Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: High MALAT1 expression is associated with poor prostate cancer prognosis. A, Venn diagram displaying genes upregulated in patients with metastatic prostate cancer compared with localized cases in three publicly available gene expression omnibus (GEO) datasets namely, GSE35988, GSE6919, and GSE6752. B, Dot plot with superimposed violin plot showing MALAT1 expression in patients with benign, primary, and metastatic prostate cancer in the GSE6919 dataset. MALAT1 transcript is reported as log 2 median-centered ratio. C, Same as B, except GSE35988 dataset. D, Same as B, except GSE6752 dataset. E, Kaplan–Meier curves for BCR-free survival in the TCGA-PRAD dataset categorized as “ MALAT1 -high” ( n = 223) and “ MALAT1 -low” ( n = 219) groups based on the median expression of MALAT1 . Blue line represents patients with higher expression of MALAT1 whereas the green line represents cases with patients with lower expression of MALAT1 . F, Same as E, except relapse-free survival for the TCGA-PRAD dataset. Data represent mean ± SEM. For B and C, one-way ANOVA with Tukey multiple comparison test was applied, while for D, two-tailed unpaired Student t test was applied. The P values for E and F were computed by the log-rank test.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Expressing, Gene Expression, Comparison, Two Tailed Test

Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: MALAT1 promotes EMT, stemness, and chemoresistance in prostate cancer. A, Immunoblots showing the expression of EMT markers in sh MALAT1 and shSCRM prostate cancer cells. β-Actin was used as an internal control. B, Boyden chamber Matrigel migration assay using the same cells as in A. Representative fields with the migrated cells are shown in the inset. The bar plot depicts the alteration in migratory potential of the prostate cancer cells upon MALAT1 knockdown. C, Flow cytometry analysis showing expression of CD117 (c-KIT) and CD133 in 22RV1-sh MALAT1 and shSCRM control cells. D, Immunofluorescence images displaying the expression of CD117 and CD44 and in the same cells as in C. Scale bar, 20 µm. E, Dot plot represents quantification of CD117 and CD44 mean fluorescence intensity (MFI) per unit area shown as arbitrary units (AU). F, Representative phase-contrast images for the prostatospheres formed using 22RV1-sh MALAT1 and shSCRM control cells on the indicated days. Scale bar, 100 µm. G, Bar plot superimposed with dots represents the mean area of the prostatospheres and percentage sphere formation efficiency. H, qPCR depicting the expression of stem cell markers in the prostatospheres derived from the same cells as in E. Expression level for each gene was normalized to GAPDH . I, Flow cytometry analysis showing expression of ATP-binding cassette superfamily G member 2 (CD338/ABCG2) using the same cells as in C. J, Cell cytotoxicity assay using chemotherapeutic drugs namely, doxorubicin and 5-fluorouracil using the same cells as in C. IC 50 values were calculated by generating a dose–response curve using GraphPad Prism software. Experiments were performed with n = 3 biologically independent samples; the data represents mean ± SEM. The statistical difference among the groups was computed using one-way ANOVA with Dunnett multiple-comparison test for B, E, G, and H.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Western Blot, Expressing, Control, Migration, Knockdown, Flow Cytometry, Immunofluorescence, Fluorescence, Derivative Assay, Binding Assay, Cytotoxicity Assay, Software, Comparison

Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: MALAT1 depletion impairs HR-mediated DSB repair in prostate cancer cells. A, DAVID analysis depicting biological pathways downregulated in LNCaP-abl-si MALAT1 cells relative to LNCaP-abl–siCTL. Bars represent -log 10 ( P ) and the frequency polygon (line in orange) represents the number of genes. B, Correlogram depicting Pearson correlation coefficient ( ρ ) between DNA repair–associated genes and MALAT1 in prostate cancer patient samples from GSE35988 and GSE3325 datasets (FDR adjusted, P < 0.05). Correlation coefficients are expressed by the color from red to blue and the dot size is proportional to the strength of the correlation. Representative genes are marked on the sides of the correlogram. C, Representative confocal images for γH2AX foci (green) in control and MALAT1 -ablated 22RV1 and LNCaP cells. The nucleus was visualized by DAPI (blue). Scale bar, 10 µm. D, Quantification of the number of γH2AX-positive foci in the indicated cells. Bar plot showing the percentage of cells with the indicated number of foci/nuclei in the same cells. The P value for the χ 2 test is indicated. E, Immunoblots showing the expression of HR markers in MALAT1 -silenced and shSCRM prostate cancer cells. β-Actin was used as a loading control. F, Same as C, except immunostaining for BRCA1. G, Same as C, except immuno-staining for RAD51. H, Schematic of the pDR-GFP reporter used to monitor HR activity in 22RV1 MALAT1 -KO and shRAD51 cells. Bar plot exhibiting the percentage of GFP + cells in 22RV1- MALAT1 -KO and shRAD51 cells transfected with pDR-GFP reporter construct. Experiments were performed with n = 3 biologically independent samples; the data represent mean ± SEM. For D and F–H, one-way ANOVA with Dunnett multiple comparisons posthoc test was applied while the χ 2 test was used for D.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Control, Western Blot, Expressing, Immunostaining, Activity Assay, Transfection, Construct

Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: MALAT1 knockdown restrains cell-cycle progression and instigates apoptosis in prostate cancer cells. A, Flow cytometry analysis for accessing the cell-cycle distribution by propidium iodide (PI) DNA staining assay in MALAT1 -silenced prostate cancer cells. The percentage of cells in each phase was calculated using FlowJo software. B, Representative images depicting EdU incorporation in the same cells as in A. Nuclei were stained with Hoechst 33342. Scale bar, 20 µm. Right, bar graph showing quantification of EdU uptake in the indicated cells. C, qRT-PCR analysis showing expression of genes associated with G 1 and S-phase of the cell cycle in MALAT1 -silenced 22RV1 cells. The expression level for each gene was normalized to GAPDH . D, Immunoblot showing the change in expression of E2F1 in the same cells as in A. β-Actin was used as an internal control. E, Line graph showing cell proliferation assay using the same cells as in A, at the indicated time points. F, Flow cytometry–based apoptosis assay using Annexin V-PE and 7-AAD staining in the same cells as in A. The percentage of apoptotic cells was calculated using FlowJo software. G, Immunoblots showing a change in the expression of apoptosis markers in the same cells as in A. β-Actin was used as an internal control. H, Schematic depicting that MALAT1 is a novel regulator of HR and plays an important role in the maintenance of genome stability in prostate cancer. MALAT1 depletion induces HR deficiency by decreasing the expression of several DDR genes and results in DSB accumulation which in turn induces cell-cycle arrest and instigates apoptosis. Experiments were performed with n = 3 biologically independent samples; the data represents mean ± SEM. For B and C, one-way ANOVA with Dunnett multiple comparisons posthoc test was applied, while for E, two-way ANOVA with Tukey multiple comparisons test was applied.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Knockdown, Flow Cytometry, Staining, Software, Quantitative RT-PCR, Expressing, Western Blot, Control, Proliferation Assay, Apoptosis Assay

Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: MALAT1 modulates the expression of HR genes by sponging tumor-suppressive miR-421. A, Venn diagram displaying the miRNAs predicted to bind to MALAT1 transcript using four miRNA prediction tools binding computational tools, namely LncBaseV.2, miRanda, NPInter v4.0, and miRTar. B, Mature miR-421 sequence and its seed sites within 3′UTR of BRCA1, ATM and RAD51 . The seed sequence of miR-421 is shown in red while the target sequence is depicted in blue. C, Bar plot depicting the relative expression of miR-421 and MALAT1 in 22RV1-miR-421 cells and control cells. D, qPCR depicting relative expression of HR genes in the same cells as in C. E, Quantitative PCR depicting relative expression of HR genes in 22RV1-shSCRM and -sh MALAT1 cells transfected with nontargeting antagomiR or antagomiR-421. F, Schematic illustrating the predicted miR-421–binding sites near the 3′ end of MALAT1 . G, Illustration of luciferase reporter construct with the wild-type or mutated (transformed residues in red) miR-421–binding sites on MALAT1 3′ end downstream of the firefly luciferase reporter gene. H, Bar plots depicting the luciferase reporter activity in HEK293T cells cotransfected with MALAT1 -WT or MALAT1 -mut construct with nontargeting mimics or miR-421 mimic. I, RIP followed by real-time qPCR analyses demonstrating enrichment of MALAT1 and miR-421 with AGO2 antibody–bound beads in comparison to IgG (control antibody) in 22RV1 cells. The experiments were performed in triplicate with biologically independent samples ( n = 3); the data represent mean ± SEM. The statistical analysis of differences was calculated using one-way ANOVA with Dunnett multiple-comparisons posthoc test for panels C–E and H–I.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Expressing, Binding Assay, Sequencing, Control, Real-time Polymerase Chain Reaction, Transfection, Luciferase, Construct, Transformation Assay, Activity Assay, Comparison

Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: MALAT1 depletion induces “BRCAness” and confers sensitivity to PARPi. A, Cell cytotoxicity assay for determining IC 50 value of olaparib in SCRM control and MALAT1 -silenced prostate cancer cells. The IC 50 values were calculated by generating a dose–response curve using GraphPad Prism software. B, Line graph showing relative decrease in cell viability on olaparib treatment (10 µmol/L) in MALAT1 -silenced 22RV1 and LNCaP cells as compared with scrambled control. The drug was replenished every 24 hours at the indicated time points. C, Foci formation assay in 22RV1-shSCRM and -sh MALAT1 cells following treatment with olaparib (5 µmol/L) or vehicle control for 15 days. Inset showing representative images of foci. D, Foci formation assay in LNCaP-shSCRM and -sh MALAT1 cells following treatment with olaparib (2 µmol/L) or vehicle control for 15 days. Inset showing representative images of foci. E, Representative confocal images for EdU uptake in MALAT1 -deficient 22RV1 and LNCaP cells followed by olaparib (10 µmol/L) treatment for 48 hours. Scale bar, 50 µm. F, Bar graph showing quantification of EdU staining after 48-hour treatment with olaparib in the indicated cells. G, Representative confocal images for γH2AX foci (red) in the same cells as in C upon olaparib (10 µmol/L) treatment for 48 hours. The nucleus was visualized by Hoechst 33342 (blue). Scale bar, 10 µm. Quantification of the number of γH2AX-positive foci in the indicated cells. Bar plot showing the percentage of cells with the indicated number of foci/nuclei in the same cells. The P value for the χ 2 test is indicated. H, Same as G, except LNCaP-shSCRM and LNCaP-sh MALAT1 cells. I, Flow cytometry–based apoptosis assay using Annexin V-PE and 7-AAD staining in the same cells as in B upon olaparib (10 µmol/L) treatment for 48 hours. The percentage of the apoptotic cell population was calculated using FlowJo software. J, Same as I, except LNCaP-shSCRM and LNCaP-sh MALAT1 cells. K, Immunoblot showing the change in expression of cleaved PARP in the same cells as in B upon olaparib (10 µmol/L) treatment for 48 hours. β-Actin was used as an internal control. The experiments were performed with n = 3 biologically independent samples; the data represents mean ± SEM. Extra sums of the square F-test was used to compute the statistical significance and compare the curves in A. The statistical analysis of difference was computed using two-way ANOVA with Tukey multiple-comparison posthoc test for B–D and F–H, while the χ 2 test was used for G and H.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Cytotoxicity Assay, Control, Software, Tube Formation Assay, Staining, Flow Cytometry, Apoptosis Assay, Western Blot, Expressing, Comparison

Journal: Cancer Research Communications

Article Title: Targeting MALAT1 Augments Sensitivity to PARP Inhibition by Impairing Homologous Recombination in Prostate Cancer

doi: 10.1158/2767-9764.CRC-23-0089

Figure Lengend Snippet: MALAT1 ablation confers sensitivity to PARPi. A, Mean tumor volume of xenografts generated by implanting 22RV1-SCRM and 22RV1- MALAT1 KO cells in NOD-SCID mice and randomized into two groups ( n = 6 each), namely, vehicle control and olaparib (50 mg/kg). B, Bar plot showing percent tumor reduction in the same mice as mentioned in A. C, Scatter dot plot showing the number of cells metastasized to the lungs in xenografted mice as mentioned in A. D, Same as C, except cells metastasized to bone marrow. E, Representative images depicting IHC staining for Ki-67 in formalin-fixed paraffin-embedded tumor xenograft specimens as in A. F, Scatter dot plot showing quantification of Ki-67 expression in the tumor tissue sections of the mice xenografts as in A. G, Schema depicting that MALAT1 inhibition perturbs HR machinery via miR-421 and this in turn enhances the sensitivity toward PARPi. MALAT1 sponges miR-421 which in turn enhances the DNA repair activity of prostate cancer cells and enables them to proliferate and survive even in the presence of therapy-induced damage. While MALAT1 depletion disrupts the HR machinery, the NHEJ pathway takes over and repairs the damage, and aids in the survival of prostate cancer cells. However, when, MALAT1 -depleted cells are treated with pharmacologic inhibitors of PARP1, a type of enzyme that helps to repair damaged DNA via the NHEJ pathway, the cells are unable to repair the breaks brought on by treatment with PARP1 inhibitors and ultimately result in cell death. For A and B, the data represent mean ± SEM and the statistical difference was computed using two-way ANOVA with Tukey multiple comparison test while for C, D and F, the data are presented as median (middle line) with interquartile range and one-way ANOVA with Tukey multiple comparison test was applied.

Article Snippet: The samples were sorted on the basis of tissue type, and MALAT1 expression was plotted [log 2 (normalized count)] using GraphPad Prism version 8.0 (RRID:SCR_002798).

Techniques: Generated, Control, Immunohistochemistry, Formalin-fixed Paraffin-Embedded, Expressing, Inhibition, Activity Assay, Comparison

Journal: Scientific Reports

Article Title: Pharmacological mTOR targeting enhances the antineoplastic effects of selective PI3Kα inhibition in medulloblastoma

doi: 10.1038/s41598-019-49299-3

Figure Lengend Snippet: PIK3CA expression is elevated in the SHH-subgroup and correlates with GLI1 expression in medulloblastoma. ( A ) PIK3CA expression was analyzed in different medulloblastoma subgroups from the Northcott_2012 dataset, downloaded from the GlioVis portal ( http://gliovis.bioinfo.cnio.es/ ) and subjected to analysis in GraphPad Prism 7.0. Unpaired one-way ANOVA, **** P ≤ 0.0001. ( B ) Gene expression data from the Northcott_2012 dataset were downloaded from the GlioVis portal and GraphPad Prism 7.0 was used for correlation analysis to compare PIK3CA expression with GLI1 . Pearson’s correlation coefficient is shown, **** P ≤ 0.0001. ( C ) Medulloblastoma patient data from the Northcott_2012 dataset were subjected to KEGG pathway enrichment analysis for GLI1 using the GlioVis portal ( http://gliovis.bioinfo.cnio.es/ ). Bubble chart shows enrichment for the given pathway, where each point represents the enrichment level, the colour corresponds to the adjusted P -value ( P .adjust), and the size corresponds to the number of genes enriched (Count). Y-axis label represents pathway (see bottom panel for detailed name of pathways), and X-axis label represents enrichment factor (amount of differentially expressed genes enriched in the pathway over amount of all genes in background gene set).

Article Snippet: Figure 2 PIK3CA expression is elevated in the SHH-subgroup and correlates with GLI1 expression in medulloblastoma. ( A ) PIK3CA expression was analyzed in different medulloblastoma subgroups from the Northcott_2012 dataset, downloaded from the GlioVis portal ( http://gliovis.bioinfo.cnio.es/ ) and subjected to analysis in GraphPad Prism 7.0.

Techniques: Expressing, Gene Expression

Journal: Scientific Reports

Article Title: Pharmacological mTOR targeting enhances the antineoplastic effects of selective PI3Kα inhibition in medulloblastoma

doi: 10.1038/s41598-019-49299-3

Figure Lengend Snippet: Inhibition of PI3Kα or PIK3CA knockdown reduces sphere formation and disrupts medulloblastoma stem cell frequencies when combined with pharmacologic mTOR inhibition. ( A,B ) DAOY ( A ) or D556 ( B ) cells were grown as spheres in CSC medium for 7 days. Spheres were dissociated and seeded at 500 cells/well into round-bottom 96-well plates in the presence of alpelisib (5 μM) and/or OSI-027 (1 μM). After 7 days, spheres were stained with acridine orange and imaged to determine cross-sectional area. Data represent means ± SEM of 3 independent experiments, each done in triplicate. Unpaired one-way ANOVA, ** P ≤ 0.01, **** P ≤ 0.0001. Representative images are shown in the top panels. Scale bar, 1,000 μm. ( C , D ) In vitro ELDA after PIK3CA knockdown in combination with mTOR inhibition. DAOY ( C ) and D556 ( D ) cells were transfected with control siRNAs (siCtrl) or siRNAs targeting PIK3CA (siCA). After 2 days, cells were dissociated with trypsin and seeded in 3–5 technical replicates (n = 3) into round-bottom 96-well plates by forward- and side scatter, single-cell sorting at densities of 10, 30, 100, 300, 1,000 or 3,000 cells per well. Cells were treated with DMSO or OSI-027 (2 μM). After 7 days, neurospheres were stained with acridine orange and imaged using a Cytation 3 Cell Imaging multi-Mode Reader with a 4x objective. Neurospheres with a diameter of ≥100 μm were scored positive for ELDA analysis ( http://bioinf.wehi.edu.au/software/elda/ ). ( E,F ) Stem cell frequencies of medulloblastoma stem-like cancer cells for DAOY ( E ) or D556 ( F ) were estimated as the ratio 1/ x with the top and bottom 95% confidence intervals, where 1 = stem cell and x = all cells. ( G,H ) P values from χ 2 analyses are shown for DAOY ( G , left panel) and D556 ( H , left panel). Whole cell lysates of DAOY ( G , right panel) and D556 ( H , right panel) were subjected to immunoblotting using antibodies against p110α to monitor knockdown of PIK3CA . Membranes were stripped and reprobed with antibodies against HSP90. Blots were analysed by autoradiography. Uncropped blots are presented in the supplement.

Article Snippet: Figure 2 PIK3CA expression is elevated in the SHH-subgroup and correlates with GLI1 expression in medulloblastoma. ( A ) PIK3CA expression was analyzed in different medulloblastoma subgroups from the Northcott_2012 dataset, downloaded from the GlioVis portal ( http://gliovis.bioinfo.cnio.es/ ) and subjected to analysis in GraphPad Prism 7.0.

Techniques: Inhibition, Knockdown, Staining, In Vitro, Transfection, Control, FACS, Imaging, Software, Western Blot, Autoradiography

Journal: PLoS ONE

Article Title: Gene Expression Profiling of Preovulatory Follicle in the Buffalo Cow: Effects of Increased IGF-I Concentration on Periovulatory Events

doi: 10.1371/journal.pone.0020754

Figure Lengend Snippet: (A) Venn diagram representing the details of differentially expressed genes identified after microarray analysis of granulosa cells collected from the ovulatory follicle at −2, 1 and 22 h post peak LH surge. Data analyzed with ≥2 fold change cut-off and statistics. The number of differentially expressed genes found common between −2 vs. 1 h (red circle) and −2 vs. 22 h (blue circle) post peak LH surge, as well as comparison of differentially expressed genes between 1 vs. 22 h (green circle) post peak LH surge are presented. (B) Schematic representation of differentially expressed genes at different time points post peak LH surge. Red hexagon represents genes that were up regulated both at 1 and 22 h as well as those genes that were down regulated at 1 h but were up regulated at 22 h time point. Green hexagon represents differentially expressed genes that were down regulated both at 1 and 22 h time points as well as those genes that were up regulated at 1 h but were down regulated at 22 h time point. Also provided is the number of genes (represented as open hexagon) that were differentially expressed at 1 h time point but not at 22 h. (C) The number of differentially expressed genes listed as alphabetical groups in are further represented in tabular form providing details on the number of genes in each group, pattern of expression change at both time points post peak LH surge as well as classification based on their ontological distribution within each ‘biological processes’ terms having high scores indicative of processes associated with each group.

Article Snippet: The processed image files (.cel) were normalized across arrays using the robust multichip average (RMA) algorithm and log-transformed (base 2), thus allowing direct comparison of probe set values between all samples used in the experiment normalization, GeneSifter (VizX Labs; Seattle, WA) microarray expression analysis software was used to identify differentially expressed transcripts.

Techniques: Microarray, Comparison, Expressing

Journal: PLoS ONE

Article Title: Gene Expression Profiling of Preovulatory Follicle in the Buffalo Cow: Effects of Increased IGF-I Concentration on Periovulatory Events

doi: 10.1371/journal.pone.0020754

Figure Lengend Snippet: (A) Microarray fold expression changes at 1 and 22 h post peak LH surge in granulosa cells for few of the selected up and down regulated genes associated with specific biological process. (B) The genes selected by microarray analysis were also subjected to qPCR analysis and fold expression changes for granulosa cells and follicular wall collected at different time points are represented as bar graphs. Bar for each gene represents mean ± SEM fold expression change value at each time point (n = 3 animals). For each gene, bars with different alphabets above them are significantly different (p<0.05). More details on the analysis are provided in the section.

Article Snippet: The processed image files (.cel) were normalized across arrays using the robust multichip average (RMA) algorithm and log-transformed (base 2), thus allowing direct comparison of probe set values between all samples used in the experiment normalization, GeneSifter (VizX Labs; Seattle, WA) microarray expression analysis software was used to identify differentially expressed transcripts.

Techniques: Microarray, Expressing

Journal: PLoS ONE

Article Title: Gene Expression Profiling of Preovulatory Follicle in the Buffalo Cow: Effects of Increased IGF-I Concentration on Periovulatory Events

doi: 10.1371/journal.pone.0020754

Figure Lengend Snippet: (A) Microarray fold expression changes at 1 and 22 h post peak LH surge in granulosa cells for few of the selected up and down regulated genes associated with specific biological process like cell survival, apoptosis and blood coagulation are represented as bar graph. (B) The genes selected by microarray analysis were also subjected to qPCR analysis and the fold expression changes for granulosa cells and follicular wall collected at different time points are represented in bar graphs. Bar for each gene represents the mean ± SEM fold expression change values at each time point (n = 3 animals). Bars with different alphabets above them are significantly different (p<0.05). More details on the analysis are provided in the section.

Article Snippet: The processed image files (.cel) were normalized across arrays using the robust multichip average (RMA) algorithm and log-transformed (base 2), thus allowing direct comparison of probe set values between all samples used in the experiment normalization, GeneSifter (VizX Labs; Seattle, WA) microarray expression analysis software was used to identify differentially expressed transcripts.

Techniques: Microarray, Expressing, Coagulation

Journal: Annals of Clinical and Translational Neurology

Article Title: Deep sequencing of mitochondrial genomes reveals increased mutation load in Friedreich's ataxia

doi: 10.1002/acn3.322

Figure Lengend Snippet: Increased mtDNA damage in FRDA fibroblasts. (A) PCR analysis of GAA repeat length in FRDA and control fibroblasts; M1 = HyperLadder Plus 1 kbp ladder, C1–C5 = controls, F1–F5 = FRDA. (B) qRT‐PCR analysis of FXN mRNA expression in fibroblast lines used in this study; C1–C5 shown in black, F1–F5 shown in gray. FXN expression was normalized to GAPDH mRNA level. (C) Left panel: Representative agarose gel electrophoresis and amplicons for mtDNA damage qPCR products; M1 = HyperLadder ™ 1 kb Plus ladder (catalog # BIO‐33068, Bioline, Taunton, MA); M2 = HyperLadder ™ 100 bp Plus ladder (catalog # BIO‐33071, Bioline); long = long PCR product, ~8.8 kbp; short = short PCR product, 222 bp. Long amplicon shown in gray, short amplicon shown in black. Right panel: qPCR analysis of mtDNA copy number in control (C) and FRDA (F) fibroblasts. Results shown are from two independent experiments with five biological replicates for each group. (D) mtDNA damage qPCR analyses of short and long fragments were performed for control and FRDA fibroblasts; results from at least three independent experiments are shown. Controls (C1–C5) are depicted in black and FRDA (F1–F5) are depicted in gray. Cumulative analysis of the data for entire C and F cohort is shown; **** indicates P < 0.0001.

Article Snippet: Pearson's correlation of NTLH1 expression with FXN expression was calculated in GraphPad Prism 6.

Techniques: Control, Quantitative RT-PCR, Expressing, Agarose Gel Electrophoresis, Amplification

Journal: Annals of Clinical and Translational Neurology

Article Title: Deep sequencing of mitochondrial genomes reveals increased mutation load in Friedreich's ataxia

doi: 10.1002/acn3.322

Figure Lengend Snippet: Mitochondrial DNA mutation frequency is increased in FRDA fibroblasts. (A) Schematic amplicons and representative agarose gel electrophoresis analysis of PCR products used in MiSeq sequencing of the mtDNA. M1 = HyperLadder ™ 1 kb Plus ladder, MTL1 = ~9 kbp, MTL2 = ~11 kbp. (B) Quantitation of cumulative mtDNA mutation frequency, respectively, for C (red bar) and F (blue bar) cohorts, ** P = 0.003. (C) Quantitation of mtDNA mutation frequency in individual C1–C5 samples (red circles) and F1–F5 samples (blue squares); ** P = 0.003. (D) Correlation between mutation frequency and FXN expression determined by RNAs‐seq signal for C1–C5 (red circles), F1–3, F5 (blue squares). Pearson's correlation coefficient ( r ) and statistical significance ( P ) values are indicated on the graph. (E) Mutation frequency depicted as an area plot for control (shown in red) and FRDA (shown in blue) fibroblasts. The X‐axis shows individual positions in mtDNA; the region spanning mtDNA from position 16,024 to 576 (total of 1122 bp) containing two hypervariable segments has been excluded from analyses. Mutation frequency was calculated by normalizing the number of mutations (transitions, transversions, and single‐nucleotide indels) to the library size factor for each sample. On the Y‐axis, 1 represents a combined total number of mutations identified at each position of the mtDNA in both C and F cohorts. The blue area represents the fraction of total mutations identified in FRDA cells while red depicts the fraction of total mutations identified in control fibroblasts.

Article Snippet: Pearson's correlation of NTLH1 expression with FXN expression was calculated in GraphPad Prism 6.

Techniques: Mutagenesis, Agarose Gel Electrophoresis, Sequencing, Quantitation Assay, Expressing, Control

Journal: Annals of Clinical and Translational Neurology

Article Title: Deep sequencing of mitochondrial genomes reveals increased mutation load in Friedreich's ataxia

doi: 10.1002/acn3.322

Figure Lengend Snippet: Downregulation of NTHL1 expression in FRDA fibroblasts. (A) NTHL1 expression was determined by RNA‐seq (control cohort‐circles, FRDA cohort‐squares) or by qRT‐PCR (triangles; in the case where no RNA‐seq analysis was available). Samples C1–C5 and F1–F5 used in mtDNA damage and frequency analyses are indicated in gray; **** P = 0.0002 and was calculated for RNA‐seq samples only. (B) The Pearson correlation coefficient was determined for FXN mRNA and NTHL1 mRNA expression in control and FRDA fibroblasts. Designations of symbols as described in A. Pearson's correlation coefficient ( r ) and statistical significance ( P ) values are indicated on the graph.

Article Snippet: Pearson's correlation of NTLH1 expression with FXN expression was calculated in GraphPad Prism 6.

Techniques: Expressing, RNA Sequencing, Control, Quantitative RT-PCR