
==== Front
Biochem Biophys Rep
Biochem Biophys Rep
Biochemistry and Biophysics Reports
2405-5808
Elsevier

S2405-5808(24)00171-7
10.1016/j.bbrep.2024.101807
101807
Research Article
Human Antigen R -mediated modulation of Transforming Growth Factor Beta 1 expression in retinal pathological milieu
Mohan Sruthi Priya ab
Nagarajan Hemavathy c
Vetrivel Umashankar d
Ramasubramanyan Sharada drsharada@snmail.org
a⁎
a R.S. Mehta Jain Department of Biochemistry and Cell Biology, KBIRVO, Vision Research Foundation, Chennai, India
b School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India
c Centre for Bioinformatics, KBIRVO, Vision Research Foundation, Chennai, India
d Virology & Biotechnology/Bioinformatics Division, ICMR-National Institute for Research in Tuberculosis, Chennai, India
⁎ Corresponding author. R.S. Mehta Jain Department of Biochemistry and Cell Biology, KBIRVO, Vision Research Foundation, No. 41 (old 18), College Road, Chennai, 600 006, Tamil Nadu, India. drsharada@snmail.org
12 8 2024
9 2024
12 8 2024
39 1018078 5 2024
27 7 2024
2 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The fate and stability of messenger RNA (mRNA), from transcription to degradation is regulated by a dynamic shuttle of epigenetic modifications and RNA binding proteins in maintaining healthy cellular homeostasis and disease development. While Transforming Growth Factor Beta 1 (TGFβ1) has been implicated as a key regulator for diabetic retinopathy, a microvascular complication of diabetes, the RNA binding proteins post-transcriptionally regulating its expression remain unreported in the ocular context. Further, dysfunction of TGFβ1 signalling is also strongly associated with angiogenesis, inflammatory responses and tissue fibrosis in many eye conditions leading to vision loss. In this study, computational and molecular simulations were initially carried out to identify Human Antigen R (HuR) binding sites in TGFβ1 mRNA and predict the structural stability of these RNA-protein interactions. These findings were further validated through in vitro experiments utilizing Cobalt Chloride (CoCl2) as a hypoxia mimetic agent in human retinal microvascular endothelial cells (HRMVEC). In silico analysis revealed that HuR preferentially binds to the 5′-UTR of TGFβ1 and displayed more stable interaction than the 3′UTR. Consistent with in silico analysis, RNA immunoprecipitation demonstrated a robust association between HuR and TGFβ1 mRNA specifically under hypoxic conditions. Further, silencing of HuR significantly reduced TGFβ1 protein expression upon CoCl2 treatment. Thus, for the first time in ocular pathological milieu, direct evidence of HuR- TGFβ1 mRNA interaction under conditions of hypoxia has been reported in this study providing valuable insights into RNA binding proteins as therapeutic targets for ocular diseases associated with TGFβ1 dysregulation.

Highlights

• In silico analysis unravelled HuR binding sites both in 3′ and 5′UTR of TGFβ1 mRNA.

• HuR binding to TGFβ1 mRNA increased in hypoxic HRMVEC.

• Silencing of HuR decreased the TGFβ1 protein levels particularly in CoCl2 treated HRMVEC.

Keywords

Post-transcriptional regulation
Human Antigen R
Hypoxia
TGFβ1
Molecular docking
Molecular dynamics
Abbreviations

mRNA messenger Ribose nucleic acid

MMP9 Matrix Metallopeptidase 9

COX2 Cyclooxygenase-2

CNTF Ciliary Neurotrophic Factor

bFGF basic Fibroblast Growth Factor

NCBI National Center for Biotechnology Information

DMSO Dimethyl sulfoxide

cDNA complementary Deoxyribose nucleic acid

FBS Fetal Bovine Serum

rRNA ribosomal RNA

MTT 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide

siRNA silencing RNA

PVDF Polyvinylidene fluoride

BSA Bovine Serum Albumin

HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

KCl Potassium Chloride

MgCl2 Magnesium Chloride

DTT Dithiothreitol

NP-40 Nonidet P-40

IgG Immunoglobulin G

PCR Polymerase Chain Reaction

ANOVA Analysis of Variance

RRM RNA Recognition Motifs

vdW van der Waals

H-bond Hydrogen bond
==== Body
pmc1 Introduction

As the burden of diabetes mellitus (DM) as a global epidemic is on a sharp rise [1], intraocular complications can be seen in one-third of diabetic patients with an annual incidence of diabetic retinopathy (DR) ranging from 2.2 to 12.7 % [2] and is one of the leading causes of vision loss in the elderly [3]. The pathological angiogenic switch in DR is triggered mainly under conditions of hypoxia initiated by stabilization of Hypoxia-inducible factor-1α (HIF-1α), which then pilots the up-regulation of several core angiogenic proteins such as vascular endothelial growth factor (VEGF) in promoting abnormal proliferation of retinal blood vessels [4]. The current modality of treatment revolves around anti-VEGF therapies, however with growing concerns about its cost, safety, and efficacy [[5], [6], [7], [8]]. In recent years, RNA binding proteins (RBPs) are emerging as master regulators for diabetes and its related complications and therefore, open a new class of potential therapeutic targets for their treatment [9,10].

Of particular interest is Human Antigen R (HuR) that regulates key cellular processes including cell proliferation, apoptosis, mRNA trafficking, and protein translation ([11]; J. [12]). HuR binds to adenine- and uridine-rich elements (ARE) in the 3′-untranslated region (UTR) of target mRNAs and regulate their stability and translation. Most importantly, HuR bound to VEGF at a 40bp RNA element sequence at the 3′UTR [13] and this interaction was also observed in HeLa cells treated with a CoCl2 by a pull-down assay to identify RNA-binding partners for VEGF mRNA [14]. Additionally, the other angiogenic molecules that are directly regulated by HuR include HIF1α, COX2, and MMP9 ([15,16]; H. [17]) indicating a pivotal role of HuR in the molecular pathophysiology of DR. Indeed, in diabetic rats, the signalling cascade involving Protein Kinase C beta (PKCβ) and activation of HuR protein led to enhanced VEGF expression in the retina [18]. Delivery of HuR siRNA using lipopolyplexes (LPPs) into the eye significantly lowered the VEGF levels in streptozotocin (STZ)-induced diabetic rats, highlighting the therapeutic potential of regulating RBPs in combating pathological angiogenesis and associated vascular remodelling in DR [19,20]

Transforming Growth Factor β (TGF β) signalling is yet another perpetuator of DR, wherein increased TGFβ1 levels have been reported in both aqueous[21] and vitreous humor of DR patients [22,23]; TGFβ lead to increased vascular permeability by decreasing the expression levels of VE-Cadherin and Claudin 5 [24]; and TGFβ induces expression of Connective Tissue Growth Factor (CTGF) thereby regulating angio-fibrotic switch in late proliferative diabetic retinopathy (PDR) [[25], [26], [27]]. The therapeutic efficacy of the anti-VEGF drug, Bevacizumab is thwarted by sub-retinal fibrosis that develops in a significant number of patients with up-regulated levels of pro-fibrotic factors such as CTGF, TGFβ2, CNTF, and bFGF [[28], [29], [30]]. While TGFβ downstream signalling has been well characterized, the RBPs including HuR that exert their influence in its expression, particularly in association with ocular pathology remains elusive. Gene expression analysis as a quantitative measure holds key information on the regulatory networks, epigenetic regulation and translation of proteins involved in disease pathogenesis [31]. Advancement in computational and molecular technologies such as cross-linked and immunoprecipitation (CLIP) (protein-centric) and RNA affinity pull down followed by mass-spectrometric analysis (RNA-centric), allows for investigating regulation of gene expression by RBPs and its influence on disease progression [32].

Understanding the structural dynamics of RBP-RNA binding interface in the last few years has been instrumental in developing novel therapeutic moieties targeting the interaction motif. Thus, the primary objective of this study was to capture if HuR can bind and post-transcriptionally regulate TGFβ1 mRNA expression in retinal endothelial cells under hypoxic conditions. Through in silico and in vitro approaches, it is observed that HuR could specifically bind to TGFβ1 mRNA and its protein expression significantly down-regulated upon HuR silencing. This is the first study in an ocular context to investigate the RBP-TGFβ1 signaling axis and also report direct binding and regulation of TGFβ1 expression by HuR.

2 Materials and methods

2.1 In silico analysis of HuR-TGFβ1 interaction motifs

The TGFβ1 mRNA sequence was retrieved from NCBI (NM_000660.7) and subjected to HuR binding site prediction using RBPmap (http://rbpmap.technion.ac.il/index.html) [33] and BRIO (http://brio.bio.uniroma2.it/) [34] respectively. Amongst the sequences, the HuR binding motif sequences spanning the 5′UTR and 3′UTR of TGFβ1 were considered for both secondary and tertiary structure prediction using RNAfold [35] and RNA composer [36], respectively. Molecular docking of protein-RNA complexes was performed for the modelled RNA structures with HuR (PDB ID: 4ED5_ChainA) (J. [12]) using HADDOCK 2.2 [37]. Further, the docked protein-RNA complexes (best docking score) were analyzed for MMGBSA using the PRIME module of the Schrodinger suite [38,39], while the interaction analysis was performed using NUCPLOT [40] and PLIP [41].

Molecular dynamics (MD) simulation of the docked protein-RNA complexes was performed using GROMACS v2021 with CHARMM c36m as the force field. Before the simulation, the complexes were preprocessed using the Input generator-solution builder module of CHARMM-GUI [42] to generate the input files for the molecular dynamics simulation. The net charge of the system was kept neutral by adding counter ions. The system was solvated using TIP3P water molecules in a cubic box in which the edges of the protein and the solvated system will not be closer than 10 Å. Simulations are performed under periodic boundary conditions (PBC) and with the Particle Mesh Ewald (PME) method for long-range electrostatic interactions. The van der Waals interactions are smoothly switched off at 12 Å. The solvated system was minimized for 1000 steps to remove the contact clashes (atomic) in the system. Followed by energy minimization, and equilibration, the production runs were performed with an integration time-step of 2 fs (fs), and all the bond lengths involving hydrogen atoms were fixed using the SHAKE algorithm. The system was equilibrated using an NPT ensemble at 1 atm pressure and 310 K temperature with constraints and finally, MD production simulations were carried out for 200ns without any constraints and the trajectory was saved for every 10 ps. The resulting MD trajectories of the complexes were analyzed for plotting root mean square deviation (RMSD) and root mean square fluctuations (RMSF), inter-hydrogen bond interactions within the systems through its entire trajectories.

2.2 Cell culture and treatment conditions

Primary Human Retinal Microvascular Endothelial Cells (HRMVEC; # ACBRI 181) were procured from Cell Systems (Kirkland, USA) and cultured in Endothelial Growth Medium-2 (EGM-2, Lonza, Switzerland) in a humidified incubator at 37 °C with 5 % CO2. For all experimental conditions, passage 6–8 cells were serum starved overnight in Endothelial Basal Medium (EBM) with 1 % FBS and treated with 0–200 μM of CoCl2 (Sigma).

2.3 Silencing of HuR

Silencer select validated siRNAs for HuR (s4610) and negative control (4404021) were purchased from Thermo Fisher Scientific (Waltham, MA) and transfected into HRMVEC using Lipofectamine RNAiMAX (Life Technologies, Carlsbad, CA) for 24 h. The transfected cells were subsequently treated for 24 h using CoCl2 for hypoxia induction and expression analysis. Cell viability was determined using MTT assay and performed as described previously [43,44].

2.4 Gene expression analysis

Total RNA was extracted from the cells using Trizol (Ambion) and converted to cDNA using the iSCRIPT cDNA synthesis kit (Bio-rad, USA). Using pre-designed primers (Supplementary Table S3), the relative expression of each target gene was normalized to 18s rRNA and fold change values were calculated using the 2 −ΔΔCt method.

2.5 Western blotting

Total cell lysates were prepared using (RIPA) lysis buffer (150 mM sodium chloride, 0.1 % TritonX-100, 0.5 % sodium deoxycholate, 0.1 % SDS (sodium dodecyl sulfate) 50 mM Tris, pH 8.0) with protease inhibitors and phosphatase inhibitor cocktail (Roche). 50 μg of protein was separated on 8–15 % SDS-PAGE gels and transferred to PVDF membrane (GE Healthcare, UK). The membranes were blocked for 1 h and immuno-blotted using commercially available antibodies and chemi-luminescence method of detection. Primary antibodies include Anti-HIF1α (Santacruz Biotechnology, USA), Anti-HuR (Santacruz Biotechnology, USA), Anti- TGFβ1 (Cell signalling technologies) and Anti-β-actin (Santacruz Biotechnology, USA). Images were captured on a FluorChem FC3 (ProteinSimple, USA) and quantification done using ImageJ software.

2.6 RNA immunoprecipitation (RIP)

5*107 cells were lysed in RIP lysis buffer (10 mM HEPES pH-8, 40 mM KCl, 3 mM MgCl2, 5 % glycerol, 2 mM DTT, and 0.5%NP-40) along with Proteinase Inhibitor cocktail (Roche) and RNAse inhibitors (Ambion). 10 % of the lysate was removed as input and the remaining lysate was incubated with magnetic beads conjugated with anti-HuR antibody or control IgG overnight at 4 °C. The beads were washed 5 times with NT-2 buffer (50 mM Tris pH:7.4, 150 mM NaCl, 1 mM MgCl2, and 0.05 % NP-40) and processed for RNA isolation using the Trizol method. qPCR was performed for the target genes and fold enrichment was calculated as follows. ΔCt [normalized RIP] = Ct [RIP] – (Ct [Input] – Log2 (Input Dilution Factor)) where Input Dilution Factor = (fraction of the input RNA saved). % Input = 2(-ΔCt [ normalized RIP]). ΔΔCt [RIP/IgG] = ΔCt [normalized RIP] – ΔCt [normalized IgG] and Fold Enrichment = 2 (-ΔΔCt [RIP/IgG])

2.7 RNA stability assay

To HRMVEC treated with CoCl2 for 24 h, 5 μg/ml of Actinomycin D (Sigma) was added to measure mRNA decay over varying time points at 0, 2 and 4 h following treatment. RNA was extracted by trizol method and by qPCR, the mRNA abundance was calculated as follows.ΔCt = (Average Ct of each time point - Average Ct of t = 0).

Relative mRNA abundance = 2(-ΔCt).

The mRNA decay rate was determined by non-linear regression curve fitting (one-phase decay) using GraphPad Prism.

2.8 Statistical analysis

Data were presented as mean ± S.E.M. and the statistical significance between groups was analyzed using GraphPad Prism5 software (GraphPad Software, Inc., San Diego, CA, USA). Student's t-test was used for the comparison of parameters between two groups and one-way ANOVA was used for the comparison of parameters between more than two groups. P-value of <0.05 was considered to be significant. For all tests, three levels of significance (*P < 0.05, **P < 0.01, ***P < 0.001) were applied.

3 Results

To determine if HuR could bind and post-transcriptionally regulate TGFβ1 expression, in silico analysis was carried out to identify the binding sites of HuR in the UTR regions of TGFβ1 and model their interaction dynamics.

3.1 In silico analysis of HuR-TGFβ1 interaction

As indicated in Table 1, 5 RBP sites were identified, of which RBP sites 1 and 5 were located within the 5′UTR and 3′UTR regions of TGFβ1 mRNA respectively. Thus, these two regions were further considered for secondary structure and tertiary structure prediction. The RBP site 1 (TGFβ1-5′UTR) and RBP site 5 (TGFβ1-3′UTR) secondary structures were predicted as shown in Fig. S1, upon which the tertiary structure was modelled for HuR binding motif including 3bp upstream and downstream of the motif as shown in Fig. 1A & B. On docking these modelled UTR segments to the HuR protein, it was observed that for TGFβ1-5′UTR, a total of 119 structures were clustered into 12 cluster(s) for the HuR- TGFβ1-5′UTR complex which represented 59.5 % of the water-refined models. In the case of the TGFβ1-3′UTR segment, HADDOCK clustered 130 structures into 12 cluster(s) for the HuR- TGFβ1-3′UTR complex representing 65.0 % of the water-refined models.Table 1 Prediction of the HuR binding site of TGFβ1.

Table 1

Fig. 1 Predicted three-dimensional structure of TGFβ1 binding segments (A) 5′UTR (B) 3′UTR The docked HuR in complex with TGFβ1 segments (C) 5′UTR (D) 3′UTR. Initial interactions of the docked complexes generated by NUCPLOT (E) HuR-TGFβ1-5′UTR (F) HuR-TGFβ1-3′UTR.

Fig. 1

Based on the molecular docking analysis (Table 2), the TGFβ1-3′UTR was observed to have a better binding affinity profile with a high HADDOCK score of −121.3 ± 13.1 than the TGFβ1-5′UTR (−107.9 ± 13.8), in addition to the least RMSD value. Moreover, the average standard deviation (negative Z score) among clusters of HuR with TGFβ1-3′UTR indicates better docking among complexes TGFβ1-5′UTR (Table 2). Besides that, the terms electrostatic energy and van der Waals energy favored the protein-RNA complex formation, while desolvation energy hindered the same. Interestingly amongst the docked complexes, the TGFβ1-5′UTR was inferred to have stable interactions with HuR, with higher hydrogen bond formation (H-bonds: 28) majorly with the RRM1 domain residues and crucial T-type π-Stacking. TGFβ1-3′UTR showed major hydrogen bond interactions with the RRM2 domain than RRM1 domain residues, along with stable hydrophobic interactions with Lys34, and Leu35 residues of the RRM1 domain. Furthermore, the binding energy was also observed to be higher in the case of the HuR-TGFβ1-5′UTR complex, wherein the Coulomb, H-bond, and vdW energies have highly favored the complex formation when compared to the HuR-TGFβ1-3′UTR complex (see Table 3).Table 2 Molecular docking profiles of HuR in complex with TGFβ1 segments.

Table 2Molecular Docking Profile	HuR_TGFβ1-5′UTR	HuR_TGFβ1-3′UTR	
Cluster	cluster4	cluster2	
HADDOCK score [a.u]	−107.9 ± 13.8	−121.3 ± 13.1	
Cluster size	15	17	
RMSD from the overall lowest-energy structure (Å)	3.4 ± 2.6	1.1 ± 0.7	
Van der Waals energy (kcal/mol)	−75.0 ± 6.4	−84.7 ± 8.6	
Electrostatic energy (kcal/mol)	−339.1 ± 68.1	−421.2 ± 37.3	
Desolvation energy (kcal/mol)	21.0 ± 4.1	33.9 ± 8.1	
Restraints violation energy (kcal/mol)	138.3 ± 46.92	137.5 ± 26.59	
Buried Surface Area (kcal/mol)	1755.6 ± 129.2	2137.2 ± 121.0	
Z-Score	−1.6	−2	

Table 3 Binding energy components from protein-RNA complex.

Table 3MMGBSA ΔG binding energy (kcal/mol)	HuR_TGFβ1-5′UTR	HuR_TGFβ1-3′UTR	
Binding energy	−119.66	−111.64	
Coulomb energy	−257.67	−132.82	
Covalent energy	23.91	6.9	
H-bond energy	−16	−9.26	
Lipophilic energy	−9.39	−18.78	
Solv_GB	260.12	168.96	
vdW Energy	−125.84	−125.75	

3.2 Stability analysis of the protein-RNA complexes

On comparative RMSD analysis (Fig. 2A) of the HuR-RNA complexes, it was observed that the TGFβ1-5′UTR bound HuR complex was found to be stabilized and to have maintained the least deviations (∼0.6 nm) throughout the MD production run. Whereas the TGFβ1-3′UTR bound HuR complex, has tried to equilibrate at the initial 50 ns with the least RMS deviations of ∼0.5 nm till 140ns, but has attained higher deviations of ∼1.1 nm at 150ns, yet has stabilized in the last 40 ns. The higher deviations of the complex can be highly attributed to the higher fluctuations of the HuR in the complex with TGFβ1-3′UTR (Fig. 2B), which has also shown higher RMSD deviations than the TGFβ1-5′UTR bound state (Fig. 2D–F). From the RMSF plot (Fig. S3) of the TGFβ1-UTR segments, both the UTR segments have the least fluctuations. On analyzing the inter-hydrogen bond interactions of both HuR and TGFβ1 UTR segments, 5′UTR of TGFβ1 was observed to maintain higher hydrogen bonds (∼25 H-bonds) throughout the MD than 3′UTR (∼15 H-bonds). The interaction analysis of the minimum potential energy structures (Fig. 3 & Table S2) of HuR-TGFβ1 UTR complexes (Fig. S4), TGFβ1-5′UTR has formed crucial π-Cation and π-Stacking (P-type stacking) with the RRM1 domain residues of HuR, while the RRM2 domain residue Arg120 has formed a π-Cation interaction with TGFβ1-3′UTR. Apart from these, TGFβ1-5′UTR has maintained stable hydrogen bonds and non-bonded interactions with HuR than the TGFβ1-3′UTR. Having established stable interaction of HuR to TGFβ1 mRNA by computational analysis, in vitro validation was performed in HRMVEC, especially under conditions of hypoxia to direct evidence HuR binding to TGFβ1.Fig. 2 Molecular dynamics simulation analysis of the complexes (A) comparative RMSD plot (B) RMSF plot of HuR (C) Inter hydrogen bond plot (D) RMSD plot of HuR bound to TGFβ1- UTRs, comparative RMSD plots of HuR and TGFβ1-UTRs (E) HuR-TGFβ1-5′UTR (F) HuR-TGFβ1-3′UTR.

Fig. 2

Fig. 3 Interactions of the minimum potential complexes generated by NUCPLOT (A) HuR-TGFβ1-5′UTR (B) HuR-TGFβ1-3′UTR.

Fig. 3

3.3 Novel binding of HuR to TGFβ1 in hypoxia-induced HMRVEC

HRMVEC treated with 200 μM of CoCl2 up to 24 h did not show any cytotoxicity and significantly enhanced the expression of HIF1α protein as expected (Figure S5 &Fig. 4A). Increased TGFβ1 expression at both gene and protein level could also be observed in hypoxic HRMVEC, with TGFβ1 mRNA stabilizing under the stress conditions (Fig. 4B, C & D). RNA immunoprecipitation assay using HuR antibody shows a novel binding of HuR to TGFβ1 mRNA, more so in cells that are under hypoxic stress (Fig. 4E–G).Fig. 4 HuR interaction with TGFβ1 in hypoxic HRMVEC. HRMVEC were treated with either Control (C)/CoCl2 and subjected to (A) western blotting for HIF1α and TGFβ1 with β-actin as loading control. (B) Relative protein expression of TGFβ1 normalized to β-actin. (C) qPCR for TGFβ1 with 18s rRNA as an internal control (D) mRNA stability assay using ActinomycinD for TGFβ1. (E) Schematic representation of RNA immunoprecipitation (F) western blotting for HuR in the immunoprecipitated samples [I-Input (10 %)]. (G) RNA immunoprecipitation followed by real-time PCR for TGFβ1 (n = 3; *p < 0.05).

Fig. 4

3.4 Silencing HuR reduces TGFβ1 expression

To further delineate the specific association of HuR to TGFβ1 mRNA upon hypoxia in HRMVEC, HuR was silenced and its effect on the expression of TGFβ1 was analyzed. Using validated siRNA oligos at concentrations not toxic to the cells (Fig. S6), a significant reduction in the expression of HuR could be observed as shown in Fig. 5A. Intriguingly, in line with the hypothesis that HuR binding would affect the post-transcriptional levels of TGFβ1, HuR silencing in CoCl2 treatment led to a significant decrease in protein expression of TGFβ1, but not its mRNA levels (Fig. 5C–E & B).Fig. 5 siRNA experiments for HuR. HRMVEC were treated with either Scramble (Scr), Scr + CoCl2, SiHuR, or SiHuR + CoCl2 and subjected to (A, B) Real-time PCR for HUR and TGFβ1 with 18s rRNA as an internal control (n = 3; ***p < 0.001 Scr Vs SiHuR; ##p < 0.01 Scr + CoCl2Vs SiHuR + CoCl2, ns-not significant) (C) western blotting for HuR and TGFβ1 with β-actin as the loading control. (D & E) relative protein expression of HuR and TGFβ1 normalized to β-actin (n = 3, #p < 0.05 Scr + CoCl2Vs SiHuR + CoCl2, ns-not significant).

Fig. 5

4 Discussion

The multi-faceted molecular networks of intraocular vascular diseases warrant that the therapeutic targets to combat ocular angiogenesis be not concentrated on molecules in individual pathways, but rather seek a convergence point for the various signaling pathways. HuR is an RNA-binding protein that is at the central dogma of angiogenesis and has been shown to modulate and influence expression levels of many pro-angiogenic factors including proteins that facilitate endothelial cell proliferation and migration. However, little is known about the influence of RNA binding proteins, particularly HuR in post-transcriptional regulation of TGFβ1 expression and its associated angio-fibrotic switch in DR which is addressed in this study.

In silico analysis revealed HuR binding sites both in the 3′UTR and the 5′ UTR regions of TGFβ1 mRNA. Computational simulations showed that the 5′UTR segment has a higher binding affinity and stable interactions towards HuR than 3′UTR. Amongst the UTRs, only the binding of 3′UTR has induced the conformational changes of HuR, since it had major interactions with the inter-domain linker segment and RRM2 domain. Wherein the binding of 5′UTR has aided in the stability of the HuR protein with increased binding affinity. This reveals that the binding of both 5′ and 3′UTR has their respective substantial contacts towards HuR conformational changes that may be attributed to its cascade of functional mechanisms. HuR is known to predominantly bind at the 3′UTR in promoting mRNA stability and regulating the translation efficiency of target genes [16]. While the association of HuR with the 5′UTRs of some target substrates caused repression in their translation [45,46], in some it had an opposite effect leading to enhanced expression of the target proteins such as HIF1α [47,48]. Thus, the impact of HuR binding to 5′UTRs and the associated fate of the mRNAs needs further studies to understand the functional significance of RBP-mRNA interaction. But of importance is in silico analysis to simulate the binding of HuR to mRNA of interest assists in understanding both the structural and functional significance of the interaction, which could be targeted by competitive inhibitors as alternate therapy.

RBPs oversee and regulate post-transcriptional regulation of mRNA including their stability, transport, alternative splicing, polyadenylation and the rate of translation [49]. In fact, Neuronal Protein 3.1 (P311) is an RBP that preferentially binds to the 5′-UTR of TGF-β1-3 mRNAs through its RRM (RNA recognition motif-like motif) domain and recruits the mRNAs to the translation machinery. This leads to enhanced translation efficiency and up-regulation of TGFβ 1–3 protein levels [50]. We report a similar observation wherein our in silico analysis demonstrate preferential binding of HuR to the 5-UTR of TGF-β1 mRNA. We hypothesized that this HuR-TGFβ1 mRNA interaction would lead to stabilization of the mRNA and thereby increase the translation efficiency of TGFβ1 in HRMVECs treated with CoCl2. Indeed, RIP experiments provided direct evidence for HuR binding to TGFβ1 mRNA in hypoxia-induced retinal endothelial cells. In line with our hypothesis, silencing HuR in CoCl2 treated HRMVEC cells did not alter TGFβ1 mRNA levels but rather significantly reducing its protein expression. This is further supported by previous studies wherein HuR association with TGFβ1 mRNA led to increased secretory levels of TGFβ1 [[51], [52], [53]]. Interestingly, while Galban et al., reported that HuR overexpression was found to increase binding to the target mRNAs upon stress insults [47], we didn't observe any significant increase in HuR levels in HRMVECs upon CoCl2 treatment (Supplementary Fig. S7) but impacted its association with the target mRNA. In this study at least, the binding affinity of HuR to target mRNAs did not depend on its protein expression levels.

This study captures for the first time in retinal cells, RBP that can post-transcriptionally regulate TGFβ1 signalling, indicating a crucial role of HuR in facilitating the angio-fibrotic switch associated with DR progression. An intricate and complex circuit of intersecting signalling pathways defines the pathogenesis of diabetes and its complications, with TGFβ signalling perpetuating the microvascular abnormalities and cellular dysfunction in DR. Thus, novel interfacial peptide inhibitors towards the HuR- TGFβ1 mRNA interaction sites would better serve as a potential therapeutic modality not limited to DR, but also in other eye conditions with TGFβ1 dysregulation.

CRediT authorship contribution statement

Sruthi Priya Mohan: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis. Hemavathy Nagarajan: Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis. Umashankar Vetrivel: Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Sharada Ramasubramanyan: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Dr Sharada Ramasubramanyan reports financial support was provided by the Science and Engineering Research Board, Department of Science and Technology, India. Sruthi Priya Mohan reports financial support was provided by Indian Council of Medical Research - Senior Research Fellowship. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

5. Acknowledgments

This work was supported by the 10.13039/501100001843 Science and Engineering Research Board , Department of Science and Technology, India [ECRA- ECR/2016/001729 ] and the Indian Council of Medical Research (ICMR) [2020–6636 CMB/BMS].

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbrep.2024.101807.
==== Refs
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