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Notes on cytokines

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  1. +6
    -7
      doc/04-merge-cytokines.Rmd
  2. +54
    -4
      progress.org

+ 6
- 7
doc/04-merge-cytokines.Rmd Zobrazit soubor

@@ -35,7 +35,7 @@ sort(cytokines$SampleID)[1:10]
```

```{r}
cytokines %>%
cytokines %>%
filter(SampleID=="16K0007")
```

@@ -49,7 +49,7 @@ c1 = data %>%
Oh, an error because the column names are different! Let's fix that by renaming the SampleID to subjectid

```{r}
cytokines = cytokines %>%
cytokines = cytokines %>%
rename(subjectid=SampleID)
c1 = data %>%
inner_join(cytokines, by="subjectid")
@@ -73,7 +73,7 @@ c1 %>%
"C+21"="21",
"DoD"="30"
))) %>%
ggplot(aes(t,IFNg,colour=phenotype)) +
ggplot(aes(t,IFNg,colour=phenotype)) +
geom_point()
```

@@ -81,8 +81,7 @@ Now let's try a linear regression


```{r}
ggplot(c1,aes(phenotype,IFNg,colour=Timepoint)) +
geom_point()
```
ggplot(c1,aes(phenotype,IFNg,colour=Timepoint)) +
geom_point()

```

+ 54
- 4
progress.org Zobrazit soubor

@@ -7,9 +7,9 @@ datasets.
+ [X] Install Rstudio
+ [X] Install packages described in [[README.md]]

- [-] Learning R and loading data
- [X] Learning R and loading data
+ [X] Read 'R for Data Science'
+ [ ] Read 'Discovering Statistics with R
+ [X] Read 'Discovering Statistics with R
+ [X] Set current working directory in http://git.genenetwork.org/African-genetics/malaria-infect/src/branch/master/doc/02-load-data.Rmd#L13
+ [X] Read file in http://git.genenetwork.org/African-genetics/malaria-infect/src/branch/master/doc/02-load-data.Rmd#L27
+ [X] Some data attributes and simple plot in http://git.genenetwork.org/African-genetics/malaria-infect/src/branch/master/doc/02-load-data.Rmd#L62
@@ -25,7 +25,7 @@ datasets.
+ [X] Describe datasets in http://git.genenetwork.org/African-genetics/malaria-infect/src/branch/master/doc/01-datasets.md
+ [ ] Assess new dataset with covariates

- [-] Modelling
- [-] Modeling
+ [X] Schizont/ELISA vs time to diagnosis, see https://biogems.info/KEMRI/Malaria/03-using-tidyverse.html
+ [X] Merge cytokines with covariates
+ [ ] Explore cytokines responses with groups
@@ -50,7 +50,12 @@ Francis Ndungu & Pjotr Prins
units)
+ ELISA maps to Schizont column (log10 transformed)
+ anti-schizont antibody is measured before infection
- lumefantrine log10 too
- Lumefantrine log10 too. Lumefantrine is an antimalarial drug. It is
only used in combination with artemether. The term "co-artemether"
is sometimes used to describe this combination. Lumefantrine has a
much longer half-life compared to artemether, and is therefore
thought to clear any residual parasites that remain after
combination treatment.
- Treg_freq – The frequency of Tregs as a proportion of CD4+ T cells
- Treg_Ki67_FC – The fold change in the number of Tregs expressing
Ki-67 (a proliferation marker) between C-1 and C+14
@@ -62,6 +67,8 @@ Francis Ndungu & Pjotr Prins
cell marker)
- Cytokine_status_cm1 – Detectable level of pro-inflammatory cytokines
at C-1 (1 = yes, 0 = no)
- Measured cytokines [[https://en.wikipedia.org/wiki/Interferon_type_I][IFNa]], [[https://en.wikipedia.org/wiki/Interferon_gamma][IFNg]], [[https://en.wikipedia.org/wiki/Interleukin_1_beta][IL1.b]], [[https://en.wikipedia.org/wiki/Interleukin_2][IL.2]], [[https://en.wikipedia.org/wiki/Interleukin_4][LI.4]], [[https://en.wikipedia.org/wiki/Interleukin_17][IL.17]], [[https://en.wikipedia.org/wiki/Tumor_necrosis_factor][TNFa]],
[[https://en.wikipedia.org/wiki/Transforming_growth_factor_beta][TGFb]], [[https://en.wikipedia.org/wiki/Interleukin_10][IL10]].
- Screening PCR – Whether the volunteer had detectable parasitaemia at
screening by qPCR (1 = yes, 0 = no)
- Symptoms_DoD – (Treated group only) whether the volunteer was
@@ -71,3 +78,46 @@ Francis Ndungu & Pjotr Prins
yes, 0 = no)
- IL10_DoD – Whether the volunteer had detectable IL-10 at the day of
Diagnosis (1 = yes, 0 = no)

* Notes

** Cytokines

Cytokines are a broad and loose category of small proteins (~5–20 kDa)
important in cell signaling. Cytokines are peptides and cannot cross
the lipid bilayer of cells to enter the cytoplasm. A given cytokine
may be produced by more than one type of cell. They act through
cell surface receptors and are especially important in the immune
system; cytokines modulate the balance between humoral and cell-based
immune responses.

For malaria the relative balance between Th1 and Th2 cytokines appears
crucial. Th1 cytokines, interleukin-12 (IL-12) and gamma interferon
(IFN-γ), and anti-inflammatory Th2 cytokines, IL-4 and IL-10 play a
role.

Interleukin 10 (IL-10), also known as human cytokine synthesis
inhibitory factor (CSIF), is an anti-inflammatory cytokine. In humans,
interleukin 10 is encoded by the IL10 gene. IL-10 downregulates the
expression of Th1 cytokines, MHC class II antigens, and co-stimulatory
molecules on macrophages. It also enhances B cell survival,
proliferation, and antibody production. IL-10 can block NF-κB
activity, and is involved in the regulation of the JAK-STAT signaling
pathway. IL-10 counteracts the hyperactive immune response in the
human body.

* T helper cells (CD4+)

The T helper cells (Th cells), also known as CD4+ cells, are a type of
T cell that play an important role in the immune system, particularly
in the adaptive immune system. They help the activity of other immune
cells by releasing T cell cytokines. These cells help suppress or
regulate immune responses.

* Regulatory T cells (Tregs)

The regulatory T cells (Tregs /ˈtiːrɛɡ/ or Treg cells), formerly known
as suppressor T cells, are a subpopulation of T cells that modulate
the immune system, maintain tolerance to self-antigens, and prevent
autoimmune disease. Tregs are immunosuppressive and generally suppress
or downregulate induction and proliferation of effector T cells.

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