Correlation of proteasome inhibition and reactive oxygen species generation in cells forming protein aggregates

Correlation of proteasome inhibition and reactive oxygen species generation in cells forming protein aggregates Nina Wilkinson (06039191) Supervisor: Professor Doug Gray Institute for Ageing and Health Masters of Research: 2010/2011 [8088 words]

Abstract Abnormal protein build up, protein dysfunction and the production of reactive oxygen species (ROS) inside neurons are associated with age-related neurodegenerative diseases, however the relationships between these features have not been fully established. Huntington s disease (HD) is an example of a protein misfolding disorder caused by an extended polyglutamine tract (polyq) in the protein huntingtin (htt). Aberrant htt may interact with one another and with other abnormal or damaged proteins to form inclusion bodies (IBs). The role of IBs is debatable as evidence suggests their formation may be protective, damaging or negligible. Using a cell model of HD with fluorescent reporters, fixed and live cell imaging, image analysis and statistics we aim to examine the correlations and temporal relationships between the sequestration of polyq into IBs, proteasome inhibition and ROS production. In cells forming IBs, proteasome inhibition and ROS production preceded IB formation. There was a statistically significant, positive correlation between polyq expression and proteasome inhibition in fixed cells 24 and 36 hours after transfection indicating a linear relationship between these factors. ROS levels decreased in cells forming IBs which supports the evidence that IBs initially promote cell survival. The timing of ROS production and the decrease in ROS levels indicates that it is not the IBs that produce ROS it is proteasome inhibition or oligomers (smaller aggregates that precede IB formation). Our work reinforces the importance of proteasome inhibition and ROS in the formation of IBs and further work must be completed to fully determine this relationship. 2

Table of Contents Abstract... 2 List of Figures... 5 List of Tables... 8 List of Abbreviations... 9 1. Introduction... 10 1.1 Huntington s disease... 10 1.1.1 Genetics of Huntington s Disease... 10 1.1.2 Pathology of Huntington s Disease... 11 1.2 Reactive Oxygen Species (ROS) and oxidative stress in the Central Nervous System (CNS) 11 1.3 The Proteasome... 12 1.4 Proteasome inhibition and oxidative stress... 13 1.5 Inclusion body formation... 14 1.6 Inclusion bodies may be neuroprotective... 15 1.7 Aims of Project... 17 1.8 Hypothesis... 17 2. Methods... 19 2.1. Expression Constructs... 19 2.2. Cell Culture... 20 2.3. Transfections... 20 2.4. Plasmids... 20 2.5. Inducible PC12 cells... 20 2.6. ROS Detection... 21 2.7. Image Acquisition... 21 2.8. Image Analysis... 22 2.9. Statistical Analysis... 22 3. Results... 24 3.1. Transfection efficiency and choice of cell line... 24 3.2. Fixed Cell imaging... 24 3.3. Correlation between the levels of green, red and deep red fluorescence in 293T cells... 26 3.4. Average fluorescent intensity of 293T cells at 18, 24 and 32 hours after transfection... 33 3.5. Tracking the intensity of cells for images acquired from the spinning disc microscope... 35 3

3.5.1. Tracking Q103 expression and proteasome inhibition in 293T cells forming protein aggregates... 35 3.5.2. Tracking Q103 expression and ROS generation in PC12 cells forming protein aggregates... 38 4. Discussion... 41 4.1. Limitations of the study... 43 4.2. Therapeutic Interventions... 46 5. Conclusion... 48 6. Future Work... 49 7. Acknowledgements and statement of originality... 50 8. Bibliography... 51 9. Appendix... 54 9.1. Example of fixed images of 293T cells at 18 and 32 hours after transfection... 54 9.2. Images of the separate channels for the live cell images... 56 4

List of Figures Figure 1: The mutant protein with the extended polyglutamine tract changes structure to form a β- sheet structure which can then form various oligomers and can lead to the formation of amyloid-like structures [27]. The intermediates can cause cytotoxicity. For example the soluble β-sheet monomers and the β-sheet oligomers and maybe the amyloid-like fibrils may be responsible [27]. This image was adapted from [27].... 16 Figure 2: Possible interactions between inclusion body formation, ROS generation and proteasome inhibition, adapted from [14]. It could be that smaller polyglutamine aggregates cause proteasome inhibition which leads to the production of ROS which can activate p38mapk and can lead to more oxidatively damaged proteins being produced which can contribute to proteasome inhibition and the generation of more ROS [14]. Another possibility is that proteasome inhibition encourages the formation of IBs and this improves proteasome function in the short term and that IBs can contribute to ROS production [14].... 18 Figure 3: Picture to show structure of expression construct taken from [14].... 19 Figure 4: A representative example of many images taken using a widefield microscope of 293T cells that have been transfected with Q103. This particular image is taken 24 hours after transfection. A: DAPI has been used to stain the nuclei which is shown as blue. B: Q103 expression is shown as green and the small intense spots of green are inclusions. C: Red shows proteasome inhibition. D: ROS are shown in magenta. E: Images A to D merged.... 25 Figure 5: A representitive example of many images taken using a widefield microscope of 293T cells that have been transfected with Q25. This particular image is taken 24 hours after transfection. A: DAPI has been used to stain the nuclei which is shown as blue. B: Q25 expression is shown as green. C: Red shows proteasome inhibition. D: ROS are shown in magenta. E: Images A to D merged.... 26 Figure 6: Scatter plots showing the relationship between Q103 expression (green) and proteasome inhibition (red) in 293T cells with and without inclusions at 18 hours, 24 hours and 32 hours after transfection with Q103 plasmids.... 29 Figure 7: Scatter plots showing the relationship between proteasome inhibition (red) and ROS (deep red) in 293T cells with and without inclusions at 18 hours, 24 hours and 32 hours after transfection with Q103 plasmids.... 30 5

Figure 8: Scatter plots showing the relationship between Q103 expression (green) and ROS (deep red) in 293T cells with and without inclusions at 18 hours, 24 hours and 32 hours after transfection with Q103 plasmids.... 31 Figure 9: Correlation after 24 hours between the levels of fluorescence in 293T cells which have been transfected with Q25 and therefore will not form protein aggregates.... 32 Figure 10: The average intensity of green, red and deep red intensity at 18, 24 and 32 hours after transfection for cells with and without inclusions. Error bars are plotted showing the mean±1.96(se).... 34 Figure 11: Images from the spinning disc microscope of green and red fluorescence over time. t stands for time in minutes. The cells are labelled A-D which corresponds to the graphs in Figure 12. To see the channels separately please see the Appendix Figure 19.... 36 Figure 12: Tracking of green and red fluorescence in 293T cells over time. A: Inclusion is visible from the start. B: Inclusion is first visible at 95 minutes. C: Inclusion is visible from the start. D: No inclusion forms during this time period.... 36 Figure 13: Images from the spinning disc microscope of green and red fluorescence over time. t stands for time in minutes. The cells are labelled A-D which corresponds to the graphs in Figure 14. To see the channels separately please see the Appendix Figure 20.... 37 Figure 14: Tracking of green and red fluorescence in 293T cells over time. A: Multiple inclusions visible from the start. B: Multiple inclusions visible from the start. C: Inclusion is visible from the start. D: Inclusion is visible from the start.... 37 Figure 15: Images from the spinning disc microscope of Q103 expression (green) and ROS (deep red) over time in PC12 cells. t stands for time in minutes. The cells are labelled A-D which corresponds to the graphs in Figure 16. To see the channels separately please see the Appendix Figure 21.... 39 Figure 16: Tracking of green and deep red fluorescence in PC12 cells. A: A double inclusion begins to form at 178 minutes. B: An inclusion begins to form at 108 minutes. C: An inclusion begins to form at 164 minutes. D: An inclusion does not form during this time period. It can be seen in all graphs A:D that the amount of ROS drops over the time period. These graphs correspond to the images in Figure 15.... 40 Figure 17: An example of many images taken using a widefield microscope of 293T cells that have been transfected with Q103. This particular image is taken 18 hours after transfection. A: DAPI has 6

been used to stain the nuclei which is shown as blue. B: Q103 expression is shown as green and the small intense spots of green are inclusions. C: Red shows proteasome inhibition. D: ROS are shown in magenta. E: Images A to D merged.... 54 Figure 18: An example of many images taken using a widefield microscope of 293T cells that have been transfected with Q103. This particular image is taken 32 hours after transfection. A: DAPI has been used to stain the nuclei which is shown as blue. B: Q103 expression is shown as green and the small intense spots of green are inclusions. C: Red shows proteasome inhibition. D: ROS are shown in magenta. E: Images A to D merged.... 55 Figure 19: Images from the spinning disc microscope of Q103 expression (green) and proteasome inhibition (red) over time in 293T cells. t stands for time in minutes.... 56 Figure 20: Images from the spinning disc microscope of Q103 expression (green) and proteasome inhibition (red) over time in 293T cells. t stands for time in minutes.... 56 Figure 21: Images from the spinning disc microscope of Q103 expression (green) and ROS (deep red) over time in PC12 cells. t stands for time in minutes.... 57 7

List of Tables Table 1: Transfection efficiencies obtained for HT4 and 293T cells.... 24 Table 2: Table showing the Pearsons Product Moment correlation coefficient between the difference pairs of fluorescence. The value in brackets is the p value which is calculated using a correlation test in R [31] where the test statistic is based on a t distribution with number of pairs of observations minus two degrees of freedom. Significant results are highlighted in yellow (p<0.0014). Note that to allow for multiple t tests we have corrected the p value range (Bonferroni correction) that we accept from 0.05 so that overall when we perform multiple t tests we still only have 5% chance of a false positive. If the correlation is more than 0.5 in magnitude it is in bold... 32 Table 3: Table showing the p values for Welsh 2 sample t tests between cells with and without inclusions at 18, 24 and 32 hours for green, red and deep red fluorescence. Note that we have used a Bonferroni correction so account for multiple t tests so we consider a value significant if p<0.0014. Significant results are highlighted yellow.... 34 8

List of Abbreviations 4',6-diamidino-2-phenylindole (DAPI) Adenosine triphosphate (ATP) Central Nervous System (CNS) Dulbecco s modified eagle s medium (DMEM) Endoplasmic reticulum (ER) Foetal calf serum (FCS) Heat shock proteins (HSPs) Huntingtin (htt) Huntington s disease (HD) Inclusion bodies (IBs) Inclusion body (IB) Oxygen free radical (OFR) Oxygen free radicals (OFRs) Polyglutamine (polyq) Reactive oxygen species (ROS) Standard error (SE) Ubiquitin-proteasome system (UPS) Yellow fluorescent reporter protein (YFP) 9

1. Introduction Age-related neurodegenerative diseases typically feature accumulation of abnormal protein deposits, loss of proteolytic efficiency and generation of reactive oxygen species (ROS) within neurons, but cause and effect relationships have been difficult to establish. A cell model system of Huntington s disease (HD) has been devised and can be used to study correlations and the temporal relationships of these factors. 1.1 Huntington s disease HD is an autosomal dominant neurodegenerative disease which is inherited and tends to affect people between 30 and 50 years old but can happen at any age [1-3]. The progression of HD occurs over around 15 years from when symptoms begin and the disorder results in death [1, 3]. There are 5-10 cases of HD in every 100,000 people and it is estimated that 5-6 thousand people suffer from the disease in the United Kingdom [3]. The disease leads to motor problems including chorea, lack of co-ordination, dysarthria, gait and eye movement problems and if individuals are young when symptoms first occur then symptoms may include rigidity, bradykinesia and dystonia [3]. Cognitive symptoms include memory and attention problems and intellectual processes slow down but verbal memory is well preserved [1, 3]. There are also psychiatric features which consist of an altered personality, moodiness, isolation and depression [3]. 1.1.1 Genetics of Huntington s Disease Abnormality of a gene on chromosome 4 that codes for the protein huntingtin (htt) is the cause of HD. HD occurs when the gene encodes an aberrant htt product [1-3]. An abnormal gene has more than 36 CAG trinucleotide repeats in exon 1 and as CAG codes for glutamine 10

the mutation causes an extended polyglutamine (polyq) tract in htt [1-3]. The higher the number of repeats, the earlier the disease will start showing symptoms, the worse the disease will be and more protein aggregation will occur [4]. 1.1.2 Pathology of Huntington s Disease Aberrant htt may not be folded correctly which can lead to the formation of protein aggregates in the nucleus and the cytoplasm due to interactions between misfolded, damaged and other proteins that have not been degraded [1]. Heat shock proteins (HSPs) may prevent this by refolding or making the htt soluble so that degradation can occur by the ubiquitin-proteasome system (UPS) or by autophagy which is a lysosomal pathway [1]. However caspases may cleave the abnormal htt which could make htt fragments which are less soluble and promote aggregation [1]. The development of aggregates and the actual aggregates can have effects on cellular processes which can cause cellular dysfunction and diseases of the nervous system [1]. Other contributors to the pathology of HD are mitochondrial dysfunction, apoptosis, excitotoxicity and changes in transcription [1]. 1.2 Reactive Oxygen Species (ROS) and oxidative stress in the Central Nervous System (CNS) ROS is an expression used to describe oxygen free radicals (OFRs) (oxygen molecules with an unpaired election) and molecules containing oxygen which are part of oxygen free radical (OFR) production [5]. ROS are very reactive and they can be responsible for chain reactions occurring as they take electrons from other molecules which results in the formation of other free radicals [5]. ROS are produced in all cells during metabolic reactions, for example in the mitochondria or they can be produced by extracellular sources, for example UV radiation or ionising radiation [5, 6]. Systems exist within a cell to remove ROS, for example 11

the antioxidant system, to help stop oxidative damage occurring but naturally within cells there is a small amount of oxidative damage which does not seem to cause any problems [6]. However, it has been shown in many papers that in the CNS with age increased oxidative damage occurs which can contribute to further cellular dysfunction, cellular gain of function effects and the production of toxins [6]. Oxidative stress refers to the condition in cells wherein ROS begins to cause adverse effects [6]. There are many possible reasons behind the build up of oxidative damage, including an increase in ROS leading to more oxidative damage occurring, the inability to remove or repair oxidatively damaged molecules or synthesise new molecules to replace the old ones [6]. By eliminating oxidatively damaged proteins the proteasome acts like an antioxidant and is thought to help control the levels of oxidative damage in ageing and neurodegenerative disease including HD. While the exact mechanisms are unknown it appears that proteasome inhibition can contribute to oxidative stress as it has been suggested that proteasome inhibition leads to more ROS being produced from mitochondria [6, 7]. However, it may be that the two are interconnected and that ROS damage the proteasome leading to proteasome inhibition which can lead to further ROS production and a build up of oxidatively damaged proteins [6]. 1.3 The Proteasome The proteasome is a multisubunit protease that exists in the cytoplasm and nucleus of all eukaryotic cells. Its activity is dependent on the hydrolysis of adenosine triphosphate (ATP) [6, 8]. In addition to soluble substrates in the nucleus and cytoplasm abnormal proteins from the endoplasmic reticulum (ER) are transported to the cytosol to be degraded by the proteasome [8]. 12

The 20S proteasome is shaped like a cylinder and is made from 4 rings which each consist of 7 subunits [6, 9]. There are 2 kinds of subunits α and β. The upper and lower two rings consist of the α subunits which keep the proteasome stable and the two inside rings consist of the β subunits which is where the protease active sites reside [6]. The 26S proteasome is made from the 20S proteasome with 2 caps which bind to the α subunits of the 20S proteasome [6]. The caps function is to recognise and unfold ubiquitinated substrates and feed them into the 20S proteasome [6]. 1.4 Proteasome inhibition and oxidative stress Multiple sources have shown that proteasome inhibition occurs in HD, however there is controversy over whether changes in the UPS are the contributing factors in the neurodegeneration that occurs in HD [6, 10]. One paper shows that in neurons proteasome inhibition can lower protein synthesis which may be a mechanism to help alleviate the problems associated with the build up of damaged proteins in the short term [11]. Conversely, if there is too much of a decrease in protein synthesis then this will eventually lead to the inability to maintain homeostasis, for example, there may no longer be enough chaperones to meet the demand which could contribute to aggregation, cellular dysfunction and neuropathology in HD [11]. It has been shown that proteasome subunits and ubiquitin are present in inclusion bodies (IBs) in HD human brains after death and in mouse and cell models [12, 13] which shows us that the UPS may play an important role in the development of symptoms in HD. 13

1.5 Inclusion body formation IBs are large protein aggregates which are either in the nucleus or cytoplasm. They are mostly made from materials reflecting a particular disease, for example abnormal htt and parts of the UPS which indicate damage to the UPS [14]. The link between inclusion body (IB) formation and neurodegeneration is controversial as there has been evidence supporting the ideas that IBs cause neurodegeneration, their presence is coincidental or they are a coping mechanism [15]. One paper shows that by interfering with the formation of oligomers (smaller order aggregates that precede IB formation) with Congo red the UPS is protected and the removal of mutant htt is favoured [16] whereas another paper reports that IBs lead to better survival of neurons and reduced abnormal htt within cells [15]. It is also suggested that the IBs may be present due to deterioration of the UPS with age and this may quicken up the process of age-associated disease [14]. Using human brain tissue it has been shown that the number of IBs inside the nucleus of neurons is correlated with the number of CAG repeats [17], but it has also been shown that the amount of IBs does not correlate with the parts of the brain in which HD-associated loss of function is most severe [2, 18]. Many studies using cell lines have shown that aggregate formation is linked to cytotoxicity and often leads to cell death [2]. Using transgenic mouse models of HD, IBs have been shown to develop in an organised manor and this development appeared to be connected to development of HD symptoms [19]. Chaperones decrease aggregation and death of cells in many cell lines and transgenic mice for HD, an example is over expression of heat-shock protein 104 which caused fewer aggregates to form and the mice lived 20% longer [2, 20]. Conversely it could be the case that there are 14

fewer IBs due to chaperones stopping oligomer formation and that the preceding oligomers are more toxic [2]. We must note that some papers show evidence against the idea that aggregates are related to cellular toxicity, for example more and larger aggregates are developed through the use of a compound in one paper in which it was found that the effects of the development of HD were reduced [2, 21]. Another paper crossed HD R6/1 mice with tissue transglutaminase knockout mice and they found that the mice live longer than HD R6/1 mice and they have better motor function even though there are more IBs inside the nuclei [2, 22]. 1.6 Inclusion bodies may be neuroprotective It has been shown that cell death may occur without IB formation as one paper has shown that neuronally differentiated cells died faster if they contained soluble oligomers in comparison to cells with IBs or containing monomers and they also found that oligomers were very quickly made a part of an IB [23, 24]. It has also been found that proteins which form aggregates have a higher rate of turnover than those who do not form aggregates [25]. This all provides evidence that IBs may be neuroprotective and promote cell survival as in HD they may sequester toxic abnormal Htt into one single site which can confine any detrimental effects to one area and reduce toxicity. Conversely, through live cell imaging and stochastic modelling it has been demonstrated that IB formation provides at best a transient relief of proteasome inhibition [14]. Many papers have tried to assess whether oligomerisation of abnormal polyq proteins is the cause of toxicity to cells. For example one paper found that soluble polyq oligomers within cells reduced cell survival in comparison to cells containing IBs [23, 24]. By stopping 15

the formation of oligomers there is reduced cell death [24]. This has been shown by using inhibitors, for example antibodies or drugs which prevent oligomerisation [24]. One paper overexpressed cytosolic chaperonin which stopped Htt aggregation and decreased the death of neurons. When they blocked cytosolic chaperonin from forming a complex they found an increase in the formation of aggregates and increased levels of neuronal death [26]. This evidence indicates that it is the oligomers that form before IBs that are toxic to cells [24]. Other publications suggest that the monomer form of the polyq proteins is damaging to cells [24]. One study has shown that the soluble β-sheet monomer is toxic to cells by microinjecting the different polyq proteins at different stages of formation into cells [27]. This suggests a hypothetical process of cytotoxicity as shown in Figure 1. Figure 1: The mutant protein with the extended polyglutamine tract changes structure to form a β-sheet structure which can then form various oligomers and can lead to the formation of amyloid-like structures [27]. The intermediates can cause cytotoxicity. For 16

example the soluble β-sheet monomers and the β-sheet oligomers and maybe the amyloid-like fibrils may be responsible [27]. This image was adapted from [27]. 1.7 Aims of Project We aimed to investigate whether proteasome inhibition leads to the formation of ROS in cells developing inclusions. It has been suggested that p38mapk is an important mediator of cell death in cells that are producing polyq proteins and that ROS may activate p38mapk as when p38mapk was inhibited cell death due to polyq no longer occurred and p38mapk promotes IBs to be produced and proteasome not to function correctly [14]. Our objective was to use image analysis techniques to determine whether ROS are generated due to polyq proteins causing proteasome inhibition or whether the polyq proteins cause ROS production and therefore ROS production occurs before proteasome inhibition. By doing this it should be possible to determine the temporal order of events between proteasome inhibition, ROS generation and IB formation. Possible interactions between these factors are shown in Figure 2. 1.8 Hypothesis Proteasome inhibition precedes IB formation and ROS are produced during IB formation. 17

Figure 2: Possible interactions between IB formation, ROS generation and proteasome inhibition, adapted from [14]. It could be that smaller polyglutamine aggregates cause proteasome inhibition which leads to the production of ROS which can activate p38mapk and can lead to more oxidatively damaged proteins being produced which can contribute to proteasome inhibition and the generation of more ROS [14]. Another possibility is that proteasome inhibition encourages the formation of IBs and this improves proteasome function in the short term and that IBs can contribute to ROS production [14]. 18

2. Methods 2.1. Expression Constructs Htt-Q103 expression constructs (obtained from Dr. Ron Kopito, Stanford University) have the first exon1 of a disease-associated huntingtin gene including the expanded polyglutamine repeat [14]. This allele has 103 CAG repeats and is designated Q103 [14]. A yellow fluorescent reporter protein (YFP) has been fused to the repeat sequence which we have designated Htt-Q103YFP [14]. We are also using a monomeric red fluorescent reporter protein (RFP) which was engineered in the Gray laboratory to work as a reporter of proteasome inhibition. It was made by PCR wherein the degron sequence was moved from the GFP U reporter to C terminus of RFP [14]. Usually the 26S proteasome degrades mrfp U very fast, however when proteasome inhibition occurs, less is degraded and it builds up to a level that can be seen using fluorescence microscopes [14]. In order that the RFP can be expressed from the same transcript as Q103YFP it was placed 3 to an internal ribosome entry site (IRES) [14]. See Figure 3. We also utilized the equivalent expression construct with a polyglutamine tract of 25Q to use as a control (obtained from Dr. Ron Kopito, Stanford University) which also has the yellow and red fluorescent reporter proteins to show Q25 expression and proteasome inhibition respectively. Figure 3: Picture to show structure of expression construct taken from [14]. 19

2.2. Cell Culture We used HT4 mouse neuroblastoma, 293T neural endocrine and PC12 rat pheochromocytoma cancer cells which we cultured in Dulbecco s modified Eagle s medium (DMEM) with 10% foetal calf serum (FCS) in an incubator at 37 C with 5% carbon dioxide and full humidity [14]. The media for PC12 cells was augmented with 10% horse serum. All work done with cells was completed in aseptic conditions in a tissue culture flow hood. 2.3. Transfections We plated HT4 or 293T cells 24 hours before transfection into 4 well dishes, 6 well dishes or single wells at a density between 1x10 4 to 5x10 4 cells per ml depending on the cell line and it s growth [14]. We used 5µl FuGene reagent (Promega) or Gene-Juice reagent (Merck) as per manufacturer s protocols and 1µg of plasmid DNA per well [14]. 2.4. Plasmids The plasmids used to transfect the HT4 or 293T cells were prepared using the Promega PureYield Maxiprep System according to the manufacturer s protocol. During optimisation we also used Qiagen Maxiprep kit according to the manufacturer s protocol. 2.5. Inducible PC12 cells The PC12 cells (obtained from Dr. Robert C. Cumming, University of Western Ontario, London, Canada) are engineered to contain hybrid ecdysone receptors (through stable transfection) as follows [28]. The plasmid constructs for PC12 cells contain exon 1 of Htt and either 25 or 103 polyq repeats which are fused to enhanced GFP reporter protein at the C terminus [28]. Amplification of exons 2-5 was completed using gene-specific primers and 20

subcloning was used to obtain plasmids containing exon 1 of Htt [28]. Further subcloning was used to transfer constructs to pind (Invitrogen) which gives the inducible Q103 or Q25 plasmids [28]. The inducible Q103 or Q25 plasmids were stably transfected into PC12 cells and the cells were sorted so that only the top expressing cells are used [28]. Cells will express the polyglutamine proteins upon addition of 2.5µg per ml Tebufenozide (an insecticide also known as POSTANAL).Tebufenozide binds to the ecdysone receptors which stimulate Q103 or Q25 gene expression. Induced Q103 cells will start to form inclusions by 24 hours and all cells will be dead 2 days after the addition of the drug. 2.6. ROS Detection To detect ROS we used CellROX deep red reagent which is a fluorogenic probe (Invitrogen) as described in the manufacturer s instructions [29]. We added 2µl CellROX per well 18, 24 or 32 hours after transfection and incubated at 37 C for 30 minutes before fixing cells with paraformaldehyde (4%) to mount with Vectashield. CellROX deep red works by becoming fluorescent when it is oxidised by ROS and it is not fluorescent while it is in a reduced state [29]. We tested the use of CellROX by adding hydrogen peroxide (either 5mM or 10mM) to the media of 293T cells and we did see increased levels of deep red fluorescence for higher concentrations of hydrogen peroxide. 2.7. Image Acquisition After transfection cells were left between 18 hours and 48 hours before fixing cells with 4% paraformaldehyde and mounting in a solution containing the DNA stain 4',6-diamidino-2- phenylindole (DAPI). We used a Leica widefield microscope to take images of the 4 fluorescent channels with a 20X objective. Filter cubes optimized for DAPI, green 21

fluorescent protein, Cy3, and Texas Red were used to detect DAPI-stained nuclei, polyq expression, proteasome inhibition, and CellROX respectively. We took 5 images per well. We completed live cell imaging using a Zeiss Cell Observer spinning disc microscope, taking a series of images over one and a half to two hours of cells forming inclusions with a 63X oil objective. 2.8. Image Analysis We used Image Pro Analyzer software (Media Cybernetics). This software can identify objects and measure the intensity of the different colour channels and can track cells over a series of images. This is done by manually selecting a threshold so the software can separate object pixels from background pixels. We used a watershed split to automatically separate objects that were touching and manually split any other cells that were not picked up automatically. Our live cell images proved difficult to complete automatic tracking on due to there being no colour channel that was constantly showing the complete area of the cell throughout the images, so we completed the tracking manually in Image J [30] to get more accurate results by drawing around the cells individually. 2.9. Statistical Analysis Statistical analysis was completed using R [31]. We imported the data into R and we used a combination of linear models, correlation tests and Welsh 2 sample t tests (allows for unequal variance) with a Bonferroni correction to analyse the data. The Bonferroni correction is used as when you perform multiple t tests the chance of a false positive increases so you need to allow for this by lowering the range of p values you will accept which is done using a simple formula called the Bonferroni correction. Individual results 22

were considered significant if p<0.0014 which gives us an overall chance of a false positive at 5%. In the linear modelling we used QQ plots, residual plots and cooks distance plots to assess the suitability of the data for linear regression and identify influential points. We used the mean intensity of fluorescence in our analysis because it took into account all areas of fluorescence in the cell, unlike the maximum. The sum of intensities depended too much on the area of the cell to be used. 23

3. Results 3.1. Transfection efficiency and choice of cell line Being derived from the neural lineage HT4 cells have more physiological relevance and a more characteristic morphology under a microscope, but we changed from HT4 cells to 293T cells to increase the transfection efficiency. We optimised the method of plasmid production, varied transfection reagents (FuGene and GeneJuice) and the amounts of plasmids and reagents used however the transfection rate did not improve to the level desired. We expected more than 50% transfection efficiency but we were getting less than 1% in a majority of transfections (See Table 1). The 293T cells have a much better transfection rate, however their attachment to glass is not as strong and care was taken to not wash the cells to the edges of wells when we were fixing them. The 293T cells also grow in clusters which can make imaging more difficult as it is harder to get all of the cells in focus at once. Note that we have used green to depict Q103 or Q25 expression even though we used a yellow fluorescent reporter protein as we used the green fluorescent protein filter cube on the widefield microscope to detect it. Minimum transfection efficiency obtained HT4 Maximum transfection efficiency obtained Minimum transfection efficiency obtained 293T Maximum transfection efficiency obtained <1% 24.5% 17.07% 51.21% Table 1: Transfection efficiencies obtained for HT4 and 293T cells. 3.2. Fixed Cell imaging Figure 4 shows that 293T cells that were transfected with Q103 plasmids were showing levels of green and red fluorescence, however the deep red fluorescence was not being detected and the small amount of magenta colour is difficult to see in our images. We 24

believe this is due to the microscope filters being less than optimal for CellROX or CellROX being washed away during fixation of cells or that the CellROX was not left on long enough. Figure 5 shows our control which does not develop IBs (the green fluorescence is not intense small spots it is a wider less intense area). There are levels of proteasome inhibition but there does not seem to be much ROS present but this may be due to lack of detection. Example images from 18 and 32 hours after transfection are shown in Figure 17 and Figure 18 in the Appendix. Figure 4: A representative example of many images taken using a widefield microscope of 293T cells that have been transfected with Q103. This particular image is taken 24 hours after transfection. A: DAPI has been used to stain the nuclei which is shown as blue. B: Q103 expression is shown as green and the small intense spots of green are IBs. C: Red shows proteasome inhibition. D: ROS are shown in magenta. E: Images A to D merged. 25

Figure 5: A representative example of many images taken using a widefield microscope of 293T cells that have been transfected with Q25. This particular image is taken 24 hours after transfection. A: DAPI has been used to stain the nuclei which is shown as blue. B: Q25 expression is shown as green. C: Red shows proteasome inhibition. D: ROS are shown in magenta. E: Images A to D merged. 3.3. Correlation between the levels of green, red and deep red fluorescence in 293T cells We tried to complete a regression analysis with Q103 expression, proteasome inhibition and ROS generation, however ROS levels were not significant in the model which indicated that we should remove this variable from the model. We have produced scatter plots of the 26

intensities of the green, red and deep red fluorescence against each other in pairs to see if there are any patterns at 18, 24 and 32 hours after transfection, for example a linear pattern (See Figure 6, Figure 7, Figure 8 and Figure 9). We would expect that the colours are all mixed and evenly dispersed to show consistency within the repeats, which is the case for some plots, for example those in Figure 6 A to E and Figure 9, however others have blocks of colours which represents a difference between the repeats which is particularly noticeable in Figure 7 B, D and F and in Figure 8 B, D and F. To do a linear analysis we require the plots to show homoscedastic behaviour, however there are plots where the variance is not constant. For example in Figure 7 F, the variance increases with intensity which is noticeable by the cone shape of the data. Therefore the data in Figure 7 and Figure 8 must be treated cautiously. Figure 6 shows a linear relationship existing between the green and red fluorescence which is not significant in A but is significant for B to F. The correlations and P values of all our correlation tests are given in Table 2 and you can see that by 32 hours after transfection the correlation between red and green fluorescence is 0.71 for cells with inclusions and 0.88 for cells without inclusions (p<0.0001). We expect this as this means that cells with a greater level of green fluorescence have a greater level of red fluorescence so cells with more Q103 expression have a greater level of proteasome inhibition. The reason this relationship may be non-significant for cells with inclusions at 18 hours (A) is that at this time point there are very few cells with inclusions and so the test has low power. We expect Q103 expression and proteasome inhibition to be strongly related since the proteasome will have to work hard to remove the abnormal polyq proteins causing it to be inhibited so the more polyq there is the more the proteasome will be inhibited. A linear relationship is shown in our 27

control (see Figure 9 A) which shows that higher levels of Q25 production are associated with higher levels of proteasome inhibition. The graphs shown in Figure 6 include all our data points, however it is clear from looking at residual plots, QQ plots and plots of the cooks distance that there are some points that are highly influential points, outliers or high leverage points. To see the effect of these points on the analysis we tried removing these points one at a time, fitting the linear model, looking at the correlations, residual plots, cooks distance plots and the adjusted R squared values. We found that removing these points did not change the results of the linear analysis apart from to give us slightly better correlations. As we did not have any reason to remove these points we stuck to our original analysis. It appears that these influential points tend to have particularly high red or green values. For the data from cells without IBs it could be that the cells were very close to forming an IB and for the data with IBs we believe it to be due to the thresholding focussing on the IB instead of the entire cell if proteasome levels are not very high so the green fluorescence is not diluted when an average is taken. From Figure 7 there is no obvious relationship between red and deep red. Cells with IBs have relatively constant ROS levels for increasing proteasome inhibition at 24 hours. We will need to carry out more experiments with ROS detection as the widefield microscope does not seem to be able to detect the deep red very well, which may be affecting our results. There also appears not to be a linear relationship between green and deep red as the correlations are very low and we also have the problem of data that is heteroscedastic which means it is not suitable for linear regression (see Figure 8). We did try transforming our data but this did not improve the fit of the model. The relationship between Q103 28

expression, proteasome inhibition and ROS is probably far too complex to be represented in such a simple way. Figure 6: Scatter plots showing the relationship between Q103 expression (green) and proteasome inhibition (red) in 293T cells with and without IBs at 18 hours, 24 hours and 32 hours after transfection with Q103 plasmids. 29

Figure 7: Scatter plots showing the relationship between proteasome inhibition (red) and ROS (deep red) in 293T cells with and without IBs at 18 hours, 24 hours and 32 hours after transfection with Q103 plasmids. 30

Figure 8: Scatter plots showing the relationship between Q103 expression (green) and ROS (deep red) in 293T cells with and without IBs at 18 hours, 24 hours and 32 hours after transfection with Q103 plasmids. 31

Figure 9: Correlation after 24 hours between the levels of fluorescence in 293T cells which have been transfected with Q25 and therefore will not form protein aggregates. Red and Green Correlation Green and Deep Red Correlation Red and Deep Red Correlation 18 hours 24 hours 32 hours 24 hours Inclusion 0.32295 (p=0.3059) -0.46895 (p=0.1241) 0.26494 (p=0.4053) Not Inclusion 0.50119 (p<0.0001) -0.02070 (p=0.7354) 0.71487 (p<0.0001) Inclusion 0.47974 (p=0.0011) 0.02221 (p=0.8876) 0.37620 (p=0.0129) Not Inclusion 0.84896 (p<0.0001) -0.41276 (p<0.0001) -0.15971 (p=0.0215) Inclusion 0.70952 (p<0.0001) 0.16184 (p=0.0813) 0.15240 (p=0.1009) Not inclusion 0.88373 (p<0.0001) 0.12208 (p=0.0051) 0.11705 (p=0.0073) Control 0.65414 (p<0.0001) -0.22395 (p=0.0012) 0.23653 (p=0.0062) Table 2: Table showing the Pearsons Product Moment correlation coefficient between the difference pairs of fluorescence. The value in brackets is the p value which is calculated using a correlation test in R [31] where the test statistic is based on a t distribution with number of pairs of observations minus two degrees of freedom. Significant results are 32

highlighted in yellow (p<0.0014). Note that to allow for multiple t tests we have corrected the p value range (Bonferroni correction) that we accept from 0.05 so that overall when we perform multiple t tests we still only have 5% chance of a false positive. If the correlation is more than 0.5 in magnitude it is in bold. 3.4. Average fluorescent intensity of 293T cells at 18, 24 and 32 hours after transfection We calculated the average intensity at each time point from our data (See Figure 10). The level of green, red and deep red fluorescence at 24 hours in cells that had not formed inclusions (Figure 10 B) and our 24 hour control were not significantly different (p values 0.02536, 0.835 and 0.722 respectively). It has been shown that as the repeat length increases the rate of degradation decreases so we would expect our control to have less green fluorescence [32]. It appears that our control has a higher average green intensity which suggests that this protein is being produced faster due to it having a lower repeat length (average intensity for our control at 24 hours was 21.52 arbitrary fluorescent intensities compared to 10.44 arbitrary fluorescent intensities for 24 hours without IBs for cells transfected with Q103). It is also less important to degrade this protein as it is not toxic to the cell and it does not produce aggregates. The levels of green fluorescence between cells with IBs (Figure 10 A) and without (Figure 10 B) is significantly different at 18 and 24 hours (see Table 3), however it is not significant at 32 hours. We expect the green fluorescence to be different as cells with inclusions should have higher levels of Q103 expression. There was a difference between the levels of red and deep red at 24 hours in cells with inclusions (Figure 10 A) but this is not significant in comparison to the difference present at 18 (p=0.097) and 32 hours (p=0.807). 33

Figure 10: The average intensity of green, red and deep red intensity at 18, 24 and 32 hours after transfection for cells with and without IBs. Error bars are plotted showing the mean±1.96(se). Green Red Deep Red 18 hours p=0.00104 p=0.3628 p=0.4609 24 hours p=0.00018 p=0.1861 p=0.3358 32 hours p=0.05687 p=0.3514 p=0.8939 Table 3: Table showing the p values for Welsh 2 sample t tests between cells with and without IBs at 18, 24 and 32 hours for green, red and deep red fluorescence. Note that we have used a Bonferroni correction so account for multiple t tests so we consider a value significant if p<0.0014. Significant results are highlighted yellow. 34

3.5. Tracking the intensity of cells for images acquired from the spinning disc microscope 3.5.1. Tracking Q103 expression and proteasome inhibition in 293T cells forming protein aggregates In Figure 11 cell A the IB is visible from the beginning and becomes brighter over time. In the corresponding graph it appears that red fluorescence seems to increase as green fluorescence increases (See Figure 12). For cell B the IB does not become visible until about t=95 minutes and from this point the red and green seem to increase together as the IB forms. From C we can again see that green and red fluorescence seem to follow each other and when one increases the other one does and the same for if the level of one decreases. Cell D does not form an IB and the levels of red and green fluorescence remain fairly constant. In Figure 13 A and B multiple IBs are visible and in the corresponding graphs in Figure 14 A and B green and red fluorescent levels seem to follow each other. This is also shown in Figure 14 C and D where IBs are visible from the start. In D the levels of green and red fluorescence remain fairly constant which shows that once an IB is fully formed, proteasome inhibition remains fairly constant. We only have a few cells forming IBs in our images which are all at different stages of IB formation and some cells form one large IB while others form multiple smaller IBs so it is difficult to do any form of statistical analysis. However, from our videos it appears that proteasome inhibition occurs before IB formation and that the lines in the graphs tend to follow one another so that if green fluorescence increases so does red fluorescence which backs up our findings that they are highly correlated in Figure 6. We did create videos with the CellROX fluorescent probe to measure ROS levels as well as Q103 expression and 35

proteasome inhibition, however there was cross talk between the red and deep red so the videos could not be used. Figure 11: Images from the spinning disc microscope of Q103 expression (green) and proteasome inhibition (red) over time in 293T cells. t stands for time in minutes. The cells are labelled A-D which corresponds to the graphs in Figure 12. To see the channels separately please see the Appendix Figure 19. Figure 12: Tracking of green and red fluorescence in 293T cells over time. A: IB is visible 36

from the start. B: IB is first visible at 95 minutes. C: IB is visible from the start. D: No IB forms during this time period. Figure 13: Images from the spinning disc microscope of Q103 expression (green) and proteasome inhibition (red) over time in 293T cells. t stands for time in minutes. The cells are labelled A-D which corresponds to the graphs in Figure 14. To see the channels separately please see the Appendix Figure 20. Figure 14: Tracking of green and red fluorescence in 293T cells over time. A: Multiple IBs visible from the start. B: Multiple IBs visible from the start. C: IB is visible from the start. D: IB is visible from the start. 37

3.5.2. Tracking Q103 expression and ROS generation in PC12 cells forming protein aggregates Our final experiments used PC12 cells because we believe there was a level of cross talk between the red and deep red fluorescence when we were imaging using the spinning disc microscope and these cells do not have the red fluorescent reporter protein for proteasome inhibition. These cells had been stably transfected with inducible Q103 or Q25 plasmids and will express the polyglutamine proteins upon addition of 2.5µg per ml Tebufenozide (an insecticide also known as POSTANAL). Induced Q103 cells start to form inclusions by 24 hours and all cells are dead 2 days after the addition of the drug. PC12 cells also do not attach to untreated plastic or glass surfaces as well as HT4 cells. We obtained a video with just Q103 expression and ROS detection and we were correct about the cross talk as this time when we had no red fluorescent reporter protein the deep red was visible even within the individual mitochondria (See Figure 15). This has shown us that the CellROX was working and that in our images taken with the widefield microscope it was not being detected which may be due to the filter set in the microscope. Alternatively the CellROX could be left on longer than suggested in the protocol (30 minutes) before fixing to increase the signal. This meant that we had to discard our fixed images taken with PC12 with the widefield microscope as it was not detecting the far red fluorescent levels in the cells. The cells labelled in Figure 15 correspond to the graphs in Figure 16. Figure 16 indicates that in cells producing polyq proteins ROS levels are decreasing as IBs form and after their initial formation. ROS levels are also decreasing in cells which are expressing polyq proteins but do not produce an IB during the time we are taking images as shown in Figure 16 D. It could be 38

that this cell would produce an aggregate shortly after we stopped taking images and the decrease in ROS would be due to the fact that an IB is soon to form. This decrease in ROS indicates that IBs are not contributing to ROS generation at least in the short term, which supports the idea that they are beneficial to cell survival. This also gives the impression that ROS are produced earlier on maybe due to proteasome inhibition or toxic oligomers. D Figure 15: Images from the spinning disc microscope of Q103 expression (green) and ROS (deep red) over time in PC12 cells. t stands for time in minutes. The cells are labelled A-D which corresponds to the graphs in Figure 16. To see the channels separately please see the Appendix Figure 21. 39

Figure 16: Tracking of green and deep red fluorescence in PC12 cells. A: A double IB begins to form at 178 minutes. B: An IB begins to form at 108 minutes. C: An IB begins to form at 164 minutes. D: An IB does not form during this time period. It can be seen in all graphs A:D that the amount of ROS drops over the time period. These graphs correspond to the images in Figure 15. 40