New insights into kidney disease with tropical frog models

Date:
November 5, 2021
Source:
University of Zurich
Summary:
Using cutting-edge genetic engineering, researchers have developed a
model to study hereditary kidney disease with the help of tropical
frogs.

The method allows them to collect large amounts of data
on anomalies, which can then be analyzed using artificial
intelligence. The research opens up new opportunities in the search
for new treatment approaches for the hitherto incurable disease.



FULL STORY ========================================================================== Using cutting-edge genetic engineering, researchers have developed a model
to study hereditary kidney disease with the help of tropical frogs. The
method allows them to collect large amounts of data on anomalies, which
can then be analyzed using artificial intelligence. The research opens
up new opportunities in the search for new treatment approaches for the hitherto incurable disease.


========================================================================== Frogs' anatomy and organ function are strikingly similar to those of
humans. An international team led by Soeren Lienkamp, professor at
the Institute of Anatomy at UZH, has now exploited this similarity
by using a tiny tropical frog called Xenopus tropicalis to model
human genetic diseases. The researchers focused on polycystic kidney
disease, a congenital and currently incurable form of progressive kidney deterioration, and replicated it in frogs.

Observing disease processes in real time Using CRISPR/Cas9, a methodology
for turning off gene function, the scientists targeted genes known to
play a role in cystic kidney disease. "Our novel frog models develop
cysts in the kidneys within only a few days, allowing us to observe these disease processes in real time for the first time," says lead author
Thomas Naert. While most genetic studies are performed on mice, frogs
have features that make them well-suited for larger scale studies. "One
frog couple can produce hundreds or even thousands of eggs," says
Naert. "That's why you see such large numbers of tadpoles in the Swiss
lakes in springtime." Similarly, in the lab large numbers ofXenopus
tropicalis tadpoles can be manipulated to develop cystic kidney diseases.

AI analyzes data from light-sheet microscopy To analyze the data from
such a large number of animals, the team employed a technique called light-sheet microscopy, which produced a 3D reconstruction of the
entire tadpole and all its organs. Much like magnetic resonance imaging, light-sheet techniques make it possible to see through tissues in tadpoles
to find disease-affected organs. The collected data was then processed
using artificial intelligence to allow rapid, automated assessment of
disease. "While it would normally take my team several days or even weeks
to analyze data from hundreds of tadpoles, artificial intelligence can
now do this task in a matter of hours," says Lienkamp.

The findings from frog models analyzed in this way provide new insights
into the early processes of polycystic kidney disease. These insights
will form the basis for developing new treatment approaches for affected patients.

========================================================================== Story Source: Materials provided by University_of_Zurich. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Thomas Naert, O"zgu"n C,ic,ek, Paulina Ogar, Max Bu"rgi, Nikko-Ideen
Shaidani, Michael M. Kaminski, Yuxiao Xu, Kelli Grand, Marko
Vujanovic, Daniel Prata, Friedhelm Hildebrandt, Thomas Brox, Olaf
Ronneberger, Fabian F. Voigt, Fritjof Helmchen, Johannes Loffing,
Marko E. Horb, Helen Rankin Willsey, Soeren S. Lienkamp. Deep
learning is widely applicable to phenotyping embryonic development
and disease. Development, 2021; 148 (21) DOI: 10.1242/dev.199664 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211105084101.htm

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