The peptide β -Amyloid ( β -A) is known to be one of the primary factors causing neurodegeneration in the Alzheimer disease. Hence, one would like to know the factors that would increase or decrease the toxicity of β -Amyloid in the brain. One of the factors that are debated in the literature is cholesterol, where it is not clear if modulating the levels of cholesterol would affect the degree of toxicity of β-Amyloid on neuron cells in the brain. In order to investigate this problem, data were collected and analyzed for three types of experiments: 1) Correspondence between cholesterol and methyl-β-cyclodextrin (MβCD) measurements; 2) measurements of the relative fluorescence unit (RFU) with respect to MβCD concentration (with/without β-A); and 3) RFU measurements with respect to β-A concentration (with/without MβCD). HT22 hippocampal neurons immortalized with the simian virus SV-40 large T-antigen plasmid vector were used to conduct the experiments. Mito-ID Membrane potential cytotoxicity was used as a measure of mitochondrial potential change. The statistical analysis of the presented experimental results indicates that cholesterol has no statistically significant effect on the degree of toxicity of β-Amyloid.
Alzheimer’s Disease (AD) is the most prevalent of neurodegenerative diseases among the elderly. Health care costs related to Alzheimer’s disease for just this year are estimated to be $226 billion. Spending is estimated to increase and reach $1.1 trillion by 2050. Research to cure Alzheimer’s is rather underfunded. Currently, the USA government spends ~$1 billion per year on research―quite less than the cost on health care (source: http://curealz.org). Despite the costs to society and the high prevalence of AD, several decades of research have managed to advance our knowledge on the mechanisms of AD pathology (from its underlying genetics, to the molecular biology and clinical pathology, e.g., [
The peptide β-Amyloid (β-A) is known to be one of the primary factors causing neurodegeneration in AD. Typically, AD appears with extracellular plaques of β-A and neurofibrillary tangles in the intracellular environment. There are several theories about the properties of the neuronal plasma membrane that affect the way β-A interacts with the cell surface. We can observe an increase in oxidative stress as a result of β-A, but the pathway by which that stress is induced is unclear. There are three main theories about the mechanism behind the ion balance disruption [
The plasma membrane of a cell is highly functionalized for cell signaling. Many different markers exist on the surface of the cell and interact with extracellular molecules regularly. Cell signaling is not isolated to biomarkers; it also is related to the degree of fluidity in the membrane. Fluidity in the microenvironment can influence cell communication through alteration of diffusion and transfer of cell signaling compounds. This fluidity is determined by the ratio of types of lipids composing the membrane. Increased amounts of cholesterol at physiological temperatures decrease the fluidity of the membrane [
Simulations and modeling suggest that β-A tends to aggregate along the perimeter of these lipid-enriched domains (e.g., see [
The role of cholesterol in the appearance of AD was particularly studied during the last two decades (
In order to elaborate on the first theory, removal of cholesterol is possibly negative if we relate it back to the theory that β-A aggregates along the perimeter of lipid rafts. If cholesterol were removed from the plasma membrane, lipid rafts would first start fractioning into smaller islands. These smaller lipid rafts would have larger total perimeter than the original lipid raft from which it originated. Thus, according to simulations suggesting aggregation along the boundaries of lipid rafts [
In the literature, there is substantial support for all three theories, which only adds to the ambiguity of β-A’s interactions with the neuronal plasma membrane and the necessity to explore this issue further. Though the literature reports a mixture of results, the mixture may exist due to differences in cell types used as well as the type of assay used to measure oxidative stress, cell viability, or other indicators of β-A peptides’ toxic effect on neuronal cells.
Plasma levels of total and LDL-cholesterol are well known to increase with normal aging both in humans and rodents, while the plasma clearance of LDL has been shown to decrease with age in both humans and rodents (see [
The purpose of this paper is to investigate whether the cholesterol has effects on the degree of toxicity of β-Amyloid. In Section 2, we provide information about the materials and reagents used in our experiments. In Section 3, we describe in detail the methods of the three experiments, the procedure, and the collected datasets. Methyl-β-cyclodextrin (MβCD) is used to deplete cholesterol, while in experiment-1, we find the exact correspondence between them; experiment-2 measures the Relative Fluorescence Unit (RFU) for various values of MβCD concentration (with/without β-A); experiment-3 measures RFU for various values of β-A concentration (with/without MβCD). In Section 4, we provide the results of the three experiments, leading to the conclusion that there are no statistically significant differences of the reaction of β-A (RFU) with changes in cholesterol content of the plasma membrane. Finally, in Section 5, we discuss the results and briefly summarize the conclusions.
HT22 hippocampal neurons immortalized with the simian virus SV-40 large T-antigen plasmid vector [
We modified the protocol and reagent mixture with no effect on the efficacy of the Mito-ID in detecting changes in mitochondrial potential. (435 uL ddH2O + 50 uL 10X buffer + 10 uL 50X buffer + 5 uL Mito-ID MP Detection Reagent; 50 uL into each well for a total volume of 100 uL; diluted in 50 uL of 0% FBS DMEM-F12; the dye was allowed to incubate for 90 min before taking the baseline reading instead of the suggested incubation time of 30 min). A fluorescence microplate reader from Molecular Devices was used to collect readings (excitation 485 nm, emission at 530 and 590 nm).
Cells were plated into a 96-well clear-bottomed black tissue culture plate about 24 hours before the start of assay to allow the cells to adhere completely to the tissue culture plate. At the start of the assay, cells were verified to be at 70-80% confluency. The media was aspirated off and cells were rinsed with 100 uL of 0% FBS DMEM-F12 media supplemented with 1% pen-strep and 1% Glutamax. Cells received 100 µL of 0% FBS DMEM-F12 media solutions of 0 to 2.5 mM of MβCD. The cells were placed back in the incubator for 60 minutes at 37˚C. After 60 minutes, the media containing the MβCD was removed and 50 µL of 0% FBS DMEM-F12 1% p-s and 1% L-Gln was added. 50 µL of our Mito-ID mixture was added and the cells were allowed incubate for 90 minutes. After 90 min, the baseline reading was taken with an excitation of 485 nm and emission read at 530 and 590 nm. Within 5 min of the baseline reading, β-A (1 - 42) peptide was added in varying amounts to the cells. The first reading was taken 15 minutes after the final addition of the β-A. For the first 60 minutes, readings were taken every 15 minutes. Thereafter, readings were taken every 30 minutes for up to 8 hours post-baseline reading.
The relative fluorescence unit (RFU) represents the mitochondrial activation. Mito-ID is a fluorescence dye that measures the fluctuation of the mitochondrial membrane potential. It uses a cationic dual emission dye that fluoresces green (530 nm) when in its monomer form, and orange (590 nm) when it forms J-aggregates due to increasing concentrations within the mitochondria (e.g., [
In this paper, we will use RFU to measure the cellular variability of neuron cells. The data was normalized to 100%, thus the baseline of a healthy neuron started at 100% RFU units. The RFU(t) response values are normalized to the initial RFU(t = 0), i.e.,
NormalizedRFU ( t ) = ( 1 00 % ) ⋅ RFU ( t ) / RFU ( t = 0 ) . (1)
Consequently, an increase in relative fluorescence unit represents an increase in mitochondrial activation.
We operated three types of experiments: 1) Cholesterol and MβCD measurements; 2) RFU measurements with respect to MβCD concentration (with/without β-A); and 3) RFU measurements with respect to β-A concentration (with/without MβCD).
All experiments had been repeated multiple times in order to estimate the sample average values and uncertainties. In experiment (1), for each MβCD concentration, we have measured RFU and Cholesterol six times. In experiments (2) and (3), at each t [min] = 0, 15, 30, 45, 60, 90, 120, 150, 180, 210, 250, 270 (or, 1060 for (b)), and for all the concentrations of MβCD and β-A, we have measured RFU three times.
・ In experiment-1, we first produce the reference curve between Cholesterol [μg] (x) and normalized RFU (y), that is, y = 41,224x + 559.23. The averaged values/standard deviations of MβCD [μM] and normalized RFU are shown in
・ In experiment-2, the averaged values and standard deviations of the normalized RFU(t) (for all t), for MβCD [μM]: 0, 0.5, 1, 1.5, 2, 2.5, and both the cases of 0 β-A and 2.5 β-A, are shown in
・ In experiment-3, the averaged values and standard deviations of the normalized RFU(t) (for all t), for β-A: 0, 1, 2, 2.5, 3, 4, 5, 7, 10, 15, and the three cases of MβCD [μM]: 0, 1, 2.5 β-A, are shown in
Using
MβCD [μM] | RFU Normalized | Cholesterol [μg] |
---|---|---|
0 | 11,160 ± 290 | 0.260 ± 0.007 |
0.5 | 10,000 ± 300 | 0.227 ± 0.008 |
1 | 6800 ± 300 | 0.150 ± 0.008 |
1.5 | 4500 ± 600 | 0.090 ± 0.014 |
2 | 3500 ± 600 | 0.070 ± 0.015 |
2.5 | 3200 ± 700 | 0.060 ± 0.017 |
t [min] = 15 | t [min] = 30 | |||
---|---|---|---|---|
MβCD [μM] | 0 β-A | 2.5 β-A | 0 β-A | 2.5 β-A |
0 | 111.5 ± 0.8 | 150 ± 30 | 118.2 ± 1.5 | 150 ± 30 |
0.5 | 110.5 ± 2.4 | 195 ± 14 | 114.8 ± 2.0 | 206 ± 9 |
1 | 112 ± 4 | 174 ± 16 | 115 ± 6 | 175 ± 14 |
1.5 | 111.4 ± 1.0 | 182 ± 4 | 116.5 ± 1.0 | 192 ± 7 |
2 | 111.7 ± 1.0 | 176 ± 22 | 117.7 ± 2.2 | 182 ± 26 |
2.5 | 119 ± 8 | 168 ± 15 | 122 ± 8 | 178 ± 20 |
t [min] = 45 | t [min] = 60 | |||
MβCD [μM] | 0 β-A | 2.5 β-A | 0 β-A | 2.5 β-A |
0 | 121.8 ± 1.1 | 160 ± 30 | 129.1 ± 1.6 | 170 ± 30 |
0.5 | 119.9 ± 2.4 | 220 ± 9 | 125.0 ± 2.9 | 234 ± 12 |
1 | 123 ± 6 | 182 ± 16 | 128 ± 8 | 194 ± 20 |
1.5 | 123.1 ± 0.7 | 199 ± 9 | 129.3 ± 1.4 | 209 ± 10 |
2 | 122.3 ± 2.5 | 183 ± 23 | 129 ± 4 | 200 ± 30 |
2.5 | 131 ± 8 | 192 ± 26 | 138 ± 9 | 200 ± 30 |
t [min] = 90 | t [min] = 120 | |||
MβCD[μM] | 0 β-A | 2.5 β-A | 0 β-A | 2.5 β-A |
0 | 121.1 ± 1.8 | 150 ± 30 | 112.5 ± 2.5 | 141 ± 28 |
0.5 | 120.4 ± 2.4 | 217 ± 10 | 108.1 ± 0.9 | 203 ± 9 |
1 | 122 ± 7 | 187 ± 16 | 112 ± 5 | 168 ± 16 |
1.5 | 124 ± 4 | 203 ± 7 | 112 ± 4 | 185 ± 6 |
2 | 122 ± 4 | 217 ± 23 | 108 ± 4 | 167 ± 18 |
2.5 | 131 ± 8 | 196 ± 20 | 108 ± 5 | 175 ± 23 |
t [min] = 150 | t [min] = 180 | |||
MβCD [μM] | 0 β-A | 2.5 β-A | 0 β-A | 2.5 β-A |
0 | 111.1 ± 2.7 | 170 ± 30 | 108 ± 3 | 143 ± 23 |
0.5 | 105.5 ± 1.4 | 193 ± 8 | 101.8 ± 0.8 | 183 ± 7 |
1 | 108 ± 5 | 162 ± 15 | 103.5 ± 3.2 | 158 ± 12 |
1.5 | 109 ± 3 | 177 ± 7 | 104 ± 4 | 167 ± 8 |
2 | 104 ± 3 | 165 ± 24 | 99 ± 4 | 155 ± 19 |
2.5 | 107 ± 8 | 168 ± 22 | 101 ± 6 | 158 ± 19 |
t [min] = 210 | t [min] = 240 | |||
MβCD [μM] | 0 β-A | 2.5 β-A | 0 β-A | 2.5 β-A |
0 | 107 ± 4 | 140 ± 23 | 116 ± 4 | 155 ± 26 |
0.5 | 99.5 ± 1.5 | 178 ± 8 | 107.9 ± 2.0 | 197 ± 8 |
1 | 101 ± 5 | 154 ± 12 | 110 ± 7 | 164 ± 16 |
---|---|---|---|---|
1.5 | 101 ± 3 | 163 ± 5 | 111 ± 3 | 177 ± 7 |
2 | 96.2 ± 2.9 | 159 ± 24 | 107 ± 4 | 176 ± 34 |
2.5 | 102 ± 8 | 157 ± 20 | 116 ± 9 | 169 ± 25 |
t [min] = 1060 | ||||
MβCD [μM] | 0 β-A | 2.5 β-A | ||
0 | 97.6 ± 1.0 | 127 ± 20 | ||
0.5 | 85.4 ± 2.4 | 169 ± 9 | ||
1 | 84 ± 9 | 122 ± 19 | ||
1.5 | 95 ± 4 | 143 ± 7 | ||
2 | 83 ± 4 | 120 ± 30 | ||
2.5 | 90 ± 6 | 120 ± 30 |
t [min] = 15 | t [min] = 30 | |||||
---|---|---|---|---|---|---|
β-A | 0 MβCD | 1 MβCD | 2.5 MβCD | 0 MβCD | 1 MβCD | 2.5 MβCD |
0 | 108.0 ± 1.9 | 107.8 ± 1.5 | 105.7 ± 1.3 | 111.1 ± 1.5 | 111.5 ± 1.2 | 107.7 ± 2.7 |
1 | 108.4 ± 2.0 | 106.1 ± 2.7 | 110 ± 6 | 111.4 ± 1.5 | 110.7 ± 2.4 | 114 ± 7 |
2 | 133 ± 15 | 170 ± 50 | 180 ± 30 | 133 ± 15 | 170 ± 50 | 180 ± 30 |
2.5 | 112 ± 5 | 111 ± 6 | 122 ± 16 | 122.6 ± 2.3 | 117 ± 9 | 125 ± 16 |
3 | 185 ± 24 | 200 ± 60 | 190 ± 50 | 187 ± 27 | 200 ± 60 | 200 ± 60 |
4 | 212 ± 34 | 190 ± 50 | 210 ± 80 | 208 ± 29 | 210 ± 70 | 210 ± 70 |
5 | 201 ± 30 | 200 ± 50 | 200 ± 50 | 194 ± 25 | 200 ± 40 | 200 ± 50 |
7 | 166 ± 25 | 157 ± 21 | 144 ± 16 | 172 ± 24 | 172 ± 22 | 162 ± 27 |
10 | 410 ± 120 | 450 ± 150 | 340 ± 110 | 420 ± 120 | 450 ± 140 | 350 ± 100 |
15 | 310 ± 30 | 250 ± 50 | 202 ± 16 | 330 ± 30 | 260 ± 50 | 208 ± 16 |
t [min] = 45 | t [min] = 60 | |||||
β-A | 0 MβCD | 1 MβCD | 2.5 MβCD | 0 MβCD | 1 MβCD | 2.5 MβCD |
0 | 112.5 ± 1.6 | 113.4 ± 2.3 | 111.0 ± 2.9 | 117 ± 3 | 117.2 ± 1.6 | 116 ± 5 |
1 | 113.5 ± 2.4 | 117 ± 6 | 117 ± 8 | 117.0 ± 2.6 | 121 ± 6 | 121 ± 10 |
2 | 140 ± 19 | 170 ± 50 | 173 ± 30 | 144 ± 18 | 180 ± 50 | 180 ± 29 |
2.5 | 123 ± 3 | 118 ± 10 | 127.1 ± 17 | 124.3 ± 1.8 | 118 ± 11 | 129 ± 19 |
3 | 210 ± 40 | 200 ± 60 | 190 ± 50 | 220 ± 50 | 210 ± 70 | 210 ± 50 |
4 | 209 ± 27 | 220 ± 70 | 210 ± 70 | 210 ± 30 | 230 ± 80 | 220 ± 70 |
5 | 194 ± 24 | 210 ± 40 | 200 ± 40 | 198 ± 27 | 210 ± 50 | 200 ± 50 |
7 | 180 ± 30 | 176 ± 25 | 163 ± 29 | 180 ± 30 | 181 ± 29 | 170 ± 30 |
10 | 430 ± 120 | 460 ± 140 | 360 ± 110 | 450 ± 130 | 470 ± 150 | 370 ± 120 |
15 | 325 ± 37 | 260 ± 50 | 212 ± 14 | 329 ± 29 | 270 ± 50 | 214 ± 17 |
---|---|---|---|---|---|---|
t [min] = 90 | t [min] = 120 | |||||
β-A | 0 MβCD | 1 MβCD | 2.5 MβCD | 0 MβCD | 1 MβCD | 2.5 MβCD |
0 | 107.2 ± 2.5 | 105.4 ± 1.7 | 101.5 ± 2.7 | 103.2 ± 2.9 | 100.3 ± 2.5 | 97 ± 4 |
1 | 107.7 ± 2.9 | 110 ± 7 | 110 ± 6 | 105 ± 3 | 104 ± 7 | 103 ± 4 |
2 | 132 ± 14 | 160 ± 50 | 160 ± 30 | 125 ± 12 | 160 ± 50 | 150 ± 40 |
2.5 | 120.7 ± 2.7 | 111 ± 14 | 121 ± 23 | 122 ± 10 | 106 ± 15 | 116 ± 25 |
3 | 200 ± 50 | 190 ± 60 | 190 ± 50 | 200 ± 50 | 180 ± 60 | 180 ± 60 |
4 | 190 ± 40 | 210 ± 70 | 200 ± 70 | 180 ± 40 | 200 ± 70 | 190 ± 70 |
5 | 184 ± 23 | 200 ± 40 | 190 ± 40 | 179 ± 23 | 190 ± 40 | 180 ± 40 |
7 | 170 ± 27 | 170 ± 30 | 160 ± 30 | 170 ± 29 | 160 ± 30 | 150 ± 40 |
10 | 420 ± 130 | 450 ± 150 | 350 ± 120 | 410 ± 130 | 430 ± 150 | 340.5 ± 120 |
15 | 309 ± 23 | 250 ± 60 | 205 ± 23 | 298 ± 12 | 240 ± 70 | 190 ± 30 |
t [min] = 150 | t [min] = 180 | |||||
β-A | 0 MβCD | 1 MβCD | 2.5 MβCD | 0 MβCD | 1 MβCD | 2.5 MβCD |
0 | 102 ± 3 | 98.9 ± 3 | 94 ± 5 | 111 ± 4 | 106 ± 3 | 100 ± 4 |
1 | 103 ± 3 | 105.0 ± 8 | 100 ± 3 | 110 ± 4 | 114 ± 11 | 105 ± 5 |
2 | 120 ± 10 | 153.8 ± 50 | 150 ± 40 | 132 ± 14 | 160 ± 50 | 150 ± 40 |
2.5 | 119 ± 8 | 104.1 ± 14 | 115 ± 24 | 123 ± 11 | 107 ± 14 | 117 ± 25 |
3 | 190 ± 50 | 176.7 ± 70 | 170 ± 60 | 200 ± 50 | 190 ± 70 | 180 ± 60 |
4 | 180 ± 40 | 196.5 ± 60 | 190 ± 70 | 190 ± 30 | 210 ± 70 | 200 ± 70 |
5 | 176 ± 24 | 184.4 ± 50 | 180 ± 40 | 184 ± 25 | 190 ± 50 | 180 ± 50 |
7 | 168 ± 29 | 160.0 ± 40 | 150 ± 40 | 170 ± 30 | 160 ± 40 | 150 ± 40 |
10 | 400 ± 140 | 430.8 ± 150 | 350 ± 140 | 420 ± 140 | 450 ± 160 | 370 ± 140 |
15 | 291 ± 9 | 233.6 ± 80 | 187.8 ± 30 | 302 ± 14 | 240 ± 80 | 190 ± 40 |
t [min] = 210 | t [min] = 240 | |||||
β-A | 0 MβCD | 1 MβCD | 2.5 MβCD | 0 MβCD | 1 MβCD | 2.5 MβCD |
0 | 112 ± 3 | 107.9 ± 2.9 | 101.1 ± 2.7 | 114 ± 7 | 109 ± 6 | 101 ± 4 |
1 | 110 ± 5 | 108 ± 6 | 106 ± 6 | 109 ± 6 | 108 ± 9 | 107 ± 7 |
2 | 130 ± 20 | 160 ± 40 | 150 ± 30 | 136 ± 18 | 160 ± 50 | 160 ± 40 |
2.5 | 125 ± 10 | 108 ± 16 | 119 ± 26 | 121 ± 13 | 105 ± 17 | 118 ± 25 |
3 | 197 ± 40 | 180 ± 60 | 180 ± 50 | 205.3 ± 40 | 190 ± 60 | 190 ± 60 |
4 | 184 ± 29 | 200 ± 70 | 190 ± 70 | 192 ± 28 | 210 ± 70 | 200 ± 70 |
5 | 182 ± 22 | 190 ± 40 | 180 ± 40 | 181 ± 25 | 190 ± 50 | 180 ± 40 |
7 | 177 ± 30 | 160 ± 50 | 150 ± 50 | 170 ± 30 | 160 ± 50 | 150 ± 50 |
10 | 420 ± 120 | 440 ± 140 | 350 ± 130 | 430 ± 130 | 450 ± 150 | 370 ± 140 |
15 | 309 ± 17 | 250 ± 80 | 190 ± 40 | 301 ± 7 | 240 ± 90 | 190 ± 40 |
t [min] = 270 | ||||||
---|---|---|---|---|---|---|
β-A | 0 MβCD | 1 MβCD | 2.5 MβCD | |||
0 | 111 ± 7 | 107 ± 7 | 98 ± 5 | |||
1 | 106 ± 6 | 106 ± 10 | 104 ± 8 | |||
2 | 132 ± 14 | 160 ± 50 | 160 ± 40 | |||
2.5 | 115 ± 15 | 101 ± 19 | 119 ± 22 | |||
3 | 220 ± 30 | 190 ± 70 | 180 ± 60 | |||
4 | 190 ± 30 | 210 ± 80 | 200 ± 80 | |||
5 | 180 ± 27 | 190 ± 50 | 180 ± 50 | |||
7 | 170 ± 30 | 160 ± 50 | 140 ± 50 | |||
10 | 430 ± 140 | 460 ± 160 | 370 ± 150 | |||
15 | 291.3 ± 1.5 | 240 ± 90 | 190 ± 50 |
lex Red cholesterol assay revealed a dose dependent decrease in cholesterol according to the MβCD concentration, as shown in
In the statistical tests that will follow, we use the RFU normalized values in order to determine if the statistical population is invariant with respect to changes of the MβCD concentration. In order to perform this examination, statistical tests are applied to the following null-hypothesis:
- H0: The response RFU is invariant under variations of MβCD.
Next, we apply a standard statistical analysis (e.g., [
- For the chi-square method, the goodness of a fit is estimated by the reduced chi-square value, χ red 2 = 1 M χ est 2 , where M are the degrees of freedom. The
meaning of χ red 2 is the portion of χ 2 that corresponds to each of the degrees of freedom, and this has to be ~1 for a good fit. Therefore, a fit is characterized as “good” when χ red 2 ~ 1, otherwise there is an overestimation, ( χ red 2 < 1), or underestimation, ( χ red 2 > 1), of the errors.
For the p-value method, the goodness of the fit is evaluated by comparing the estimated chi-square value, χ est 2 , and the chi-square distribution,
P ( χ 2 ; M ) = [ 2 M 2 Γ ( M 2 ) ] − 1 e − 1 2 χ 2 ( χ 2 ) M 2 − 1 , (2a)
that is, the distribution of all the possible χ 2 values (parameterized by the degrees of freedom M). The likelihood of having a χ 2 value, equal to or larger than the estimated value χ est 2 , is given by the complementary cumulative distribution. The probability of taking a result χ 2 , larger than the estimated value χ est 2 , defines the p-value that equals
P ( χ est 2 ≤ χ 2 < ∞ ) = ∫ χ est 2 ∞ P ( χ 2 ; M ) d χ 2 . (2b)
The larger the p-value, the better the fit: a p-value larger than 0.5 corresponds to χ est 2 < M or χ red 2 < 1. Larger p-values, up to p = 1, correspond to smaller chi-squares, down to χ red 2 ~ 0. Thus, an increasing p-value above the threshold of 0.5 cannot lead to a better fitting. Rather, it leads to a worse fit, similar to a decreasing χ red 2 < 1. For this reason, the “p-value of the extremes” is used. According to this method, the probability of taking a result χ 2 , that is more extreme than the observed value χ est 2 , defines the p-value that equals the minimum between the two probabilities, P ( 0 ≤ χ 2 ≤ χ est 2 ) and its complementary, P ( χ est 2 ≤ χ 2 < ∞ ) . A null hypothesis associated with a p-value smaller than the significance level of ~0.05 is typically rejected.
As shown in
( T + 1 ) / 2 = ( 2 p ) log 2 . (3)
Finally, in
β-A = 0 | β-A = 2.5 | |||||||
---|---|---|---|---|---|---|---|---|
t [min] | RFU | χ red 2 | p-value | T-value | RFU | χ red 2 | p-value | T-value |
15 | 111.5 ± 0.6 | 0.2 | 0.04 | -0.06 | 181 ± 6 | 0.62 | 0.32 | 0.75 |
30 | 116.8 ± 0.9 | 0.55 | 0.26 | 0.64 | 192 ± 8 | 1.25 | 0.28 | 0.68 |
45 | 122.6 ± 0.8 | 0.68 | 0.36 | 0.81 | 203 ± 9 | 1.6 | 0.15 | 0.39 |
60 | 128.9 ± 1.3 | 0.57 | 0.27 | 0.66 | 213 ± 11 | 1.26 | 0.28 | 0.68 |
SS90 | 121.8 ± 1.5 | 0.52 | 0.24 | 0.60 | 204 ± 8 | 1.27 | 0.28 | 0.68 |
120 | 108.8 ± 1.3 | 0.84 | 0.48 | 0.98 | 186 ± 8 | 1.85 | 0.1 | 0.23 |
150 | 106.6 ± 1.6 | 0.84 | 0.48 | 0.98 | 180 ± 7 | 1.08 | 0.37 | 0.83 |
180 | 102.1 ± 1.2 | 0.85 | 0.49 | 0.99 | 170 ± 7 | 1.35 | 0.24 | 0.60 |
210 | 100 ± 1.8 | 1.13 | 0.34 | 0.78 | 165 ± 6 | 0.96 | 0.44 | 0.92 |
240 | 110 ± 2.1 | 0.98 | 0.43 | 0.91 | 182 ± 8 | 1.31 | 0.26 | 0.64 |
1060 | 95 ± 3 | 6.81 | 2.3E-06 | -0.95 | 148 ± 10 | 2.08 | 0.06 | 0.06 |
in the scaling. In other words, the graph of RFU(t) for β-A = 2.5 is a scaled replica of the graph of the graph of RFU(t) for β-A = 0. Both the graphs appear to have linear behavior during the growth, while they decay exponential after the maximum RFU achieved for t ~ 60 minutes. Therefore, the graphs of RFU(t) can be described by the same model, that is,
RFU ( t ) = { RFU 0 + b lin ⋅ t , t ≤ t max RFU ∞ + b exp ⋅ e − λ ⋅ | t − t max | γ , t ≥ t max , (4)
Where tmax = 60 min, λ = 0.00075, γ = 1.7 for both the cases of β-A = 0 and β-A = 2.5; the values of RFU0, blin, RFU∞, and bexp = RFUmax − RFU∞ are given by 105.5, 0.38, 99, 29.5 and 170.5, 0.72, 165, 48.5, respectively for β-A = 0 and β-A = 2.5.
We observe that the addition of β-amyloid increased the mitochondrial activation. After 60 min, the mitochondrial activation was at its peak, however it continued to fluctuate thereon after (
In order to demonstrate the independence of RFU(β-A) on MβCD, we proceed as follows: First, we derive the linear fitting of all the graphs of RFU(β-A), that is for the three different MβCD amountsi=0, 1, 2.5 mM, and all the times t [min] = 0, 15, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270 (
Then, we average the three optimal values ai and bi, for i = 1, 2, 3. The averaged values a ± δa and b ± δb, are shown in
β-A = 0 | β-A = 2.5 | |||||
---|---|---|---|---|---|---|
t [min] | a | p-value | T-value | b | p-value | T-value |
15 | 2.014 ± 0.007 | 0.08 | 0.14 | 0.024 ± 0.005 | 0.27 | 0.66 |
30 | 2.038 ± 0.008 | 0.28 | 0.68 | 0.024 ± 0.004 | 0.44 | 0.93 |
45 | 2.047 ± 0.009 | 0.22 | 0.55 | 0.024 ± 0.005 | 0.24 | 0.59 |
60 | 2.061 ± 0.014 | 0.20 | 0.51 | 0.023 ± 0.005 | 0.22 | 0.57 |
90 | 2.018 ± 0.008 | 0.11 | 0.28 | 0.030 ± 0.004 | 0.46 | 0.95 |
120 | 2.002 ± 0.011 | 0.21 | 0.53 | 0.031 ± 0.003 | 0.41 | 0.88 |
150 | 1.994 ± 0.012 | 0.31 | 0.74 | 0.0309 ± 0.0029 | 0.36 | 0.81 |
180 | 2.023 ± 0.014 | 0.47 | 0.97 | 0.030 ± 0.003 | 0.27 | 0.66 |
210 | 2.025 ± 0.014 | 0.32 | 0.75 | 0.030 ± 0.003 | 0.18 | 0.48 |
240 | 2.026 ± 0.019 | 0.40 | 0.87 | 0.029 ± 0.003 | 0.22 | 0.56 |
270 | 2.017 ± 0.022 | 0.38 | 0.85 | 0.029 ± 0.003 | 0.25 | 0.62 |
Notes. Fitting parameters a and b, averaged over their three values ai and bi derived for three different MβCD amounts i = 0, 1, 2.5 mM, all at each time t, with the statistical p-values and T-values of each averaging also shown.
- H0: the three linear fits are the same, namely, a1 = a2 = a3 and b1 = b2 = b3.
For this hypothesis to be accepted or rejected is necessary to check the p-values of the fitting, as in Experiment-2. The estimated p-values, as well as the derived T-values, are also shown in
We observe that all the p-values approve the statistical hypothesis that the linear behavior of log(RFU) with β-A is independent of the amount of the MβCD, for all the times of the experiment. In particular, all the p-values are larger of the confident level of 0.05 (
Data for three types of experiments, as regards the influence of cholesterol on the β-Amyloid, were collected and statistically analyzed. The three experiments are the following: 1) Correspondence between cholesterol and methyl-β-cyclodextrin (MβCD) measurements; 2) measurements of the relative fluorescence unit (RFU) with respect to MβCD concentration (with/without β-A); and 3) RFU measurements with respect to β-A concentration (with/without MβCD). The statistical analysis of the presented experimental datasets affirms that cholesterol
has no statistically significant effect on the degree of toxicity of β-Amyloid. This also confirms the conclusion of Dayeh et al. [
We did observe an increase in mitochondrial activation with increasing concentrations of β-A as evidenced by the data in
Over time, we did not see a statistically significant decrease in mitochondrial activity due to β-A’s toxicity. The negative control also had fluctuations in its mitochondrial activity over time. This lack of collapse in the mitochondrial activation indicated that the cells did not apoptose. We used HT22 hippocampal cells that are treated with a simian virus making them tumor cells. Since these hippocampal cells replicate, the effects of β-A can be compromised with the assay we used. The Mito-ID only provides information about the general population of cells. Due to heterogenous responses among HT22 cells, the assay was not sensitive enough to detect cellular-level differences in response to β-A and changes in cholesterol content. The cells also secrete growth factors that have protected them from the oxidative stress.
Cholesterol has many important roles in cellular function. It provides structure for the neuronal membrane, and plays an important part in cellular signaling. Our initial hypothesis is that cholesterol in the lipid membrane would play a protective role against oxidative stress. With this particular model, cholesterol content in the lipid layer does not seem to play any detectable role in modulating the effects of beta amyloid on oxidative stress response.
・ We observe an increase in mitochondrial activation with increasing concentrations of beta amyloid. Dose response shows increasing relative mitochondrial activation.
・ We did not see statistically significant differences with changes in cholesterol content of the plasma membrane. There are no statistically significant differences in mitochondrial activation at the same beta amyloid concentration between cells with different cholesterol contents.
・ We did check that the cholesterol content of the membrane decreased with increasing treatment concentrations of MβCD. The plasma membrane contains approximately 50% of the cell’s cholesterol, so a reduction of 50% or 75% of the overall cholesterol significantly reduces plasma membrane cholesterol content.
・ We observed lack of collapse of mitochondrial potential over time. Possibly, the dividing cells protect themselves against beta amyloid.
・ Cholesterol content does not seem to play a significant role in modulating the effects of beta amyloid on oxidative stress response.
・ HT22 neurons treated with human β-amyloid (1 - 40) showed oxidative stress.
We conclude with what’s next for improving and extending the presented analysis. Other investigations may involve other effects of membrane cholesterol changes. For example, Nicholson and collaborators suggested that age-depen- dent changes in membrane cholesterol might, at least in part, modulate the susceptibility of hippocampal neurons to Aβ-induced Ca2+ influx, calpain activation, and subsequent Tau toxicity in an NMDA receptor-dependent manner [
This work was supported by a grant from King Abdul Aziz University, S. A.
Livadiotis, G., Assas, L., Dayeh, M.A., Elaydi, S., Phea, C., Roberts, J.L., Samman, Y. and Tchen, R. (2017) Experimental Analysis of Interacting HT22 Plasma Membrane Cholesterol and β-Amyloid. Advances in Alzheimer’s Disease, 6, 75-96. https://doi.org/10.4236/aad.2017.64006