Table of Contents Author Guidelines Submit a Manuscript
Journal of Diabetes Research
Volume 2017, Article ID 8103904, 11 pages
Research Article

Gene Expression Profiling Identifies Downregulation of the Neurotrophin-MAPK Signaling Pathway in Female Diabetic Peripheral Neuropathy Patients

1Zhejiang Key Laboratory of Pathophysiology, Medical School of Ningbo University, Ningbo 315211, China
2Laboratory of Behavioral Neuroscience, Ningbo Addiction Research and Treatment Center, Medical School of Ningbo University, Ningbo 315211, China
3Department of Preventive Medicine, Medical School of Ningbo University, Ningbo 315211, China
4Department of Research and Teaching, Ningbo No. 2 Hospital, Ningbo 315010, China
5Department of Biochemistry, Medical School of Ningbo University, Ningbo 315211, China

Correspondence should be addressed to Jin Xu; nc.ude.ubn@1nijux and Lin-Dan Ji; nc.ude.ubn@nadnilij

Received 14 April 2017; Revised 14 June 2017; Accepted 2 July 2017; Published 16 August 2017

Academic Editor: Joseph F. Ndisang

Copyright © 2017 Lin Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). It is not diagnosed or managed properly in the majority of patients because its pathogenesis remains controversial. In this study, human whole genome microarrays identified 2898 and 4493 differentially expressed genes (DEGs) in DM and DPN patients, respectively. A further KEGG pathway analysis indicated that DPN and DM share four pathways, including apoptosis, B cell receptor signaling pathway, endocytosis, and Toll-like receptor signaling pathway. The DEGs identified through comparison of DPN and DM were significantly enriched in MAPK signaling pathway, NOD-like receptor signaling pathway, and neurotrophin signaling pathway, while the “neurotrophin-MAPK signaling pathway” was notably downregulated. Seven DEGs from the neurotrophin-MAPK signaling pathway were validated in additional 78 samples, and the results confirmed the initial microarray findings. These findings demonstrated that downregulation of the neurotrophin-MAPK signaling pathway may be the major mechanism of DPN pathogenesis, thus providing a potential approach for DPN treatment.

1. Introduction

Diabetes mellitus (DM) has become one of the largest global public health problems of this century. According to the World Health Organization global report on diabetes, over 8.5% of the global population, approximately 422 million adults, have diabetes [1]. Diabetes of all types can lead to complications in many parts of the body and can increase the overall risk of premature death. Potential complications include cardiomyopathy, vasculopathy, neuropathy, retinopathy, nephropathy, dermatopathy, and encephalopathy [2, 3]. Diabetic peripheral neuropathy (DPN) is characterized as a chronic symmetrical, progressive disorder with early symptoms of pain, allodynia, and paresthesia, and it affects approximately 50% of people with considerable morbidity, mortality, and diminished quality of life [4]. Despite the development of diagnostic methods and therapeutic modalities, DPN is still not diagnosed or managed properly in most patients [5].

Over the past decade, there have been many achievements in understanding the pathogenesis of DPN. However, controversy remains because of a multifactorial etiology involving both metabolic and vascular factors [6]. Chronic hyperglycemia used to be regarded as the major factor in the initiation of various metabolic events underlying DPN. Results from the Diabetes Control and Complication Trial (DCCT) support the hypothesis that DPN develops as a result of increased blood glucose and have suggested that treating hyperglycemia in type 1 DM can markedly decrease the incidence of DPN, by up to 60 to 70% [7]. However, more recent studies have reported that intensive glucose control in patients with type 2 DM has no significant effect on the development of DPN [8]. Over 40% of type 2 DM patients develop DPN despite good glucose control, thus suggesting that hyperglycemia is only one of the many key events that cause nerve and microvascular injury [9]. Nevertheless, hyperglycemia has effects on several major, well-characterized biochemical pathways that include activation of the polyol pathway [10], formation of advanced glycation end products (AGEs) and their receptors [11], activation of protein kinase C (PKC) [12] and inducible nitric oxide synthase [13], increased poly(ADP-ribose) polymerase (PARP) activity [14], and elevated inflammation [15]. Furthermore, oxidative stress and mitochondrial dysfunction [16], hypoxia and ischemia [17], elevated cytokines [18], and deficiencies in neurotrophic factors [19] also play significant etiologic roles in DPN. Despite advances in delineating the etiology of DPN, few effective therapies exist to manage, delay, or prevent the development of painful DPN. Therefore, identifying precise mechanisms and related therapeutic drugs remains paramount.

Systems biology approaches, such as whole genome expression profiling, may provide new insights into the molecular mechanisms of DPN without preset bias. However, previous studies have identified global transcriptomic changes only in animal models [20]. In the present study, we used high-throughput genome-wide expression microarrays to identify alterations in the transcriptome, both common and distinct, between type 2 DM and DPN. The results may provide relevant information for the future development of new mechanism-based diagnostics and therapies.

2. Material and Methods

2.1. Study Design

A workflow was designed to identify candidate genes whose expression levels may differentiate among healthy controls (CN), DM patients, and DPN patients (Figure 1). After obtaining three individual differentially expressed gene (DEG) datasets (DM versus CN, DPN versus CN, and DPN versus DM), we used a Venn diagram to identify the shared and distinct gene expression changes between DPN and DM. Subsequently, several gene annotation databases were used to identify the potential biological functions and signaling pathways involved in DPN. Finally, DEGs of interest were further validated in an independent sample set.

Figure 1: The workflow of the study. The asterisks represent statistical significance () compared with the control group.
2.2. Participants

The participants were recruited from hospitals affiliated with Ningbo University from April 2015 to June 2016. The healthy controls had no family history of diabetes or neurologic disorders, exhibited normal glucose tolerance, and were free of any major chronic diseases. Type 2 DM cases were defined as meeting at least one of the following criteria: fasting plasma glucose ≥7 mmol/L, 2 h plasma glucose after oral glucose tolerance test (OGTT) ≥ 11.1 mmol/L, and use of glucose-lowering drugs or physician-diagnosed diabetes. Diabetic peripheral neuropathy was diagnosed by a positive assessment through neurologic examinations and nerve conduction studies, as previously described [20]. In brief, the criteria for DPN were as follows: (1) confirmed type 2 DM patients; (2) decreased sensation and positive neuropathic sensory symptoms (including pricking, burning, stabbing, or aching pain) in the toes, feet, or legs; (3) decreased distal sensation and unequivocally decreased or absent ankle reflexes; (4) and abnormal motor and sensory nerve conduction. Patients with type 1 DM, cardiovascular diseases, previous history of neurologic disorders, peripheral vascular occlusive disease, autoimmune disease, or any other possible causes of peripheral neuropathy were excluded from all groups. Patients with any clinically observable diabetic complications were excluded from the DM group. The DPN patients involved in the present study were all new cases without any antineuropathy medication. Patients with any other major acute or chronic complication associated with diabetes or vitamin B12/folic acid deficiencies were excluded from the DPN group. The protocol of this study was approved by the medical ethics committee of Ningbo University. The health records and blood samples (2 mL) of the participants were collected after informed written consents were provided by the subjects. A total of 6 healthy controls, 6 DM patients, and 6 DPN patients were recruited for the microarray analysis. In the validation stage, another 26 healthy controls, 26 DM patients, and 26 DPN patients were included. An overview of the clinical and demographic characteristics of the participants can be found in Table 1.

Table 1: Clinical and demographic characteristics of the participants.
2.3. RNA Preparation

Total RNA was isolated from peripheral blood mononuclear cells (PBMCs) by using a TaKaRa MiniBEST Universal RNA Extraction Kit according to the manufacturer’s instructions. The RNA quality was determined on the basis of an optical density (OD) 260/280 ratio ≥ 1.8 and OD260/230 ratio ≥ 2.0 using a NanoDrop ND-1000 spectrophotometer. RNA integrity was determined using an Agilent 2100 Bioanalyzer. The intensity of the 18S and 28S rRNA bands was examined on a 1% formaldehyde-agarose gel. RNA samples with an RNA integrity number (RIN) of ≥6.0 and 28S/18S > 1.5 were subjected to microarray analysis.

2.4. Microarray Analysis

The microarray analysis was performed by BGI Inc., including RNA amplification, probe labeling, hybridization, and data extraction. Briefly, aliquots (100 ng) of total RNA were amplified and transcribed into fluorescent Cy5-labeled antisense RNAs (aRNAs) by using a OneArray Amino Allyl aRNA Amplification Kit (Phalanx Biotech, San Diego, CA) according to the manufacturer’s instructions. The Cy5-labeled aRNAs were fragmented and then hybridized to the Human Whole Genome OneArray Version 6.0 (Phalanx Biotech, San Diego, CA). Nonspecific binding targets were washed out three times. The arrays were scanned by an Agilent G2505C Microarray Scanner (Agilent Technologies, Wilmington, DE). The fluorescence intensities of each spot were analyzed with Feature Extraction software (Agilent Technologies, Wilmington, DE). Overall, 18 microarray chips were analyzed in this study.

2.5. Statistical Analysis of the Microarray Data

The microarray data were normalized using the R/Bioconductor Limma package [21]. An empirical Bayes model was used to compare DEGs between groups, and the criteria for DEGs were FDR < 0.05 and |log2 (ratio)| ≥ 1. Hierarchical clustering was performed to visualize distinguishable gene expression profiles among the samples. The gene annotation and biological interpretation of the identified DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 [22]. Biological functions, represented by Gene Ontology terms ( and Kyoto Encyclopedia of Genes and Genomes pathways (, were deemed significant at a Benjamini-Hochberg-corrected The R/Bioconductor Pathview package was used for pathway-based gene data integration and visualization [23].

2.6. Quantitative Real-Time PCR (qRT-PCR)

Seven representative genes selected from DEGs involved in the neurotrophin-MAPK signaling pathway were validated by qRT-PCR in another 26 independent samples from each group (CN, DM, and DPN). Total RNA was extracted as described above, and double-stranded cDNA was synthesized using a PrimeScript RT Reagent Kit (TaKaRa Biotechnology, Dalian, China) according to the manufacturer’s instructions. Real-time PCR was performed using LightCycler 480 SYBR Green I Master mix (Roche, Mannheim, Germany). The 2−ΔΔCT method was used to quantify the relative expression of each gene, using GAPDH expression to normalize each sample. All experiments were run in triplicate and repeated three times. Differences in expression among groups were evaluated with a one-way analysis of variance (ANOVA) using SPSS 16.0 software. A significant difference was considered to be indicated by .

3. Results

3.1. Identification of DEGs among DM Patients, DPN Patients, and Healthy Controls

The gene expression data from this study are available in the Gene Expression Omnibus (GEO, accession number GSE95849). Compared with healthy individuals, patients with DM and DPN exhibited 2795 and 3606 upregulated genes, respectively, whereas 103 genes and 887 genes were expressed at a lower level in DM and DPN patients compared with the healthy controls (Table 2). Among these DEGs, DM and DPN patients shared 1947 upregulated and 95 downregulated genes. In DPN patients, compared with DM patients, 1942 genes were differentially expressed, including 931 upregulated and 1011 downregulated genes (Table 2).

Table 2: Number of differentially expressed genes in different groups.
3.2. Functional Annotation and Enrichment Analysis of the DEGs

According to the GO analysis, the DEGs of DM and DPN patients were enriched in many similar GO terms as well as distinct GO terms (Figure 2). Compared with the profile of healthy controls, the DM-associated gene expression profile primarily included DEGs related to intracellular transport, immune response, cell activation, protein localization, and inflammatory response (Figure 2(a)). The DPN patients exhibited differential expression of genes involved in protein transport, protein localization, leukocyte activation, immune response, protein kinase cascades, and cell death (Figure 2(b)). Between the DPN and DM group, the differentially expressed genes were enriched in cell activation, cellular response to stress, and cell death as well as in the regulation of transcription and translation (Figure 2(c)).

Figure 2: The top 10 biological processes identified by GO analysis of the DEGs: (a) DPN versus CN, (b) DM versus CN, and (c) DPN versus DM. Bars represent −log ( value).

KEGG pathway analysis revealed that, in comparison with healthy controls, DPN and DM shared four pathways (Table 2), including apoptosis (hsa04210), B cell receptor signaling pathway (hsa04662), endocytosis (hsa04144), and Toll-like receptor signaling pathway (hsa04620). Moreover, Fc gamma R-mediated phagocytosis (hsa04666), chemokine signaling pathway (hsa04062), and insulin signaling pathway (hsa04910) were enriched in the DM group, and NOD-like receptor signaling pathway (hsa04621), lysosome (hsa04142), valine, leucine and isoleucine degradation (hsa00280), and amino sugar and nucleotide sugar metabolism (hsa00520) were enriched in the DPN group. In contrast, for DEGs between DPN and DM patients, the most enriched pathway was MAPK signaling pathway (hsa04010), followed by NOD-like receptor signaling pathway (hsa04621) and neurotrophin signaling pathway (hsa04722). For the gene set lists of these three pathways, see electronic Supplementary Table 1 available online at When the DEGs identified between DPN and DM patients were further analyzed with regard to upregulation and downregulation, the upregulated genes were enriched in the infectious disease category, whereas the downregulated genes were enriched in the “neurotrophin signaling pathway” and “MAPK signaling pathway” (Table 3). A KEGG Pathview analysis also showed that downregulation of the neurotrophin-MAPK signaling pathway was the major pathogenesic pathway in diabetic peripheral neuropathy patients (Figure 3).

Table 3: The significant KEGG pathways for the DEGs of DPN versus DM.
Figure 3: Downregulation of the neurotrophin-MAPK signaling pathway in DPN.
3.3. Validation of DEGs in the Neurotrophin-MAPK Signaling Pathway

Seven DEGs selected from the “neurotrophin-MAPK signaling pathway” were further validated in an additional 78 participants by qRT-PCR. These genes are BDNF (brain-derived neurotrophic factor), NTRK2 (neurotrophic tyrosine kinase type 2 receptor, also known as TrkB), SH2B2 (SH2B adaptor protein 2, also known as rAPS), MAPK3 (mitogen-activated protein kinase 3, also known as Erk), MAPK12 (mitogen-activated protein kinase 12, also known as p38), MAPKAPK2 (mitogen-activated protein kinase-activated protein kinase 2), and ATF4 (activating transcription factor 4, also known as CREB). The results confirmed the initial microarray findings for all of the genes except MAPK3, whose expression was not changed in DM patients or in DPN patients (Figure 4).

Figure 4: Microarray data were validated by qRT-PCR. The results are expressed as the mean ± SD. The asterisks represent statistical significance () compared with the control group.

4. Discussion

DPN is the most common complication of diabetes, affecting up to 50% of patients, and it contributes significantly to pain, loss of sensation, numbness, injury, and lower extremity amputation [4]. Because complex pathways are implicated in the pathophysiology of DPN, there are still no specific treatments and no means of predicting or preventing DPN onset or progression. Gene expression microarrays have been widely used in diabetic studies, because alterations in transcriptional profiles provide a robust and sensitive way to better understand the mechanisms of the disease and its complications. Microarray analyses have previously been used to investigate the mechanisms underlying diabetic cardiomyopathy [24], diabetic nephropathy [25], diabetic bone disease [26], and diabetic periodontitis [27] in diabetic patients or animal models. However, only one study has been conducted in patients with diabetic neuropathy to identify the DEGs and the related biological pathways responsible for the progression of diabetic neuropathy [28]. That study comprised a microarray experiment performed on human sural nerves collected from 50 patients with diabetic neuropathy during a 52-week clinical trial. Hyperglycemia may cause damage to the majority of the peripheral nerve system, not just the sural nerve. Moreover, as mentioned in that article, it is highly unlikely that future studies of diabetic neuropathy in patients will include the collection of sural nerve biopsies [28]. Therefore, performing new transcriptional microarray analyses with commonly used human sample material, such as PBMCs, to identify the gene expression signatures of DPN is necessary. Actually, transcriptome studies in PBMCs have been widely used in diabetic studies. Gene expression profiles in PBMCs can clinically stratify patients with recent-onset type 1 DM [29] and reflect the pathophysiology of type 2 DM [30]. In PBMCs from children with diabetes, the gene expression microarrays identified that type 1 and type 2 DM likely shared a common pathway for β-cell dysfunction that includes secretion of IL-1β and prostaglandins by immune effector cells [31]. Furthermore, a microarray study of gene expression in PBMCs identified that THBS1 and COX1 genes were upregulated, while MMP9 and COX2 genes were downregulated in patients with diabetic nephropathy [32].

The abovementioned microarray study on DPN identified DEGs that are functionally enriched in inflammatory responses and defense response pathways and may potentially be responsible for the progression of diabetic neuropathy [28]. In the current study, we found that DPN and DM shared four pathways, at least two of which were directly associated with immune-related functions, the “B cell receptor signaling pathway” and “Toll-like receptor signaling pathway.” It is widely accepted that inflammation and immunity are crucially involved in diabetes and a majority of diabetic complications [33, 34]; therefore, changes in the immune response may not be a distinct mechanism of DPN. We further compared the expression profiles of DPN and DM patients and identified that DPN-specific DEGs were significantly enriched in “MAPK signaling pathway”, “NOD-like receptor signaling pathway”, and “neurotrophin signaling pathway”. A stratification analysis further indicated that “neurotrophin signaling pathway” and “MAPK signaling pathway” were the top two pathways with downregulated DEGs. Although the DEGs in neurotrophin-MAPK signaling pathway are all located in autosome, caution should still be taken since only female subjects were involved in the present study, and more experiments are required to confirm it in male patients.

Neurotrophic factors are essential molecules that develop and maintain the nervous system by promoting the growth and survival of neurons. The neurotrophin family of growth factors includes nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and neurotrophin- (NT-) 3/4/5. Glial cell-derived neurotrophic factors (GDNF) form a second family and include GDNF, neurturin, artemin, and persephin [35, 36]. Deficiencies in NGF and NT-3 have long been reported in both the tissue and sera of DPN patients. Diabetes also decreases the anterograde and retrograde axonal transport of BDNF, NGF, and NT-3 in peripheral nerves [3739]. In the current study, the expression levels of NGF, NT-3, and NT-4 remained unchanged. However, the BDNF level decreased significantly. BDNF plays important roles in regulating the survival and growth of neurons and influences synaptic efficiency and plasticity. The human BDNF gene consists of 11 exons, and the majority of the BDNF transcripts are detected not only in the brain but also in blood cells [40]. BDNF mediates neuronal differentiation and survival by binding and activating tropomyosin receptor kinase B (TrkB), an important member of the larger Trk family [41]. In the current study, the NTRK2 gene, which encodes TrkB in humans, was specifically downregulated in DPN patients but not in DM patients, thus strongly suggesting that downregulation of the BDNF-TrkB signaling pathway is associated with DPN.

The binding of BDNF to TrkB leads to the dimerization and autophosphorylation of tyrosine residues in the intracellular domain of the receptor [42], which in turn leads to phosphorylation of tyrosine residues in the juxtamembrane domain or the C-terminus of the receptor [43]. These tyrosine residues serve as docking sites for multiple adaptor molecules, including Shc adaptor proteins, fibroblast growth factor receptor substrate 2 (FRS2), phospholipase Cγ (PLCγ), SH2B adaptor proteins, ankyrin repeat-rich membrane spanning (ARMS), Csk homology kinase (CHK), insulin receptor substrate 1 (IRS1), and c-ABL1 [43]. Among these adaptor molecules, only the expression level of SH2B2 (also known as rAPS) decreased in DPN patients, probably due to the downregulation of TrkB. TrkB activates three major intracellular signaling cascades: the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol 3-kinase (PI3K)-Akt pathway, and the PLCγ-Ca2+ pathway [44]. However, in the current study, only the MAPK pathway was affected in DPN patients. Among the three major MAPK family members, the Erk1/2 and p38 pathways were found to be downregulated in our original microarray study. However, after qRT-PCR validation, only the expression level of p38 decreased. This result is actually reasonable, because the direct downstream target of p38, MAPKAPK2, was also downregulated, whereas the downstream targets of Erk1/2, MSK1, and p90RSK were unchanged.

In neuronal development and function, neurotrophins activate the p38 MAPK/MAPKAPK2 pathway through Trk receptor-mediated signaling mechanisms [45]. Subsequently, MAPKAPK2 phosphorylates CREB (cAMP response element binding protein) and other transcription factors [46]. These transcription factors in turn regulate the expression of genes whose products are involved in many aspects of neural development and function, including cell fate decisions, axon growth, dendrite pruning, synaptic function, and plasticity [45]. In the present study, 2898 genes exhibited altered expression in DM patients, whereas this number in DPN patients was 4493. Moreover, in DPN patients, compared with DM patients, the number of downregulated genes increased 7 times, possibly because of decreased CREB expression mediated by the downregulation of the neurotrophin-MAPK signaling pathway.

In addition to the “neurotrophin signaling pathway” and “MAPK signaling pathway,” the present study also showed that the “NOD-like receptor signaling pathway” was affected in DPN patients. Nucleotide-binding oligomerization domain containing 2 (NOD2), a member of the NOD-like receptor family, has been found to be one of the critical components of a signal transduction pathway linking renal injury to inflammation and podocyte insulin resistance in diabetic nephropathy [47]. A recent study has also suggested that the formation and activation of the NOD-like receptor protein 1 (NLRP1) inflammasome induces neuroinflammation and neuron injury during hyperglycemia, thus representing a novel mechanism of diabetes-associated neuron injury [48]. Interestingly, our microarray data indicated that the expression of NOD2 and NLRP1 remained unchanged in DM patients but increased significantly in DPN patients (3.76-fold for NOD2 and 2.07-fold for NLRP1). Therefore, the NOD-like receptor signaling pathway may be an important mechanism in the pathogenesis of diabetic complications.

It is now widely accepted that increased inflammation is a key etiological factor in the development of many chronic diseases, including diabetes [49]. Similar to Nod-like receptors, Toll-like receptors (TLRs) belong to another major family of pattern recognition receptors, which have been demonstrated to play a critical role in the innate immune system [50]. Among these TLRs, TLR4 plays an important role in many inflammatory disorders, and system inflammation facilitated by TLR4 is involved in the pathophysiological process of diabetes [51]. A recent study has further demonstrated that TLR4 may be a potential diagnostic biomarker for DPN [52]. In the current microarray study, TLR4 expression increased in both DM and DPN patients, and there was even higher expression in the DPN patients (Table 4), in accordance with the results of the abovementioned study [52]. Moreover, it has been suggested that the inflammatory effects of high glucose may be mediated through the modulation of inflammatory responses resulting from TLR activation in diabetes [53]. The present microarray study showed that the “Toll-like receptor signaling pathway” was significantly enriched, and all TLR family genes were upregulated in both DPN and DM patients (Tables 4 and 5). These results strongly suggest that although some TLR genes, such as TLR4 and TLR7, were expressed at much higher levels in DPN patients, they may not be sensitive biomarkers for DPN, because the TLR signaling pathway is involved in diabetes and multiple diabetic complications.

Table 4: Expression of TLR family genes.
Table 5: The significant KEGG pathways for the DEGs of different groups.

5. Conclusions

In summary, these findings provide the first demonstration that downregulation of the neurotrophin-MAPK signaling pathway may be the major pathogenesis of DPN. The use of growth factors in treating DPN has been extensively explored [54], and NGF, BDNF, and NT-3 have been assessed in various levels of clinical trials of DPN, with limited success [35]. The current study suggests that pharmacological targeting of both the neurotrophin signaling pathway and the MAPK signaling pathway at multiple levels may provide a potential approach for the treatment of DPN.


BDNF:Brain-derived neurotrophic factor
CREB:cAMP response element binding protein
DAVID:Database for Annotation, Visualization and Integrated Discovery
DEG:Differentially expressed gene
DM:Diabetes mellitus
DPN:Diabetic peripheral neuropathy
GO:Gene Ontology
KEGG:Kyoto Encyclopedia of Genes and Genomes
NGF:Nerve growth factor
OGTT:Oral glucose tolerance test
qRT-PCR:Quantitative real-time PCR
RIN:RNA integrity number
TLR:Toll-like receptor.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


The authors thank all the study participants and the clinical staffs from the affiliated hospitals of Ningbo University for their contribution to this research study. This work was supported by the Applied Research Project on Nonprofit Technology of Zhejiang Province (2015C33309), the National Natural Science Foundation of China (81541039, 81402747), the Natural Science Foundation of Ningbo (2016A610085, 2017A610199), and the K.C. Wong Magna Fund of Ningbo University.


  1. World Health Organization, Global Report on Diabetes, Geneva, 2016.
  2. R. Singh, L. Kishore, and N. Kaur, “Diabetic peripheral neuropathy: current perspective and future directions,” Pharmacological Research, vol. 80, pp. 21–35, 2014. View at Google Scholar
  3. J. M. Forbes and M. E. Cooper, “Mechanisms of diabetic complications,” Physiological Reviews, vol. 93, no. 1, pp. 137–188, 2013. View at Google Scholar
  4. J. L. Edwards, A. M. Vincent, H. T. Cheng, and E. L. Feldman, “Diabetic neuropathy: mechanisms to management,” Pharmacology & Therapeutics, vol. 120, no. 1, pp. 1–34, 2008. View at Google Scholar
  5. S. Tesfaye and D. Selvarajah, “Advances in the epidemiology, pathogenesis and management of diabetic peripheral neuropathy,” Diabetes/Metabolism Research and Reviews, vol. 28, Supplement 1, pp. 8–14, 2012. View at Google Scholar
  6. N. E. Cameron, S. E. Eaton, M. A. Cotter, and S. Tesfaye, “Vascular factors and metabolic interactions in the pathogenesis of diabetic neuropathy,” Diabetologia, vol. 44, no. 11, pp. 1973–1988, 2001. View at Google Scholar
  7. DCCT Research Group, “The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group,” The New England Journal of Medicine, vol. 329, no. 14, pp. 977–986, 1993. View at Google Scholar
  8. W. Duckworth, C. Abraira, T. Moritz et al., “Glucose control and vascular complications in veterans with type 2 diabetes,” The New England Journal of Medicine, vol. 360, no. 2, pp. 129–139, 2009. View at Google Scholar
  9. K. Juster-Switlyk and A. G. Smith, “Updates in diabetic peripheral neuropathy,” F1000Res, vol. 5, 2016. View at Google Scholar
  10. P. J. Oates, “Polyol pathway and diabetic peripheral neuropathy,” International Review of Neurobiology, vol. 50, pp. 325–392, 2002. View at Google Scholar
  11. S. Y. Goh and M. E. Cooper, “Clinical review: the role of advanced glycation end products in progression and complications of diabetes,” The Journal of Clinical Endocrinology and Metabolism, vol. 93, no. 4, pp. 1143–1152, 2008. View at Google Scholar
  12. I. G. Obrosova, “Diabetes and the peripheral nerve,” Biochimica et Biophysica Acta, vol. 1792, no. 10, pp. 931–940, 2009. View at Google Scholar
  13. I. Vareniuk, I. A. Pavlov, and I. G. Obrosova, “Inducible nitric oxide synthase gene deficiency counteracts multiple manifestations of peripheral neuropathy in a streptozotocin-induced mouse model of diabetes,” Diabetologia, vol. 51, no. 11, pp. 2126–2133, 2008. View at Google Scholar
  14. I. G. Obrosova, W. Xu, V. V. Lyzogubov et al., “PARP inhibition or gene deficiency counteracts intraepidermal nerve fiber loss and neuropathic pain in advanced diabetic neuropathy,” Free Radical Biology & Medicine, vol. 44, no. 6, pp. 972–981, 2008. View at Google Scholar
  15. N. E. Cameron and M. A. Cotter, “Pro-inflammatory mechanisms in diabetic neuropathy: focus on the nuclear factor kappa B pathway,” Current Drug Targets, vol. 9, no. 1, pp. 60–67, 2008. View at Google Scholar
  16. A. M. Vincent, J. W. Russell, P. Low, and E. L. Feldman, “Oxidative stress in the pathogenesis of diabetic neuropathy,” Endocrine Reviews, vol. 25, no. 4, pp. 612–628, 2004. View at Google Scholar
  17. P. J. Dyck and C. Giannini, “Pathologic alterations in the diabetic neuropathies of humans: a review,” Journal of Neuropathology and Experimental Neurology, vol. 55, no. 12, pp. 1181–1193, 1996. View at Google Scholar
  18. J. Doupis, T. E. Lyons, S. Wu, C. Gnardellis, T. Dinh, and A. Veves, “Microvascular reactivity and inflammatory cytokines in painful and painless peripheral diabetic neuropathy,” The Journal of Clinical Endocrinology and Metabolism, vol. 94, no. 6, pp. 2157–2163, 2009. View at Google Scholar
  19. S. C. Apfel, “Nerve growth factor for the treatment of diabetic neuropathy: what went wrong, what went right, and what does the future hold?” International Review of Neurobiology, vol. 50, pp. 393–413, 2002. View at Google Scholar
  20. J. Hur, P. D. O'Brien, V. Nair et al., “Transcriptional networks of murine diabetic peripheral neuropathy and nephropathy: common and distinct gene expression patterns,” Diabetologia, vol. 59, no. 6, pp. 1297–1306, 2016. View at Google Scholar
  21. G. K. Smyth, Limma: Linear Models for Microarray Data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer, 2005.
  22. W. Huang da, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at Google Scholar
  23. W. Luo and C. Brouwer, “Pathview: an R/Bioconductor package for pathway-based data integration and visualization,” Bioinformatics, vol. 29, no. 14, pp. 1830-1831, 2013. View at Google Scholar
  24. J. Lasheras, M. Vila, M. Zamora et al., “Gene expression profiling in hearts of diabetic mice uncovers a potential role of estrogen-related receptor gamma in diabetic cardiomyopathy,” Molecular and Cellular Endocrinology, vol. 430, pp. 77–88, 2016. View at Google Scholar
  25. M. L. Caramori, Y. Kim, A. B. Goldfine et al., “Differential gene expression in diabetic nephropathy in individuals with type 1 diabetes,” The Journal of Clinical Endocrinology and Metabolism, vol. 100, no. 6, pp. E876–E882, 2015. View at Google Scholar
  26. A. T. Haug, K. F. Braun, S. Ehnert et al., “Gene expression changes in cancellous bone of type 2 diabetics: a biomolecular basis for diabetic bone disease,” Langenbeck's Archives of Surgery, vol. 399, no. 5, pp. 639–647, 2014. View at Google Scholar
  27. O. M. Andriankaja, J. Galicia, G. Dong, W. Xiao, F. Alawi, and D. T. Graves, “Gene expression dynamics during diabetic periodontitis,” Journal of Dental Research, vol. 91, no. 12, pp. 1160–1165, 2012. View at Google Scholar
  28. J. Hur, K. A. Sullivan, M. Pande et al., “The identification of gene expression profiles associated with progression of human diabetic neuropathy,” Brain, vol. 134, Part 11, pp. 3222–3235, 2011. View at Google Scholar
  29. K. M. Irvine, P. Gallego, X. An et al., “Peripheral blood monocyte gene expression profile clinically stratifies patients with recent-onset type 1 diabetes,” Diabetes, vol. 61, no. 5, pp. 1281–1290, 2012. View at Google Scholar
  30. T. Takamura, M. Honda, Y. Sakai et al., “Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetes,” Biochemical and Biophysical Research Communications, vol. 361, no. 2, pp. 379–384, 2007. View at Google Scholar
  31. E. C. Kaizer, C. L. Glaser, D. Chaussabel, J. Banchereau, V. Pascual, and P. C. White, “Gene expression in peripheral blood mononuclear cells from children with diabetes,” The Journal of Clinical Endocrinology and Metabolism, vol. 92, no. 9, pp. 3705–3711, 2007. View at Google Scholar
  32. D. K. Moczulski, H. Fojcik, A. Wielgorecki et al., “Expression pattern of genes in peripheral blood mononuclear cells in diabetic nephropathy,” Diabetic Medicine, vol. 24, no. 3, pp. 266–271, 2007. View at Google Scholar
  33. J. Wada and H. Makino, “Innate immunity in diabetes and diabetic nephropathy,” Nature Reviews Nephrology, vol. 12, no. 1, pp. 13–26, 2016. View at Google Scholar
  34. J. Nilsson, E. Bengtsson, G. N. Fredrikson, and H. Bjorkbacka, “Inflammation and immunity in diabetic vascular complications,” Current Opinion in Lipidology, vol. 19, no. 5, pp. 519–524, 2008. View at Google Scholar
  35. K. L. Farmer, C. Li, and R. T. Dobrowsky, “Diabetic peripheral neuropathy: should a chaperone accompany our therapeutic approach?” Pharmacological Reviews, vol. 64, no. 4, pp. 880–900, 2012. View at Google Scholar
  36. T. J. Boucher and S. B. McMahon, “Neurotrophic factors and neuropathic pain,” Current Opinion in Pharmacology, vol. 1, no. 1, pp. 66–72, 2001. View at Google Scholar
  37. R. Hellweg and H. D. Hartung, “Endogenous levels of nerve growth factor (NGF) are altered in experimental diabetes mellitus: a possible role for NGF in the pathogenesis of diabetic neuropathy,” Journal of Neuroscience Research, vol. 26, no. 2, pp. 258–267, 1990. View at Google Scholar
  38. R. Hellweg, G. Raivich, H. D. Hartung, C. Hock, and G. W. Kreutzberg, “Axonal transport of endogenous nerve growth factor (NGF) and NGF receptor in experimental diabetic neuropathy,” Experimental Neurology, vol. 130, no. 1, pp. 24–30, 1994. View at Google Scholar
  39. A. P. Mizisin, N. A. Calcutt, D. R. Tomlinson, A. Gallagher, and P. Fernyhough, “Neurotrophin-3 reverses nerve conduction velocity deficits in streptozotocin-diabetic rats,” Journal of the Peripheral Nervous System, vol. 4, no. 3-4, pp. 211–221, 1999. View at Google Scholar
  40. A. Cattaneo, N. Cattane, V. Begni, C. M. Pariante, and M. A. Riva, “The human BDNF gene: peripheral gene expression and protein levels as biomarkers for psychiatric disorders,” Translational Psychiatry, vol. 6, no. 11, article e958, 2016. View at Google Scholar
  41. B. Xu, W. Gottschalk, A. Chow et al., “The role of brain-derived neurotrophic factor receptors in the mature hippocampus: modulation of long-term potentiation through a presynaptic mechanism involving TrkB,” The Journal of Neuroscience, vol. 20, no. 18, pp. 6888–6897, 2000. View at Google Scholar
  42. M. E. Cunningham and L. A. Greene, “A function-structure model for NGF-activated TRK,” The EMBO Journal, vol. 17, no. 24, pp. 7282–7293, 1998. View at Google Scholar
  43. L. Minichiello, “TrkB signalling pathways in LTP and learning,” Nature Reviews Neuroscience, vol. 10, no. 12, pp. 850–860, 2009. View at Google Scholar
  44. E. J. Huang and L. F. Reichardt, “Trk receptors: roles in neuronal signal transduction,” Annual Review of Biochemistry, vol. 72, pp. 609–642, 2003. View at Google Scholar
  45. E. J. Huang and L. F. Reichardt, “Neurotrophins: roles in neuronal development and function,” Annual Review of Neuroscience, vol. 24, pp. 677–736, 2001. View at Google Scholar
  46. B. Mayr and M. Montminy, “Transcriptional regulation by the phosphorylation-dependent factor CREB,” Nature Reviews Molecular Cell Biology, vol. 2, no. 8, pp. 599–609, 2001. View at Google Scholar
  47. P. Du, B. Fan, H. Han et al., “NOD2 promotes renal injury by exacerbating inflammation and podocyte insulin resistance in diabetic nephropathy,” Kidney International, vol. 84, no. 2, pp. 265–276, 2013. View at Google Scholar
  48. X. F. Meng, X. L. Wang, X. J. Tian et al., “Nod-like receptor protein 1 inflammasome mediates neuron injury under high glucose,” Molecular Neurobiology, vol. 49, no. 2, pp. 673–684, 2014. View at Google Scholar
  49. M. Y. Donath, “Multiple benefits of targeting inflammation in the treatment of type 2 diabetes,” Diabetologia, vol. 59, no. 4, pp. 679–682, 2016. View at Google Scholar
  50. S. Akira and K. Takeda, “Toll-like receptor signalling,” Nature Reviews Immunology, vol. 4, no. 7, pp. 499–511, 2004. View at Google Scholar
  51. M. R. Dasu, S. Devaraj, S. Park, and I. Jialal, “Increased Toll-like receptor (TLR) activation and TLR ligands in recently diagnosed type 2 diabetic subjects,” Diabetes Care, vol. 33, no. 4, pp. 861–868, 2010. View at Google Scholar
  52. T. Zhu, Q. Meng, J. Ji, X. Lou, and L. Zhang, “Toll-like receptor 4 and tumor necrosis factor-alpha as diagnostic biomarkers for diabetic peripheral neuropathy,” Neuroscience Letters, vol. 585, pp. 28–32, 2015. View at Google Scholar
  53. M. R. Dasu, S. Devaraj, L. Zhao, D. H. Hwang, and I. Jialal, “High glucose induces Toll-like receptor expression in human monocytes: mechanism of activation,” Diabetes, vol. 57, no. 11, pp. 3090–3098, 2008. View at Google Scholar
  54. N. A. Calcutt, C. G. Jolivalt, and P. Fernyhough, “Growth factors as therapeutics for diabetic neuropathy,” Current Drug Targets, vol. 9, no. 1, pp. 47–59, 2008. View at Google Scholar