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Proteomic enrichment analysis of psychotic and affective disorders reveals common signatures in presynaptic glutamatergic signaling and energy metabolism.

Gottschalk MG, Wesseling H, Guest PC, Bahn S - Int. J. Neuropsychopharmacol. (2014)

Bottom Line: Profiling studies have identified candidate protein markers associated with specific disorders of the psychoaffective spectrum, but this has always been done in a selective fashion without accounting for the entire proteome composition of the system under investigation.Independent in silico analyses of biological annotations revealed common pathways across the diseases, associated with presynaptic glutamatergic neurotransmission and energy metabolism.We suggest a disease model in which disturbances of the glutamatergic system and ensuing adaptations of neuronal energy metabolism are linked to distinct psychiatric symptom dimensions, delivering novel evidence for targeted treatment approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK (Drs Gottschalk, Wesseling, and Drs Guest and Bahn); Department of Neuroscience, Erasmus Medical Center, Rotterdam, The Netherlands (Dr Bahn).

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(A–E) Volcano plots of group comparisons showing the adjusted significance p value (log2) versus fold change (log2). The plots show an increased magnitude of differential protein abundances in BD/CT, MDD/CT, and MDD-P/MDD compared to SZ/CT and MDD-P/CT. BD/CT and MDD/CT revealed a prominent trend towards increases in protein levels and decreases in the MDD-P/MDD comparison. Horizontal grey lines indicate an adjusted p value threshold of 0.05; vertical grey dotted lines indicate a fold change threshold of 10%. Red dots indicate positive fold changes; green dots indicate negative fold changes. (F) Correlation map identifying overlapping protein abundance patterns between diseases. Spearman correlation coefficents (p < 0.001) were estimated for the fold changes of all significantly-changed proteins overlapping between any two comparisons (see Supplementary Figure S1A–J). Positive correlations indicate fold change patterns in the same direction.
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Figure 1: (A–E) Volcano plots of group comparisons showing the adjusted significance p value (log2) versus fold change (log2). The plots show an increased magnitude of differential protein abundances in BD/CT, MDD/CT, and MDD-P/MDD compared to SZ/CT and MDD-P/CT. BD/CT and MDD/CT revealed a prominent trend towards increases in protein levels and decreases in the MDD-P/MDD comparison. Horizontal grey lines indicate an adjusted p value threshold of 0.05; vertical grey dotted lines indicate a fold change threshold of 10%. Red dots indicate positive fold changes; green dots indicate negative fold changes. (F) Correlation map identifying overlapping protein abundance patterns between diseases. Spearman correlation coefficents (p < 0.001) were estimated for the fold changes of all significantly-changed proteins overlapping between any two comparisons (see Supplementary Figure S1A–J). Positive correlations indicate fold change patterns in the same direction.

Mentions: Proteomic analysis identified a total of 1 310 unique quantifiable proteins, based on 22 629 unique peptides, across all brain samples. Based on linear modelling, we found that 241 proteins (54 proteins ≥ 2 unique peptides) were differentially expressed in SZ patients, 664 proteins (368 proteins ≥ 2 unique peptides) in BD, 524 proteins (274 proteins ≥ 2 unique peptides) in MDD, and 224 proteins (44 proteins ≥ 2 unique peptides) in MDD-P, in comparison to CT individuals. In order to identify characteristic changes associated with psychotic features in affective disorders, we compared MDD-P to MDD and found 437 proteins (193 proteins ≥ 2 unique peptides) significantly differentially expressed. Characteristic expression profiles for each comparison are shown in Figure 1A–E. Based on similarities in the fold change direction (e.g. BD and MDD showed a tendency for positive fold changes) and to identify shared significant protein alteration patterns across disease entities, we cross-compared the findings using linear correlation (Figure 1F; correlation plots can be found in Supplementary Figure S1A–J). This enabled us to detect disease-specific and overlapping disease signatures at the protein level (Supplementary Table S4; Supplementary Figure S2). As expected, protein changes reflecting affective psychosis (MDD-P/MDD) overlapped with markers of MDD with psychotic features (MDD-P/CT), although no significant correlation between MDD-P/CT and MDD/CT was found. Proteins significantly changed in the MDD-P/MDD comparison showed opposite directional change in BD/CT, MDD/CT and SZ/CT. BD/CT and SZ/CT did not show significantly overlapping protein changes, yet biomarker patterns for both disorders were positively correlated to changes identified in MDD/CT (Figure 1F).


Proteomic enrichment analysis of psychotic and affective disorders reveals common signatures in presynaptic glutamatergic signaling and energy metabolism.

Gottschalk MG, Wesseling H, Guest PC, Bahn S - Int. J. Neuropsychopharmacol. (2014)

(A–E) Volcano plots of group comparisons showing the adjusted significance p value (log2) versus fold change (log2). The plots show an increased magnitude of differential protein abundances in BD/CT, MDD/CT, and MDD-P/MDD compared to SZ/CT and MDD-P/CT. BD/CT and MDD/CT revealed a prominent trend towards increases in protein levels and decreases in the MDD-P/MDD comparison. Horizontal grey lines indicate an adjusted p value threshold of 0.05; vertical grey dotted lines indicate a fold change threshold of 10%. Red dots indicate positive fold changes; green dots indicate negative fold changes. (F) Correlation map identifying overlapping protein abundance patterns between diseases. Spearman correlation coefficents (p < 0.001) were estimated for the fold changes of all significantly-changed proteins overlapping between any two comparisons (see Supplementary Figure S1A–J). Positive correlations indicate fold change patterns in the same direction.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4368887&req=5

Figure 1: (A–E) Volcano plots of group comparisons showing the adjusted significance p value (log2) versus fold change (log2). The plots show an increased magnitude of differential protein abundances in BD/CT, MDD/CT, and MDD-P/MDD compared to SZ/CT and MDD-P/CT. BD/CT and MDD/CT revealed a prominent trend towards increases in protein levels and decreases in the MDD-P/MDD comparison. Horizontal grey lines indicate an adjusted p value threshold of 0.05; vertical grey dotted lines indicate a fold change threshold of 10%. Red dots indicate positive fold changes; green dots indicate negative fold changes. (F) Correlation map identifying overlapping protein abundance patterns between diseases. Spearman correlation coefficents (p < 0.001) were estimated for the fold changes of all significantly-changed proteins overlapping between any two comparisons (see Supplementary Figure S1A–J). Positive correlations indicate fold change patterns in the same direction.
Mentions: Proteomic analysis identified a total of 1 310 unique quantifiable proteins, based on 22 629 unique peptides, across all brain samples. Based on linear modelling, we found that 241 proteins (54 proteins ≥ 2 unique peptides) were differentially expressed in SZ patients, 664 proteins (368 proteins ≥ 2 unique peptides) in BD, 524 proteins (274 proteins ≥ 2 unique peptides) in MDD, and 224 proteins (44 proteins ≥ 2 unique peptides) in MDD-P, in comparison to CT individuals. In order to identify characteristic changes associated with psychotic features in affective disorders, we compared MDD-P to MDD and found 437 proteins (193 proteins ≥ 2 unique peptides) significantly differentially expressed. Characteristic expression profiles for each comparison are shown in Figure 1A–E. Based on similarities in the fold change direction (e.g. BD and MDD showed a tendency for positive fold changes) and to identify shared significant protein alteration patterns across disease entities, we cross-compared the findings using linear correlation (Figure 1F; correlation plots can be found in Supplementary Figure S1A–J). This enabled us to detect disease-specific and overlapping disease signatures at the protein level (Supplementary Table S4; Supplementary Figure S2). As expected, protein changes reflecting affective psychosis (MDD-P/MDD) overlapped with markers of MDD with psychotic features (MDD-P/CT), although no significant correlation between MDD-P/CT and MDD/CT was found. Proteins significantly changed in the MDD-P/MDD comparison showed opposite directional change in BD/CT, MDD/CT and SZ/CT. BD/CT and SZ/CT did not show significantly overlapping protein changes, yet biomarker patterns for both disorders were positively correlated to changes identified in MDD/CT (Figure 1F).

Bottom Line: Profiling studies have identified candidate protein markers associated with specific disorders of the psychoaffective spectrum, but this has always been done in a selective fashion without accounting for the entire proteome composition of the system under investigation.Independent in silico analyses of biological annotations revealed common pathways across the diseases, associated with presynaptic glutamatergic neurotransmission and energy metabolism.We suggest a disease model in which disturbances of the glutamatergic system and ensuing adaptations of neuronal energy metabolism are linked to distinct psychiatric symptom dimensions, delivering novel evidence for targeted treatment approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK (Drs Gottschalk, Wesseling, and Drs Guest and Bahn); Department of Neuroscience, Erasmus Medical Center, Rotterdam, The Netherlands (Dr Bahn).

Show MeSH
Related in: MedlinePlus