University:
Amherst College
Course:
BIOL 220 | Developmental Biology
Academic year:

2024
Views:

323

Pages:

25
Author:

pigamdomo81d

Regeneration and Patterning Regeneration: pp. 560-571 Modeling: pp. 116-117; 697-698 Regeneration • “I’d give my right arm to know the secret of regeneration” -Oscar Schotté • Making a new organ when one is removed – – – – salamander limb starfish “arms” flatworms and hydra vertebrate liver Regeneration Terminology • Epimorphosis (e.g. limb, Hydra) – growth – dedifferentiation and respecification • Morphallaxis (e.g. flatworm) – no growth – repatterning of existing tissues • Compensatory (e.g. liver) – cell division – no dedifferentiation Limb Regeneration • Epimorphic • Wound heals forming an ectodermal cap • Cells beneath dedifferentiate forming a blastema • Cell division in blastema followed by redifferentiation into muscle, cartilage and bone, etc. Limb • Blastema re-patterns limb like a new progress zone – requires enervation to make growth factors stimulating cell division – retinoic acid made by wound epidermis plays a role in specification • Same order: proximal to distal Liver • Compensatory regeneration • Hepatocyte growth factor stimulates cell division of remaining cells • Each liver cell type retains its character but enters the cell cycle • Many are arrested in G2 and can rapidly enter mitosis Flatworm Regeneration • Morphallactic • Bisect – maintains polarity • Trisect – same • Same tissue can make head or tail Flatworm Regeneration • Small piece may show no polarity • Given time it develops a gradation • Head commitment at the first cut • Try some in lab! Hydra • Fresh water coelenterate • Reproduces sexually and also by budding • Epimorphic • Regeneration experiments suggest morphogen gradients Hydra • Abraham Trembley (1740) • Is it a plant or animal? • Moves, eats, so if you cut it up, it should die – wrong! • Parlor games of the 18th century – (before TV) – music – science Historical Comment • Réaumur – “During the summer of 1741, lizards, frogs, worms, snakes, eels, crabs, butterflies, and lobsters were threatened by dangers to which they had never been exposed before.” – “When I saw for the first time two polyps form little by little from the one I had cut in two, I could hardly believe my eyes: and it is a fact which I am not at all accustomed to seeing, after having seen it over again hundreds and hundreds of times.” Head and Base Activating Regions • Head or region near head induces outgrowth from host • Base induces base • Each “neutralizes” the other Head Inhibition of Heads • Head induces almost anywhere • Subhead does not induce if near head • Remove head allows induction • Gradient of inhibition Long Distance Inhibition Recipe 1. Paste together five Hydra to make a giant. 2. Make another and decapitate. 3. Wait a while. Model of Function of Inhibitor in Budding Loss of distal inhibition upon growth Wnt Expression • Wnt signals mark the future head region Recall Schotté’s Experiment: Positional Information Positional Information • One interpretation – There is information specifying only relative position – What a cell makes depends on how it reads the signal – This depends on • its capabilities – its genes – and its history Wolpert’s French Flag • French and American flags have different “genetics” or rules • Gradient of positional information can be the same left to right • Rules of interpretation different Applied to Chick Wing ZPA Patterning Models • Where does the gradient come from? – Could be preformed • could be gradient of receptor concentrations on cell surface or a source of localized source of morphogen diffusing through tissue • begs the question? – Could be generated • but what could generate a gradient of positional information from an initially homogeneous field? Mathematical Models • Turing’s reaction diffusion model – chemical analogue in oscillating reactions • Need 2 substances – the inhibitor suppresses the other (activator) – the activator promotes itself and the inhibitor – inhibitor diffuses more rapidly than the activator • Random fluctuations will generate pattern Patterning Models • Initial random fluctuations • Eventually a series of peaks is generated in a pattern • Can predict complex patterns Patterning Models Patterning Models • Some people like them, some people don’t.

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