Serine and glycine physiology reversibly modulate retinal and peripheral nerve function.

Metabolic homeostasis is maintained by redundant pathways to ensure adequate nutrient supply during fasting and other stresses. These pathways are regulated locally in tissues and systemically via the liver, kidney, and circulation. Here, we characterize how serine, glycine, and one-carbon (SGOC) metabolism fluxes across the eye, liver, and kidney sustain retinal amino acid levels and function. Individuals with macular telangiectasia (MacTel), an age-related retinal disease with reduced circulating serine and glycine, carrying deleterious alleles in SGOC metabolic enzymes exhibit an exaggerated reduction in circulating serine. A Phgdh+/- mouse model of this haploinsufficiency experiences accelerated retinal defects upon dietary serine/glycine restriction, highlighting how otherwise silent haploinsufficiencies can impact retinal health. We demonstrate that serine-associated retinopathy and peripheral neuropathy are reversible, as both are restored in mice upon serine supplementation. These data provide molecular insights into the genetic and metabolic drivers of neuro-retinal dysfunction while highlighting therapeutic opportunities to ameliorate this pathogenesis.

Development of a Semi-automated Computer-based Tool for the Quantification of Vascular Tortuosity in the Murine Retina.

Purpose: The murine oxygen-induced retinopathy (OIR) model is one of the most widely used animal models of ischemic retinopathy, mimicking hallmark pathophysiology of initial vaso-obliteration (VO) resulting in ischemia that drives neovascularization (NV). In addition to NV and VO, human ischemic retinopathies, including retinopathy of prematurity (ROP), are characterized by increased vascular tortuosity. Vascular tortuosity is an indicator of disease severity, need to treat, and treatment response in ROP. Current literature investigating novel therapeutics in the OIR model often report their effects on NV and VO, and measurements of vascular tortuosity are less commonly performed. No standardized quantification of vascular tortuosity exists to date despite this metric's relevance to human disease. This proof-of-concept study aimed to apply a previously published semi-automated computer-based image analysis approach (iROP-Assist) to develop a new tool to quantify vascular tortuosity in mouse models. Design: Experimental study. Subjects: C57BL/6J mice subjected to the OIR model. Methods: In a pilot study, vasculature was manually segmented on flat-mount images of OIR and normoxic (NOX) mice retinas and segmentations were analyzed with iROP-Assist to quantify vascular tortuosity metrics. In a large cohort of age-matched (postnatal day 12 [P12], P17, P25) NOX and OIR mice retinas, NV, VO, and vascular tortuosity were quantified and compared. In a third experiment, vascular tortuosity in OIR mice retinas was quantified on P17 following intravitreal injection with anti-VEGF (aflibercept) or Immunoglobulin G isotype control on P12. Main Outcome Measures: Vascular tortuosity. Results: Cumulative tortuosity index was the best metric produced by iROP-Assist for discriminating between OIR mice and NOX controls. Increased vascular tortuosity correlated with disease activity in OIR. Treatment of OIR mice with aflibercept rescued vascular tortuosity. Conclusions: Vascular tortuosity is a quantifiable feature of the OIR model that correlates with disease severity and may be quickly and accurately quantified using the iROP-Assist algorithm. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

Importance: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. Objective: To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. Design, Setting, and Participants: This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Exposures: Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). Main Outcomes and Measures: The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. Results: A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. Conclusions and Relevance: The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading.

PURPOSE: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP). DESIGN: We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale. PARTICIPANTS: A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants. METHODS: We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale. MAIN OUTCOME MEASURES: Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP. RESULTS: The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values. CONCLUSIONS: Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.

Objective: To develop a generative adversarial network (GAN) to segment major blood vessels from retinal flat-mount images from oxygen-induced retinopathy (OIR) and demonstrate the utility of these GAN-generated vessel segmentations in quantifying vascular tortuosity. Design: Development and validation of GAN. Subjects: Three datasets containing 1084, 50, and 20 flat-mount mice retina images with various stains used and ages at sacrifice acquired from previously published manuscripts. Methods: Four graders manually segmented major blood vessels from flat-mount images of retinas from OIR mice. Pix2Pix, a high-resolution GAN, was trained on 984 pairs of raw flat-mount images and manual vessel segmentations and then tested on 100 and 50 image pairs from a held-out and external test set, respectively. GAN-generated and manual vessel segmentations were then used as an input into a previously published algorithm (iROP-Assist) to generate a vascular cumulative tortuosity index (CTI) for 20 image pairs containing mouse eyes treated with aflibercept versus control. Main Outcome Measures: Mean dice coefficients were used to compare segmentation accuracy between the GAN-generated and manually annotated segmentation maps. For the image pairs treated with aflibercept versus control, mean CTIs were also calculated for both GAN-generated and manual vessel maps. Statistical significance was evaluated using Wilcoxon signed-rank tests (P ≤ 0.05 threshold for significance). Results: The dice coefficient for the GAN-generated versus manual vessel segmentations was 0.75 ± 0.27 and 0.77 ± 0.17 for the held-out test set and external test set, respectively. The mean CTI generated from the GAN-generated and manual vessel segmentations was 1.12 ± 0.07 versus 1.03 ± 0.02 (P = 0.003) and 1.06 ± 0.04 versus 1.01 ± 0.01 (P < 0.001), respectively, for eyes treated with aflibercept versus control, demonstrating that vascular tortuosity was rescued by aflibercept when quantified by GAN-generated and manual vessel segmentations. Conclusions: GANs can be used to accurately generate vessel map segmentations from flat-mount images. These vessel maps may be used to evaluate novel metrics of vascular tortuosity in OIR, such as CTI, and have the potential to accelerate research in treatments for ischemic retinopathies. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Imbalanced unfolded protein response signaling contributes to 1-deoxysphingolipid retinal toxicity.

The accumulation of atypical, cytotoxic 1-deoxysphingolipids (1-dSLs) has been linked to retinal diseases such as diabetic retinopathy and Macular Telangiectasia Type 2. However, the molecular mechanisms by which 1-dSLs induce toxicity in retinal cells remain poorly understood. Here, we integrate bulk and single-nucleus RNA-sequencing to define biological pathways that modulate 1-dSL toxicity in human retinal organoids. Our results demonstrate that 1-dSLs differentially activate signaling arms of the unfolded protein response (UPR) in photoreceptor cells and Müller glia. Using a combination of pharmacologic activators and inhibitors, we show that sustained PERK signaling through the integrated stress response (ISR) and deficiencies in signaling through the protective ATF6 arm of the UPR are implicated in 1-dSL-induced photoreceptor toxicity. Further, we demonstrate that pharmacologic activation of ATF6 mitigates 1-dSL toxicity without impacting PERK/ISR signaling. Collectively, our results identify new opportunities to intervene in 1-dSL linked diseases through targeting different arms of the UPR.

Integrating human iPSC-derived macrophage progenitors into retinal organoids to generate a mature retinal microglial niche.

In the retina, microglia are resident immune cells that are essential for development and function. Retinal microglia play a central role in mediating pathological degeneration in diseases such as glaucoma, retinitis pigmentosa, age-related neurodegeneration, ischemic retinopathy, and diabetic retinopathy. Current models of mature human retinal organoids (ROs) derived from iPS cell (hiPSC) do not contain resident microglia integrated into retinal layers. Increasing cellular diversity in ROs by including resident microglia would more accurately represent the native retina and better model diseases in which microglia play a key role. In this study, we develop a new 3D in vitro tissue model of microglia-containing retinal organoids by co-culturing ROs and hiPSC-derived macrophage precursor cells (MPCs). We optimized the parameters for successful integration of MPCs into retinal organoids. We show that while in the ROs, MPCs migrate to the equivalent of the outer plexiform layer where retinal microglia cells reside in healthy retinal tissue. While there, they develop a mature morphology characterized by small cell bodies and long branching processes which is only observed in vivo. During this maturation process these MPCs cycle through an activated phase followed by a stable mature microglial phase as seen by the down regulation of pro-inflammatory cytokines and upregulation of anti-inflammatory cytokines. Finally, we characterized mature ROs with integrated MPCs using RNAseq showing an enrichment of cell-type specific microglia markers. We propose that this co-culture system may be useful for understanding the pathogenesis of retinal diseases involving retinal microglia and for drug discovery directly in human tissue.

iPSC-derived retinal pigmented epithelial cells from patients with macular telangiectasia show decreased mitochondrial function.

Patient-derived induced pluripotent stem cells (iPSCs) provide a powerful tool for identifying cellular and molecular mechanisms of disease. Macular telangiectasia type 2 (MacTel) is a rare, late-onset degenerative retinal disease with an extremely heterogeneous genetic architecture, lending itself to the use of iPSCs. Whole-exome sequencing screens and pedigree analyses have identified rare causative mutations that account for less than 5% of cases. Metabolomic surveys of patient populations and GWAS have linked MacTel to decreased circulating levels of serine and elevated levels of neurotoxic 1-deoxysphingolipids (1-dSLs). However, retina-specific, disease-contributing factors have yet to be identified. Here, we used iPSC-differentiated retinal pigmented epithelial (iRPE) cells derived from donors with or without MacTel to screen for novel cell-intrinsic pathological mechanisms. We show that MacTel iRPE cells mimicked the low serine levels observed in serum from patients with MacTel. Through RNA-Seq and gene set enrichment pathway analysis, we determined that MacTel iRPE cells are enriched in cellular stress pathways and dysregulation of central carbon metabolism. Using respirometry and mitochondrial stress testing, we functionally validated that MacTel iRPE cells had a reduction in mitochondrial function that was independent of defects in serine biosynthesis and 1-dSL accumulation. Thus, we identified phenotypes that may constitute alternative disease mechanisms beyond the known serine/sphingolipid pathway.

Divergent amino acid and sphingolipid metabolism in patients with inherited neuro-retinal disease.

OBJECTIVES: The non-essential amino acids serine, glycine, and alanine, as well as diverse sphingolipid species, are implicated in inherited neuro-retinal disorders and are metabolically linked by serine palmitoyltransferase (SPT), a key enzyme in membrane lipid biogenesis. To gain insight into the pathophysiological mechanisms linking these pathways to neuro-retinal diseases we compared patients diagnosed with two metabolically intertwined diseases: macular telangiectasia type II (MacTel), hereditary sensory autonomic neuropathy type 1 (HSAN1), or both. METHODS: We performed targeted metabolomic analyses of amino acids and broad sphingolipids in sera from a cohort of MacTel (205), HSAN1 (25) and Control (151) participants. RESULTS: MacTel patients exhibited broad alterations of amino acids, including changes in serine, glycine, alanine, glutamate, and branched-chain amino acids reminiscent of diabetes. MacTel patients had elevated 1-deoxysphingolipids but reduced levels of complex sphingolipids in circulation. A mouse model of retinopathy indicates dietary serine and glycine restriction can drive this depletion in complex sphingolipids. HSAN1 patients exhibited elevated serine, lower alanine, and a reduction in canonical ceramides and sphingomyelins compared to controls. Those patients diagnosed with both HSAN1 and MacTel showed the most significant decrease in circulating sphingomyelins. CONCLUSIONS: These results highlight metabolic distinctions between MacTel and HSAN1, emphasize the importance of membrane lipids in the progression of MacTel, and suggest distinct therapeutic approaches for these two neurodegenerative diseases.

Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10.

Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development. Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel. Methods: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity. Main Outcome Measures: The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes. Results: The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. Conclusions: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Description of a patient cohort with Hereditary Sensory Neuropathy type 1 without retinal disease Macular Telangiectasia type 2 - implications for retinal screening in HSN1.

Pathogenic variants in the genes encoding serine palmitoyl transferase (SPTLC1 or SPTLC2) are the most common causes of the rare peripheral nerve disorder Hereditary Sensory Neuropathy Type 1 (HSN1). Macular telangiectasia type 2 (MacTel), a retinal disorder associated with disordered serine-glycine metabolism, has been described in some patients with HSN1. This study aims to further investigate this association in a cohort of people with HSN1. Fourteen patients with a clinically and genetically confirmed diagnosis of HSN1 from the National Hospital for Neurology and Neurosurgery (NHNN, University College London Hospitals NHS Foundation Trust, London, United Kingdom) were recruited to the MacTel Registry, between July 2018 and April 2019. Two additional patients were identified from the dataset of the international clinical registry study (www.lmri.net). Ocular examination included fundus autofluorescence, blue light and infrared reflectance, macular pigment optical density mapping and optical coherence tomography. Twelve patients had a pathogenic variant in the SPTLC1 gene, with p.Cys133Trp in 11 cases (92%) and p.Cys133Tyr in one case (8%). Four patients had a variant in the SPTLC2 gene. None of the patients showed clinical evidence of MacTel. The link between HSN1 and MacTel seems more complex than can solely be explained by the genetic variants. An extension of the spectrum of SPTLC1/2-related disease with phenotypic pleiotropy is proposed. HSN1 patients should be screened for visual symptoms and referred for specialist retinal screening, but the association of the two diseases is likely to be variable and remains unexplained.

Deletion of Tgfß signal in activated microglia prolongs hypoxia-induced retinal neovascularization enhancing Igf1 expression and retinal leukostasis.

Retinal neovascularization (NV) is the major cause of severe visual impairment in patients with ischemic eye diseases. While it is known that retinal microglia contribute to both physiological and pathological angiogenesis, the molecular mechanisms by which these glia regulate pathological NV have not been fully elucidated. In this study, we utilized a retinal microglia-specific Transforming Growth Factor-ß (Tgfß) receptor knock out mouse model and human iPSC-derived microglia to examine the role of Tgfß signaling in activated microglia during retinal NV. Using a tamoxifen-inducible, microglia-specific Tgfß receptor type 2 (Tgfßr2) knockout mouse [Tgfßr2 KO (ΔMG)] we show that Tgfß signaling in microglia actively represses leukostasis in retinal vessels. Furthermore, we show that Tgfß signaling represses expression of the pro-angiogenic factor, Insulin-like growth factor 1 (Igf1), independent of Vegf regulation. Using the mouse model of oxygen-induced retinopathy (OIR) we show that Tgfß signaling in activated microglia plays a role in hypoxia-induced NV where a loss in Tgfß signaling microglia exacerbates and prolongs retinal NV in OIR. Using human iPSC-derived microglia cells in an in vitro assay, we validate the role of Transforming Growth Factor-ß1 (Tgfß1) in regulating Igf1 expression in hypoxic conditions. Finally, we show that Tgfß signaling in microglia is essential for microglial homeostasis and that the disruption of Tgfß signaling in microglia exacerbates retinal NV in OIR by promoting leukostasis and Igf1 expression.

Bioactive extracellular vesicles from a subset of endothelial progenitor cells rescue retinal ischemia and neurodegeneration.

Disruption of the neurovascular unit (NVU) underlies the pathophysiology of various CNS diseases. One strategy to repair NVU dysfunction uses stem/progenitor cells to provide trophic support to the NVU's functionally coupled and interdependent vasculature and surrounding CNS parenchyma. A subset of endothelial progenitor cells, endothelial colony-forming cells (ECFCs) with high expression of the CD44 hyaluronan receptor (CD44hi), provides such neurovasculotrophic support via a paracrine mechanism. Here, we report that bioactive extracellular vesicles from CD44hi ECFCs (EVshi) are paracrine mediators, recapitulating the effects of intact cell therapy in murine models of ischemic/neurodegenerative retinopathy; vesicles from ECFCs with low expression levels of CD44 (EVslo) were ineffective. Small RNA sequencing comparing the microRNA cargo from EVshi and EVslo identified candidate microRNAs that contribute to these effects. EVshi may be used to repair NVU dysfunction through multiple mechanisms to stabilize hypoxic vasculature, promote vascular growth, and support neural cells.

Cell-specific cis-regulatory elements and mechanisms of non-coding genetic disease in human retina and retinal organoids.

Cis-regulatory elements (CREs) play a critical role in the development and disease-states of all human cell types. In the retina, CREs have been implicated in several inherited disorders. To better characterize human retinal CREs, we performed single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) and single-nucleus RNA sequencing (snRNA-seq) on the developing and adult human retina and on induced pluripotent stem cell (iPSC)-derived retinal organoids. These analyses identified developmentally dynamic, cell-class-specific CREs, enriched transcription-factor-binding motifs, and putative target genes. CREs in the retina and organoids are highly correlated at the single-cell level, and this supports the use of organoids as a model for studying disease-associated CREs. As a proof of concept, we disrupted a disease-associated CRE at 5q14.3, confirming its principal target gene as the miR-9-2 primary transcript and demonstrating its role in neurogenesis and gene regulation in mature glia. This study provides a resource for characterizing human retinal CREs and showcases organoids as a model to study the function of CREs that influence development and disease.

Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.

AIM: To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2). METHODS: The dataset consisted of 99 eyes from 67 participants enrolled in an international, multicentre, phase 2 MacTel2 clinical trial (NCT01949324). Each eye was imaged with spectral-domain OCT at three time points over 2 years. Retinal cavitations were manually segmented by a trained Reader and the retinal cavitation volume was calculated. Two convolutional neural networks (CNNs) were developed that operated in sequential stages. In the first stage, CNN1 classified whether a B-scan contained any retinal cavitations. In the second stage, CNN2 segmented the retinal cavitations in a B-scan. We evaluated the performance of the proposed method against alternative methods using several performance metrics and manual segmentations as the gold standard. RESULTS: The proposed method was computationally efficient and accurately classified and segmented retinal cavitations on OCT images, with a sensitivity of 0.94, specificity of 0.80 and average Dice similarity coefficient of 0.94±0.07 across all time points. The proposed method produced measurements that were highly correlated with the manual measurements of retinal cavitation volume and change in retinal cavitation volume over time. CONCLUSION: The proposed method will be useful to help clinicians quantify retinal cavitations, assess changes over time and further investigate the clinical significance of these early structural changes observed in MacTel2.

Interferon-γ inhibits retinal neovascularization in a mouse model of ischemic retinopathy.

Interferon-γ (IFNG) is one of the key cytokines that regulates both innate and adaptive immune responses in the body. However, the role of IFNG in the regulation of vascularization, especially in the context of Vascular endothelial growth factor A (VEGFa)-induced angiogenesis is not clarified. Here, we report that IFNG shows potent anti-angiogenic potential against VEGFa-induced angiogenesis. IFNG significantly inhibited proliferation, migration, and tube formation of Human umbilical vein endothelial cells (HUVECs) both under basal and VEGFa-treated conditions. Intriguingly, Knockdown (KD) of STAT1 abolished the inhibitory effect of IFNG on VEGFa-induced angiogenic processes in HUVECs. Furthermore, IFNG exhibited potent anti-angiogenic efficacy in the mouse model of oxygen-induced retinopathy (OIR), an in vivo model for hypoxia-induced retinal neovascularization, without induction of functional side effects. Taken together, these results show that IFNG plays a crucial role in the regulation of VEGFa-dependent angiogenesis, suggesting its potential therapeutic applicability in neovascular diseases.

Identification of genetic factors influencing metabolic dysregulation and retinal support for MacTel, a retinal disorder.

Macular Telangiectasia Type 2 (MacTel) is a rare degenerative retinal disease with complex genetic architecture. We performed a genome-wide association study on 1,067 MacTel patients and 3,799 controls, which identified eight novel genome-wide significant loci (p < 5 × 10-8), and confirmed all three previously reported loci. Using MAGMA, eQTL and transcriptome-wide association analysis, we prioritised 48 genes implicated in serine-glycine biosynthesis, metabolite transport, and retinal vasculature and thickness. Mendelian randomization indicated a likely causative role of serine (FDR = 3.9 × 10-47) and glycine depletion (FDR = 0.006) as well as alanine abundance (FDR = 0.009). Polygenic risk scoring achieved an accuracy of 0.74 and was associated in UKBiobank with retinal damage (p = 0.009). This represents the largest genetic study on MacTel to date and further highlights genetically-induced systemic and tissue-specific metabolic dysregulation in MacTel patients, which impinges on retinal health.