On the crucial cerebellar wound healing-related pathways and their cross-talks after traumatic brain injury in Danio rerio

Abstract

Upon injury, the direct damage and the subsequent secondary injury in the brain often result in chronic neurological disorders. Due to multifactorial nature of secondary injury and subsequent complex cellular responses, much of the underlying mechanisms are unclear. This study used an adult zebrafish cerebellum injury model to investigate the phenotypes and the secondary injury responses for recovery mechanisms of injured brain. Using the time course microarray analysis, a candidate protein-protein interaction (PPI) network was refined as cerebellar wound healing PPI network by dynamic modeling and big data mining. Pathway enrichment and ontological analysis were incorporated into the refined network to highlight the main molecular scheme of cerebellar wound healing. Several significant pathways, including chemokine, Phosphatidylinositide 3-kinases, and axon guidance signaling pathway and their cross-talks through PI3K, PAK2, and PLXNA3 were identified to coordinate for neurogenesis and angiogenesis, which are essential for the restoration of the injured brain. Our finding provides an insight into the molecular restoration mechanisms after traumatic brain injury, and open up new opportunity to devise the treatment for traumatic brain injury in human.

Figure 1. The diagram of the stab lesion assay.

A: Schematic diagram of the stab lesion. A 27G syringe was used to create a cranial wound of depth 1.5 mm in the cerebellum (OB, olfactory bulb; Tel, telencephalon; OT, optic tectum; Ce, cerebellum; SC, spinal cord). B–E: Bright field images of the cranium (B, D) and exposed brains (C, E) of control fish and after the stab lesion. B and C show intact cranium and cerebellum of the control (no stab lesion) and D and E show the injured cranium and cerebellum of an experimental animal at 0.5 hour post-lesion (hpl). The fresh wound can be seen clearly on both the cranium and the cerebellum (D, E; the white arrow shows the lesion site on the cranium and the black arrow shows the wound on the exposed cerebellum).

Figure 2. Regeneration of cerebellum at different day-post-lesion and the time-course microarray data in cerebellar wound healing process.

A-H: Bright field images of the craniums (A, C, E, G) and cerebellums (B, D, F, H) at different time points post lesion. The cranial and cerebellum were intact before stab lesion (A, B). At 3dpl, the wound can be seen on the cranial and cerebellum. (C, D) At 7dpl, the cranial is seal and scar is observed in the cerebellum. (E, F) At 14 dpl, the scar was hardly seen. (Scale bar, 100 µm.). (I) The differentially expressed genes (≥1.5 or ≤0.67 fold change) are hierarchically clustered (5839 genes). Columns represent the day-post-lesion (dpl), and the rows represent the genes. The blocks indicate temporally up-regulated and down-regulated genes, respectively. The color bar represents the log value of the ratio relative to the intensity at 0 dpl.

Figure 3. Equipment setup for behavior analysis.

A: Close-up views of the video camera. B: Close-up views of the aquarium. C: The camera and aquarium setup. D: Swim sample paths of zebrafish after TBI. E: Quantitative behavior index (zebrafish movement index, ZMI) of injured zebrafish describes the degree of disability in the behavior of the injured zebrafish. The greater the index value, the greater the degree of disability.

Figure 4. Flowchart for constructing the wound healing-related PPI network using a dynamic network model and big data mining.

The network is constructed by PPI information from databases mining and systems identification according to the time-course microarray data (as shown in the boxes). Open access systems biology tools STEM and PANTHER are used to identify significantly temporal patterns and enriched cerebellar wound healing-related pathways.

Figure 5. The constructed zebrafish cerebellar wound healing-related cellular PPI network by dynamic network modeling via microarray data and big data mining.

The cerebellar wound healing-related PPI network of zebrafish contains 5270 PPIs among 802 proteins. The red, green, and blue nodes belong to group N, A, and P (see Fig.6 and the context for details). The information to draw the network is summarized in the File S2.

Figure 6. Significantly temporal patterns in the wound healing process.

(A) Using STEM, significantly temporal patterns are identified (color background) (FDR corrected p-value <0.05). According to the STEM results and ZMI, we focused on group 29, 40, and 11, which are positively correlated with ZMI (B), acute response (C), and negatively correlated with ZMI (D).

Figure 7. Cross-talks among significantly enriched pathways in the three significantly temporal groups of proteins.

(A) Cross-talks between groups A and P: Cross-talks between PI3K pathways (enriched in group A) and the cell cycle (enriched in group P) dominated sub-network AP1. Cross-talks between endo-cannabinoid pathways (enriched in group A) and neurotransmitter-related pathways (enriched in group P) dominated sub-network AP2. (B) Cross-talks between groups A and N: Cross-talks between G-protein pathways (enriched in proteins from both groups A and N) dominated sub-network AN1. The cross-talks between G-proteins and T and B cell activation pathways (enriched in group A) and neurotransmitter-related pathways (enriched in group N) dominated the sub-network AN2. (C) Cross-talks between groups N and P: Cross-talks between neurotransmitter-related pathways (enriched in group N) and cell cycle pathways (enriched in group P) dominated sub-network NP1. Cross-talks between G-protein and neurodegenerative diseases pathways (enriched in group N) and inflammation pathways (enriched in group P) dominated sub-network NP2. Cross-talks between transcription pathways (enriched in both groups N and P) dominated sub-network NP3. Cross-talks between axon guidance pathways (enriched in both groups N and P) dominated sub-network NP4. Red lines indicate the cross-talks between groups and are listed in Table S5 in File S1.

Figure 8. Schematic diagram of wound healing-related cellular pathways and their cross-talks among inflammation, neurogenesis, and angiogenesis.

(A) Pathways shown in blocks were enriched in the sub-networks (Fig. 7). Their cross-talks were observed in the cerebellar wound healing PPI network of zebrafish (solid arrows indicate cross-talks involving proteins in groups A, N, and P; dashed arrows indicate cross-talks involving proteins not in these groups). To establish cell communication and defense mechanisms (inflammation and immune response) after brain injury, cell communication was related to neurotransmitter-related pathways (through AP2 and AN2), the axon guidance pathway (through NP4 indirectly), and itself (through AN1). Defense mechanisms were related to the cell cycle pathway (through AP1), neurotransmitter-related pathways (through AN2), the cell communication pathway (through NP2), and neurodegenerative diseases (through NP2). Cell cycle pathways also interacted with gene regulation pathways through NP3 indirectly. (B) The cross-talks are found among pathways of inflammation, neurogenesis, and angiogenesis in cerebellar wound healing process. Inflammation is mediated by chemokine signaling pathway. New neural and vascular cells generation is mediated by PI3K-AKT signaling pathway and axon guidance pathway. PI3K in chemokine signaling pathway also regulates the PI3K-AKT signaling pathway and axon guidance pathway. PAK2 in axon guidance pathway also maintains the stability of vascular.