Showing posts with label qPCR. Show all posts
Showing posts with label qPCR. Show all posts

Monday, July 20, 2015

7 17 2015 PGE/EP4 qPCR

Today I ran more targets that may be of interest due expected changes in expression. Here I explore PGE/EP4 which is expected to have decreased expression due to exposure to increased temperatures. 

Primers:

1638PGE/EP4_FWDACAGCGACGGACGATTTTCTJH5/21/20152055O.luridaProstaglandin E2 receptor EP4 subtype (PGE receptor EP4 subtype) (PGE2 receptor EP4 subtype) (Prostanoid EP4 receptor)P32240
1637PGE/EP4_REVATGGCAGACGTTACCCAACAJH5/21/20152055O.luridaProstaglandin E2 receptor EP4 subtype (PGE receptor EP4 subtype) (PGE2 receptor EP4 subtype) (Prostanoid EP4 receptor)P32240
Reagent Table:
VolumeReactions X116
Ssofast Evagreen MM101160
FWD Primer0.558
REV Primer0.558
1:9 cDNA9
  1. Added reagents from greatest to least volume
  2. Vortexed
  3. Centrifuged briefly
  4. Pipetted 11 ul Master Mix to each tube
  5. Pipetted 9 ul of 1:9 cDNA each column using a channel pipetter
  6. Centrifuged plate at 2000 rpm for 1 minute
  7. Ran Program Below
Program:
StepTemperatureTime
Initiation95 C10 min
Elongation95 C30 sec
60 C1 min
Read
72 C30 sec
Read
Repeat Elongation 39 times
Termination95 C1 min
55 C1 sec
Melt Curve Manual ramp 0.2C per sec Read 0.5 C55 - 95 C30 sec
21 C10 min
End
Plate Layout:
1234567
DNased 42215 HC1DNased 42215 NC1DNased 42215 SC1DNased 42215 HT1 1DNased 42215 NT1 1DNased 42215 ST1 1NTC
DNased 42215 HC2DNased 42215 NC2DNased 42215 SC2DNased 42215 HT1 2DNased 42215 NT1 2DNased 42215 ST1 2NTC
DNased 42215 HC3DNased 42215 NC3DNased 42215 SC3DNased 42215 HT1 3DNased 42215 NT1 3DNased 42215 ST1 3NTC
DNased 42215 HC4DNased 42215 NC4DNased 42215 SC4DNased 42215 HT1 4DNased 42215 NT1 4DNased 42215 ST1 4NTC
DNased 42215 HC5DNased 42215 NC5DNased 42215 SC5DNased 42215 HT1 5DNased 42215 NT1 5DNased 42215 ST1 5
DNased 42215 HC6DNased 42215 NC6DNased 42215 SC6DNased 42215 HT1 6DNased 42215 NT1 6DNased 42215 ST1 6
DNased 42215 HC7DNased 42215 NC7DNased 42215 SC7DNased 42215 HT1 7DNased 42215 NT1 7DNased 42215 ST1 7
DNased 42215 HC8DNased 42215 NC8DNased 42215 SC8DNased 42215 HT1 8DNased 42215 NT1 8DNased 42215 ST1 8
Results:

All samples
NTCs

The expression curves look great. There is no amplification in the NTCs which is spectacular. To better understand the data I ran it through my script to generate expression bar graphs, Two Way ANOVA, One Way ANOVA for the populations, and T-Test for the control and treatment in each population. 

Adjusted Expression Graph

TWO WAY ANOVA and Tukey's TEST

Call:
   aov(formula = expression ~ Pop + Treat + Pop:Treat, data = rep2res2)

Terms:
                         Pop        Treat    Pop:Treat    Residuals
Sum of Squares  1.324664e-21 1.935831e-20 1.181360e-22 2.539439e-20
Deg. of Freedom            2            1            2           39

Residual standard error: 2.551741e-11
Estimated effects may be unbalanced
> TukeyHSD(fit)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = expression ~ Pop + Treat + Pop:Treat, data = rep2res2)

$Pop
             diff           lwr          upr     p adj
N-H  1.298872e-11 -9.354413e-12 3.533185e-11 0.3425329
S-H  5.317103e-12 -1.778535e-11 2.841956e-11 0.8415922
S-N -7.671613e-12 -3.042287e-11 1.507964e-11 0.6920871

$Treat
             diff          lwr           upr   p adj
T-C -4.150351e-11 -5.69261e-11 -2.608093e-11 3.1e-06

$`Pop:Treat`
                 diff           lwr           upr     p adj
N:C-H:C  1.583187e-11 -2.239305e-11  5.405680e-11 0.8140892
S:C-H:C  7.332959e-12 -3.089197e-11  4.555789e-11 0.9921358
H:T-H:C -3.803279e-11 -7.759935e-11  1.533774e-12 0.0656382
N:T-H:C -2.535171e-11 -6.357664e-11  1.287322e-11 0.3678867
S:T-H:C -3.878418e-11 -8.007183e-11  2.503458e-12 0.0762124
S:C-N:C -8.498916e-12 -4.672384e-11  2.972601e-11 0.9846364
H:T-N:C -5.386466e-11 -9.343122e-11 -1.429810e-11 0.0027726
N:T-N:C -4.118358e-11 -7.940851e-11 -2.958655e-12 0.0282920
S:T-N:C -5.461606e-11 -9.590370e-11 -1.332842e-11 0.0038668
H:T-S:C -4.536575e-11 -8.493231e-11 -5.799185e-12 0.0165674
N:T-S:C -3.268467e-11 -7.090960e-11  5.540260e-12 0.1314674
S:T-S:C -4.611714e-11 -8.740479e-11 -4.829501e-12 0.0208893
N:T-H:T  1.268108e-11 -2.688548e-11  5.224764e-11 0.9276071
S:T-H:T -7.513978e-13 -4.328417e-11  4.178138e-11 0.9999999
S:T-N:T -1.343247e-11 -5.472012e-11  2.785517e-11 0.9232037



ONE WAY ANOVA COMPARING POPULATIONS CONTROLS
> fit2<-aov(expression~Pop, data=rep2res2[Treat=="C"])
> fit2
Call:
   aov(formula = expression ~ Pop, data = rep2res2[Treat == "C"])

Terms:
                         Pop    Residuals
Sum of Squares  1.004406e-21 2.342257e-20
Deg. of Freedom            2           21

Residual standard error: 3.339701e-11
Estimated effects may be unbalanced
> TukeyHSD(fit2)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = expression ~ Pop, data = rep2res2[Treat == "C"])

$Pop
             diff           lwr          upr     p adj
N-H  1.583187e-11 -2.625788e-11 5.792163e-11 0.6167331
S-H  7.332959e-12 -3.475680e-11 4.942272e-11 0.8996597
S-N -8.498916e-12 -5.058867e-11 3.359084e-11 0.8678189


ONE WAY ANOVA COMPARING POPULATIONS TREATMENT
> fit3<-aov(expression~Pop, data=rep2res2[Treat=="T"])
> fit3
Call:
   aov(formula = expression ~ Pop, data = rep2res2[Treat == "T"])

Terms:
                         Pop    Residuals
Sum of Squares  8.423697e-22 1.971827e-21
Deg. of Freedom            2           18

Residual standard error: 1.046642e-11
Estimated effects may be unbalanced
> TukeyHSD(fit3)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = expression ~ Pop, data = rep2res2[Treat == "T"])

$Pop
             diff           lwr          upr     p adj
N-H  1.268108e-11 -1.143704e-12 2.650586e-11 0.0754135
S-H -7.513978e-13 -1.561259e-11 1.410979e-11 0.9908666
S-N -1.343247e-11 -2.785861e-11 9.936614e-13 0.0704802

T-TEST for DABOB 
> fit4<-t.test(expression~Treat, data=rep2res2[Pop=="H"])
> fit4

Welch Two Sample t-test

data:  expression by Treat
t = 4.1437, df = 7.652, p-value = 0.003565
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1.669890e-11 5.936667e-11
sample estimates:
mean in group C mean in group T 
   4.545505e-11    7.422266e-12 

T-TEST for FIDALGO
> fit5<-t.test(expression~Treat, data=rep2res2[Pop=="N"])
> fit5

Welch Two Sample t-test

data:  expression by Treat
t = 2.901, df = 9.418, p-value = 0.01678
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 9.285075e-12 7.308209e-11
sample estimates:
mean in group C mean in group T 
   6.128693e-11    2.010334e-11 

T-TEST for OYSTER BAY
> fit6<-t.test(expression~Treat, data=rep2res2[Pop=="S"])
> fit6

Welch Two Sample t-test

data:  expression by Treat
t = 3.5341, df = 7.296, p-value = 0.008921
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1.551289e-11 7.672140e-11
sample estimates:
mean in group C mean in group T 
   5.278801e-11    6.670868e-12 

So there isn't a difference between populations but there is a significant difference between treatment and control in all three populations. So PGE/EP4 is suppressed due to heat exposure equally in all populations. This effect can be seen very well in the associated box plot. 

You can see the raw data here. 
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Friday, July 17, 2015

7 17 2015 HSPb11 qPCR

Today I ran more targets that may be of interest due expected changes in expression. Here I explore HSPb11 which is expected to have decreased expression due to exposure to increased temperatures. 

Primers:
1650Hspb11_FWDATGTTTCCTGGTCTCCGTCAJH5/21/20152055O.luridaHeat shock protein beta-11 (Hspb11) (Placental protein 25) (PP25)Q9Y547
1649Hspb11_REVCATCAACGCCAGGGGAACTTJH5/21/20152055O.luridaHeat shock protein beta-11 (Hspb11) (Placental protein 25) (PP25)Q9Y547
Reagent Table:
VolumeReactions X116
Ssofast Evagreen MM101160
FWD Primer0.558
REV Primer0.558
1:9 cDNA9
  1. Added reagents from greatest to least volume
  2. Vortexed
  3. Centrifuged briefly
  4. Pipetted 11 ul Master Mix to each tube
  5. Pipetted 9 ul of 1:9 cDNA each column using a channel pipetter
  6. Centrifuged plate at 2000 rpm for 1 minute
  7. Ran Program Below
Program:
StepTemperatureTime
Initiation95 C10 min
Elongation95 C30 sec
60 C1 min
Read
72 C30 sec
Read
Repeat Elongation 39 times
Termination95 C1 min
55 C1 sec
Melt Curve Manual ramp 0.2C per sec Read 0.5 C55 - 95 C30 sec
21 C10 min
End
Plate Layout:
1234567
DNased 42215 HC1DNased 42215 NC1DNased 42215 SC1DNased 42215 HT1 1DNased 42215 NT1 1DNased 42215 ST1 1NTC
DNased 42215 HC2DNased 42215 NC2DNased 42215 SC2DNased 42215 HT1 2DNased 42215 NT1 2DNased 42215 ST1 2NTC
DNased 42215 HC3DNased 42215 NC3DNased 42215 SC3DNased 42215 HT1 3DNased 42215 NT1 3DNased 42215 ST1 3NTC
DNased 42215 HC4DNased 42215 NC4DNased 42215 SC4DNased 42215 HT1 4DNased 42215 NT1 4DNased 42215 ST1 4NTC
DNased 42215 HC5DNased 42215 NC5DNased 42215 SC5DNased 42215 HT1 5DNased 42215 NT1 5DNased 42215 ST1 5
DNased 42215 HC6DNased 42215 NC6DNased 42215 SC6DNased 42215 HT1 6DNased 42215 NT1 6DNased 42215 ST1 6
DNased 42215 HC7DNased 42215 NC7DNased 42215 SC7DNased 42215 HT1 7DNased 42215 NT1 7DNased 42215 ST1 7
DNased 42215 HC8DNased 42215 NC8DNased 42215 SC8DNased 42215 HT1 8DNased 42215 NT1 8DNased 42215 ST1 8
Results:

All samples

NTCs


These curves look good with only minor amplification in 1 of the 4 NTCs. The product of which is smaller than the target so it seems like some errant amplification. 

To better understand what this data means I ran it through a script to produce expression bar graphs, Two Way ANOVA, One Way ANOVA, and T-Test to determine significant differences between populations. 

Adjusted Expression Bar Graph

Adjusted ANOVAs and T-Tests

TWO WAY ANOVA OVERVIEW
Call:
   aov(formula = expression ~ Pop + Treat + Pop:Treat, data = rep2res2)

Terms:
                         Pop        Treat    Pop:Treat    Residuals
Sum of Squares  1.691436e-19 8.907630e-20 2.216630e-20 5.453249e-19
Deg. of Freedom            2            1            2           41

Residual standard error: 1.153283e-10
Estimated effects may be unbalanced
> TukeyHSD(fit)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = expression ~ Pop + Treat + Pop:Treat, data = rep2res2)

$Pop
               p adj
N-H   0.0057786
S-H   0.9104947
S-N  0.0169218

$Treat
               p adj
T-C  0.0133392

$`Pop:Treat`
                   p adj
N:C-H:C   0.0408428
S:C-H:C   0.9998018
H:T-H:C  0.9080577
N:T-H:C   0.9977017
S:T-H:C  0.9803808
S:C-N:C  0.0767914
H:T-N:C  0.0025122
N:T-N:C  0.1404431
S:T-N:C  0.0062859
H:T-S:C  0.7895896
N:T-S:C   0.9999679
S:T-S:C  0.9244313
N:T-H:T   0.7094196
S:T-H:T   0.9995389
S:T-N:T  0.8690780

ONE WAY ANOVA for CONTROL

> fit2<-aov(expression~Pop, data=rep2res2[Treat=="C"])
> fit2
Call:
   aov(formula = expression ~ Pop, data = rep2res2[Treat == "C"])

Terms:
                         Pop    Residuals
Sum of Squares  1.540748e-19 3.284131e-19
Deg. of Freedom            2           21

Residual standard error: 1.250549e-10
Estimated effects may be unbalanced
> TukeyHSD(fit2)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = expression ~ Pop, data = rep2res2[Treat == "C"])

$Pop
              p adj
N-H   0.0258634
S-H   0.9672109
S-N  0.0436003

ONE WAY ANOVA FOR TREATMENT

> fit3<-aov(expression~Pop, data=rep2res2[Treat=="T"])
> fit3
Call:
   aov(formula = expression ~ Pop, data = rep2res2[Treat == "T"])

Terms:
                         Pop    Residuals
Sum of Squares  2.958426e-20 2.169118e-19
Deg. of Freedom            2           20

Residual standard error: 1.041422e-10
Estimated effects may be unbalanced
> TukeyHSD(fit3)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = expression ~ Pop, data = rep2res2[Treat == "T"])

$Pop
                p adj
N-H   0.2759237
S-H   0.9344962
S-N  0.4424321

T-TEST FOR DABOB POPULATION

> fit4<-t.test(expression~Treat, data=rep2res2[Pop=="H"])
> fit4

Welch Two Sample t-test

data:  expression by Treat
t = 1.6786, df = 13.27, p-value = 0.1166
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.674696e-11  1.345441e-10
sample estimates:
mean in group C mean in group T 
   1.130039e-10    5.410532e-11 

T-TEST FOR FIDALGO POPULATION
 
> fit5<-t.test(expression~Treat, data=rep2res2[Pop=="N"])
> fit5

Welch Two Sample t-test

data:  expression by Treat
t = 1.811, df = 12.883, p-value = 0.09351
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.924424e-11  3.307085e-10
sample estimates:
mean in group C mean in group T 
   2.901428e-10    1.394107e-10 

T-TEST FOR OYSTER BAY POPULATION

> fit6<-t.test(expression~Treat, data=rep2res2[Pop=="S"])
> fit6

Welch Two Sample t-test

data:  expression by Treat
t = 1.1686, df = 13.888, p-value = 0.2622
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -4.685627e-11  1.588582e-10
sample estimates:
mean in group C mean in group T 
   1.283930e-10    7.239206e-11 


As you can see in the ANOVA overview and the One way ANOVA for Control samples there is a significant difference between the Fidalgo population and the Dabob and Oyster Bay populations. This difference is only apparent in control samples and does not appear in the treated samples. 

The boxplot below exemplifies these findings.

This looks to be of some interest in regards to our population level differences. 

You can see the raw data here.
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