Hexbyte Glen Cove How proteins help yeast adapt to changing conditions thumbnail

Hexbyte Glen Cove How proteins help yeast adapt to changing conditions

Hexbyte Glen Cove

Credit: CC0 Public Domain

Proteins in the brain called prions are well known for their involvement in causing disease, but a study published today in eLife suggests they may help yeast cope with rapidly changing environmental conditions.

The findings show that prions may be part of an important epigenetic mechanism for controlling in changing conditions. Further insight into this role could aid our understanding of diseases that involve abnormal cell growth or .

Prions are proteins that are abnormally folded into different shapes. Prions can spread or be passed on to new cells. They have famously been linked to two deadly brain diseases, Creutzfeldt-Jakob and Mad Cow disease. But some prions can be helpful. Each shape of prion may perform a different task in the cell, in a similar way to a Swiss Army knife.

“While scientists have known about prions for decades, we don’t yet know what distinguishes beneficial prions from harmful ones,” says co-first author David Garcia, Ph.D., who was a postdoctoral fellow at the Department of Chemical Systems Biology at Stanford University School of Medicine, California, US, and is now Assistant Professor at the Institute of Molecular Biology, University of Oregon, US.

To learn more, Garcia and colleagues studied a yeast enzyme called pseudouridine synthase that can take on two . They found that, in its alter-ego prion form, this enzyme causes yeast to multiply and grow more quickly, although these changes come at the cost of a shorter lifespan for the yeast.

Through computer modelling, the team then showed that the changes brought about by the prion are beneficial when environmental resources are abundant, but harmful when resources are scarce. By reducing a so-called protein ‘chaperone’, they also showed that the prion can revert to its original enzyme shape. Since protein chaperones themselves fluctuate during changing conditions, they propose that this might be a way to turn the prion on or off when desirable.

“We’ve identified a new role for prions in which they can transform cell growth and survival,” says co-first author Edgar Campbell, a Ph.D. student in Chemical and Systems Biology at Stanford Medicine. “These findings suggest that prions may be another form of epigenetic control of cells.”

Epigenetic changes can alter the behaviour of cells without changing their DNA, can be passed on to new generations of cells, and may be turned on or off by environmental conditions. The authors suggest that learning more about the role of prions in epigenetic control may be critical to improving our understanding of prion diseases.

“These types of epigenetic changes are missed when we sequence genomes but can still have a major influence on cell growth,” concludes senior author Daniel Jarosz, Ph.D., Associate Professor of Chemical and Systems Biology and Developmental Biology at Stanford Medicine. “It is critical to learn more about the consequences of -driven epigenetic changes in and find new ways to search for them in yeast and other organisms.”



More information:
David M Garcia et al, A prion accelerates proliferation at the expense of lifespan, eLife (2021). DOI: 10.7554/eLife.60917

Journal information:
eLife



Citation:
How proteins help yeast adapt to changing conditions (2021, September 21)
retrieved 22 September 2021
from https://phys.org/news/2021-09-proteins-yeast-conditions.html

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Hexbyte Glen Cove A powerful computational tool for efficient analysis of cell division 4-D image data thumbnail

Hexbyte Glen Cove A powerful computational tool for efficient analysis of cell division 4-D image data

Hexbyte Glen Cove

3D projections of images of nuclei (green and white dots) and membranes (red lines) at different time points. Credit: DOI number: 10.1038/s41467-020-19863-x

A joint research team co-led by City University of Hong Kong (CityU) has developed a novel computational tool that can reconstruct and visualize three-dimensional (3-D) shapes and temporal changes of cells, speeding up the analyzing process from hundreds of hours by hand to a few hours by the computer. Revolutionizing the way biologists analyze image data, this tool can advance further studies in developmental and cell biology, such as the growth of cancer cells.

The was co-led by Professor Yan Hong, Chair Professor of Computer Engineering and Wong Chung Hong Professor of Data Engineering in the Department of Electrical Engineering (EE) at CityU, together with biologists from Hong Kong Baptist University (HKBU) and Peking University. Their findings have been published in the scientific journal Nature Communications, titled “Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4-D segmentation.”

The tool developed by the team is called “CShaper.” “It is a powerful that can segment and analyze cell images systematically at the single-cell level, which is much needed for the study of cell division, and cell and gene functions,” described Professor Yan.

Bottleneck in analyzing massive amount of cell division data

Biologists have been investigating how animals grow from a single cell, a fertilized egg, into organs and the whole body through countless cell divisions. In particular, they want to know the gene functions, such as the specific genes involved in cell divisions for forming different organs, or what causes the abnormal cell divisions leading to tumourous growth.

Morphological dynamics of cell division of C. elegans embryo cell at single-cell resolution. Credit: DOI number: 10.1038/s41467-020-19863-x

A way to find the answer is to use the gene knockout technique. With all genes present, researchers first obtain cell images and the lineage tree. Then they “knock out” (remove) a gene from the DNA sequence, and compare the two lineage trees to analyze changes in the and infer . Then they repeat the experiment with other genes being knocked out.

In the study, the collaborating biologist team used Caenorhabditis elegans (C. elegans) embryos to produce terabytes of data for Professor Yan’s team to perform computational analysis. C. elegans is a type of worm which share many essential biological characteristics with humans and provide a valuable model for studying the tumor growth process in humans.

“With estimated 20,000 genes in C. elegans, it means nearly 20,000 experiments would be needed if knocking out one gene at a time. And there would be an enormous amount of data. So it is essential to use an automated image analysis system. And this drives us to develop a more efficient one,” he said.

Breakthrough in segmenting cell images automatically

Cell images are usually obtained by laser beam scanning. The existing image analysis systems can only detect cell nucleus well with a poor cell membrane image quality, hampering reconstruction of cell shapes. Also, there is a lack of reliable algorithm for the segmentation of time-lapsed 3-D images (i.e. 4-D images) of cell division. Image segmentation is a critical process in computer vision that involves dividing a visual input into segments to simplify image analysis. But researchers have to spend hundreds of hours labeling many cell images manually.

The framework of CShaper. With deep-learning-based DMapNet, time-lapse 3D cell shapes across the development with defined cell identity are generated (shown in the right green box). Credit: DOI number: 10.1038/s41467-020-19863-x

The breakthrough in CShaper is that it can detect cell membranes, build up cell shapes in 3-D, and more importantly, automatically segment the cell images at the cell level. “Using CShaper, biologists can decipher the contents of these images within a few hours. It can characterize cell shapes and surface structures, and provide 3-D views of cells at different time points,” said Cao Jianfeng, a Ph.D. student in Professor Yan’s group, and a co-first author of the paper.

To achieve this, the deep-learning-based model DMapNet developed by the team plays a key role in the CShaper system. “By learning to capture multiple discrete distances between image pixels, DMapNet extracts the membrane contour while considering shape information, rather than just intensity features. Therefore CShaper achieved a 95.95% accuracy of identifying the cells, which outperformed other methods substantially,” he explained.

With CShaper, the team generated a time-lapse 3-D atlas of cell morphology for the C. elegans embryo from the 4- to 350-cell stages, including cell shape, volume, surface area, migration, nucleus position and cell-cell contact with confirmed cell identities.

Advancing further studies in tumor growth

“To the best of our knowledge, CShaper is the first computational system for segmenting and analyzing the images of C. elegans embryo systematically at the single-cell level,” said Mr Cao. “Through close collaborations with biologists, we proudly developed a useful computer tool for automated analysis of a massive amount of cell . We believe it can promote further studies in developmental and , in particular in understanding the origination and growth of cancer cells,” Professor Yan added.

They also tested CShaper on plant tissue cells, showing promising results. They believe the computer tool can be adopted to other biological studies.



More information:
Jianfeng Cao et al, Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation, Nature Communications (2020). DOI: 10.1038/s41467-020-19863-x

Provided by
City University of Hong Kong

Citation:
A powerful computational tool for efficient analysis of cell division 4-D image data (2020, December 22)
retrieved 25 December 2020
from https://phys.org/news/2020-12-powerful-tool-efficient-analysis-cell.html

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