An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

Abstract

Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine.

Figure 1. Overview of the ciliary landscape.

(a) HEK293T cells stained with the ciliary marker ARL13B (green), the transition zone marker RPGRIP1L (purple), and the axonemal marker acetylated alpha-tubulin (red). Scale bar, 20 μm. In the magnifications the scale bar represents 5 μm. (b) Complexes/proteins identified in this study are depicted by circles and rounded boxes. Rounded boxes show complexes/proteins in the Syscilia gold-standard ciliary proteins. The edge thickness is proportional to the socioaffinity index, and proteins/complexes are coloured according to whether they have ciliary phenotypes. The border thickness is proportional to the number of variants in UK10K ciliopathy patients.

Figure 2. Complexes and networks within the landscape.

(a) Detailed network of IFT-B1/2 and MAK/ICK/KIAA0556; IFT-B is linked to IFT-A by NUDC, and to complex KIF3 by SSNA1. The IFT-B protein TTC30B interacts with multiple membrane proteins. One of those, TMEM41B, was further analysed and shows a ciliary length phenotype upon modulation of expression by siRNA knock-down and overexpression. For both, knockdown and overexpression, biological triplicates were analysed and a t-test was performed. P values below 0.01 are represented by ** and below 0.001 by ***. Error bars represent the s.e.m. (b) Detailed interaction network of the KIF3 complex and Complex:15, with SLC9A2R2 bridging ciliary processes. (c) Detailed interactions between Dynein and Dynactin intermediated by the HSF1/HSPA1L/MAPRE2 linker complex. (d) Muskelin/RanBP9/CTLH complex (GID complex in Yeast) network showing core, attachments and links to several other complexes, mediated by RAB8A. Immunofluorescence demonstrates the localization of two GID components, GID8 and MKLN1 (red arrows in the network) to the ciliary base: MKLN1 in kidney tubule epithelial cells (anti-MKLN1, left panel, red); GID8 (right panel, green) in multi-ciliated brain ependymal cells. DAPI staining (blue) marks the nucleus, GT335 co-staining (green or red) marks the cilium. Scale bars represent 10 μm. (e) Complex:21 and 31 containing several ubiquitin conjugating or modifying enzymes in interaction with the GID and exocyst complexes. (f) Elaborated view of the sub-network involving the NPHP1-NPHP4-RPGRIPL/PDE6D/RPGR complex, and its association with the complexes IQ/CALM1, KIF3, COP9 signalosome, and Complex:15. (g) Ciliogenesis transcription factor FOXJ1 stably interacts with PLK1 (Polo-like kinase 1) and CFAP20, and is linked to the FOXJ/RFX complex and Complex:15.

Figure 3. Identification of IFT-B sub-complexes and edgetic variants.

(a) Socioaffinity-weighted, spring-embedded (cytoscape) layout of IFT-B proteins with two sub-complexes indicated. (b) Cumulative elution profiles for IFT-B1/B2 proteins FLAG-purified and analysed by EPASIS in HEK293 cells stably expressing IFT88 or IFT27. Green and blue lines show components of IFT-B1 and -B2 sub-complexes, respectively. (c) Networks showing protein depletions in IFT-B comparing mutant to wild type with TAP-MS. Red arrows denote proteins with variants, and protein size is proportional negative fold-change. Top left, IFT88 p.R607H, a heterozygous MKS patient variant leads to a loss of IFT-B1. Bottom left, HSPB11 p.T41I (heterozygous MKS) at the IFT27 interface, leads to the loss of IFT-B1. Top right, IFT-B2 subunit, IFT172 p.E1153G (heterozygous in MKS) leads to a loss of IFT-B2. Bottom right, HSPB11 p.R61S (heterozygous JATD), on the surface, potentially interacting with an unknown partner, though not at any known interface affects only HSPB11 itself. Green and blue nodes represent components of the IFT-B1 and -B2 sub-complexes, respectively.

Figure 4. 3M Syndrome is a ciliopathy.

(a) Schematic showing comparison of interactome to disease genes with the top scoring diseases shown (orange denotes known ciliopathies). The three 3M associated proteins are shown right, with the baits that retrieved them, and within the network (below). Matrix relationships are not shown in the table. (b) siRNA down-regulation of these genes in mpkCCD cells reveals that 3M genes are involved in ciliogenesis (knockdown of IFT88, known to affect ciliogenesis, is shown for comparison). (c) Fibroblasts from a 3M patient have fewer cilia than controls. The transition zone marker MKS1 is shown in red, acetylated tubulin in green and nuclear staining with DAPI in blue. Scale bars represent 5 μm. (d) Quantification of differences in ciliated cell count comparing 3M fibroblasts to those transformed with wild-type or mutant (p.H1464P) CUL7 or empty vector. Wild-type CUL7 restored ciliogenesis, while mutant CUL7 did not. (b–d) For all experiments biological triplicates with technical duplicates were performed. Error bars represent the s.e.m.