Dynamics of developmental fate decisions - Luís A. Nunes Amaral
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This document summarizes a presentation on developmental fate decisions. It discusses: - Early experiments in fruit fly development by Thomas Morgan that established genetics as a field - The process of development from a single cell to a multicellular organism - Models of developmental processes like Waddington's epigenetic landscape and studies of C. elegans development - Conserved genes and signaling pathways that control cell specialization, communication, patterning and other developmental mechanisms - Studies of Drosophila eye development that provided insights into processes like cell fate specification and lateral inhibition - Recent studies tracking expression of Yan and Pnt transcription factors that control cell fate in the Drosophila eye using live imaging - Preliminary modeling of gene
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Dynamics of developmental fate decisions - Luís A. Nunes Amaral
- 1. DYNAMICS OF DEVELOPMENTAL FATE DECISIONS Luís A. Nunes Amaral Northwestern University Biological Physics Seminar
- 2. Good to get some historical perspective 2
- 3. From ‘Lords of the fly’ 3 ‘… experimentalists trust and value most highly results that they can put to use productively in their own experimental work.’
- 4. A bit of history 4 Thomas Hunt Morgan (September 25, 1866 – December 4, 1945), an American evolutionary biologist, geneticist, embryologist, won the Nobel Prize in Physiology or Medicine in 1933 for discoveries elucidating the role that the chromosome plays in heredity. Morgan began to study the genetic characteristics of the fruit fly Drosophila melanogaster and demonstrated that genes are carried on chromosomes and are the mechanical basis of heredity. These discoveries formed the basis of the modern science of genetics. As a result of his work, Drosophila became a major model organism in contemporary genetics.
- 5. From ‘Lords of the fly’ 5 ‘Morgan’s discovery of the autocatalytic property of large-scale breeding – the breeder reactor – was a turning point in Drosophila’s natural history.’
- 6. The wonder of development
- 7. An egg is the organic vessel containing the zygote in which an animal embryo develops until it can survive on its own.
- 8. From the egg 8 Human fertilized egg 0.1 mm 0.5 mm 20 mm
- 9. To a multicellular organism 9 Spider mite: 0.4 mm Human: 1,700 mm Chicken: 200 mm Fruit fly: 3.5 mm
- 14. C. elegans 14
- 16. Conservation of important genes 16
- 17. How do these mechanisms work?
- 21. Signaling pathways are highly conserved 21
- 23. Patterning 23
- 26. Patterning of the complex eye
- 27. 27 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427658/ Studies of […] eye mutants have provided key insights into the areas of cell fate specification, lateral inhibition, signal transduction, transcription factor networks, planar cell polarity, cell proliferation and programmed cell death just to name a few.
- 28. The structure of the complex eye 28 Each complex eye comprises 475-490 ommatidia. The core of the adult ommatidium contains 8 photoreceptor neurons, 4 lens secreting cone cells and 2 primary pigment cells. Each ommatidium shares 6 secondary pigment cells, 3 tertiary pigment cells and 3 mechano-sensory bristle complexes with its surrounding neighbors. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427658/
- 29. Ommatidia formation 29 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427658/ All cells that are not incorporated into the pre- clusters undergo a single additional round of mitosis. This second mitotic wave is required to generate the remaining cells needed to produce mature ommatidia
- 32. Recruitment of other photo-receptors 32
- 33. Recruitment of other photo-receptors 33 http://dev.biologists.org/content/137/14/2265.full Bistable expression of Yan and Pointed regulates the transit to differentiation Concentration Time Pointed (Activator) EGFR Signaling Yan Pointed Yan (Repressor) Multipotent State Differentiating State Genes Differentiation Yan Genes Differentiation Pnt 7 Remake plot
- 34. Recruitment of other photo-receptors 34 0.5 1.0 1.5 2.0 anConcentration(AU) Concentration Time 0.5 1.0 1.5 2 .0 0.5 1.0 1.5 2 .0 − 2 0 0.0 PntC PntConcentration(A.U.) PntConcentration(A.U.) D E Time (h) -10 0 10 2 0 3 0 40 5 0 Multipotent Cells R2/R5 Neurons tedConcentration(AU) R3/R4 Neur Concentration Pointed Time Multipotent Yan Differentiated Yan Pnt
- 35. How where these results obtained?
- 36. Null Mutants! Staining using antibodies
- 37. Summary of previous lecture 37
- 38. Summary of previous lecture 38
- 39. Summary of previous lecture 39 0.5 1.0 1.5 2.0 YanConcentration(AU) Concentration Time 0.5 1.0 1.5 2 .0 PntConcentration(A.U.) D Time (h) -10 0 10 2 0 3 0 40 5 0 Multipotent Cells R2/R5 Neurons R3/R4 Concentration Pointed Time Multipotent Yan Differentiated Yan Pnt
- 40. The human team 40 Sebastian Bernasek Chem. Engineer Neda Bagheri Electr. Engineer Rich Carthew Biologist Justin Cassidi Biologist Nicolas Pelaez Biologist Ilaria Rebay Biologist
- 41. Dynamics of cell fate determinants 41 Peláez et al., eLife 4, e08924 (2015)
- 42. The first challenge 42 Qi et al., Int Conf Signal Process Proc. 2013, 670-674 (2014)
- 43. Performance of segmentation algorithm 43 Qi et al., Int Conf Signal Process Proc. 2013, 670-674 (2014) Original image Gold standard Our segmentation Comparison
- 44. Our images are more challenging 44 Peláez et al., eLife 4, e08924 (2015)
- 45. Flies that express Histone-RFP and Yan-YFP 45 Peláez et al., eLife 4, e08924 (2015)
- 46. So, what did we find?
- 47. This is the expectation! 47 0.5 1.0 1.5 2.0 YanConcentration(AU) Concentration Time 0.5 1.0 1.5 2 .0 PntConcentration(A.U.) D Time (h) -10 0 10 2 0 3 0 40 5 0 Multipotent Cells R2/R5 Neurons R3/R4 Concentration Pointed Time Multipotent Yan Differentiated Yan Pnt
- 48. This is the observed outcome for Yan 48 Approximately exponential decay Peláez et al., eLife 4, e08924 (2015)
- 49. This is the observed outcome for Pnt 49 Unpublished (2017)
- 50. Let’s look at Yan in more detail
- 51. Process is insensitive to absolute Yan levels 51
- 52. Rates of decay of Yan levels 52
- 53. Variability across cells highest at ramping up 53
- 54. What is going on between Yan and Pnt?
- 55. Comparison of levels within same cells 55 Unpublished (2017)
- 56. We have not yet developed modeling approach, but feel we now have the data to attempt to validate models
- 57. Why this may be better Yan and Pnt are very powerful transcription factors. Probably not safe to have high levels of either for long! System is re-usable: Whether particular fate is chosen or not, levels of Yan and Pnt return to baseline.
- 58. Why are gene regulatory networks so large? 58 Unpublished (2017)
- 59. Because otherwise things breakdown… 59 miR-7 +miR-7 - Unpublished (2017)
- 60. But what if we slow things down? 60 Glucose restriction Unpublished (2017)
- 61. Lots of good things happen! 61 Unpublished (2017)
- 62. What if we slow things down by reducing number of ribosomes?
- 63. Lots of good things happen! 63 Unpublished (2017)
- 64. Even when you remove a big system! 64 Unpublished (2017)
- 65. Modeling approach 65 Unpublished (2017) D is activation level of gene in DNA R is number of mRNAs available for translations P is number level of proteins Assumptions: D takes continuous value between 0 and some maximum R and P are integer but large enough that can be seen as continuous Formation Degradation
- 66. Feedback control 66 Unpublished (2017)
- 67. Redundant feedback control 67 Unpublished (2017)
- 68. Challenges of analysis of results 68 Unpublished (2017) We are not modeling any particular system System is almost certainly nonlinear Change in metabolic rate will likely affect different parameters differently We cannot know values of any parameters Parameters are effective anyway
- 69. Addressing challenges 69 Unpublished (2017) Scan many possible parameter values (at least order an order of magnitude change for each parameter value) Consider linear and nonlinear version of equations Consider different possibilities for how change in metabolic rate will affect different parameters
- 70. Analysis of simulation results 70 Unpublished (2017)
- 71. Modeling approach 71 Unpublished (2017)
- 72. Redundancy allows for speed Gene regulation network redundancy enables organisms to increase metabolic rate while avoiding developmental mistakes. Shorter developmental times, increase fitness by reducing time organism is helpless. Provides driving force for increasing complexity.
- 73. PROMPTING IS CURRENTLY THANK YOU! NORTHWESTERN MEMORIAL
Editor's Notes
- Production of atonal protein on cells in the morphogenic furrow is progressively localized to a ‘rosette’ of 10-15 cells, then to a pre-cluster group of 5 cells, and, finally, to a single cell which becomes an R8. Selection of the single R8 is regulated by an inhibitory loop involving two other proteins, ro (rough) and sens (senseless).
- Since the cells of the ommatidium are not related by lineage it was proposed that the ommatidium was built by a series of inductive events. Based on the developmental history of the ommatidium and the physical cell-cell contacts that are made between the different cell types, the R8 was predicted to recruit R2/5 which in turn would recruit R3/4 and so on. An inductive signal would, by necessity, involve cell-cell communication and the use of a ligand-receptor signaling complex.
- Production of atonal protein on cells in the morphogenic furrow is progressively localized to a ‘rosette’ of 10-15 cells, then to a pre-cluster group of 5 cells, and, finally, to a single cell which becomes an R8. Selection of the single R8 is regulated by an inhibitory loop involving two other proteins, ro (rough) and sens (senseless).