Maternal_Serum
Lindsay Hayes
Extended Data Figures
ELISA data for maternal serum IL-6 response three hours after PIC treatment at 0, 5, 10, or 20 mg/kg to pregnant dams at E9.5. Units of data are pg/mL.
Samples
## mg/kg PIC
## pups survived? 0 5 10 15 20
## died 7 1 29 1 5
## n 21 0 81 11 22
## y 133 8 146 0 28##
## died n y
## CON 2 12 81
## CON:CON 2 6 27
## CON:PLX 3 3 24
## MIA 9 41 98
## MIA:CON 13 48 47
## MIA:PLX 14 25 38Plot MIA vs CON dose response
Fig ED 10e
ANOVA for dose effect
Survive %>%
group_by(Dose) %>%
summarise(n = n())## # A tibble: 4 × 2
## Dose n
## <fct> <int>
## 1 0 81
## 2 5 8
## 3 10 62
## 4 20 28Survive %>%
group_by(Dose) %>%
shapiro_test(log2.IL6)## # A tibble: 4 × 4
## Dose variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 0 log2.IL6 0.837 3.60e- 7
## 2 5 log2.IL6 0.791 2.30e- 2
## 3 10 log2.IL6 0.669 1.54e-10
## 4 20 log2.IL6 0.719 5.29e- 6Survive %>%
group_by(Dose) %>%
shapiro_test(IL.6.pg.mL)## # A tibble: 4 × 4
## Dose variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 0 IL.6.pg.mL 0.546 3.56e-13
## 2 5 IL.6.pg.mL 0.856 1.11e- 1
## 3 10 IL.6.pg.mL 0.921 7.11e- 4
## 4 20 IL.6.pg.mL 0.919 3.30e- 2Survive %>%
levene_test(IL.6.pg.mL ~ Dose)## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 162 38.0 1.23e-18aov.out <- aov(IL.6.pg.mL ~ Dose, data = Survive)
summary(aov.out)## Df Sum Sq Mean Sq F value Pr(>F)
## Dose 3 2.021e+09 673611978 45.17 <2e-16 ***
## Residuals 162 2.416e+09 14912393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 13 observations deleted due to missingnesssummary(glht(aov.out, linfct = mcp(Dose = "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = IL.6.pg.mL ~ Dose, data = Survive)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 5 - 0 == 0 2425.4 1443.4 1.680 0.32118
## 10 - 0 == 0 6633.9 678.1 9.783 < 0.001 ***
## 20 - 0 == 0 8153.4 867.1 9.403 < 0.001 ***
## 10 - 5 == 0 4208.5 1450.7 2.901 0.02019 *
## 20 - 5 == 0 5728.0 1548.1 3.700 0.00141 **
## 20 - 10 == 0 1519.5 879.3 1.728 0.29681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)ELISA data for maternal serum IL-6 response three hours after PIC treatment along with prenatal PLX treatment. Units of data are pg/mL.
Maternal Response with PLX treatment
Fig ED 7f
ANOVA for Rescue effect
Rescue %>% group_by(Group) %>% summarise(n = n())## # A tibble: 4 × 2
## Group n
## <fct> <int>
## 1 CON:CON 27
## 2 CON:PLX 24
## 3 MIA:CON 33
## 4 MIA:PLX 27Rescue %>% group_by(Group) %>% shapiro_test(log2.IL6)## # A tibble: 4 × 4
## Group variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 CON:CON log2.IL6 0.388 0.000000165
## 2 CON:PLX log2.IL6 0.582 0.00000448
## 3 MIA:CON log2.IL6 0.977 0.684
## 4 MIA:PLX log2.IL6 0.939 0.115Rescue %>% levene_test(log2.IL6 ~ Group)## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 91 1.21 0.310aov.out <- aov(log2.IL6 ~ Group, data=Rescue)
summary(aov.out)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 3 2462.9 821.0 230.7 <2e-16 ***
## Residuals 91 323.8 3.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingnesssummary(glht(aov.out, linfct = mcp(Group = c("CON:PLX - CON:CON == 0", "MIA:CON - CON:CON == 0", "MIA:PLX - CON:CON == 0", "MIA:PLX - MIA:CON == 0"))))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Fit: aov(formula = log2.IL6 ~ Group, data = Rescue)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## CON:PLX - CON:CON == 0 1.0057 0.6380 1.576 0.320
## MIA:CON - CON:CON == 0 11.1876 0.5632 19.866 <1e-04 ***
## MIA:PLX - CON:CON == 0 10.8833 0.5840 18.634 <1e-04 ***
## MIA:PLX - MIA:CON == 0 -0.3043 0.4895 -0.622 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)sessionInfo()## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rstatix_0.7.0 multcomp_1.4-19 TH.data_1.1-1 MASS_7.3-57
## [5] survival_3.3-1 mvtnorm_1.1-3 cowplot_1.1.1 forcats_0.5.1
## [9] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
## [13] tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.3 sass_0.4.1 jsonlite_1.8.0 splines_4.2.0
## [5] carData_3.0-5 modelr_0.1.8 bslib_0.3.1 assertthat_0.2.1
## [9] highr_0.9 cellranger_1.1.0 yaml_2.3.5 pillar_1.7.0
## [13] backports_1.4.1 lattice_0.20-45 glue_1.6.2 digest_0.6.29
## [17] rvest_1.0.2 colorspace_2.0-3 sandwich_3.0-2 htmltools_0.5.2
## [21] Matrix_1.4-1 pkgconfig_2.0.3 broom_0.8.0 haven_2.5.0
## [25] scales_1.2.0 tzdb_0.3.0 generics_0.1.2 farver_2.1.0
## [29] car_3.1-0 ellipsis_0.3.2 withr_2.5.0 cli_3.3.0
## [33] magrittr_2.0.3 crayon_1.5.1 readxl_1.4.0 evaluate_0.15
## [37] fs_1.5.2 fansi_1.0.3 xml2_1.3.3 tools_4.2.0
## [41] hms_1.1.1 formatR_1.12 lifecycle_1.0.1 munsell_0.5.0
## [45] reprex_2.0.1 compiler_4.2.0 jquerylib_0.1.4 rlang_1.0.2
## [49] grid_4.2.0 rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.14
## [53] gtable_0.3.0 codetools_0.2-18 abind_1.4-5 DBI_1.1.2
## [57] R6_2.5.1 zoo_1.8-10 lubridate_1.8.0 knitr_1.39
## [61] fastmap_1.1.0 utf8_1.2.2 stringi_1.7.6 vctrs_0.4.1
## [65] dbplyr_2.1.1 tidyselect_1.1.2 xfun_0.31