An SPM-Enriched Marine Oil Supplement Shifted Microglia Polarization toward M2, Ameliorating Retinal Degeneration in rd10 Mice

Olivares-González, Lorena; Velasco, Sheyla; Gallego, Idoia; Esteban-Medina, Marina; Puras, Gustavo; Loucera, Carlos; Martínez-Romero, Alicia; Peña‐Chilet, María; Pedraz, José Luís; Rodrigo, Regina

doi:10.3390/antiox12010098

Cited by 7 publications

(11 citation statements)

References 103 publications

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“…6C share a neuroprotective effect, also appearing to be related to fatty acid processes. Omega 3-related compounds, also predicted by our model, appear to have an impact on the progression of RP as discussed in Olivares-Gonzalez L. and cols (44). These ndings as well as the further use of the de ned RP actionable mechanistic map, have the potential to discover uncharted therapeutic alternatives for mitigating the detrimental consequences of RP.…”

Section: Discussionsupporting

confidence: 61%

“…In RP, oxidative stress is initiated by the release of ROS from dying photoreceptor cells and microglia cells. The accumulation of ROS leads to the activation of additional stress pathways, including the unfolded protein, leading to cell death (44,(68)(69)(70).…”

Section: Discussionmentioning

confidence: 99%

“…5). In addition, we previously evaluated the role of target genes lipoxygenase 5 (Alox5) and elongase 4 (Elovl4), both related to fatty acid metabolism, in the mechanisms of RP at early postnatal days (51). We observed a signi cant downregulation of Slc12a5 gene (Fig.…”

Section: Machine Learning Model Performancementioning

confidence: 90%

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The mechanistic functional landscape of Retinitis Pigmentosa: a Machine Learning driven approach to drug Repurposing.

Esteban-Medina

Loucera

Rian

et al. 2023

Preprint

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Background Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating drug repurposing in RP. Methods By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Six of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1, Slc12a5, KCC2, Grin1, Glr2a. As a result, we provide a resource to evaluate drugs with the potential to be repurposed in Retinitis Pigmentosa. Conclusions The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing target candidates for drug repositioning. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying six promising target candidates for drug repositioning. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

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Section: Discussionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

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The mechanistic functional landscape of Retinitis Pigmentosa: a Machine Learning driven approach to drug Repurposing.

Esteban-Medina

Loucera

Rian

et al. 2023

Preprint

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“…The Pde6 β gene mutation in rd10 mouse was associated with oxidative stress and ER stress, leading to the gradual photoreceptor degeneration. [ 44 ] Thus, either regenerative or protective effects for the ONL neurons after RPCs_Nb 2 C_NIR treatment may ascribe to the unique antioxidative stress of Nb 2 C Mxene and Nb 2 C Mxene‐induced efficient photoreceptor differentiation.…”

Section: Resultsmentioning

confidence: 99%

Bifunctional MXene‐Augmented Retinal Progenitor Cell Transplantation for Retinal Degeneration

et al. 2023

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Retinal degeneration, characterized by the progressive loss of retinal neurons, is the leading cause of incurable visual impairment. Retinal progenitor cells (RPCs)‐based transplantation can facilitate sight restoration, but the clinical efficacy of this process is compromised by the imprecise neurogenic differentiation of RPCs and undermining function of transplanted cells surrounded by severely oxidative retinal lesions. Here, it is shown that ultrathin niobium carbide (Nb2C) MXene enables performance enhancement of RPCs for retinal regeneration. Nb2C MXene with moderate photothermal effect markedly improves retinal neuronal differentiation of RPCs by activating intracellular signaling, in addition to the highly effective RPC protection by scavenging free radicals concurrently, which has been solidly evidenced by the comprehensive biomedical assessments and theoretical calculations. A dramatically increased neuronal differentiation is observed upon subretinal transplantation of MXene‐assisted RPCs into the typical retinal degeneration 10 (rd10) mice, thereby contributing to the efficient restoration of retinal architecture and visual function. The dual‐intrinsic function of MXene synergistically aids RPC transplantation, which represents an intriguing paradigm in vision‐restoration research filed, and will broaden the multifunctionality horizon of nanomedicine.

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Section: Discussionmentioning

confidence: 99%

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

Esteban-Medina,

Loucera,

Rian

et al. 2024

J Transl Med

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Background Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. Methods By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

show abstract

An SPM-Enriched Marine Oil Supplement Shifted Microglia Polarization toward M2, Ameliorating Retinal Degeneration in rd10 Mice

Cited by 7 publications

References 103 publications

The mechanistic functional landscape of Retinitis Pigmentosa: a Machine Learning driven approach to drug Repurposing.

The mechanistic functional landscape of Retinitis Pigmentosa: a Machine Learning driven approach to drug Repurposing.

Bifunctional MXene‐Augmented Retinal Progenitor Cell Transplantation for Retinal Degeneration

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

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