Fig. 5

Quantitative label-free proteomics was applied to mechanically isolated OSs of 5 month old zygotic mutants and sibling controls. a Semi-thin plastic sections stained with Richardson′s solution show persistent OSs with morphological abnormalities in zgbbs1^−/− mutants (right image) at 5 month compared to controls (left image). The blow-up images show examples of isolated OSs used for proteomic analysis. b Bar plot showing the sum of normalized peptide LFQ-intensities reported by MaxQuant of all detected BBSome components. Note significant reduction or complete absence (#) of the indicated BBSome components. Independent multiple two-sided T test on LFQ-intensities; Sig:**FDR(q value) < 0.01; Sig: ***FDR(q value) < 0.001; Sig: ****FDR(q value) < 0.0001; sample size (n = 4 Mut and n = 4 Ctrl Samples, each sample is a pool of 7 retinae from 4 animals) exact P values can be found in Supplementary Data 6; Error bars show standard deviation around the mean. c Linear mixed effect models are used to estimate fold changes and P values, which are adjusted for multiple testing by the Benjamini-Hochberg procedure. Binned frequency distribution plot of proteins based on their fold changes (zgbbs1^−/− over control) revealing an overall accumulation of proteins (FC > ± 2). Out of the 169 enriched proteins, the abundance of 115 proteins was significantly increased (adj. P < 0.05) while 36 out of the 59 reduced proteins were significantly decreased. d GO-ORA analysis of all proteins (FC > ± 2 & adj. P < 0.05) reveals an overrepresentation of “membrane-associated proteins” for the cellular component term. The majority of these proteins have a positive FC and are enriched in the mutant OSs. In the biological process and molecular function terms, we found an overrepresentation of proteins associated with lipid homeostasis that accumulate in mutant OSs. Complete plots are found in Supplementary Fig. 12. Scale bars: (a) 25 µm, zoom: 10 µm. Abbreviations: RPE retinal pigment epithelium, OS outer segment, ONL outer nuclear layer, GO-ORA Gene ontology over representation analysis, LFQ label-free quantification.