Registered report: IDH mutation impairs histone demethylation and results in a block to cell differentiation

The Reproducibility Project: Cancer Biology seeks to address growing concerns about reproducibility in scientific research by conducting replications of selected experiments from a number of high-profile papers in the field of cancer biology. The papers, which were published between 2010 and 2012, were selected on the basis of citations and Altmetric scores (Errington et al., 2014). This Registered Report describes the proposed replication plan of key experiments from “IDH mutation impairs histone demethylation and results in a block to cell differentiation” by Lu and colleagues, published in Nature in 2012 (Lu et al., 2012). The experiments that will be replicated are those reported in Figures 1B, 2A, 2B, 2D and 4D. Lu and colleagues demonstrated that expression of mutant forms of IDH1 or IDH2 caused global increases in histone methylation and increased levels of 2 hydroxyglutarate (Figure 1B). This was correlated with a block in differentiation (Figures 2A, B and D). This effect appeared to be mediated by the histone demethylase KDM4C (Figure 4D). The Reproducibility Project: Cancer Biology is a collaboration between the Center for Open Scienceand Science Exchange, and the results of the replications will be published by eLife. DOI: http://dx.doi.org/10.7554/eLife.10860.001


Introduction
Mutations in the metabolic proteins IDH1 and IDH2 are associated with gliomas, acute myeloid leukemias, chondrosarcomas, intrahepatic cholangiocarcinomas, lymphomas, melanomas and colon, thyroid and prostate cancers (for review, see Krell et al., 2013). Previous work has shown that these mutations change the specificity of the reaction catalyzed by IDH proteins; instead of producing aketoglutarate from isocitrate, they produce 2-hydroxyglutarate (2HG), a metabolite that can have oncogenic effects (Krell et al., 2013;McKenney and Levine, 2013;Ward et al., 2010;Xu et al., 2011;Zhang et al., 2013). Lu and colleagues expand upon this work to identify a potential mechanism for how 2HG can effect major changes in cell behavior. They present evidence that 2HG interferes with global demethylation that is required for progenitor cells to complete terminal differentiation. Transfection of 3T3-L1 cells with the mutant forms of IDH1 and IDH2 that produce 2HG lead to an increase in global methylation levels and prevented normal in vitro differentiation into adipocytes. The 2HG-sensitive histone demethylase KDM4C appeared to be required for this process, as knockdown of KDM4C recapitulated the phenotype of 2HG production. Examination of glioma samples showed a correlation between IDH mutation status and level of overall methylation . Taken together, Lu and colleagues' findings help explain how mutations in IDH1 and IDH2 potentially interface with cancer development and progression.
In Figure 1B, Lu and colleagues examined the effects of mutations in IDH1 and IDH2 on global levels of methylation by transfecting mutant and wild type forms of the genes into 293T cells and using Western blot to assess the levels of various methylation markers. They also confirmed that introduction of the mutated forms of IDH1 and IDH2 correlated with increased intracellular levels of the oncometabolite 2HG. Their findings suggest that mutations in IDH1 and IDH2 correlate with increased levels of many methylation markers, and this key finding is replicated in Protocol 1.
In order to understand the effects of hypermethylation more fully, Lu and colleagues turned to an in vitro model of differentiation; when treated with appropriate signals, 3T3-L1 cells undergo epigenetic changes required for them to differentiation into adipocytes. In Figure 2A and B, they transfect undifferentiated 3T3-L1 cells with the wild type and mutant forms of IDH2 and assess the cells' ability to differentiate into adipocytes, as determined by staining for lipid droplets with Oil-Red-O. While differentiated 3T3-L1 cells transfected with vector only or wild type IDH2 showed robust Oil-Red-O staining, cells transfected with mutant IDH2 did not, indicating a block in differentiation. qRT-PCR confirmed that cells transfected with mutant IDH variants did not express high levels of known adipocyte markers ( Figure 2D). This key finding will be replicated in Protocol 2.
Lu and colleagues identified a histone demethylase, KDM4C, expressed as 3T3-L1 differentiation progressed, that appeared to be sensitive to 2HG. In Figure 4D, they use siRNAs to knock down levels of KDM4C in differentiating 3T3-L1 cells. Western blot analysis and Oil-Red-O staining confirmed that loss of KDM4C increased global methylation levels and inhibited differentiation. This key finding will be replicated in Protocol 3.
Several aspects of Lu's findings have been corroborated by other work. Multiple groups have demonstrated that perturbations in IDH proteins alter methylation levels; overexpression of the IDH1 R132H allele in human tumor cells lines increased global histone methylation levels (Duncan et al., 2012), exogenous IDH2 R140Q increased methylation levels in erythroleukemia progenitor cells (Kernytsky et al., 2015) and an immortalized astrocyte cell line expressing IDH1 R132H also demonstrated increased levels of methylation . Members of the Thompson lab (authors of this study) have confirmed that expression of mutant variants of IDH proteins in 3T3-L1 cells blocked differentiation into adipocytes (Londono Gentile et al., 2013;Ward et al., 2013). Sasaki and colleagues have shown that mutant IDH1 expression increased levels of methylation in mice (Sasaki et al., 2012), while Akbay and colleagues published a similar observation for mutant forms of IDH2 (Akbay et al., 2014). This effect may even hold true for human patients, as there is a marked increase in H3K9me3 levels associated with IDH mutations in oligodendromas and high grade astrocytomas (Venneti et al., 2013).

Materials and methods
Unless otherwise noted, all protocol information was derived from the original paper, references from the original paper, or information obtained directly from the authors. An asterisk (*) indicates data or information provided by the Reproducibility Project: Cancer Biology core team. A hashtag (#) indicates information provided by the replicating lab.
Protocol 1: Assessing the methylation status and 2HG production of 293T cells transfected with mutant forms of IDH1 and IDH2 This protocol describes how to transfect 293T cells with wild-type and mutant forms of IDH1 and IDH2 and assess levels of global methylation and 2HG production, as seen in Figure 1B  Sampling . Experiment will be repeated a total of 6 times for a minimum power of 80%. The metabolite data is qualitative, thus to determine an appropriate number of replicates to initially perform, sample sizes based on a range of potential variance was determined.
See "power alculations' for details. The metabolite data displayed in the bottom of Figure 1B were derived from Figure 3B of Figueroa and colleagues (Figueroa et al., 2010).
. Each experiment consists of five cohorts: 2. *# Quantify protein concentration using a Bradford Assay. 3. # Load~50 mg of protein per well and separate proteins on a 10% NuPAGE 4-12% gradient gel. 4. # Transfer to nitrocellulose membrane using an XCell II blot module at 25 V for 1-2 hr (start at 100 mA per gel). 5. *Perform a Ponceau stain and image to confirm transfer of proteins.
a. Incubate with HRP-conjugated secondary antibodies # diluted in TBST for 1 hr at room temperature. #* Use manufacturer recommended dilutions. 9. Wash three times with TBST. 10. Detect signal # using ECL plus according to the manufacturer's instructions. 11. Quantify band intensities with ImageJ.
a. Normalize methylation band intensities to total H3. b. Divide normalized band intensities by the vector control band intensity.
. Gas chromatography-mass spectrometry analysis of 2HG levels. Note; the data in the original paper and the methodology are derived from Figueroa and colleagues (Figueroa et al., 2010). 1. Gently remove culture medium from cells 3 days after transfection, # wash cells quickly three times with 2 ml ice-cold PBS, and add # 0.5 ml ice-cold 80% methanol containing 20 mM L-norvaline per well of a 6-well plate to the cells. a. # Quantify protein concentration using the # Bio-Rad Quick Start Bradford Assay. 2. Incubate 20 min at -80˚C. 3. Centrifuge at 14000xg for 20 min at 4˚C. a. # Counter-extract samples with chloroform to remove nonpolar metabolites. 4. Collect supernatant and dry using a # MiVac. 5. Redissolve dried extracts in # 60 mL of a 1:1 mixture of acetonitrile and N-methyl-N-tertbutyldimethylsilyltrifluoroacetamide (MTBSTFA). 6. Heat the samples for 75 min at 70˚C. 7. GC-MS analysis: a. # A Shimadzu QP2010 Plus GC-MS is programmed with an injection temperature of 250˚C, injection split ratio 1/10, with injection volume 0.3-1 ml. GC oven temperature starts at 130˚C for 4 min, rising to 243˚C at 6˚C/min and to 280˚C at 60˚C/min with a final hold at this temperature for 2 min. GC flow rate with helium carrier gas was 50 cm/s. The GC column used is a 15 m x 0.25 mm x 0.25 mm Rxi-5ms (Restek). GC-MS interface temperature is 300˚C and (electron impact) ion source temperature is 200˚C, with 70 V/ 70 mA ionization voltage/ current. The mass spectrometer is set to scan m/z range 150-600, with~1 kV detector sensitivity (modified as necessary).
8. *# In parallel to the sample, run a standard curve of known amounts of 2HG. 9. Confirm and *# quantify 2HG metabolite peak using standard curve. 10. Analyze and *# quantify 2HG and glutamate signal (identified by elution time and mass fragment pattern) intensities by integration of peak areas. .

Repeat independently from
Step 4 onwards an additional five times.

Deliverables
. Data to be collected: Sequence files and agarose gel images confirming vector identity Full gel images of western blots with ladder (as seen in Figure 1B) &

Images of Ponceau stained membranes
Quantification of band intensities (as seen in Supplemental Figure 1A) GC-MS data Quantification of signal intensities of 2HG and glutamate (as seen in Figure 1B)

Confirmatory analysis plan
. Statistical Analysis of the Replication Data: Note: At the time of analysis we will calculate Pearson's r to check for correlation between the six dependent variables, normalized intensities measured for each of the histone lysine methylations, for the Western blot data. We will also perform the Shapiro-Wilk test and generate a quantile-quantile plot to assess the normality of the Western blot data and 2HG/glutamate ratios. We will also perform Levene's test to assess homoscedasticity. If the data appear skewed we will perform a log transformation in order to proceed with the proposed statistical analysis. If the log transformation does not result in similar variance across groups, we will perform the equivalent non-parametric test listed in Power Calculations for this protocol.

Western blot:
& MANOVA (six dependent variables are the normalized intensities for each of the histone lysine methylations; four independent variables are the IDH1 and IDH2 variants (all normalized to vector) with the following planned comparisons using Bonferroni's correction: . Wild-type IDH1 compared to IDH1 R132H , for H3K9me2. . Wild-type IDH2 compared to IDH2 R172K , for H3K9me2. 2HG/glutamate ratios: & One-way ANOVA (one dependent variable is the 2HG/glutamate ratio; four independent variables are the IDH1 and IDH2 variants) with the following planned comparisons using Bonferroni's correction: . Meta-analysis of original and replication attempt effect sizes: For Western blot:

&
The replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.

&
The replication data will be presented as a mean with 95% confidence intervals and will include the original data point, calculated directly from the representative image, as a single point on the same plot for comparison.
. Additional exploratory analysis: Correlation analysis (Pearson's r) of each of the six relative histone methylation levels to 2HG/glutamate levels using Bonferroni 's correction (as seen in Supplemental Figure 1B).
. The replication attempt will quantify total amounts of 2HG in addition to the ratio of 2HG to glutamate.
. Aspects of the Western blot protocol are provided by the replicating lab; complete details of the original protocol were unavailable.

Provisions for quality control
All data obtained from the experiment -raw data, data analysis, control data and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/vfsbo/).
. Sequence files and agarose gel images confirming vector identity and integrity . Ponceau stains confirming protein transfer to membranes . STR profiling and mycoplasma testing results

Protocol 2: Examining the effects of mutations in IDH2 on differentiation of 3T3-L1 cells
This protocol describes how to induce the differentiation of 3T3-L1 cells into adipocytes, which involves extensive chromatin remodeling, after transfection with wild type and mutant forms of IDH2 and assess the level of differentiation by Oil-Red-O staining, as seen in Figure 2A and B, and adipocyte marker expression, as seen in Figure 2D.

Sampling
. This experiment will use 5 biological replicates for a minimum power of 80%. The metabolite data is qualitative, thus to determine an appropriate number of replicates to initially perform, sample sizes based on a range of potential variance was determined.
See Power Calculations for details. . Each experiment will consist of three cohorts: Cohort 1: 3T3-L1 cells transduced with vector Cohort 2: 3T3-L1 cells transduced with wild-type IDH2 Cohort 3: 3T3-L1 cells transduced with IDH2 R172K . Each cohort will have 5 plates per biological replicate: One plate will be used to assess IDH2 expression by Western blot.
The second plate will be used to assess intracellular levels of 2HG.
The third plate will be assessed for adipogenesis by Oil-Red-O staining. The fourth and fifth plates will have mRNA harvested for qRT-PCR analysis. a. Centrifuge at 500xg for 10 min at room temperature to pellet debris. b. Filter supernatant through a 0.45 mm syringe filter, aliquot and store at -80˚C.

Materials and reagents
. Transduce 3T3-L1 cells with viral supernatant. 1. # Seed cells in 6-well plates and incubate overnight. a. Cells should be 50-60% confluent the next day.
2. # Add viral supernatant to medium. a. Supernatant will be added at varying concentrations to determine optimal transduction efficiency; 1:5 to 1:10 -150 to 300 mL per well. 3. # Adjust media volume to 1.4 ml per well. 4. # Add polybrene in 100 mL of media into each well for a final concentration of 8 mg/ml. 5. # Spinoculate by spinning at 1000xg for 60 min at room temperature.
a. Incubate overnight. 6. # Change media to remove viral transduction media.
a. Replace with fresh media. 7. Grow cells with 2.5 mg/ml puromycin for 7 days to select for stable expression of either wild-type or mutant IDH2. a. Maintain cells in puromycin. b. Also treat a non-transduced well of 3T3-L1 cells as a control showing susceptibility to puromycin. c. Split each biological replicate into 5 plates for the four assays being performed.
i. Plate 1 is for Western blot ii. Plate 2 is for GC-MS iii. Plate 3 is for Oil-Red-O staining (harvested 7 days after differentiation) iv. Plate 4 and 5 are for qRT-PCR (harvested 0 and 4 days after differentiation) . Generate whole cell lysates from the first plate of each cohort: 1. Lyse cells and sonicate in RIPA buffer. a. RIPA buffer: 1% sodium deoxycholate, 0.1% SDS, 1% Triton X-100, 0.01 M Tris pH 8.0 and 0.14 M NaCl b. # Sonicate for 1 min, at 180 watts with rounds of 10 sec on/10 sec off. Keep sample on ice during sonication. 2. Centrifuge lysates at 14000xg for 10 min at 4˚C. 3. Collect supernatant and measure total protein concentration # using a Bradford assay. 4. Perform Western blot as outlined in Protocol 1 Step 6 using the following primary antibodies * at the manufacturer's recommended dilution: a. Anti-IDH1 b. Anti-IDH2 . Harvest the second plate for metabolite analysis by mass spectrometry as described in Protocol 1 Step 4.
. Induce 3T3-L1 cells to differentiate into adipocytes. 1. Incubate cells for 2 days with a differentiation cocktail composed of 0.5 mM isobutylmethylxanthine, 1 mM dexamethasone, 5 mg/ml insulin and 5 mM troglitazone supplementing the standard media. 2. After 3 days, maintain cells with 5 mg/ml insulin until harvested.
. After 7 days of differentiation, assess adipogenesis by Oil-Red-O staining in the third plate from each cohort. Deliverables . Data to be collected: Whole gel images of Western blots with ladder (as seen in Figure 2A) * Densitometric quantification of bands & Also normalized to the loading control.
Images of wells stained with Oil-Red-O (as seen in Figure 2B) * Quantification of Oil-Red-O levels for each cohort All raw qRT-PCR data Graph of gene expression over time for each of the three adipocyte markers (as seen in Figure 2D)

Confirmatory analysis plan
. Statistical Analysis of the Replication Data: Note: At the time of analysis we will calculate Pearson's r to check for correlation between the three dependent variables, normalized gene expression for each of the adipocyte markers, for the qRT-PCR data. We will also perform the Shapiro-Wilk test and generate a quantile-quantile plot to assess the normality of the qRT-PCR data and 2HG/glutamate ratios. We will also perform Levene's test to assess homoscedasticity. If the data appears skewed we will perform a log transformation in order to proceed with the proposed statistical analysis. If this is not possible we will perform the equivalent non-parametric test listed in Power Calculations for this protocol. . IDH2 R172K compared to IDH2 WT qRT-PCR: & One-way MANOVA (three dependent variables are the normalized gene expression of each of the adipocyte markers on day 4; three independent variables are the vector and IDH2 variants) with the following planned comparisons using Bonferroni's correction: . IDH2 R172 compared to vector for each gene (three comparisons total) . IDH2 R172K compared to IDH2 WT for each gene (three comparisons total) . Meta-analysis of original and replication attempt effect sizes: For qRT-PCR:

&
The replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.

&
The replication data will be presented as a mean with 95% confidence intervals and will include the original data point, calculated directly from the representative image, as a single point on the same plot for comparison.
. Additional exploratory analysis: Oil-Red-O staining: & One-way ANOVA (one dependent variable is the A 500 readings; three independent variables are the vector and IDH2 variants) with the following planned comparison using Fisher's LSD correction: . IDH2 R172K compared to IDH2 WT . Aspects of the Western blot protocol are provided by the replicating lab; complete details of the original protocol were unavailable.
. Aspects of the viral production protocol are adapted from the replicating lab's in-house protocol.
. Viral supernatant will be collected only at 48 hr post-transection and will not be combined with viral supernatant collected at 72 hr.
. In addition to imaging Oil-Red-O stained plates, the replication attempt will quantify the amount of Oil-Red-O staining spectrophotometrically.

Provisions for quality control
All data obtained from the experiment -raw data, data analysis, control data and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/vfsbo/).
. Sequence files and agarose gel images confirming vector identity and integrity . Ponceau stains confirming protein transfer to membranes . STR profiling and mycoplasma testing results . Absorbance data for RNA and cDNA

Protocol 3: Assessing the role of KDM4C on differentiation of 3T3-L1 cells
This protocol describes how to treat 3T3-L1 cells with an siRNA against the histone demethylase KDM4C, whose activity is inhibited by 2HG, and assess the effect of loss of KDM4C activity on methylation and differentiation, as seen in Figure 4D and Supplemental Figure 8.

Sampling
. This experiment will be repeated 3 times for a minimum power of 80%. The Western blot data is qualitative, thus to determine an appropriate number of replicates to initially perform, sample sizes based on a range of potential variance was determined. i. One will be harvested on Day 3 of differentiation for Western blot analysis.
ii. One will be used on Day 7 of differentiation for Oil-Red-O analysis. 2. Transfect with the following siRNAs at a final concentration of 40 nM using Lipofectamine RNAiMAX according to the manufacturer's instructions. a. Transfect control wells with a scrambled control siRNA. b. Also plate control wells with no transfection. 3. Incubate for 3 days.
. Induce differentiation of control siRNA and antisense siRNA transduced 3T3-L1 cells as specified in Protocol 2 Step 6.
. 3 days after differentiation, harvest one plate from each treatment and prepare whole cell lysates as specified in Protocol 2 Step 7.
. Perform Western blot analysis on all whole cell lysates from Day 3 as described in Protocol 2 Step 7. Deliverables . Data to be collected: Whole gel images of all Western blots with ladder (as seen in the top of Figure 4D) Images of Oil-Red-O stained wells (as seen in the bottom half of Figure 4D) . Quantification of Oil-Red-O staining at Day 7 of differentiation (compare to Supplemental Figure 8B)

Confirmatory analysis plan
. Statistical Analysis of the Replication Data: Note: At the time of analysis we will calculate Pearson's r to check for correlation between the two dependent variables, normalized intensities measured for KDM4C and H3K9me3, for the Western blot data. We will also perform the Shapiro-Wilk test and generate a quantilequantile plot to assess the normality of the Western blot and Oil-Red-O data. We will also perform Levene's test to assess homoscedasticity. If the data appears skewed we will perform a log transformation in order to proceed with the proposed statistical analysis. If this is not possible we will perform the equivalent non-parametric test listed in Power Calculations for this protocol. This replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.
. There is no originally reported data from siRNA #3, therefore it will not be included.
Western Blot: The replication data will be presented as a mean with 95% confidence intervals and will include the original data point, calculated directly from the representative image, as a single point on the same plot for comparison.

Known differences from the original study
The replication will perform the Oil-Red-O quantification for all three siRNAs, not just #1 and #2 as presented in Supplemental Figure 8.

Provisions for quality control
All data obtained from the experiment -raw data, data analysis, control data and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/vfsbo/).
. Ponceau stains confirming protein transfer to membranes . STR profiling and mycoplasma testing results

Power calculations
Note: details of all power calculations can be found at https://osf.io/rb32p/

Protocol 1 Summary of original data
Note: data estimated from published figures. Test family . Western blot; Figure 1B/Supplemental Figure 1A: Note: Since we do not have the raw data, we were unable to perform power calculations using a MANOVA. We are approximating sample sizes with corrected one-way ANOVAs for each DV (normalized histone methylations).
. Note: Only H3K9me2 is being included since this is the histone modification with the largest effect size reported. A correlation among all the histone methylations will also be performed prior to performing the proposed analysis plan.
. 2HG/glutamate ratios; Figure 1B Power calculations . Power calculations were performed using R software (version 3.2.2) (R Core Team, 2015) and G*Power (version 3.1.7) (Faul et al., 2007) . Partial h 2 calculated as in Lakens (2013) . Western blot calculations: . Note: Due to the large variance, these parametric tests are only used for comparison purposes. The sample size is based on the non-parametric tests also listed. For the ANOVA a Kruskal-Wallis would be performed as the non-parametric alternative, which would require an~15% increase in sample size calculated for the parametric test listed. In order to produce quantitative replication data, we will run the experiment six times. Each time we will quantify the 2HG/glutamate ratio. We will determine the standard deviation across the biological replicates and combine this with the reported value from the original study to simulate the original effect size. We will use this simulated effect size to determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure that the experiment has more than 80% power to detect the original effect.

Protocol 2 Summary of original data
Note: data estimated from published figures.  . qRT-PCR; Figure 2D: Note: Since we did not have the raw data, we were unable to perform power calculations using a MANOVA. We are approximating the sample sizes with corrected one-way ANOVAs for each DV (gene).  Team, 2015) and G*Power (version 3.1.7) (Faul et al., 2007) . Partial h 2 calculated as in Lakens (2013 In order to produce quantitative replication data, we will run the experiment five times. Each time we will quantify the 2HG/glutamate ratio. We will determine the standard deviation across the biological replicates and combine this with the reported value from the original study to simulate the original effect size. We will use this simulated effect size to determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure that the experiment has more than 80% power to detect the original effect.
. qRT-PCR: Note: Due to the large variance, these parametric tests are only used for comparison purposes. The sample size is based on the non-parametric tests also listed. For the ANOVA a Kruskal-Wallis would be performed as the non-parametric alternative, which would require an~15% increase in sample size calculated for the parametric test listed. These values were normalized to ß-Actin as seen in Supplemental Figure 8A.
2 These values were normalized to total H3 as seen in Figure 4D. Also there is no data for siRNAs #1 and #2. We have assumed similar values for siRNA #3 for the purposes of these calculations. Test family . Western blot; Figure 4D and S8A: Note: Since we did not have the raw data, we were unable to perform power calculations using a MANOVA. We are approximating the sample sizes with corrected one-way ANOVAs for each DV (normalized protein).  Team, 2015) and G*Power (version 3.1.7) (Faul et al., 2007).
. Partial h 2 calculated as in Lakens (2013) In order to produce quantitative replication data, we will run the experiment three times. Each time we will quantify band intensity. We will determine the standard deviation of band intensity across the biological replicates and combine this with the reported value from the original study to simulate the original effect size. We will use this simulated effect size to determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure that the experiment has more than 80% power to detect the original effect.
. Oil-Red-O staining: Note: Due to the large variance, these parametric tests are only used for comparison purposes. The sample size is based on the non-parametric tests also listed. For the ANOVA a Kruskal-Wallis would be performed as the non-parametric alternative, which would require an~15% increase in sample size calculated for the parametric test listed. Author contributions ADR, DAS, OZ, PA-B, C-CC, DAR-G, Drafting or revising the article; RP:CB, Conception and design, Drafting or revising the article