Characterization of a novel pathogenic variant in the FECH gene associated with erythropoietic protoporphyria

Kieke, Michele C.; Klemm, Jacob; Tondin, Arthur Rech; Alencar, Victor Hugo Medeiros; Johnson, Nathan M.; Driver, Ashley M.; Lentz, Thomas L.; Fischer, Gregory J.; Caporale, Diane A.; Drury, Luke J.

doi:10.1016/j.ymgmr.2019.100481

Cited by 2 publications

(2 citation statements)

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“…CA1 is not typically a primary target for immune-mediated diseases, but the role of members of the carbonic anhydrase family in managing acid-base balance or bone resorption in such conditions could be of interest [ 62 ]. Similarly, while FECH is not a well-documented target for immune-mediated diseases, potential relevance may exist for certain red blood cell disorders or porphyrias, necessitating further investigation [ 63 ]. In summary, BCL2L1 is the gene with the clearest therapeutic relevance for immune-mediated diseases among this group, due to its direct role in lymphocyte apoptosis.…”

Section: Resultsmentioning

confidence: 99%

Harnessing large language models (LLMs) for candidate gene prioritization and selection

Toufiq,

Rinchai,

Bettacchioli

et al. 2023

J Transl Med

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Background Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection. Methods In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene’s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene. Results Of the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module. Conclusions Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

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Section: Resultsmentioning

confidence: 99%

Harnessing large language models (LLMs) for candidate gene prioritization and selection

Toufiq,

Rinchai,

Bettacchioli

et al. 2023

J Transl Med

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“…FECH-deficient EPP is usually caused by loss-of-function mutations in FECH in trans to the splice modulating single nucleotide variant c. [315-48T > C] ( Gouya et al, 2002 ). Both the wild type FECH c.[315-48T] and c.[315-48C] variants are associated with a partial aberrant splicing of the FECH mRNA, leading to nonsense-mediated decay caused by a premature stop codon in the retained intronic sequence which is more pronounced in the presence of the c.[315-48T > C] genotype ( Rüfenacht et al, 1998 ; Kieke et al, 2019 ). Further, in vitro studies in the erythroleukemic cell line K562 and lymphoblastoid cell lines derived from patients with EPP and healthy controls showed a dose-dependent increase in aberrant FECH mRNA splicing under iron-depleted cell culture conditions that is more pronounced in the more common c.[315-48T] genotype ( Barman-Aksözen et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Heme Biosynthetic Gene Expression Analysis With dPCR in Erythropoietic Protoporphyria Patients

et al. 2022

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Background: The heme biosynthesis (HB) involves eight subsequent enzymatic steps. Erythropoietic protoporphyria (EPP) is caused by loss-of-function mutations in the ferrochelatase (FECH) gene, which in the last HB step inserts ferrous iron into protoporphyrin IX (PPIX) to form heme.Aim and method: The aim of this work was to for the first time analyze the mRNA expression of all HB genes in peripheral blood samples of patients with EPP having the same genotype FECH c.[215dupT]; [315-48T > C] as compared to healthy controls by highly sensitive and specific digital PCR assays (dPCR).Results: We confirmed a decreased FECH mRNA expression in patients with EPP. Further, we found increased ALAS2 and decreased ALAS1, CPOX, PPOX and HMBS mRNA expression in patients with EPP compared to healthy controls. ALAS2 correlated with FECH mRNA expression (EPP: r = 0.63, p = 0.03 and controls: r = 0.68, p = 0.02) and blood parameters like PPIX (EPP: r = 0.58 p = 0.06).Conclusion: Our method is the first that accurately quantifies HB mRNA from blood samples with potential applications in the monitoring of treatment effects of mRNA modifying therapies in vivo, or investigation of the HB pathway and its regulation. However, our findings should be studied in separated blood cell fractions and on the enzymatic level.

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Characterization of a novel pathogenic variant in the FECH gene associated with erythropoietic protoporphyria

Cited by 2 publications

References 4 publications

Harnessing large language models (LLMs) for candidate gene prioritization and selection

Harnessing large language models (LLMs) for candidate gene prioritization and selection

Heme Biosynthetic Gene Expression Analysis With dPCR in Erythropoietic Protoporphyria Patients

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