Evidence map›Paper›PMID 41181063›Full record

ArticleInternational journal of tryptophan research : IJTR2025

Development of a Novel Method to Quantify Quinolinic Acid in Biological Samples.

Masatsuna Tasaka, Hidetsugu Fujigaki, Sayaka Sugiura, Suwako Fujigaki, Akihiro Ikuno, Yasuko Yamamoto, Masao Takemura, Akio Kimura, Kuniaki Saito

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Article in International journal of tryptophan research : IJTR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Masatsuna TasakaDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.
Hidetsugu FujigakiDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.ORCID https://orcid.org/0000-0001-9655-1074
Sayaka SugiuraDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.
Suwako FujigakiDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.
Akihiro IkunoDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.ORCID https://orcid.org/0000-0002-7798-439X
Yasuko YamamotoDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.
Masao TakemuraDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.
Akio KimuraDepartment of Neurology, Gifu University Graduate School of Medicine, Japan.
Kuniaki SaitoDepartment of Advanced Diagnostic System Development, Fujita Health University Graduate School of Medical Sciences, Toyoake, Aichi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The accumulation of quinolinic acid (QUIN) in cerebrospinal fluid and serum may be used as a biomarker for various neuropsychiatric and inflammatory diseases. In this study, we developed a highly sensitive method to measure QUIN. Methods: A reverse-phase high-performance liquid chromatography (HPLC) with fluorescence detection was established based on the enzymatic conversion of QUIN to nicotinic acid mononucleotide by recombinant quinolinic acid phosphoribosyltransferase, followed by the formation of fluorescent (BODIPY)-labeled deamido-NAD by recombinant nicotinic acid mononucleotide adenyltransferase. Results: BODIPY-deamido-NAD was isocratically eluted within 6 minutes using reverse-phase chromatography and its chromatographic peak was resolved. The calibration range, precision, and analytical recovery of the QUIN assay are suitable for the analysis of biological fluids. Compared with published quantitation limits for QUIN measurement by HPLC, this method is at least 30-fold more sensitive and has a lower limit of detection of 5.0 nmol/L. The sensitivity was comparable to that previously reported for gas chromatography/mass spectrometry (GC/MS) and the quantitation results of QUIN from samples of cerebrospinal fluid correlated well with that of the GC/MS method. Conclusions: We established a novel method to quantify QUIN in biological samples. Due to its high sensitivity and the fact that it does not rely on MS instrumentation, this method has the potential for widespread adoption in research laboratories.

Indexed as

enzymatic conversionhigh performance liquid chromatographykynurenine pathwayquinolinic acidtryptophan metabolism

Identifiers

PMID41181063
PMCPMC12579120

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.