Snapshot: What is Autophagy?

Autophagy is an important disposal mechanism in our bodies, and it is not as scary as the word sounds. The word autophagy is derived from Greek, with ‘auto’ referring to ‘self’ and ‘phagy’ meaning ‘eating’. Autophagy is important for the growth and development of our cells. It helps to restructure our cells and plays a role in our body’s response to stress, like infection or starvation.

 How does autophagy work?

Autophagy is best compared to a very small recycling depot. Our cells are made of many large and small molecules, also called cellular components. For the sake of our analogy, think of these components as the paper and glue that make up the shipping package from an Amazon delivery. When your Amazon box either is damaged or you no longer have a use for it, you put it in your recycling bin. You will monitor how full or empty it is. When full, it is placed at the curb to be picked up on recycling day.

A high-resolution electron-microscopy image of what an autophagosome looks like in cells. Image from Liza Gross retrieved from Wikimedia.

Your neighbourhood’s recycling pick-up day signals to the city workers what part of the city they need to travel to. The bins outside further specify which houses need their recycling picked up. The contents are taken to the depot, where they are sorted, and the materials can be recycled. Special machinery is used to shred and pulp the cardboard, breaking it down into fibers which are used to make new boxes and other paper products.

When cell components are no longer working properly, like the Amazon box, they must be removed and replaced. Molecules, called protein kinases, monitor how well the cell is working – this is you! Deficits activate these proteins, signaling the need for replacement components and thereby placing your recycling out for pick up. In response to this signal, small pieces of membrane are recruited to an assembly site where they combine to form a half circle – the city workers are here to pick up your recycling! This half circle engulfs the defective cell component(s) and then assembles the rest of the circle to form an autophagosome – your box has finally made it to the depot.

The autophagosome then fuses with the membrane of another storage compartment, called a lysosome, which contains digestive enzymes. These enzymes, like the machinery used to shred cardboard, eat away at the inner membrane of the autophagosome. Once they have worked their way through, they can then target its contents – the cardboard is now being worked into a pulp. The enzymes reduce the damaged goods, inclusive of proteins, into amino acids, sugars, and nucleotides. These products can be either removed from the cell or used for nutrients and energy, just like the processed cardboard fibers reused for other products.

What happens when autophagy does not work?

Looking at spinocerebellar ataxias specifically, some are caused by genes that have a polyglutamine expansion. This type of mutation gives cells the wrong instructions for protein folding. These misfolded proteins huddle together in the brain stem which regulates basic functions like breathing; the cerebellum which coordinates voluntary movements; and the basil ganglia which fine tunes these voluntary movements. When autophagy is working properly, the protein clumps can be cleared away easily. When autophagy is defective, as in spinocerebellar ataxias, the cell cannot get rid of these clusters. The proteins continue to accumulate and are toxic to neurons, contributing to the pathology of neurodegenerative disease.

If you would like to learn more about autophagy, take a look at these resources by the Merriam-Webster and our past Snapshot on Protein Degradation.

Snapshot written by Katie Neuman and edited by Celeste Suart.

Snapshot: What is the Rotarod Test?

Patients with ataxia share many common symptoms, including a loss of coordination. While these symptoms might be easy to see in patients, testing movement ability is not as straightforward in mouse models of ataxia. Because of this, researchers use something called the “rotarod performance test” to assess motor coordination and performance in mice.

How do researchers measure ataxia symptoms in mice? Image courtesy of WikiMedia.

What is a Rotarod?

A rotarod is a machine that is similar to a treadmill. It consists of a rod that rotates at a set speed and accelerates for a designated amount of time. During testing, mice are placed on the rod. The spinning of the rod causes the mice to jog. Researchers record the amount of time the mouse spends on the rod.

Mice on a rotating rod, seperated into lanes. Underneath the rod is padding for when the mice fall.
Diagram of Mice on a Rotarod. Image created with BioRender by Eder Xhako.

If a mouse spends less time on the rotarod, it suggests that the mouse has motor coordination deficits that are similar to an ataxia patient’s. Researchers also use the rotarod to assess motor learning in mice: the test is performed over multiple days. This is so they can determine how much motor learning has occurred by comparing how the mice did on the first day versus the last day.

Why do researchers use the Rotarod?

The rotarod allows researchers to measure the coordination and motor learning ability of mice. Having a consistent system of measurement also allows scientists to compare the results of different mouse models. For example, we can see how a new mouse model of ataxia performs compared to mice without ataxia. This allows us to see how severe the motor symptoms are in that specific model. This gives us the ability to confirm that the new mouse strain really does model ataxia.

More excitingly, though, we can use the rotarod to see what effect different treatments have on ataxia symptoms. If a treated mouse is able to spend more time on the rotarod than an untreated mouse, it suggests that the treatment helps improve motor symptoms. Seeing treatment results in a mouse model of ataxia gives researchers more confidence that the same treatment could be useful for patients with ataxia.

If you would like to learn more about the rotarod test, take a look at this video by the Maze Engineers.

Snapshot written by Eder Xhako and edited by Carrie Sheeler.

Snapshot: How Do Scientific Articles Get Published?

The process of publishing a scientific article begins when a group of scientists set out to answer an outstanding question in their field. They then design and conduct a set of experiments to answer this question. Once the scientists feel that their results answer their questions, one of them – usually the one who did the largest number of experiments in the project – writes a first draft of their article.

Writing the Draft

This article draft is then read and edited by the other researchers who contributed to the experiments described in the paper. They will also be listed as its authors. Once all the article’s co-authors have agreed on a version of the article that they are satisfied with, they may choose to post it on a preprint server. This is an online forum where researchers can post scientific articles that have not yet been accepted for publication in a scientific journal. You can learn more about the differences between preprints and peer-reviewed articles in our past Snapshot on Preprints.

Getting Feedback: The Peer-Review Process

Whether or not the authors decide to post their article to a preprint server, they eventually send it to a scientific journal for publication. Different journals publish different types of articles, and the first thing that the journal’s editor will do is to check that it fits with what that journal usually publishes. This includes considerations like the field of science that the paper falls into, or the techniques used. If the editor accepts the paper, they then send it to a panel of scientists – usually two or three – who are experts in the article’s topic of research. These scientists – known as reviewers – read the paper and assess its quality. This includes asking questions like:

Did the authors do the right experiments to answer the questions that they were asking?

Were the experiments done correctly, or were mistakes made?

Do the results of the authors’ experiments mean what the authors claim that they mean?

The reviewers then send the editors a list of comments about the paper. These comments may include questions about the experiments, disagreements about what the experiments’ results mean, and requests for the authors to do new experiments to strengthen their conclusions.

If the reviewers think that it would take too much work to make the paper ready for publication in the journal, they will recommend that the editor reject the article. If this happens, the authors choose another journal to send their article to. That journal’s editor distributes the article to a new set of reviewers, and the review process begins again.

A notebook and pen lay next to a laptop with a fresh mug of coffee next to them.
What does it take to get a scientific paper published? There is a lot of writing and rewriting involved. Photo by Pixabay on Pexels.com

Revisions and Acceptance

If, on the other hand, the reviewers do not reject the article, the authors are given a set amount of time – usually several months – in which to respond to the reviewers’ comments. This could include doing new experiments, rewriting sections of the paper, and/or writing a response to the reviewers’ comments. The article may be sent between authors and the journal’s reviewers several times. However, once the reviewers all agree that their concerns about the paper have been addressed, the paper is deemed ready for publication. After additional formatting by copy editors, the paper is published in the next virtual and/or physical issue of the journal.

Between writing and rewriting a paper, having it read by multiple people, and doing new experiments, the process of publishing a scientific article can take months or even years! This is especially true if it ends up being sent to multiple journals. In the end, though, this process holds scientists accountable to their peers, allowing us all to be more confident in the findings of scientific research.

If you would like to learn more about scientific publisishing, take a look at this resource by the Understand Science.

Snapshot written by Amy Smith-Dijak  and edited by Celeste Suart.

Snapshot: What is Protein Degradation?

The Life Cycle of a Protein

No protein is made to last forever. Just as DNA and RNA direct a coordinated process for protein creation, there is also a process for proteins to be broken down by the cell. We call this proteolysis or protein degradation.

Proteins are broken down for a number of reasons. First and foremost, it’s a strategy for quality control. After a string of protein building blocks are put together, they are bent and folded into a  specific shape that allows the protein to interact with other proteins in a useful way. You can think of it like a daisy in a daisy chain- the stem needs to be carefully folded and tied or the daisy chain falls apart entirely. Cells have tools to identify misfolded proteins and break them down quickly to prevent problems.

Even beyond quality control, proteins have a certain lifespan within the cell. Regular protein recycling ensures that there is always an available supply of protein building blocks for the creation of new proteins. Removing older proteins also gives cells flexibility in terms of adjusting to environmental changes.

A bright blue plastic recycling bin.
Reuse and recycle. Protein degradation is how your cells break down old or broken proteins so their parts can be reused.
Continue reading “Snapshot: What is Protein Degradation?”

Snapshot: What is Statistical Significance?

What is statistical significance?

Anyone interested in research, be it experiments testing the effects of new medications or studies of human behaviour, is bound to eventually encounter the term statistical significance. Despite being a fundamental feature of research, the concept of statistical significance is often a source of confusion beyond the laboratories and classrooms in which it is frequently discussed. This confusion stems partially from the fact that the word “significant” has different meanings in and outside of research. In everyday language, the term significant typically refers to something important or considerable. Significance in research, or statistical significance, refers to the likelihood that a result can be explained by chance. The distinction between statistically significant and important should not be overlooked – a result that is not statistically significant may still be quite meaningful and have far-reaching implications!

 Research findings are always a matter of probability, not certainty. Researchers can never be entirely sure of a particular finding; they can only have some degree of confidence in it. Statistical significance relates to the amount of confidence that a researcher can have in a given result and whether this confidence is sufficient to accept the result as accurate.

person typing on a laptop
Not every research finding is real – many can be explained solely by chance. Statistical significance is a tool that allows researchers to identify results that are unlikely to occur by chance and, therefore, are likely meaningful. Photo by Ruthson Zimmerman on Unsplash

How do researchers evaluate statistical significance?

Researchers evaluate statistical significance through hypothesis testing, in which one tests data against a null hypothesis. For any experiment, the null hypothesis essentially states that there is no real difference between groups of interest. Accordingly, if the null hypothesis is true, any observed group differences can be attributed to chance. Hypothesis testing yields a test statistic called a p-value, which represents the probability of obtaining a result as or more extreme as that observed if the null hypothesis were true. The larger the p-value (from 0 to 1), the more likely the corresponding result occurred by chance.

Researchers often set their criterion for statistical significance at p<0.05, meaning that they will accept a result as significant if there is less than a 5% chance of obtaining it by chance. When a p-value is larger than the cut-off value for statistical significance, the data is considered to be consistent with the null hypothesis and unlikely to contain real relationships between variables of interest. Conversely, when a p-value is less than the cut-off value for significance, we can conclude that the finding is due to a real relationship between variables. In other words, we can reject the null hypothesis.

Why is statistical significance important?

Consider a researcher interested in whether a new drug improves motor control in adults with cerebellar ataxia. She runs an experiment in which half of the participants receives the new drug and the other half receives a placebo (which does nothing). She observes improved SARA scores in the participants who received the drug compared to those who received the placebo. This finding may indicate that the drug improves motor control. Alternatively, this finding may simply have occurred by chance, perhaps due to lucky sampling and group assignment. To determine whether the improvement in SARA scores is significant, the researcher must compare the p-value associated with the result to her criterion for significance. For example, if the p-value is 0.03 and the criterion for significance is p<0.05, the researcher can conclude with 95% certainty that the result is statistically significant, and there is a real relationship between the drug and improved SARA scores.

Probability graph visually showing the percentage likelihood of an event occurring due to random chance. Detailed description available in the image caption.
Graph illustrating the probability of possible results for a given experiment, with a criterion for statistical significance of p<0.05 and a result with a p value of 0.03. Based on a p value of 0.03, there is a 3% chance that a researcher would, by chance, obtain a result equal to or more extreme than the result observed (shaded green region). Figure made by Chloe Soutar.

A researcher can be more or less conservative in estimating statistical significance by applying different criteria. For instance, if we set the criterion for significance at p<0.01, we accept values as significant only if we would expect them to occur less than 1% of the time by chance. The criterion for statistical significance dictates how confident we can be in a given result. Understanding statistical significance thus allows us to make sound judgements about research findings and, in turn, how we invest our time, energy, and money.

To learn more about statistical significance, check out these articles and videos from Laerd Statistics and Khan Academy.

Snapshot written by Dr. Chloe Soutar and edited by Celeste Suart