Table 1.
Significant components of phosphoprotein-cytokine signaling network
| Interactions | MI | PCR | Lit. | Comment | Interactions | MI | PCR | Lit. | Comment | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
G-CSF |
NF-κB |
✓ |
✓ |
✓[44] |
|
IL-6 |
RSK |
✓ |
✘ |
✓[45] |
- IL-6 has the lowest R
2
-value. This may be due to avoiding the linearity assumption in computing the mutual information. |
| (Pro-inflammatory) |
JNK lg |
✓ |
✓ |
✓[46] |
(Anti-inflammatory and pro-inflammatory) |
JNK lg |
✓ |
✓ |
✓[47] |
||
| JNK sh |
✓ |
✓ |
✓[46] |
JNK sh |
✓ |
✓ |
✓[47] |
||||
| P38 |
✓ |
✓ |
✘ |
P38 |
✓ |
✘ |
✓[48] |
||||
| PKCμ2 |
✓ |
✘ |
✓[49] |
PKCμ2 |
✓ |
✘ |
✓[50] |
||||
| ERK1 |
✓ |
✘ |
✓[51] |
NF-κB |
✓ |
✓ |
✓[52] |
||||
| ERK2 |
✓ |
✘ |
✓[51] |
ERK1 |
✓ |
✘ |
✓[45] |
||||
| RSK |
✓ |
✘ |
✓[53] |
ERK2 |
✓ |
✘ |
✓[45] |
||||
|
Finding: Our model confirms the regulatory effect of P38, which has been suggested by the PCR minimal model [34]. |
cAMP |
✓ |
✓ |
✓[54] |
|||||||
|
TNFα |
RSK |
✓ |
✘ |
✘ |
- TNFα has the largest network. This study identifies the regulatory effect of 12 phosphoproteins on TNFα. |
IL-10 |
JNK lg |
✓ |
✓ |
✘ |
|
| (Pro-inflammatory) |
AKT |
✓ |
✓ |
✓[55] |
(Anti-inflammatory) |
P38 |
✓ |
✘ |
✓[56] |
||
| P38 |
✓ |
✓ |
✓[57] |
||||||||
| RPS6 |
✓ |
✘ |
✓[58] |
||||||||
| GSK3A |
✓ |
✘ |
✓[59] |
- TNFα yields the highest coefficient of determination R
2
= 0.62 |
|||||||
| GSK3B |
✘ |
✘ |
✓[59] |
||||||||
| PKCμ2 |
✓ |
✘ |
✓[60] |
ERK1 |
✓ |
✘ |
✓[61] |
||||
| cAMP |
✓ |
✓ |
✓[62] |
ERK2 |
✓ |
✘ |
✓[61] |
||||
| NF-κB |
✓ |
✓ |
✓[58] |
JNK sh |
✓ |
✓ |
✘ |
||||
| JNK lg |
✓ |
✓ |
✓[63] |
NF-κB |
✓ |
✓ |
✓[64] |
||||
| JNK sh |
✓ |
✓ |
✓[63] |
PKCμ2 |
✓ |
✘ |
✓[65] |
||||
| ERK2 |
✓ |
✘ |
✓[66] |
RSK |
✓ |
✘ |
✓[67] |
||||
| ERK1 |
✓ |
✘ |
✓[66] |
Finding: Our study confirms the regulatory effects of JNK sh/ lg on IL-10. |
|||||||
|
Finding: Our study suggests the novel regulatory effect of RSK on TNFα. | |||||||||||
|
MIP-1α |
P38 |
✓ |
✓ |
✓[57] |
- This study doesn’t confirm the regulatory effects of STAT1α/β/3 on MIP-1α suggested by one study [68]. The PCR minimal model [34] identifies the impact of STAT1α on MIP-1a. |
RANTES |
STAT3 |
✓ |
✘ |
✓[69] |
- Unlike the PCR minimal model [34], this study successfully captures the regulatory impacts of STAT1α/3/5 on RANTES. |
| (Anti-inflammatory) |
NF-κB |
✓ |
✓ |
✓[70] |
(Anti-inflammatory) |
STAT5 |
✓ |
✘ |
✓[71] |
||
| cAMP |
✓ |
✓ |
✓[72] |
STAT1α |
✓ |
✓ |
✓[73] |
||||
| RSK |
✓ |
✘ |
✓[74] |
NF-κB |
✓ |
✓ |
✓[75] |
||||
| JNK lg |
✓ |
✓ |
✓[76] |
P38 |
✓ |
✘ |
✓[77] |
||||
| JNK sh |
✓ |
✓ |
✓[76] |
PKCμ2 |
✓ |
✘ |
✓[78] |
||||
| AKT |
✓ |
✘ |
✓[79] |
JNK sh |
✓ |
✓ |
✓[75] |
||||
| ERK1 |
✓ |
✘ |
✓[74] |
JNK lg |
✓ |
✓ |
✓[75] |
||||
| ERK2 |
✓ |
✘ |
✓[74] |
RSK |
✓ |
✘ |
✓[80] |
||||
| STAT1α |
✘ |
✓ |
✓[68] |
ERK2 |
✓ |
✘ |
✓[80] |
||||
| STAT1β |
✘ |
✘ |
✓[68] |
ERK1 |
✓ |
✘ |
✓[80] |
||||
| STAT3 |
✘ |
✘ |
✓[68] |
IL-1α |
ERK2 |
✓ |
✘ |
✓[81] |
|||
| (Pro-inflammatory) | ERK1 |
✓ |
✘ |
✓[81] |
|||||||
| RSK |
✓ |
✘ |
✓[82] |
||||||||
| P38 |
✓ |
✘ |
✓[83] |
||||||||
| JNK lg |
✓ |
✓ |
✓[84] |
||||||||
| JNK sh |
✓ |
✓ |
✓[84] |
||||||||
| NF-κB | ✓ | ✓ | ✓[85] | ||||||||
A comparison of phosphoprotein-cytokine regulatory connections identified by information theoretic approach (‘MI’), PCR (‘PCR’) and the literature knowledge (‘Lit.’). Identified interactions are shown by ✓ and cells marked by ✘ indicate missed interactions by a method.