Downregulation of MerTK in Circulating T cells of Non-Proliferative Diabetic Retinopathy Patients

doi:10.21203/rs.3.rs-4791734/v1

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Downregulation of MerTK in Circulating T cells of Non-Proliferative Diabetic Retinopathy Patients

https://doi.org/10.21203/rs.3.rs-4791734/v1

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Objective

To explore the differential gene expression in peripheral blood immune cells of individuals with type 2 diabetes mellitus (DM), comparing those with and without non-proliferative diabetic retinopathy (NPDR).

Methods

From 117 potential participants, 51 were selected for detailed analysis: 9 healthy donors (HDs), 19 with DM, and 23 with NPDR. We analyzed peripheral blood mononuclear cells (PBMCs) using RNA sequencing and qPCR to identify differentially expressed genes (DEGs) and used flow cytometry to assess protein expression.

Results

In NPDR patients compared to those with DM alone, MerTK—a gene linked to inherited retinal dystrophies—was notably downregulated in PBMCs. Flow cytometry revealed MerTK predominantly in monocytes and myeloid-derived suppressor cells (MDSCs), with reduced expression in CD4 + and CD8 + T cells and natural killer T (NKT) cells. DM patients showed significant deviations in PBMC composition, especially in B cells, CD4 + T cells, and NK cells, compared to HDs.

Conclusions

The study indicates that MerTK expression in T cells within PBMCs could act as a viable blood biomarker for NPDR risk in DM patients. Furthermore, the regulation of T cells by MerTK might represent a critical pathway through which DM evolves into NPDR.

Health sciences/Diseases/Eye diseases/Retinal diseases

Health sciences/Biomarkers/Predictive markers

Biological sciences/Immunology/Inflammation

Type 2 diabetes mellitus

non-proliferative diabetic retinopathy

MerTK

peripheral immune cells

inflammation

Diabetic retinopathy (DR), a major ocular complication of diabetes mellitus (DM), is a primary cause of vision loss, especially in individuals over 50 years old[1]. Clinically, DR is classified into non-proliferative diabetic retinopathy (NPDR) and advanced proliferative diabetic retinopathy (PDR)[2]. NPDR involves vascular basement membrane thickening and microaneurysm formation, progressing to PDR with severe risks due to neovascularization[3]. Current DR treatments mainly address symptoms and show varied patient outcomes. Early retinal function and cellular changes preceding visible microangiopathy in DR suggest the necessity for early intervention[4].

Research has shown that individuals with diabetes mellitus and DR experience changes in the composition of immune cells in their bloodstream[5, 6]. Bo Li et al have unveiled a significant association between 35 distinct immune cell phenotypes and the risk of developing DR[7]. This alteration in peripheral immune cells is believed to play a crucial role in chronic inflammation, setting the stage for the development and progression of DR[8, 9]. This process is not only pivotal in the early stages of NPDR but also in the progression to more severe forms of the condition, such as PDR. PDR is marked by severe complications, including neovascularization, vitreous hemorrhage, and tractional retinal detachment[10]. Peripheral inflammation is thought to exacerbate retinopathy by altering the intraocular environment and disrupting the vascular protective mechanisms in the retina, as indicated by Yokomizo et al[11]. Thus, targeting the activity of circulating immune cells presents a potential strategy for the early diagnosis and prevention of DR.

Circulating immune cells have systemic effects on inflammation and can penetrate tissues by crossing the parenchyma-blood barriers, occurring during both health and disease states[12]. The movement of specific immune cell subsets into and out of tissues is vital for health and disease progression. Particularly in the retina, the interactions between these immune cells and retinal cells can either worsen or alleviate disease-related changes[13–15], highlighting the critical role of peripheral immune regulation in retinopathy's development and progression. Understanding the mechanisms by which these cells affect retinal diseases is crucial for creating targeted treatments. Such therapies could adjust immune responses to prevent or manage retinopathy effectively, opening new pathways for intervention.

This study seeks to elucidate the pivotal molecular alterations in circulating immune cells that drive the advancement of DM to NPDR. We analyzed peripheral blood mononuclear cells (PBMCs) from patients with and without NPDR to achieve this. Through RNA sequencing (RNA-seq), we discovered a significant reduction in the expression of the Mer tyrosine kinase (MerTK) gene in patients with NPDR, particularly noting its marked decrease in circulating T cells. MerTK, a member of the Tyro/Axl/Mer receptor family, is predominantly expressed on various immune cells such as monocytes/macrophages, dendritic cells, and microglia[16]. It plays a vital role in phagocytosis and clearing apoptotic cells, thereby preventing prolonged inflammation. This phagocytic function is critical in maintaining tissue functionality and internal environment stability[17]. MerTK has been extensively researched in the context of retinal pigment epithelium and macrophages, where its mutations are linked to severe retinitis pigmentosa and the dysregulation of macrophage functions in phagocytosis and inflammation. Therefore, our findings suggest a critical role for MerTK in circulating T cells, potentially influencing the development of diabetic retinopathy.

2.1 Study subjects

We enrolled 117 donors from the First People’s Hospital of Guangzhou between 2022 and 2023, and 9 HDs, 19 DM patients (type 2 diabetes mellitus ), and 23 NPDR patients (type 2 diabetes mellitus with non-proliferative diabetic retinopathy) were selected for analysis (Fig. 1). Diagnostic criteria adhered to the Clinical Diagnosis and Treatment Criteria for Diabetic Retinopathy in China. Inclusion criteria included confirmed DM diagnosis and accurate clinical diagnosis of DR grading; the duration of diabetes ranged from 1 to 20 years; and the participants' ages ranged from 40 to 90 years. Exclusion criteria encompassed other diabetic microangiopathies like nephropathy or neuropathy; autoimmune, neurological, or malignant diseases; major cardiovascular and cerebrovascular events; other systemic infections or inflammatory diseases; recent usage of hormones, immunosuppressants, or anti-inflammatory drugs; and laser photocoagulation, steroid or anti-vascular endothelial growth factor treatment in the past week. This study complied with the Declaration of Helsinki for research involving human subjects and was approved by the research ethics committee of the institute (K-2022-157-01). Informed consent was obtained from all participants per ethical standards. Flow chart of participants enrollment is in Fig. 1. Detail of participants are in Supplementary Table 1–3.

2.2 PBMCs preparation

Peripheral blood (5mL) was collected in EDTA-containing tubes (Sanli, Liuyang, China) and processed within 2 hours. Lymphocyte Separation Medium (LSM, biosharp, Beijing, China) was added to the blood before centrifugation[18]. The PBMCs layer was transferred to a new tube, washed with PBS (labgic, Beijing, China), and treated with red blood cell lysis buffer (solarbio, Beijing, China) at room temperature for 5 minutes. After a final PBS wash, the PBMCs were obtained[19].

2.3 Flow cytometry

PBMCs were incubated with a mixture of antibodies for 25 minutes at 4°C in the dark. After PBS washing, cells were suspended in 300uL PBS and analyzed using a BD flow cytometer. FlowJo 10.8.1 software was used for data analysis. Those antibodies used are in Supplementary Table 4.

2.4 Gating Strategies

Flow cytometry-acquired cells were gated and classified into various immune cells. Myeloid cells were identified as monocytes (CD14+) and their subpopulations, and lymphoid lineage cells included B cells (CD19+), T cells (CD3 + and its subpopulations), NK cells (CD56+), and NKT cells (CD3 + CD56+). The details are in Fig. 3B.

2.5 Quantitative RT-PCR

RNA was extracted from PBMCs using TRIzol (Invitrogen, Waltham, MA, USA) and quantified spectrophotometrically. cDNA was synthesized from 1µg of RNA using a kit (agbio, Hunan, China). qPCR was performed using SYBR-Green PCR Master Mix (agbio, Hunan, China) on a QuantStudioTM 5 system (Thermo Fisher Scientific, Waltham, MA, USA). MerTK gene expression was normalized to GAPDH and analyzed using the 2-∆∆Ct method.

MerTK

Forward primer GTACCAGCCTGCCTTGATGT

Reverse primer GTGTTTGAAGGCAAGAGGCG

GAPDH

Forward primer AAAGCCTGCCGGTGACTAAC

Reverse primer TAGGAAAAGCATCACCCGGAG

2.6 RNA-seq and analysis

RNA integrity was checked on a 1% agarose gel and quantified with NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). Samples with RIN > 7 were sequenced on Illumina HiSeq/Novaseq/MGI2000 instruments (2 x 150 PE). Data were quality-checked, filtered, and aligned to the reference genome.

This study utilized R 4.3.0 for bioinformatics analysis. The expression matrix was derived from raw sequencing data and analyzed using the "deseq2" package in R. The negative binomial regression model was fitted, and hypothesis testing was performed using the Wald test or likelihood ratio test to identify DEGs. Upregulated genes were selected based on the criteria of |log2FC| ≥ 1 and adjusted P < 0.05, while downregulated genes were selected based on |log2FC| ≤ -1 and adjusted P < 0.05. Volcano plot was generated using the “ggplot2” package, and heatmap was created with the “pheatmap” package. For Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we used the “ClusterProfiler” package in R software.

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2021), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: HRA007945) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

2.7 Statistical analyses

Data analysis was conducted using GraphPad Prism 8.0. T-tests and one-way ANOVA were employed for comparisons, with the Mann-Whitney test or Kruskal-Wallis test used when assumptions of normal distribution or homoscedasticity were not met. Mean ± SEM was reported, with P < 0.05 considered significant.

3.1 Downregulation of MerTK in Circulating Immune Cells of NPDR Patients

Following our inclusion and exclusion criteria, we selected four patients each from the NPDR and DM groups for RNA-seq analysis (Fig. 1). This analysis revealed seven differentially expressed genes (Fig. 2A and B), highlighting alterations in biological processes like neutrophil homeostasis and apoptotic cell clearance (Fig. 2C). Notably, we observed a significant downregulation of MerTK in the NPDR group compared to the DM group (adjusted P = 0.02, |log2FC| = -1.232, Fig. 2A and 2D). This reduction in MerTK expression at both transcriptional and translational levels was confirmed using qPCR and flow cytometry (Fig. 3A-D), indicating a consistent decline of MerTK in NPDR patients' PBMCs.

3.2 Specific Decline of MerTK Expression in T Cells of NPDR Patients

Our analysis extended to various immune cell types to investigate MerTK expression differences. Using UMAP analysis, we identified nine distinct immune cell clusters (Fig. 4A and B). MerTK expression was primarily noted in monocytes and MDSCs, with lesser amounts in NK and NKT cells, and minimal in T cells, including CD4 + and CD8 + T cells, and the lowest in B cells (Fig. 4C and D). In the NPDR group, significant reductions in MerTK expression were observed specifically in CD4+, CD8+, and NK T cells (P < 0.01, P < 0.05, P < 0.05, respectively) (Fig. 4E and F). Conversely, monocytes, B cells, NK cells, and MDSCs did not show significant expression changes between DM and NPDR groups (Fig. 4G).

3.3 Immune Cell Population Changes in DM and NPDR

Our study analyzed peripheral blood from 9 HDs, 15 DM patients (without NPDR), and 18 NPDR patients. We found significant alterations in the immune cell populations between the DM or NPDR groups and HDs, with no notable differences between DM and NPDR groups. Specifically, B cells increased significantly in both DM and NPDR groups compared to HDs, while NK cells showed a significant increase only in the DM group (Fig. 5A). T cells, monocytes, low-density eosinophils, low-density neutrophils, NKT cells, and MDSCs did not significantly differ among the three groups (Fig. 5B and C). Further analysis of T cell and monocyte subtypes revealed an increase only in CD4 + T cells in the DM and NPDR groups, with no significant changes in CD8 + T cells or in classical, non-classical, and intermediate monocytes (Fig. 5D and E). These findings suggest that hyperglycemia-induced dysregulation of circulating immune cells may contribute to the onset of NPDR.

MerTK is known for its role in retinal pigment epithelium (RPE), mediating rapid phagocytosis and clearance of photoreceptor debris, with its dysfunction leading to retinal dystrophy and retinitis pigmentosa[20–22]. Contrary to its well-documented presence in RPE cells, our study found MerTK to be significantly downregulated in the circulating T cells of NPDR patients compared to those with DM, particularly in CD4 + and CD8 + T cells, as well as NKT cells. Given that T cells are implicated in retinal infiltration during DR, our findings suggest a potential role for MerTK in modulating T cell functions, contributing to the progression from DM to NPDR.

MerTK is a crucial cell surface receptor involved in the innate immune system, involved in the function of a variety of immune cells. One of the most well-known functions of MerTK in macrophages is mediating the clearance of apoptotic cells in vitro and vivo[23]. Cai et al. found that the signal from MerTK in macrophages can activate the ERK-dependent pathway and inhibit the phosphorylation of 5-lipoxygenase, thus promoting the production of specialized proresolving mediators and promoting the regression of inflammation[24]. Meanwhile, TAM family kinases can suppress antitumour immunity and promote resistance to immune-checkpoint inhibitors[25]. In dendritic cells, MerTK regulates antigen presentation and immune activation, influencing initial activation and immune tolerance of CD8 + T cells[26]. The research between MerTK with T cells is also very crucial. Previous research has indicated that MerTK negatively regulates T cell activation[27] and serves as a co-stimulatory receptor on CD8 + T cells[28]. Studies in pediatric T-cell acute lymphoblastic leukemia showed increased MerTK expression[29], while inhibition of MerTK curtailed T-cell precursor expansion and induced apoptosis[30, 31]. These insights bolster our hypothesis that MerTK downregulation could enhance T cell activation and proliferation, intensifying T-cell-mediated immune responses and potentially hastening the transition from DM to DR.

The relationship between DR and DM underscores a complex interplay of factors, including the vitreous environment[11] and systemic inflammation[10, 32] from prolonged hyperglycemia. Our results raise the question of whether changes in circulating T cells mirror alterations within the vitreous environment. The infiltration of these cells through the blood-retina barrier suggests their substantial influence on the vitreous immune response, either protective or harmful[33]. Furthermore, the common signaling pathways in blood and retinal T cells hint at a shared mechanism in their regulation.

While the influence of immune cell-mediated inflammation on DR pathogenesis is widely recognized, there is a lack of detailed quantitative analyses comparing immune cell profiles among DM patients, those with NPDR, and HD[34]. Our study’s comparison of immune cell profiles among HD, DM, and NPDR groups revealed significant changes, particularly in immune cells like B cells, NK cells and T cell subtypes, suggesting their involvement in DR pathogenesis. Interestingly, we observed increased NK and B cell activities in DM patients, which might play a role in DR's early stages[35, 36]. The adaptive immune response, represented by CD4 + T cell, also showed significant increase, while CD8 + cell showed a decrease, aligning with literature indicating their varied involvement in DR progression[34]. Follicular helper T cells, a subpopulation of CD4 + T cells, have also been found to be elevated in DR patients and diabetic mice, and inhibition of this population attenuates vascular inflammation and neovascularization[37]. Some studies have indicated that CD8 + T cells can penetrate into retina and concentration significantly higher in vitreous and macular edema in patients with DR, which is associated with poor visual prognosis[38, 39]. These can further underline the significance of these cells in DR.

Despite these findings, our study has limitations. We could not fully assess the impact of diabetes medication on PBMCs, with drugs like metformin known to modulate immune responses[40, 41]. Additionally, our patient cohort’s demographic skew towards postmenopausal women may influence the observed immune changes. Furthermore, it has been reported that the expression of MerTK in the lymphopoietic system is 59.3% higher in men than in women[42]. In our study, although there was no statistical difference in patient gender between the DM and NPDR groups, there were more men in the NPDR group. Therefore, it is necessary to investigate the changes in MerTK expression across different genders. Besides, future research should delve into MerTK’s functional role in T cells and its impact on NPDR development.

In conclusion, our study elucidates the nuanced differences in immune cell behavior between HD, DM, and NPDR groups, highlighting T cell dysregulation and MerTK downregulation as potential factors in DR’s pathogenesis. These findings pave the way for further investigation into the role of immune cells in early DR development.

Acknowledgment

This work was supported by National Natural Science Foundation of China [Grant No. 8217060280 to Y.L. and No. 82071188 to Y.U.L.], Natural Science Foundation of Guangdong Province [Grant No. 2023A1515010376 to Y.Z.], and Science and Technology Projects in Guangzhou [Grant No. 2023A03J0479 to Y.Z. and No. 2023A03J0578 to Y.Y.].

Declaration of Competing interest

The authors declare no conflict of interest regarding the publication of this paper.

CRediT authorship contribution statement

Shimiao Bu: Data curation, Investigation, Writing – original draft. Zheng Zhao, Xiaojun Wu: Investigation, Methodology, Software, Visualization. Liting Zhang: Methodology, Visualization. Xiangyu Shi: Data curation. Lang Huang, Zongqin Xiang: Validation, Supervision. Ying Yang: Funding acquisition, Resources. Yong U. Liu: Funding acquisition, Project administration, Resources, Writing – review & editing. Yufeng Liu: Funding acquisition, Project administration, Resources. Yuehong Zhang: Funding acquisition, Project administration, Resources.

Bressler SB, Scanlon PH, Pearce E. Why is continued vision loss still a problem in some patients with diabetic retinopathy, despite treatment? JAMA Ophthalmol 2022;140:308–9. https://doi.org/10.1001/jamaophthalmol.2022.0135.
Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet 2010;376:124–36. https://doi.org/10.1016/S0140-6736(09)62124-3.
Wong TY, Cheung CMG, Larsen M, Sharma S, Simó R. Diabetic retinopathy. Nat Rev Dis Primers 2016;2:16012. https://doi.org/10.1038/nrdp.2016.12.
Honasoge A, Nudleman E, Smith M, Rajagopal R. Emerging insights and interventions for diabetic retinopathy. Curr Diab Rep 2019;19:100. https://doi.org/10.1007/s11892-019-1218-2.
Liao D, Fan W, Li N, Li R, Wang X, Liu J, et al. A single cell atlas of circulating immune cells involved in diabetic retinopathy. iScience 2024;27:109003. https://doi.org/10.1016/j.isci.2024.109003.
Berbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 diabetes and its impact on the immune system. CDR 2020;16:442–9. https://doi.org/10.2174/1573399815666191024085838.
Li B, Zhao X, Hong Z, Ding Y, Zhang Y. Circulating immune cell phenotyping is potentially relevant for diabetic retinopathy risk assessment. Diabetes Research and Clinical Practice 2024.
Binet F, Cagnone G, Crespo-Garcia S, Hata M, Neault M, Dejda A, et al. Neutrophil extracellular traps target senescent vasculature for tissue remodeling in retinopathy. Science 2020;369:eaay5356. https://doi.org/10.1126/science.aay5356.
Forrester JV, Kuffova L, Delibegovic M. The role of inflammation in diabetic retinopathy. Front Immunol 2020;11:583687. https://doi.org/10.3389/fimmu.2020.583687.
Kovoor E, Chauhan SK, Hajrasouliha A. Role of inflammatory cells in pathophysiology and management of diabetic retinopathy. Survey of Ophthalmology 2022;67:1563–73. https://doi.org/10.1016/j.survophthal.2022.07.008.
Yokomizo H, Maeda Y, Park K, Clermont AC, Hernandez SL, Fickweiler W, et al. Retinol binding protein 3 is increased in the retina of patients with diabetes resistant to diabetic retinopathy. Science Translational Medicine 2019;11:eaau6627. https://doi.org/10.1126/scitranslmed.aau6627.
Luster AD, Alon R, Von Andrian UH. Immune cell migration in inflammation: present and future therapeutic targets. Nat Immunol 2005;6:1182–90. https://doi.org/10.1038/ni1275.
Meng Z, Chen Y, Wu W, Yan B, Meng Y, Liang Y, et al. Exploring the immune infiltration landscape and M2 macrophage-related biomarkers of proliferative diabetic retinopathy. Front Endocrinol (Lausanne) 2022;13:841813. https://doi.org/10.3389/fendo.2022.841813.
Bhutto IA, Vaidya T, Ghosh S, Padmanabhan A, Yazdankhah M, Shang P, et al. Infiltrating immune cells and pathophysiology in diabetic retinopathy (DR). Investigative Ophthalmology & Visual Science 2019;60:2683.
Xu H, Chen M. Diabetic retinopathy and dysregulated innate immunity. Vision Research 2017;139:39–46. https://doi.org/10.1016/j.visres.2017.04.013.
Behrens EM, Gadue P, Gong S, Garrett S, Stein PL, Cohen PL. The mer receptor tyrosine kinase: expression and function suggest a role in innate immunity. European Journal of Immunology 2003;33:2160–7. https://doi.org/10.1002/eji.200324076.
Cai B, Thorp EB, Doran AC, Subramanian M, Sansbury BE, Lin C-S, et al. MerTK cleavage limits proresolving mediator biosynthesis and exacerbates tissue inflammation. Proc Natl Acad Sci U S A 2016;113:6526–31. https://doi.org/10.1073/pnas.1524292113.
Villani A-C, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 2017;356:eaah4573. https://doi.org/10.1126/science.aah4573.
Wang C, Yang S, Duan L, Du X, Tao J, Wang Y, et al. Adaptive immune responses and cytokine immune profiles in humans following prime and boost vaccination with the SARS-CoV-2 CoronaVac vaccine. Virol J 2022;19:223. https://doi.org/10.1186/s12985-022-01957-1.
Shelby SJ, Feathers KL, Ganios AM, Jia L, Miller JM, Thompson DA. MERTK signaling in the retinal pigment epithelium regulates the tyrosine phosphorylation of GDP dissociation inhibitor alpha from the GDI/CHM family of RAB GTPase effectors. Exp Eye Res 2015;140:28–40. https://doi.org/10.1016/j.exer.2015.08.006.
Lew DS, Mazzoni F, Finnemann SC. Microglia inhibition delays retinal degeneration due to MerTK phagocytosis receptor deficiency. Frontiers in Immunology 2020;11:1463.
Mercau ME, Akalu YT, Mazzoni F, Gyimesi G, Alberto EJ, Kong Y, et al. Inflammation of the retinal pigment epithelium drives early-onset photoreceptor degeneration in Mertk-associated retinitis pigmentosa. SCIENCE ADVANCES 2023;9:eade9459.
Wanke F, Gutbier S, Rümmelin A, Steinberg M, Hughes LD, Koenen M, et al. Ligand-dependent kinase activity of MERTK drives efferocytosis in human iPSC-derived macrophages. Cell Death Dis 2021;12:1–13. https://doi.org/10.1038/s41419-021-03770-0.
Cai B, Kasikara C, Doran AC, Ramakrishnan R, Birge RB, Tabas I. MerTK signaling in macrophages promotes the synthesis of inflammation resolution mediators by suppressing CaMKII activity. Science Signaling 2018;11:eaar3721. https://doi.org/10.1126/scisignal.aar3721.
DeRyckere D, Huelse JM, Earp HS, Graham DK. TAM family kinases as therapeutic targets at the interface of cancer and immunity. Nat Rev Clin Oncol 2023. https://doi.org/10.1038/s41571-023-00813-7.
Silva–Sanchez A, Meza–Perez S, Liu M, Stone SL, Flores–Romo L, Ubil E, et al. Activation of regulatory dendritic cells by Mertk coincides with a temporal wave of apoptosis in neonatal lungs. Sci Immunol 2023;8:eadc9081. https://doi.org/10.1126/sciimmunol.adc9081.
Cabezo´n R, Carrera-Silva EA, Flo´rez-Grau G, Errasti AE, Caldero´n-Go´mez E, Lozano JJ, et al. MERTK as negative regulator of human T cell activation. Journal of Leukocyte Biology 2015;97:751–60. https://doi.org/10.1189/jlb.3A0714-334R.
Peeters MJW, Dulkeviciute D, Draghi A, Ritter C, Rahbech A, Skadborg SK, et al. MERTK acts as a costimulatory receptor on human CD8 + T cells. Cancer Immunol Res 2019;7:1472–84. https://doi.org/10.1158/2326-6066.CIR-18-0841.
Brandao LN, Winges A, Christoph S, Sather S, Migdall-Wilson J, Schlegel J, et al. Inhibition of MerTK increases chemosensitivity and decreases oncogenic potential in T-cell acute lymphoblastic leukemia. Blood Cancer Journal 2013;3:e101–e101. https://doi.org/10.1038/bcj.2012.46.
Powell RM, Peeters MJW, Rahbech A, Aehnlich P, Seremet T, thor Straten P. Small molecule inhibitors of MERTK and FLT3 induce cell cycle arrest in human CD8 + T cells. Vaccines (Basel) 2021;9:1294. https://doi.org/10.3390/vaccines9111294.
Summers RJ, Jain J, Vasileiadi E, Smith B, Stout M, Kelvin J, et al. Therapeutic targeting of Mertk and BCL-2 in T-cell and early T-precursor acute lymphoblastic leukemia. Blood 2021;138:1184. https://doi.org/10.1182/blood-2021-151726.
Wan H, Cai Y, Wang Y, Fang S, Chen C, Chen Y, et al. The unique association between the level of peripheral blood monocytes and the prevalence of diabetic retinopathy: a cross-sectional study. J Transl Med 2020;18:248. https://doi.org/10.1186/s12967-020-02422-9.
Llorián-Salvador M, de Fuente AG, McMurran CE, Dashwood A, Dooley J, Liston A, et al. Regulatory T cells limit age-associated retinal inflammation and neurodegeneration. Molecular Neurodegeneration 2024;19:32. https://doi.org/10.1186/s13024-024-00724-w.
Obasanmi G, Lois N, Armstrong D, Lavery N-J, Hombrebueno JR, Lynch A, et al. Circulating leukocyte alterations and the development/progression of diabetic retinopathy in type 1 diabetic patients - a pilot study. Current Eye Research 2020;45:1144–54. https://doi.org/10.1080/02713683.2020.1718165.
Kim JH, Park K, Lee SB, Kang S, Park JS, Ahn CW, et al. Relationship between natural killer cell activity and glucose control in patients with type 2 diabetes and prediabetes. J Diabetes Investig 2019;10:1223–8. https://doi.org/10.1111/jdi.13002.
Liu B, Hu Y, Wu Q, Zeng Y, Xiao Y, Zeng X, et al. Qualitative and quantitative analysis of B-cell-produced antibodies in vitreous humor of type 2 diabetic patients with diabetic retinopathy. J Diabetes Res 2020;2020:4631290. https://doi.org/10.1155/2020/4631290.
Liu Y, Yang Z, Lai P, Huang Z, Sun X, Zhou T, et al. Bcl-6-directed follicular helper T cells promote vascular inflammatory injury in diabetic retinopathy. Theranostics 2020;10:4250–64. https://doi.org/10.7150/thno.43731.
Kase S, Saito W, Ohno S, Ishida S. Proliferative diabetic retinopathy with lymphocyte-rich epiretinal membrane associated with poor visual prognosis. Invest Ophthalmol Vis Sci 2009;50:5909. https://doi.org/10.1167/iovs.09-3767.
Huang J, Zhou Q. CD8 + T cell-related gene biomarkers in macular edema of diabetic retinopathy. Front Endocrinol (Lausanne) 2022;13:907396. https://doi.org/10.3389/fendo.2022.907396.
Al Dubayee MS, Alayed H, Almansour R, Alqaoud N, Alnamlah R, Obeid D, et al. Differential expression of human peripheral mononuclear cells phenotype markers in type 2 diabetic patients and type 2 diabetic patients on metformin. Front Endocrinol (Lausanne) 2018;9:537. https://doi.org/10.3389/fendo.2018.00537.
Al Dubayee M, Alshahrani A, Almalk M, Hakami A, Homoud B, Alzneidi N, et al. Metformin alters peripheral blood mononuclear cells (PBMC) senescence biomarkers gene expression in type 2 diabetic patients. J Diabetes Complications 2021;35:107758. https://doi.org/10.1016/j.jdiacomp.2020.107758.
Liu S, Wu J, Yang D, Xu J, Shi H, Xue B, et al. Big data analytics for MerTK genomics reveals its double-edged sword functions in human diseases. Redox Biol 2024;70:103061. https://doi.org/10.1016/j.redox.2024.103061.

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Downregulation of MerTK in Circulating T cells of Non-Proliferative Diabetic Retinopathy Patients

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Abstract

Figures

1. Introduction

2. Methods

2.1 Study subjects

2.2 PBMCs preparation

2.3 Flow cytometry

2.4 Gating Strategies

2.5 Quantitative RT-PCR

2.6 RNA-seq and analysis

2.7 Statistical analyses

3. Results

3.1 Downregulation of MerTK in Circulating Immune Cells of NPDR Patients

3.2 Specific Decline of MerTK Expression in T Cells of NPDR Patients

3.3 Immune Cell Population Changes in DM and NPDR

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

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