Urinary proteomics and metabolomics in the diagnosis of pediatric disorders

Current Scenario of Clinical Diagnosis to Identify Inborn Errors of Metabolism with Precision Profiling for Expanded Screening in Infancy in a Resource-limited Setting

Inborn errors of metabolism (IEM) are a diverse collection of abnormalities that cause a variety of morbidities and mortality in children and are classified as uncommon genetic diseases. Early and accurate detection of the condition can save a patient's life. By aiding families as they navigate the experience of having a child with an IEM, healthcare practitioners have the chance to reduce the burden of negative emotional consequences. New therapeutic techniques, such as enzyme replacement and small chemical therapies, organ transplantation, and cellular and gene-based therapies using whole-genome sequencing, have become available in addition to traditional medical intake and cofactor treatments. In the realm of metabolic medicine and metabolomics, the twentyfirst century is an exciting time to be alive. The availability of metabolomics and genomic analysis has led to the identification of a slew of novel diseases. Due to the rarity of individual illnesses, obtaining high-quality data for these treatments in clinical trials and real-world settings has proven difficult. Guidelines produced using standardized techniques have helped enhance treatment delivery and clinical outcomes over time. This article gives a comprehensive description of IEM and how to diagnose it in patients who have developed clinical signs early or late. The appropriate use of standard laboratory outcomes in the preliminary patient assessment is also emphasized that can aid in the ordering of specific laboratory tests to confirm a suspected diagnosis, in addition, to begin treatment as soon as possible in a resource limiting setting where genomic analysis or newborn screening facility is not available.

A proteomic analysis of urine biomarkers in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by early-onset social-communication challenges, restricted and repetitive behaviors, or unusual sensory-motor behaviors. A lack of specific biomarkers hinders the early diagnosis and treatment of this disease in many children. This study analyzes and validates potential urinary biomarkers using mass spectrometry proteomics. Global proteomics profiles of urine from 19 ASD patients and 19 healthy control subjects were compared to identify significantly changed proteins. These proteins were validated with targeted proteomics using parallel reaction monitoring (PRM) in an independent validation set consisting of samples from 40 ASD patients and 38 healthy controls. A total of 34 significantly changed proteins were found in the discovery set, among which seven proteins were identified as potential biomarkers for ASD through PRM assays in the validation set. Of these seven proteins, immunoglobulin kappa variable 4-1, immunoglobulin kappa variable 3-20, and immunoglobulin lambda variable 1-51 were up-regulated, while ATP synthase F1 subunit alpha, 10 kDa heat shock protein, apolipoprotein C-III, and arylsulfatase F were down-regulated. Six of these seven proteins support previous findings that ASD is accompanied by altered immune response and lipid metabolism, as well as mitochondrial dysfunction. This study lays the groundwork for additional research using biomarkers to clinically diagnose ASD. The proteomics and PRM raw data of this study have been deposited under the accession number IPX0002592000 at iProX. SIGNIFICANCE: This study identified 34 proteins in urine of ASD patients that were significantly changed from the urinary proteins of healthy subjects using LC-MS/MS-based proteomics in a discovery set. Seven of these proteins were validated by PRM analysis in an independent validation set. This report represents the first description of combined label-free quantitative proteomics and PRM analysis of targeted proteins for discovery of ASD urinary biomarkers. The results will be helpful for early diagnosis and can provide additional insight into the molecular mechanisms of ASD.

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.

Shotgun Proteomics of Isolated Urinary Extracellular Vesicles for Investigating Respiratory Impedance in Healthy Preschoolers

Urine proteomic applications in children suggested their potential in discriminating between healthy subjects from those with respiratory diseases. The aim of the current study was to combine protein fractionation, by urinary extracellular vesicle isolation, and proteomics analysis in order to establish whether different patterns of respiratory impedance in healthy preschoolers can be characterized from a protein fingerprint. Twenty-one 3-5-yr-old healthy children, representative of 66 recruited subjects, were selected: 12 late preterm (LP) and 9 full-term (T) born. Children underwent measurement of respiratory impedance through Forced Oscillation Technique (FOT) and no significant differences between LP and T were found. Unbiased clustering, based on proteomic signatures, stratified three groups of children (A, B, C) with significantly different patterns of respiratory impedance, which was slightly worse in group A than in groups B and C. Six proteins (Tripeptidyl peptidase I (TPP1), Cubilin (CUBN), SerpinA4, SerpinF1, Thy-1 membrane glycoprotein (THY1) and Angiopoietin-related protein 2 (ANGPTL2)) were identified in order to type the membership of subjects to the three groups. The differential levels of the six proteins in groups A, B and C suggest that proteomic-based profiles of urinary fractionated exosomes could represent a link between respiratory impedance and underlying biological profiles in healthy preschool children.

Changes in Water Soluble Uremic Toxins and Urinary Acute Kidney Injury Biomarkers After 10- and 100-km Runs

Acute kidney injury (AKI) is described as a relatively common complication of exercise. In clinical practice the diagnosis of AKI is based on serum creatinine, the level of which is dependent not only on glomerular filtration rate but also on muscle mass and injury. Therefore, the diagnosis of AKI is overestimated after physical exercise. The aim of this study was to determine changes in uremic toxins: creatinine, urea, uric acid, asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), trimethylamine N-oxide (TMAO) and urinary makers of AKI: albumin, neutrophil gelatinase-associated lipocalin (uNGAL), kidney injury molecule-1 and cystatin-C (uCyst-C) after long runs. Sixteen runners, mean age 36.7 ± 8.2 years, (2 women, 14 men) participating in 10- and 100-km races were studied. Blood and urine were taken before and after the races to assess markers of AKI. A statistically significant increase in creatinine, urea, uric acid, SDMA and all studied urinary AKI markers was observed. TMAO and ADMA levels did not change. The changes in studied markers seem to be a physiological reaction, because they were observed almost in every runner. The diagnosis of kidney failure after exercise is challenging. The most valuable novel markers which can help in post-exercise AKI diagnosis are uCyst-C and uNGAL.

Urinary prostaglandin metabolites as Duchenne muscular dystrophy progression markers

BACKGROUND

Patients with Duchenne muscular dystrophy (DMD) exhibit increased prostaglandin D₂ (PGD₂) expression in necrotic muscle and increased PGD₂ metabolites in their urine. In mouse models, inhibiting PGD₂ production suppresses muscle necrosis, suggesting a possible intervention through PGD₂-mediated activities.

OBJECTIVE

We investigated the involvement of PGD₂ and its potential use as a marker of pathological progression in DMD.

METHODS

Sixty-one male children with DMD and thirty-five age-matched controls were enrolled in the study. DMD patients were divided into "ambulant" and "non-ambulant" groups, which were further subdivided into "steroid" and "non-steroid" therapy groups. Levels of the PGD₂ metabolite tetranor-PGDM (t-PGDM) and creatinine were measured in both spot and 24-hour urine samples, with comparisons between groups made according to geometric mean values.

RESULTS

DMD patients had significantly higher levels of creatinine-corrected t-PGDM in spot urine samples as compared with the control group. Additionally, both ambulant and non-ambulant DMD groups had significantly higher levels of t-PGDM as compared with controls, with no significant difference in t-PGDM levels observed between steroid and non-steroid groups. Moreover, total creatinine excretion in 24-hour urine samples was significantly lower in DMD patients as compared with controls, and although DMD patients had lower muscle mass than controls, their overall levels of t-PGDM did not differ significantly from those in the non-ambulant and control groups.

CONCLUSION

PGD₂ might help explain the progression and symptomatic presentations (e.g., ambulatory difficulty) associated with DMD, suggesting it as a useful pathological marker and use of a selective PGD₂ inhibitor as a potential treatment modality.

Discovery of biomarkers in rare diseases: innovative approaches by predictive and personalized medicine

There are more than 8000 rare diseases (RDs) that affect >5 % of the world’s population. Many of the RDs have no effective treatment and lack of knowledge creates delayed diagnosis making management difficult. The emerging concept of the personalized medicine allows for early screening, diagnosis, and individualized treatment of human diseases. In this context, the discovery of biomarkers in RDs will be of prime importance to enable timely prevention and effective treatment. Since 80 % of RDs are of genetic origin, identification of new genes and causative mutations become valuable biomarkers. Furthermore, dynamic markers such as expressed genes, metabolites, and proteins are also very important to follow prognosis and response the therapy. Recent advances in omics technologies and their use in combination can define pathophysiological pathways that can be drug targets. Biomarker discovery and their use in diagnosis in RDs is a major pillar in RD research.

Lupus nephritis biomarkers

Lupus nephritis (LN), a potentially destructive outcome of SLE, is a real challenge in the management of SLE because of the difficulty in diagnosing its subclinical onset and identifying relapses before serious complications set in. Conventional clinical parameters such as proteinuria, GFR, urine sediments, anti-dsDNA and complement levels are not sensitive or specific enough for detecting ongoing disease activity in lupus kidneys and early relapse of nephritis. There has long been a need for biomarkers of disease activity in LN. Such markers ideally should be capable of predicting early sub-clinical flares and could be used to gauge response to therapy, thus obviating the need for serial renal biopsies with their possible hazardous complications. Since urine can be readily obtained, it lends itself as an obvious biological substrate. In this review, the use of urine and serum as sources of lupus nephritis biomarkers is described, and the results of biomarker discovery studies using candidate and proteomic approaches are summarized.

Investigation of urine proteome of preterm newborns with respiratory pathologies

A serious problem during intensive care and nursing of premature infants is the invasiveness of many examination methods. Urine is an excellent source of potential biomarkers due to the safety of the collection procedure. The purpose of this study was to determine the features specific for the urine proteome of preterm newborns and their changes under respiratory pathologies of infectious and non-infectious origin. The urine proteome of 37 preterm neonates with respiratory diseases and 10 full-term newborns as a control group were investigated using the LC-MS/MS method. The total number of identified proteins and unique peptides was 813 and 3672 respectively. In order to further specify the defined infant-specific dataset these proteins were compared with urine proteome of healthy adults (11 men and 11 pregnant women) resulting in 94 proteins found only in infants. Pairwise analysis performed for label-free proteomic data revealed 36 proteins which reliably distinguished newborns with respiratory disorders of infectious genesis from those with non-infectious pathologies, including: proteins involved in cell adhesion (CDH-2,-5,-11, NCAM1, TRY1, DSG2), metabolism (LAMP1, AGRN, TPP1, GPX3, APOD, CUBN, IDH1), regulation of enzymatic activity (SERPINA4, VASN, GAPDH), inflammatory and stress response (CD55, CD 93, NGAL, HP, TNFR, LCN2, AGT, S100P, SERPINA1/C1/B1/F1).

Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders

Metabolic profiling provides a powerful diagnostic tool complementary to genomics and proteomics. The pain, discomfort and probable iatrogenic injury associated with invasive or minimally invasive diagnostic methods, render them unsuitable in terms of patient compliance and participation. Metabolic profiling of biomatrices like urine, breath, saliva, sweat and feces, which can be collected in a painless manner, could be used for noninvasive diagnosis. This review article covers the noninvasive metabolic profiling studies that have exhibited diagnostic potential for diseases and disorders. Their potential applications are evident in different forms of cancer, metabolic disorders, infectious diseases, neurodegenerative disorders, rheumatic diseases and pulmonary diseases. Large scale clinical validation of such diagnostic methods is necessary in future.