In the treatment of complex diseases, multi-target therapies have received increasing attention, and their effectiveness and efficiency have been significantly improved compared with monotherapy. Effective treatment of most diseases cannot be achieved through intervention of a single gene target, and multi-target drugs will have higher efficiency With the development of information technology, the combination of medical research and big data analysis is thriving [1]. More and more bioinformatics analysis including network pharmacology have been utilized to reveal the underlying pathogenesis and efficient treatment methods. Network pharmacology integrating the active ingredient - drug targets – disease genes - related diseases, has attracted more and more attention [2]. Diabetic nephropathy (DN), as a common microvascular complication of diabetes, is a complex disease with multiple disease genes. The effect of single-target drugs is very limited, and may also bring unwanted side effects. Therefore, we have been engaging in multifunctional and less-side-effect therapeutic drugs discovery [3-5]. Due to its integration of drugs, target genes, signal pathways and disease relationships, Network pharmacology has become a hot spot in the researches of DN pathogenesis and related intervention drugs.

Definition and Origin of Network Pharmacology

Network pharmacology is a methodology based on systems biology and bioinformatics. In 2007, Andrew L. Hopkins, a pharmacologist at Dundee University, UK, first proposed this concept, and since then network pharmacology was rapidly applied in many fields of research, showing important theoretical and practical application values [6]. Network pharmacology effectively combines network biology, bioinformatics and multiple pharmacology, seeks disease and drug targets based on various databases and known pathogenesis and signaling pathways, select specific signaling nodes to design multi-target drug molecular model through network analysis of biological systems, and elucidate disease’s related gene targets by database building, network establishment, network analysis, and experimental validation [7]. Network pharmacology is a remarkably effective research method, which is widely used to explore pharmacological mechanisms of drugs [8,9]. It emphasizes the multi-pathway regulation of signaling pathways, improving the therapeutic effects of drugs and reducing toxic side effects, thereby raising the success rate of clinical trials of new drugs and saving the cost of drug development.

Significance of Network Pharmacology Research

In the background of the rapid development of the field of systems biology, network pharmacology has tremendously developed as a new strategy for finding bioactive components, molecular targets and interactions [7,10]. There are many steps involved in the process of network pharmacology: identifying chemical components and targets of drugs, obtaining disease-related targets, constructing and analyzing the network of diseases-drugs-active components-key targets, performing enrichment analysis and analyzing the mechanism of drug pharmacological actions on diseases [11].

Common tools for conducting network pharmacology studies include many databases. Among them, the common screening databases for disease targets are: TTD (Therapeutic Target Database), CTD (The Comparative Toxicogenomics Database), Dis Ge NET Database (Dis Ge NET Database), Online Mendelian Inheritance in Man Database, Drug bank Database, etc.; the databases of protein interactions between disease and drug components are: Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine (TCMIP); the database of protein and protein interactions is: String Database, OPHID database (Online Predicted Human Interaction Database), DIP (Database of Interacting Proteins), etc.; component-target-pathway-disease correlations use the DAVID platform (The Database for Annotation, Visualization and Integrated Discovery) and Cytoscape software, KEGG for mapping, GO enrichment analysis and signaling pathway analysis. Validation tests are also performed to identify the signaling pathways and molecular targets involved in the drug treatment of the disease.

The current drug development has the following limitations: The safety and effectiveness of drugs need to undergo lengthy and complicated clinical trials. Clinical trials have many bias factors, such as the timeliness and effectiveness of the enrolled patients leading to low efficiency. Huge capital investment in the research process. Network pharmacology, based on databases, integrates the relationship between drugs, target genes, signal pathways and diseases involved in the occurrence and development of diseases by constructing a drug-target-signaling pathway-disease network [2,12]. Therefore, the application of network pharmacology has started a new chapter in drug research. Changing from single-target to multi-target is crucial for treating multifactorial and multi-organ damage diseases. Meanwhile, it also provides possible signaling pathways and molecular targets in drug trials, which helps to understand the mechanism of pharmaceutical effect on disease from a systematic and holistic perspective [13].

Diabetes is a common chronic metabolic disease, which lead to numerous target-organ damage and serious complications. Oxidative stress, hyperglycemia and hyperlipidemia can promote microvascular complications; and DN is one of the serious microvascular complications caused by diabetes, which also the main cause of end-stage renal disease [14]. It is estimated that there will be approximately 451 million diabetic patients (over 18 years of age) worldwide by 2017. By 2045, the number of diabetic patients is expected to reach 693 million, of which 30% -40% will develop into DN, presenting a large medical resources burden across the world [15]. The current treatment for DN focuses on blood pressure management, glycaemia and lipidemia control and lifestyle changes [16,17].

Although the long-term prognosis of patients with DN has been improved significantly, our understanding of the pathogenesis of DN still needs to be updated. The pathogenesis of DN at present encompasses: vasoactive hormone pathways (including aldosterone and endothelin, etc.) play an important role in diabetic kidney damage; metabolic pathways (mainly reactive oxygen species, ROS, and reduced nicotinamide adenine dinucleotide phosphate oxidase, NAPDH oxidase, etc.), leading to inflammation and fibrosis; the interaction between hemodynamics and metabolic pathways, the mechanism of it has not been well-studied, and may be related to the mutual influence of the two pathways respectively activated [18]. At the same time, many novel therapeutic targets have also been identified, such as sodium-glucose cotransporter 2 (SGLT-2), dipeptidyl peptidase-4 (DPP-4), glucagon-like peptide-1 (GLP-1). The pathogenesis of DN is diverse and complex, involving multiple genes and complex signal transduction pathways, and each pathway may also have mutual influences [19]. Thus, it is pivotal to develop multi-target drugs involved in multiple pathways, and further research on the pathogenesis of DN are all-important as well [20]. Network pharmacology has evolved drug discovery from single-target to multi-target and it is well suited for application to the study of treatment for complex disease like DN, which is marked by its systematic nature, relevance and predictability (Figure 1) [21].

Figure1: Network pharmacology for diabetic nephropathy. Through network pharmacology, we could integrate the drug targets with disease targets by carrying out PPI analysis and GO, KEGG functional enrichment analysis to explore the mechanisms of DN comprehensively, and perform confirmatory test to validation.

Treatment of DN

In diabetic patients, chronic exposure to hyperglycemia leads to abnormal mesangial extracellular matrix (ECM) accumulation, altered cell signaling pathways and gene expression, often accompanied by oxidative stress. Glomerular sclerosis and kidney fibrosis is generally the outcome of kidney diseases, causing severe disruption of the normal structure and function of the kidney [22]. Many signaling pathways are associated with the pathogenesis of DN, such as NF-κB, TGFβ/Smad and other classical signaling pathways. Hence, the treatment of DN needs to involve many signaling pathways and gene expression, explore the phenotypic alterations triggered by different DN-related gene expression changes, and build a complex network of gene-epigene-mRNA-protein -phenotypic changes. The crux of its treatment is to inhibit the relevant signaling pathways and delay the formation of glomerular sclerosis and kidney fibrosis.

Meanwhile, patients with DN are often suffering other complications such as cardiovascular disease. In the past, comprehensive therapies were mostly based on the drug combination, such as the combination of ramipril (an angiotensin-converting enzyme inhibitor) + sitaxsentan (an endothelin receptor antagonist) to treat renal and cardiac damage in diabetic patients with DN and cardiovascular morbidity, but the drug combination may also bring more side effects while increasing benefits [23]. Previously, the combination of angiotensin-converting enzyme inhibitors (ACEI) and angiotensin II receptor blockers (ARB) was performed to treat DN patients with cardiovascular disease. However, ACEI alone may affect all-cause mortality of subjects and raise the incidence of side effects such as hyperkalemia. Before the beginning of the era of network pharmacology, researches on pharmacological interventions for complex diseases like DN were less efficient, mostly using large-scale clinical trials and drug combinations. Through network pharmacology, we could integrate and study the targets and signaling pathways related to active compounds of drugs. By identifying therapeutic effects-related targets and side-effects-related targets, we could reduce the incidence of side-effects while improving the effectiveness of drug combination therapy, so network pharmacology is playing an epoch-making role in the study of DN therapeutics?Figure 1?.

Through GO, KEGG functional enrichment analysis and protein-protein interaction network (PPI network), network pharmacology is applied for DN pathogenesis researches, screening drug-related genes and signaling pathways to clarify the correlation of intersecting targets, finally using cellular or animal assays to verify. Currently, network pharmacology has been applied to identify genes and signaling pathways related to DN pathogenesis and active drug components through GO, KEGG functional enrichment analysis and protein-protein interaction network (PPI network). The most relevant targets will be detected, providing guidance for choosing the best signal pathway for drug intervention. By exploring the different pathogenic signaling pathways of DN, the optimal pathway for drug intervention will naturally emerge. Finally, carry out cellular or animal experiments for validation. Lili Zhang et al. used Salvia miltiorrhiza (SM) for the treatment of DN and found that SM had 66 active ingredients and 189 targets by network pharmacology analysis, of which 64 targets overlapped with diabetes-related targets, and the PPI pathway network showed that AKT1, VEGFA, IL6, TNF, MAPK1, TP53, EGFR, STAT3, MAPK14, and JUN were the 10 most relevant targets [24]. GO and KEGG analysis suggested that the common targets of DN and SM were mainly advanced glycosylation end products, oxidative stress, inflammatory response, and immune regulation. Yaping Xiao et al. used the method of network pharmacology + validation experiment to study the mechanism of total Rhizoma Coptidis alkaloids in improving DN, and its protective effect may be related to the activation of AGE-rage-TGFβ / Smad2 and PI3K-Akt signaling pathways, which provides an experimental basis for further studies. (Xiao et al., 2021)

Qiqiang Zhang et al. found through clinical trials + network pharmacology that Gandi capsule (GDC) can not only reduce urine protein levels in DN patients, but also increase glomerular filtration rate, thereby mitigating renal injury in diabetic patients [25,26]. Network pharmacological analysis revealed that GDC has six components, among which baicalin and wogonin can alleviate high glucose-induced glomerular podocyte injury by binding to HNF4A, HMGCR, JAK3 and SIRT1 protein targets to regulate AMPK and PI3K-AKT signaling pathways. Combined with the clinical efficacy and drug component analysis, it confirmed the therapeutic efficacy of GDC in DN, clarified its therapeutic principles and molecular mechanisms, and expanded the therapeutic indications of this formula.

The characteristics of multi-target and multi-layered integrated research in network pharmacology are suitable for complex diseases like DN, which can not only improve the efficiency of DN therapeutic drug discovery, but also effectively contribute to screen the active ingredients of drugs. These studies can clarify the underlying mechanism, improve DN therapeutic outcomes while minimizing drug side effects, and also facilitate to discover new indications of existing drugs, serving as productive research tools for DN therapeutic drug discovery.

Exploration of the Principles of DN Treatment

Treating DN for A Single Cause

The pathogenesis of DN includes chronic inflammation, oxidative stress and apoptosis, etc. DN treatment strategies at a certain pathogenesis are accompanied with limitations in failing to provide evident clinical benefit, making it difficult to employ the synergistic treatment of multiple pathways [27].

The failed resolution of inflammation may be responsible for the pathogenesis of diabetes and associated complications, the most common being DN. Lipoxins (LXs) may have the potential to ameliorate diabetic microangiopathy and thereby alleviate the leisions of DN. LXs are endogenous active molecules and block chronic inflammatory signaling pathways such as TGF-β1, PDGF, TNF-α, and NF-κB to alleviate DN lesions, but whether they target other DN pathogenesis is unknow [28].

In many diabetic complications, such as diabetic kidney damage, oxidative stress can significantly affect macromolecules (e.g., type IV collagen in normal basement membrane structures), thereby damaging the normal structure of the kidney and promoting organ dysfunction. Suppression of this pathway can alleviate renal damage in diabetes and delay the progression of DN. Pyridoxamine (PM) has an antioxidant effect, and it plays a renoprotective role by inhibiting the oxidative stress pathway, especially in the early stages of DN, but it is not sure whether PM can treat DN through other pathways and whether it will affect other signaling pathways then produce adverse effects [29]. Excessive production of reactive oxygen species (ROS) in mitochondria is also an essential part of oxidative stress in the pathogenesis of DN. The downregulation of PGC-1α increased ROS generation, leading to mitochondrial fragmentation, damaged network structure and mesangial cell hypertrophy [30]. The overexpression of PGC-1α may protect DN via the inhibition of ROS production. But whether it affects other mitochondrial functions remains obscure.

Oxidative stress due to ROS is a significant initiating factor in the process of apoptosis, and inhibiting oxidative stress can attenuate DN. Knockdown of thioredoxin-interacting protein (TxNIP)-related genes or inhibition of their expression can reduce apoptosis by inhibiting the oxidative stress pathway and thereby decelerate the progression of DN, but whether this treatment will affect other normal pathways and produce serious side effects are unsure [31].

Application of Network Pharmacology in DN Therapeutic Mechanisms

Therapeutic approaches targeting a single mechanism have many limitations and cannot palliate DN lesions well from a overall view of the signaling pathway network, so developing drugs targeting multiple signaling pathways based on network pharmacology is a very promising therapeutic approach.

Among many pathogenesises of DN, ROS is an important influential factor in DN [32]. It is generally regarded as a useless by-product of cellular metabolism and oxidative stress, and a potential contributor of DN. ROS can induce renal fibrosis, renal hypertrophy and proteinuria, while antioxidant therapy has not shown significant clinical benefit [33]. The intersection of signaling pathways between ROS signaling network and diabetic injury mechanisms is investigated by network pharmacology to shift antioxidant therapy from single-target pathways to multi-targets and signaling pathways, in order to promote low-dose therapy and fewer side effects. Combining network pharmacology and accurate diagnosis with necessary lifestyle modifications will transform the treatment and prevention of diabetes and its target organ damage, whilst providing a deeper understanding of its pathogenesis [27].

Using network pharmacology to explore the mechanism of some classical traditional therapies for DN can efficaciously uncover which signaling pathways are used to best achieve therapeutic effects, and provide a theoretical basis for further research. Qingxin Lianzi Yin Decoction (QXLZY), a classical traditional Chinese medicine formula, has been used to treat various chronic kidney diseases for more than 1000 years. Wen-Ya Gao utilized network pharmacology to identify baicalein, liquiritigenin, succinic acid, formononetin, wogonin as effective components for the treatment of DN, possibly through the glycolipid metabolism, oxidative stress and inflammatory response signaling pathways in patients with DN lesions [34]. Astragaloside IV (AS-IV), an active ingredient extracted from the Astragalus, has been widely used in Chinese medicine formulae. Enyu Wang et al. explored the relevant pathways and targets of the therapeutic effect of AS-IV on DN through network pharmacology approach, and after experiment validation, it was clear that the therapeutic effect of AS-IV on DN was mainly through the effects on Wnt, PI3K/Akt, NF-κB, Ras, JAK/STAT signaling pathways, alleviating oxidative stress damage and slowing down the release of inflammatory factors, thereby promoting an anti-oxidative stress and anti-inflammatory function, and finally alleviating the renal damage caused by high glucose [14].

Apart from exploring the mechanism of drug therapy for DN, network pharmacology can also be applied to detect a particular signaling pathway in the pathogenesis of DN. For example, cysteine protease 1 (caspase-1) is a member of the cysteine protease family, and caspases family proteins are widely involved in biological processes such as apoptosis, inflammation, and pyroptosis [35]. Previous studies found that inhibition of caspase-1 can reduce IL-1β and IL-18 expression, suppress tissue inflammation, and ultimately improve DN symptoms, but it remains indeterminate which signaling pathway achieves remission effect [26,36]. Network pharmacology techniques and validation assays were utilized to find that caspase-1-mediated pyroptosis plays a crucial role in the development of DN [37]. The monomeric active ingredient ginsenoside Rh2 may inhibit caspase-1-mediated cell pyroptosis through inhibition of the lysosomal pathway and ultimately ameliorate renal injury in DN, providing a new direction and molecular mechanism for further exploration of DN treatment through the caspase-1/lysosomal pathway.

The combination of network pharmacology with other technologies to investigate the therapeutic principles of DN is equally compelling. The revolution in high-throughput technological approaches, represented by transcriptomics, has transformed the focus of disease and treatment research from single gene, protein, and metabolite to genome, proteome, and metabolome [38]. The combined study of transcriptomics and kidney biopsy showed that podocyte gene (NPHS1, NPHS2, SYNPO, PODXL) are disregulated in the first stage of DN [39]. These findings help to understand the molecular mechanisms associated with diabetic kidney damage, and can also combine with DN-related targets derived from network pharmacology analysis to deliver precise targeting therapy of DN. This will lead to safer and more effective therapies for patients by minimizing drug side effects and will improve the specificity of intervention targets. Metabolomics and intestinal flora analysis have increasingly been at the forefront of research. Metabolomics allows qualitative and quantitative analysis of metabolites with relative molecular weights less than 1000 in vivo and is used in the study of the pathogenesis of type 2 diabetes [40-42]. Intestinal flora can significantly regulate insulin secretion, modulate blood glucose and other hormones, and have an important role in insulin resistance [43,44]. Some researchers combined network pharmacology analysis with metabolomics and intestinal flora analysis for Bekhogainsam decoction (BHID) and found that it could alter metabolites and the structure of intestinal flora in vivo and cause kidney damage in patients with diabetes by activating PI3K/MAPK signaling pathway [45]. Qian Guo et al. used network pharmacology analysis to find that there are 18 active ingredients in the Drug Pair of Astragalus Radix and Dioscoreae Rhizoma can treat type 2 diabetes and can act on 135 targets simutaneousely [46]. Combining metabolomics, the formula was found to regulate blood glucose and alleviate diabetic kidney damage mainly through four target proteins. Juan Wang et al in their study, used RNA sequencing technology to screen differential gene in ginsenoside intervention group rats, then applied GO and KEGG functional enrichment in network pharmacology to analyze the relationship between different gene transcript levels and disease mechanisms [47]. There were 78 differentially expressed genes between DN group rats and normal control rats, of which 52 were upregulated and 26 were downregulated; 43 differentially expressed genes between DN group rats and ginsenoside intervention group rats, of which 10 were upregulated and 33 were downregulated; 21 genes that were downregulated in DN control group were upregulated in ginsenoside intervention group, 7 genes that were upregulated in DN control group were genes were downregulated in the ginsenoside intervention group. Accordingly, by GO and KEGG functional enrichment analysis, these differential genes were mainly related to lipid metabolic pathways. High-throughput RNA sequencing technology can obtain comprehensive transcriptional data and differential genes of test subjects, and the combination with network pharmacology technology can clarify the expression of differential genes during disease development and their relationship with the pathogenesis network. The analysis of genes and pathways will supply effective data basis for specifying the pathogenesis of DN.

Thus, network pharmacology technology can not only improve the effectiveness of therapeutic drugs for DN, but also reduce the adverse effects, clarify the principles of their therapeutic functions, take into account the interactions between different signaling pathways, and can be combined with other technologies to improve the efficiency and accuracy of research, which is a very auspicious research method.

DN And Related Complications

Given that diabetes leads to many target organ damage and complications, the advantages of network pharmacology lies in improving the efficiency and accuracy of investigating multi-target drugs for the treatment of diabetes and alleviating damage to other target organs while treating DN. In a network pharmacology study on Liuwei Dihuang Pill (LWD) treating DN and DN-related osteoporosis (DNOP), the AGE-RAGE signaling pathway and MAPK signaling pathway were related with the occurrence and development of DN and its complications [48]. Kaempferol and quercetin were the most significant constituents, and miR-574 was significantly upregulated in DN and DNOP samples. The results suggest that quercetin and kaempferol may downregulate the expression of MIR-574 to exert a protective effect through MAPK signaling pathway, which provides a new supporting perspective for further research on the simultaneous treatment of DN and DNOP with LWD.

Treatment of DN with Traditional Chinese Medicine (TCM)

Traditional Chinese Medicine (TCM) has a long history and its safety and efficacy have been evaluated historically and empirically. Therapeutic drugs in traditional medicine are relatively inexpensive and may have potential new efficacy, and validation using Western scientific paradigms has begun in recent years with increasing emphasis on the use of database-based research methods [49].

TCM is also an important object of research in DN network pharmacology. The holistic view of TCM on disease understanding reflects in the network and system, emphasizes the unity and harmony between the individual and the external environment. It treats disease as a discordant change in the organism and its diagnosis and treatment are mostly based on changes in symptoms and signs indicators. TCM has a history of more than 3500 years and has received widespread attention due to its unique charm. Although standardized research on TCM has been conducted in China since the 1950s, there is still a lack of exact unified standards and its drug mechanisms are still relatively vague. Because of the complex composition of TCM formulas, the methods of pharmacological research on them are relatively limited, and the side effects are also unclear [50]. Therefore, it is essential to define the active ingredients and their pharmacological mechanisms in TCM prescriptions. Network pharmacology, which is based on a wealthy of powerful databases, can clarify the active active ingredients of herbal prescriptions and establish the relationship between the active ingredients and the targets of action, as well as the connection between different targets, and finally find the key nodes in the disease network [51-53].. The combination of TCM and modern technology helps to clarify the network relationship between numerous herbal components and diseases, and is an effective way to combine traditional medicine with western modern medical information technology [49].

DN is mainly characterized by hyperglycemia, hypertension, proteinuria, and edema, as a common microvascular complication of diabetes mellitus. Its occurrence and development are related to multiple signaling pathways. TCM has been widely used to treat DN but the studies of pharmacological mechanism is still unmet. With the help of network pharmacology we can explore the role of its active ingredients on different signaling pathways of DN, at the same time understanding the biological basis of complex diseases [11,21]. Cordyceps sinensis has the characteristics of multicomponent, multitarget, and multichannel in the treatment of DN, and can alleviate DN-related damage by targeting TNF, MAPK1, EGFR, ACE and CASP3 pathways, indirectly affecting other biological processes such as inflammatory response, apoptosis, oxidative stress and insulin resistance [54]. It demonstrates the unique advantage of network pharmacology in revealing the relationship between multiple signaling pathways in the course of DN disease and the treatment of proprietary Chinese medicines.

In addition, the molecular mechanisms of many classical Chinese herbal remedies for DN remains unknown, and the application of network pharmacology helps to clarify their specific mechanisms, action targets and related signaling pathways. Han, J et al. clarified that the main targets of peach kernel-safflower medicine for DN treatment were VEGFA, IL-6, TNF, AKT1 and TP53 through network pharmacological analysis [55]. Meanwhile, IL-17, HIF-1 signaling pathway may also be involved in the process of relieving DN pathology. Meng, X et al. used jowiseungki decoction (JSD) to treat STZ-induced DN mice, and also undertook network pharmacology analysis to clarify that the main signaling pathways of this formula were PKCα/ PI3K / Akt and NF-κB /α-SMA signaling pathways [56].

The combination of TCM and network pharmacology can help to foster the standardized developing of TCM for DN therapy: scientific data analysis methods can standardize the drug discovery process of TCM and improve research efficiency and drug safety, effectively shorten the timeline of clinical trials and optimize the use of research funds; the specific signaling pathways and targets of TCM for DN therapy can be elucidated, which is conducive to establish a scientific and efficient treatment system and reduce bias factors in trials; the therapeutic potential of proprietary Chinese medicines can be further exploited, finding evidence of the treatment for other target organ damage in diabetes whilst alleviating DN.

In conclusion, the combination of TCM and network pharmacology can provide an efficient and inexpensive DN treatment and evaluation system, which can achieve the desired efficacy and minimize the related adverse effects, and promote the In conclusion, the combination of TCM and network pharmacology can provide an efficient and inexpensive DN treatment and evaluation system, which can achieve the desired efficacy and minimize the related adverse effects, and promote the standardized, normalized and specialized development of TCM.

In the study of DN pathogenesis and treatment, network pharmacology can establish the relationship network of target gene-signaling pathway-disease-drug and improve the efficiency and accuracy of research. However, network pharmacology has the following limitations: the relevant database is still not sufficient; different algorithms will lead to different outcomes, and it is necessary to choose the appropriate method based on different purposes; some drug mechanisms and new therapeutic targets discovered by network pharmacology research in Chinese medicine are still in the qualitative research stage; there is a dose effect between drugs and diseases, and it is still difficult to perform precise quantitative research with the current network pharmacology technology; the quality of network pharmacology studies is uneven and it needs further exploration.

In general, network pharmacology still provides a multidimensional research approach to the exploration of complex diseases. We can expect that with the update of data analysis methods and databases, network pharmacology will break through the above limitations and be increasingly applied to the exploration of mechanisms of multitarget diseases and the development of related agents.

Ethical Statement

Ethical approval and consent to participate: Not applicable. This article does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication: Written informed consent for publication was obtained from all participants.

Availability of data and materials: The data and materials used and analyzed during the current study are available from the corresponding authors on reasonable request.

Competing interests: The authors declare that they have no conflict of interests.

Funding: This study was funded by the National Natural Science Foundation of China (Grant No. 31201052). The authors confirm independence from the sponsors; the content of the article has not been influenced by the sponsors.

Author contributions: CL and LL conceived and designed research. HJ and XS conducted experiments. XZ participated in the writing. CL and LL designed and prepared the figures. JH and YD proofread and polished the manuscript. All authors read and approved the manuscript

Acknowledgements: This study was funded by the National Natural Science Foundation of China (Grant No. 31201052). The authors confirm independence from the sponsors; the content of the article has not been influenced by the sponsors.

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