Metabolomics Study Helps Researchers Get Closer to Identifying Sanfilippo Biomarkers
A comprehensive analysis of compounds present in the urine of patients with Sanfilippo syndrome provides new insight into the biological processes likely involved in the disease. The research could help to eventually identify disease biomarkers.
Sanfilippo syndrome, also known as mucopolysaccharidosis type III or MPS III, is a genetic disorder characterized by a deficiency of one of four enzymes encoded by the genes GNS, HGSNAT, NAGLU, and SGSH.
Impaired activity of any of these enzymes will result in the abnormal accumulation of molecules and compounds in small vesicles called lysosomes. These molecules and compounds occur as a result of the normal metabolism of cells. Their accumulation, however, is toxic for cells and leads to multiple progressive tissue and organ damage.
Metabolism is a complex, interconnected, and finely regulated network of biochemical processes that transform certain compounds into vital products for cell, tissue, and organism function. When metabolic pathways are deregulated due to nutritional, environmental, or genetic factors, disease can occur.
Using a type of technology called metabolomics, French researchers assessed potential inborn errors of metabolism that could be involved in Sanfilippo syndrome. The metabolome refers to all metabolites, or compounds, present in a given biological system. Metabolomics allows for the understanding of the metabolome.
The team analyzed urine samples from 49 patients with different subtypes of Sanfilippo syndrome: 13 patients had MPS IIIA, 16 had MPS IIIB, 13 MPS IIIC, and 7 MPS IIID. They compared them with samples from 66 healthy volunteers.
An untargeted analysis detected a total of 854 compounds present in the samples. Of these, 25 were detected in MPS IIIA samples, 243 in MPS IIIB, 247 in MPS IIIC, and 262 in MPS IIID, compared to controls.
A more detailed analysis revealed that of these discriminant features, some had a particularly high potential to identify the different subtypes of the disease. The compound N-acetylserotonin could identify about 83 percent of all MPS IIIA and MPS IIIB samples. N-succinyl-L,L-2,6-diaminopimelate identified 73 percent of MPS IIIC cases. And octanoylglucuronide detected 79 percent of MPS IIID samples.
When researchers specifically looked for amino acids, they found that samples belonging to the same group clustered together based on the amount of each amino acid.
By combining all of the data, researchers were able to identify the main impaired metabolism pathways linked to each subtype of Sanfilippo syndrome.
In MPS IIIA and MPS IIIB patients, the most affected molecular pathways were beta-alanine, arginine-proline, aspartate metabolism, malate-aspartate shuttle, and the urea cycle. For MPS IIIC, these same pathways were affected, along with methionine metabolism. Lastly, in MPS IIID patients, the main affected pathways included the urea cycle, arginine-proline metabolism, porphyrin metabolism, and pyrimidine and purine metabolism.
Collectively, the results show that Sanfilippo syndrome is linked to “a profound metabolic modeling mainly of amino acid-related metabolism,” the researchers wrote. Additionally, they revealed urine patterns of metabolites that could be used to identify patients with this genetic disorder.
“These results may shed light on MPS III pathophysiology and could help to set more targeted studies to infer the biomarkers of the affected pathways, which is crucial for rare conditions such as MPS III,” they said.