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Research Article

Correlational Analysis of Qiming Granule with the CMKLR1-Centered Network and Systemic Inflammation via the Gut–Retina Axis in Diabetic Retinopathy

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DOI:

10.3791/72700

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September 15th, 2026

 ,  ,  ,  ,  , 
1Hospital of Chengdu University of Traditional Chinese Medicine, 2Chengdu University of Traditional Chinese Medicine, 3School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine

In This Article

Summary

This study demonstrates that Qiming Granule (QMG) ameliorates diabetic retinopathy (DR) by modulating the gut–retina axis. QMG reshaped gut microbiota, suppressed systemic inflammation, and restored the CMKLR1-centered regulatory network, thereby reversing retinal histopathological damage. These findings position QMG as a promising gut–retina therapeutic strategy for DR.

Abstract

Diabetic retinopathy (DR) is a neurovascular complication driven by chronic hyperglycemia and systemic inflammation, with emerging evidence highlighting the gut–retina axis as a critical but underexploited therapeutic target. This study investigates whether Qiming Granule (QMG) exerts its protective efficacy against DR by modulating the gut–retina axis. C57BL/6 mice were randomized into experimental groups and subjected to distinct interventions. Retinal histopathological changes were assessed by Hematoxylin-Eosin (HE) staining. Serum levels of inflammatory cytokines (IL-1β, IL-6, TNF-α) were measured by enzyme-linked immunosorbent assay (ELISA). The expression of key regulatory proteins (CMKLR1, Wnt5a, PPAR-γ, AP-1, TP53) in both retinal and colonic tissues was detected by Western blot (WB). Gut microbiota composition and diversity were characterized by full-length 16S rDNA sequencing. Correlational analyses were performed to assess relationships between gut microbiota and inflammatory factors or key proteins. QMG treatment improved retinal histopathological abnormalities, as evidenced by restoration of retinal laminar architecture, partial recovery of retinal ganglion cell numbers (P < 0.001), and reversal of retinal thinning (P < 0.01). It also markedly downregulated serum IL-1β, IL-6, and TNF-α levels (P < 0.0001). Along the gut‑retina axis, QMG reversed the abnormal expression of key regulatory proteins, notably the CMKLR1‑centered network (Wnt5a, PPAR-γ, AP-1, TP53). Furthermore, QMG restored gut microbiota composition, diversity, and function, and correlation analyses revealed that QMG couples systemic inflammation with these regulatory proteins to modulate the gut‑retina axis. This study provides preliminary pharmacological evidence that QMG ameliorates DR by coupling the CMKLR1-centered regulatory network (including Wnt5a, PPAR-γ, AP-1, and TP53) and systemic inflammation via the gut–retina axis, positioning QMG as a promising gut–retina therapeutic strategy for DR.

Introduction

Diabetic retinopathy (DR), one of the most common microvascular complications of diabetes mellitus (DM), is a leading cause of blindness among working-age adults worldwide1. Globally, DR affects approximately 20–30% of the DM population2. Chronic hyperglycemia drives the pathogenesis of DR by triggering a cascade of pathological events, including impaired neurovascular coupling, heightened inflammatory responses, and oxidative stress3. Recently, alterations in gut microbiota diversity and composition have been observed in diabetic patients with retinopathy compared to those without4,5. Consequently, the gut–retina axis—a bidirectional communication network linking intestinal homeostasis to retinal health—has emerged as a novel contributor to DR progression. However, the specific molecular mediators that translate gut–retina signals into retinal pathology remain largely uncharacterized, limiting the identification of reliable biomarkers and therapeutic targets.

The gut–retina axis delineates a complex bidirectional communication network between the intestinal microbiota and their metabolites with retinal tissues through immunological, metabolic, neuroendocrine, and vascular pathways, thereby modulating their homeostatic equilibrium6. Molecularly, the gut–retina axis is characterized by the dysregulation of key stress-responsive and cell-fate-determining proteins, specifically AP-1 and TP537,8,9. Besides, CMKLR1, a key chemokine receptor, emerges as a key mediator of endothelial dysfunction and metabolic dysregulation, establishing a critical link between gut microbiota alterations and diabetic retinopathy pathogenesis10. Importantly, Wnt signaling orchestrates dual regulatory roles in maintaining retinal vascular homeostasis and intestinal epithelial barrier function11, while PPAR signaling serves as a master regulator of inflammatory responses and metabolic processes across both ocular and intestinal tissues12,13. These molecular players collectively constitute an interconnected signaling network within the gut–retina axis, presenting novel mechanistic insights and therapeutic opportunities for diabetes-related complications. Targeting structural and functional alterations along this axis, therefore, represents a promising strategy for managing DR.

Traditional Chinese Medicine (TCM) has historically played a significant role in the treatment of diabetes and related retinopathy, with therapeutic decisions based on syndrome differentiation14. Qiming Granule (QMG), composed of Astragalus membranaceus, Pueraria lobata, Rehmannia glutinosa, Lycium barbarum, Senna obtusifolia, Leonurus japonicus, Typha angustifolia, and Whitmania pigra, is broadly applied in the treatment of DR. Approved by the National Medical Products Administration of China (Approval No. Z20090036), QMG has successfully completed clinical trials and received marketing authorization. QMG exhibits protective effects by preventing the thickening of the basement membrane in retinal capillaries, reducing damage to pericytes and retinal microvasculature, and maintaining the local pathological morphology of the eye15, making it a promising therapy for addressing DR. By targeting key proteins and inflammatory factors, QMG's active components exert anti-inflammatory, antioxidant, and anti-apoptotic effects, providing a mechanistic basis for its modulation of the gut–retina axis16. These properties provide a mechanistic basis for QMG’s potential modulation of the gut–retina axis, yet few studies have explored this link. Therefore, this study aims to validate the efficacy of QMG in modulating the gut–retina axis, providing experimental evidence for its therapeutic potential in DR.

This study bridges theoretical predictions with in vivo experimental data to validate the efficacy of QMG in modulating the gut–retina axis. These findings demonstrate that QMG ameliorates DR by coupling the CMKLR1‑centered regulatory network (including Wnt5a, PPAR-γ, AP-1, and TP53) with systemic inflammation via the gut–retina axis, positioning QMG as a promising gut–retina-targeted therapeutic strategy for DR. The graphical abstract is exhibited in Figure 1.

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Protocol

All animal experiments were performed in compliance with the ARRIVE guidelines. The study was approved by the Ethics Review Committee of Sichuan Scientist Biotechnology Co., Ltd. (IACUC ISSUE SYST-2024-011).  A detailed experimental timeline summarizing the study design is provided in Supplementary Figure 1.

Animal preparation and treatment

Mice were deeply anesthetized with 2% isoflurane inhalation. Under deep anesthesia, blood was collected via retro-orbital enucleation. Immediately after blood collection, eyeballs were enucleated for retinal tissue harvest. Subsequently, cervical dislocation was performed under deep anesthesia to ensure euthanasia. Colon tissues were then dissected immediately. Animals exhibiting adverse events were promptly removed from the study and received appropriate veterinary care.

Randomization and blinding

Male C57BL/6 mice, aged 5 weeks and weighing between 20 and 30 grams, were accommodated under strictly regulated conditions, including temperature and humidity. After one week of acclimatization, C57BL/6 mice were randomly assigned to three groups (blank, model, and intervention groups) using a computer-generated random number sequence. The randomization sequence was generated by an investigator not involved in animal handling or outcome assessment.

To ensure blinding, all animal care, drug administration, and data collection were performed by investigators unaware of group allocation. The coded group assignments were revealed only after the completion of all experiments and statistical analyses. The blinding applied to: the technician performing oral gavage; the pathologist evaluating retinal histology; and the analyst quantifying Western blot (WB) bands.

Sample size calculation

The sample size was determined based on a preliminary experiment evaluating retinal ganglion cell (RGC) counts under the same experimental conditions. Assuming a two-sided significance level (α) of 0.05 and a statistical power (1-β) of 0.80, a minimum of 5 mice per group was required to detect a clinically relevant difference in RGC counts between the model and intervention groups. Considering a potential dropout rate of approximately 20%, each group was set to include 6 mice.

Establishment of diabetic animals

The blank group received a standard diet, whereas the intervention and model groups were administered a high-fat, high-sugar diet (HFHSD) for a duration of four weeks. Following this dietary period, the intervention and model groups underwent intraperitoneal injection with a 1% Streptozotocin (STZ) solution at a dosage of 35 mg/kg17,18,19,20. After seventy-two hours of injection, mice exhibiting non-fasting blood glucose levels exceeding 16.7 mmol/L were considered to have successfully induced a DM model and were selected for further experimentation. The DM animals continued with the high-fat, high-sugar diet for an additional four months, whereas the blank group maintained their standard diet.

Taxonomic validation and quality assessment

The eight ingredients in QMG were taxonomically validated using the Modernized Plant Names Search (MPNS, http://mpns.kew.org/mpns-portal/). The quality of composition reporting for the QMG was evaluated using the ConPhYMP tool.

Administration methods

In the intervention group, the animals were orally administered QMG. QMG was purchased from Zhejiang Sansheng Mandi Pharmaceutical Co., Ltd. (Chinese National Drug Approval No. Z20090036). The QMG suspension was prepared by uniformly mixing QMG with distilled water. Mice in the intervention group were administered the suspension via oral gavage at an equivalent volume to the model and blank groups. The dosage was calculated based on the clinical single therapeutic dose of 4.5 g per 60 kg adult (the standard unit dose of the marketed formulation) using the body surface area (BSA) normalization method recommended by the FDA and Reagan-Shaw21. The conversion factor between adult humans (Km = 37) and mice (Km = 3) was 12.33, resulting in a mouse dose of 0.9 g/kg body weight. The model group and the blank group received an equivalent volume of saline via oral gavage. The administration was conducted daily between 9 AM and 11 AM for a consecutive period of 3 months.

Observation and testing indicators

General vital signs monitoring

The general vital signs and physiological indicators, including body weight, blood glucose levels, water and food consumption, urine output, activity levels and behavior, body temperature, as well as fur and skin condition, were systematically observed and recorded to comprehensively assess therapeutic interventions and detect potential complications.

Observation of morphological changes in the retina by HE staining analysis

All specimens were processed through an automatic dehydrator using a series of ethanol and xylene solutions after fixation, followed by embedding in paraffin. Subsequently, 4 µm paraffin sections were cut and stained with HE and then mounted with neutral gum. Retinal images were scanned using a 3DHISTECH Pannoramic SCAN scanner. Image analysis and data measurement were performed by Image-Pro Plus.

Detection of serum IL-6, TNF-α, and IL-1β by ELISA analysis

Blood samples were collected via retro‑orbital enucleation. Serum was separated by centrifugation at 1,000 × g for 15 min at 4 °C and stored at -80 °C until analysis. Serum levels of IL‑1β, IL‑6, and TNF‑α were measured according to standard procedures, including reagent equilibration, sample addition, incubation, washing, conjugate and substrate addition, reaction termination, optical density measurement, and concentration calculation based on a standard curve. The optical density was read at 450 nm using a microplate reader, and cytokine concentrations were calculated against standard curves.

Detection of the key regulatory proteins in retina and colon by WB analysis

Three independent biological replicates were conducted on a single membrane and sequentially probed for target proteins. All replicates were derived from a single cohort of animals sacrificed concurrently. When the molecular weight of a target protein was too close to that of GAPDH (<5 kDa difference), they were analyzed on independent membranes to prevent signal overlap. Each membrane was probed exclusively for either the target protein or GAPDH to ensure accurate quantification. The following primary antibodies were used: GAPDH polyclonal antibody (1:5000), CMKLR1 polyclonal antibody (1:1000), Wnt5a polyclonal antibody (1:1000), TP53 polyclonal antibody (1:1000), AP-1 monoclonal antibody (1:5000), and PPAR gamma (PPAR-γ) polyclonal antibody (1:1000). Total proteins were obtained by cell lysis followed by centrifugation. Protein concentrations were determined using the BCA assay. The extracted proteins were resolved by SDS-PAGE using a 12% separating gel and subsequently transferred onto membranes. The membranes were blocked in TBST containing 5% non-fat milk and incubated with the appropriate primary antibodies overnight at 4 °C. Following washing, the membranes were incubated with HRP-conjugated anti-rabbit or anti-mouse secondary antibodies at a dilution of 1:10,000. Finally, the protein bands were visualized and analyzed through chemiluminescence imaging. During visualization and data processing, blots were cropped horizontally to remove irrelevant molecular weight regions, and all sample lanes were retained without removal of inter-lane areas. Slight edge distortion may occur during cropping to remove irrelevant gel regions.

Detection of gut microbiota by 16S rDNA sequencing analysis

Fresh fecal samples were collected from each mouse prior to terminal procedures. Mice were placed individually in sterile cages for 10–15 min to allow spontaneous defecation. Fecal pellets were immediately collected with sterile forceps, transferred to sterile 1.5 mL tubes, snap-frozen in liquid nitrogen, and stored at -80 °C until DNA extraction. All procedures were performed under sterile conditions to minimize cross-contamination. The interval between defecation and freezing was maintained within 10 min to ensure microbial integrity. The fecal genomic DNA (gDNA) samples were purified using the Fecal gDNA extraction kit, followed by full-length 16S rDNA gene amplification via polymerase chain reaction (PCR) with universal primers 8F (5’-AGAGTTTGATCATGGCTCAG-3’) and 1492R (5’-CGGTTACCTTGTTACGACTT-3’). The PCR products were subsequently analyzed by agarose gel electrophoresis, purified, and quantified for downstream applications. High-throughput sequencing was conducted on the Nanopore GridION sequencer. The data analysis process included basecalling, data quality control, species annotation, phylogenetic tree construction, community analysis, Alpha and Beta diversity analysis, and differentially abundant species analysis and community function prediction. Various tools and databases, such as NanoFilt, Usearch, the R programming language, Python, and the SILVA database, were utilized during the analysis.

Correlation analysis of the gut–retina axis with systemic inflammation and multi-level gut Microbiota

To explore the potential associations between multi-level gut microbiota with serum inflammatory cytokines and key regulatory proteins, correlation analysis was performed by using the Luoning Bio-Cloud platform. Initially, the species abundance data at the Kingdom, Phylum, Class, Order, Family, Genus, and Species levels were extracted from the sequencing results. Physicochemical factors, including serum inflammatory cytokines and protein expression levels, were compiled into an environmental factor file. To ensure the reliability and visualization of the correlations, the data underwent normalization to eliminate the scale differences between microbial abundance and clinical measurement units. The correlation coefficients (R-values) and corresponding significance levels (P-values) were calculated. The results were visualized using a correlation heatmap, where color gradients indicate the strength and direction of the correlation (red representing positive correlation, blue representing negative correlation), and asterisks (**) denote statistical significance (P < 0.01).

Statistical methods

All statistical analyses and visualization were performed using GraphPad Prism. The t-test to compare means was employed for data that adhered to normal distribution. In cases where data deviated from normality, the Kruskal-Wallis test was used. Spearman's or Pearson's correlation was conducted for variable relationships. All statistical tests were conducted as two-tailed, with statistical significance set at 0.05.

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Results

General situation of experimental animals

Blood glucose levels were significantly elevated in the model group (27.21 ± 6.03 mmol/L) compared with the blank group (P < 0.001), indicating the successful induction of the DM model. During the study period, the experimental animals showed stable body weight, continuous water and food intake, normal urine output, body temperature, healthy fur and skin condition, and normal behavior during the experiment.

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Discussion

Diabetic retinopathy (DR) is a complex neurovascular complication driven by chronic hyperglycemia, inflammation, and metabolic dysregulation22. The American Academy of Ophthalmology categorizes DR into non-proliferative DR and proliferative DR, characterized by the onset of neovascularization23. Given the limitations of current therapeutic strategies, exploring novel mechanisms has emerged as a promising direction for addressing DR. Notably, recent breakthroughs have identi...

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Disclosures

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The author would like to acknowledge all of their team members who provided a great deal of support and assistance to fulfill this research.

This research was supported by the National Natural Science Foundation of China (Grant Number:82474580), and Hospital Foundation Free Exploration Project in Hospital of Chengdu University of Traditional Chinese Medicine (No. 23ZYTS1601).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
AP-1 monoclonal antibodyProteintech66313-1-IG1:5000
CMKLR1 polyclonal antibodyAffinityAF52911:1000
GAPDH polyclonal antibodyAffinityAF70211:5000
GoatAnti-Mouse IgG(H+L) HRPMULTI SCIENCESGAM00721:10000
GoatAnti-Rabbit IgG(H+L) HRPMULTI SCIENCESGAR00721:10000
GraphPad Prism/version 9.5
Image-Pro PlusMedia Cybernetics, Inc., Rockville, MD, USAversion 6.0
Luoning Bio-Cloud platform http://www.biomediv.cn/login.html
Mouse IL-1β(Interleukin 1 Beta) ELISA KitElabscienceE-EL-M0037Serum cytokine detection
Mouse IL-6(Interleukin-6) ELISA KitElabscienceE-EL-M0044Serum cytokine detection
Mouse TNF-α(Tumor Necrosis Factor Alpha) ELISA KitElabscienceE-EL-M3063Serum cytokine detection
NanoFilt/version 2.7.1
Nanopore GridION sequencerOxford Nanopore Technologieshttps://store.nanoporetech.com/gridion.html
Pannoramic SCAN II3DHISTECH Pannoramic SCAN scanner https://www.3dhistech.com/scanners/pannoramic-scan-ii-digital-scanner/
PPAR gamma (PPAR-γ) polyclonal antibody ImmunoWaybs-0530R1:1000
Python/version 3.7.4
Qiming GranuleZhejiang Sansheng Mandi Pharmaceutical Co., Ltd.Chinese National Drug Approval No. Z200900360.9 g/kg, oral gavage
R programming language/version 4.0.3
SILVA database/version 138
Streptozotocin (STZ)Sigma-AldrichS013035 mg/kg, i.p.
TP53 polyclonal antibodyAbcamab1314421:1000
Usearch/version 10.0.240
Wnt5a polyclonal antibodyProteintech55184-1-AP1:1000
Zymo Research BIOMICS DNA Microprep KitZymo ResearchD4301Fecal gDNA extraction

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