Quantitative Proteomic Profiling Identifies Differentially Regulated Pathways in Fanconi Anemia and Aplastic Anemia

Purpose: Prognosis and therapeutic treatment of Fanconi anemia (FA) and acquired aplastic anemia (AA) are different. However, delayed and incorrect treatments are frequently occurred in FA because its symptoms are very similar to those of AA. This study aimed to elucidate the differences between FA and AA at the molecular level and to identify biomarkers and pathways of FA through quantitative proteomics and bioinformatics analyses.

Methods: Samples were obtained from the bone marrow of patients with FA and AA followed the institutional guideline. Proteins were extracted, digested with Lys-C and trypsin, and chemically labeled with tandem mass tag (TMT) duplex reagents (TMT126 and 127) for mass spectrometric analyses. Proteins with fold changes of > 1.5 or < 0.667 were considered as differentially expressed proteins (DEPs). Gene ontology analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the ClueGO plug-in in Cytoscape. A DEP-associated protein-protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape. The degree and topological index of these DEPs were calculated using CentiScape. The PPI network was subjected to modular analysis using the Molecular Complex Detection plug-in in Cytoscape.

Results: A total of 114 DEPs, including 71 upregulated and 43 downregulated proteins, were discovered in the FA sample compared with those in the AA sample. The upregulated proteins were enriched in nucleosome assembly, cell or subcellular component movement, glycolysis, and gluconeogenesis pathway. The downregulated proteins were enriched in immune response, negative regulation of apoptosis, proteolysis, phagosome, and pantothenate and CoA biosynthesis. Five important modules were detected from the PPI network of DEPs. Eight hub proteins, a-enolase (ENO1), phosphoglycerate kinase 1 (PGK1), HSP90AA1, HSP90AB1, ACTC1, ACTBL2, EEF1A1, and CFL1, with a high degree of connectivity were obtained.

Conclusions: Quantitative proteomics coupled with bioinformatics analyses are useful for the identification of protein biomarkers and pathways associated with FA and AA. Some critical DEPs, such as ENO1, PGK1, ACTC1, ACTBL2, EEF1A1, and CFL1, likely play important roles in FA and could be used as serologic markers for its early diagnosis and to distinguish FA from AA.