Team 6
Introduction
Meet Team 6
We are a team of undergraduate students investigating ALS and genes associated with motor decline using the Drosophila model. Scientific research is essential for developing new concepts and furthering our understanding.
From left to right : Hailey Hartshorn, Karen Perez, Joshua Jones, Jesus Mendoza
Overview of Amyotrophic Lateral Sclerosis (ALS)
What is ALS?
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that targets nerve cells residing in the brain and spinal cord. As the brain degenerates, this causes sclerosis to occur in the lateral region of the spinal cord. This leads to the degeneration of motor neurons that communicate to muscles throughout the body, resulting in the muscle atrophy observed in ALS patients.
Goals of the Experiments, What Do We Hope to Learn?
The goal of this experiment is to test two gene candidates, HGF and Rdl, which are predicted to be involved in Amyotrophic Lateral Sclerosis (ALS) on the animal model, Drosophila. By applying different behavior tests, we can analyze fly locomotion to determine whether the two chosen genes are implicated in disease pathology. While evaluating the results of our locomotion tests, we hope to learn whether the up-regulation of the HGF gene and down-regulation of the Rdl gene, have significant effects on the motor capacity of the fly progeny and exhibit ALS-like symptoms.
Methods
Why the fruit fly?
Image of AlrmGAL4/+ female fly
Drosophila Melanogaster
Can look for visible traits to identify chromosome inheritance.
Balancer chromosomes can be used to prevent meiotic recombination.
Ability to produce numerous progeny.
Relatively short lifespan allows for observation of motor decline with age in a limited timeframe.
Drosophila have 4 chromosomes that can be used to easily track genetic transmission.
How to Identify Male vs Female Drosophila
Male vs Female
When setting up a cross for an experiment it is vital that you are able to distinguish a male fly from a female. A difference that you will find in a male is that they have a shorter more stout abdomen when compared to the longer and sharper abdomen of the female. What you will really be looking for throughout the semester however is whether or not the fly has dark genitalia. This genitalia is an obvious sign that you are looking at a male.
Virgin female flies
When looking to set up a cross for your gene of interest what you are really looking for is virgin female flies. If a female fly has mated before, the progeny resulting from your crosses may contain genetic material from previous mates. Using virgin females exclusively guarantees that the resulting progeny is between parent flies whose crosses result in your target genotype.
At a first glance it might be difficult to distinguish the difference between a virgin and a non-virgin female fly. However, with practice you will know what signs to look for when hunting for a virgin. The main giveaway in a virgin fly is the dark spot on their abdomen called the meconium. Secondary signs you should look for include the much more lighter and even translucent color of their abdomen as well as a sort of swollen appearance. It is important that you are able to determine the signs of a virgin female fly in order for your crosses to succeed.
Genotypes of the parents
HGF cross
Rdl Cross
We looked for all progeny from the HGF cross
(UAS-HGF/hsp70flp ; +/alrmGAL4)
We looked for progeny without CyO genotype from the Rdl cross
( UAS-Rdl/hsp70flp ; +/alrmGAL4)
Genes of Choice
HGF
Upregulation of HGF leads to...
HGF can promote the survival of neurons and glial cells. It is possible that overexpression of HGF is supporting damaged motor neurons allowing them to survive longer. HGF also promotes the growth of astrocytes which have been shown to lead to the death of healthy motor neurons in ALS→secrete toxic factors (can lead to MN death).
Hepatocyte Growth Factor (HGF) Fly Ortholog: CG30283
Dataset: Humphrey
Log2FC: 1.4004274 (Upregulated)
Pvalue: 7.04E-17
GABRR1
Down regulation of GABRR1...
Encodes for 1 out of 19 possible subunits for GABA receptors. As an inhibitory neurotransmitter GABA blocks action of excitatory neurotransmitter Glutamate. There are reduced levels of GABA in the motor cortex of patients with ALS. Alteration of GABA receptors can lead to disruption of inhibitory circuits, leading to cortical hyperexcitability, a hallmark feature of amyotrophic lateral sclerosis. This can result in visible symptoms such as spasticity.
Gamma-aminobutyric Acid type A Receptor Subunit 1 (GABRR1) Fly Ortholog: Rdl
Dataset: Ziff
Log2FC: -1.543544654 (Downregulated)
Pvalue: 0.0149391
How To: Unstimulated Behavior Test
Created in BioRender.com
Fly progeny are separated from the parent vial of either HGF or RDL and are transferred to a new vial for behavior tests. To conduct the behavior test, we want to make sure that the flies we want to test haven't been exposed to CO2 or ice treatment earlier in the day. When prepping for a petri dish test, the flies are then transferred from their behavior test vial to a new empty vial.
With the flies in the empty vial, they are then put in a styrofoam container with ice and are put to sleep due to the ice. They will be on the ice for 3 minutes, if the flies are still not asleep, they will be kept on for longer (we keep checking after 1-2 minute intervals but it usually doesn't take that long).
Once unconscious, flies are then transferred from the vial onto a petri dish. This petri dish has a plastic ring on the inside that extends around the diameter of the dish to prevent the flies from climbing on the sides. If they climb on the sides, it messes with the AnimalTA tracking program.
With the flies still asleep in the dish, the petri dish is placed on a microscope stage. This setup allows for a phone to record the clear bottom of the petri dish through the circular opening to see the movement of the flies. The attached lights on the side of the instrument and the Kim wipe on top of the petri dish are used to provide a clear and uniform background.
Once the flies wake up in the dish, and the setup is complete, we record the fly's movement for 120 seconds to get as much data as we can about their average velocity and distance traveled.
After ~120 seconds the setup is disassembled and the petri dish with the awake flies is transferred back to the container with ice to put them back to sleep. Earlier in our experiments, we found it was easier to put the flies back to sleep with ice instead of CO2 so we could maximize the number of flies that make it back to their behavior test vial. Once they are asleep we transfer the flies from the petri dish back to their behavior test vial.
The video is then uploaded to the AnimalTA software and the data from the video is analyzed.
Animal TA: Video Analysis
Behavior test vial Smith before it is put in the petri dish.
Animal TA Software
Smith vial in the petri dish and set up to be recorded on the microscope.
Upload videos recorded from the unstimulated behavior tests into the One Drive folder titled 'Unanalyzed videos.'
Open Animal TA software, select 'New Project', and name it by the date and genotype of the fly.
Using the software select 'add videos' and convert the videos from .MOV to .ata.
Once videos are added to the project select crop and crop the videos to 120 seconds long and the area to only be the petri dish.
Next would be to define a background, and make sure no flies are in the frame. Right-click to change the color and left-click to color over the visible flies.
Next would be to define an arena click on the ellipse and make it so that the petri dish is outlined in the frame.
Define the scale, and make a line stretching from the outer corner of the diameter to the other side, the measurement is 8.5 cm.
After all the initial settings have been adjusted you can now begin tracking your flies of interest.
Once the video is finished being analyzed view and correct the track.
To obtain the data needed from the video select prepare analysis, then navigate to 'Analysis of individual trajectories.'
All the needed information from the flies is in this section, make sure to note down speeds while moving and distance traveled
Method of Data Keeping
Excel Spreadsheet
To ensure that we had all of the information from the videos organized, our team decided to use an Excel spreadsheet as a method of record keeping as well as a tool for generating graphs and other annoying math we had to do.
We used the equations =AVERAGE() and =SUM() to help us get the information we needed from the videos. To obtain the graphs used in the next section, we highlighted the week and either the average speed or the average distance traveled and generated a scatter plot containing information like the equation and r values.
Results
UAS-HGF/hsp70flp; +/alrmGAL4
This graph contains the speeds of week one through week four flies averaged out. Within each week there are various trials of flies which is why we decided to use the standard error of the mean which measures how likely the average of the week is to be from the sample mean. The R^2 value given in the graph is relatively close to the values of 0.95 and 1, which would indicate that the relationship between the independent and dependent variables is strong and the line is a good match for the relationship of the variables.
This graph contained information covering the averages of the total distance traveled. The information in this graph was gathered in the same way as the previous one and has an even stronger R^2 value. The graphs look like how we would expect the data to turn out if it were to mimic locomotor defects, with decreasing average speed and average distance as the flies age. Overall, between the two tests, we were able to collect many trials during the different weeks of a fly's life which contributed to the reliability of our statistics.
UAS-Rdl/hsp70flp ; +/alrmGAL4
This graph contains the average speeds of one-week-old to four-week-old flies containing the Rdl gene. One statistic from the graph that stands out is the r^2 value of near zero, which means there is no linear relationship between the data points. There is also a slight increase in the trend of the line, whereas you would expect that, with age, the flies would begin to slow down. Unfortunately, due to unproductive crosses, as well as selecting this gene later on in the semester, we weren't able to collect week four data on this gene.
The image to the left depicts a graphing of the average distance traveled vs the weeks. The graph shows a nonlinear relationship between the points of data; because of this, we aren't able to determine the overall trend of the graph. To elaborate further on why the data turned out like this, we began testing this gene much later than we did with HGF. Because of this, we couldn't get many trials of unstimulated behavior, shaping the results to be a little more sporadic. With more data comes a more stable, predictable graph such as that of HGF. Also, the number of flies was non-consistent throughout the behavior tests; some trials may have had three flies, while others could have had one.
Whole Class Control Data
alrmGAL4/+
alrmGAL4/TDP43
About the Graphs
The top two tables graph the data from our negative control, w1118 x arlmGAL4 throughout various weeks. This cross is meant to represent the wild-type flies locomotor function without a transcription factor such as TDP43 having an effect. The bottom two graphs have the TDP43 transcription factor which mimics locomotor disease and models ALS. This data serves as a group we can compare with our test results. What the data should be showing is that the locomotor function decreases with age. As you can see at first glance, the results from the control flies are very unreliable. One problem with the chart is the r^2 values. The preferred range for the data to be significant is around 0.95-1.0, and as you can see, most of the graphs have an r^2 value that is closer to zero than it is to 1, which means there is no linear relationship between the values. After adding a few more data points, both the speed and distance have an increasing trend; however, this data is still unreliable because of the little amounts data collected.
Why did the data turn out this way?
There were a few factors that determined the overall unreliability of the control data. One of these factors was the limited amount of behavior tests done on control crosses. We could only collect analysis data on one set of flies per week. There was also a variation in the amount of flies per trial. While one week may have had three in each test, the next had two or one because of the flies escaping. Finally, most of the trials were conducted during the first few weeks when we were still beginning to learn how to use the software and its limitations, which rendered many initial videos unusable.
Comparison of Genes and Controls
Why no ANOVA?
Due to the limited amount of controls, we are unable to produce a one-way analysis of variance. One reason for this is the requirement of at least three reliable data points. Therefore, if you only have two or fewer, you won't be able to accurately analyze the variance within each group. The number of flies used throughout the different weeks varies, with the highest amount in a week being three flies. Then, in the following weeks, dropping down to two, then to one. Another factor is the variance between the actual speeds and/or distance of the flies. What I mean by this is that one fly might have achieved a total distance of 3.96 cm while another random fly reaches a distance of 46.4. This huge variance makes it difficult to assume any patterns, especially when paired with the limited data points of individual flies.
About the Graphs
The following graphs depict either the average speed between our genes HGF, Rdl, and controls or the average total distance traveled by a group of flies. We didn't include statistics such as a standard error/deviation bar due to the reasons mentioned above on the unreliability of certain trials. Unfortunately, for our Rdl gene, we couldn't gather and graph the data for the week, which doesn't tell us much about the predicted end behavior of the graph. With HGF, you can see a clear decrease in the line trend, meaning locomotor functions in flies get worse with time. With the controls, you can't tell the overall direction since, due to the small amount of data, one new point can completely alter the direction of the graph.
Conclusions
Upon looking at the data we were able to collect, for both genes HGF and Rdl, we cannot come up with any concrete statement due to the lack of data towards our controls as well as some missing data within our gene Rdl. Since we have this lack of data, any results we may have gotten throughout the semester are very likely to change drastically when introducing a new analysis. In other words, it's very difficult to notice a pattern and determine whether it is actually statistically significant or whether it's due to the attitude of the fly during that day. This ties in with another major problem in our results, the inconsistency in some of our behavior trials. For the questions of: "Does this gene mimic locomotor defect," "Are these genes actually statistically significant? Or, is our incomplete data creating that false conclusion?", we can only explore these genes further in the future with more standardized and controlled procedures to answer that.
Next Steps
How to improve our confidence? Next semester changes?
Obtaining more control data as a point of comparison between our gene.
Establishing crosses early on in the semester
Establish a working protocol early on in the semester, in this case, a protocol with the analysis software.
Select a method of behavior analysis sooner rather than later.
The UArizona library allows students to rent out PC's.
Inspect the genes of choice much more closely to ensure that the data from the gene is significant
VHA13 was not statistically significant,
CHANGE: do more research on specific genes to not only find a more statistically significant gene but also allow more time for crosses/development of crosses. (We would have had more weeks for RDL if we chose it earlier in the semester).
Alternative Genes of Interest: Distal-less (DLL)
DLL Functions and Description:
Dll is crucial for the development of distal limb structures.
Dysregulation of this gene can result in fusion of distal leg segments, and complete loss of medial leg segment (tibia), among other mutations in regards to Drosophila appendages.
Human Ortholog DLX1 Function and Previous Research:
DLX1 is involved in the proper development of facial structures during embryogenesis.
Plays a role in the differentiation and survival of inhibitory neurons in the forebrain. Malfunction or degeneration of inhibitory neurons can result in hyperexcitability.
May be involved in the early development of diencephalic subdivisions, which ultimately gives rise to anterior forebrain structures like the thalamus which relays motor information.
Why is DLL a good candidate?
Human Ortholog DLX1 is statistically significant and upregulated in both the Ziff and Cantanese data sets. Seeing consistent numbers across two data sets, and seeing how previous research confirms DLL's role in limb development, as well as DLX1's role in the nervous system, increases our confidence in this gene being a good target.
Limitations:
The accuracy/accessibility of the Animal TA software
Could only test a maximum of four flies at a time which limited the capacity to track the flies' movement and obtain valid data.
Initially figuring out how to use the program and making mistakes along the way limited our ability to collect analysis data within those earlier weeks.
Due to the program only being applicable on Windows computers meant that only a few were able to work with the program as well as trying to figure out fixes.
Finding new ways to test greater amounts of flies will lead to a greater understanding of the impact our gene has on locomotor functions.
First time trying a new experimental design
Naturally there are going to be flaws when experimenting with a new method.
New (and returning) students have more difficulty maintaining flies when transferring flies into different vials.
Much more initial confusion between everyone and more of trying to figure out how to work within the new design.
Required getting used to the equipment to improve our experiment skills (transferring to ice --> lost some flies, getting rid of glare on phone, etc.)
HUMAN ERROR
Spontaneity of Drosophila
Environmental effects: Drosophila exhibit a wide range of behaviors in response to sensory stimuli (light, chemicals, temperature, etc). Variation in these conditions throughout testing can affect the integrity results.
Genetic Background: While statistical significance is a useful guideline when selecting genes of interest, it is important to take into consideration the phenotypes associated with the fly ortholog as it can affect aspects of the flies' behavior and biology beyond their motor function. For instance, throughout the semester we have observed that our Rdl cross took a few extra days than other crosses when it came to producing offspring. After looking at the literature we found that overexpression of this gene can be detrimental to courtship behaviors and sleep patterns, potentially affecting the amount of offspring and maybe even behavior tests.
Accidents: Whether it be as a result of their genetic background having incredibly adverse effects on their health resulting in early death, or getting crushed by the onion ring during petri dish tests, flies will die due to a variety of reasons, reducing your sample size. Having back up crosses and doing your research will prove beneficial.
Not enough control data
leads to an unfair comparison between our genes and control