In GranderLab/miR34a_asRNA_project: The miR34AasRNAproject package facilitates the transparency and reproducibility of the miR34a asRNA project and associated publication.

packages <- c(
    "tidyverse",
    "printr",
    "ggthemes",
    "readr",
    "miR34AasRNAproject",
    "grid",
    "gtable"
)
purrr::walk(packages, library, character.only = TRUE)
rm(packages)

Introduction

miR34a asRNA over-expression was shown to increase the expression levels of miR34a. We investigated if this increase of miR34a was due to increased levels of transcription at the miR34a promoter.

Methods

Cell culture, QPCR, and ChIP

All cell lines were cultured at 5% CO2 and 37° C with Skov3 and PC3 cells in RPMI (Hyclone) and 2 mM L-glutamine and Saos2 cells cultured in DMEM high modified (Hyclone). All growth mediums were supplemented with 10% heat-inactivated FBS and 50 μg/ml of streptomycin and 50 μg/ml of penicillin. Cells were plated at 2-3x10^6 cells in a 15cm dish and incubated overnight. Cells were cross-linked in 1% formaldehyde, quenched in glycine (0.125M), resuspended in cell lysis buffer (5mM PIPES, 85mM KCL, 0.5% NP40, protease inhibitor), and sonicated (Bioruptor Sonicator Diagenode) in nucleus lysis buffer (50mM TRIS-HCL pH 8.0, 10mM EDTA, 1% SDS, protease inhibitor). The samples were incubated overnight at 4 °C with the appropriate antibody. Salmon sperm DNA/Protein A–agarose (Upstate/Millipore) was used to pull down the antibody. DNA was eluted in elution buffer (1% SDS, 100 mM NaHCO3), followed by reverse cross-linking, RNase-A and protease-K treatment. The DNA was eluted by using a Qiagen PCR purification kit. The following antibodies were used (4 μg/sample): phospho-polymerase II (abcam: ab5095). QPCR was carried out using KAPA 2G SYBRGreen (Kapa Biosystems) using the Applied Biosystems 7900HT machine with the cycling conditions: 95 °C for 3 min, 95 °C for 3 s, 60 °C for 30 s.

Analysis

QPCR was performed in technical replicate for each sample and the mean of the technical replicates was subsequently used to calculate the fraction of input. The fold change of the miR34a asRNA overexpression samples was then calculated by dividing the values by the values for the corresponding mock samples. The fold values were then log2 transformed and the Student's t-test was used to test for significant differences under the null hypothesis that the true mean value of log2 transformed fold changes were equal to 0.

Primers

primers <- data.frame(
    name=c(
        "miR34aChIP F1",
        "miR34aChIP R1"
    ),
    sequence=c(
       "AAA GTT TGC AAA GAA GGA GGC GGG",
       "AGG GAA GAA AGA ACT AGC CGA GCA"
    )
)
primers

Results

data <- getData("Figure 3d")

#calculate means of technical replicates
data <- mutate(data, techMean = rowMeans(select(data, Quantity1, Quantity2)))

#calculate fraction of input
.fractionOfInput <- function(data, techMean, Biological.Replicate, condition) {
  bool1 <- data$`Biological Replicate` == Biological.Replicate
  bool2 <- data$condition == condition
  bool3 <- data$sample == "input"
  bool <- bool1 & bool2 & bool3
  techMean / pull(data, techMean)[bool]
}

data <- data %>%
  group_by(`Biological Replicate`, condition, sample) %>%
  mutate(fractionInput = .fractionOfInput(data, techMean, `Biological Replicate`, condition)) %>%
  ungroup() %>%
  filter(sample != "input")

#calculate fold over mock
.foldOverControl <- function(data, fractionInput, Biological.Replicate) {
  bool1 <- data$`Biological Replicate` == 3
  bool2 <- data$condition == "mock"
  bool <- bool1 & bool2
  fractionInput / pull(data, fractionInput)[bool]
}

data <- data %>%
  mutate(
    foldOverControl = .foldOverControl(data, fractionInput, `Biological Replicate`),
    logfoldOverControl= log2(foldOverControl)
  ) %>%
  ungroup()

#calculate stats 
stats <- data %>%
  group_by(condition) %>%
  summarize(
    n = n(),
    mean = mean(logfoldOverControl),
    CI95l =t.test(logfoldOverControl)$conf.int[1],
    CI95h =t.test(logfoldOverControl)$conf.int[2],
    pValue = t.test(logfoldOverControl, mu = 0)$p.value
  ) %>%
  pFormat(.) %>%
  mutate(pFormat = case_when(
    condition == "mock" ~ "", 
    TRUE ~ pFormat
  ))

p <- ggplot(data = NULL) +
  geom_dotplot(
    data = data, 
    aes(
      x = condition, 
      y = logfoldOverControl, 
      fill = condition
    ),
    binwidth = 1/10,
    #alpha = 0.5,
    colour = NA,
    position = position_dodge(width = 0.9), 
    binaxis='y', 
    stackdir='center',
    dotsize = 2,
    show.legend = TRUE
  ) +
  scale_y_continuous("log2(Fold)") +
  labs(
    x = "Cell line",
    y = "log2(Fold polII enrichment)",
    title = "Phosphorylated Poll II ChIP"
  ) +
  guides(fill = FALSE)

markdown <- p +
    geom_linerange(
    data = stats,
    aes(x = condition, ymin = CI95l, ymax = CI95h),
    colour = "gray20",
    show.legend = FALSE
  ) +
  geom_point(
    data = stats,
    aes(x = condition, y = mean),
    colour = "gray20",
    shape = 95,
    size = 6,
    show.legend = FALSE
  ) +
  geom_segment(
    data = filter(stats, condition == "lncTAM34a"),
    aes(x = 1, y = CI95h + 0.25, xend = 2, yend = CI95h + 0.25),
    colour = "grey43"
  ) +
  geom_label(
    data = stats,
    aes(x = 1.5, y = CI95h + 0.45, label = pFormat),
    label.size = 0,
    label.padding = unit(0.01, "lines"),
    show.legend = FALSE,
    fill = "white",
    size = 5,
    family = "Arial Unicode MS"
  )

plotRmarkdown(markdown)

pdf <- p +
    geom_linerange(
    data = stats,
    aes(x = condition, ymin = CI95l, ymax = CI95h),
    colour = "grey30",
    size = 0.3,
    show.legend = FALSE
  ) +
  geom_point(
    data = stats,
    aes(x = condition, y = mean),
    colour = "grey30",
    shape = 95,
    size = 4,
    show.legend = FALSE
  ) +
  geom_segment(
    data = filter(stats, condition == "lncTAM34a"),
    aes(x = 1, y = CI95h + 0.25, xend = 2, yend = CI95h + 0.25),
    colour = "grey30",
    size = 0.3
  ) +
  geom_label(
    data = stats,
    aes(x = 1.5, y = CI95h + 0.45, label = pFormat),
    label.size = 0,
    label.padding = unit(0.01, "lines"),
    show.legend = FALSE,
    fill = "white",
    colour = "grey30",
    size = 2.5,
    family = "Arial Unicode MS"
  )

figure <- plotPDF(pdf)

# path <- file.path("~/GitHub/miR34a_asRNA_project/inst", fileMap(type = "pdf")["Figure 3d"][[1]])
# ggsave(
#   plot = figure,
#   filename = path,
#   device = cairo_pdf,
#   height = 60,
#   width = 43,
#   units = "mm",
#   dpi = 10000000
# )

Phosphorylated Poll II ChIP in miR34a asRNA over-expressing PC3 stable cell lines compared to their respective mock control. The points represent the values obtained from each independant experiment (n = 4). 95% confidence interval (vertical black lines), mean (horizontal black lines), and p-values are shown with the horizontal line under the p-value indicating the comparison that was tested.

Conclusion

Phosphorylated pol-II was increased at the miR34a promoter in miR34a asRNA over-expressing cell lines indicating that the regulation of miR34a by miR34a asRNA is transcriptional.

sessionInfo()

GranderLab/miR34a_asRNA_project documentation built on May 26, 2019, 7:26 a.m.