A map of human cancer signaling

We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancer-signaling architectural and functional organization. From the network, we extracted an oncogene-signaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few ‘oncogene-signaling-dependent blocks' in which frequently used oncogene-signaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancer-associated genes and biomarkers.
Introduction
Cells use sophisticated communication between proteins in order to initiate and maintain basic cellular functions such as growth, survival, proliferation and development. Traditionally, cell signaling is described via linear diagrams and signaling pathways. As many more ‘cross-talks' between signaling pathways have been identified (Natarajan et al, 2006), a network view of cell signaling emerged: the signaling proteins rarely operate in isolation through linear pathways, but rather through a large and complex network. As cell signaling is crucial to affect cell responses such as growth and survival, alterations of cellular signaling events, such as those that arise by mutations, can result in tumor development. Indeed, cancer is largely a genetic disease that is caused by acquiring genomic alterations in somatic cells. Alterations to the genes that encode key signaling proteins, such as RAS and PI3K, are commonly observed in many types of cancers. During tumor progression, it is proposed that a malignant tumor arises from a single cell, which undergoes a series of evolutionary processes of genetic or epigenetic changes and selections so that a cell within the population can acquire additional selective advantages for cellular growth or survival, resulting in progressive clonal expansion (Nowell, 1976).
Genetic mutations of the signaling proteins might overactivate key cell-signaling properties such as cell proliferation or survival and then give rise to the cell with selective advantages for uncontrolled cellular growth and promoting tumor progression. In addition, mutations may also inhibit the function of tumor-suppressor proteins, resulting in a relief from normal constraints on growth. Furthermore, epigenetic alterations by promoter methylation, resulting in transcriptional repression of genes controlling tumor malignancy, is another important mechanism for the loss of gene function that can provide a selective advantage to tumor cells.
Enormous efforts have been made over the past few decades to identify mutated genes that are causally implicated in human cancer. Furthermore, a genome-wide or large-scale sequencing of tumor samples across many kinds of cancers represents a largely unbiased overview of the spectrum of mutations in human cancers (Stephens et al, 2005; Sjoblom et al, 2006; Greenman et al, 2007; Thomas et al, 2007). Most of these efforts have been made by the Cancer Genome Project (CGP, http://www.sanger.ac.uk/genetics/CGP/), which aims to identify cancer-mutated genes using genome-wide mutation-detection approaches. Similarly, genome-wide identification of epigenetic changes in cancer cells has been conducted recently (Ohm et al, 2007; Schlesinger et al, 2007; Widschwendter et al, 2007). These studies showed that a substantial fraction of the cancer-associated mutated and methylated genes is involved in cell signaling, which is in agreement with the previous finding that the most common domain encoded by cancer genes is the protein kinase domain (Futreal et al, 2004). Although there is a wealth of knowledge regarding molecular signaling in cancer, the complexity of human cancer prevents us from gaining an overall picture of the mechanisms by which these genetic and epigenetic events affect cancer cell signaling and tumor progression. Where are the oncogenic stimuli embedded in the network architecture? What are the principles by which genetic and epigenetic alterations trigger oncogene-signaling events? Given that so many genes have genetic and epigenetic aberrations in cancer signaling, what is the architecture of cancer signaling? Do any tumor-driven signaling events represent ‘oncogenic dependence' (the phenomenon by which certain cancer cells become dependent on certain signaling cascades for growth or survival)? Who are the central players in oncogene signaling? Are there any signaling partnerships generally used to generate tumor phenotypes? To answer these questions, we conducted a comprehensive analysis of cancer mutated and methylated genes on a human signaling network, focusing on network structural aspects and quantitative analysis of gene mutations on the network.
Results and discussion
The architecture and the relationships among the proteins of a signaling network are important for determining the sites at which oncogenic stimuli occur and through which oncogenic stimuli are transduced. Extensive signaling studies during the past decades have yielded an enormous amount of information regarding regulation of signaling proteins for more than 200 signaling pathways, most of which have been assembled and collected in public databases in diagrams. We manually curated the data on signaling proteins and their relations (activation and inhibitory and physical interactions) from the BioCarta database and the Cancer Cell Map database (see Materials and methods). We merged the curated data with another literature-mined signaling network that contains ∼500 proteins (Ma'ayan et al, 2005). As a result, we have built a human signaling network containing 1634 nodes and 5089 links. Integrative network analyses have provided numerous biological insights (Wuchty et al, 2003; Han et al, 2004; Ihmels et al, 2004; Luscombe et al, 2004; Kharchenko et al, 2005; Wang and Purisima, 2005; Cui et al, 2006). Thus, the integration of the data on mutated and methylated cancer-associated genes onto the network will help us to identify critical sites involved in tumorigenesis and increase our understanding of the underlying mechanisms in cancer signaling.
To integrate mutated and methylated genes onto the network, we first collected the cancer mutated genes from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, which collects the cancer mutated genes through literature curation and large-scale sequencing of tumor samples in the CGP. We then combined these data with the cancer mutated genes derived from other genome-wide and high-throughput sequencing of tumor samples (Stephens et al, 2005; Sjoblom et al, 2006; Greenman et al, 2007; Thomas et al, 2007). The merged gene set represents a mixture of the past directed approach and current systematic screening of cancer mutations. The cancer-associated methylated genes were taken from the genome-wide identification of the DNA methylated genes in cancer stem cells (Ohm et al, 2007; Schlesinger et al, 2007; Widschwendter et al, 2007). Finally, 227 cancer mutated genes and 93 DNA methylated genes were mapped onto the network. Among the 227 cancer mutated genes, 218 (96%) and 55 (24%) genes were derived from large-scale gene sequencing of tumors and literature curation, respectively (see Materials and methods, Figure 1A). In general, cancer genes can be divided into two groups: positive regulators (oncogenes) that promote cancer cell proliferation and the negative regulators (tumor suppressors) that restrain it. By comparing the mutated genes with the known tumor suppressors, we found that only 6.6% (15 genes) of the mutated genes are known tumor suppressors and that the majority of the mutated genes are oncogenes (Supplementary Figure 1). On the other hand, methylated genes are mainly found to encode tumor suppressors in cancer cells (Supplementary Figure 1) (Ohm et al, 2007; Widschwendter et al, 2007).
Cancer mutated genes are enriched in signaling hubs but not in neutral hubs
Genes that, when mutated or silenced, result in tumorigenesis often lead to the aberrant activation of certain downstream signaling nodes resulting in dysregulated growth, survival and/or differentiation. The architecture of a signaling network is important for determining the site at which a genetic defect is involved in cancer. To discover where the critical tumor signaling stimuli occur on the network, we explored the network characteristics of the mutated and methylated genes. The signaling network is presented as a graph, in which nodes represent proteins. Directed links are operationally defined to represent effector actions such as activation or inhibition, whereas undirected links represent protein physical interactions that are not characterized as either activating or inhibitory. For example, scaffold proteins do not directly activate or inhibit other proteins but provide regional organization for activation or inhibition between other proteins through protein interactions. In this case, undirected links are used to represent the interactions between scaffold proteins and others. On the other hand, adaptor proteins are able to activate or inhibit other proteins through direct protein interactions. In this situation, directed links are used to represent these relations. There are two kinds of directed links. An incoming link represents a signal from another node. The sum of the number of incoming links of a node is called the indegree of that node. An outgoing link represents a signal to another node. The sum of the number of outgoing links of a node is called the outdegree of that node. We call incoming and outgoing links as signal links, whereas the physical links are neutral links. We first examined the characteristics of the nodes that represent mutated genes on the network. We compared the average indegree of the mutated genes with that of the nodes in the whole network. We found that the average indegree of the mutated nodes is significantly higher than that of the network nodes (P<1.1 × 10−6, Wilcoxon test, Supplementary Figure 2). A similar result was obtained for the average outdegree of the mutated nodes (P<6.0 × 10−14, Wilcoxon test, Supplementary Figure 2). In contrast, there is no difference of the average neutral degrees between the mutated nodes and other nodes in the network. To refine these results further, we calculated the correlations between the indegree, outdegree and neutral degree of the network nodes. We found a significant correlation between the indegree and the outdegree of the network nodes (R=0.41, P<2.2 × 10−16, Spearman's correlation), but no correlation between the indegree and neutral degree of the nodes (R=−0.02, P=0.54, Spearman's correlation). Taken together, these results suggest that cancer mutations most likely occur in signaling proteins that are acting as signaling hubs (i.e., RAS) actively sending or receiving signals rather than in nodes that are simply involved in passive physical interactions with other proteins. As these hubs are focal nodes that are shared by, and/or are central in, many signaling pathways, alterations of these nodes, or signaling hubs, are predicted to affect more signaling events, resulting in cancer or other diseases. In previous studies, we found that cancer-associated genes are enriched in hubs (Awan et al, 2007). However, these results indicate that cancer-associated genes are enriched in signaling hubs but not neutral hubs.
We also investigated the relations between the node degree and the methylated genes in the network. Methylated gene nodes do not appear to differ significantly from the network nodes with regard to their indegree, outdegree and neutral degree, respectively (P=0.32, P=0.16, P=0.09, Supplementary Figure 2). These results suggest that cancer mutated genes and methylation-silenced genes have different regulatory mechanisms in oncogene signaling.
Activating and inhibitory signals enhance and alleviate oncogene-signaling flows, respectively
Signaling flow branching represents the splitting of one signal at a source node (Figure 1B), whereas signaling flow convergence represents the consolidation of the signals at a target node from two source nodes (Figure 1B). Both types of the signaling flows are the basic elements of the network architectural organization. In the network, when the upstream and downstream nodes of a particular signal transduction event get altered either genetically or epigenetically, we considered the transduction event (link) to be most likely selected and used in cancer signaling and defined it as an oncogenic signal transduction event (Figure 1B). If a particular oncogenic signal transduction event is frequently found in many tumor samples, we infer that the tumor cells are ‘dependent' on this highly used signaling event and call it ‘oncogene-signaling-dependent event' (Figure 1B). To investigate how cancer signaling is distributed on these signal transduction routes, we extracted all the branching and convergent signaling flow units that contain at least one oncogenic signal transduction event and conducted a quantitative analysis by overlaying the gene mutation frequency onto these units. The mutation frequency of a gene was defined as the number of tumor samples that contain that mutated gene divided by the total number of the tumor samples that are used to screen the mutations for that gene. The mutation frequency of each mutated gene was obtained by using the COSMIC database, which contains the data on more than 200 000 tumor samples screened for cancer gene mutations. For the signaling branching units, we divided the signaling flows into two groups: activating and inhibitory group (Figure 1B) and compared the gene mutation frequencies of the upstream nodes with those of the downstream nodes in each group. Interestingly, in the activating group, the upstream nodes often have lower mutation frequencies than those of the downstream nodes. In contrast, in the inhibitory group, the upstream nodes often have higher mutation frequencies than those of the downstream nodes (Table I). Statistical tests confirmed that these observations are statistically significant (Table I). Similar results were obtained for the signaling convergent units as well (Figure 1B, Table I). These results suggest that the oncogene-signaling event triggered by mutations is preferentially associated with activating downstream signaling paths or conduits. Conversely, oncogene-signaling event triggered by mutations are less likely to be associated with downstream inhibitory signaling paths.
In general, there are far more activating signaling flows than inhibitory ones in the network. Thus, we hypothesized that the downstream genes of the network, especially the genes of the output layer of the network, would have a higher mutation frequency. To test this possibility, we compared the average gene mutation frequency of the nuclear proteins, which represent the output layer members of the network, with that of the other network genes. Indeed, the nuclear genes have higher mutation frequency than others (P=0.01, Wilcoxon test), which complements with our previous finding that cancer-associated genes are enriched in the nuclear proteins (Awan et al, 2007). In contrast, the distributions of the methylated genes have no such preference, suggesting that DNA methylated genes do not tend to directly affect the output layer of the network. These results strongly suggest that the genes in the output layer of the network, which play direct and important roles in determining phenotypic outputs, are frequent targets for activating mutations. The importance of this output layer is reinforced by our previous observations that the expression of the output layer genes of the signaling network is heavily regulated by microRNAs (Cui et al, 2006).
Mutated and methylated genes are enriched in positive and negative regulatory loops, respectively
The complex architecture of signaling networks can be regarded as consisting of interacting network motifs, which are statistically overrepresented subgraphs that appear recurrently in networks. A signaling network motif, also known as regulatory loops in biology, is a group of interacting proteins capable of signal processing. They bear specific regulatory properties and mechanisms (Babu et al, 2004; Wang and Purisima, 2005). The structure and the intrinsic properties of the frequently occurring network regulatory motifs give us a functional view of the organization of signaling networks. Thus, the study of the distributions of the mutated and methylated genes in the network motifs will provide insights into cancer-signaling regulatory mechanisms. We first examined the mutated genes on the feed-forward loops, in which the first protein regulates the second protein, and both proteins regulate the third protein. We classified the feed-forward loops into four subgroups (labeled 0–3) based on the number of nodes that are mutated genes. We calculated the ratio (Ra) of positive (activating) links to the total directed (positive and negative) links in each subgroup and compared it with the average Ra in all the feed-forward loops, which is shown as a horizontal line in Supplementary Figure 3. The Ra (∼0.7) in subgroup 0 is less than the average Ra (∼0.74) of all the feed-forward loops (P<1.9 × 10−9, Fisher's test). However, as the number of mutated nodes rises, the Ra for the corresponding group increases to a maximum of ∼0.93 (Supplementary Figure 3, Supplementary Table 1). We obtained similar results, when we extended the same analysis to all the 3-node- and 4-node-size network motifs (Figure 2, Supplementary Table 1). These motifs show a clear positive correlation between positive link ratio and the number of mutated genes in the motifs. These results suggest that cancer gene mutations occur preferentially in positive regulatory motifs. In contrast, all the 3-node and 4-node size motifs show an obviously negative correlation between positive link ratio and the number of methylated genes in the motifs (Figure 2, Supplementary Table 2). These results suggest that cancer gene methylation preferentially occurs in negative regulatory motifs. A similar trend was found for the 15 known tumor suppressors (Supplementary Figure 4a–d), which is in agreement with the notion that cancer-associated methylated genes play roles as tumor suppressors. Collectively, these facts suggest that mutated and methylated genes have different regulatory mechanisms in cancer signaling and support the notion that gene mutations and methylations are strongly selected in tumor samples.
Signaling information propagates through a series of built-in regulatory motifs to contribute to cellular phenotypic functions (Ma'ayan et al, 2005). The transition from a normal cellular state into a long-term deregulated state such as cancer is often driven by prolonged activation of downstream proteins, which are regulated by upstream proteins or regulatory motifs or circuits. Positive regulatory loops (Ferrell, 2002) could amplify signals, promote the persistence of signals, serve as information storage and evoke biological responses to generate phenotypes such as cancer. In cancer cells, constitutive activation of the oncogene signaling is necessary. Neutral mutations do not affect protein function, whereas missense mutations may have positive or negative effects on protein activity. The enrichment of gene mutations in positive regulatory loops suggests that the mutants in the motifs must have gain of function or increase their biochemical activities compared to the wild-type genes in order to constitutively activate downstream proteins. Indeed, a recent survey showed that 14 out of the 15 PI3K mutants in tumors have gain of function (Gymnopoulos et al, 2007). Gain-of-function mutants in a positive regulatory loop afford the amplification of weak input stimuli and serve as information storage to extend the duration of activation of the affected downstream proteins. This might allow the downstream signaling cascades to persistently hold and transfer information leading to tumor phenotypes.
Promoter gene methylation is a known mechanism of inducing loss of function by inhibiting the expression of genes (Ohm et al, 2007; Widschwendter et al, 2007). Negative regulatory loops controlled by tumor-suppressor proteins repress positive signals and play an important role in maintaining cellular homeostasis and restraining the cellular state transitions (Ma'ayan et al, 2005). A loss of function of gene methylation in a negative regulatory loop could break the negative feedbacks, thereby releasing the restrained activation signals and promoting oncogenic state transitions. Homeostasis relies on the balance between positive and negative signals in crucial components of the network. Both the gain-of-function mutated genes in positive regulatory loops and the loss-of-function methylated genes in negative regulatory loops could break this delicate balance, thus promoting state transitions and generating tumor phenotypes. Therefore, both mutated and methylated genes and their regulatory loops (oncogenic regulatory loops) are critical components of the network where the oncogenic stimuli occur.
An oncogene-signaling map emerges from the network
In the language of networks, genes whose mutations or epigenetic silencing are crucial to trigger oncogene signaling might link together as components in the network. Identification of such components will help us to discover the relationships and structural organizations of the oncogenic proteins. To uncover the architecture of cancer signaling and to gain insights into the higher-order regulatory relationships among signaling proteins that govern oncogenic signal stimuli, we mapped all genetic mutations and epigenetically silenced genes onto the network. We found that most of these genes (67%) are connected together to form a giant, linked network component. Randomization tests confirmed that such a component is unlikely to be formed by chance (P<2 × 10−4). To build an oncogenic map, we included other mutated and methylated genes that are not present in the composition of the component into the giant network component based on node connectivity (see Materials and methods). The resulting oncogene-signaling map consists of approximately 20% of the signaling network nodes (326 nodes, 892 links) and includes almost 90% of the mutated and methylated genes (Figure 3). The map showed different network topological characteristics from the signaling network. For example, the average length of the map is less than that of the signaling network (5 versus 6, P<2 × 10−16, Wilcoxon test). On the other hand, the average clustering coefficient of the map is greater than that of the signaling network (0.08 versus 0.04, P=0.06, Wilcoxon test). These results suggest that oncogenic proteins tend to have more interactions and signaling regulatory relationships. The emerging oncogene-signaling map represents a ‘hot area' where extensive oncogene-signaling events might occur. As a proof of concept, we found that the MAPK kinase and TGFβ pathways, which are well-known cancer-signaling pathways, are embedded in the map. For example, 50 out of 87 proteins in the MAPK kinase pathway (Supplementary Table 3) and 22 out of 52 proteins in the TGFβ pathway (Supplementary Table 4), respectively, are included in the map. More importantly, in addition to known oncogenic pathways, there are many other novel candidate cancer-signaling cascades present in the map. For a given gene mutation in a tumor, one could use this map to generate testable hypotheses to discover the underlying oncogene-signaling cascades in that tumor.
As mentioned above, oncogene-signaling-dependent events, which we define as the interactions between the cancer mutated or methylated genes, are frequently found in tumor samples and represent various oncogene-driving events that could play more critical roles in generating tumor phenotypes. To systematically identify such events and discover how they are organized in the map, we charted the gene mutation frequency onto the map and highlighted the signaling links between any two genes that have high mutation frequencies. Most genes have mutation frequencies lower than 2%, whereas a handful of genes have very high mutation frequencies, such as p53 (41%), PI3K (10%) and RAS (15%) (see Materials and methods). Therefore, a gene mutation frequency equal to or greater than 2% was considered as high. Interestingly, nearly 10% of the links in the map are oncogene-signaling-dependent events. Certain signaling events such as Pten-PI3K and RAS-PI3K in the map are well-known oncogene-signaling-dependent events/cascades that are frequently used in various cancers.
As shown in Figure 3, most oncogene-signaling-dependent events are connected, and three major regions that contain densely connected oncogene-signaling-dependent events emerge in the map: the first region (p53 region) contains mainly tumor suppressors such as p53, Rb, BRCA1, BRCA2 and p14 (CDKN2A) etc.; the second region (RAS region) contains mainly well-known oncogenes such as RAS, EGFR and PI3K etc.; and the third region (TGFβ region) contains SMAD3, SMAD4 and a few other TGFβ-signaling proteins. Interestingly, genes in the p53 and TGFβ regions are also heavily methylated in cancer stem cells, suggesting that these regions are involved in the early stage of oncogenesis. Other methylated genes are intertwined with the mutated genes in the map, suggesting that they share some oncogene-signaling cascades and might be regulated to cooperate in cancer signaling via gene mutation and/or methylation. Notably, it seems that, in cancer stem cells, TGFβ-signaling pathway is shut down, supporting its known role as a tumor suppressor in the early stages of tumorigenesis (Hanahan and Weinberg, 2000; Siegel and Massague, 2003). These results suggest that the crucial players of oncogene signaling tend to be closely clustered and regionalized. This map uncovers the architectural structure of the basic oncogene signaling and highlights the signaling events that are highly conserved in generating tumor phenotypes.
Functional collaboration of genes between oncogene-signaling blocks
The oncogene-signaling map can be decomposed into several network communities or network themes (Zhang et al, 2005), in which each network community contains a set of more closely linked nodes and ties to particular biological functions. To discover such network communities, we implemented and applied an algorithm that detects network communities to the map. As a result, 12 network communities, ranging in size from 11–65 nodes (Supplementary Table 5), called ‘oncogene-signaling blocks', were found in the map. Structurally, the nodes within each block have more links and signaling regulatory relations among themselves than others. The genes in each block share similar biological functions such as cell proliferation, development and apoptosis (Supplementary Table 5). We further performed Gene Ontology (GO) enrichment analysis for each oncogene-signaling block using DAVID Tools (http://david.abcc.ncifcrf.gov/home.jsp). Most of the oncogene-signaling blocks are enriched with protein serine/threonine kinase activity (Supplementary Table 6), which is well known to take part in tumorigenesis. Notably, Block 1 is enriched with cell surface receptor-linked signaling, whereas Block 10 is enriched with intracellular signaling cascades. Block 11 is enriched with tumor suppressors and biological processes such as apoptosis and cell cycle. These results suggest that certain blocks are taking part in different parts/kinds of signaling, that is, cell surface receptor-related signaling, intracellular signaling, cascade signaling and apoptotic signaling. However, three oncogene-signaling blocks have no GO enrichment detected. One of the reasons is that a fraction of the genes in these blocks is not well annotated yet. For example, about one-third of genes in Block 6 have no GO term associated.
We asked if the genes in each block could operate in a compensatory or concerted manner to govern a set of similar functions. Toward this end, we surveyed the gene mutations in tumor samples where at least two genes are screened for mutations. As a result, the co-occurrence in tumor samples of 25 mutated gene pairs is found to be statistically significant (Supplementary Table 7). Significantly, only three collaborative gene pairs came from the same block, whereas other collaborative gene pairs came from two different blocks, with predominantly one of them arising from Block 11 (defined as p53 block), which contains p53, Rb, p14, BRCA1, BRCA2 and several other genes involved in control of DNA damage repair and cell division. Collectively, these results suggest that the signaling genes from different blocks most likely work together in a complementary way to generate tumor phenotypes.
We further asked which oncogene-signaling blocks work together to produce a tumor phenotype. To address this question, we surveyed the gene mutations in the tumor samples where at least two gene mutations are found. In total, 592 tumor samples fit this criterion. We used the 592 samples to build a matrix (M) where samples are rows and the signaling blocks are columns. If a gene of a particular signaling block (b) gets mutated in a tumor sample(s), we set Ms,b to 1, otherwise we set Ms,b to 0. A heatmap was generated using the matrix (Figure 4A). If a sample contains statistically significant co-occurring mutated gene pairs (see Supplementary Table 7), these pairs were highlighted in the heatmap. Samples were organized based on the cancer types they belong to. Several cancer types such as breast, central nervous system, blood, lung, pancreas and skin tumors that have relatively more samples were also highlighted in the heatmap. As shown in Figure 4A, two signaling blocks have statistically significant enrichment of gene mutations (P<2 × 10−4, randomization tests), suggesting that genes in these two signaling blocks are predominantly used to generate tumor phenotypes. One oncogene-signaling block (Block 1, defined as RAS block) contains genes like RAS, EGFR and PI3K etc., which share similar biological functions such as cell proliferation, cell survival and cell growth, whereas the other is the p53 block, which share similar biological functions such as cell cycle checkpoint control, apoptosis and affecting genomic instability (Supplementary Table 5). These two blocks also represent the two oncogene-signaling-dependent regions (p53 and RAS regions) in Figure 3, respectively. When a tumor sample has a mutation in a gene from the RAS-signaling block, it is also most likely to contain a mutation in a gene from the p53 block (P<2 × 10−4). To check if this phenomenon is primarily due to a particular pair of genes, we calculated the likelihood of co-occurrence for each pair of the genes, of which one gene is mutated in one block and the other gene is mutated in the other block. We found that the P-values for gene pairs are always significantly greater than that for the pair of Blocks RAS and p53. For example, the P-value of co-occurrence of RAS (in Block RAS) and p53 (in Block p53) mutations is 0.01, which is greater than that of the two blocks (P<2 × 10−4). This indicates that these two oncogene-signaling blocks collaborate to generate tumor phenotypes for most tumors. Experimental examples have shown similar gene collaboration in tumorigenesis: activation of RAS (RAS block) and inactivation of p53 (p53 block) induce lung tumors (Meuwissen and Berns, 2005), whereas activation of RAS (RAS block) and inactivation of p16 (p53 block) induce pancreatic tumors (Obata et al, 1998). In general, tumor cells exhibit either elevated cell proliferation or reduced differentiation or apoptosis relative to normal cells. The oncogenic blocks we have identified, especially the RAS and p53 blocks, encode functions that are tumor-related, such as cell cycle control, cell proliferation and apoptosis (Supplementary Figure 5). Activation of genes in the RAS block promotes the cell proliferation, whereas inactivation of genes in the p53 block prevents apoptosis. Thus, a functional collaboration between the genes in these two blocks would promote synergistic cancer signaling and foster tumorigenesis.
Notably, we found that at least one gene mutation in the p53 block had occurred in the tumor samples we examined. In other words, the p53 block is involved in generating tumors for most cancers. This result suggests that the p53 block is a central oncogene-signaling player and essential in tumorigenesis. This finding is further supported by the following observations. (a) To become oncogenic, tumor suppressors require loss-of-function mutations, which occurs more often than gain-of-function mutations (Gymnopoulos et al, 2007). Indeed, the average gene mutation frequency in the p53 block is higher than that of other signaling blocks including the RAS block. (b) The methylation of genes in the cancer stem cells resulting in long-term loss of expression represents the early stage of the tumorigenesis. In fact, most of the members of the p53 block are methylated in cancer stem cells. These facts further support that the p53 block might play an important role in the earlier stages of oncogenesis. (c) Gene methylation or inactivating mutations of DNA damage checkpoint genes such as p53 induce genome instability and thus increase the chance of other gene mutations, including the genes of other oncogene-signaling blocks that could functionally collaborate with the p53 block genes to generate tumor phenotypes.
Using the map as a framework, we benchmarked the mutated genes in the NCI-60 cell lines, which represent a panel of well characterized cancer cell lines and various cancer types. A systematic mutation analysis of 24 known cancer genes showed that most NCI-60 cell lines have at least two mutations among the cancer genes examined (Ikediobi et al, 2006). We built a matrix and constructed a heatmap using these cell lines and their mutated genes as described above (Figure 4B). Overall, the pattern obtained from the NCI-60 panel resembles that of the 592 tumor panel with both RAS and p53 blocks enriched with gene mutations and exhibiting statistically significant collaborations in these cell lines (Figure 4B, P<2 × 10−4), which is in agreement with the earlier observations. We also benchmarked the mutated genes derived from a genome-wide sequencing of 22 tumor samples (Sjoblom et al, 2006). Among these 22 samples, 10 breast and 10 colon tumor samples have at least two gene mutations in the map. As shown in Figure 4C and D, the p53 block is enriched with gene mutations. For the 10 colon tumor samples, collaboration between Block 6 and Block p53 is established, but for the 10 breast tumors, collaborative patterns between multiple blocks emerged.
To further examine the block collaborative patterns in individual tumor types in higher resolutions, from the heatmap (Figure 4A) we extracted the sub-heatmap for several tumor types that are better represented among the 592 tumor samples, that is, they have relatively more samples within the 592 samples (Figure 5). As shown in Figure 5, signaling block collaborative patterns are tissue dependent and are classified into two groups. One group contains pancreas, skin, central nervous system and blood tumors that have simple block collaborative patterns. In these tumors, signaling collaborations are mainly between Block p53, Block RAS with some minor contributions from Blocks 5, 6 or 7, suggesting that they predominantly use these oncogene-signaling routes to generate tumors resulting in relatively homogenous cancer cell types. The other group contains breast and lung tumors that also contain large proportions of mutations from the p53 block, but also have complex patterns of collaborations between assortments of multiple blocks, suggesting that these tumors may have a larger variety of oncogene-signaling routes, which may explain, in part, the heterogeneous nature of the tumor subtypes in this category. These results might also explain why both lung and breast cancers are the most common types of human tumors.
In this study, the cancer mutated genes were collected from a ‘directed approach' (i.e., mutational analysis of specific genes, such as p53) and a ‘large-scale approach' (i.e., large-scale sequencing of tumor samples). We tested whether the mutated genes from the directed approach introduce bias to our analysis. Literature-curated cancer mutated genes (directed approach) have been assembled in the Cancer Gene Census (Futreal et al, 2004), of which 115 genes were found in the human signaling network. As of November 30th, 2006, among the 115 Cancer Gene Census genes, mutations in 55 of them have been further validated by additional experimental evidence (i.e., other independent experiments confirming the mutation of these specific genes in cancer samples have been documented in the COSMIC database), whereas 60 of them have no such evidence in the COSMIC database (see Materials and methods). In fact, we included only these 55 literature-curated genes in the cancer mutated gene set (227 genes) used in all of our analyses above (see Materials and methods). Of the 55 literature-curated genes, only 9 were not already present in the output of large-scale sequencing of tumor samples (Figure 1A). We removed these 9 genes from the cancer mutated gene set (227 genes), mapped rest of the genes onto the human signaling network and rebuilt an oncogene-signaling map, oncogene-signaling blocks and a heatmap. On the other hand, we added the 60 literature-curated genes, which have no independent supporting evidence in the COSMIC database, to the 227-gene set and obtained 287 genes. Using these 287 genes, we reran the analyses mentioned above. In these two analyses, although the gene members of each oncogene-signaling block have some minor differences with those of the original blocks, the major collaboration patterns of oncogene-signaling blocks remain largely unchanged (Supplementary Figure 6a and b), suggesting that our findings are robust to addition or removal of the cancer mutated genes derived from the directed approach.
The mutated genes in the network provide a predictive power
A substantial number (∼20%) of mutated genes were found in the network. We asked if a gene that has more links to mutated genes in the network is most likely to be cancer associated. To answer this question, we extracted the nonmutated genes that have more than one link to the mutated genes and then grouped them based on their link numbers to the mutated genes. We interrogated a cancer-associated gene set (Supplementary Table 8) compiled from literature mining (see Materials and methods) to find out how many genes in each group are cancer associated. As shown in Figure 6, the more mutated genes a gene links to, the more probably it is cancer associated. When the link number of the network genes is more than six, ∼80% of them are cancer-associated genes. For example, SHC, a gene that has been implicated in cancer metastasis (Jackson et al, 2000), has numerous links to the mutated genes in the network. To further investigate the predictive power of the mutated genes in the network, we took the 14 network genes, which not only have at least four links to the mutated genes, but also are not implicated in cancer in the literature, to perform a survival analysis using a microarray data set that contains the gene expression profiles and survival information for 295 breast tumor samples. As a result, the expression profiles of 5 out of the 14 genes (36%) are able to discriminate ‘good' and ‘bad' tumors (i.e., patients having ‘bad' tumors have higher chance of tumor recurrence and short survival time). Therefore, these genes are potentially novel biomarkers. In contrast, less than 10% of the nonmutated network genes have similar discriminatory power. These results suggest that the network genes, which have more links to the mutated genes, have more chance to be perturbed in tumorigenesis and be associated with cancer. Practically, the mutated genes in the network provide a predictive power that can be used to discover novel biomarkers of tumors.
Concluding remarks
Although a wide variety of genetic and epigenetic events contribute to the signaling of tumorigenesis, it has been challenging to gain a global view of where and how they affect the signaling alterations to generate tumors on the entire signaling network. By integrative analysis of the human signaling network with cancer-associated mutated and methylated genes, we uncovered an overall picture of the network architecture where the oncogenic stimuli occur and the regulatory mechanisms involving mutated and methylated genes. Mutations, the majority of which are activating, preferentially occur in the signaling hub genes (but not neutral hubs) and the genes of the positive regulatory loops, whereas methylated alterations tend to occur in the genes of the negative regulatory loops. Cancer and cell signaling have been well established, and extensive efforts have been made to illustrate cancer signaling during the past few decades. However, it has been a struggle to get clues of how the oncogene signaling is structurally and functionally organized. In this analysis, we extracted an oncogene-signaling map, which provides a blueprint of the oncogene signaling in cancer cells. From the map, we discerned that the oncogene-signaling-dependent events form three highly connected regions that resemble oncogene-signaling superhighways frequently used in tumorigenesis. Topologically, the map has been divided into 12 oncogene-signaling blocks. Functional collaborations between subsets of these blocks are underlying tumorigenesis. In most tumors, genes in both p53 and RAS blocks often get mutated, although the combinations of p53 with other signaling blocks are also found in a small fraction of tumors. Analysis of the NCI-60 cell line panel mutations showed the enrichment of gene mutations in p53 and RAS blocks, which is similar to the patterns found in the 592 samples. Furthermore, we can dissect some of this functional collaboration among different tumor types. These facts indicate that at least two signaling gene mutations, one from the p53 block and the other from another block, are necessary for tumorigenesis. This fact supports the notion that both the prevention of cell death (p53 block) and the promotion of cell proliferation (RAS or other blocks) are necessary to generate most tumors.
At present, a number of researchers doubt or even argue against the value of large-scale human cancer genome sequencing as a meaningful or efficient strategy in cancer research. Their arguments are based on the following observations (Chng, 2007): (a) previous large-scale human cancer genome sequencing revealed that each tumor has a different mutation pattern, and the prevalence and patterns of somatic mutations in human cancers are tremendously diverse and complex (Kaiser, 2006; Sjoblom et al, 2006; Greenman et al, 2007); (b) the interpretation of such complex somatic alterations is a formidable challenge (Chanock and Thomas, 2007; Thomas et al, 2007). We mapped the mutation data from the genome-wide sequencing tumor samples (Sjoblom et al, 2006) using the oncogene-signaling map as a framework. Although the number of mutated genes is impressive in toto, most signaling gene mutations are limited to 2–3 critical mutations, divided among several signaling blocks, per individual tumor. This result suggests that the mutations in the samples of the same tumor type might share a similar underlying signaling mechanism, because each oncogene-signaling block contains a set of genes linked together through shared regulatory relations and key input and/or output signaling nodes that are involved in tumorigenesis. These findings imply that although the mutations seem tremendously diverse and complex at the gene level, clear patterns emerge recurrently at the network level in most tumors. Therefore, with proper bioinformatics analysis, large-scale cancer genome sequencing efforts would be fruitful in finding appropriate combinations of biological targets for cancer diagnostic and therapeutics.
In summary, this work revealed novel insights into the oncogenic regulatory mechanisms, oncogene-signaling network architecture and oncogene-signaling cooperative relationships that drive cancer development and progression. It also highlights the emergence of the central players in cancer signaling. Cancer studies have integrated microarray, knowledge, pathways and networks (Liu and Lemberger, 2007), but not genetic and epigenetic data yet. However, as the next generation of genome sequencing technology becomes more accessible and affordable, much more efforts involving genome-wide sequencing of large number of tumor genomes will be conducted. Our work provides a conceptual and technical framework for incorporating the genome sequencing outputs and other types of data such as microarray profiles to get more insights into the cancer-signaling mechanisms that will facilitate the identification of key genes for biomarkers and drug development.
Materials and methods
Data sets used in this study
Human signaling network
To build up the human signaling network, we manually curated the signaling molecules (most of them are proteins) and the interactions between these molecules from the most comprehensive signaling pathway database, BioCarta (http://www.biocarta.com/). The pathways in the database are illustrated as diagrams. We manually recorded the names, functions, cellular locations, biochemical classifications and the regulatory (including activating and inhibitory) and interaction relations of the signaling molecules for each signaling pathway. To ensure the accuracy of the curation, all the data have been crosschecked four times by different researchers. After combining the curated information with another literature-mined signaling network that contains ∼500 signaling molecules (Ma'ayan et al, 2005), we obtained a signaling network containing ∼1100 proteins (Awan et al, 2007). We further extended this network by extracting and adding the signaling molecules and their relations from the Cancer Cell Map (http://cancer.cellmap.org/cellmap/), a database that contains 10 manually curated signaling pathways for cancer. As a result, the network contains 1634 nodes and 5089 links that include 2403 activation links (positive links), 741 inhibitory links (negative links), 1915 physical links (neutral links) and 30 links whose types are unknown (Supplementary Table 9). To our knowledge, this network is the biggest cellular signaling network at present.
Cancer mutated genes
The cancer mutated genes were taken from the COSMIC database (http://www.sanger.ac.uk/genetics/CGP/cosmic/) and other large-scale or genome-wide sequencing of tumor samples (Sjoblom et al, 2006; Greenman et al, 2007; Thomas et al, 2007). COSMIC database contains manually curated cancer mutated genes and the information of tumor samples, mutated sequences from literature and the output from the CGP's large-scale sequencing of tumor samples (Davies et al, 2005; Stephens et al, 2005; Greenman et al, 2007). The literature-curated genes were compiled as the Cancer Gene Census (Futreal et al, 2004), which is accessible in COSMIC database. The CGP is using human genome sequences and high-throughput mutation detection techniques to identify somatically acquired sequence mutations and hence to identify genes critical in the development of human cancers. A few recent publications (Davies et al, 2005; Stephens et al, 2005; Greenman et al, 2007) represent a small fraction of the CGP output. In addition, COSMIC database has provided mutation frequencies for most of the cancer mutated genes. The cancer gene mutation frequency of a gene is defined as the ratio of samples containing the mutated gene to the total samples screened for that gene. In the database, about one-third of the literature-curated mutated genes (Cancer Gene Census genes) have nonzero mutation frequencies, suggesting that the literature curation of these genes (i.e., included them into the Cancer Gene Census) has been supported by one or more other independent experiments.
For the network analysis in this study, we first intersected the network genes with the literature-curated mutated genes. As a result, we obtained 115 genes (Supplementary Table 10), of which 55 genes (Supplementary Table 10) have nonzero mutation frequencies. Meanwhile, we intersected the network genes with the mutated genes derived from the CGP large-scale sequencing output and several other genome-wide and high-throughput sequencing of tumor samples (Stephens et al, 2005; Sjoblom et al, 2006; Greenman et al, 2007; Thomas et al, 2007). As a result, we obtained another gene set containing 218 genes. Finally, we obtained 227 genes by merging the 55 genes and the 218 genes mentioned above. Among these 227 genes, 218 (96%) and 55 (24%) genes were collected from the large-scale sequencing of tumors and literature curation, respectively (Figure 1A). Notably, 46 genes (84%) of the literature-curated genes were overlapped with the mutated genes derived from the large-scale gene sequencing of tumors. The genes and their mutation frequencies from sequencing of tumors and literature were collected in Supplementary Table 10.
Methylated genes in cancer stem cells
We obtained 287 DNA-methylated genes from the three recent genome-wide determinations of the methylated genes from cancer stem cells (Ohm et al, 2007; Schlesinger et al, 2007; Widschwendter et al, 2007). Out of the 287 genes, 93 were mapped onto the human signaling network (Supplementary Table 11).
Cancer-associated gene set
The cancer-associated gene set contains the following data sources: (a) the cancer mutated genes we mentioned above; (b) a literature-mined breast cancer gene set from plasmID database (http://plasmid.hms.harvard.edu/GetCollectionList.do); (c) the genes extracted from the NCBI's Online Mendelian Inheritance in Man (OMIM) data set using the keywords such as ‘cancer', ‘tumor' and ‘onco' etc. The cancer-associated gene list contains 2128 genes (Supplementary Table 8).
Microarray data
Gene expression profiles and the patients' survival data for the 295 breast tumor samples were obtained from Chang et al (2005).
Oncogenic map extraction
To extract an oncogenic map from the human signaling network, we mapped all the mutated and methylated genes onto the network. As a result, 67% of these genes are connected together to form a giant, linked network component. To include the mutated and methylated genes that are not present in this network component, we first found one shortest path between such a gene and a component node. If the length of the shortest path is 2 (i.e., the gene reaches one of the component nodes via a nonmutated network node), we linked that gene and the node on the shortest path into the component. A Java program had been written to implement this procedure (Supplementary File 1).
Network analysis
To extract the members of the branching and convergent units and 3-node- and 4-node-size network motifs, mfinder program (Kashtan et al, 2004) was used. To detect the signaling network communities from the oncogene-signaling map, we applied a network community algorithm (Newman, 2006).
Analyzing the enrichment of the mutated and methylated genes in the network motifs
We mapped the mutated and methylated genes onto each type of the motifs. We then counted the number of mutated or methylated genes in each motif and classified each type of motif into several subgroups based on the number of nodes that are mutated or methylated genes. We then calculated the ratio (Ra) of the activation links to the total activation and inhibitory links in each subgroup.
Randomization tests
We performed randomization tests to evaluate the statistical significance of the observations. A more detailed explanation of the randomization tests was described previously by Wang and Purisima (2005).
Survival analysis
To evaluate the prognostic value of a gene based on the gene expression profiles and the survival information of the tumor samples, we performed Kaplan–Meier analysis by implementing the Cox–Mantel log-rank test using R, a statistical computing language (http://www.r-project.org/). If the P-value is less than 0.05, the gene was thought as statistically significant to classify the tumor samples into ‘good' and ‘bad' groups.
Supplementary Material
Supplementary Figures
Supplementary Table 1
Supplementary Table 2
Supplementary Table 3
Supplementary Table 4
Supplementary Table 5
Supplementary Table 6
Supplementary Table 7
Supplementary Table 8
Supplementary Table 9
Supplementary Table 10
Supplementary Table 11
Supplementary Java File
Supplementary Tables and Java File Legends

cancer signaling
signaling network
gene mutation
tumorigenesis
dna methylation
cancer-signaling map
oncogene-signaling dependence


Neuronal Chemokines: Versatile Messengers In Central Nervous System Cell Interaction

Whereas chemokines are well known for their ability to induce cell migration, only recently it became evident that chemokines also control a variety of other cell functions and are versatile messengers in the interaction between a diversity of cell types. In the central nervous system (CNS), chemokines are generally found under both physiological and pathological conditions. Whereas many reports describe chemokine expression in astrocytes and microglia and their role in the migration of leukocytes into the CNS, only few studies describe chemokine expression in neurons. Nevertheless, the expression of neuronal chemokines and the corresponding chemokine receptors in CNS cells under physiological and pathological conditions indicates that neuronal chemokines contribute to CNS cell interaction. In this study, we review recent studies describing neuronal chemokine expression and discuss potential roles of neuronal chemokines in neuron–astrocyte, neuron–microglia, and neuron–neuron interaction.
Introduction
Chemokines are small proteins that are able to induce a chemotactic response in cells expressing the corresponding chemokine receptors. Since the discovery of the first protein with chemotactic activity [1], the chemokine family has expanded to approximately 50 chemokines [2] and 20 receptors [3]. Chemokines have been divided into four groups based on the position of four conserved cysteine residues in the N-terminal region of the protein. The two largest groups are CXC and CC. The first two cysteines in the CXC group are separated by one amino acid residue, whereas the first two cysteines in the CC group are adjacent to each other [4, 5]. The two small groups are the C chemokines, with only one cysteine in the N-terminal region, and the CX3C chemokine, where the first two cysteines are separated by three amino acid residues [5]. Chemokine receptors are designated according to the chemokine group they preferentially bind. For example, CC chemokines bind to CC receptors and so on. There has yet only been one exception reported, namely CCL21, that, in addition to CCR7, also binds to CXCR3 [6–8]. All chemokine receptors belong to the family of G-protein coupled receptors (GPCRs). In general, GPCRs can bind many different G-proteins, allowing for a great variety of intracellular signaling pathways (for excellent review, see [9]). The majority of chemokine-induced responses are inhibited by pertussis toxin (PTX), indicating that  mediate many effects [10]. Chemokine receptors can activate intracellular targets like adenylcyclase, phospholipases, GTPases like Rho, Rac, and Cdc42 and pathways of major kinases like mitogen-activated protein kinase (MAPK) and phosphatidyl inositol-3 kinase (PI3-K) [11, 12]. This diversity of intracellular signaling shows that chemokine receptors, in addition to pathways involved in cell migration, also activate other pathways and may, in that way, control a great spectrum of cellular functions [13, 14].
Chemokines are well-known regulators of peripheral immune cell trafficking under both physiological and pathological conditions (reviewed by [15–17]). In addition to chemo-attraction of immune cells, chemokines have been implicated in a variety of cell functions, such as early development, formation of secondary lymphoid organs, wound healing, angiogenesis and angiostasis, regulation of adhesion molecule expression, development of Th1/Th2 profiles, tumor growth, and metastasis [5, 14, 18–24]. Thus, from being molecules thought to solely orchestrate immune cell migration, chemokines are now considered versatile messengers with the ability to control the interaction between a wide diversity of cell types.
In addition to their presence in the periphery, numerous studies have demonstrated that chemokines are also expressed in the central nervous system (CNS), where they play a crucial role in physiological and pathological conditions, such as development, synaptic transmission, homeostasis, injury, and disease-associated neuroinflammation [19, 25, 26]. Although astrocytes and microglia are the primary source of chemokines, there is evidence that neurons express and secrete chemokines as well, indicative of a neuronal contribution to chemokine signaling. In this paper, we review recent studies describing neuronal chemokine expression and discuss the potential roles of neuronal chemokines in neuron–astrocyte, neuron–microglia, and neuron–neuron interaction.
Neuronal Chemokine Expression
Approximately 60 studies describe chemokine expression in neurons under physiological and pathological conditions (see Table 1). These studies, of which the majority is published in the last 3 years, are reviewed in the following sections.
Table 1Neuronal chemokine expressionChemokineSpeciesConditionRNAProteinReferencesCCL2hBrain+[39]Spinal cord, ALS↑[35]Monoculture↑+[41]Cell line+[38]rBrain+[37]Brain, cranial nerve injury↑↑[30]Brain, ischemia↑↑[28]Retina, ischemia↑[168]Spinal cord, peripheral nerve injury↑↑[31–34]Monoculture+[164]mBrain, ischemia↑[27, 29]Spinal cord, ALS model↑[36]Monoculture, West Nile virus↑[43]CCL3hBrain, AD≈[40]Monoculture↑+[41]rRetina, ischemia↑[168]Monoculture+[51]mMonoculture↑[42]CCL4hMonoculture↑+[41]rRetina, ischemia↑[168]mMonoculture↑[42]CCL5hMonoculture↑+[41]mMonoculture↑↑[43, 44]CCL20rMonoculture+/↑[51]Trigeminal neuron culture↓[169]CCL21mBrain, ischemia↑[45]Monoculture↑↑[45, 46]Neonatal hippocampal slice culture↑[46]CXCL1/2/3rMonoculture++[51]CXCL8hMonoculture+[41]CXCL9mMonoculture+[43]CXCL10hBrain, HIV↑[54]Mixed brain culture+[54]macBrain, HIV↑[54]rBrain, ischemia↑[52]mBrain, entorhinal cortex lesion↑[53]Brain, West Nile virus↑↑[43]Monoculture++[43]CXCL11mMonoculture+[43]CXCL12hBrain, HIV↑[59]Monoculture+[59]rBrain++[55, 60–62]Monoculture++[58, 60, 87]mBrain, ischemia≈/↓[56]Brain, LPS injection≈[56]Mixed brain culture+[59]CX3CL1hBrain, MS↑[113]Brain, HIV↑[170]Spinal cord+[66]Monoculture≈≈/↑/↓[74, 75, 138]Cell line≈↑/↓a[66, 75, 138]macBrain+[66]rBrain and spinal cord, EAE≈[63, 65, 66, 68]Spinal cord, peripheral nerve injury≈≈[171, 172]Brain, LPS injection≈[67]Brain, KA injection≈[67]Monoculture≈↑/↓a[64, 65, 73, 76, 78, 118]mBrain++[69]Brain, prion disease≈[67]Brain, LPS injection≈[67]Brain, KA injection≈[67]Brain, EAE≈[66]Monoculture≈≈[77]Cell line≈≈[77]h Human, mac macaque, r rat, m mouse, ALS amyotrophic lateral sclerosis, AD Alzheimer’s disease, HIV human immunodeficiency virus, MS multiple sclerosis, EAE experimental autoimmune encephalomyelitis, LPS lipopolysaccharide, KA kanaic acid; + present, ≈ present without change in mentioned conditions, ↑ present with increase in mentioned conditions, ↓ present with decrease in mentioned conditions,aIncrease in soluble CX3CL1 and decrease in membrane-bound CX3CL1
CC Chemokines
CCL2
CCL2 is currently the most extensively described neuronal chemokine. The majority of reports describing neuronal CCL2 expression are focused on pathological conditions. An induction of neuronal CCL2 expression was described upon ischemia [27–29], after axonal injury [30–34] and in motoneurons of patients with amyotrophic lateral sclerosis (ALS), and in mouse models for ALS [35, 36]. Interestingly, neuronal CCL2 expression in response to ischemia was detectable within 2 h, whereas CCL2 expression in astrocytes was detected only after 2 days [27]. Although most reports show induction of neuronal CCL2 expression under pathological conditions, a recent study has shown constitutive CCL2 expression in neurons throughout the rat brain [37]. This study demonstrated that, depending on the brain region, up to 100% of the neurons were positive for CCL2 [37]. CCL2 was mainly detected in neuronal cell bodies and costaining-depicted colocalization with various neurotransmitters and neuropeptides, corroborating a population-specific expression of CCL2 [37]. Constitutive neuronal CCL2 expression was also shown in a human neuronal cell line [38] and during human CNS development [39].
CCL3, CCL4, and CCL5
At present, there is only one study describing neuronal CCL3 expression in situ, depicting protein expression in adult human brain [40]. Further, expression of CCL3, CCL4, and CCL5 was described in cultured forebrain neurons derived from human first trimester embryos. These chemokines showed increased expression after exposure to immunological stimuli [41]. CCL3 and CCL4 expression were induced in mouse cerebellar granule neurons after infection with Toxoplasma gondii [42], as was CCL5 expression after viral infections [43, 44].
CCL21
In a middle cerebral artery occlusion (MCAO) mouse model of brain ischemia, cortical neurons rapidly expressed CCL21 in the penumbra of the ischemic core. Because control brain tissue did not express CCL21, CCL21 was assumed to be specifically expressed in endangered neurons [45]. In accordance with the in vivo findings, CCL21 expression was induced in cortical neurons in vitro within 2 h after excitotoxicity [45, 46]. The CCL21 expression in endangered neurons was rather surprising, as CCL21 is well known for its constitutive expression in secondary lymphoid organs, controlling the homing of mature dendritic cells and naïve T cells [47] and is, therefore, generally considered a homeostatic chemokine linked to the development and maintenance of secondary lymphoid organs [48]. The rapid CCL21 expression in endangered neurons after injury indicates a brain specific role of CCL21. This assumption is corroborated by findings in transgenic mice in which CCL21 was expressed ectopically in various tissues. CCL21 expression in the brain induced a massive brain inflammation that killed the animals within 3 days after the expression onset [49], whereas CCL21 expression in the skin induced the formation of secondary lymphoid structures [50].
Other CC Chemokines
A single study has demonstrated a constitutive and inducible expression of CCL20 in rat cerebellar granule neurons in vitro, which was suggested to play a role in neuronal apoptosis [51]. Expression of other CC chemokines has not yet been observed in neurons.
CXC Chemokines
CXCL10
CXCL10 expression was first described in cortical neurons in rat in response to MCAO-induced brain ischemia [52]. Remarkably, neuronal CXCL10 expression was transient and appeared rapidly after stroke (within 3–12 h), whereas CXCL10 expression in astrocytes was detectable later and persisted up to 15 days after MCAO [52]. Correspondingly, neurons also showed a rapid CXCL10 expression after entorhinal cortex lesion [53]. Further, neuronal CXCL10 expression and release was induced after viral infection in vitro and in vivo [43, 54].
CXCL12
The CXCL12 gene contains three splice variants, termed stromal cell-derived factor-1 (SDF-1) α, β, and γ. SDF-1γ was cloned from rat brain and showed constitutive neuronal mRNA expression with almost no change in level after peripheral nerve injury [55]. In addition, SDF-1α showed neuronal mRNA expression with almost no change in level after brain ischemia or intracerebral LPS injection [56]. In contrast, SDF-1 β mRNA expression was not detected in neurons [56]. As little is known about the role of SDF splice variants, and most studies did not specify the splice variants, CXCL12 is used for all SDF splice variants henceforth.
Like CCL2, but in contrast to most of the other neuronal chemokines, CXCL12 is expressed constitutively in specific neuronal populations. Neuronal CXCL12 expression in vitro was observed in cultured cortical, hippocampal, and cerebellar neurons from human, rat, and mouse [57–60]. Neuronal CXCL12 expression in vivo was studied in detail in the adult rat brain, showing CXCL12 mRNA and protein expression in cholinergic, dopaminergic, and vasopressin containing neurons throughout the brain [61, 62].
Other CXC Chemokines
Studies describing the expression of other CXC chemokines in neurons are limited. Most notably, in vitro neuronal mRNA expression of CXCL1 [51], CXCL8 [41], CXCL9, and CXCL11 [43] has been illustrated. Expression of other CXC chemokines has not yet been described in neurons.
CX3CL1
CX3CL1 was the first chemokine shown to be expressed in neurons [63–66]. Because microglia were shown to express the corresponding receptor CX3CR1, a role of CX3CL1–CX3CR1 signaling in neuron–microglia interaction was suggested [63–65]. CX3CL1 is constitutively expressed in human, macaque, rat, and mouse neurons in vitro and in vivo, with high expression in cerebral cortex, hippocampus, caudate putamen, thalamus, and olfactory bulb [63, 65, 66, 68, 69]. CX3CL1 appears to be the only chemokine with a higher expression level in brain than in peripheral organs [70]. It is membrane bound and can be cleaved from the cell surface by proteases of the A Disintegrin and Metalloprotease (ADAM) family [71, 72]. The neuronal CX3CL1 mRNA expression remained relatively stable in response to both neuron-damaging stimuli in vitro [73–77] and during neuroinflammation in vivo [66], whereas in vitro neurons released CX3CL1 protein after glutamate-induced damage [73, 74, 78]. Furthermore, CX3CL1 concentrations higher than 300 pg/mg were described in aqueous extracts of the brain [79], indicating that CX3CL1 can be cleaved from the neuronal membrane and released into the extracellular environment. It is yet unknown which ADAM protease cleaves CX3CL1 in neurons and whether CX3CL1 protein expression changes during in vivo neuroinflammation or degeneration.
Potential Roles of Neuronal Chemokines in Neuron–Astrocyte, Neuron–Microglia, and Neuron–Neuron Interaction
Astrocytes, microglia, and neurons have been shown to express chemokine receptors in vitro under physiological and pathological conditions and in vivo. These would include CCR2 for CCL2, CXCR3 for CCL21 and CXCL10, CXCR4 for CXCL12, and CX3CR1 for CX3CL1. Studies describing the expression of these chemokine receptors on astrocytes, microglia, and neurons (see Table 2) and studies indicating a role for these chemokine–chemokine receptor pairs in CNS cell interaction are discussed in the following sections on neuron–astrocyte, neuron–microglia, and neuron–neuron interaction.
Table 2Chemokine receptor expression in astrocytes, microglia, and neuronsChemokineReceptorCell typeSpeciesConditionRNAProteinReferencesCCL2CCR2astrocytehBrain, MS, HIV↑[96, 97, 173]Monoculture↑↑[83, 99, 174–176]macMonoculture↑[175]rBrain, EAE, LPS injection↑[177, 178]microgliahBrain, MS, HIV↑[96, 173, 179]Monoculture↓+[173, 180]Glia culture+[99]rBrain, tumor, LPS injection, NMDA injection↑[177, 181, 182]Monoculture↑[88]mSpinal cord, peripheral nerve injury↑[183]neuronhBrain, HIV+[184]Monoculture++[38]Cell line++[38]rBrain and spinal cord++[161, 177, 185]Monoculture++[161, 164]CXCL10/CCL21CXCR3astrocytehBrain, MS, HIV↑[95, 97, 179, 186]Astrocyte culture↑↑[84, 98, 175]Mixed glial culture+[95]macMonoculture+[175]mMonoculture+[84]microgliahMonoculture++[7, 84, 98, 114]Cell line↑↑[98]rCell line↑/↓[187]mBrain, various infectious agents, axotomy≈/↑≈/↑[125]Monoculture+↓[45, 84]Cell line↑/↓[188]neuronhBrain, AD+≈[95, 179, 189]Monoculture++[38]Cell line++[38]macBrain, HIV+[54]rMonoculture+[163]CXCL12CXCR4astrocytehBrain, HIV↑[173, 190, 191]Monoculture↑↑[85, 90, 98, 99, 175, 192–196]macMonoculture↑↑[85, 175]rBrain+[197]Monoculture↑/↓↑/↓[57, 58, 102, 198]mMonoculture↑/↓↑/↓[86, 101, 104, 199, 200]microgliahBrain and spinal cord, HIV++[173, 179, 190, 191, 201, 202]Monoculture+↓[98, 99, 191, 202–205]Cell line≈[98]babMonoculture↑[206]rBrain+[197]Monoculture≈/↑+[58, 207, 198]mCell line+[86]neuronhBrain, HIV+≈/↑[179, 184, 191, 197, 202, 204, 208]Monoculture++[38, 85]Mixed brain culture++[202]Cell line++[38, 191, 209]macBrain+[210, 211]Monoculture+[85]rBrain++[57]Monoculture++[64, 158]CX3CL1CX3CR1astrocytehBrain, MS≈[113]Monoculture↑+[113, 175]macMonoculture↑[175]rMonoculture≈/↑↑[76, 198, 212]mMonoculture↓↓[77, 81, 200]microgliahBrain, MS≈[113]Brain, HIV↑[170]Monoculture++[75, 113]rBrain, ischemia, prion disease, cranial nerve injury, EAE↑↑[63, 67, 68, 213]Brain, LPS injection, KA injection≈[67]Spinal cord, peripheral nerve injury↑↑[171, 172]Monoculture↑/↓↑[63, 65, 198, 212, 214]Cell line↓[187]Brain, LPS injection, KA injection≈[67]mMonoculture≈≈[77]neuronhMonoculture++[75]Cell line≈↑[75]rBrain, LPS injection, KA injection≈[67]Monoculture++[64, 139]mBrain, prion disease↓[67]Brain, LPS injection, KA injection≈[67]h human, mac macaque, bab baboon, r rat, m mouse, MS multiple sclerosis, HIV human immunodeficiency virus, EAE experimental autoimmune encephalomyelitis, LPS lipopolysaccharide, NMDAN-methyl-d-aspartatic acid, AD Alzheimer’s disease, KA kainic acid; + present, ≈ present without change in mentioned conditions, ↑ present with increase in mentioned conditions, ↓ present with decrease in mentioned conditions
Neuron–Astrocyte Interaction
Astrocytes comprise the largest group of CNS-residing cells and are not only essential in development, homeostasis, maintenance of the blood–brain barrier, and regulation of central blood flow but are also involved in the immune defense of the CNS. Furthermore, astrocytes are considered to be involved in neuronal information processing [80].
It is becoming clear that astrocytes play an active role in the intricate chemokine network of the CNS. Not only has it been shown that astrocytes express a wide variety of constitutive and inducible chemokines in vivo and in vitro, there is also extensive evidence that they express a repertoire of chemokine receptors under physiological and pathological conditions (see reviews [81, 82]).
Neuronal Chemokines Induce Calcium Transients in Astrocytes
The activation of intracellular calcium transients is a hallmark in chemokine receptor signaling, a mechanism that also holds true for astrocytes [57, 76, 83–86]. Activation of GPCRs, including chemokine receptors, results in a rapid release of calcium from the endoplasmatic reticulum (ER) through the activation of inositol-1,4,5-triphosphate receptors on the ER membrane. One of the first chemokines described to induce calcium transients in astrocytes is CXCL12 [57, 85–87]. CXCL12 concentrations ranging from 0.1 to 100 ng/ml [85, 86] or 10–100 nM [57, 87] induced calcium fluxes in in vitro human, rat, and mouse astrocytes. In all cases, CXCL12-induced calcium mobilization was PTX-sensitive, indicating that this process is Gαi-protein mediated. Similar results were found for CXCL10 [84], CCL2 [83, 88], and CX3CL1 [76].
In astrocytes, intracellular calcium transients not only function as a second messenger in multiple intracellular signaling pathways but are also implicated in astrocyte–astrocyte signal propagation, astrocyte–neuron synaptic transmission, and neurotransmitter release (see reviews [80, 89]). Recent findings corroborate that chemokines could also be involved in astrocyte-mediated neurotransmitter release. CXCL12 induced calcium-dependent release of glutamate from astrocytes in human and rat astrocyte cultures and rat hippocampal slice cultures [90]. Moreover, reports that investigated the effects of CXCL12 on the electrophysiological properties of neurons in brain slice cultures suggest that CXCL12-induced effects on neurons at least partly depend on astrocytic glutamate release [91–93]. Whether this astrocytic glutamate release was induced by CXCR4 activation or via other pathways was not investigated.
Neuronal Chemokines Induce Astrocyte Proliferation and Migration in Vitro: Implications for Astrogliosis?
Astrocytes respond to CNS injury or neuroinflammation by enhanced GFAP expression, proliferation, and possibly, migration, a process known as astrogliosis (see review [94]). In these reactive astrocytes, enhanced expression of chemokine receptors has been described under various pathological conditions, such as multiple sclerosis (MS), human immunodeficiency virus (HIV) infection, ischemia, and neoplasm [95–97]. Under these conditions, CXCR3 was mainly found in reactive astrocytes in the proximity of the lesion sites, suggesting that induction of CXCR3 expression in astrocytes is limited to damaged areas of the brain [95–97]. A comparable induction of CCR2 expression was found in reactive astrocytes in MS patients [97].
Interestingly, both CCL2 and CXCL10 are implicated in astrocyte proliferation in vitro [98, 99]. In addition, CXCL12 has been shown to induce astrocyte proliferation in vitro, a process that is dependent on activation of extracellular signal-regulated kinases ERK1 and ERK2 [87, 100–102]. Both CXCL12-induced astrocyte proliferation and ERK1/2 activation was inhibited by PTX and wortmannin, suggesting that they are dependent on upstream activation of Gαi proteins and PI3-K [87].
As chemokines are primarily known for their capacity to induce cell migration, migration assays have been used to determine chemokine receptor functionality in astrocytes [103]. Accordingly, astrocyte migration was demonstrated in vitro in response to CCL2, CXCL10, and CXCL12 [83, 84, 86, 103, 104]. Thus, reactive astrocytes express various chemokine receptors and activation of these receptors in vitro induces proliferation and migration, cellular reactions that are generally involved in astrogliosis. Therefore, it is tempting to speculate that chemokines are involved in the regulation of astrogliosis upon CNS injury or neuroinflammation. Whether neuronal chemokines are indeed responsible for either proliferation or migration of astrocytes in vivo is yet unknown.
Neuron–Microglia Interaction
Microglia in the healthy CNS are ramified cells that continually survey their environment by moving their processes. Upon injury, they quickly protrude their processes toward the damaged site and subsequently transform into amoeboid cells, reflecting a fast activation [105, 106]. Activated microglia form a first line of defense in CNS injury through their capacity to migrate, proliferate, secrete inflammatory and neurotrophic factors, phagocytose-damaged cells and debris, and present antigens [82, 107]. Although activated microglia were initially considered to be detrimental in CNS injury, recent findings indicate a prominent neuroprotective activity as well, suggesting a balance between neurotoxic and neuroprotective microglia activity (see for recent review, [108]). Therefore, it is of particular interest to gain insight into the process of microglia activation. Until now, it is largely unknown which environmental signals mediate microglia surveillance and activation. Almost 10 years ago, chemokines were indicated as promising candidates for neuron–microglia signaling [63–65]. Because then, various studies have described constitutive chemokine expression in neurons and rapid changes in expression levels upon injury. Parallel to this, corresponding chemokine receptors were described in resting and/or activated microglia. In addition, there is increasing evidence that neurons play an important role in microglia activity, which is at least partly mediated by chemokines.
Microglia Activity Upon Neuronal Damage
Upon CNS injury, activated microglia retract their protrusions, transforming into amoeboid cells with migratory and/or proliferative capacities [109–111]. It is known that damaged neurons are accompanied by prominent activated microglia within hours after injury, suggesting that neurons emit signals that attract microglia [111]. Several findings support the notion that these signals are primarily chemokines. Microglia express various chemokine receptors, and cell migration is induced upon exposure to chemokines in vitro [7, 45, 84, 112–115]. Moreover, damaged neurons in culture express and release chemokines like CX3CL1 [73, 74, 78], CCL21 [45, 46], and CXCL10 [43, 54], all of which are able to induce microglia migration [7, 45, 46, 73, 76, 84, 113, 115]. In accordance with this, inhibition of chemokine function diminished microglia migration in response to supernatants from damaged neurons [73]. Thus, in vitro results suggest a role of neuronal chemokines in neuron–microglia activation.
The issue of chemokine-mediated neuron–microglia activation has been further investigated using genetically modified mice. Mice deficient for either CX3CR1 [116] and CX3CL1 [117] have been studied in various CNS injury and neuroinflammation models. Although CX3CR1 deficiency did not influence microglia activity in response to facial nerve lesion [116], CX3CR1 deficiency was uniformly associated with higher levels of microglia activity in LPS-induced neuroinflammation, 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridin-induced neurotoxicity, and in the SOD1G93A-model of motoneuronal death in the spinal cord [79]. Interestingly, enhanced microglia activity in the last three models was accompanied by increased neuronal death, indicating that, in wild-type mice, neurotoxic microglia activity is inhibited by CX3CL1–CX3CR1 signaling [79]. These findings are corroborated by several in vitro findings. Exposure of a neuron–microglia coculture to CX3CL1 reduced inflammation-related neuronal death, accompanied by suppressed nitric oxide and proinflammatory cytokine production [118]. In conjunction with these findings, inhibition of endogenous CX3CL1 increased neuronal cell death in cocultures [77]. Moreover, in vitro exposure to CX3CL1 supported microglia survival under basal culture conditions and reduced Fas-ligand induced apoptosis considerably [119]. Thus, exposure of microglia to CX3CL1 reduced microglia toxicity and protected microglia from apoptosis under inflammatory conditions. In contrast to these results, CX3CL1 deficiency reduced the infarct volume and mortality after transient focal cerebral ischemia [117]. However, microglia activity in CX3CL1-deficient and wild-type mice was not compared in this study, making it difficult to determine whether disturbed neuron–microglia signaling was responsible for the differences [117].
Increased microglia toxicity by CXCR3 and its ligands CXCL10 and/or CCL21 is suggested by findings derived from the entorhinal cortex lesion (ECL) model, in which CXCR3 deficiency was associated with reduced microglia activity and reduced loss of secondary neurons in the hippocampal formation [53]. An interesting aspect of chemokines in neuron–microglia signaling is acknowledged in the ECL model. In this paper, microglia activity is specifically found within the midmolecular layer of the dentate gyrus, which is the projection site of the transected neurons (see for review, [120]). The microglia activity at a site distant from the primary lesion indicates transport of the chemokine signal. Recent data reinforced this notion, showing that CCL2 that was induced in dorsal root ganglion (DRG) neurons after peripheral nerve injury was transported to afferent terminals in the spinal cord [34]. Moreover, in vitro neuronal CCL21 was sorted into vesicles, transported into neuronal processes, and even reached presynaptic terminals [46]. The finding of CCL21 protein in neuronal vesicles is a strong indication that neuronal chemokines may be the signals responsible for microglia activity at sites distant from the primary lesion, a phenomenon that has been observed also in humans [121, 122].
A role of CCL2 in microglia activity after neuronal death is suggested by a delayed microglia activity in the thalamus of CCL2-deficient mice in response to cortical injury [123]. The delayed microglia activity was accompanied by a transient improvement of neuron survival in the thalamus, which may indicate that CCL2 is involved in neurotoxic microglia activity [123]. However, it is not yet clear if this effect is due to disturbed neuron–microglia interaction. Damaged neurons are capable to express CCL2, as was found after axotomy in sympathetic ganglia [31] and facial nerve lesion [30], but cortical injury predominantly induced CCL2 mRNA expression in astrocytes [124]. Whether interference with CCL2 signaling would affect microglia activity in the first two models has not yet been investigated.
It is clear that the assumption that neuronal chemokines are involved in neuron–microglia signaling is no longer based on the finding that damaged neurons rapidly alter chemokine expression patterns and that microglia express the corresponding receptors [63–65]. Studies now show that microglia activation is reduced in mice with genetically disturbed chemokine function, indicating an important role of chemokines in microglia activation. Recent data even suggest that neurosupportive and neurotoxic microglia activity are associated with chemokine receptor expression [125]. The ultimate effects of neuronal chemokines are likely dependent on injury type, brain region, and disruption of the blood–brain barrier [53, 79, 116]. Whereas the exact role of neuronal chemokines in neuron–microglia signaling remains obscure, their importance in regulating damage responses is becoming apparent.
Neuron–Neuron Interaction
Various reports indicate that chemokines influence neuronal development, differentiation [126, 127], survival [128–130], electrophysiological properties [93, 131, 132], and synaptic transmission [26, 92, 133]. Because neurons can express numerous chemokines, autocrine and paracrine contributions of neuronal chemokines are likely.
Neuroprotection
Neuronal cell death, the ultimate consequence of all neuroinflammatory conditions, has been studied extensively in vitro. However, there are relatively few in vitro models that can be extrapolated to pathological conditions leading to neuronal death in vivo. One of the most prominent models is glutamate or NMDA-induced neurotoxicity, a model for excitotoxicity, which is most likely involved in various neurodegenerative diseases. β-amyloid-induced neuronal death serves as a model that may explain the loss of neurons in Alzheimer’s disease, whereas exposure of neuronal cultures to HIV proteins gp120 or HIVtat are aimed to elucidate neuronal death in HIV-dependent neurodegeneration [134–137]. Several reports indicate that neuronal chemokines may protect neurons from these toxic conditions. In vitro, CX3CL1 is known to protect neurons from glutamate-induced toxicity [78, 138], gp120-induced neuronal death [64, 139], and death induced by deprivation of trophic support [140].
Similar to CX3CL1, CCL2 exposure is shown to protect neurons from glutamate- and HIV-tat-induced neurotoxicity [141, 142]. However, CCL2 exposure was not protective in β-amyloid-dependent neuronal death [141]. As exposure of neuronal cells to chemokines is known to activate the putatively neuroprotective MEK/ERK and PI3-K/Akt signaling pathways [78, 138–140], it is reasonable to argue that chemokine-dependent protection is mediated by these pathways. Indeed, inhibition of both pathways completely abolished the neuroprotective effects of CX3CL1 in gp120- and glutamate-induced neurotoxicity in hippocampal neurons [64, 78]. Interestingly, in case of glutamate-dependent neurotoxicity, the involvement of MEK/ERK and PI3-K/Akt signaling pathways was only evident when CX3CL1 was applied together with glutamate [78]. CX3CL1 exposure was shown to be protective even when the chemokine was applied up to 8 h after the glutamate stimulus. However, an inhibition of MEK/ERK and PI3-K/Akt pathways did not affect the protective activity of delayed CX3CL1 exposure, indicating that CX3CL1 may activate additional pathways in neurons that lead to neuroprotection [78]. The effect of CXCL12 on neuronal death is contradictory. Although several reports indicate that CXCL12 exposure may protect neurons from gp120-induced neuronal death, most papers describe a toxic effect of CXCL12 in neuronal cultures (see below) [64, 143].
Neurotoxicity
Approximately 10% of HIV-infected patients develop HIV-1 associated dementia (HAD). It has been shown that the viral protein gp120 itself is neurotoxic [144], indicating that the neuronal loss in HAD is not only due to inflammation occurring after the virus enters the brain but also because of direct toxic effects of viral proteins (see for recent review, [145]). It was shown in 1998 that the neurotoxic effect of gp120 is mediated via the chemokine receptor CXCR4 [146], findings that have been corroborated in subsequent years by various groups [64, 102, 147, 148]. The viral protein gp120 binds and activates CXCR4, the main coreceptor utilized by HIV-1 to infect T cells. CXCR4 has subsequently become the best investigated chemokine receptor with respect to neurotoxicity, and its involvement in neurotoxic signaling has been demonstrated by use of the specific CXCR4 antagonist AMD31000 [102, 147]. The HIV-derived protein gp120 shows agonist activity on CXCR4, and therefore, it is not surprising that its ligand CXCL12 has also been described to be neurotoxic [102, 143, 146, 148, 149]. Currently, there is little information on intracellular signaling pathways that are activated by CXCR4 and subsequently lead to neuronal death. One recent report indicates the involvement of Src activity in CXCL12-induced apoptosis in a neuronal cell line, whereas gp120-induced apoptosis in these cells was independent of Src activity [149]. Interestingly, CXCL12 and gp120 had different effects on ERK activation in neurons and astrocytes [102], indicating that CXCR4 signaling exerts both ligand and cell-type specific effects. The effect of CXCL12 is further complicated by matrix metalloproteinase-2, which was shown to remove the first four amino acids of CXCL12, resulting in a truncated form of CXCL12 [150]. This truncated form was found to be highly neurotoxic compared to the full-length CXCL12, which remarkably was not mediated by CXCR4 but by a yet unknown PTX-sensitive receptor [150]. Because MMP-2 has also been described in HIV-infected patients, it is reasonable to assume that truncated CXCL12 may be a neurotoxic player in HAD [151].
CXCL12 is not the only neuronal chemokine that exerts neurotoxic effects. Neuronal cell lines and primary human neurons respond to high concentrations of CXCL10 with intracellular calcium transients, caspase activity, and apoptosis [54, 152, 153]. The direct involvement of CXCR3 was demonstrated by the use of an antibody that prevents the activation of CXCR3 and subsequently inhibited CXCL10-dependent neurotoxicity [153].
Chemokinergic Effects on Synaptic Transmission
Recent data show that CXCR4 activation by either gp120 or CXCL12 significantly enhanced giant depolarizing potentials (GDP) in rat neonatal hippocampus [154]. These GDPs only occur in the developing hippocampus and are involved in growth and synapse formation. These data may explain why HIV infections have a greater impact in the developing brain than in adults [152] and show that neuronal chemokines may change the electrophysiological properties of neurons, thereby corroborating earlier findings [131, 132, 155, 156].
The electrophysiological properties of neuronal chemokine receptors have predominantly been studied in cultured primary neurons or neuronal cell lines and brain slice cultures [157]. Remarkably, in cultures of DRG, cerebellar granule or Purkinje neurons, and hippocampal pyramidal cells, chemokines induced changes in the electrophysiological properties of only 10–20% of the neurons [64, 140, 158, 159]. In addition, several effects of chemokines in neurons were not sensitive to PTX, in contrast to hematopoietic cells, suggesting that chemokine receptors in neurons, although generally accepted, are not solely coupled to Gαi proteins [128, 140, 149]. Whether these chemokinergic PTX-insensitive effects are mediated by neuronal Gz-subunits is yet unclear [160]. Cultured cerebellar and DRG neurons respond to various chemokines with intracellular calcium transients [140]. In DRG neurons, exposure to CX3CL1 and CXCL12 also increased their excitability [158]. Although chemokinergic effects of CX3CL1, CXCL12, CCL2, and CXCL10 in neurons have been reported to modulate the frequency of both spontaneous and activity-dependent neuronal firing, a direct effect on the induction of action potentials has not yet been described [159, 161–164]. Similar to the effects on isolated neurons, CX3CL1, CXCL12, and CXCL10 also affected neuronal signaling in brain slice cultures [26, 78, 91–93, 133, 134, 165, 166]. However, the presence of glia cells (astrocytes, microglia, and oligodendrocytes) in these slice cultures makes it difficult to determine whether the electrophysiological effects of chemokines are mediated by chemokine receptors on neurons and/or on glia cells, as glia cells may also induce electrophysiological changes in neurons [26, 133, 156]. Whether the effects of CXCL12 in brain slice cultures are mediated via chemokine receptors on neurons and/or on glia cells may depend on the concentration, as concentrations up to 1 nM caused a direct decrease in peak and discharge frequency of evoked action potentials in neurons and concentrations higher than 10 nM activated an indirect GABA-mediated hyperpolarization of neurons [92].
Future Directions
As discussed here, neuronal chemokines appear to be versatile messengers in CNS cell interaction. However, several important issues need to be addressed in future studies. To begin with, neuronal CX3CR1 expression in vivo remains controversial. Immunohistochemical analysis revealed CX3CR1 positive neurons in mouse brain sections with little changes under pathological conditions [67], whereas neuronal CX3CR1 expression was never described in studies using genetically modified mice in which CX3CR1-expressing cells are also positive for EGFP [116]. Different microscopic techniques and models of neurodegeneration have been explored in these mice, demonstrating only CX3CR1 expression in resting and activated microglia [79, 105, 106, 116]. An explanation may be that neuronal CX3CR1 expression is at such a low level that detection is difficult to achieve with microscopic techniques. This may also be the case for CXCR3 expression in microglia. Although CXCR3 expression has yet not been described in microglia in vivo, functional evidence derived from CXCR3-deficient mice strongly indicates that microglia do express CXCR3 in vivo [53, 115]. Therefore, it seems appropriate that future experiments concerning the expression of chemokine receptors in CNS cells in vivo also include functional analysis.
Another issue that needs to be addressed in more detail regards cellular localization. Neurons are highly polarized cells, as their function is largely dependent on their morphology and contacts with other cells (e.g. synapses with other neurons). Although neuronal signaling molecules, such as neurotransmitters, neuropeptides, and neurotrophins, are generally found at specific sites, most reports describing neuronal chemokine expression did not address this issue. Interestingly, a few recent publications do suggest a localized expression of chemokines comparable to other neuronal signaling molecules. Our group demonstrated that neuronal CCL21 is transported in vesicles, reaching presynaptic terminals in cortical neurons in vitro [46]. In subsequent studies, these vesicles appeared to be of the large-dense core type, in which other neuronal peptides are also found (e.g. neurotrophins; Stanulovic et al., manuscript in preparation). Moreover, it has been described for several neuronal populations in vivo that CCL2 and CXCL12 colocalize with other neurotransmitters and neuropeptides in synaptic regions [37, 62]. Like neuronal chemokine expression, a site-specific expression of chemokine receptors may exist, as is suggested by CXCR4 redistribution in the axonal and dendritic compartment of hippocampal neurons after prolonged CXCL12 exposure [167]. Because a localized expression of chemokines and their receptors may have a consequence for their role in cell interaction, future studies on neuronal chemokine expression may address this issue.
At last, as all reports indicating that chemokine exposure alters the excitability of neurons used exogenous chemokines, it is yet unknown whether chemokines released from neurons have similar effects.
Conclusion
Knowledge on the spatial and temporal expression of neuronal chemokines and their regulation under physiological and pathological conditions is increasing rapidly. As CNS cells can express the corresponding chemokine receptors, contribution of these neuronal chemokines to CNS cell interaction is conceivable. This assumption is corroborated by various in vitro and in vivo studies. For example, the following effects of neuronal chemokines were observed in vitro: in astrocytes proliferation and migration, in microglia migration and neurotoxic and neuroprotective activity and in neurons electrophysiological changes, neurotoxicity, and neuroprotection. Further, the synaptic transmission between neurons seems to be influenced by the action of neuronal chemokines on neurons and/or glia cells. In vivo studies support the important role of chemokines in migration and neurotoxic and neuroprotective activity of microglia upon CNS injury and neuroinflammation. Further exploration of the roles of neuronal chemokines in CNS cell interaction is needful, as insight into the role of neuronal chemokines in CNS injury and neuroinflammation may contribute to the development of therapeutic strategies.

neurons
chemokines
central nervous system
cell interaction
cns
astrocytes
microglia