Proximity labelling identifies pro-migratory endocytic recycling cargo and machinery of the Rab4 and Rab11 families
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Abstract
Endocytic recycling controls the return of internalised cargos to the plasma membrane to coordinate their positioning, availability and downstream signalling. The Rab4 and Rab11 small GTPase families regulate distinct recycling routes, broadly classified as fast recycling from early endosomes (Rab4) and slow recycling from perinuclear recycling endosomes (Rab11), and both routes handle a broad range of overlapping cargos to regulate cell behaviour. We adopted a proximity labelling approach, BioID, to identify and compare the protein complexes recruited by Rab4a, Rab11a and Rab25 (a Rab11 family member implicated in cancer aggressiveness), revealing statistically robust protein-protein interaction networks of both new and well characterised cargos and trafficking machinery in migratory cancer cells. Gene ontological analysis of these interconnected networks revealed that these endocytic recycling pathways are intrinsically connected to cell motility and cell adhesion. Using a knock sideways relocalisation approach we were further able to confirm novel links between Rab11/25 and the ESCPE-1 and retromer multiprotein sorting complexes and identify new endocytic recycling machinery associated with Rab4, Rab11 and Rab25 that regulates cancer cell migration in 3D-matrix.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The manuscript entitled "Proximity labelling identifies pro-migratory endocytic recycling cargo and machinery of the Rab4 and Rab11 families" by Wilson et al, presents an approach, BioID, able to identify and characterize protein complexes associated with Rab proteins in an ovarian cancer cell line. They started the study by coupling a proximity labelling method to mass spectrometry. By doing so they identified the interactomes associated with Rab4a, Rab11a and Rab25. Next, the authors proceeded to detect directly biotinylated peptides. Then, using knock sideways experiments, the authors validated novel links between …
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Referee #3
Evidence, reproducibility and clarity
Summary:
The manuscript entitled "Proximity labelling identifies pro-migratory endocytic recycling cargo and machinery of the Rab4 and Rab11 families" by Wilson et al, presents an approach, BioID, able to identify and characterize protein complexes associated with Rab proteins in an ovarian cancer cell line. They started the study by coupling a proximity labelling method to mass spectrometry. By doing so they identified the interactomes associated with Rab4a, Rab11a and Rab25. Next, the authors proceeded to detect directly biotinylated peptides. Then, using knock sideways experiments, the authors validated novel links between Rab11/Rab25 and some of the direct interactors identified. Lastly, they propose that SH3BP5L and CRACR2A are required for migration of ovarian cancer cells, in 3D-cell derived matrix.
Major comments:
A major limitation of the study is the reliance on a single migratory cell line, the A2780 cell line. As such the authors should include additional cell lines for their key experiments throughout the manuscript.
The authors state that BioID-Rabs are expressed at a "level close to endogenous". This should be quantified. Also, authors should clearly show that BioID-Rabs co-localize with the endogenous Rabs. So, immunofluorescence labelling of endogenous Rabs and markers for Early Endosomes (e.g. Rab5, EEA1, etc) and Recycling Endosomes should be performed.
In the dot-plot of the high-confidence proximal analysis, the average intensity (represented in the circle colour) should be normalized by the abundance of protein.
The knock sideways experiments validated high affinity prey interactions, including of sorting nexins with Rab4/11/25. SNX1 and SNX3 showed that they would only significantly redistribute in FKBP-GFP-Rab11a and FKBP-GFP-Rab25, respectively. Authors should comment on why the role of SNX1 and SNX3 was not assessed in migration studies.
Knock sideways showed that Rab4 was unable to induce significant re-localization of CLINT1. This would suggest that CLINT1 would be a candidate less robust than others identified by BioID and validated by knock sideways experiments. Why did the authors decide to proceed to assess the role of CLINT1 in migration studies?
Although the authors reported a lack of significant re-localization of CLINT1 by Rab4a, they state that "CLINT1 plays a role in Rab4 (but not Rab25) dependent migration in 3D-CDM". Can the authors comment on this?
"CLINT1 was identified as a Rab4, -11 and -25 proximal protein (Figure 2)". The study would benefit from additional evidence showing that CLINT1 does not act downstream of Rab11 to control migration of A2780 cells.
Authors should include immunofluorescence studies to better characterise the role of Rab4a, Rab11a and Rab25 networks in migration, adhesion and leading-edge related processes. Focal adhesions should be quantified, and actin cytoskeleton described. Such studies should be coupled to the cell migration studies. These would validate and support the conclusions drawn from the GO analysis.
In the discussion, the authors mention two other papers in which "proximity labelling methods have proven an excellent tool for identification of protein complexes, including for Rab4 and Rab11". The authors should also discuss if there are overlapping results.
Minor comments:
Figure 1: Panel A is too small. Insets are hard to interpret. The size of the whole panel should be increased.
Description of results regarding the trafficking machinery associated with Rab4a, Rab11a and Rab25 does not follow the same organization and structure as in Figure 2. The authors should try to match the organization of data and its description to improve readability.
In Figure 4B and S4C there are two labels for 1 and 2.
Figure S4E merge of GFP-FKBP Rab11a cells shows poor overlap. A replacement should be considered.
There are several typos in the discussion and in Figure 7 ("CRACRA" should be CRACR2A)
Significance
• The manuscript presents an approach that allow the identification of Rab-associated networks and the direct comparison between GTAses. This is of relevance since we still lack robust methodologies to identify the endosomal trafficking machinery underlying migration in cancer cells. By not targeting Rab4 specific machinery (e.g. TBC1D5), the authors missed the opportunity to expand the knowledge regarding the machinery sustaining Rab4-dependent migration in cancer cells.
• The work targets an audience interested in endosomal trafficking and protein recycling in cancer cell migration.
• The reviewer is a translational cancer biologist with expertise in cytoskeleton, endosomal recycling, signaling and cancer.
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Referee #2
Evidence, reproducibility and clarity
Wilson et al. submitted a paper entitled: "Proximity labelling identifies pro-migratory endocytic recycling cargo and machinery of the Rab4 and Rab11 families". The goal of the paper is to identify new interactors for RAB4/11 and 25 that could be involved in Rab-dependent migration. To do so, they used BioID of the aforementioned 3 Rabs in mesenchymal and migratory ovarian cancer cell line A2780. They validate some of the interactors using the knock-sideways technique and test the requirement of some interactor for migration/invasion in a 3D matrix. This is a very descriptive paper that could benefit from more in-depth mechanistic …
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Referee #2
Evidence, reproducibility and clarity
Wilson et al. submitted a paper entitled: "Proximity labelling identifies pro-migratory endocytic recycling cargo and machinery of the Rab4 and Rab11 families". The goal of the paper is to identify new interactors for RAB4/11 and 25 that could be involved in Rab-dependent migration. To do so, they used BioID of the aforementioned 3 Rabs in mesenchymal and migratory ovarian cancer cell line A2780. They validate some of the interactors using the knock-sideways technique and test the requirement of some interactor for migration/invasion in a 3D matrix. This is a very descriptive paper that could benefit from more in-depth mechanistic analysis of fewer candidates.
Major comments:
For the most part, the data appears of excellent quality and most of the conclusions or interpretations are correct (see below for a few points that can be improved). Some key concepts are missing in the introduction. For example, the concept of GEFs and GAPs only appears later in the experimental section - this should be introduced earlier.
The description of the BioID data is poorly structured and descriptive, a recurring challenge with big data paper. One suggestion to improve the manuscript would be to exploit the best-known interactions to clearly benchmark the efficiency of the screens. Next the new interactions could be described and figure 2 could be better exploited in that respect (the mentioned complexes could be better drawn etc.). The text could also be more focused on fewer interactors such that it is more digestible for the readers. The major weakness of the manuscript, in my opinion, is the lack of depth in testing functionally some of the uncovered novel interactions.
Some additional experiments that would be needed to support the claim of novelty in the paper include testing the function of some of the tested interactions. For example, the novel GEF interactions would benefit from biochemical testing in addition to BioID. Likewise, the section on biotinylation and interaction domain mapping is interesting but is, as presented, a theory. Using one interaction to dissect in more details to support this claim is needed. Alternatively, can the authors demonstrate that this approach can be used to confirmed known protein domains involved in protein-protein interactions of these Rabs? Finally, the authors end their manuscript by screening candidates issued from their BioID which have not been implicated in migration/invasion before. This is somewhat preliminary and fails to provide some depth into the function of one of these potential interactions (domain mapping, knockdown rescue of wt or mutants etc.).
The authors use the knock-sideways technique to validate the strength of their interaction. This is a clever way to validate interaction in cellulo which could be difficult using conventional IP. However, it looks like the expression of FRB-MITO leads to mitochondria fragmentation and aggregation. Is it possible that this cause a bias in their quantification analysis because it becomes difficult to clearly delineate individual mitochondria? In some cases (ex. Fig 5C), the recruitment of the candidate is obvious. However, in other cases (ex. Figure 5A) the recruitment to the mitochondria is not very convincing and looks more like the candidates collapse around the aggregated mitochondria. The authors should therefore describe the limitations in more details.
Minor comments:
The authors aim to identify new interactors involved in migration, but they performed the BioID on confluent cells where cell migration is likely limited. Would comparing a BioID performed on confluent cells with one where the cells are sparse enough to migrate possibly interesting to conduct? This could be discussed.
In Figure 1C, it is difficult to read the name on the candidates. The authors should fit the entire name in the nodes (maybe use an ellipse instead of a circle).
In Figure 1C and 2 the known interactors could be in a different color emphasize the new potential interactors.
Figure 4 is very heavy and the images are small making difficult to see the results clearly. Instead of showing 10 time points per condition, 3 or 4 time point with higher resolution images would have been more appropriate.
Methods: The methods are well described. It is a bit surprising that the BioID samples are run on SDS-PAGE and that bands are cut when on beads digestion is currently done by many lab for this technique.
Statistics: Statistics should be provided for all quantification, not only the one that are significant. For the non-significant, the P-value should be indicated on the figure.
The authors looked at endogenous Rab11 vs BioID-Rab11. Why no do it for the other 2 Rabs. Also, quantification of endo/exo expression should be done.
Significance
The advance of this work is to expand the potential functional interactome of three Rabs involved in slow recycling of endosomes. Some novel interactions are reported and some screening approaches have been use to reveal functional ones (this could be improved).
This work is potentially important and part of the priorities in the field to ascribe the overlapping and specific interactions/functions of Rab subfamilies. Similar work has been done for Rho proteins and selected Ras oncogenes.
The work presented here would be of broad interest for people in the cell biology field.
The expertise of this reviewer is in Ras-superfamily proteins, proteomics, cell migration/invasion and as such was qualified to assess this manuscript in its entirety.
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Referee #1
Evidence, reproducibility and clarity
From methodology and reproducibility point of view it is an excellent manuscript. It is also well-written.
Significance
- This is a manuscript that presents an in-depth analysis of potential interactome and cross-interactome of Rab11a, Rab4a and Rab25 GTPases. BioID and knock-sideway data presented in first half of the manuscript is very interesting and undoubtedly will be of a good use for many laboratories. However, authors tend to over-interpret some of their data, suggesting functional connections between specific Rabs and BioID hits without any additional data. Authors admit themselves that there are some disconnects between …
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
From methodology and reproducibility point of view it is an excellent manuscript. It is also well-written.
Significance
- This is a manuscript that presents an in-depth analysis of potential interactome and cross-interactome of Rab11a, Rab4a and Rab25 GTPases. BioID and knock-sideway data presented in first half of the manuscript is very interesting and undoubtedly will be of a good use for many laboratories. However, authors tend to over-interpret some of their data, suggesting functional connections between specific Rabs and BioID hits without any additional data. Authors admit themselves that there are some disconnects between BioID and knock-sideways data. Furthermore, BioID measure proximity and not functional connection.
- Second half of the manuscript focuses on taking some of the BioID hits and testing whether they are required for mediating cell migration. By itself, it is a great idea since that would provide that functional connection that is missing in the original BioID screen. Unfortunately, the data is limited to few knock-downs without any further analyses of the involvement of these proteins in regulating migration. Consequently, as it stands, this manuscript is essentially a BioID screen with limited insights or validation of specific "hits", thus, does not really lead to any new conclusions about cross-function of Rab11, Rab25 and Rab4 networks.
- Additional comments:
There are no blots shown (only boxes) for Figure S1C-D. Data in Figure S1 doe shown that BirA-Rab11a is expressed in similar levels as endogenous Rab11. However, no data supporting similar statement for Rab4 and Rab25 is shown.
The presence of specific proteins in BioID does not mean that they either directly bind or regulate particular BirA-Rab. For example, authors state "DENND4C, related to Drosophila Rab11 GEF CRAG, was enriched to Rab11a, suggesting that this could be an alternate GEF for Rab11". There is no data supporting such a statement in this manuscript. Actually, DENND4C is better known GEF for Rab10. Rab10 is also known as Rab present in recycling endosomes, thus, could have easily be present in Rab11a-positive recycling endosomes. There are numerous similar statements in the manuscript that implies functional connections between Rabs and BioID "hits" without providing any other functional data.
Authors should not use RCP term to refer to Rab11FIP1 since Rab11FIP1 is its established name and using other names only creates confusion. RCP term was first used to indicate that Rab11FIP1 can bind to both Rab4 and Rab11, the hypothesis that since then was proven to be incorrect.
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