Metabolic alterations in the visual pathway of retinitis pigmentosa rats: A longitudinal multimodal magnetic resonance imaging study with histopathological validation

Duan, Fei; Xiao, Zhang; Wang, Yuzhe; Sun, Xinghuai; Tang, Zuohua; Wang, Rong; Guo, Li; Tang, Weijun; Liu, Tingting; Wang, Peng; Zhan, Yang

doi:10.1002/nbm.4751

Cited by 3 publications

(2 citation statements)

References 61 publications

(118 reference statements)

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“…Thus, stimuli and sensory transduction also plays a role in RP progression, making photoreceptor cells and other neural cells susceptible to excitotoxic neurotransmitters, such as glutamate, or to the action of other neurotransmitters, like GABA or glycine [ 67 ]. As mentioned above, RP leads to a progressive loss of photoreceptor cells, but other neural cells suffer morphologic and metabolic changes resulting in retinal remodeling including loss of retinal ganglion cells (RGCs) or network rewiring [ 68 ].…”

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.

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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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show abstract

“…The visual cortex is one of the most important cortical structures in the brain and is primarily associated with the processing of visual information. Studies have revealed that eye disorders associated with retinal degeneration, including glaucoma [6], retinitis pigmentosa [7] and age-related macular degeneration [8], and systemic diseases, such as diabetes [9] and systemic lupus erythematosus [10] can trigger interactive structural and functional reorganisations of the visual cortex. The current therapeutic approaches for retinal degenerative diseases are limited and mainly aim to restore or substitute the retinal input.…”

Section: Small Animal Models Have Become Important Tools For Broadeni...mentioning

confidence: 99%

Project-Oriented RF Coil Comparison and Optimization for Preclinical, Single-Voxel MR Spectroscopy of the Rat Visual Cortex at 9.4 T

et al. 2023

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As one of the most important cortical structures in the brain, the visual cortex plays a primary role in processing visual data. Observing the metabolic changes in this region using single-voxel MR spectroscopy (MRS) can provide a plethora of information on the disease stage and progress in vivo. For single-voxel MRS experiments, a target voxel is always chosen and specifically investigated. The volume of the voxel must be sufficiently small to avoid any interference from adjacent areas but large enough to maximise its signal-to-noise ratio (SNR). Of the factors used to improve SNR, e.g. enlarging voxel size or increasing the number of signal averaging, the optimisation of the RF coil can contribute significantly to the SNR improvement. In this work, we compared and optimised RF coils which include a circularly-polarised (CP) high-pass birdcage coil and two CP surface coils with different dimensions. The coils were particularly designed for the entire rat head, brain and the target region (visual cortex), respectively. The performance of these coils was evaluated based on the project-specific purpose of investigating metabolite concentration changes in the visual cortex of the in vivo rat brain, assessed by means of single-voxel MRS at 9.4 T. In the SNR comparison, the values obtained using the optimised small loop coil were found to be approximately 5 and 1.35 times higher than those using the birdcage volume head coil and the medium-sized loop coil, respectively. In other words, using the optimised coil rather than, for example, the birdcage coil allows the acquisition to be completed more quickly by a factor of 25 times. In conclusion, by using the optimised setup, we can i) benefit either from reduced measurement time or in the acquisition of high-quality MR images or ii) obtain spectra with a minimum quantification error.

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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.

show abstract

Metabolic alterations in the visual pathway of retinitis pigmentosa rats: A longitudinal multimodal magnetic resonance imaging study with histopathological validation

Cited by 3 publications

References 61 publications

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

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

Project-Oriented RF Coil Comparison and Optimization for Preclinical, Single-Voxel MR Spectroscopy of the Rat Visual Cortex at 9.4 T

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

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