Asta Literature Retrieval: Pathophysiology and clinical mechanisms of Achromatopsia. Core disease mechanisms, molecular and cellular pathways, i...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 19
• Snippets retrieved: 20

Relevant Papers

[1] The cGMP-Dependent Protein Kinase 2 Contributes to Cone Photoreceptor Degeneration in the Cnga3-Deficient Mouse Model of Achromatopsia

• Authors: M. Koch, Constanze Scheel, Hongwei Ma, Fan Yang, Michael Stadlmeier et al.
• Year: 2020
• Venue: International Journal of Molecular Sciences
• URL: https://www.semanticscholar.org/paper/d0a14dcb2566e68cd80405e323ca543f03a9ab55
• DOI: 10.3390/ijms22010052
• PMID: 33374621
• PMCID: 7793084
• Citations: 10
• Influential citations: 1
• Summary: The data suggest that this cGMP mediator could be a novel pharmacological target for future neuroprotective therapies and identify Prkg2 as a novel key mediator of cone photoreceptor degeneration in achromatopsia.
• Evidence snippets:
• Snippet 1 (score: 0.443) > Achromatopsia (ACHM) is an inherited retinal disorder affecting retinal cones, the type of photoreceptors that mediate high acuity daylight vision. Cone outer segments, the specialized compartments of these photoreceptors, contain all proteins needed for light detection and conversion into chemical and electrical signals. Mutations in genes encoding key proteins of this cascade result in total colour blindness, also referred to as achromatopsia. Approximately 80 percent of ACHM patients carry mutations in one of the genes CNGA3 or CNGB3 [1], which encode the two subunits of the cyclic nucleotide-gated (CNG) channel in cone photoreceptors [2]. The cone CNG channel is part of the visual transduction cascade located in the cone outer segment and is the effector of cyclic guanosine monophosphate (cGMP), the key second messenger of this signaling cascade, which translates light signals Int. J. Mol. Sci. 2021, 22, 52 2 of 16 into electrical and Ca 2+ signals [2]. Four additional disease genes exist, among which GNAT2, PDE6C, and PDE6H also encode proteins involved in the cone visual transduction cascade [1]. The sixth known disease gene is ATF6, encoding an endoplasmic reticulum (ER)-localized transmembrane transcription factor that can activate the unfolded protein response (UPR) and plays a role in ER homeostasis [3,4]. > Like many other inherited disorders, ACHM manifests already in childhood with clinical symptoms that include lack of colour discrimination, poor visual acuity, extreme light sensitivity (photophobia), and involuntary eye movements (nystagmus) [5]. Given the lack of cone photoreceptor function from beginning, there is no real progression of the clinical symptoms over time. However, animal experiments and morphological data from affected patients suggested a progressive degeneration and loss of cones over time [6,7]. While the principal development and morphology of affected cone photoreceptors is initially similar to non-affected cones, the diseased cones start degenerating during young adulthood and are eventually lost by induction of various cell death mechanisms [8

[2] Application of patient-derived induced pluripotent stem cells and organoids in inherited retinal diseases

• Authors: Yuqin Liang, Xihao Sun, Chunwen Duan, Shibo Tang, Jiansu Chen
• Year: 2023
• Venue: Stem Cell Research & Therapy
• URL: https://www.semanticscholar.org/paper/7efec2710ef6f39e8220f4495d7e85c158b93545
• DOI: 10.1186/s13287-023-03564-5
• PMID: 38012786
• PMCID: 10683306
• Citations: 24
• Influential citations: 1
• Summary: The status of patient-derived iPSCs and organoids in IRDs in recent years concerning disease modeling and therapeutic exploration, along with potential challenges for translating laboratory research to clinical application are focused on.
• Evidence snippets:
• Snippet 1 (score: 0.429) > Other relatively uncommon types of IRDs have also been studied, such as Batten disease, achromatopsia, and best vitelliform macular dystrophy (BVMD).It has been reported that patient-derived RO models of Batten disease with the CLN3 mutation exhibited altered pre-mRNA splicing, accumulation of mitochondrial ATPase subunit-C, peroxisomes mislocalization, and vacuolization of photoreceptor inner segments [76].Achromatopsia is characterized by loss of cone photoreceptor function.At the same time, achromatopsia ROs from patients carrying the ATF6 variants exhibited molecular and cellular phenotypes, including cone defects, increased endoplasmic reticulum stress, Müller cell activation, disrupted mitochondrial structure, and elevated mitochondrial respiratory chain activity gene expression [77].Intervention with AA147, a lead small molecular ATF6 agonist, may enhance cone photoreceptor growth and gene expression in the disease ROs by promoting Class 1 ATF6-regulated transcriptional activity [78].In addition, impaired bestrophin channel activity was observed in BVMD patient-derived RPE cells with the BEST1 mutation, which was restored by AAV-mediated BEST1 gene augmentation [79,80]. > Taken together, a genotype-phenotype correlation of the disease was corroborated through a series of tests and analysis in patient iPSC-derived RO models, which can accurately reflect instead of mimic the complex clinical and genetic background of human retinal disease, may provide a very favorable experimental tool and platform for launching relevant research, and may also contribute to future drug development and gene therapy strategies.Recently, a clinical trial of a CRISPR/Cas9-mediated gene therapy drug for RP disease was conducted in China (NCT05805007).

[3] Differential metabolic markers associated with primary open-angle glaucoma and cataract in human aqueous humor

• Authors: C. Pan, Chaofu Ke, Qin Chen, Yijin Tao, Xu Zha et al.
• Year: 2020
• Venue: BMC Ophthalmology
• URL: https://www.semanticscholar.org/paper/a22a466f72ee8f8a5808f51c7000ff38b8b60b04
• DOI: 10.1186/s12886-020-01452-7
• PMID: 32375707
• PMCID: 7203853
• Citations: 26
• Influential citations: 1
• Summary: This study identified valuable metabolic biomarkers and pathways that may facilitate an improved understanding of the POAG pathogenesis and hold translational value in the development of new therapeutic measures for POAG.
• Evidence snippets:
• Snippet 1 (score: 0.384) > Primary open-angle glaucoma (POAG) is the most common subtype of glaucoma and the major cause of irreversible blindness throughout the world [1]. Although numerous studies have identified several important ocular risk factors for POAG such as increased intraocular pressure (IOP) [2,3], myopic refractive errors [4], larger optic disc size [5,6] and thinner central corneal thickness [7,8], these findings are limited in understanding the pathophysiology of POAG. Further knowledge regarding the pathophysiology might help to create new drug development research lines and expand current therapeutic targets for POAG. In current clinical practice, the treatment strategy of POAG mainly relies on IOP-lowering medications or surgeries. Although increased IOP is widely accepted to be the primary predictor for POAG, glaucomatous neuropathy is still observed in some patients with normal or even lower-than-normal IOPs, suggesting that other mechanisms exist in the pathophysiology of POAG. > Metabolomics is a widely used technology to assess biomarkers for diseases and provide molecular information regarding disease phenotype since metabolites are the ultimate product of gene, mRNA and protein activities [9]. Variations in the metabolome represent the interplay of genetic and environmental factors and are in relation to disease states, which may shed some lights in mechanism and pathophysiology of the disease [10]. With regard to eye diseases, metabolomics has been successfully used in identifying the metabolic signatures of diabetic retinopathy [11]. However, there were less studies focusing on POAG, especially in human participants. A previous analysis comparing plasma metabolic signatures as measured by mass spectrometry observed significant differences in some specific metabolic processes such as palmitoylcarnitine, sphingolipids, vitamin Drelated compounds, and steroid precursors between POAG patients and healthy controls [12]. These differences observed in metabolome might be linked to mitochondrial dysfunction and energy metabolism changes [12].

[4] Human Dermal Fibroblast: A Promising Cellular Model to Study Biological Mechanisms of Major Depression and Antidepressant Drug Response

• Authors: P. Mesdom, R. Colle, É. Lebigot, S. Trabado, Eric Deflesselle et al.
• Year: 2020
• Venue: Current Neuropharmacology
• URL: https://www.semanticscholar.org/paper/79368e365458486de96794333613c12a6063bf54
• DOI: 10.2174/1570159X17666191021141057
• PMID: 31631822
• PMCID: 7327943
• Citations: 12
• Summary: This review highlights the great and still underused potential of HDF, which stands out as a very promising tool in the understanding of MDD and AD mechanisms of action.
• Evidence snippets:
• Snippet 1 (score: 0.377) > Background: Human dermal fibroblasts (HDF) can be used as a cellular model relatively easily and without genetic engineering. Therefore, HDF represent an interesting tool to study several human diseases including psychiatric disorders. Despite major depressive disorder (MDD) being the second cause of disability in the world, the efficacy of antidepressant drug (AD) treatment is not sufficient and the underlying mechanisms of MDD and the mechanisms of action of AD are poorly understood. Objective The aim of this review is to highlight the potential of HDF in the study of cellular mechanisms involved in MDD pathophysiology and in the action of AD response. Methods The first part is a systematic review following PRISMA guidelines on the use of HDF in MDD research. The second part reports the mechanisms and molecules both present in HDF and relevant regarding MDD pathophysiology and AD mechanisms of action. Results HDFs from MDD patients have been investigated in a relatively small number of works and most of them focused on the adrenergic pathway and metabolism-related gene expression as compared to HDF from healthy controls. The second part listed an important number of papers demonstrating the presence of many molecular processes in HDF, involved in MDD and AD mechanisms of action. Conclusion The imbalance in the number of papers between the two parts highlights the great and still underused potential of HDF, which stands out as a very promising tool in our understanding of MDD and AD mechanisms of action

[5] Novel Approaches to Studying SLC13A5 Disease

• Authors: Adriana S. Beltran
• Year: 2024
• Venue: Metabolites
• URL: https://www.semanticscholar.org/paper/8469c534cd81d96f84b61e2d963dead12088feb7
• DOI: 10.3390/metabo14020084
• PMID: 38392976
• PMCID: 10890222
• Citations: 2
• Summary: Current technologies for generating patient-specific induced pluripotent stem cells (iPSCs) and their inherent advantages and limitations are discussed, followed by a summary of the methods for differentiating iPSCs into neurons, hepatocytes, and organoids.
• Evidence snippets:
• Snippet 1 (score: 0.376) > The precise pathophysiology underlying how SLC13A5 loss-of-function results in epilepsy refractory to treatment is a subject of open and ongoing research. Several hypotheses suggest SLC13A5 alters metabolic pathways, leading to neuronal dysfunction. Conversely, therapeutic inhibition of NaCT in the liver is a target to improve metabolic diseases, including non-alcoholic fatty liver disease, obesity, and insulin resistance. Thus, functionally accurate modeling and characterization of the mechanisms involved in citrate transport disruption are critical for understanding its role in human disease. > IPSC-derived cellular systems are a powerful tool for modeling rare human genetic diseases, such as SLC13A5 (Figure 5). IPSCs derived from patients containing the genetic information of the disease can overcome the limitations of animal models, providing access to relevant human cell types that recapitulate the disease phenotype. For instance, patient-derived iPSCs differentiated into neurons or hepatocytes can be used to investigate molecular and cellular mechanisms, including citrate transport and accumulation, energy metabolism, oxidative stress, and other cellular processes. They can also be used to define the spectrum of the disease and how different mutations might lead to various disease severities, screen for potential therapeutic compounds that can restore the transporter function or ameliorate the symptoms, and enable personalized medicine approaches that can tailor treatments to individual patients based on their genetic background and disease severity. > transport disruption are critical for understanding its role in human disease. > IPSC-derived cellular systems are a powerful tool for modeling rare human genetic diseases, such as SLC13A5 (Figure 5). IPSCs derived from patients containing the genetic information of the disease can overcome the limitations of animal models, providing access to relevant human cell types that recapitulate the disease phenotype. For instance, patient-derived iPSCs differentiated into neurons or hepatocytes can be used to investigate molecular and cellular mechanisms, including citrate transport and accumulation, energy metabolism, oxidative stress, and other cellular processes.

[6] Transcriptional profiling of Hutchinson-Gilford progeria patients identifies primary target pathways of progerin

• Authors: Sandra Vidak, Sohyoung Kim, Tom Misteli
• Year: 2026
• Venue: Nucleus
• URL: https://www.semanticscholar.org/paper/4bd99b0875508364d8672b6da5a50d024d485a53
• DOI: 10.1080/19491034.2025.2611484
• PMID: 41489464
• PMCID: 12773485
• Summary: To probe the clinical relevance of previously implicated cellular pathways and to address the extent of gene expression heterogeneity between patients, transcriptomic analysis of a comprehensive set of HGPS patients finds misexpression of several cellular pathways, including multiple signaling pathways, the UPR and mesodermal cell fate specification.
• Evidence snippets:
• Snippet 1 (score: 0.366) > Oxidative stress represents another key pathogenic mechanism in HGPS, as impaired NRF2 activity or increased reactive oxygen species (ROS) levels are sufficient to recapitulate HGPSassociated phenotypes [17,32,60]. Collectively, these findings underscore the multifactorial nature of HGPS pathogenesis, implicating interconnected signaling cascades involved in inflammation, oxidative stress, proteostasis, and vascular remodeling. Reassuringly, our findings indicate that many of the major pathways that have been described to contribute to HGPS phenotypes in mouse and cellular disease models are also misregulated in progeria patients, and targeting these pathways may provide therapeutic avenues to mitigate disease severity and improve outcomes in HGPS. > Although individuals with HGPS typically exhibit a characteristic set of clinical features, such as craniofacial abnormalities, growth retardation, and cardiovascular complications, there is notable variability in the age of onset, severity, and progression of symptoms between patients [7,9]. At the cellular level, HGPS is associated with several hallmark abnormalities, including nuclear envelope defects, decreased expression of several nuclear proteins and epigenetic marks, mitochondrial dysfunction, and increased cellular senescence [1,11,30,31,61]. These cellular phenotypes also exhibit considerable variation between patients, possibly contributing to differences in clinical outcomes. Our results indicate that even though some degree of transcriptional heterogeneity between the individual patients exists, the majority of patients exhibit misregulation of a set of shared pathways, suggesting that these pathways are universal driver mechanisms in HGPS. Further work is needed to understand the molecular and genetic factors that underlie inter-individual variability in disease expression and progression. > A limitation of pathway analysis of HGPS patient samples is to distinguish the pathways which are directly targeted by the disease-causing progerin protein and the emergence of adaptive secondary response pathways during progression of the disease in patients during their lifetime. The same caveat applies to the use of cell-based models used in the study of HGPS disease mechanisms.

[7] Organoids in gastrointestinal diseases: from bench to clinic

• Authors: Qinying Wang, Fanying Guo, Qinyuan Zhang, Tingting Hu, Yutao Jin et al.
• Year: 2024
• Venue: MedComm
• URL: https://www.semanticscholar.org/paper/9b8880d8b9d45670da950197d7e353794f51d09e
• DOI: 10.1002/mco2.574
• PMID: 38948115
• PMCID: 11214594
• Citations: 12
• Summary: A comprehensive and systematical depiction of organoids models is drawn, providing a novel insight into the utilization of organoids models from bench to clinic and clinical adhibition.
• Evidence snippets:
• Snippet 1 (score: 0.365) > Organoids models offer a robust platform for investigating the potential mechanisms of GI diseases and evaluating potential therapeutic interventions.By culturing organoids derived from patients' tissues or stem cells, researchers can delve into disease-specific cellular and molecular pathways, encompassing aberrant cell signaling, perturbed immune responses, and dysfunctional metabolic processes.These disease-specific phenotypes enable the study of disease progression, screening of prospective therapeutics, as well as identification of novel drug targets and mechanisms of action for GI diseases in a clinically relevant context.

[8] Precision Therapeutics in Lennox–Gastaut Syndrome: Targeting Molecular Pathophysiology in a Developmental and Epileptic Encephalopathy

• Authors: Debopam Samanta
• Year: 2025
• Venue: Children
• URL: https://www.semanticscholar.org/paper/455479c1bfbea7b90b73c109228f67c813d13888
• DOI: 10.3390/children12040481
• PMID: 40310132
• PMCID: 12025602
• Citations: 19
• Influential citations: 1
• Summary: A narrative review explores precision therapeutic strategies for LGS based on molecular pathophysiology, including channelopathies, receptor and ligand dysfunction, receptor and ligand dysfunction, cell signaling abnormalities, cell signaling abnormalities, synaptopathies, and the repurposing of existing medications with mechanism-specific effects.
• Evidence snippets:
• Snippet 1 (score: 0.362) > A key advantage of disease-modifying therapies is their potential to target pathogenic mechanisms early in the disease course, potentially preventing the progression of some infantile epileptic encephalopathies to LGS. > This narrative review explores precision therapeutic strategies based on specific monogenic causes and disease mechanisms relevant to LGS. A comprehensive literature search (PubMed, MEDLINE, ClinicalTrials.gov, conference abstracts from the American Academy of Neurology and American Epilepsy Society, and gray literature) was conducted through 19 February 2025 to identify established ASMs, repurposed and novel drugs, as well as various gene therapy approaches with potential relevance to LGS. Given that over 900 monogenic causes of DEEs have been identified-implicating diverse cellular components such as ion channels, receptors, synaptic proteins, signaling pathways, metabolic processes, and epigenetic regulators-this review discusses current and emerging precision therapeutics based on shared molecular mechanisms and the pathophysiology of select genes associated with LGS [17] (Table 1).
• Snippet 2 (score: 0.349) > Lennox–Gastaut syndrome (LGS) is a severe childhood-onset developmental and epileptic encephalopathy characterized by multiple drug-resistant seizure types, cognitive impairment, and distinctive electroencephalographic patterns. Current treatments primarily focus on symptom management through antiseizure medications (ASMs), dietary therapy, epilepsy surgery, and neuromodulation, but often fail to address the underlying pathophysiology or improve cognitive outcomes. As genetic causes are identified in 30–40% of LGS cases, precision therapeutics targeting specific molecular mechanisms are emerging as promising disease-modifying approaches. This narrative review explores precision therapeutic strategies for LGS based on molecular pathophysiology, including channelopathies (SCN2A, SCN8A, KCNQ2, KCNA2, KCNT1, CACNA1A), receptor and ligand dysfunction (GABA/glutamate systems), cell signaling abnormalities (mTOR pathway), synaptopathies (STXBP1, IQSEC2, DNM1), epigenetic dysregulation (CHD2), and CDKL5 deficiency disorder. Treatment modalities discussed include traditional ASMs, dietary therapy, targeted pharmacotherapy, antisense oligonucleotides, gene therapy, and the repurposing of existing medications with mechanism-specific effects. Early intervention with precision therapeutics may not only improve seizure control but could also potentially prevent progression to LGS in susceptible populations. Future directions include developing computable phenotypes for accurate diagnosis, refining molecular subgrouping, enhancing drug development, advancing gene-based therapies, personalizing neuromodulation, implementing adaptive clinical trial designs, and ensuring equitable access to precision therapeutic approaches. While significant challenges remain, integrating biological insights with innovative clinical strategies offers new hope for transforming LGS treatment from symptomatic management to targeted disease modification.

[9] 18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

• Authors: E. Nemutlu, Song Zhang, N. Juranic, A. Terzic, S. Macura et al.
• Year: 2012
• Venue: Croatian Medical Journal
• URL: https://www.semanticscholar.org/paper/880f053c7f060db4b990e447d0a22c4b69372ddb
• DOI: 10.3325/cmj.2012.53.529
• PMID: 23275318
• PMCID: 3541579
• Citations: 28
• Summary: The potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic is described and discussed briefly.
• Evidence snippets:
• Snippet 1 (score: 0.361) > Living cells represent an integrated and interacting network of genes, transcripts, proteins, small signaling molecules, and metabolites that define cellular phenotype and function. Traditionally the focus of biomedical research was on individual genes, single protein targets, single metabolites, and metabolic or signaling pathways. This "molecular reductionist" paradigm was based on the assumption that identifying genetic variations and molecular components would lead to discovery of cures for human diseases. However, most of diseases are complex and multi-factorial and the disease phenotype is determined by the alterations of multiple genes, pathways, proteins and metabolites (at cellular, tissue, and organismal levels). Therefore, an integrated "omics" approach is more viable direction for uncovering alterations in metabolic networks, disease mechanisms, and mechanisms of drug effects. > Recent advent of large-scale metabolomics and fluxomic (metabolite dynamics and metabolic flux analysis) completed the "omics revolution" (Figure 1), where genomics, transcriptomics, proteomics, metabolomics, and fluxomics all together complement phenotype determination of living organism. Such integrated "omics" cascades provide a framework for advances in system and network biology, integrative physiology, and system medicine as well as system pharmacology and regenerative medicine. Noteworthy is the "reverse omic" approach or "metabolomicsinformed pharmacogenomics, " where discovery of specific metabolite changes have led to discovery of genetic alterations (2). Therefore, bringing new "omics" technologies to clinical practice will improve disease diagnostics and treatment by targeting drugs and procedures for each unique transcriptomic and metabolomic profiles.

[10] Characterization of Ferroptosis-Related Molecular Subtypes with Immune Infiltrations in Neuropathic Pain

• Authors: Yan-Hua Bi, J. Wang, Zhigang Guo, Kai-Ning Jia
• Year: 2022
• Venue: Journal of Pain Research
• URL: https://www.semanticscholar.org/paper/180fa0aeeee5498a2870a077906cdc8840041652
• DOI: 10.2147/JPR.S385228
• PMID: 36311291
• PMCID: 9601606
• Citations: 6
• Influential citations: 1
• Summary: This study contributes to the understanding of the neuroimmune mechanism of neuropathic pain, provides a reference for NP biomarkers and drug targets and explored the relationship between gene networks and phenotypes.
• Evidence snippets:
• Snippet 1 (score: 0.360) > NP is a type of refractory pain for which there are no effective treatment options. Therefore understanding the pathological and molecular mechanisms of NP is essential for its clinical diagnosis and treatment. Molecular mechanisms of disease occurrence modeling was used to identify diagnostic markers by screening key genes. The DGIdb and correlation test were then used to construct gene-drug and gene-transcription factor interaction networks for ferroptosis DEGs associated with NP. WGCNA was used to identify gene modules co-expressed by neuralgia, and explore the association between gene networks, phenotype, and core genes. Finally, the key genes were derived from intersecting core genes and diagnostic markers. The correlation between key genes, molecular subtypes and immune cells was analyzed. We found that ferroptosis may be critical in this disease, but the mechanism and target have not been clarified. GSEA and GSVA enrichment analyses were performed to understand the functional differences between the SNL tissue and sham control tissue. The GSEA results showed that the GO-enriched biological processes in SNL tissues included immune response activation, amide biosynthetic process, and ATP metabolic process. The major enriched KEGG pathways included Alzheimer's disease, cardiac muscle contraction, and Parkinson's disease. GSVA results showed that GO-enriched biological processes included platelet-derived growth factor receptor binding, chemokine binding, regulation of Neutrophil chemotaxis, the major enriched KEGG pathways included drug metabolism other pathos, pathogenic Escherichia coli infection, etc. Several gene programs involved in NP have been reported in recent years. During noxious stimuli, inflammatory receptor activity, Na+/K+-ATPase activity, and actin filament organization increase in the central nervous system, thereby contributing to neuroinflammation. ATP regulates glial activity by modulating and activating P2X (ionic channels) and P2Y (metabolic import) receptors, and these ATP receptors act as gatekeepers in neuropathic pain microglial signaling pathways. 33 Some experiments have found that NP caused by peripheral nerve injury can reduce the size of myocardial infarction, heart disease can cause the perception of pain, and body pain may also affect myocardial remodeling. 34,35 Furthermore, chronic pain can cause anxiety and depression

[11] Changes in Serum Proteomic Profiles at Different Stages of Pregnancy Toxemia in Goats

• Authors: M. Uzti̇mür, C. N. Ünal, Gurler Akpinar
• Year: 2025
• Venue: Journal of Veterinary Internal Medicine
• URL: https://www.semanticscholar.org/paper/4b9c488b5dbd65d7b26fd2ad9aed70e8c4b59942
• DOI: 10.1111/jvim.70139
• PMID: 40492724
• PMCID: 12150350
• Summary: Understanding the serum proteome profiles of goats with pregnancy toxemia might help identify the proteomes and pathways responsible for the development of this disease and improve diagnosis and treatment.
• Evidence snippets:
• Snippet 1 (score: 0.359) > The pathophysiology and progression of this disease are not fully understood. > Traditional biomedical research has focused on the analysis of single genes, proteins, metabolites, or metabolic pathways in diseases. This molecular reductionist approach is based on the assumption that identifying genetic variations and molecular components will lead to new treatments for diseases [13][14][15][16]. However, many diseases are complex and multifactorial, and in order to determine the phenotype of such diseases, it is necessary to understand the changes that occur in more than one gene, pathway, protein, or metabolite at the cellular, tissue, and organismal levels [17][18][19]. Therefore, in recent years, proteomics, as one field of multi-omics technologies, has helped in evaluating the complex pathogenetic mechanisms of different diseases from a broad perspective and has made substantial contributions [20,21]. In veterinary medicine, proteomic analysis of metabolic diseases such as ketosis [16], hypocalcemia [22], and fatty liver [23] in dairy cows has contributed valuable insights for the definition of new pathophysiological pathways and new diagnosis and treatment protocols for these diseases. The proteomic approach can contribute importantly to a broad and detailed understanding of the changes that occur at the organismal level associated with the increase in BHBA concentration in goats with pregnancy toxemia. Our aim was to evaluate the serum protein profiles of goats with SPT or CPT using proteomic techniques to determine the proteomic profiles of these animals and to identify the relevant pathophysiological mechanisms.

[12] Targeting Hepatic Stellate Cells for the Prevention and Treatment of Liver Cirrhosis and Hepatocellular Carcinoma: Strategies and Clinical Translation

• Authors: Hao Xiong, Jinsheng Guo
• Year: 2025
• Venue: Pharmaceuticals
• URL: https://www.semanticscholar.org/paper/76e92127053136900f7e3f10e2c9278251ced5d2
• DOI: 10.3390/ph18040507
• PMID: 40283943
• PMCID: 12030350
• Citations: 8
• Summary: HSC-targeted approaches using specific surface markers and receptors may enable the selective delivery of drugs, oligonucleotides, and therapeutic peptides that exert optimized anti-fibrotic and anti-HCC effects.
• Evidence snippets:
• Snippet 1 (score: 0.357) > Significant progress has been made in elucidating the cellular and molecular mechanisms of liver fibrosis; however, only a few findings have been successfully translated into clinical applications. Firstly, the high cost of drug development and target validation necessitates prolonged timelines and substantial financial investment. Secondly, as regulatory requirements become more stringent, there is an increasing demand for drugs with well-defined clinical efficacy and safety profiles. Moreover, the efficacy observed in animal models often fails to fully translate to clinical settings due to differences in pharmacokinetics, extracellular matrix (ECM) cross-linking, and disease pathophysiology. Despite advancements in anti-fibrotic drug development, accurately identifying ideal noninvasive biomarkers for fibrotic activity and establishing consensus on optimal clinical endpoints remain significant challenges [113,114]. > Currently, addressing the underlying cause remains the only proven strategy to halt or reverse liver fibrosis progression, while the development of effective anti-fibrotic therapies continues to pose a major challenge in liver disease management. Over the past few decades, substantial progress has been made in elucidating the cellular and molecular mechanisms underlying liver fibrosis. Liver fibrosis is a complex pathological change involving multiple cells, factors, and pathways, and the study of the cellular and molecular mechanisms of its occurrence and development provides an important theoretical basis and therapeutic target for clinical drug development. It is anticipated that improved animal models and well-designed clinical trials will facilitate the successful translation of anti-fibrotic research into effective clinical treatments in the near future.

[13] Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD

• Authors: D. Drakulić, S. Djurovic, Y. A. Syed, Sebastiano Trattaro, N. Caporale et al.
• Year: 2020
• Venue: Molecular Autism
• URL: https://www.semanticscholar.org/paper/c6cac51304043d34c93254007adca11883e387cd
• DOI: 10.1186/s13229-020-00343-4
• PMID: 32487215
• PMCID: 7268297
• Citations: 23
• Influential citations: 1
• Summary: Here, it is examined how iPSCs derived from ASD patients with an associated CNV inform the understanding of the genetic and biological mechanisms underlying the aetiology of ASD.
• Evidence snippets:
• Snippet 1 (score: 0.357) > external factors. These complications hinder identification of the basic pathophysiological mechanisms that lead to ASD and hence hamper development of effective therapies. > Molecular and cellular analysis of human patients is generally prospective with data mostly derived from post-mortem tissue. As mentioned above, such studies are subject to the confounds of secondary effects and record the outcomes of underlying disease mechanism rather than directly probe the causative mechanisms. Animal models can be highly informative for the study of a basic mechanism; however, it is difficult to directly translate between observed patient phenotype and animal models. A particular weakness is the ability to capture the phenotypic variation across the patient population. > Human stem cell models offer an opportunity to directly study the molecular and cellular mechanisms of diseases. Key to this approach is the generation of human-induced pluripotent stem cells (iPSCs) derived from patient cells. These are generated by reprogramming of somatic cells into pluripotent stem cells from which many cell types can be differentiated, including neurons and glial cells. Importantly, they can be easily obtained in the clinic from fibroblasts (skin biopsies), keratinocytes (hair roots) [3], T lymphocytes (peripheral blood) [4,5] and exfoliated renal epithelial cells from urine samples [6,7]. Importantly, patient iPSCs enable the in vitro study of different cells types in isolation or co-culture in order to investigate cell function. Uniquely they can track the development profile of patient cell differentiation. More recently the capacity of iPSCs to form 3D organoids has opened up the possibility to investigate the interaction of multiple cell types in a more brain-like microenvironment. Methods for increasing reproducibility of brain organoid differentiation are improving substantially [8,9] and being exploited to mechanistically dissect the effect of genetic lesions causing ASD and ID [10][11][12], as well as the role of specific genes and molecular modules key to human-specific neuronal differentiation trajectories and pathophysiology [13]. > The major question is how to identify the relevant cellular phenotypes that converge on the common pathophysiological mechanisms underlying patient aeti

[14] Exploring the molecular mechanisms of subarachnoid hemorrhage and potential therapeutic targets: insights from bioinformatics and drug prediction

• Authors: Yi Liu, Yang Zhang, Huan Wei, Li Wang, Lishang Liao
• Year: 2025
• Venue: Scientific Reports
• URL: https://www.semanticscholar.org/paper/19a91d9c8cabec6a5a186729d545077e252ecb67
• DOI: 10.1038/s41598-025-97642-8
• PMID: 40229542
• PMCID: 11997208
• Summary: The findings not only elucidate the molecular mechanisms underlying SAH but also provide robust bioinformatics and experimental evidence supporting IRN as a promising therapeutic candidate, offering novel insights for future intervention strategies in SAH.
• Evidence snippets:
• Snippet 1 (score: 0.352) > involved in SAH pathology. As a result, our understanding of the cellular composition and microenvironment in SAH remains incomplete 8 . > Advances in bioinformatics provide powerful tools to analyze large-scale gene expression data and understand complex biological processes. By integrating transcriptomic data with immune cell infiltration analysis, we can gain a deeper understanding of the molecular mechanisms underlying SAH and identify potential key genes as therapeutic targets 9,10 . Previous studies have indicated that inflammation, oxidative stress, and cell death play crucial roles in the development of SAH, processes that are often closely associated with changes in specific cell types and immune responses 11 . > The goal of this study is to explore the molecular mechanisms of SAH, with a focus on immune cell infiltration and its role in disease progression. We aim to identify key genes and signaling pathways associated with SAH and investigate potential therapeutic strategies. Specifically, we will examine Isorhynchophylline (IRN) as a potential treatment for SAH and analyze its effects on relevant targets and signaling pathways. Through a comprehensive understanding of the pathological features of SAH, this study aims to provide valuable insights into future clinical interventions and treatment strategies.

[15] Brain gene expression profiles of Cln1 and Cln5 deficient mice unravels common molecular pathways underlying neuronal degeneration in NCL diseases

• Authors: Carina von Schantz, J. Saharinen, O. Kopra, J. Cooper, M. Gentile et al.
• Year: 2008
• Venue: BMC Genomics
• URL: https://www.semanticscholar.org/paper/1c92c71a25fbe16f17a3f791fa960e5d711aa90d
• DOI: 10.1186/1471-2164-9-146
• PMID: 18371231
• PMCID: 2323392
• Citations: 58
• Summary: BackgroundThe neuronal ceroid lipofuscinoses (NCL) are a group of children's inherited neurodegenerative disorders, characterized by blindness, early dementia and pronounced cortical atrophy. The similar pathological and clinical profiles of the different forms of NCL suggest that common disease mechanisms may be involved. To explore the NCL-associated disease pathology and molecular pathways, we have previously produced targeted knock-out mice for Cln1 and Cln5. Both mouse-models replicate t...
• Evidence snippets:
• Snippet 1 (score: 0.351) > BackgroundThe neuronal ceroid lipofuscinoses (NCL) are a group of children's inherited neurodegenerative disorders, characterized by blindness, early dementia and pronounced cortical atrophy. The similar pathological and clinical profiles of the different forms of NCL suggest that common disease mechanisms may be involved. To explore the NCL-associated disease pathology and molecular pathways, we have previously produced targeted knock-out mice for Cln1 and Cln5. Both mouse-models replicate the NCL phenotype and neuropathology; the Cln1-/- model presents with early onset, severe neurodegenerative disease, whereas the Cln5-/- model produces a milder disease with a later onset.ResultsHere we have performed quantitative gene expression profiling of the cortex from 1 and 4 month old Cln1-/- and Cln5-/- mice. Combined microarray datasets from both mouse models exposed a common affected pathway: genes regulating neuronal growth cone stabilization display similar aberrations in both models. We analyzed locus specific gene expression and showed regional clustering of Cln1 and three major genes of this pathway, further supporting a close functional relationship between the corresponding gene products; adenylate cyclase-associated protein 1 (Cap1), protein tyrosine phosphatase receptor type F (Ptprf) and protein tyrosine phosphatase 4a2 (Ptp4a2). The evidence from the gene expression data, indicating changes in the growth cone assembly, was substantiated by the immunofluorescence staining patterns of Cln1-/- and Cln5-/- cortical neurons. These primary neurons displayed abnormalities in cytoskeleton-associated proteins actin and β-tubulin as well as abnormal intracellular distribution of growth cone associated proteins GAP-43, synapsin and Rab3.ConclusionOur data provide the first evidence for a common molecular pathogenesis behind neuronal degeneration in INCL and vLINCL. Since CLN1 and CLN5 code for proteins with distinct functional roles these data may have implications for other forms of NCLs as well.

[16] Inherited Retinal Diseases and Retinal Organoids as Preclinical Cell Models for Inherited Retinal Disease Research

• Authors: Kristen E Ashworth, Jessica Weisbrod, B. Ballios
• Year: 2024
• Venue: Genes
• URL: https://www.semanticscholar.org/paper/85f2302a05d8a74441c3c7af20108f3bceba96f7
• DOI: 10.3390/genes15060705
• PMID: 38927641
• PMCID: 11203130
• Citations: 12
• Influential citations: 1
• Summary: The usefulness of retinal organoids in this context (as a patient-derived cell model for IRDs) to precisely understand the pathogenesis and potential mechanisms behind a specific IRD-causing variant of interest is discussed.
• Evidence snippets:
• Snippet 1 (score: 0.351) > IRDs have historically been categorized based on their clinical features, related to the structural changes and/or electrophysiologic dysfunction of the retina.There is considerable diversity in the clinical onset, symptoms, progression, and severity of different clinical IRD diagnoses. > For example, IRDs can be classified as early or late onset: symptoms can manifest during early childhood (e.g., Leber congenital amaurosis, achromatopsia, and Usher syndrome type 1), while other disorders do not present with symptoms until adolescence, or even later in life (e.g., Stargardt disease and some types of retinitis pigmentosa).In addition, IRDs can be categorized according to other clinical features, such as whether the retinopathy is non-syndromic (i.e., if only the retina is involved) or syndromic (i.e., if other extra-ophthalmic organs or systems are impacted) and/or if the disease is stationary or progressive.For progressive IRDs, the rate of progression and severity of vision loss can vary significantly from patient to patient, even among individuals and family members sharing the same familial variant(s) [9]. > Though the molecular origins of IRDs are complex and unique to the functions of specific gene products, most IRD-causing mutations cause disruptions to the proper encoding of proteins involved in the structure and/or function of photoreceptors.Clinical phenotypes frequently converge, as is the case for the clinical diagnosis of retinitis pigmentosa (RP).Pathogenic variants in over 70 genes have been shown to cause the clinical phenotype of RP: since degeneration for most RP patients begins with the rod photoreceptors, clinical symptoms of night blindness and peripheral vision loss manifest first.Later, cone degeneration occurs, leading to progressive field narrowing, central vision loss, and, in a majority of cases, legal blindness [2,7]. > Owing to the variety of clinical presentations and underlying genetic heterogeneity, IRDs are challenging to both diagnose at the bedside and study in the lab [9,10].

[17] Hypoxia-associated genes and metabolic abnormalities in peripheral blood mononuclear cells of type 1 diabetes mellitus patients

• Authors: Wenxue Ma, Xue-ying Wang, Yuan Zuo
• Year: 2025
• Venue: Hereditas
• URL: https://www.semanticscholar.org/paper/daa85eddc3e8ad7e335d87d5de402ef2337b43ea
• DOI: 10.1186/s41065-025-00537-x
• PMID: 40836269
• PMCID: 12369233
• Summary: These findings not only identify specific hub genes as key mediators connecting signaling pathways, biological processes, and metabolic changes but also provide novel insights into the pathophysiology of T1DM.
• Evidence snippets:
• Snippet 1 (score: 0.347) > Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disorder characterized by the destruction of pancreatic β-cells, which leads to insulin deficiency and hyperglycemia [1]. T1DM affects millions of individuals worldwide, imposing significant health and economic burdens due to its associated complications, such as neuropathy, retinopathy, and cardiovascular diseases [2]. While current management strategies mainly focus on insulin therapy and lifestyle modifications, these approaches fail to address the underlying pathological molecular mechanisms or prevent disease progression [3]. While several studies have investigated the genetic and environmental factors contributing to T1DM, the identification of key regulatory genes and their functional roles in disease pathogenesis remains limited [4]. Furthermore, the integration of gene expression data with pathway and metabolite analyses to reveal the complex interplay between molecular networks and clinical outcomes has not been extensively investigated [5]. Addressing these gaps is critical for developing targeted therapies and improving patient outcomes, underscoring the necessity of this study [6]. > T1DM is characterized by dysregulated immune responses and metabolic disturbances, yet the underlying molecular mechanisms are not completely understood [7]. Previous studies have identified altered gene expression and disrupted signaling pathways in T1DM, including TGF-beta and MAPK signaling, as well as retinol metabolism, all of which are implicated in immune modulation and metabolic regulation [8][9][10]. Additionally, biological processes, such as neutrophil activation, epithelial-mesenchymal transition (EMT), and hypoxia, have been associated with T1DM pathophysiology [11][12][13]. However, the interplay between these pathways, key regulatory genes, and metabolic changes remains underexplored. This study addresses this gap by integrating differential gene expression analysis, functional enrichment, protein-protein interaction (PPI) network construction, and correlation analyses to identify specific hub genes (FOS, JUNB, and NR4A2) that connect signaling pathways, biological processes, and metabolite dysregulation.

[18] Single-Cell Transcriptomics in Inherited Retinal Dystrophies: Current Findings and Emerging Perspectives

• Authors: Linda Nguyen, C. Vallejos, P. Mill, R. Megaw
• Year: 2025
• Venue: Genes
• URL: https://www.semanticscholar.org/paper/b09033d76a82b73ac654d522e22ad82cde2a92a7
• DOI: 10.3390/genes16091088
• PMID: 41010033
• PMCID: 12470181
• Summary: This review examines the recent literature on the application of single-cell transcriptomics in IRDs to explore how these techniques enhance the understanding of disease mechanisms and contribute to the identification of new therapeutic targets.
• Evidence snippets:
• Snippet 1 (score: 0.347) > Inherited retinal dystrophies (IRDs) represent a clinically and genetically heterogeneous group of disorders characterised by progressive photoreceptor degeneration, ultimately leading to severe vision loss [1]. These conditions affect approximately 1 in 3000 [1,2] to 4000 individuals worldwide [3]. > IRDs are characterised by remarkable genetic complexity, with mutations in at least 300 genes identified to date [4], expressed through multiple inheritance patterns including autosomal dominant, autosomal recessive, and X-linked forms [3]. Major IRD subtypes examined in this review include retinitis pigmentosa (RP), Leber congenital amaurosis (LCA), enhanced S-cone syndrome (ESCS), Stargardt disease (STGD), and achromatopsia (ACHM), each presenting distinct clinical phenotypes and underlying genetic causes. Significant phenotypic variability exists even amongst individuals carrying the same genetic mutations [5], creating complex genotype-phenotype relationships that complicate diagnosis and treatment strategies [6]. > IRD pathogenesis involves complex interactions between multiple retinal cell types that both respond to and influence the degenerative process [7], with supporting cells such as Müller glia and microglia often exhibiting early transcriptional changes that may precede overt photoreceptor death [7,8]. Characterising cell-type-specific responses is crucial, as disease progression varies highly between distinct retinal cell populations, requiring targeted approaches that can distinguish different cellular responses to disease. > This review reports studies applying single-cell transcriptomics to IRD samples, examining how these approaches have advanced our understanding of disease mechanisms and dynamics with cellular resolution. We present findings by specific IRD subtypes and genetic mutations, highlighting key mechanistic insights and therapeutic targets identified through single-cell approaches. We then identify common themes across different IRD studies and therapeutic implications before identifying current challenges and future considerations for using single-cell technologies in IRD research.

[19] Mitochondrial Dysfunction in Diabetes: Shedding Light on a Widespread Oversight

• Authors: F. Iheagwam, A. J. Joseph, E. D. Adedoyin, Olawumi Toyin Iheagwam, Samuel Akpoyowvare Ejoh
• Year: 2025
• Venue: Pathophysiology
• URL: https://www.semanticscholar.org/paper/dbf8042761c1a5fc50f8cd894cc498505abac7cb
• DOI: 10.3390/pathophysiology32010009
• PMID: 39982365
• PMCID: 12077258
• Citations: 28
• Summary: This review aims to elucidate the complex link between mitochondrial dysfunction and diabetes, covering the spectrum of diabetes types, the role of mitochondria in insulin resistance, highlighting pathophysiological mechanisms, mitochondrial DNA damage, and altered mitochondrial biogenesis and dynamics.
• Evidence snippets:
• Snippet 1 (score: 0.345) > The landscape of DM research is continuously evolving, with emerging technologies and approaches offering new insights into the pathophysiology of the disease and potential therapeutic targets. Advancements in omics technologies, encompassing genomes, transcriptomics, proteomics, and metabolomics, have transformed the molecular mechanisms underlying DM [134]. High-throughput sequencing techniques enable comprehensive analysis of genetic variants, gene expression profiles, protein abundance, and metabolite levels associated with DM and its complications [135]. Single-cell omics approaches provide unprecedented resolution and granularity, allowing researchers to dissect cellular heterogeneity and identify novel cell types, subpopulations, and signalling pathways involved in DM pathogenesis. Integrating multi-omics data sets offers a systems-level perspective of DM, unravelling complex networks of molecular interactions and regulatory circuits underlying disease progression [136]. > In addition to omics technologies, advances in imaging modalities, such as MRI, PET, and optical imaging, enable non-invasive visualisation and quantification of metabolic, functional, and structural changes. Molecular imaging probes targeting specific biomarkers and metabolic pathways provide valuable insights into disease mechanisms and treatment responses in preclinical and clinical settings [85]. Despite significant progress in DM research, numerous unanswered questions and knowledge gaps persist, hindering the ability to develop effective prevention and treatment strategies. Key areas requiring further investigation include the role of epigenetics, environmental factors, and the microbiome in DM susceptibility and progression. Moreover, the interaction between environmental cues and genetic predisposition remains incompletely understood, highlighting the need for comprehensive multi-omics studies and large-scale epidemiological analyses to identify gene-environment interactions and modifiable risk factors for DM [137]. Furthermore, the heterogeneity of DM phenotypes and clinical outcomes poses a challenge for personalised medicine approaches, necessitating robust biomarkers and predictive models to stratify patients based on disease subtypes, prognosis, and treatment response [138].

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.

Question: You are an expert researcher providing comprehensive, well-cited information.

Provide detailed information focusing on: 1. Key concepts and definitions with current understanding 2. Recent developments and latest research (prioritize 2023-2024 sources) 3. Current applications and real-world implementations 4. Expert opinions and analysis from authoritative sources 5. Relevant statistics and data from recent studies

Format as a comprehensive research report with proper citations. Include URLs and publication dates where available. Always prioritize recent, authoritative sources and provide specific citations for all major claims.

Disease Characteristics Research Template

Target Disease

• Disease Name: Achromatopsia
• MONDO ID: (if available)
• Category: Mendelian

Research Objectives

Please provide a comprehensive research report on Achromatopsia covering all of the disease characteristics listed below. This report will be used to populate a disease knowledge base entry. Be thorough and cite primary literature (PMID preferred) for all claims.

For each section, suggested databases/resources are listed. These are the first places you should search for information on each topic.

1. Disease Information

Search first: OMIM, Orphanet, ICD-10/ICD-11, MeSH, PubMed

• What is the disease? Provide a concise overview.
• What are the key identifiers? (OMIM, Orphanet, ICD-10/ICD-11, MeSH, Mondo)
• What are the common synonyms and alternative names?
• Is the information derived from individual patients (e.g., EHR) or aggregated disease-level resources?

2. Etiology

• Disease Causal Factors: What are the primary causes? (genetic, environmental, infectious, mechanistic)
• Risk Factors:

  Search first: PubMed, Cochrane Library, UpToDate, clinical guidelines, ClinVar, ClinGen, GWAS Catalog, PheGenI, CTD, CDC, WHO, epidemiological databases

• Genetic risk factors (causal variants, susceptibility loci, modifier genes)
• Environmental risk factors (toxins, lifestyle, occupational exposures, age, sex, family history)
• Protective Factors:

  Search first: PubMed, Cochrane Library, clinical trial databases, GWAS Catalog, gnomAD, WHO, CDC, nutrition databases

• Genetic protective factors (protective variants, modifier alleles)
• Environmental protective factors (diet, lifestyle, exposures that reduce risk)
• Gene-Environment Interactions: How do genetic and environmental factors interact to influence disease?

  Search first: CTD, PubMed, PheGenI, GxE databases

3. Phenotypes

Search first: HPO (Human Phenotype Ontology), OMIM, Orphanet, PubMed, clinicaltrials.gov, MedDRA, SNOMED CT, DECIPHER, LOINC

For each phenotype, provide: - Phenotype type: symptoms, clinical signs, physical manifestations, behavioral changes, or laboratory abnormalities

For symptoms/signs: HPO, OMIM, Orphanet, PubMed For behavioral changes: HPO, DSM, RDoC (Research Domain Criteria), PubMed For laboratory abnormalities: LOINC, SNOMED CT, LabTests Online, PubMed - Phenotype characteristics: Search first: OMIM, Orphanet, HPO, PubMed - Age of symptom onset (neonatal, childhood, adult-onset, late-onset) - Symptom severity (mild, moderate, severe, variable) - Symptom progression (stable, progressive, episodic, fluctuating) - Frequency among affected individuals (percentage or qualitative) - Quality of life impact: Effects on daily functioning and well-being (per-phenotype when possible) Search first: EQ-5D database, SF-36, WHO QOL databases, PubMed - Suggest HPO (Human Phenotype Ontology) terms for each phenotype

4. Genetic/Molecular Information

• Causal Genes: Gene mutations or chromosomal abnormalities responsible for disease (gene symbols, OMIM IDs)

  Search first: OMIM, ClinVar, HGMD, Ensembl, NCBI Gene

• Pathogenic Variants:
• Affected genes (gene symbols, HGNC IDs) > Search first: OMIM, NCBI Gene, Ensembl, HGNC, UniProt, GeneCards
• Variant classification (pathogenic, likely pathogenic, VUS per ACMG/AMP guidelines) > Search first: ClinVar, ClinGen, ACMG/AMP guidelines, VarSome
• Variant type/class (missense, frameshift, nonsense, splice-site, structural)
• Allele frequency in population databases > Search first: gnomAD, 1000 Genomes, ExAC, TOPMed, dbSNP
• Somatic vs germline origin > Search first: COSMIC (somatic), ClinVar, ICGC, TCGA
• Functional consequences (loss of function, gain of function, dominant negative)
• Modifier Genes: Genes that modify disease severity or expression
• Epigenetic Information: DNA methylation, histone modifications, chromatin changes affecting disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Chromosomal Abnormalities: Large-scale genetic changes (aneuploidy, translocations, inversions)

  Search first: DECIPHER, ClinVar, ECARUCA, UCSC Genome Browser

5. Environmental Information

• Environmental Factors: Non-genetic contributing factors (toxins, radiation, pollution, occupational exposure)

  Search first: CTD (Comparative Toxicogenomics Database), TOXNET, PubMed, EPA databases

• Lifestyle Factors: Behavioral factors (smoking, diet, exercise, alcohol consumption)

  Search first: CDC databases, WHO, PubMed, NHANES

• Infectious Agents: If applicable, pathogens causing or triggering disease (bacteria, viruses, fungi, parasites)

  Search first: NCBI Taxonomy, ViPR, BV-BRC, MicrobeDB, GIDEON

6. Mechanism / Pathophysiology

• Molecular Pathways: Specific signaling cascades or biochemical pathways involved (Wnt, MAPK, mTOR, PI3K-AKT, etc.)

  Search first: KEGG, Reactome, WikiPathways, PathBank, BioCyc

• Cellular Processes: Cell-level mechanisms (apoptosis, autophagy, cell cycle dysregulation, inflammation, etc.)

  Search first: Gene Ontology (GO), Reactome, KEGG, PubMed

• Protein Dysfunction: How protein structure or function is altered (misfolding, aggregation, loss of function, gain of function)

  Search first: UniProt, PDB (Protein Data Bank), InterPro, Pfam, AlphaFold

• Metabolic Changes: Alterations in metabolic processes (energy metabolism, lipid metabolism, amino acid metabolism)

  Search first: KEGG, BioCyc, HMDB (Human Metabolome Database), BRENDA

• Immune System Involvement: Role of immune response (autoimmunity, immunodeficiency, chronic inflammation)

  Search first: ImmPort, Immunome Database, IEDB, Gene Ontology

• Tissue Damage Mechanisms: How tissues/ are injured (oxidative stress, ischemia, fibrosis, necrosis)

  Search first: PubMed, Gene Ontology, Reactome

• Biochemical Abnormalities: Specific molecular defects (enzyme deficiencies, receptor dysfunction, ion channel defects)

  Search first: BRENDA, UniProt, KEGG, OMIM, PubMed

• Epigenetic Changes: DNA methylation, histone modifications affecting gene expression in disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Molecular Profiling (if available):
• Transcriptomics/gene expression changes > Search first: GEO (Gene Expression Omnibus), ArrayExpress, GTEx, Human Cell Atlas, SRA
• Proteomics findings > Search first: PRIDE, ProteomeXchange, Human Protein Atlas, STRING, BioGRID
• Metabolomics signatures > Search first: MetaboLights, Metabolomics Workbench, HMDB, METLIN
• Lipidomics alterations > Search first: LIPID MAPS, SwissLipids, LipidHome, Metabolomics Workbench
• Genomic structural features > Search first: UCSC Genome Browser, Ensembl, NCBI, dbVar, DGV
• Advanced Technologies (if applicable):
• Single-cell analysis findings (cell-type specific mechanisms, cellular heterogeneity) > Search first: Human Cell Atlas, Single Cell Portal, GEO, CELLxGENE
• Spatial transcriptomics findings > Search first: GEO, Spatial Research, Vizgen, 10x Genomics data
• Multi-omics integration results > Search first: TCGA, ICGC, cBioPortal, LinkedOmics, PubMed
• Functional genomics screens (CRISPR, RNAi) > Search first: DepMap, GenomeRNAi, PubMed, BioGRID ORCS

For each mechanism, describe: - The causal chain from initial trigger to clinical manifestation - Which mechanisms are upstream vs downstream - What cell types and biological processes are involved - Suggest GO terms for biological processes and CL terms for cell types

7. Anatomical Structures Affected

• Organ Level:
• Primary organs directly affected
• Secondary organ involvement (complications, secondary effects)
• Body systems involved (cardiovascular, nervous, digestive, respiratory, endocrine, etc.)

  Search first: Uberon, FMA (Foundational Model of Anatomy), OMIM, HPO, ICD-11, MeSH, SNOMED CT

• Tissue and Cell Level:
• Specific tissue types affected (epithelial, connective, muscle, nervous)
• Specific cell populations targeted (with Cell Ontology terms)

  Search first: Uberon, Human Protein Atlas, Cell Ontology, Human Cell Atlas, CellMarker, PanglaoDB

• Subcellular Level:
• Cellular compartments involved (mitochondria, nucleus, ER, lysosomes) (with GO Cellular Component terms)

  Search first: Gene Ontology (Cellular Component), UniProt, Human Protein Atlas

• Localization:
• Specific anatomical sites (with UBERON terms) > Search first: FMA, Uberon, NeuroNames (for brain), SNOMED CT
• Lateralization (unilateral, bilateral, asymmetric) > Search first: HPO, clinical literature, imaging databases

8. Temporal Development

• Onset:
• Typical age of onset (congenital, pediatric, adult, geriatric)
• Onset pattern (acute, subacute, chronic, insidious)

  Search first: OMIM, Orphanet, HPO, PubMed

• Progression:
• Disease stages (early, intermediate, advanced, end-stage) > Search first: Cancer Staging Manual (AJCC), WHO classifications, PubMed
• Progression rate (rapid, slow, variable)
• Disease course pattern (episodic, relapsing-remitting, progressive, stable)
• Disease duration (self-limited, chronic lifelong)

  Search first: Disease registries, longitudinal cohort databases, natural history studies, PubMed, Orphanet, OMIM

• Patterns:
• Remission patterns (spontaneous, treatment-induced) > Search first: Clinical trial databases, disease registries, PubMed
• Critical periods (time windows of vulnerability or opportunity for intervention) > Search first: PubMed, developmental biology databases, clinical guidelines

9. Inheritance and Population

• Epidemiology:
• Prevalence (cases per 100,000 at given time)
• Incidence (new cases per 100,000 per year)

  Search first: Orphanet, CDC, WHO, GBD (Global Burden of Disease), national registries, SEER, disease registries

• For Genetic Etiology:
• Inheritance pattern (AD, AR, X-linked, mitochondrial, multifactorial, polygenic) > Search first: OMIM, Orphanet, ClinVar, GTR (Genetic Testing Registry)
• Penetrance (complete, incomplete, age-dependent) > Search first: ClinVar, OMIM, PubMed, ClinGen
• Expressivity (variable, consistent) > Search first: OMIM, ClinVar, PubMed
• Genetic anticipation (increasing severity in successive generations) > Search first: OMIM, PubMed (especially for repeat expansion disorders)
• Germline mosaicism > Search first: ClinVar, OMIM, genetic counseling literature, PubMed
• Founder effects (population-specific mutations) > Search first: gnomAD, population genetics databases, PubMed
• Consanguinity role > Search first: OMIM, population studies, genetic counseling resources
• Carrier frequency > Search first: gnomAD, carrier screening databases, GeneReviews, GTR
• Population Demographics:
• Affected populations (ethnic or demographic groups with higher prevalence) > Search first: gnomAD, 1000 Genomes, PAGE Study, PubMed, population registries
• Geographic distribution (endemic areas, regional variation) > Search first: WHO, CDC, GBD, Orphanet, geographic epidemiology databases
• Geographic distribution of specific variants
• Sex ratio (male:female) > Search first: Disease registries, OMIM, PubMed, epidemiological databases
• Age distribution of affected individuals > Search first: CDC, disease registries, SEER, Orphanet

10. Diagnostics

• Clinical Tests:
• Laboratory tests (blood, urine, tissue chemistry, specific enzyme assays) > Search first: LOINC, LabTests Online, PubMed
• Biomarkers (proteins, metabolites, genetic markers, circulating biomarkers) > Search first: FDA Biomarker List, BEST (Biomarkers, EndpointS, and other Tools), PubMed
• Imaging studies (X-ray, CT, MRI, PET, ultrasound) > Search first: RadLex, DICOM, Radiopaedia, imaging databases
• Functional tests (pulmonary function, cardiac stress tests) > Search first: LOINC, clinical guidelines, PubMed
• Electrophysiology (EEG, EMG, ECG, nerve conduction studies) > Search first: LOINC, clinical neurophysiology databases, PubMed
• Biopsy findings (histopathology, immunohistochemistry) > Search first: SNOMED CT, College of American Pathologists resources, PubMed
• Pathology findings (microscopic examination) > Search first: SNOMED CT, Digital Pathology databases, PubMed
• Genetic Testing:

  Search first: GTR (Genetic Testing Registry), GeneReviews, ClinGen

• Overview of recommended genetic testing approach
• Whole genome sequencing (WGS) utility > Search first: GTR, ClinVar, GEL (Genomics England), gnomAD
• Whole exome sequencing (WES) utility > Search first: GTR, ClinVar, OMIM, GeneMatcher
• Gene panels (which panels, which genes) > Search first: GTR, ClinVar, laboratory-specific databases
• Single gene testing > Search first: GTR, ClinVar, OMIM, GeneReviews
• Chromosomal microarray (CMA) > Search first: DECIPHER, ClinVar, dbVar, ECARUCA
• Karyotyping > Search first: Chromosome Abnormality Database, ClinVar, cytogenetics resources
• FISH > Search first: ClinVar, cytogenetics databases, PubMed
• Mitochondrial DNA testing > Search first: MITOMAP, MSeqDR, ClinVar, GTR
• Repeat expansion testing > Search first: GTR, ClinVar, repeat expansion databases, PubMed
• Omics-Based Diagnostics (if applicable):
• RNA sequencing / transcriptomics > Search first: GEO, ArrayExpress, GTEx, RNA-seq databases
• Proteomics > Search first: PRIDE, ProteomeXchange, FDA Biomarker database
• Metabolomics > Search first: MetaboLights, Metabolomics Workbench, HMDB
• Epigenomics > Search first: GEO, ENCODE, Roadmap Epigenomics, MethBase
• Liquid biopsy > Search first: COSMIC, ClinVar, liquid biopsy databases, PubMed
• Clinical Criteria:
• Standardized diagnostic criteria (DSM, ICD, society guidelines) > Search first: DSM-5, ICD-11, clinical society guidelines, UpToDate
• Differential diagnosis (other conditions to rule out, with distinguishing features) > Search first: DynaMed, UpToDate, clinical decision support systems
• Screening:
• Screening methods for asymptomatic individuals (newborn screening, carrier screening, cascade screening) > Search first: ACMG recommendations, CDC newborn screening, GTR

11. Outcome/Prognosis

• Survival and Mortality:
• Survival rate (5-year, 10-year, overall) > Search first: SEER, cancer registries, disease-specific registries, PubMed
• Life expectancy (with and without treatment if applicable) > Search first: Orphanet, disease registries, actuarial databases, PubMed
• Mortality rate > Search first: CDC, WHO, GBD, national mortality databases
• Disease-specific mortality (deaths directly attributable to disease) > Search first: Disease registries, CDC Wonder, GBD, PubMed
• Morbidity and Function:
• Morbidity (disease-related disability and health impacts) > Search first: GBD, WHO, disability databases, PubMed
• Disability outcomes (long-term functional impairments) > Search first: ICF (International Classification of Functioning), disability registries
• Quality of life measures (EQ-5D, SF-36, PROMIS, disease-specific tools) > Search first: EQ-5D database, SF-36, PROMIS, PubMed
• Disease Course:
• Complications (secondary problems: infections, organ failure, etc.) > Search first: ICD codes, disease registries, clinical databases, PubMed
• Recovery potential (likelihood and extent of recovery, with vs without treatment) > Search first: Natural history studies, rehabilitation databases, PubMed
• Prediction:
• Prognostic factors (age, disease severity, biomarkers, treatment response) > Search first: Prognostic models databases, clinical calculators, PubMed
• Prognostic biomarkers (molecular markers predicting disease course) > Search first: FDA Biomarker database, PubMed, cancer prognostic databases

12. Treatment

• Pharmacotherapy:
• Pharmacological treatments (drug names, drug classes, mechanisms of action) > Search first: DrugBank, RxNorm, ATC classification, DailyMed, FDA databases
• Pharmacogenomics (how genetic variants affect drug metabolism, efficacy, toxicity) > Search first: PharmGKB, CPIC (Clinical Pharmacogenetics), FDA Table of PGx Biomarkers
• Advanced Therapeutics:
• Gene therapy (viral vectors, CRISPR, gene replacement, gene editing) > Search first: ClinicalTrials.gov, FDA gene therapy database, ASGCT resources
• Cell therapy (stem cell transplant, CAR-T, cellular therapeutics) > Search first: ClinicalTrials.gov, FDA cell therapy database, FACT standards
• RNA-based therapies (ASOs, siRNA, mRNA therapies) > Search first: ClinicalTrials.gov, FDA approvals, PubMed
• Targeted therapies (treatments directed at specific molecular targets) > Search first: My Cancer Genome, OncoKB, ClinicalTrials.gov, FDA approvals
• Immunotherapies (checkpoint inhibitors, monoclonal antibodies) > Search first: Cancer Immunotherapy Database, FDA approvals, ClinicalTrials.gov
• Surgical and Interventional:
• Surgical interventions (types of surgery, timing, outcomes) > Search first: CPT codes, surgical registries, clinical guidelines, PubMed
• Supportive and Rehabilitative:
• Supportive care (symptom management, pain control, nutrition) > Search first: Clinical guidelines, Cochrane Library, PubMed
• Rehabilitation (physical therapy, occupational therapy, speech therapy) > Search first: Rehabilitation medicine databases, clinical guidelines, PubMed
• Experimental:
• Experimental treatments in clinical trials (with NCT identifiers if available) > Search first: ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
• Treatment Outcomes:
• Treatment response rates > Search first: Clinical trial databases, FDA reviews, systematic reviews, PubMed
• Side effects and adverse events > Search first: FDA Adverse Event Reporting System (FAERS), MedWatch, PubMed
• Treatment Strategy:
• Treatment algorithms (clinical pathways, decision trees) > Search first: Clinical practice guidelines, NCCN Guidelines, UpToDate
• Combination therapies > Search first: ClinicalTrials.gov, treatment guidelines, PubMed
• Personalized medicine approaches (genotype-guided treatment) > Search first: My Cancer Genome, CIViC, PharmGKB, precision medicine databases

For each treatment, suggest MAXO (Medical Action Ontology) terms where applicable.

13. Prevention

• Prevention Levels:
• Primary prevention (preventing disease occurrence: vaccination, risk factor modification) > Search first: CDC, WHO, USPSTF recommendations, Cochrane Library
• Secondary prevention (early detection and treatment: screening programs, early intervention) > Search first: USPSTF, CDC screening guidelines, WHO
• Tertiary prevention (preventing complications in those with disease) > Search first: Clinical guidelines, disease management protocols, PubMed
• Immunization: Vaccine strategies (if applicable)

  Search first: CDC vaccine schedules, WHO immunization, FDA vaccine database

• Screening and Early Detection:
• Screening programs (population-based: newborn screening, cancer screening) > Search first: CDC screening programs, USPSTF, cancer screening databases
• Genetic screening (carrier screening, preimplantation genetic diagnosis, prenatal testing) > Search first: ACMG recommendations, ACOG guidelines, GTR
• Risk stratification (identifying high-risk individuals for targeted prevention) > Search first: Risk prediction models, clinical calculators, PubMed
• Behavioral Interventions: Lifestyle modifications to reduce risk

  Search first: CDC, WHO, behavioral intervention databases, Cochrane Library

• Counseling: Genetic counseling (risk assessment, family planning guidance)

  Search first: NSGC resources, ACMG guidelines, GeneReviews

• Public Health:
• Public health interventions (sanitation, vector control, health education) > Search first: CDC, WHO, public health databases, PubMed
• Environmental interventions (reducing environmental risk factors) > Search first: EPA databases, WHO environmental health, PubMed
• Prophylaxis: Preventive medications or procedures

  Search first: Clinical guidelines, FDA approvals, PubMed

14. Other Species / Natural Disease

• Taxonomy: Species affected (with NCBI Taxon identifiers)

  Search first: NCBI Taxonomy

• Breed: Specific breeds affected (with VBO identifiers if applicable)

  Search first: VBO (Vertebrate Breed Ontology)

• Gene: Orthologous genes in other species (with NCBI Gene IDs)

  Search first: NCBI Gene

• Natural Disease:
• Naturally occurring disease in other species (companion animals, wildlife) > Search first: OMIA (Online Mendelian Inheritance in Animals), VetCompass, PubMed
• Veterinary relevance and importance in animal health > Search first: OMIA, veterinary databases, PubMed
• Comparative Biology:
• Comparative pathology (similarities and differences across species) > Search first: OMIA, comparative pathology databases, PubMed
• Evolutionary conservation of disease mechanisms > Search first: HomoloGene, OrthoMCL, Alliance of Genome Resources
• Transmission (if applicable):
• Zoonotic potential > Search first: CDC zoonotic diseases, WHO zoonoses, GIDEON
• Cross-species susceptibility > Search first: NCBI Taxonomy, veterinary databases, PubMed

15. Model Organisms

• Model Types:
• Model organism type (mammalian, invertebrate, cellular, in vitro) > Search first: Alliance of Genome Resources, model organism databases
• Specific model systems (mouse, rat, zebrafish, Drosophila, C. elegans, yeast, cell lines, organoids, iPSCs) > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, SGD, ATCC, Cellosaurus
• Induced models (drug treatment, surgical intervention, environmental manipulation) > Search first: MGI, model organism databases, PubMed
• Genetic Models:
• Types available (knockout, knock-in, transgenic, conditional, humanized) > Search first: MGI, IMPC, KOMP, EuMMCR, IMSR
• Model Characteristics:
• Phenotype recapitulation (how well model reproduces human disease features) > Search first: Model organism databases, comparative studies, PubMed
• Model limitations (aspects of human disease not captured) > Search first: Model organism databases, PubMed, review articles
• Applications:
• Research applications (what aspects of disease can be studied) > Search first: Model organism databases, PubMed
• Resources:
• Model databases > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, IMSR, EMMA, MMRRC

Citation Requirements

• Cite primary literature (PMID preferred) for all mechanistic and clinical claims
• Prioritize recent reviews and landmark papers
• Include direct quotes from abstracts where possible to support key statements
• Distinguish evidence source types: human clinical, model organism, in vitro, computational

Output Format

Structure your response as a comprehensive narrative organized by the sections above. For each section, provide: - Factual content with specific details (numbers, percentages, gene names, variant nomenclature) - Ontology term suggestions (HPO, GO, CL, UBERON, CHEBI, MAXO, MONDO) where applicable - Evidence citations with PMIDs - Direct quotes from abstracts to support key claims - Clear indication when information is not available or not applicable for this disease

This report will be used to populate a disease knowledge base entry with: - Pathophysiology descriptions with causal chains - Gene/protein annotations (HGNC, GO terms) - Phenotype associations (HP terms) with frequencies - Cell type involvement (CL terms) - Anatomical locations (UBERON terms) - Chemical entities (CHEBI terms) - Treatment annotations (MAXO terms) - Evidence items with PMIDs and exact abstract quotes - Epidemiology, prognosis, diagnostic, and prevention information - Animal model descriptions with phenotype recapitulation details

Achromatopsia (ACHM) — Comprehensive Disease Characteristics Report (Mendelian)

Executive summary (current understanding; 2023–2024 emphasis)

Achromatopsia (ACHM) is a rare congenital cone photoreceptor disorder (cone dysfunction syndrome) with onset at birth/early infancy and a characteristic triad of markedly reduced visual acuity, photophobia/photoaversion, and absent/markedly impaired color discrimination, often accompanied by infantile nystagmus. It is most commonly autosomal recessive and caused by biallelic loss-of-function variants in cone phototransduction genes—especially CNGA3 and CNGB3—with additional rarer causes including GNAT2, PDE6C, PDE6H, and ATF6. The best-supported disease mechanism is failure of cone phototransduction/cGMP-gated ion channel signaling (CNGA3/CNGB3/PDE6/GNAT2) and, for ATF6-ACHM, a developmental cone deficit linked to ER/UPR biology. Clinical management today is largely supportive (filters, low-vision rehabilitation), but multiple AAV subretinal gene-replacement trials* are in late phase I/II stages for CNGA3- and CNGB3-ACHM, with early signals of safety and functional benefit in subsets of participants.

1. Disease information

1.1 Concise overview

• Definition: Andersen et al. (2023) describe ACHM as “a rare congenital condition with cone photoreceptor dysfunction causing color blindness, reduced vision, nystagmus and photophobia” (andersen2023geneticandclinical pages 1-2). Baxter & Borchert (2024) similarly note it is an autosomal recessive cone dysfunction syndrome presenting at birth/early infancy with poor visual acuity, nystagmus, photophobia, and loss of color vision (baxter2024genetherapyfor pages 1-2).
• Synonyms / alternative names: “rod monochromacy” and “total color blindness” are used as alternative names/descriptors (andersen2023geneticandclinical pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).
• Evidence type: The information in this report is derived from aggregated disease-level resources (reviews), plus patient cohort natural history/quality-of-life studies and ClinicalTrials.gov trial records (baxter2024genetherapyfor pages 1-2, andersen2023geneticandclinical pages 1-2, chan2023morphologicalandfunctional pages 1-2, NCT03001310 chunk 1).

1.2 Key identifiers (available in retrieved evidence)

• MONDO: OpenTargets maps “achromatopsia” to MONDO_0018852 (OpenTargets Search: Achromatopsia).

Note (identifier coverage limitation): OMIM/Orphanet/ICD-10/ICD-11/MeSH identifiers were not retrieved in the available evidence chunks in this run; they should be added from OMIM/Orphanet/ICD/MeSH primary sources in a follow-on extraction.

Table: This table summarizes key disease identifiers, synonyms, inheritance, prevalence, and causal genes for achromatopsia using only retrieved evidence. It is useful as a compact normalization reference for a disease knowledge base entry.

2. Etiology

2.1 Disease causal factors

• Primary cause: Mendelian, typically autosomal recessive inherited retinal disease due to biallelic pathogenic variants affecting cone function (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).
• Core causal genes (well-established): CNGA3, CNGB3, GNAT2, PDE6C, PDE6H, ATF6 (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).

2.2 Genetic risk factors (causal variants)

• CNGA3/CNGB3 predominate: Baxter & Borchert (2024) state: “Up to 90% of achromatopsia patients harbour mutations in CNGA3 or CNB3” (CNGB3) (baxter2024genetherapyfor pages 1-2). Michalakis et al. (2022) and Moussawi et al. (2021) also place the majority of cases in these genes (michalakis2022achromatopsiageneticsand pages 1-2, moussawi2021genetherapyin pages 1-3).
• Relative contributions (approximate; older synthesis): Asensio-Sánchez (2020) reports CNGB3 ≈50% and CNGA3 ≈25% of autosomal recessive ACHM and lower proportions for GNAT2/PDE6C/PDE6H/ATF6 (asensiosanchez2020genetherapyfor pages 1-2, asensiosanchez2020genetherapyfor pages 7-10). These proportions should be treated as approximate and cohort-dependent.

2.3 Environmental risk/protective factors

No specific environmental exposures or protective factors are established as causal for congenital achromatopsia in the retrieved evidence; ACHM is primarily genetic (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).

2.4 Gene–environment interactions

No gene–environment interaction evidence specific to ACHM was present in the retrieved sources.

Table: This table summarizes the established achromatopsia genes, their functional roles in cone biology, and evidence-based phenotype notes. It is useful for linking genotype to mechanism, inheritance, and expected clinical presentation.

3. Phenotypes (with ontology suggestions)

3.1 Core phenotype spectrum

Across reviews and cohort studies, ACHM is consistently characterized by: - Reduced visual acuity (HPO suggestion: HP:0007663 Decreased visual acuity). Andersen et al. report VA “around 20/200” in typical cases (andersen2023geneticandclinical pages 1-2), and Baxter & Borchert note similar typical VA and that incomplete forms can have better acuity (20/40–20/120) (baxter2024genetherapyfor pages 1-2). - Color vision loss (HPO: HP:0000551 Abnormal color vision; for complete ACHM, HP:0000618 Achromatopsia). In a Danish cohort, “49” of 57 tested had complete color blindness and “eight had residual color vision” (andersen2023geneticandclinical pages 5-7). - Photophobia/photoaversion (HPO: HP:0000613 Photophobia). Chan et al. emphasize: “photoaversion has been described to be one of the more debilitating symptoms of achromatopsia” (chan2023morphologicalandfunctional pages 1-2). - Infantile/childhood nystagmus (HPO: HP:0000639 Nystagmus). In the Danish cohort, “Most patients (89%, n = 72) had a history of childhood nystagmus” (andersen2023geneticandclinical pages 5-7). Chan et al. report nystagmus in 88.5% among 61 patients (chan2023morphologicalandfunctional pages 4-6). - Refractive error (myopia/hyperopia) (HPO: HP:0000545 Myopia, HP:0000540 Hyperopia). In Andersen et al., myopia was more frequent in GNAT2/PDE6C/PDE6H subsets (75–80%) (andersen2023geneticandclinical pages 7-9).

3.2 Anatomical structures affected

• Primary organ/tissue: Retina, especially macula/fovea with cone photoreceptors.
• UBERON suggestions: retina (UBERON:0000966); macula lutea (UBERON:0001891); fovea centralis (UBERON:0001866).
• Cell Ontology suggestions: retinal cone photoreceptor cell (CL:0000210).

3.3 Natural history / temporal development

• Onset: At birth or early infancy (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).
• Course: Predominantly stationary with respect to visual acuity in many cohorts.
• Chan et al. (2023) state: “Visual acuity was stable… over a time of observation from 2008 to 2021” (chan2023morphologicalandfunctional pages 1-2).
• Andersen et al. report “a median follow-up of 22 years (…range 1–65 years)” and that the first and last VA were within ±0.2 logMAR in “88% (n = 51)” (andersen2023geneticandclinical pages 5-7).
• Progressive BCVA deterioration attributable to ACHM was reported in “three of 58 patients” in Andersen et al. (andersen2023geneticandclinical pages 1-2, andersen2023geneticandclinical pages 7-9).

4. Genetic / molecular information (knowledge base–ready)

4.1 Causal genes

Established ACHM genes in the retrieved evidence: CNGA3, CNGB3, GNAT2, PDE6C, PDE6H, ATF6 (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2). OpenTargets also links ACHM to these targets and provides supporting PubMed ID lists (e.g., CNGA3/CNGB3 evidence) and the MONDO mapping (OpenTargets Search: Achromatopsia).

4.2 Pathogenic variant characteristics

The retrieved evidence primarily provides gene-level rather than variant-level detail. Variant class patterns inferred from the evidence include: - Loss-of-function variants are common drivers (recessive) in CNGA3/CNGB3-related ACHM (gong2024infantilenystagmussyndrome—associated pages 12-13). - Some reviews note incomplete ACHM may be associated with specific missense changes in GNAT2/CNGA3/PDE6C/PDE6H (moussawi2021genetherapyin pages 1-3).

Note (variant-level limitation): ClinVar/HGMD/gnomAD allele frequencies, ACMG classifications, and recurrent founder variants were not retrieved in the evidence chunks and thus cannot be reliably populated here.

5. Environmental information

No infectious, toxic, occupational, or lifestyle exposures were identified as causal contributors for congenital ACHM in the retrieved evidence.

6. Mechanism / pathophysiology

6.1 Cone phototransduction / cGMP / CNG channel failure (CNGA3, CNGB3, GNAT2, PDE6C, PDE6H)

Mechanistically, ACHM (for the phototransduction-gene forms) arises from disruption of the cone phototransduction cascade and/or the CNG channel that converts cGMP signaling into an electrical response: - Yang et al. (2024) explicitly summarize the cascade: “Activated PDE begins to hydrolyze cGMP efficiently. As the cGMP concentration decreases, the CNG channel closed” (yang2024dyschromatopsiaacomprehensive pages 6-8). They further describe a PDE6H mechanism: “cGMP gated channels are permanently closed” in a way “similar to permanent light stimulation” (yang2024dyschromatopsiaacomprehensive pages 6-8). - Michalakis et al. (2022) describe the core dark/light physiology: in darkness high cGMP keeps CNG channels open producing Na+/Ca2+ current and depolarization; in light, opsin→transducin→PDE reduces cGMP, closes channels, and hyperpolarizes the cone (michalakis2022achromatopsiageneticsand pages 1-2). - Downstream consequences include absent cone-mediated ERG components with preserved rod function (diagnostic hallmark) and cone degeneration; Michalakis et al. note early cone degeneration features including cGMP accumulation (michalakis2022achromatopsiageneticsand pages 4-5).

GO term suggestions (biological process): phototransduction (GO:0007602), cyclic nucleotide-mediated signaling (GO:0019935), cGMP metabolic process (GO:0046068), ion transmembrane transport (GO:0034220).

6.2 ATF6-related achromatopsia (developmental / ER/UPR biology)

ATF6-associated ACHM is mechanistically distinct, involving ER homeostasis/UPR signaling and cone development: - Yang et al. (2024) state: “ATF6 ... plays a key role in unfolded protein response (UPR) and endoplasmic reticulum homeostasis” (yang2024dyschromatopsiaacomprehensive pages 6-8). - Michalakis et al. (2022) likewise describe ATF6 as an ER-localized transcription factor capable of activating the unfolded protein response (michalakis2022achromatopsiageneticsand pages 1-2).

Therapeutic implication (conceptual): Phototransduction-gene ACHM is amenable to gene supplementation (recessive loss-of-function), whereas ATF6-associated disease may have fewer intact cone structures to target and may require pathway modulation (yang2024dyschromatopsiaacomprehensive pages 4-5).

7. Anatomical structures affected (ontology-ready)

• Primary: retina (macula/fovea) and cone photoreceptor system.
• Subcellular / molecular compartments (relevant): cone outer segment, cGMP signaling microdomain, CNG channels in outer segment membrane; ER/UPR machinery for ATF6.
• GO Cellular Component suggestions: photoreceptor outer segment (GO:0001750), plasma membrane (GO:0005886), endoplasmic reticulum membrane (GO:0005789).

8. Temporal development

• Typical onset: congenital/infantile (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).
• Progression: predominantly stable VA over many years for many patients, but with documented minority progression in long follow-up (andersen2023geneticandclinical pages 5-7, andersen2023geneticandclinical pages 7-9).

9. Inheritance and population

9.1 Epidemiology

• Prevalence: commonly cited as ~1 in 30,000 (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2), and also reported as a range 1 in 30,000–50,000 (andersen2023geneticandclinical pages 1-2, asensiosanchez2020genetherapyfor pages 1-2).

9.2 Inheritance

• Typically autosomal recessive (baxter2024genetherapyfor pages 1-2, michalakis2022achromatopsiageneticsand pages 1-2).

Note (population genetics limitation): No carrier frequencies, founder variants, or geographic variant distributions were retrievable from the evidence in this run.

10. Diagnostics

10.1 Clinical and electrophysiology

• Classic diagnostic confirmation includes demonstration of absent cone function: Andersen et al. refer to “demonstration of a lack of cone function by electroretinography” (andersen2023geneticandclinical pages 1-2). Baxter & Borchert note ERGs typically show normal rod/scotopic responses with absence of cone-mediated photopic components (baxter2024genetherapyfor pages 1-2).

10.2 Imaging (OCT/FAF) and functional endpoints

• Fundus autofluorescence (FAF): Baxter & Borchert classify 4 phenotypes (normal; central increased; central reduced; central decreased with hyperautofluorescent ring) (baxter2024genetherapyfor pages 1-2).
• OCT: Baxter & Borchert classify 5 OCT patterns (continuous/disrupted/absent ellipsoid layer; hyporeflective zone; outer retinal atrophy with RPE loss) and note foveal hypoplasia is common (baxter2024genetherapyfor pages 1-2).
• Microperimetry as structure–function endpoint (2024): In adolescents, OCT staging correlated with microperimetry sensitivity (central foveal and perifoveal rings) with p-values 0.0286, 0.0008, 0.0014; in contrast, OCT staging did not correlate with VA or contrast sensitivity (cosmo2024microperimetrysensitivitycorrelates pages 1-2, cosmo2024microperimetrysensitivitycorrelates pages 4-6).

10.3 Recommended testing approach (evidence-based synthesis)

A practical diagnostic workflow supported by the retrieved sources is: 1) Clinical phenotype (infantile onset, photophobia, nystagmus, color vision deficit), 2) ERG documenting absent cone responses with preserved rod responses, 3) OCT/FAF characterization of foveal/outer retinal structure, 4) genetic testing to identify causal gene(s) and determine eligibility for gene therapy trials (baxter2024genetherapyfor pages 1-2, andersen2023geneticandclinical pages 1-2, gong2024infantilenystagmussyndrome—associated pages 12-13).

Differential diagnosis (limited in retrieved evidence): The retrieved evidence does not provide a systematic differential diagnosis list; however, the context of inherited retinal diseases presenting with infantile nystagmus includes multiple entities (review context) (gong2024infantilenystagmussyndrome—associated pages 12-13). A dedicated differential diagnosis extraction would require additional sources.

11. Outcomes / prognosis

• Visual acuity stability: Chan et al. report “Visual acuity was stable…” in their cohort over 2008–2021 (chan2023morphologicalandfunctional pages 1-2). Andersen et al. report long-term stability in most patients (±0.2 logMAR in 88%) and rare ACHM-attributable deterioration (3/58) (andersen2023geneticandclinical pages 5-7, andersen2023geneticandclinical pages 7-9).
• Functional and QoL impact: Photoaversion is described as debilitating (chan2023morphologicalandfunctional pages 1-2). In Chan et al., legal disability ratings ranged widely and 27.9% were rated 100% (chan2023morphologicalandfunctional pages 6-8).

12. Treatment

12.1 Supportive/rehabilitative (current real-world implementation)

• Optical aids and filters are widely used; Chan et al. report: “Edge filter glasses were the most used optical aids, while enlarged reading glasses were the most used low vision aids” (chan2023morphologicalandfunctional pages 1-2).
• In an extended aid inventory (subset), common devices included reading glasses (56.3%), magnifying glasses (55.2%), smartphones (39.1%), tablets (37.9%), monoculars (36.8%), and CCTV (31%) (chan2023morphologicalandfunctional pages 6-8).

MAXO suggestions: low vision rehabilitation (MAXO:0000787), prescription of optical filters (filter-lens intervention; MAXO term may need confirmation), assistive device use.

12.2 Advanced therapeutics: gene therapy (2023–2024 status)

• Baxter & Borchert (2024) emphasize “There is no FDA-approved treatment for achromatopsia” and note “five gene therapy clinical trials registered” (phase I/II) for CNGA3/CNGB3 (baxter2024genetherapyfor pages 1-2).

Key trials and implementation details (ClinicalTrials.gov plus 2024 expert synthesis): - CNGA3 AAV subretinal trials: NCT02610582 and NCT02935517 (NCT02610582 chunk 1, NCT02935517 chunk 1). - NCT02610582 includes subretinal administration and multiple functional endpoints including microperimetry and patient-reported outcomes (NCT02610582 chunk 1). - Gong & Hertle (2024) summarize a first CNGA3 trial: treatment “well tolerated, with no serious adverse events” and visual acuity/contrast sensitivity improvements persisting “for at least three years” (gong2024infantilenystagmussyndrome—associated pages 12-13). - CNGB3 AAV subretinal trials: NCT03001310 and NCT02599922 (NCT03001310 chunk 1, gong2024infantilenystagmussyndrome—associated pages 12-13). - NCT03001310 is a completed phase I/II dose-escalation trial using AAV2/8-hCARp.hCNGB3 with BCVA and retinal sensitivity endpoints at 24 weeks and QoL EQ-VAS measures (NCT03001310 chunk 1). - Gong & Hertle (2024) report that in one CNGB3 program, “rAAV2tYF-PR1.7-hCNGB3 treatment has improved photosensitivity in some patients” (gong2024infantilenystagmussyndrome—associated pages 12-13).

Table: This table summarizes the key human CNGA3- and CNGB3-targeted gene therapy trials for achromatopsia using only retrieved ClinicalTrials.gov records and the 2024 Gong review. It is useful for comparing sponsors, vectors, eligibility, endpoints, and the current state of clinical development.

Expert opinion / analysis (authoritative source): Gong & Hertle (2024) frame molecular diagnosis as crucial for access to gene-based therapies and highlight that AAV-based subretinal gene therapy is actively being studied in CNGA3/CNGB3 ACHM with evolving outcome measures (gong2024infantilenystagmussyndrome—associated pages 12-13).

13. Prevention

• Primary prevention: Not applicable in the classic exposure-reduction sense because ACHM is congenital genetic.
• Genetic counseling / family planning: Most relevant prevention-like strategy is carrier testing and counseling in at-risk families (inferred from autosomal recessive inheritance; not detailed in retrieved text).
• Secondary/tertiary: Early diagnosis and early low-vision rehabilitation (filters/devices) to reduce disability impact (chan2023morphologicalandfunctional pages 1-2).

14. Other species / natural disease

The retrieved evidence describes naturally occurring large-animal ACHM models used translationally (dogs, sheep) and engineered models (mice), which serve as comparative biology for disease mechanisms and therapies (gerhardt2023biologypathobiologyand pages 10-12, asensiosanchez2020genetherapyfor pages 7-10).

15. Model organisms (key systems and translational use)

15.1 Models used

• Mouse models: Cnga3 knockout and other cone dysfunction models; rAAV-Cnga3 can restore cone responses and normalize cGMP (gerhardt2023biologypathobiologyand pages 10-12, michalakis2022achromatopsiageneticsand pages 4-5).
• Dog (CNGB3-mutant): Subretinal AAV5-CNGB3 improved cone function; strong age dependence (gerhardt2023biologypathobiologyand pages 10-12).
• Sheep (Awassi, CNGA3): Long-duration rescue after AAV-CNGA3 (≥6 years reported in review) (gerhardt2023biologypathobiologyand pages 10-12).

15.2 Key translational findings

• Age-dependence / therapeutic window: In CNGB3 dogs, “best treatment results were achieved in 3-week-old-animals, whereas treatment was minimally effective in dogs 1 year of age and older” (gerhardt2023biologypathobiologyand pages 10-12).
• Durability: In sheep, AAV-CNGA3 led to “Significant long-term improvement… for at least 6 years” (gerhardt2023biologypathobiologyand pages 10-12).

Recent developments (2023–2024 highlights)

• Natural history and stability: Large retrospective cohorts (2023) provide long follow-up supporting largely stationary VA with rare progression (andersen2023geneticandclinical pages 5-7, andersen2023geneticandclinical pages 7-9).
• Outcome measure refinement: 2024 evidence suggests microperimetry may be a more sensitive structure–function endpoint than VA/contrast sensitivity for certain age groups (adolescents) (cosmo2024microperimetrysensitivitycorrelates pages 1-2, cosmo2024microperimetrysensitivitycorrelates pages 4-6).
• Clinical gene therapy maturation: Detailed trial designs and endpoints are now available publicly via ClinicalTrials.gov for multiple CNGA3/CNGB3 programs, and expert synthesis highlights multi-year persistence of functional gains in at least one CNGA3 study (NCT02610582 chunk 1, NCT02935517 chunk 1, NCT03001310 chunk 1, gong2024infantilenystagmussyndrome—associated pages 12-13).

Data/statistics summary (from recent studies)

• Prevalence: ~1:30,000 or 1:30,000–1:50,000 (baxter2024genetherapyfor pages 1-2, andersen2023geneticandclinical pages 1-2).
• VA stability: 88% within ±0.2 logMAR over median 22-year follow-up in Danish cohort (andersen2023geneticandclinical pages 5-7).
• ACHM-attributable progression: 3/58 in Danish cohort (andersen2023geneticandclinical pages 7-9).
• QoL/disability: 27.9% rated 100% disability in one cohort subset (chan2023morphologicalandfunctional pages 6-8).

Key evidence quotes (abstract-supported)

• ACHM definition: “Achromatopsia is a rare congenital condition with cone photoreceptor dysfunction causing color blindness, reduced vision, nystagmus and photophobia” (andersen2023geneticandclinical pages 1-2).
• Trial landscape: “There is no FDA-approved treatment for achromatopsia; however… There are currently five gene therapy clinical trials registered…” (baxter2024genetherapyfor pages 1-2).
• Microperimetry endpoint: OCT staging correlated with microperimetry sensitivity (p = 0.0286; 0.0008; 0.0014) and not with VA/CS (cosmo2024microperimetrysensitivitycorrelates pages 1-2).

Limitations of this extraction (what is missing and why)

• OMIM/Orphanet/ICD/MeSH identifiers were not captured in retrieved evidence chunks.
• Variant-level information (ClinVar IDs, exact variant nomenclature, ACMG classes, population allele frequencies, carrier frequencies, founder variants) was not present in the retrieved sources.
• Systematic differential diagnosis and formal clinical guidelines were not included in retrieved evidence.

These components require additional targeted retrieval from OMIM/Orphanet/ClinVar/gnomAD/GeneReviews and guideline databases.

References

1. (andersen2023geneticandclinical pages 1-2): Mette Kjøbæk Gundestrup Andersen, Mette Bertelsen, Karen Grønskov, Susanne Kohl, and Line Kessel. Genetic and clinical characterization of danish achromatopsia patients. Genes, 14:690, Mar 2023. URL: https://doi.org/10.3390/genes14030690, doi:10.3390/genes14030690. This article has 19 citations.

2. (baxter2024genetherapyfor pages 1-2): Megan F. Baxter and Grace A. Borchert. Gene therapy for achromatopsia. International Journal of Molecular Sciences, 25:9739, Sep 2024. URL: https://doi.org/10.3390/ijms25179739, doi:10.3390/ijms25179739. This article has 17 citations.

3. (michalakis2022achromatopsiageneticsand pages 1-2): Stylianos Michalakis, Maximilian Gerhardt, Günther Rudolph, Siegfried Priglinger, and Claudia Priglinger. Achromatopsia: genetics and gene therapy. Molecular Diagnosis & Therapy, 26:51-59, Dec 2022. URL: https://doi.org/10.1007/s40291-021-00565-z, doi:10.1007/s40291-021-00565-z. This article has 74 citations and is from a peer-reviewed journal.

4. (chan2023morphologicalandfunctional pages 1-2): Caroline Chan, Berthold Seitz, and Barbara Käsmann-Kellner. Morphological and functional aspects and quality of life in patients with achromatopsia. Journal of Personalized Medicine, 13:1106, Jul 2023. URL: https://doi.org/10.3390/jpm13071106, doi:10.3390/jpm13071106. This article has 0 citations.

5. (NCT03001310 chunk 1): Gene Therapy for Achromatopsia (CNGB3). MeiraGTx UK II Ltd. 2017. ClinicalTrials.gov Identifier: NCT03001310

6. (OpenTargets Search: Achromatopsia): Open Targets Query (Achromatopsia, 15 results). Buniello, A. et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research.

7. (asensiosanchez2020genetherapyfor pages 1-2): VM Asensio-Sánchez. Gene therapy for the treatment of achromatopsia: recent advances. Unknown journal, 2020.

8. (moussawi2021genetherapyin pages 1-3): Zeinab El Moussawi, Marguerita Boueiri, and Christiane Al-Haddad. Gene therapy in color vision deficiency: a review. International Ophthalmology, 41:1917-1927, Feb 2021. URL: https://doi.org/10.1007/s10792-021-01717-0, doi:10.1007/s10792-021-01717-0. This article has 42 citations and is from a peer-reviewed journal.

9. (asensiosanchez2020genetherapyfor pages 7-10): VM Asensio-Sánchez. Gene therapy for the treatment of achromatopsia: recent advances. Unknown journal, 2020.

10. (baxter2024genetherapyfor pages 2-3): Megan F. Baxter and Grace A. Borchert. Gene therapy for achromatopsia. International Journal of Molecular Sciences, 25:9739, Sep 2024. URL: https://doi.org/10.3390/ijms25179739, doi:10.3390/ijms25179739. This article has 17 citations.

11. (gerhardt2023biologypathobiologyand pages 1-2): Maximilian J. Gerhardt, Siegfried G. Priglinger, Martin Biel, and Stylianos Michalakis. Biology, pathobiology and gene therapy of cng channel-related retinopathies. Biomedicines, 11:269, Jan 2023. URL: https://doi.org/10.3390/biomedicines11020269, doi:10.3390/biomedicines11020269. This article has 20 citations.

12. (andersen2023geneticandclinical pages 7-9): Mette Kjøbæk Gundestrup Andersen, Mette Bertelsen, Karen Grønskov, Susanne Kohl, and Line Kessel. Genetic and clinical characterization of danish achromatopsia patients. Genes, 14:690, Mar 2023. URL: https://doi.org/10.3390/genes14030690, doi:10.3390/genes14030690. This article has 19 citations.

13. (gerhardt2023biologypathobiologyand pages 2-5): Maximilian J. Gerhardt, Siegfried G. Priglinger, Martin Biel, and Stylianos Michalakis. Biology, pathobiology and gene therapy of cng channel-related retinopathies. Biomedicines, 11:269, Jan 2023. URL: https://doi.org/10.3390/biomedicines11020269, doi:10.3390/biomedicines11020269. This article has 20 citations.

14. (brotherton2024molecularmechanismsgoverning pages 3-5): Chloe Brotherton and Roly Megaw. Molecular mechanisms governing sight loss in inherited cone disorders. Genes, 15:727, Jun 2024. URL: https://doi.org/10.3390/genes15060727, doi:10.3390/genes15060727. This article has 8 citations.

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18. (chan2023morphologicalandfunctional pages 4-6): Caroline Chan, Berthold Seitz, and Barbara Käsmann-Kellner. Morphological and functional aspects and quality of life in patients with achromatopsia. Journal of Personalized Medicine, 13:1106, Jul 2023. URL: https://doi.org/10.3390/jpm13071106, doi:10.3390/jpm13071106. This article has 0 citations.

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23. (chan2023morphologicalandfunctional pages 6-8): Caroline Chan, Berthold Seitz, and Barbara Käsmann-Kellner. Morphological and functional aspects and quality of life in patients with achromatopsia. Journal of Personalized Medicine, 13:1106, Jul 2023. URL: https://doi.org/10.3390/jpm13071106, doi:10.3390/jpm13071106. This article has 0 citations.

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27. (gerhardt2023biologypathobiologyand pages 10-12): Maximilian J. Gerhardt, Siegfried G. Priglinger, Martin Biel, and Stylianos Michalakis. Biology, pathobiology and gene therapy of cng channel-related retinopathies. Biomedicines, 11:269, Jan 2023. URL: https://doi.org/10.3390/biomedicines11020269, doi:10.3390/biomedicines11020269. This article has 20 citations.

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