The Hydrodynamic Model of Giardia lamblia Attachment
Creator
Picou, Theodore Julius
Advisor
Elmendorf, Heidi G
Abstract
Giardia lamblia is a flagellated, intestinal parasite that resists peristalsis by attaching to the intestinal wall. Despite investigation over the past 40 years, the mechanism of attachment remains controversial. Here we provide experimental and computational evidence in support of an active hydrodynamic attachment mechanism. Using spinning disk confocal microscopy, we show that fluorescent quantum dots move in a directed manner under the ventral surface of the parasite: entering under the anterior end of the ventral disk through a small opening created by the overlap of the disk’s spiral array of microtubules before exiting under the flexible posterior zone of the disk and through the ventral groove. On average, the quantum dots traveled with a speed of ~5 m/s—as predicted by our computational model—which translates into a negative pressure differential (with respect to atmospheric pressure) sufficient to provide the previously measured force of attachment1. Additionally, our model predicts that Giardia should be capable of attaching to uneven and porous substrates (akin to intestinal cell microvilli), and we provide evidence that Giardia can attach with roughly equivalent forces to low porosity polyacrylamide and glass surfaces under flow, but increasing porosity compromises the attachment strength. We computationally verified these data using a morphologically accurate 2-D finite element model of the parasite’s ventral surface and found that the ventral flagella’s waveform and beat frequency are sufficient to generate the observed flow. The experimentally measured fluid flow at Giardia’s ventral surface, the attachment of trophozoites to porous surfaces, and the computationally predicted flagellar-driven fluid flow in Giardia’s ventral surface geometry provide a clearer understanding of the role that fluid dynamics play in Giardia lamblia’s attachment mechanism and support our proposed hydrodynamic model.
Description
Ph.D.
Permanent Link
http://hdl.handle.net/10822/1043816Date Published
2017Subject
Type
Publisher
Georgetown University
Extent
141 leaves
Collections
Metadata
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