A highly accurate mass spectrometry method for the quantification of phenylalanine and tyrosine on dried blood spots: Combination of liquid chromatography, phenylalanine/tyrosine-free blood calibrators and multi-point/dynamic calibration

Quantification of derivatized phenylalanine and tyrosine in dried blood spots using liquid chromatography with tandem spectrometry for newborn screening of phenylketonuria

Phenylketonuria (PKU) is an autosomal genetic disorder caused by a deficiency of the phenylalanine hydroxylase (PAH) enzyme. The lack of PAH results in the inability of phenylalanine (PHE) to transform into tyrosine (TYR). Consequently, this leads to the accumulation of PHE in the blood samples of newborns causing metabolic diseases such as irreversible neurological problems. An analysis was required for determining the values of PHE and TYR in blood samples from newborn babies. In this study, therefore, we developed a derivatized method to monitor PHE and TYR in plasma samples using liquid phase chromatography linked with quadrupole mass spectrometry. Accessible formaldehyde isotopes and cyanoborohydride were used to react with PHE and TYR amino groups to generate h2-formaldehyde-modified PHE and TYR (as standards) and d2-formaldehyde-modified PHE and TYR (as internal standards). We used tandem mass spectrometry for multiple reaction monitoring. We demonstrated a derivatized method suitable for the PKU screening of newborns. The recoveries for PHE and TYR were 85% and 90%, respectively. Furthermore, we compared the values of PHE and TYR in different human plasma sample storage methods, including direct plasma and dried blood spots, and the results showed no significant difference.

Systems Biology and Inborn Error of Metabolism: Analytical Strategy in Investigating Different Biochemical/Genetic Parameters

Inborn errors of metabolism (IEM) are a group of about 500 rare genetic diseases with large diversity and complexity due to number of metabolic pathways involved in. Establishing a correct diagnosis and identifying the specific clinical phenotype is consequently a difficult task. However, an inclusive diagnosis able in capturing the different clinical phenotypes is mandatory for successful treatment. However, in contrast with Garrod's basic assumption "one-gene one-disease," no "simple" correlation between genotype-phenotype can be vindicated in IEMs. An illustrative example of IEM is Phenylketonuria (PKU), an autosomal recessive inborn error of L-phenylalanine (Phe) metabolism, ascribed to variants of the phenylalanine hydroxylase (PAH) gene encoding for the enzyme complex phenylalanine-hydroxylase. Blood values of Phe allow classifying PKU into different clinical phenotypes, albeit the participation of other genetic/biochemical pathways in the pathogenetic mechanisms remains elusive. Indeed, it has been shown that the most serious complications, such as cognitive impairment, are not only related to the gene dysfunction but also to the patient's background and the participation of several nongenetic factors.Therefore, a Systems Biology-based strategy is required in addressing IEM complexity, and in identifying the interplay between different pathways in shaping the clinical phenotype. Such an approach should entail the concerted investigation of genomic, transcriptomics, proteomics, metabolomics profiles altogether with phenylalanine and amino acids metabolism. Noticeably, this "omic" perspective could be instrumental in planning personalized treatment, tailored accordingly to the disease profile and prognosis.

Fully Integrated Point-of-Care Platform for the Self-Monitoring of Phenylalanine in Finger-Prick Blood

Development of point-of-care platforms combining reliability and ease of use is a challenge for the evolution of sensing in healthcare technologies. Here, we report the development and testing of a fully integrated enzymatic colorimetric assay for the sensing of phenylalanine in blood samples from phenylketonuria patients. The platform works with a customized mobile app for data acquisition and visualization and comprises an electronic system and a disposable sensor. The sensing approach is based on specific enzymatic phenylalanine recognition, and the optical transduction method is based on in situ gold nanostructure formation. The phenylketonuria (PKU) smart sensor platform is conceived to perform self-monitoring on phenylalanine levels and real-time therapy tuning, thanks to the direct connection with clinicians. Validation of the technologies with a population of patients affected by PKU, together with the concurrent validation of the platform through centralized laboratories, has confirmed the good analytical performances in terms of sensitivity and specificity, robustness, and utility for phenylalanine sensing. The self-monitoring of phenylalanine for the daily identification of abnormal health conditions could facilitate rapid therapy tuning, improving the wellness of PKU patients.