Proteomics in Inherited Metabolic Disorders

Expanded inherited metabolic diseases screening by tandem mass spectrophotometry: The first report from Iran

Inherited metabolic diseases (IMD) are a group of rare genetic disorders that can present with a variety of symptoms. Since these disorders are hard to treat once the symptoms occur, neonatal screening may be a logical strategy. Here we evaluate the first results of national expanded IMD screening in Iran. A total of 46 IMDs were screened in this national program. Between April 2018 and March 2022, all infants who underwent national IMD screening at Shahid Beheshti University of Medical Sciences were included in this study. History and Physical examinations of infants, screening results, recall rate, response rate, and prevalence of IMDs were evaluated. A total of 125,819 infants were screened during this period. The recall rate of the test was 0.81%. 124 cases were diagnosed with a definite IMD and the raw overall prevalence of IMDs was estimated to be 1:1015. Aminoacidopathies were the most commonly detected disorders and Hyperphenylalaninemia/PKU was the most prevalent disorder among all groups. Since IMDs vary from region even in a single country, screening for IMDs is crucial in societies with a high rate of consanguineous marriages. More studies are essential for figuring out the most efficient combination of diseases to be screened based on countries' facilities.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Prevalence of inherited metabolic disorders among newborns in Zhuzhou, a southern city in China

Background and aims: Defective enzymes, cofactors, or transporters of metabolic pathways cause inherited metabolic disorders (IMDs), a group of genetic disorders. Several IMDs have serious consequences for the affected neonates. Newborn screening for IMDs is conducted by measuring specific metabolites between 3 and 7 days of life. Herein, we analyzed the incidence, spectrum, and genetic characteristics of IMDs in newborns in the Zhuzhou area. Methods: Tandem mass spectrometry was conducted on 90,829 newborns who were admitted to the Women and Children Healthcare Hospital of Zhuzhou and requested for screening for IMDs. These newborns were subsequently subjected to next-generation sequencing and further validated using Sanger sequencing. Results: 30 IMDs cases were found in 90,829 cases of newborns screened for IMDs, and the overall incidence was 1/3,027. The incidence of amino acid, organic acid, fatty acid oxidation and urea cycle disorders were 1/8,257, 1/18,165, 1/7,569, and 1/45,414, respectively. Additionally, 9 cases of maternal IMDs were found in our study, and unreported gene mutations of 3 cases IMDs were identified. Conclusion: Our data indicated that IMDs are never uncommon in zhuzhou, meanwhile, we also found that primary carnitine deficiency was the only disorder of fatty acid oxidation in Zhuzhou, and the incidence (1/7,569) was higher than the national level, organic acid metabolic diseases are mostly inherited. Therefore, our study has clarified the disease spectrum and genetic backgrounds, contributing to the treatment and prenatal genetic counseling of these disorders in this region.

Determination of Normal Range of Acylcarnitine in Neonatal Dried Blood Spots using LC-MS/MS

Background

Acylcarnitine is one of the crucial markers of fatty acid metabolism, and examination of their level in infants can reveal several Inherited Metabolic Disorders (IDM) or Inborn errors of Metabolism (IEM). Because of the great importance of hereditary, metabolic, and other inherited disorders early diagnosis before the appearance of clinical symptoms, this study was carried out to establish a reference range for carnitine analytes and to identify acylcarnitine profiles in normal weight neonatal dried blood spots (DBS) specimens.

Methods

By using liquid chromatography tandem mass spectrometry (LC-MS/MS) for neonatal screening and eventually the examination and analysis of LC-MS/MS results, 34 acylcarnitine derivatives were identified.

Results

The normal range for acylcarnitine analytes with carbon numbers ranging from zero to 18, both main and the branched ones, were ultimately measured. Afterward, they were compared with the results of some other diagnostic laboratories to be verified.

Conclusions

This study differed from the other findings, which could be due to diversity in population and work methods. However, the reference range of most acylcarnitine derivatives in Tehran closely aligned with this study's findings.

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.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Understanding the age-related alterations in the testis-specific proteome

INTRODUCTION

Fertility rates in developing countries have declined over the past decades, and the trend of delayed fatherhood is rising as societies develop. The reasons behind the decline in male fertility with advancing age remain mysterious, making it a compelling and crucial area for further research. However, the limited number of studies dedicated to unraveling this enigma poses a challenge. Thus, our objective is to illuminate some of the upregulated and downregulated mechanisms in the male testis during the aging process.

AREAS COVERED

Herein, we present a critical overview of the studies addressing the alterations of testicular proteome through the aging process, starting from sexually matured young males to end-of-life-expectancy aged males. The comparative studies of the proteomic testicular profile of men with and without spermatogenic impairment are also discussed and key proteins and pathways involved are highlighted.

EXPERT OPINION

The difficulty of making age-comparative studies, especially of advanced-age study subjects, makes this topic of study quite challenging. Another topic worth mentioning is the heterogeneous nature and vast cellular composition of testicular tissue, which makes proteome data interpretation tricky. The cell type sorting and comorbidities testing in the testicular tissue of the studied subjects would help mitigate these problems.