A Shotgun Proteomic Platform for a Global Mapping of Lymphoblastoid Cells to Gain Insight into Nasu-Hakola Disease

Abstract

Nasu-Hakola Disease (NHD) is a recessively inherited systemic leukodystrophy disorder characterized by a combination of frontotemporal presenile dementia and lytic bone lesions. NHD is known to be genetically related to a structural defect of TREM2 and DAP12, two genes that encode for different subunits of the membrane receptor signaling complex expressed by microglia and osteoclast cells. Because of its rarity, molecular or proteomic studies on this disorder are absent or scarce, only case reports based on neuropsychological and genetic tests being reported. In light of this, the aim of this paper is to provide evidence on the potential of a label-free proteomic platform based on the Multidimensional Protein Identification Technology (MudPIT), combined with in-house software and on-line bioinformatics tools, to characterize the protein expression trends and the most involved pathways in NHD. The application of this approach on the Lymphoblastoid cells from a family composed of individuals affected by NHD, healthy carriers and control subjects allowed for the identification of about 3000 distinct proteins within the three analyzed groups, among which proteins anomalous to each category were identified. Of note, several differentially expressed proteins were associated with neurodegenerative processes. Moreover, the protein networks highlighted some molecular pathways that may be involved in the onset or progression of this rare frontotemporal disorder. Therefore, this fully automated MudPIT platform which allowed, for the first time, the generation of the whole protein profile of Lymphoblastoid cells from Nasu-Hakola subjects, could be a valid approach for the investigation of similar neurodegenerative diseases.

Keywords: Lymphoblastoid cells; MudPIT; Nasu-Hakola Disease; TREM2; frontotemporal dementia; proteomics.

Figure 1

Venn Diagram. The figure shows the distribution of all identified proteins within three investigated categories (Wild type, Wt, green circle; Heterozygote, He, violet circle; Homozygote, Ho, orange circle). About 1400 proteins were shared between the three groups of subjects and approximately 400 proteins were identified as anomalous of each category.

Figure 2

Hierarchical Clustering. Dendrogram of protein lists from 7 Nasu-Hakola subjects and their technical replicates. Clustering was performed by computing the average spectral count (aSpC) value of proteins selected by Linear Discriminant Analysis (LDA); Euclidean’s distance metric and Ward’s methods were applied. Categories were reported in different colours: He subjects in violet, Ho subjects in orange and wt subjects in green.

Figure 3

LDA-SPs and DEPs Diagram. The figure shows the portion of LDA Significant Proteins (LDA-SPs) and differentially expressed proteins (DEPs) compared to the total identified distinct proteins in all processed samples and replicates.

Figure 4

Protein–Protein Interaction (PPI) Network. Interactome network built for Wild type (Wt), Heterozygote (He) and Homozygote (Ho) subjects through the mapping of proteins found to be differentially expressed by LDA and MAProMa analysis. The color code of distinct nodes reflects the normalized SpC values of each examined condition.

Figure 5

Trem2 Interactor Proteins. (A) Three different proteins associated with the TREM2 network were highlighted: RAC1, PLCG2 and VAV2. These interactor proteins were upregulated in NHD patients (Ho) in comparison with healthy subjects (Wt). (B) Western blotting of VAV2 protein. * Statistically significant value.