containing an actin- binding domain and a rod domain with six tandem repeats of a 100-residue motif with an immunoglobulin fold. While the same arrangements of repeats exist within the mammalian filamin, ddFLN is significantly shortened (24 repeats in mammalian versus 6 in Dictyostelium filamin) (Popowicz et al., 2004).
The molecular structure of the rod domains with repeats 4, 5, and 6 of ddFLN exist within PDB 1WLH, Molecular Structure of the Rod Domain of Dictyostelium filamin
(Popowicz et al., 2004). No files exist for repeats 1, 2, and 3, or for the actin-binding domain; however, a reconstruction exists summarizing the current structural knowledge of the ddFLN dimer, which was used for the basis of the 3D model. An actin-binding domain exists for mammalian filamin, and although the structures of rod domains 1-3 have not been determined experimentally yet, their sequences closely resembled that of rod domains 4 and
5. Thus, filamin was built from the PDB files 4B7L, Crystal Structure of Human Filamin B
Actin Binding Domain with 1st Filamin Repeat (Sawyer and Sutherland-Smith, 2012), and
1WLH, which contains the dimeric structure of rod domains 4-6. Thus, the conformation of all the domains is either known or can be modeled.
! First, 1WLH was opened within PyMOL. One chain was duplicated, and the sixth rod domain was selected and deleted. The resulting chain with duplicated rod domains 4-5 was aligned with the original rod domains 4-6 based on the homodimer model from
Popowicz et al, 2004 (Figure 29). Next, the first rod domain and actin-binding domain were aligned with the model using PDB file 4B7L. The final structure was imported into Cinema
4D to generate high-resolution and low-resolution models (Figure 30).
A
Figure 29. Comparison of filamin models. Note that the actin-binding domains differ, but overall structure remains close. For the scale that filamin will be shown in the interactive, the differences are negligible. A) Reproduced from Popowicz et al., 2004 from the Journal of Molecular Biology. B) Final structure in PyMOL. Text not intended to be read.
A
� 2019 Cecilia Johnson
Figure 30. A) High-resolution and B) low-resolution models of filamin. Filamin may bend at the center of the model where the two anti-parallel chains interact. Filamin in the final cortex model exhibit varying degrees of bend.
! Building Anchoring Protein: Cortexillin-I
Cortexillin-I is an example of a protein in Dictyostelium that cross-links actin similarly to alpha-actinin, but also likely anchors actin filaments to the membrane. There are no crystal structures of cortexillin-I, but based on its domain analysis, cortexillin-I has an actin- binding region and a lipid-binding region. Cortexillin-I is a dimeric protein. The two subunits join in a parallel coiled coil, which gives rise to a 19 nm tail. The N-termini each contain an actin-binding sequence within a globular head. Each of the two globular head measures approximately 4-6 nm in diameter. Some cortexillin-I display small, globular domains around
2-3 nm diameter at their tail. The total length of the tails, including the globular domain, is approximately 23 nm (Faix et al., 1996) (Figure 31).
Figure 31. Electron microscope analysis and predicted structure of cortexillin-I molecules used as reference for building the cortexillin-I 3D model. Image reproduced from Faix et al., 1996, from Elsevier publishing. Text not intended to be read.
48 The coiled-coil tail of cortexillin-I was built using the previously built coiled-coil tail from myosin-II. The rod portion of Dictyostelium myosin-II measures approximately 186 nm in length and 5.7 nm in width, and the cortexillin-I measures approximately 19 nm in length and 5.7 nm in width. Given that the length of the coiled-coil tail for myosin-I within Cinema
4D was 1800 cm, the value for the length of the coiled-coil tail of cortexillin-I was reduced to 184 cm.
186 nm / 19 nm = 9.8 1800 cm / 9.8 = 184 cm
After building the coiled coil tail, globular heads and tails were added based off the known diameters of the structures, using published electron microscope analysis as reference
(Figures 31, 32).
Figure 32. Final 3D structure of cortexillin-I (blue) next to six subunits of an actin filament (green). No high-resolution model exists of cortexillin-I because this model was not built using crystallization data.
49 Populating the Cortex
After all the components of the cortex were built, they needed to be assembled into the cortex, considering their individual concentrations.
Cortical Actin Filament Concentration Determination
The following calculations were used to determine the number of actin filaments within the cortex:
The volume of the cortex is 30 x 25 x 30 m in Cinema 4D. Using the conversion rate, in meaningful units, this would be 300 x 250 x 300 nm, or 0.3 x 0.25 x 0.3 µm.
The volume V of a rectangular solid then is length L x width W x thickness T, yielding V = 0.0225 µm3
Volume conversion: 1 Liter equals 1000 cm3. 1 cm equals 104 µm; thus, 1 cm3 = 1012 µm3. Thus, 1L = 1015 µm3.
[actin] in the cortex: 90 µM
Concentration of actin filaments: Since the concentration of actin subunits in polymer form is 90 µM, then the actin filament concentration is 90 µM actin subunits in polymer form / 34 actins/polymer = 2.65 µM actin filaments that are an average of 94 nm in length.
Number of actin filaments in the volume: 2.65 µM = 2.65 x10-6 mol/L. Given that a mol = 6.02x1023 particles, then 2.65 x10-6 mol/L x 6.02x1023 particles/L = 16 x 1017 particles/L.
As a result, the cortex would contain 16 x 1017 filaments/1015 µm3 or 1600 filaments/µm3. Since the cortex volume being depicted has a volume of 0.0225 µm3, then the volume should contain 1600 filaments/µm3 x 0.0225 µm3 = 36 filaments.
Thus, using mograph cloner in Cinema 4D, 36 filaments were set to fill the previously built cortex volume. Their rotations were randomized to 180° around all 3 axes.
Clones of multiple lengths were included. This caused many clones to stick out in all directions of the cortex (Figure 33), so the clones that were sticking out of the cortex were
50 selected using mograph selection, and were applied to a plane effector, which altered their positions to remain inside the defined box.
Figure 33. Cortex with 36 actin filaments and additional actin monomers distributed to the volume of the cube. Actin monomers stick out of the sides of the cube unless they are individually edited with the mograph selection tool.
Cortical Crosslinker Concentration Determination
The same calculation process was applied to the crosslinkers and anchoring proteins.
The density for each type of crosslinking protein is approximately 1 µM:
1 µM = 1 x 10-6 mol/L. Given that a mol = 6.02x1023 particles, then 1 x10-6 mol/L x 6.02x1023 particles/L = 6.02 x 1017 particles/L.
As a result, the cortex would contain 6.02 x 1017 crosslinkers/1015 µm3 or 602 crosslinkers/µm3. Since the cortex cube has a volume of 0.0225 µm3, then the volume should contain 602 crosslinkers/µm3 x 0.0225 µm3 = 13.5 crosslinkers. The cortex to cytoplasm ratio is typically 1.2 to 1.5, so the number of crosslinkers represented within the cortex cube should be between 16-20 for each type of crosslinker. Thus, alpha-actinin,
51 filamin, and cortexillin-I were each placed within mograph cloners, set to clone the objects to the actin cortex cube to a value of 20 (Figure 34).
Figure 34. Actin cortex cube reflecting concentrations of the actin filaments, crosslinkers, and anchoring proteins.
Cortical Myosin-II Concentration Determination
The average cellular concentration of myosin-II is 3.4 µM, and about 75% of the total myosin-II remains cytoplasmic throughout interphase and through cell division
(Robinson et al., 2002). Due to the concentration ratio between the cortex and the cytoplasm, the cortical myosin-II concentration has been determined to be 4 µM. Because the fraction of myosin-II that is assembled is ~20-25% and myosin-II must be able to assemble to localize to the cortex, we presume that the large majority of the myosin-II in the cortex is in the assembled state though it can exchange readily with the free pool. In fact, the recovery (turnover) time is ~5 s (Reichl et al., 2008).
52 To determine the concentration of myosin-II bipolar thick filaments (BTFs), as opposed to the concentration of total myosin-II found in the cortex, we consider the anticipated size distribution of the BTFs based upon simulation. In this case, the distribution has an expected mean size of ~20 myosin-II hexameric monomers (subunits) per BTF (Ren et al., 2009). From the 4 µM concentration of total myosin-II, we then expect that the concentration of BTFs to be 200 nM (4 µM/20). Because we estimated the concentration of actin polymers to be 2 µM (not just actin monomers in polymer form), a reasonable upper limit estimation is that the cortex will contain one myosin-II BTF for every
10 or so actin filaments. In fact, the cortex model includes 13 actin filaments per myosin-II
BTF, which allows for the balance of the myosin-II to be monomers that diffuse into the region to exchange with the myosin-IIs in the BTFs (Figure 35).
Figure 35. Actin cortex reflecting calculated concentrations of actin filaments, crosslinkers, anchoring proteins, and myosin-II BTFs. Nothing is interacting in this version of the cortex; thus, the resulting cortex appears sparse and disjointed. In addition, proteins are not staying within the volume of the cortex cube.
53 The resulting model represented the correct densities of individual components, but not the correct binding affinities. Thus, this version of the cortex appeared too disconnected and sparse. As a result, calculations were made for each of the individual components to determine how many crosslinkers and actin filaments should be bound to one another to represent a normal cortex at rest. In addition, future models of the actin cortex were rendered with only one bipolar thick filament, but included two dimers and one monomer, for clarity of other elements in the cortex and to further emphasize that the system is dynamic.
Binding Affinities Calculation
To determine how many actin filaments each crosslinker should be bound to (Table
3), the following was calculated. Given that tot = total, f = free (unbound), and AB = AB in complex with each other:
KD = [A]f[B]f / [AB]
[A]tot = [A]f + [AB] [A]f = [A]tot - [AB]
[B]tot = [B]f + [AB] [B]f = [B]tot - [AB]
KD = (([A]tot - [AB])([B]tot - [AB]))/[AB]
2 Equation 1. AB - (Atot + Btot + KD)(AB) + (Atot)(Btot) =0
Here AB = x Atot = actin = 90 µM Btot= crosslinker = 1 µM KD= 5 µM
Plugging in: x2 - 96x+ 90 = 0 x = 0.95 µM bound
54 Based on these results, 95% of the crosslinkers should be bound to at least one actin filament. To determine how many crosslinkers should be bound to two actin filaments, the following was calculated:
Assume:
[actin] now = 8 µM; if crosslinkers are bound to a single actin filament, it sees a lower concentration of actin due to only being able to access binding sites of the actin filaments within a restricted volume.
[crosslinker] = 0.95 µM KD = 5 µM
Plugging these values in Equation 1 above, x2 - 13.95x + 7.6 = 0 x = 0.57 µM
Thus, the concentration of crosslinkers bound to two actin filaments is 0.57 µM, or ~60%.
The concentration of crosslinkers bound to only one actin filament is 0.95 - 0.57 = 0.38 µM, or ~40%.
Filaments Cortexillin Alpha-Actinin Filamin
Total number 40 20 20 20 within Cortex
% bound to 2 actin filaments - 60% 60% 60%
# bound to 2 actin filaments 12 12 12
% bound to 1 actin filament - 40% 40% 40%
# bound to 1 actin filament 8 8 8
Table 3. Values showing approximation of number of crosslinker bound to one or two filaments.
55 Next, combinations of crosslinkers and actin-filaments were created, and ratios of the combinations were tested to obtain the desired 60:40 ratio. Combinations included: one actin filament bound to one crosslinker (FC), one actin filament bound to two crosslinkers
(F2C), two actin filaments bound to one crosslinker (2FC), two actin filaments bound to two crosslinkers (2F2C), one actin filament bound to three crosslinkers (F3C), and two actin filaments bound to three crosslinkers (2F3C). Only increments divisible by 3 were tested to account for the three types of crosslinkers. The cortex filament value of 36 was rounded up to 40, due to feedback that the cortex looked slightly underpopulated with actin filaments and for easier calculations for the subsequent steps.
The resulting combination was 3 2F2C, 3 2FC, and 12 2F3C, totaling 36 bound filaments and 54 bound crosslinkers (Table 4). This allows 4 unbound actin filaments and 6 unbound crosslinkers within the cortex to show a dynamic cell state.
Amount in Cortex Total # Bound Filaments Total # Bound Crosslinkers FC 0 0 0 F2C 0 0 0 2FC 3 6 6 2F2C 3 6 3 F3C 0 0 0 2F3C 12 24 36 36 54
Table 4. Number of filament-crosslinker combinations. Combinations including fewer actin filaments than crosslinkers were not favored because they increased the ratio of crosslinkers to actin filaments in the cortex, which should be kept at 1.5:1. Given the results, actin-crosslinker combinations were built (Figures 36-38) and positioned throughout the cortex using instances set to “Render Instances,” which allowed for faster calculating speeds within Cinema 4D.
56
Figure 36. Examples of the 2FC models. Each contains two filaments and one variant of crosslinker.
Figure 37. Examples of the 2F2C models. Eachs contain two filaments and two crosslinkers.
Figure 38. Examples of the 2F3C models. Each contains two filaments and three crosslinkers. Variation was introduced to these models to avoid a repetitive appearance within the actin cortex model.
A random effector was applied to the resulting geometry within the cortex. When set to object mode, the random effector does not need to be applied to a mograph object and can be applied to any objects in the scene as long as it is the child or sibling of the objects you wish to affect. With the random effector set to noise, a slight Brownian motion was applied to the objects within the cortex. Various styles for the actin cortex block were developed (Figure 39).
57 A
B
C
� 2019 Cecilia Johnson
Figure 39. Actin cortex styles. A) Early attempt to show the cortex as a transparent object with texture and refraction. A displacement map on the cortex obscures the figures inside. B) Cortex with decreased displacement. The internal reflection of the cortex block was disorienting when the cortex was rotating. C) Removal of internal reflection. A slight displacement on the cube and membrane remains.
! Whole Cell Model
To build the 3D model for an entire cell required significant planning, testing, and editing. The whole cell 3D model is a Dictyostelium cell, therefore all the features of the cell were based off known Dictyostelium ratios and volumes. The components included in the whole cell model are the plasma membrane, the cortical actin, the cytoplasmic actin, microtubules, and the nucleus. Dictyostelium do not have intermediate filaments; therefore, they were not included in this model.
Building the Whole Cell Model Shape
The shape of the Dictyostelium cell was built first because it provides a volume to clone actin filaments to in subsequent steps. The cell shape was modeled after a scanning electron micrograph (SEM) of a wild-type Dictyostelium cell (Figure 40). Although the membrane is not shown in that image, it provided a starting point to model the shape of an average Dictyostelium cell during interphase. This image was imported into Cinema 4D and applied as a texture to a simple plane for use as a reference image (Figure 41). A simple icosahedron sphere was placed in Cinema 4D, and the sphere was modeled into using
Cinema 4D’s native sculpting tools. This model was imported into ZBrush numerous times throughout the sculpting process to reduce polygons and optimize the mesh.
Further modeling was performed on the plasma membrane following the completion of the actin cortex, but this initial step provided a starting point for building other components of the whole cell interactive.
59 Figure 40. Scanning Electron Micrograph of a wild-type interphase Dictyostelium cell. Scale bar 10 µM. Image reproduced from Reichl et al., 2008.
Figure 41. Cinema 4D interface showing the use of the Dictyostelium SEM to model the shape of the cell. Text not intended to be read.
60 Building the Actin Cortex of the Whole Cell Model
Initial tests for the actin cortex attempted to mimic the texture of the cortex as it appears in the SEM of a Dictyostelium cell using displacers and bump maps or a technique of mapping filaments to the surface of a sphere (Figure 42).
A B
C D
E
Figure 42. Early tests for the actin cortex texture using Cinema 4D R20’s new Volume Builder and Volume Mesher. Texture was attempting to replicate that of Figure 40. Filaments were mapped to a sphere or simple cell shape and the Volume Builder and Mesher fused overlapping filaments. E) A close-up of the fused meshwork of geometry.
61 The tests produced results that visually resembled the texture of the 2D images of the actin cortex; however, there were many disadvantages to using the Cinema 4D Volume
Builder and Mesher approach. With this approach, Cinema 4D had slow calculating times, created a large number of unnecessary polygons, and lacked control of the thickness of structures. These tests produced a cortex was thinner and the actin filaments that were larger than the relative sizes of these structures in actual cells. Decreasing the filament size further caused Cinema 4D to freeze or crash.
Rather than using Volume Builder and Mesher to mimic a cortex texture, attempts were made to rebuild the whole cortex using only the mograph cloner. Removing the
Volume Builder and Mesher allows a significant increase in the number of filaments and a significant decrease in the size of the filaments without crashing Cinema 4D.
Initial attempts to construct the cortex involved cloning individual filaments to the cell shape surface and applying a random effector to the cloner. This method still lacked the ability to control the thickness of the actin cortex. In addition, when mapping the cortex to the cell shape, this method produced undesirable arrangements of actin filaments where the cell model exhibited steep curves (Figure 43).
62
Figure 43. Example of one early test cloning filaments to the cell shape model. The model is hidden so only the filaments are visible. Note that the cortex appears as a thin sheet rather than a layer with depth, and at the edges of the cell, actin filaments stick outwards.
In future steps, rather than cloning individual filaments to the cell shape surface, the previously built actin cortex block was cloned to the membrane. This method is preferred over the previous methods because cloning the cortex block provides the cortex with depth.
In addition, the actin cortex block, due to the calculations presented in previous steps, already represents the correct concentration of actin filaments, the average length of an actin filament, and the correct depth of the cortex in relation to the length of the actin filament.
Conversely, the previously built actin cortex contains too many polygons for the whole cell model. Cloning the cortex model in its current state would result in billions of polygons, causing long calculation times that result in Cinema 4d freezing and crashing. To be able to use the actin cortex as the clone, the actin crosslinkers and anchoring proteins were removed from the cortex block. Due to their small size, they would not be visible on the scale of the whole cell interactive. Although this significantly decreased the amount of geometry in the cortex, removing the crosslinkers and anchoring proteins still left the cortex
63 with a significant amount of unnecessary geometry. The next step in reducing the cortex geometry was to replace the actin filaments with simple triangular prisms (Figure 44).
Figure 44. Example of the low-polygon actin cortex without any crosslinkers, anchoring proteins, or myosins. The actin filaments have been replaced with simple triangular prisms of the same size and length.
This low-resolution cortex was scaled down in size and cloned to the surface of the cell shape model using mograph cloner. Mapping to the surface cloned the cortex block randomly to the surface of the cell, which produced areas of high density and low density within the cell. There is no setting to clone with an even distribution to the surface, so rather than cloning to the surface, the cloner was set to clone to points. This means that every single point within the cell shape model will contain a cortex block clone. The number of points for even distribution of the cortex around the cell was determined through trial and error. The cortex block was cloned to the cell shape model and scaled until the cloned blocks touch but do not overlap; thus, the number of points in the plasma membrane directly affects the depth of the cortex in relation to the size of the cell (Figures 45, 46).
64 The cytoskeleton composition is approximately 15% cortical actin and 85% cytoplasmic actin. There is some flexibility in these values because there is no sharp distinction between the cortical actin and cytoplasmic actin. An open source plug-in for
Cinema 4D, Area and Volume, was used to calculate the percentage of cortical actin to cytoplasmic actin. After the actin cortex was scaled so that the cortex blocks were not overlapping, the cell shape model was duplicated and used to measure the relative volumes of the whole cell and the actin cortex. By scaling the duplicated cell shape models to the outer surface of the actin cortex and the inner surface of the actin cortex, the Area and
Volume plugin can be used to obtain the volume of each cell shape model. By dividing the relative volumes, the percentage of the cortical actin could be calculated. Depending if the percentage of the cortical actin volume is high or low, the number of points in the cell shape model can be increased or decreased within ZBrush using ZRemesher (Figure 47).
The resulting volume of the outer cell shape model was recorded as 934724835.493 cm³ and the volume of the inner cell shape model was recorded as 789347792.279 cm³.
(Inner) = (7.89 x 108 cm³) = 0.84 = 84% Outer 9.34 x 108 cm³
65
Figure 45. Cell shape model in ZBrush before ZRemesher. Note that points are unevenly distributed. If cortex blocks are cloned to the points in this model, there would be areas of higher actin filament density. This is especially apparent at the cell protrusions, where the number of points is quadruple that of the number of points at the top of the cell.
A B
Figure 46. Two ZRemeshed cell shape models. A) has fewer points than B) and thus, would result in a thicker actin cortex. B) has more points, therefore, for no overlapping between cortex blocks, each cortex clone would be significantly smaller, resulting in a thinner actin cortex.
66 A B
C D
Figure 47. Results of cloning the cortex block to cell shape models of varying point counts. The cell shape model is hidden to better appreciate the cortex. A) Low point count: actin cortex represents approximately 60% of the cell volume. B) Intermediate point count: note that the actin filaments are much smaller in relation to the size of the cell, and the cell appears denser throughout the entire model. Actin cortex represents approximately 40% of the cell volume. C) Top-down view of the actin cortex thickness, visible by the darker ring around the edge of the cell: the actin cortex represents approximately 25% of the cell volume. D) Final actin cortex thickness, approximately 16% the volume of the whole cell: note that the actin cortex in this model lacks the patchiness of previous models and appears to almost be a solid surface.
! The final version of the model included low polygon myosin-II BTF in addition to the actin filaments (Figures 48, 49).
!
Figure 48. Low polygon myosin-II BTFs for the whole cell model. These were added to the actin cortex due to their large size and importance in the cortex.
Figure 49. Final cortex model including lighting, materials, and textures. Close-up view in inset. This model reflects the myosin-II BTF inclusion. Blue represents actin filaments. Purple represents the myosin-II BTF. At this scale, it is more important to distinguish between the actin cortex and other layers of the cell, rather than between the structures within the cortex, so there is a subtle distinction between the actin filaments and myosin-II BTF. The inclusion of the myosin-II BTF are a detail in the cortex model rather than a main teaching point.
! Because the actin cortex is dense, it was important that within the interactive, users would have the capability of toggling the cortex from the full model to a cross-section, for the ability to see deeper inside the cell. To achieve this, the cell shape object which the cortex is cloned to was duplicated and a primitive cube was positioned covering half of the cell (Figure 50). The duplicated model was placed inside a boole with the cube, and the boole was set to “A without B” (Figure 51). When the user toggles on the cross-section of the cell cortex, the cortex block can be set to clone to the booled object, rather than the original cell shape model (Figure 52).
Figure 50. Boole set up to achieve a cortex cross-section: Everywhere the cube overlaps with the cell shape object will be subtracted from the geometry. The result is the cortex will only map to the visible cell shape, rather than the original geometry.
69 Figure 51. Boole object settings to generate a cortex cross-section. The cortex cloner can be set to clone to the boole object rather than the original object. Boolean type must be set to “A without B” rather than “A subtract B,” which will create a closed face where the two geometries intersect.
Figure 52. Final cross-section of the actin cortex, including color, texture, and lighting.
! Building Cytoplasmic Actin
The actin cortex is more densely packed with actin filaments along the cell surface than in the rest of the cell cytoplasm. The cortical actin and cytoplasmic actin networks are continuous with each other, but for didactic purposes, they are represented in the interactive models with a distinct border. The density of actin filaments within the non-cortical cytoplasm is approximately 73% of the density of actin filaments within the cortex. Because the density is less than that of the cortex, the same actin cortex block cloning method cannot be used. Instead, individual actin filaments were cloned to the volume of the cytoplasm.
The cell shape object used to generate the actin cortex was duplicated and scaled down in size until the new shape intersected with the inner border of the actin cortex model.
For clarity in future steps, the duplicated cell shape object will be referred to as the
“cytoplasm cloner object.” A mograph cloner was used to fill the cytoplasm cloner object volume with actin filaments. Actin filaments within the cytoplasm may be longer than the actin filaments within the cortex; a variety of actin filament lengths were cloned to the cytoplasm for diversity. The number of clones to include was chosen based on the number of actin filaments within a cytoplasmic actin network of a Dictyostelium cell of this size.
To determine the number of actin filaments within the cytoplasmic actin network, the following was calculated:
Based on earlier calculations (see: Cortical Actin Filament Concentration
Determination), there are approximately 1600 actin filaments/µm3 within the actin cortex.
Therefore, at 73%, the cytoplasmic actin would have 1168 actin filaments/µm3.
The Dictyostelium cell model has a diameter and height of approximately 9 µm and 6
µm, respectably. The volume of the cytoplasm, which is the volume of the cell not including
71 the 0.3 µm thick cortex, is 232 µm3. At 1168 actin filaments/µm 3 for a volume of 232 µm3, there would be approximately 270,000 actin filaments in the cytoplasm.
In a normal Dictyostelium cell, other organelles exist within the cytoplasm that would reduce the available volume for actin filaments to fill. The estimated available 232 µm3 is assuming there are no other organelles or structures within the cell, therefore the resulting
270,000 actin filaments is a high estimate.
The Dictyostelium nucleus has a diameter of 2 µm, which is a volume of 4 µm3.
Contractile vacuoles with Dictyostelium cells are recorded to be three times that of the nucleus, or 12 µm3. This reduces the available volume of the cell from 232 µm3 to 216 µm3.
Mitochondria in Dictyostelium cells can range from approximately 5-25% of the cell volume.
Estimating the amount at 15%, that reduces 216 µm3 to approximately 180 µm3. In addition, the cell would contain endoplasmic reticulum (ER), Golgi apparatus, vesicles, ribosomes, and additional cytoskeletal components. Cytosol is estimated to be around 50-60% of the cell by volume, and the remaining 40-50% of the cell can be accounted for 20% mitochondria, 10% rough ER, 6% smooth ER, and 3% peroxisomes, lysosomes, and endosomes by volume. Estimating an additional 20% of the cytoplasm due to rough ER and smooth ER, then the available volume is approximately 138 µm3.
With the new estimate of the cytosol volume where filaments can reside as 138 µm3, then 138 µm3 * 1168 actin filaments/µm3 = ~160,000 actin filaments.
To emphasize the density difference between the cortical actin and cytoplasmic actin, and also for added visibility in the cell model, 160,000 actin filaments was reduced to
100,000 actin filaments.
72 Just as with the cortical actin, a feature within the interactive would be to change the cytoplasmic actin from the full structure to a cross-section. The same method of a boole subtracting a cube from the cytoplasm cloner object was used (Figure 53).
A B
Figure 53. Resulting cytoplasmic actin density of A) the full model and B) the cross- sectional model.
Building the Nucleus and Microtubules
Based on florescence microscopy imaging, the Dictyostelium nucleus measures approximately
2 �m. The nucleus was built in Cinema 4d de novo with a sphere icosahedron primitive.
Texture was added to the sphere using a displacement deformer and noise. To keep geometry low, characteristic nuclear pores were implied with noise material (Figure 54).
A B
!
Figure 54. A comparison of the nucleus A) prior to and B) after a noise material was applied.
! +'! Microtubules were built de novo using splines and a sweep deformer. Dictyostelium cells have an average of 8-12 microtubules, so for the complete cell model, a total of ten microtubules were modeled. Splines were drawn from an origin point in front of the nucleus, extending to various points along the inner edge of the cell membrane, based on immunofluorescence staining images of Dictyostelium (Figure 55).
Figure 55. Immunofluorescence staining of a Dictyostelium cell showing (a) Dynein- GFP signal (b) tubulin (microtubule monomer) staining and (c) overlay of (a) and (b) (Shou and Chisholm, 2002). The arrow points to an example of a microtubule plus end. These images were used to model the pathways of the microtubule splines.
Microtubules have a diameter of approximately 25 nm. Actin filaments have a diameter of approximately 8 nm. Therefore, the diameter of a microtubule is approximately
3 times the diameter of an actin filament. The actin filament radius within Cinema 4D is 0.3 cm, so microtubule radius was set to 1 cm. (Figure 56).
74
Figure 56. Cinema 4D settings used to generate the microtubules. A spline was drawn to generate the length of the microtubule. An n-Side spline was used to generate the thickness of the microtubule, with a radius set to 1 cm. The number of sides of the microtubules was set to 5 to keep polygon count low. Both the n-Side and spline were set as children of the sweep deformer, which sweeps the n-Side along the length of the spline to create geometry.
A microtubule organizing center (MTOC) was added as a simple sphere from which the microtubules extend (Figure 57). Dictyostelium do not have centrioles, so they were not included in this model.
A B
Figure 57. Resulting model of microtubules and the MTOC A) with and B) without the nucleus.
! +)! Building the Plasma Membrane
The plasma membrane was modeled from the existing cell shape object used to populate the cortex and cytoplasm with actin filaments. The cell shape object was duplicated and scaled up in size until it was slightly larger than the cell cortex. Because this object was originally only used to define volumes for the cortex and cytoplasmic actin networks, additional modeling was performed in order for the plasma membrane to exhibit characteristics such as lamellipodia, filipodia, and membrane folds. Within ZBrush, the membrane was sculpted further, modeled after a SEM of a Dictyostelium cell (Figures 58, 59).
Figure 58. SEM of a Dictyostelium cell plasma membrane. Imaging techniques exaggerate the cell surface topography, which was noted when modeling the plasma membrane.
76 A B
C D
Figure 59. Iterations of the plasma membrane model within ZBrush. A) An early version of the cell shape used to generate the actin cortex and cytoplasm. B) An attempt within ZBrush to adhere the plasma membrane to the underlying cell cortex, using the ZProject brush. This method caused the appearance of small holes in the membrane. C) An early sculp of the plasma membrane lamellipodia and filopodia based off SEM imaging and known values of the average number of protrusions within a Dictyostelium cell. D) The final sculpted plasma membrane model including surface texture within ZBrush.
Because the cortex and plasma membrane are anchored together, it was necessary for the plasma membrane to be as close to the cortex as possible without any actin filaments showing through the plasma membrane. Early attempts of modeling the plasma membrane in ZBrush involved using the ZProject brush, a tool which enables users to adapt the form of one surface onto a sample surface lying directly beneath it. While this tool directly
77 adhered the plasma membrane to close underlying actin filaments in the cortex, not every area of the membrane was directly above actin filaments. Those areas exhibited deep pits in the membrane, which in some cases appeared as holes. This method was discarded for a more general approach to modeling the membrane within Cinema 4D.
The plasma membrane size was increased until most actin filaments could not be seen through the membrane, however, in textured areas of the plasma membrane, cortex was still visible outside of the cell. Rather than editing the plasma membrane, which was carefully modeled, the hidden cell surface shape to which the cortex was cloned to was edited. Using
Cinema 4D’s native sculpting tools, the underlying cell shape could be adjusted, and the cell cortex (cloned to the cell shape model) would adjust as well. For areas where the plasma membrane was thinner than the cortex, protrusions on the plasma membrane needed to be slightly modified. In these cases, the soft selection tool was used for subtle editing of the membrane (Figure 60).
When importing the membrane model from ZBrush to Cinema 4D, the texture of the cell surface appeared smoother. Additional steps were taken to recover the modeled texture by applying a custom bump map. The bump map was generated from the SEM of a
Dictyostelium cell membrane, as seen in Figure 58.
78 A
B
Figure 60. Editing the plasma membrane to reduce intersections with the cortex. A) Screenshot of the plasma membrane model in Cinema 4D: green areas are locations of cortex visible through the membrane. B) Soft selection editing of the plasma membrane.
Feedback from the intended audience on the cell model was that the cell appeared
“too wet.” Alternate styles were generated and tested for the plasma membrane (Appendix
A). A small noise material was added to imply texture of a lipid bilayer on the cell surface and reduce the perceived wet quality of the membrane.
Next, a cross-section of the plasma membrane was generated. First, the plasma membrane cross-section was generated using the same boole technique as the previous cross-sections. This approach revealed a significant amount of space between the actin cortex and the membrane. The space between the cortex and membrane could not be
79 diminished without actin filaments showing through the opposite side of the membrane, so an alternative approach was used.
First, the plasma membrane cross-section was imported into ZBrush. ZBrush does not automatically recognize internal geometry, so when looking at the inner face of the plasma membrane cross section, there appeared to be a large hole. To see inner geometry, the display Figure 61. Display property settings to properties of the plasma membrane cross-section see the inner geometry of an object. were set to Double (Figure 61).
Next, the cut edge of the cross-sectional plasma membrane was masked using
Masking > MaskByFeature > Border (Figure 62). This mask was smoothed and inversed, so that everything except for the cut edge of the cross section was masked. This protects the whole cell from being edited, allowing transformations to be applied to only the unmasked
areas. Using gizmo 3D in ZBrush, the unmasked
edge was transformed forward and scaled down
in size. This process was repeated with varying
levels of masking on the edge of the cell, to
produce a slight inwards curve of the membrane
(Figure 63). This inwards curve functions to
hide the extra space between the plasma
membrane and the cortex. In addition, this
Figure 62. MaskByFeature: Border removes the harsh thin edge of the booled setting within ZBrush plasma membrane object.
80 A B
C D
Figure 63. Modeling the plasma membrane cross section edge, A) The original booled plasma membrane with the border masked. Note how the membrane edge appears paper- thin and harsh in this model. B) The gizmo 3D tool affects only the unmasked areas, which is the inverse of the border mask. C) Moving only select geometry on the edge produces harsh, low-polygon effects in stark contrast to the rest of the cell membrane. Using Deformation > Polish on the unmasked edge smooths geometry without reducing texture in the rest of the cell. D) The final cross-sectional plasma membrane object is shown.
Interactive Capabilities
Interactive capabilities of the 3D models were generated in Blender using Verge3D, a real-time renderer used for creating web-based 3D interactive experiences. Initial tests performed followed the standard methods of importing 3D models from Cinema 4D into
Blender, then exporting the models from Blender as glTF files. Interactivity functions were added using Verge3D’s visual coding interface Puzzles.
81 To export the 3D assets from Cinema 4D to Blender, animations of the Brownian motion of molecules were baked using mograph cache. Cinema 4D cannot export directly to glTF, so 3D assets were exported in FBX format including instances and animation tracks
(Figure 64). Checking instances ensures that the export recognizes instanced objects. If this box is not checked, instanced objects will not be exported.
Figure 64. Settings used to export animations from Cinema 4D. Instances are exported as well as animation frames.
To import the FBX files into Blender, import > FBX was chosen, and import normals and import animation were both checked (Figure 65).
82
Figure 65. Settings used to import FBX animations into Blender. Import normals and import animation must be checked.
Unfortunately, when exporting from Cinema 4D, all instanced objects are replaced with geometry. This significantly slows rendering times, and the resulting models are too large to load real-time on the web. Various methods were researched for how to maintain instanced geometry (Page 95).
Components in the mechanobiome models were exported as separate FBX files and set up as individual layers within Blender. Puzzles were used to generate functionality to toggle layer visibilities, to switch between the whole cell model and the cortex block, and to zoom and rotate the models (Figures 66-72).
83
Figure 66. Early test of the whole cell interactive, exported to the web. Note that tests were done early in the development of the 3D models, to ensure that Verge3D and the WebGL format would function with the high polygon counts of the models.
Figure 67. Early iteration of the interactive displaying functionality of the Membrane Half button. Buttons were added using Puzzles. Clicking on the “half” button underneath the “membrane” button shows a cross-section of the cell’s plasma membrane.
84
Figure 68. Early iteration of the interactive displaying functionality of the Cortex Half button. Functionality was added to halve the actin cortex and cytoplasmic actin, allowing viewers to see structures underneath. If viewers click “full” for either element, the full actin cortex or cytoplasmic actin will reappear.
Figure 69. Interactive functionality tested for early iterations of the cortex block. Note that the plasma membrane and crosslinkers were not yet generated at this stage, so the cortex block only shows actin filaments and actin monomers. Users navigate to this page using a “Zoom In / Out” button in the upper left-hand corner. A “Play/Pause Animation” button was coded to appear when the user switches to the cortex model. This button was coded using Puzzles to play or pause the Brownian motion of the molecules upon click.
85
Figure 70. Example of Puzzles within Verge3D. Above Puzzles code for the “Play/Pause Animation” button appearance, which is separate from the button functionality.
Figure 71. Puzzles used to generate functionality for the “Play/Pause Animation” button.
86
Figure 72. Puzzles used to generate functionality of the “Zoom In/Out” button. This button hides the current 3D model to switch to the whole cell or cortex model.
87 Given that the 3D mechanobiome models are extremely complex, various experiments were performed to optimize moving the models from 3D software into a web browser. One positive of Verge3D is that files are exported as glTF files for the final web interactive, which significantly reduces file size and boosts browser loading times for real- time loading. However, the 3D models generated in Cinema 4D are still large files to run on a web application. Various methods for the interactive capabilities of both 3D models were tested for the feasibility of generating a working 3D interactive. Steps can be taken to reduce file size, including optimizing the meshes of the plasma membrane model, and stripping away the normals data of the exported models. Instead, normals can be calculated at load.
Further optimization of the model can be performed through identifying identical objects, something of which the 3D models have an extensive amount. Both models consist of many identical objects including actin filaments, crosslinking proteins, myosin-II filaments, and the cortex block. The only difference between each identical object is their individual position, scale, and rotation (PSR). The actual geometry of each object is identical, so there is no need to upload that geometry to the GPU more than once.
Instead of exporting instances from Cinema 4D as FBX files, as described previously, each instance may be converted into a null object with the same position, scale, and rotation (PSR). Null objects are empty objects which have no geometry, but their PSR information is still saved upon export. Although this method was not tested, it provides a potential method for moving forward with developing a fully interactive web module.
88 Information Architecture
Information Architecture (IA) was created to organize the content of the website into vital teaching components (Figures 73-77). The final IA was determined based on several iterations after earlier rounds of feedback and discussion (Appendix B)
Figure 73. Final IA. Legible text on subsequent pages.
89
Figure 74. IA of the 3D Viewer.
90
Figure 75. IA of the Loading Screen, Introduction, and Key Concepts.
91
Figure 76. IA of the News and Repository.
92
Figure 77. IA of Appendix and About.
93 Wireframes
A set of wireframes for each page of the web resource was developed for layout and content design in the creation of the web resource (Figure 78). These wireframes were amended from earlier iterations to reflect altered IA, design, and content development (Appendix B).
Figure 78. Final wireframes for select pages of the 3D interactive, Key Concepts, News, Appendix and About sections. Text not intended to be read.
94 Color Palette Development
Various color palettes were explored for the website. Four color palette options were developed (Figure 79). After feedback from colleagues and the intended audience, the final color palette used was a combination of A and D. Palette D was chosen for the interactive because the gray minimizes distractions, but palette A was kept for all other pages. The color palettes are sufficiently similar that the combination maintains a cohesive theme.
A
B A
C
D
Figure 79. Developed web interface palettes. Text not intended to be read.
95 Colorblind Accessibility
Colorblind accessibility for both the cortex block (Figure 80) and whole cell interactives
(Figure 81) were tested in the online colorblindness simulator Colblis of Colblindor). Tests were performed for the three most common types of color-blindness: protanomaly (red- weak), deuteranomaly (green-weak), and tritanomaly (blue-weak).
A
� 2019 Cecilia Johnson
Figure 80. Cortex colorblindness simulation results for A) normal color B) protanomaly C) deuteranomaly and D) tritanomaly. Color contrast for the actin filaments and myosin-II filaments is decreased in both deuteranomaly and tritanomaly; however, the structures differ significantly and colorblind users should have no trouble distinguishing between the two proteins without a sharp color contrast. In addition, when clicking on a type of protein within the cortex, all other structures fade to white coloration, further emphasizing the clicked protein.
! ! Figure 81. Whole cell colorblindness simulation results for A) normal color B) protanomaly C) deuteranomaly and D) tritanomaly. Color contrast for the cortical actin network, microtubules, and nucleus are decreased in both deuteranomaly and tritanomaly. Colorblind users may struggle differentiating between the cortical actin network and microtubules; however, users have the ability to hide either structure within the interactive. In addition, when clicking on a layer within the cell, all other structures fade to white coloration, further emphasizing the clicked layer.
Software and Equipment Overview
A variety of programs were used in the creation of the 2D and 3D assets and the final web prototype (Table 5). Design production began with Draw.io, an open source application for building diagrams, and Adobe Illustrator. Cinema 4D was used to generate
3D assets for the interactive models, as well as those for additional 3D animations. 3D models in Cinema 4D were also rendered as the basis for 2D art, edited within Photoshop.
Pixelogic ZBrush was used to refine 3D models and reduce polygon count. Interactive models and 3D interactivity were tested within Blender and Verge3D. The final website was
! designed within Illustrator, and the web prototype was animated using After Effects.
Storyboards for the introduction animation were generated using Storyboarder.
Software Equipment Draw.io Wacom Intuos Pro tablet Adobe Illustrator CC iMac 27 inch Adobe Photoshop CC Microsoft Surface Pro 4 Abobe After Effects CC Maxon Cinema 4D Pixelogic ZBrush Blender Verge3D Storyboarder Microsoft Word
Table 5. Software and Equipment used in the creation of 2D and 3D assets.
98 Results
Novel Mathematically-Derived 3D Models
Two novel theoretical models were developed describing the key components of the mechanobiome, one at the cellular level and one at the nanoscale level, in physiologically relevant proportions. These models integrate available data to produce a working hypothesis for the three-dimensional structure of the cell’s mechanical system.
The 3D model at the cellular level, the whole cell model (Figure 82), consists of separate objects for the cell membrane, cortical actin, cytoplasmic actin, microtubules, and nucleus (Figure 83). The cell membrane, cortical actin, cytoplasmic actin, and microtubules have cross-sectional versions (Figure 84). The cell membrane has an additional transparent version (Figure 85).
The 3D model at the nanoscale level, the cortex block, consist of separate objects for the cell membrane, cortex volume, actin filaments, actin monomers, myosin-II bipolar thick filament, myosin dimers, myosin monomer, alpha-actinin, filamin, and cortexillin-I (Figure
86). Several versions of the cortex block exist with each component highlighted in didactic coloration.
C
Figure 82. Whole cell model A) Side view B) Oblique top view C) Back view.
! A
B
!
Figure 83. Whole cell model cross section and transparent membrane. A) Side view transparent membrane. B) Top view cross-sectional membrane. C) Side view cross-sectional membrane.
! A
!
Figure 84. Cross section of the whole cell model A) Front view B) Oblique view without cytoplasmic actin C) Side view
! !
!
!
Figure 85. Whole cell model additional visibility options A) Cortex cross-section and membrane transparent B) Cortex and cytoplasmic actin cross-section C) Cortex and cytoplasmic actin hidden
!
!
Figure 86. Final cortex model A) front view, B) oblique view, C) highlighting the membrane D) highlighting actin filaments and monomers, and E) highlighting crosslinking proteins.
! Website Prototype
A platform was prototyped to present these novel 3D visuals as interactives on an accessible web-based educational resource, “Mapping the Mechanobiome” (Figures 87.1-87.8). The resource hosts the theoretical models of the complete cell and the cortex block as didactic interactives. Users can rotate the models to understand the complex 3D relationships of mechanobiome components. In addition, users can click on each component of the mechanobiome to learn about their introductory principles. Viewing options for each of the mechanobiome components include visible, cross-section, transparent, or hidden.
The resource also provides review-style descriptions of fundamental concepts in mechanobiome research with accompanying visual media derived from the mathematically- derived models, a News section for breakthroughs in mechanobiome research, an Appendix of mechanobiome terminology, References, Resources, a visual Repository of published mechanobiome visuals, and an About section.
104
Figure 87.1. Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.2 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.3 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.4 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.5 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.6 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.7 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
!
Figure 88.8 Mapping the Mechanobiome interface. See Appendix D for didactic text accompanying the interactive models.
! Button Style
Buttons were developed to change various states of visibility for components in the cell and cortex models (Figure 88). Clicking on the buttons changes the component to invisible, cross-sectional, transparent, or fully visible. The buttons are color-coded to match the cellular components they represent. In addition, the buttons act as icons to reflect the visibility state of the corresponding cellular component.
Figure 88. Icon styles for the web prototype.
113 Visualizations Derived from the Novel 3D Models ! Visualizations were created to demonstrate additional didactic capabilities of the 3D models, prior to full functionality of the web resource. The following images are examples of visual media that may populate the review-style Concepts section of the web repository, accompanied by didactic text. In addition, these images will be published in an upcoming review article written by Priyanka Kothari for the Journal of Cell Science, describing fundamental concepts of the mechanobiome.
A key illustration summarizes the components of the mechanobiome, teaching (1) areas of the cell in which the mechanobiome functions and (2) key proteins in the mechanobiome (Figure 89). This illustration emphasizes (1) the significant space mechanobiome proteins encompass in the cell, (2) the small size of proteins relative to the size of the cell, and (3) the interconnectivity of proteins within the mechanobiome.
100 nm
1 �m
Figure 89. Illustration summarizing mechanobiome components
! A turntable animation of the animated cell cortex was generated to accompany the review article. The turntable teaches (1) key proteins in the cortex, (2) densities of each protein, (3) average sizes and lengths of each protein, (4) number of associations between actin filaments and crosslinking proteins in a cell “at rest,” in other words, when not experiencing mechanical force. The speed of molecular Brownian motion within the cortex is slower than the speed required for crosslinking proteins to dissociate from and re- associate with actin filaments. Thus, crosslinkers and actin filaments remain in stable states, but exhibit Brownian motion to emphasize that the cortex is dynamic.
115 � 2019 Cecilia Johnson
� 2019 Cecilia Johnson
Figure 90. Stills from cortex block turntable animation A) Front view, B) Oblique view
! A third visualization derived from the 3D models is a short gif describing the recruitment of contractile machinery, intended to be supported by text in the review article.
This animation teaches that when cells experience mechanical force, additional myosin-II and cortexillin-I, or “contractile machinery,” are recruited to the area to which mechanical forces are being applied. The recruitment of contractile machinery enables changes in the cell shape to respond to the mechanical forces. This concept is visualized through a migrating cell experiencing contractile forces at its rear end to help propel the cell forward
(Figure 91). As the cell is migrating, the camera zooms into the cell to depict the actions of the molecular machinery (Figure 92). Because the animation is built from the mathematically-derived 3D models, the animation depicts the recruitment of molecular machinery at physiologically relevant levels.
117 Figure 91. Stills from didactic animation teaching the recruitment of contractile machinery featuring a migrating cell. The camera zooms into the location where contractions occur to see the underlying contractile machinery.
! !
2019 Cecilia Johnson
! Figure 92. Stills from didactic animation teaching the recruitment of contractile machinery depicting the increase in number of myosin-II BTF.
!
! Introduction Animation for Web Resource ! A script, storyboards, and animatic were developed to introduce the term
“mechanobiome” to unfamiliar audiences (Appendix E). Approximately 45 seconds in length, this animation is designed to be displayed on the home page of the web resource.
Select scenes from the animation were developed (Figures 93.1, 93.2). The animation begins with cells depicted as empty water sacks in isolated environments, to emphasize through contrast that cells exist within dynamic environments, and with highly packed, coordinated interiors. This introductory animation covers intracellular and extracellular activity and addresses various cell states, including disease.
! Figure 93.1 Introduction animation A) depicting cytokinesis and B) cell movement. Sample scenes of the animation depicting style. Full animation storyboarded (Appendix D).
!
A B
C
Figure 93.2 Introduction animation A) depicting phagocytosis B) the dynamic environment of cells undergoing cytokinesis and C) leukocyte rolling and extravasation. Sample scenes of the animation depicting style. Full animation storyboarded (Appendix D).
!
! User Testing
Feedback was gathered from potential stakeholders and the intended audience at key iterations of development. Feedback on the final prototype and the two models (complete cell and cortex block) and the supplemental visualizations was collected from two senior researchers and six graduate students in the field of cell mechanics. Overall, responses for the novel 3D models and web-based resource were positive. Individuals noted that visualizing the density of the actin cortex helped to emphasize the packed environment. One senior researcher provided feedback that the model stimulated perspectives not previously considered. For example, the cortex model provided a new appreciation for the density of the mechanobiome, which prompted the individual to express interest in exploring how large structures such as the myosin-II BTFs navigate through the highly interconnected, woven environments depicted. All users responded positively to features of the cortex block interactive such as the refraction index and Brownian motion. Several respondents noted that in switching between the interactive models, it was difficult to make the leap from the whole cell to the cortex models. As a result, an icon was developed to direct viewers where in the whole-cell image the cortex block was located. Multiple respondents observed that they needed more cues to realize the size, density, and concentrations of the components of the models were derived from published values and additional calculations. Finally, senior researchers identified additional components to include in future iterations of the cortex block, specifically the nucleating complex Arp 2/3.
122 Access to Assets Resulting from this Thesis
The demonstration animations can be reviewed at: http://robinsonlab.cellbio.jhmi.edu/videos
The prototype can be accessed through: http://cjostudios.com/#thesis
A copy of this thesis and all its assets is located in the Johns Hopkins University Department of Art as Applied to Medicine.
123 Discussion and Conclusion
Novel Visualizations of the Mechanobiome
The mechanobiome is a novel term with an expansive role in cell mechanics and vast implications in health and physiology. Most individuals introduced to the concept of the mechanobiome must contend with learning what a mechanobiome is, in addition to learning related key concepts vital to mechanobiome processes, such as mechanosensation, contractility kits, force-sharing, and force-point control. Prior to this project, no didactic mechanobiome visualizations existed; this lack contributed to challenges in understanding the large amount of specialized information related to this term. Omes can be challenging to new learners, as they encompass a systems-level view of a vast body of information. The mechanobiome is an especially challenging ome due to the conceptual nature of mechanical forces. In addition, the lack of mechanobiology concepts being taught at introductory levels leaves many individuals unprepared to understand the mechanobiome at first explanation.
The innovative, didactic images (3D models, animations, and 2D illustrations) of the mechanobiome produced for this project are anticipated to help explain introductory and vital components of the mechanobiome to individuals from a variety of backgrounds and levels.
A large number of dynamic and interconnected macromolecules on the nanoscale level convey properties of cellular level activity. To understand the mechanobiome, it is necessary to understand the density and distribution of macromolecules on the cellular level, as well as the proteins on a nanoscale, and how the two scales interplay. To address this, the visualizations of the mechanobiome include both scales, to highlight the interplay between molecular composition and cellular response. The cellular level depicts key components of
124 the cell involved in the mechanobiome as well as true-to-life cell properties. The molecular level depicts individual proteins not visible at the cellular scale. The actions of the proteins visible on the molecular level convey mechanical properties to the structures on the cellular level, as seen in the visuals created.
The images of the mechanobiome created for this project simplify concepts without sacrificing accuracy or complex spatial relationships, features vital to the functionality of the mechanobiome. This was done through the inclusion of only one or two proteins as archetypes of categories, such as choosing cortexillin-I as the anchoring protein, rather than including every known protein, which would be visually disorienting and provide little additional information for big picture concepts. The resulting models fill a significant gap in didactic visualization of mechanical cell properties.
Mathematically-Derived Models
Prior to the generation of these visualizations, researchers in the field of mechanobiology expressed concern that cell mechanics comprise more components than people initially assume. Individuals underestimate the number of molecular players involved, how densely packed the cells are, and how organelles and proteins within the cell need to move with respect to the densely packed cytoskeleton. This misconception in visualization arises, in part, from highly simplified illustrations and animations of cell activity. The 3D models generated for this project address these concerns by being built on a foundation of published datasets of crystal structures, average protein polymer lengths, protein concentrations, dissociation constants, volumes and more, to depict the most accurate visualization of the cell’s mechanical system to date.
125 Building the mathematically-derived models required a balance between accuracy and complexity. There are hundreds of thousands of actin filaments within a cell, so care was taken to produce results that were accurate, while not creating a file too large for the computer to load. One such solution was the inclusion of a separate cortex block model, rather than generating one high resolution mechanobiome model that the user could zoom into. This decision allowed for a more detailed, accurate visualization of the actin cortex, although it required removing the cortex from the whole cell context.
Although the inclusion of mathematically-derived visualization was time intensive and required much trial and error, it was essential to provide a full, accurate understanding of the mechanobiome. The complex 3D spatial relationships of the mechanobiome are a feature that gives the mechanobiome its functionality. Variations in the number of proteins in the cortex imply variation in cell state, for example cellular response to mechanical force or disease. Therefore, it was important to show proteins in physiologically relevant proportions. This consideration of the data-driven, complex 3D relationships of the mechanobiome may help researchers gain insight into the full picture of activity on a cellular and molecular level.
A Visual Resource to Teach Mechanobiome Concepts
The inclusion of mechanobiome 3D models into an online didactic interactive provides an accessible, visual, well-organized mechanobiome resource, which integrates and aggregates disperse information in the field of cell mechanics. The web resource was designed for users to explore concepts at their own pace, so that learners from various backgrounds and levels of understanding can benefit. The inclusion of an Appendix with additional resources, references, and a glossary of terms further accommodates different
126 backgrounds of learners, allowing for a variety of levels or types of previous experience.
Finally, the review-style Concepts section allows users to explore topics of interest; each section contains visualizations generated from the mathematically-derived models. Because significant consideration and calculation was used to build physiologically relevant mechanobiome models, with slight adjustments to the models or additional animation, these models can be used to efficiently generate new physiologically relevant teaching assets. As new concepts related to the mechanobiome emerge, additional teaching assets can be generated from the existing models and used to populate the web resource.
Future Directions
This thesis developed a novel workflow for the production of an accessible, interactive, data-driven educational mechanobiome resource. Future development of the project could focus on the coding and full implementation of the interactive and web resource. The main pathways developed for the teaching components focused on introductory concepts of the mechanobiome, such as the role of key players. Thus, additional pathways depicting more complex concepts, such as the mechanobiome’s role in metabolism, mechanotransduction pathways, and various control systems of the mechanobiome, could be further developed and implemented on the web resource.
The 3D interactives depict a Dictyostelium cell in interphase experiences no mechanical stress. Future 3D models could be developed to explore the mechanobiome:
• Under states of pressure; i.e. undergoing micropipette aspiration, a lab technique commonly performed to study cell shape response • During additional states of mitosis or undergoing cytokinesis • With disease states; i.e. describing how components of the mechanobiome vary in disease processes such as metastasis • In other model organisms, such as a mammalian model
127 Both 3D models – the whole cell model and the cortex block – were developed focusing on key teaching introductory components. Further iterations of the actin cortex block could include additional components such as nucleators, specifically Arp 2/3, and capping proteins. The whole cell model could include structures secondary to the mechanical properties of the cell, such as mitochondria, endoplasmic reticulum, and lysosomes. The addition of secondary organelles would emphasize the expansive role the mechanobiome plays within a cell.
Conclusion
A mechanobiome resource will contribute to discussions on the forefront of research in cell mechanics, provide a comprehensive understanding for new researchers in the field, and advance research efforts by highlighting the significance of fundamental mechanical properties. Because the mechanobiome combines research from the fields of biology, chemistry, physics, mathematics, and engineering, the resource accommodates users of diverse backgrounds who may not share a common language, despite a common interest in cell mechanics. The novel, mathematically-derived models created in this project have the potential to reveal aspects of the mechanobiome not previously considered due to the lack of accurate visualization of the full working system. This novel web resource provides a platform to further enhance our understanding of the role of cell mechanics in health and disease.
128 Appendix
Appendix A: Plasma Membrane Style Development
The plasma membrane is the first component of the mechanobiome that a viewer sees when opening the interactive module, therefore, significant consideration was placed into the style of the cell’s appearance.
B
E
Figure 94. Membrane texture style and mapping. A) No texture map, B) UV mapping, C) Transparency without texture map, D) Transparency frontal mapping, E) Transparency UV mapping.
!
� 2019 Cecilia Johnson
Figure 95. Materials tested for the plasma membrane
! Appendix B: Early Information Architecture
Early iterations of the IA included a “Mechanobiome Interactions Map” to highlight interactions between components of the mechanobiome. Discussion of the Interactions Map concluded that research and full implementation would be too time intensive to create and that similar concept maps already existed. The Interactions Map idea was amended to a
Concepts Map, which would teach fundamental concepts of the mechanobiome, rather than focus on individual protein interactions. An addition to the final IA included a News section, to display recent additions to the Repository as well as include articles about exciting research in the field.
131
Figure 96. Early IA Legible text on subsequent images.
132
Figure 97. Left half of the information architecture, including the Loading Screen, 3D Viewer, and Mechanobiome Interactions Map.
133
Figure 98. Right half of the information architecture, including the Repository and Help sections.
134 Appendix C: Early Wireframes
Figure 99. Early website wireframes. Wireframes display early concept for the Loading page, the Home page, and the 3D interactives. Text not intended to be read.
135 Appendix D: Text Accompanying the Interactive 3D Models
Dictyostelium discoideum
A powerful tool in cell mechanics
The cell to the right is modeled after the social amoeba Dictyostelium discoideum.
Dictyostelium is commonly used to study cell motility, cytokinesis, chemotaxis, phagocytosis, endocytic vesicle traffic, cell adhesion, pattern formation, caspase-independent cell death, and more.
Many of the proteins in Dictyostelium are evolutionarily conserved or have similar mammalian homologs. Dictyostelium harbors at least 33 homologs of human disease genes.
You may notice several features of the Dictyostelium cell that differ from mammalian cells.
Keep in mind that:
1. Dictyostelium do not have intermediate filaments
2. Dictyostelium nuclei are smaller than many mammalian cell nuclei
3. Density of proteins, concentrations, and filament lengths reflect those of
Dictyostelium
Learn more about Dictyostelium discoideum in research here.
Cortex
Force generation and sensation at the surface of cells
The actin cortex (cell cortex, actomyosin cortex) is a thin network directly beneath the plasma membrane containing a network of actin filaments and actin-binding proteins.
The actin cortex affects membrane behavior and drives mechanical changes at the surface of cells. Fluctuations in the actin cortex tension directly affect cell surface tension at the
136 membrane, which drives changes in cell shape. Gradients of this surface tension may result in contractions or deformations within the plasma membrane. This has the ability to drive cellular processes such as cell migration and cytokinesis. In cell migration, contraction towards the back of the cell aids in generating the forces responsible for propelling migrating cells forward. During cytokinesis, the cortex produces the contractile ring necessary for the formation of two daughter cells.
To learn more about the actin cortex, click here.
Cytoplasmic Actin
Intracellular force
The cellular functions of cytoplasmic actin are generally less focused on than the functions of the actin cortex; however, cytoplasmic actin is also important in specific processes and may have more general roles. Functions range from the assembly and positioning of meiotic spindles to the prevention of cytoplasmic streaming. The bulk cytoplasmic actin has roles in meiosis of oocytes and possibly mitosis of early embryos.
To learn more about cytoplasmic actin, click here.
Membrane
The point of contact between a cell and its external environment
The lipid bilayer membrane is a fundamental aspect of cell mechanics. It separates the cell interior from the physical environment, allowing selective permeability to molecules and ions. As the physical boundary between the intracellular and extracellular environments, any interaction between a cell and its external environment must occur through the plasma membrane. Mechanical, chemical, or electrical signals may interact with the plasma
137 membrane, activating signal transduction pathways to initiate cellular response, via the activation of membrane-associated proteins or secondary messengers. The plasma membrane is associated with an underlying meshwork forming the composite cell cortex, which provides the cell with resistance to deformations and provides contractile abilities.
The composite cell cortex can be thought of as a viscoelastic material with the ability to resist and respond to deformations yet the cortex has expansive and contractile abilities. The values of viscoelasticity and contractibility vary with each cell type and may change based on any signaling the cell encounters. Separation of the membrane from the cortex can result in cell blebbing, which is involved in controlled cell movement and uncontrolled cancer-spread.
To learn more about the membrane, click here.
Nucleus
Triggers gene expression patterns
For cells to adapt to dynamic physical environments, they must be able to respond to mechanical signals by changing their gene expression patterns. Biochemical signal pathways involving force-induced deformations to the plasma membrane can rapidly transduce a signal to the nucleus via the cytoskeleton. The nucleus houses the cell’s genetic information and serves as the site of DNA and RNA synthesis, transcription, and processing; thus, it plays a vital role in cell mechanics and the cytoskeleton architecture. Mechanical signaling from the plasma membrane to the nucleus occurs rapidly on timescales of seconds or less, impacting this gene expression and protein synthesis. Due to its size and rigidity (the nucleus is often the cell’s largest organelle and can be up to 5-10 times stiffer than the cytoplasmic cytoskeleton) the mechanical properties of the nucleus will often be an overriding influence on cell behavior during physiological deformations. A number of diseases, including cancers
138 and cardiovascular disease, are associated with defective nuclear mechanosignaling, giving rise to deregulated gene expression.
To learn more about the nucleus, click here.
Microtubules
Organizes intracellular material
Microtubules are stiffer and more elongated than actin filaments. Microtubules are critical in cell polarization and building the mitotic spindle during mitosis. Tracks can span the length of a cell, and form radial arrays during cell division, which function as tracks for intracellular movement. Microtubules assemble stably and disassemble rapidly, which helps cells separate chromosomes during mitosis through rapid reorganization. Thus, microtubules play a large role in intracellular forces and the organization of intracellular materials.
To learn more about microtubules, click here.
Actin
Actin is the most abundant protein in the cytoskeleton and one of the most abundant proteins in most eukaryotic cells. It participates in more protein-protein interactions than any other known protein. Globular actin (G-actin) has the ability to polymerize into filaments (F-actin), generating forces that alter cell shape change and, in combination with molecular motors, organizes organelles and vesicles within the cell. F-actin is particularly concentrated within the actin cortex, which lies just beneath the cell membrane and drives mechanical change at the cell surface.
To learn more about actin, click here.
139 Myosin-II
Myosin-II is considered the active force generator that drives constriction in all animal cells. Myosin-II comes from a family of myosins (motor proteins) which cover a large range of cellular function, including actin polymerization (myosin-I) and vesicle trafficking
(myosins-V and VI). All myosins contain a common motor domain that allow the myosin to perform mechanical work. The basic motile phase of myosin involves swinging of a lever arm forward, translocating the myosin ~10 nm relative to the actin filament. Although the mechanism is the same, each type of myosin reacts to mechanical force uniquely, which allows them to perform specific tasks, such as muscle contraction.
Myosin-II is formed from two heavy chains, where each chain contains a globular head, lever arm and coiled-coil tail. Myosin-II associates into dimers and tetramers, but the functional contractile unit of myosin-II is the bipolar thick filament (BTF). BTF size ranges from as few as eight to as many as 400 monomers, depending on the organism and tissue type.
To learn more about myosin-II, click here.
Actin Crosslinkers
Actin-crosslinking proteins organize actin filaments into dynamic network meshes which orchestrate vital mechanical functions. Actin crosslinker meshes provide mechanical viscoelasticity, supporting various cellular activities including cell motility and the ability to form protrusions. Without crosslinkers, actin filaments cannot tether to each other, preventing formation of a network that myosin-II can contract. Crosslinkers include proteins such as alpha-actinin, filamin, and cortexillin-I. Alpha-actinin and cortexillin-I have the ability to link actin filaments into both highly organized parallel bundles and random
140 meshworks. The important roles of crosslinkers in the contraction of actin filaments can be demonstrated through the result that increased crosslinking that slows cleavage furrow ingression during cytokinesis.
To learn more about crosslinkers, click here.
Anchoring Proteins
Anchoring proteins physically link the actin network to the cell membrane.
Cortexillin, shown here, acts as both an anchoring protein and a crosslinker, as it has both actin and lipid binding domains. Without anchoring proteins, actin filaments cannot tether to the membrane, so the actin network would be unable to sense when stress is applied to the cell or move the plasma membrane. Anchoring proteins act as regulators of signaling from deformations in the plasma membrane to the actin cortex. Without anchoring proteins, blebs, separations of the membrane from the cytoskeleton, form in cells. Blebs can be controlled or disastrous to cells; they are the main feature of cells undergoing apoptosis
(controlled-cell death), but they also play roles in locomotion and division.
To learn more about anchoring proteins, click here.
Other
Several classes of proteins interact with actin to regulate f-actin organization, polymerization, and movement. Mechanical forces can affect the activity of these regulatory proteins, which in turn affects the mechanical actin network properties, such as flexibility and adaptability. Nucleation-promoting factors initiate f-actin formation, polymerases promote the polymerization of g-actin into f-actin, and capping proteins terminate f-actin polymerization. If any of these regulatory proteins is altered, the result is significant impact
141 on the mechanical properties of cell. For example, some nucleators are essential in cell division. In the case of capping proteins, if depleted, cortical thickness increases and cortical tension decreases, suggesting that f-actin length directly impacts tension. Depletion of depolymerases is correlated with excessive cortical actin accumulation, which causes cytokinesis failure.
142 Appendix E: Introduction Animation Script and Storyboards
Script
Every biological process, from cytokinesis, to cell migration, to metabolism, is dependent upon a cell’s ability to sense and adapt to its dynamic environment. Mechanical forces, transmitted via the cytoskeleton, can be converted into biochemical signals which guide cell behavior. The integrated network of macromolecules that allows cells to sense, respond, and generate intra- and extracellular forces is called the mechanobiome. Understanding principles that govern the mechanobiome is helping researchers gain insight into normal biological processes, as well as cancer, COPD, and cardiovascular disease. Click on the 3D Viewer or the Key Concepts to learn more.
Storyboards
! %(' ! %(( ! Figure 100. Introduction animation storyboards
! %() Glossary of Mechanobiome terms
Actin - A multi-functional protein found in essentially all eukaryotic cells. Actin participates in cellular processes including muscle contraction, cell motility, cell division, and organelle movement. Anchoring protein - Proteins containing actin-filament and lipid binding domains. Anchoring proteins connect the actin cortex to the cell membrane. Apoptosis - Programmed cell death which occurs as a normal part of development. Bleb - A bulge in the plasma membrane of a cell due to decoupling of the cytoskeleton from the plasma membrane. Capping protein - A protein while regulates actin filament length by binding to the growing end of filaments and blocking addition or loss of actin subunits. Chemotaxis - The directional locomotion of a cell towards the source of a chemical gradient. Cleavage furrow - The surface indentation at the center of a dividing cell, which ingresses into a bridge that connects the two daughter cells. Compressive stress - The net stress that acts against the outward flow of cytoplasm from the cleavage furrow. It comprises the Laplace-like pressure from the daughter cells, polar cortical contractions, and viscoelastic cytoplasm. Contractility kit - pre-assembled complexes of proteins which are primed to respond to signaling cues. Control System - A series of interconnected components which regulate the behavior or properties of a biological system. Cortex - The region of the cell underlying the plasma membrane that is rich in actin cytoskeleton, including myosin-II. Cortical Tension - The force in the cell cortex that serves to minimize the surface area to volume ratio. Crosslinker - Proteins that bind actin filaments for organization into coordinated networks. Cytokinesis - The physical separation of a mother cell into two daughter cells. Cytoskeleton - The network of protein filaments and tubules within the cell cytoplasm that plays important roles in cell movement, shape, growth, division, differentiation, and internal movement of organelles. Depolymerization - the process of converting a polymer into simpler compounds such as monomers or a mixture of monomers.
146 Differentiation - The process by which cells become progressively more specialized. Equatorial cortex - The central region of the cell cortex that generally gives rise to the cleavage furrow. Force-sharing - The distribution of force across motor proteins and actin crosslinkers. Global cortex - The region of the cortex outside of the cleavage furrow. This is also referred to as the polar cortex Homeostasis - the tendency towards a stable equilibrium between interdependent elements Homeostatic imbalance - the inability to maintain a stable equilibrium, usually causing or resulting from a disease process Homolog - A gene similar in structure and evolutionary origin to a gene in another species Isoform - A member of a set of similar proteins with the same function and similar amino acid sequence, encoded by different genes Laplace pressure - The pressure generated at a curved fluid surface due to surface tension. It serves to minimize the surface area to volume ratio. Mechanical Stress - The pressure (force per unit area) applied to a material. Mechanobiome (mechanome) - The totality of macromolecules that sense, respond to, and generate mechanical forces. Mechanosensation - The ability to sense and respond to mechanical inputs. Mechanotransduction - The conversion of a mechanical input into a biochemical signaling pathway Microtubule - Dynamic polymers of tubulin that form part of the cytoskeleton. Microtubules provide structure and shape to the cytoplasm of many cell types. Model organism - A species that is extensively studied and widely used in research to understand biological phenomena, due to particular experimental advantages. Morphogenesis - The process in which organisms develop their shape. Myosin-II - A motor protein that binds to and performs mechanical work on actin filaments. Myosin-II forms the basis of cellular contractility. Myosin-II Bipolar Thick Filament (BTF) - the functional contractile unit of myosin-II. Nucleation - The first step in the formation of a new structure via self-assembly; i.e. the de novo formation of cytoskeletal filaments Polymerase - An enzyme that catalyze the synthesis of chains of polymers or nucleic acids Set-point control - A system to maintain a desired target value for an essential variable
147 Signal transduction pathways - A cascade of steps that mediate the sensing and processing of stimuli. Signal transduction pathways detect, amplify, and integrate external signals to generate responses within the cell, including changes in enzyme activity, gene expression, or ion-channel activity. Viscoelasticity - A property of a material held together by dynamic interactions. These dynamic interactions lead to time-dependent responses to imposed stresses, leading to both viscous and elastic characteristics Viscosity - The resistive property of a fluid to flow in response to an external stress.
148 References
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153 Vita
Cecilia C. Johnson was born in Pompton Plains, New Jersey. Growing up, she always fostered a huge love for all things science, which led to her induction into the first Sally Ride: Science Club for Girls in middle school and into the prestigious “Gold Academy” in high school. Cecilia continued her studies at The College of New Jersey (TCNJ), with Biology as her primary major. She performed research for two years in the Ornithology lab of Dr. Luke K. Butler, including her thesis work titled “Prealternate molt of the Western Tanager: age and sex differences in timing, latitude, and intensity,” for which she was awarded a travel grant to present at the North American Ornithological Conference in Washington DC. She also performed research with Dr. Butler through the Mentored Undergraduate Summer Experience (MUSE) and presented at TCNJ the culmination of her summer research on the “Fitness and feather quality in White-Throated Sparrows.” Dr. Butler was her first biological illustration client, for whom she published ornithological illustrations for his chapter titled Feathers and Molt in the Johns Hopkins University Press’s textbook Ornithology: Foundation, Critique, and Application. While at TCNJ, art professor and fine artist Gregory Thielker played a vital role in Cecilia’s arts education and encouraged her to continue a career in the arts. Largely due to Thielker’s influence and encouragement, Cecilia decided to pursue a secondary, self-designed major titled Biological Illustration. The summer of 2015, Cecilia performed an Artist Assistantship with Gregory Thielker, painting pieces for his collection Under the Unminding Sky, which exhibited at Castor Gallery in Chelsea, NY. During her first year of graduate studies, Cecilia was awarded the Association of Medical Illustrators Award of Excellence in the Editorial category and the Award of Merit in the Biological Sciences category. Her studies are partially funded by the William P. Didusch Scholarship, the Elinor Widmont Bodian Scholarship, and the School of Medicine Tuition Grant. Cecilia is currently a candidate to receive a Master of Arts in Medical and Biological Illustration in May, 2019. Her thesis project was partially supported by a Vesalius Trust Research Grant.
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