Identification of a novel biomarker candidate, a 4.8-kDa peptide fragment from a neurosecretory protein VGF precursor, by proteomic analysis of cerebrospinal fluid from children with acute encephalopathy using SELDI-TOF-MS

Abstract

BackgroundAcute encephalopathy includes rapid deterioration and has a poor prognosis. Early intervention is essential to prevent progression of the disease and subsequent neurologic complications. However, in the acute period, true encephalopathy cannot easily be differentiated from febrile seizures, especially febrile seizures of the complex type. Thus, an early diagnostic marker has been sought in order to enable early intervention. The purpose of this study was to identify a novel marker candidate protein differentially expressed in the cerebrospinal fluid (CSF) of children with encephalopathy using proteomic analysis.MethodsFor detection of biomarkers, CSF samples were obtained from 13 children with acute encephalopathy and 42 children with febrile seizure. Mass spectral data were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) technology, which is currently applied in many fields of biological and medical sciences. Diagnosis was made by at least two pediatric neurologists based on the clinical findings and routine examinations. All specimens were collected for diagnostic tests and the remaining portion of the specimens were used for the SELDI-TOF MS investigations.ResultsIn experiment 1, CSF from patients with febrile seizures (n = 28), patients with encephalopathy (n = 8) (including influenza encephalopathy (n = 3), encephalopathy due to rotavirus (n = 1), human herpes virus 6 (n = 1)) were used for the SELDI analysis. In experiment 2, SELDI analysis was performed on CSF from a second set of febrile seizure patients (n = 14) and encephalopathy patients (n = 5). We found that the peak with an m/z of 4810 contributed the most to the separation of the two groups. After purification and identification of the 4.8-kDa protein, a 4.8-kDa proteolytic peptide fragment from the neurosecretory protein VGF precursor (VGF4.8) was identified as a novel biomarker for encephalopathy.ConclusionsExpression of VGF4.8 has been reported to be decreased in pathologically degenerative changes such as Alzheimer's disease, amyotrophic lateral sclerosis (ALS), frontotemporal dementia, and encephalopathy. Thus, the VGF4.8 peptide might be a novel marker for degenerative brain conditions.

VGF Changes during the Estrous Cycle: A Novel Endocrine Role for TLQP Peptides?

“…In view of the lower proteomic complexity of the cerebro-spinal fluid (CSF), the precise chemical nature of VGF derived end products has so far been mostly studied in such body fluid [36] with the addition of neuro-endocrine cells lines [33] . An array of VGF peptides and fragments have been revealed, with striking changes in neurodegenerative and other disease conditions [37] – [40] . Certain VGF peptides proved to be released in vivo in the brain upon neuronal depolarization [31] while an interactomic investigation revealed selective domains of VGF, including the TLQP-21 region, as likely binding partners for the amyloid precursor protein [41] .…”

Proteomics and Human Diseases

“…Differences in the levels of various proteins in patients with neurodegenerative diseases compared with healthy control subject have been reported. It is interesting that the expression levels of the nerve growth factor (VGF), a neurosecretory protein that is known to regulate metabolism [72] and synaptic plasticity, are reduced in AD [73,74]; in FTD [75] and ALS [76]. Reduced expression of this protein seems to be a general phenomenon in diseases characterized by premature neuronal cells death, and thus may be a general marker for neurodegeneration.…”

SELDI-TOF-MS Proteomic Profiling of Serum, Urine, and Amniotic Fluid in Neural Tube Defects

“…SELDI-TOF-MS detects proteins selectively adsorbed onto the surface of a protein chip array after nonspecifically bound proteins are washed off by stringent buffers, and has been shown to be sensitive, rapid and reliable. This technique has been successfully applied in the discovery of serum biomarkers for many cancers [5][6][7][8][9][10][11][12][13][14][15][16][17][18], nervous system diseases [19][20][21][22] and renal diseases [23][24][25][26][27][28][29][30][31][32]. Furthermore, construction of a classification tree using ''artificial intelligence'' algorithms to process SELDI data improves the accuracy to differentiate cancer patients from non-cancer groups [33][34].…”