Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.
Charcot-Marie-Tooth Disease (CMT), also known as hereditary motor sensory neuropathy (HMSN), was first reported by French neurologists Charcot and Marie and British neurologist Tooth in 1886 [
Like many other degenerative disorders, hereditary peripheral neuropathies have been difficult to treat. There are currently no effectual pharmacologic treatments for CMT, limiting historic treatment to supportive care. In nearly a decade of research, ascorbic acid, progesterone antagonists, and subcutaneous neurotrophin-3 (NT3) injections have shown initial success in animal models of CMT 1A (the most common subtype of CMT) but have failed to translate any effect in humans [
The advent of next-generation sequencing (NGS) has expanded and accelerated the analysis of various diseases at the level of genome, especially in heterogeneous disorder groups such as CMT [
Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms. In this study, we firstly made a comprehensive selection of genes genetically related to CMT. We then performed a functional enrichment analysis to identify important biological topics within these genetic factors. In order to further explore the pathogenesis of CMT, we constructed a biological process network to explore possible crosstalk among the significant biological processes. Furthermore, we made a PPI network of these CMT-related genes using MCODE [
Candidate genes associated with CMT were collected by retrieving the human genetic association studies deposited in PUBMED (
The functional features of the CMT-related genes were examined by R software [
We further performed biological process crosstalk analysis to explore the interactions among significantly enriched biological processes. To evaluate the overlap between any chosen pairs of biological processes, two measurements were introduced, that is, Select a set of biological processes for crosstalk analysis. Only the biological processes with adjusted Count the number of shared candidate genes between pairs of biological processes, and remove the pair with less than three overlapped genes Calculate the average score of the JC [ Visualize the selected biological process crosstalk with the software Cytoscape (version 3.7.1) [
In the current study, we first identified the protein-protein interaction (PPI) network relationships of the CMT-related genes from human Integrated Interaction Database (IID) (available online:
Study design and procedures. The green arrow to the right is the results.
By searching PUBMED, there were more than 600 studies related to Charcot-Marie-Tooth Disease that were collected. In these publications, it was reported that 100 genes were significantly related to CMT and formed a gene set (CMTgset) for subsequent analysis (Table
Functional enrichment analysis can show a more specific function of these genes. GO enrichment analysis was performed to investigate the biological function of 100 genes in CMTgset (Table
Top 10 significant GO terms and hub gene counts. For each term, the number of enriched genes is indicated by the bar size; while the level of significance is represented by the color. Blue indicates low significance while red represents high significance (
Identifying biochemical pathways for the enrichment of candidate genes may provide valuable hints for us to understand the molecular mechanism of CMT. We searched for enriched pathways in the CMTgset using KOBAS 3.0 and found 14 significant enrichment pathways for CMT (Table
Pathways enriched in CMTgset.
Pathways | ID | Genes included in the pathwayc | ||
---|---|---|---|---|
Aminoacyl-tRNA biosynthesis | hsa00970 | GARS1, WARS, AARS1, HARS1, MARS1, YARS1, KARS1 | ||
Metabolic pathways | hsa01100 | NAGLU, GAMT, SGPL1, COX10, COX6A1, MTMR2, PRPS1, DNMT1, SPTLC2, HADHB, HK1, DGAT2, SPTLC1, POLG | ||
Vasopressin-regulated water reabsorption | hsa04962 | DCTN1, DCTN2, DYNC1H1 | ||
Sphingolipid metabolism | hsa00600 | SGPL1, SPTLC2, SPTLC1 | ||
Amyotrophic lateral sclerosis (ALS) | hsa05014 | SOD1, NEFL, NEFH | ||
Salmonella infection | hsa05132 | PFN2, RAB7A, DYNC1H1 | ||
Huntington’s disease | hsa05016 | DCTN1, COX6A1, SOD1, DCTN2 | ||
Sphingolipid signaling pathway | hsa04071 | SGPL1, SPTLC2, SPTLC1 | ||
Apoptosis | hsa04210 | LMNA, TUBA8, AIFM1 | ||
Carbohydrate digestion and absorption | hsa04973 | HK1, ATP1A1 | ||
Endocrine and other factor-regulated calcium reabsorption | hsa04961 | ATP1A1, DNM2 | ||
Phagosome | hsa04145 | TUBA8, RAB7A, DYNC1H1 | ||
Mineral absorption | hsa04978 | ATP1A1, ATP7A | ||
Central carbon metabolism in cancer | hsa05230 | HK1, SCO2 |
CMTgset: Charcot-Marie-Tooth Disease-related gene set. a
Significant pathway enrichment of CMTgset. Dark blue represents signaling pathway, and light blue represents candidate genes.
Since CMT might involve many genes and biological processes, taking a further step to understand how significantly enriched biological processes interact with each other, we performed a biological process crosstalk analysis among the 135 significantly enriched biological processes. The method was based on the assumption that two biological processes were considered to crosstalk if they shared a proportion of CMTgset [
Biological process crosstalk among CMTgset-enriched biological processes. Nodes represent biological processes, and edges represent crosstalk between biological processes. Edge-width corresponds to the score of specific biological process pair. Larger edge-width indicates higher score. (a) Represents one correlative module. (b) Represents two separate modules.
The correlative module also could be grouped into five modules, which could be defined as nervous system-related biological processes (e.g., peripheral nervous system development, ensheathment of neurons, axon ensheathment, and myelination), antigen presentation processes (e.g., antigen processing and presentation of exogenous peptide antigen, antigen processing and presentation of exogenous peptide antigen via MHC class II, and antigen processing and presentation of peptide antigen via MHC class II), transport processes (e.g., transport along microtubule, cytoskeleton-dependent intracellular transport, and axonal transport), oxidation processes (e.g., response to reactive oxygen species, response to oxidative stress, and cellular response to toxic substance), and mitochondrial related biological processes (e.g., mitochondrion organization, purine ribonucleotide metabolic process, and ATP metabolic process). At the same time, these five modules were not independent, but connected by the interaction of several biological processes. The other two separate modules, one related to autophagy (e.g., positive regulation of autophagy and macroautophagy) and one related to tRNA (e.g., tRNA aminoacylation for protein translation, tRNA aminoacylation, and tRNA metabolic process).
Through the online PPI analysis of IID, 88 of the 100 candidate genes can be mapped to the human interactome network, and 1698 predicted genes were screened out. Pathway-based MCODE cluster analysis was used to analyze the topological characteristics of PPI to help understand the potential biological mechanisms associated with the network. The higher the clustering scores, the more important the biological function of this clustering in the development of CMT. Then, we analyzed in detail the three clusters with the highest clustering scores. The results are shown in Figure
The red nodes are genes of CMTgset, and the green nodes are nonoriginal/extended genes.
Genes included in CMT top three specific PPI networks but not in the CMTgset.
Gene symbol | Gene name | Cluster |
---|---|---|
MTMR1 | Myotubularin-related protein 1 | Clusters A, B, and C |
AATK | Apoptosis-associated tyrosine kinase | Clusters A, B, and C |
HSF1 | Heat shock transcription factor 1 | Clusters A, B, and C |
PNISR | PNN interacting serine and arginine-rich protein | Clusters A, B, and C |
HSPB6 | Heat shock protein family B (small) member 6 | Clusters A, B, and C |
CRYAB | Crystallin alpha B | Clusters A, B, and C |
HSPA4 | Heat shock protein family A (Hsp70) member 4 | Clusters A, B, and C |
STUB1 | STIP1 homology and U-box containing protein 1 | Clusters A, B, and C |
MLF2 | Myeloid leukemia factor 2 | Clusters A, B, and C |
HSP90AB1 | Heat shock protein 90 alpha family class B member 1 | Clusters A, B, and C |
SQSTM1 | Sequestosome 1 | Clusters A, B, and C |
MAGED1 | MAGE family member D1 | Clusters A, B, and C |
IRAK1 | Interleukin 1 receptor-associated kinase 1 | Clusters A, B, and C |
EGFR | Epidermal growth factor receptor | Clusters A, B, and C |
SNW1 | SNW domain containing 1 | Clusters A, B, and C |
CDC5L | Cell division cycle 5 like | Clusters A, B, and C |
PKN1 | Protein kinase N1 | Clusters A, B, and C |
DYNLT1 | Dynein light chain Tctex-type 1 | Clusters A and B |
GRB2 | Growth factor receptor bound protein 2 | Clusters A and B |
NINL | Ninein like | Clusters A and B |
DCTN5 | Dynactin subunit 5 | Clusters A and B |
BICD2 | BICD cargo adaptor 2 | Clusters A and B |
SCLT1 | Sodium channel and clathrin linker 1 | Clusters A and B |
ACTR1A | Actin-related protein 1A | Clusters A and B |
MAPRE1 | Microtubule-associated protein RP/EB family member 1 | Clusters A and B |
NIN | Ninein | Clusters A and B |
DCTN4 | Dynactin subunit 4 | Clusters A and B |
HSP90AA1 | Heat shock protein 90 alpha family class A member 1 | Clusters A and B |
CNTRL | Centriolin | Cluster B |
TMEM17 | Transmembrane protein 17 | Cluster B |
CMT = Charcot-Marie-Tooth Disease; PPI = protein-protein interaction.
In the past few decades, much has been learnt about the molecular mechanisms underlying Charcot-Marie-Tooth Disease from studies on cell models, animals, or human subjects [
Although genetic association and biochemical studies based on candidate genes have provided us with the knowledge of factors involved in CMT, the systematic approach depicted in this work has clear advantages. Above all, we have comprehensively collected genes potentially genetically related to CMT in our study, which provided a valuable resource for further analysis. From the perspective of molecular network level, it is very important to explore the biological characteristics of genes related to CMT. Moreover, functional enrichment analysis considering the biological relevance of genes can more robustly deal with possible false positives caused by different genes in various studies, and combining with network analysis could provide a more comprehensive view of the molecular mechanism of CMT.
Biological function enrichment analysis revealed the specific biological processes involved by CMTgset. GO enrichment analysis showed that these genes for CMT participated in myelin sheath and axon-related processes, peripheral nervous system, Schwann cell, mitochondrial function, various metabolic processes, and autophagy. In addition, terms such as ensheathment of neurons, axon ensheathment, myelination, tRNA aminoacylation for protein translation, peripheral nervous system development, tRNA aminoacylation, amino acid activation, selective autophagy, Schwann cell differentiation, and response to unfolded protein were in the top ten enriched GO terms, indicating the important roles of these activities in the pathologic processes of CMT. The above findings are basically consistent with previous reports [
At the same time, the pathway analysis showed that 14 pathways were enriched and the first of which is aminoacyl-tRNA biosynthesis, which is considered to play an important role in CMT. Aminoacyl-tRNA synthetases (ARSs) are universally expressed enzymes accountable for charging tRNAs with their cognate amino acids, so it is crucial for the first step of protein synthesis [
Of significance, in biological process crosstalk analysis, we identified a correlative module and two separate modules. The correlative module could be grouped into five modules: the first module was mainly dominated by the biological processes related to the activity of the nervous system. Among these biological processes, axon ensheathment, myelination, peripheral nervous system development, and Schwann cell development have been well studied, involving the axons, myelin sheaths, or peripheral nervous system as well as the progress of Charcot-Marie-Tooth Disease [
The first three important PPI networks of the CMT-related genes were extracted from the human reference interactome network by MCODE clustering, which extends to its neighboring nodes with the seed node as the center. Then, the nodes that may interact with the seed nodes to build a complex in the PPI network were selected [
So far, the detection and analysis approaches of CMT are diverse, including linkage analysis, functional studies, and studies in the model organisms. However, the research of this disorder’s molecular mechanism have not yet broken through, which may be due to the inability of conventional single-gene analyses to explain complicated psychiatric phenotype. This paper takes a comprehensive analysis of potential causal genes within a pathway [
Of course, this study still has a few limitations. First of all, our functional enrichment analysis and PPI network analysis results are entirely dependent on the genes reported in the retrieved literature that are related to CMT, but more evidence needs to be used to further verify specific new genes. In other words, the results obtained by/in the bioinformatics approach should be further verified in the DNA samples obtained from CMT-affected patients. Secondly, our research accepts the results of the original authors of each retrieved study. Due to the imbalance and incompleteness of these existing studies, they will certainly bias our results. Third, to reduce the false positive rate of genes, we excluded publications that were negative or irrelevant. However, some of the genes in these studies may be related to the pathogenesis of CMT and were only excluded because of the small sample size or heterogeneity or other factors. In addition, although the quantity and quality of PPI databases have been greatly improved recently, the human interaction group is still incomplete and has many false positives.
In this study, we analyzed related genes collected from selected literature deposited in PUBMED, using the system biology framework to conduct a comprehensive, systematic biological function and network-based analysis of CMT. By integrating the information from GO, pathways, and biological process crosstalk analysis, the conclusions of this study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.
The statistical data of the article used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there is no conflict of interest regarding the publication of this paper.
This work was supported by grants from (1) the diagnostic value of circRNAs in breast cancer and the ceRNA regulatory network mechanism (2019YFS0332), (2) the key technologies of development and application of based on biochemical pipeline intelligent checks and interpretation system (2019YFH0010), and (3) construction of information management and shared service platform centered on clinical sample resources (2019YFS0038), all from the Science & Technology Department of Sichuan Province.
Supplemental Table 1: list of genes associated with Charcot-Marie-Tooth Disease. Supplemental Table 2: Gene Ontology and biological process terms enriched in CMT-related genes.