Great paper! Really interesting results, especially the differences you see between the two disease presentations (CMD-LMNA and classical EDMD). I have a few questions/comments:

1. in section 'impaired cell-cell interactions in LMNA-CMD mutant muscle cells precursors' you conclude that impaired cell-cell adhesions are due in part to degradation of m-cadherin. do you have a hypothesis for why the cadherin is degrading? Is it because something else is impaired so there's just alot extra around that has no purpose?

2. Given such striking phenotypes in cell-cell adhesion, I'm kind of surprised that the functional overload testing for PLN mass and force doesn't create a bigger gap in the CMD-LMNA condition. Do you have any insight into why this might not be such a strong change?

3. Wow, figure 7 has a really cool result!

4. You worked out a really interesting mechanism for the defects observed in your cells and in the patient population. I'm wondering if you have any ideas on the direct connection between LaminA disruption and the downstream effects, like disrupted YAP signaling, impaired cell-cell junctions, etc. It is just presumed to be an effect of a poorly functioning nucleus structure (due to lamin deficiency), or is there something more specific?

Really interesting paper! I love reading about the impacts of structural features generated by proteins (i.e. a lamin network) on protein:protein interactions. A few questions/thoughts: 1. In figure 1B, do you make anything of the puncta in the FLAG channel or the smaller puncta in the DAPI channel? 2. The linker region discovery is a very nice one! I was wondering, until I read your conclusion, whether there were any interesting disease-associated mutations in that region. It would be neat to track development of a mouse model with just the linker region disrupted in different ways to see if they don't make it through development. 3. I really loved your swapping strategy. It's really handy when biology provides a useful control protein. 4. Do you have a hypothesis for the strange spatial distribution of the LaB1(head+rod)-A(linker)-B1(tail) constructs? 5. How different are the A and and B1 linkers? especially if their NLS signals are similar, that's only like 20 amino acids left. It would be cool to see if there are any predicted structure differences/flexibilities between the two regions. It would also be informative to know how the lamin network structures might differ based on the inclusion of the different linker regions. 6. The section on lamin network stability at the end really made me rethink the whole lamin:emerin interaction story. Without having much prior knowledge on lamin, I wonder if the lamin:emerin interaction in vitro is based on a single lamin monomer interacting with emerin, or if it takes multiple to form a network. Is there a way you can drive formation or disintegration of the lamin network? That might even inform your pulldown data: if you pulldown lamin, then do something to disrupt a potential network, do you still pull down emerin? 7. I'm really curious about how the lamin network structure might differ between all of these different mutants. That would form a nice parallel to all of your emerin anchorage data.

I'm struggling to work through the model of cytoplasmic organization and how that translates to disease pathogenesis. Is the hypothesis that this organization is mostly a result of disrupted cytoskeletal structure, so that the cell can't transmit forces as well? Or does crowding also play a role in this physical aspect of the cell?

I'm curious if reduced ribosome production has any impact on total protein levels within the cell, or if it just impacts cytoplasmic organization?

Interesting! Do you have any hypotheses on why there might be tissue sensitivities? And why did you choose these two to begin with?

Do you have a sense (on the molecular level) of what these two classes represent? Or how you conceptualize these two classes of proteins based on what their functions might be within a cell?

It would be interesting to see how these different categories of dissociators compare to your categories of Kd's, since they tell different sides of the story. It would be cool to see how you are adding another element of data here (and how you synthesized all of it to make a decision on what to characterize, or how one might move forward with that data).

I really like how you are able to isolate the binding event to a single spot. That makes detection so much easier. If you could track dissociation in real time (like you can in BLI), you wouldn't need to 'trap' and then image again. I wonder if some approach like that is possible. Either imaging many wells in real time, or imaging one at a time but controlling the flow of one chamber at a time so you can observe the whole span of the reaction.

Totally agree. It makes sense that the proteins that are expressed in a cell free system may behave differently than ones expressed in yeast.

Do you have a sense of the protein aggregation level or stability of your gfp-tagged proteins? It seems like if they are expressed and subsequently bound to one part of the chip, many could be aggregated and it might not be clear (though perhaps you could visually see it).

I wonder how you control for a set amount of dissociation - after you flow out any protein that has dissociated, you have disrupted equilibrium, and more will dissociate to make up for it, so you're kind of fighting this never-ending dissociation battle.

That's a very interesting (and useful) finding! Especially important if your goal is to make sure you get enough of your low-expressing proteins.

Thanks for including this technical issue! This seems like it could save many people alot of time spent troubleshooting. I wonder - if you want 4mL total volume (and don't care about replicates) if you could do this is 4x24well plates instead. The shaking dynamics might be less impacted since they would be shaking on a larger orbit, but the automation would be a little more involved.

I'm confused - do you plate just for QC, or is there a method where you pick colonies manually into a 96wp for culture growth? In the next section, it looks like you just describe starting your culture from the existing glycerol stock.

it's super awesome to see this kind of flexibility built in to an automated system!

how do you avoid contamination when your robot arm handles both bacterial cultures for your plasmid preps and expi293 cells?

I'm curious why you decided to include plasmid preps in this workflow. Are you cloning all of your own constructs or using this to just generate more high purity dna? What were the tradeoffs you examined between what you chose and just getting these genes synthesized?

Was this a reproducible loss? Was it always the same ten proteins?

That's a very simple fix! Very cool to see.

There does not appear to be any reference in the text to figure 3A-B. In addition, it would be nice to clarify the difference between 'pure A' and 'pure B' earlier on in the text! This also makes the figure confusing.

Do you have any speculations on why this might be? What do you think the rate limiting component is, then?

It would be very interesting to know the chemical composition differences between the two energy mixes. Is this proprietary? Do you have any speculation?

I really like your co-infection strategy: it highlights that many proteins require a more complex physiological environment to be studied. Do you have any data showing what happens if you try to un-bind the DCAF from the complex after purification? I wonder how it behaves if it is alone in solution - maybe the other components of the complex are required for its stability post-purification.

This is a nice bonus. And maybe is another explanation for why a few constructs didn't co-elute with the DDB1 - because the DCAF proteins weren't well folded.

it would be interesting to estimate the stoichiometry of each protein via the gel (obviously not the most quantitative, but you could get an estimate). Does the size of the complex off of the SEC suggest a 1:1:1 relationship? That would be a nice check that your are purifying what you think you are.

What is your rationale for doing scale up in Hi5 cells when you did your screening in sf9 cells?

It looks like you are eluting your protein in a solution with very high conductivity. Perhaps eluting at a more physiological condition, like 100-150mM salt + imidazole, might reveal some additional interactions. I'm impressed that you co-retained so many complexes in your elution as is, though - those interactions must be quite strong!

This could also be an issue of stoichiometry. If the interaction is strong, but the yield of expressed DDB1 for some reason was low, this could also be the case. It would be interesting to assay the amount of each DCAF bound to DDB1 versus free.

I'm curious if there was any trend or difference that you observed in co-purification with DDB1 compared to the truncated DDB1.

That's interesting. Do you have any theories why the truncations did not perform as well?

I'm curious about your bacmid preparation in high throughput. Did this require manual colony picking for recombined colonies? Also, did you shake your 24wp on a large or small orbit?

This is a really interesting point. The difference between 'preventing storage accumulation' and 'rescuing the impacts of already having storage accumulation' are definitely worth considering, and I appreciate that you chose a point that is most in line with what happens clinically.

Nice result! Do you have any idea why the sulfatase acitivity wouldn't be rescued but you can see this dramatic improvement in the downstream product levels? Is this a matter of assay sensitivity, or is something else going on?

This result is really striking! I wonder if the single condition which was not rescued (panel D) suggests maybe a specific sulfatase that still might be underperforming/underactivated?

It's intriguing that some of the sulfatase activities plateau below the point of WT, though. I'd be curious to see the different levels of the sulfatases at each expression level of the FGE as well. Perhaps there are fewer sulfatases for some reason?

Do you have a quantitative estimate of increase in expression for your lentiviral integrations vs. wild type levels? That might be interesting to see.

This study highlights the true complexity of protein processing, trafficking, and its ultimate function! I think it could benefit from some orthogonal methods of analysis - would be it be possible to try to isolate specific 'states' of processed protein (like you did with the CHX treatment) or using subcellular localization or IPs to identify exactly which glycosylation sites or cleavages have been made? Excited to see what comes next!

Some Lamp1 staining for lysosome localization could contribute to the above IF figures as well!

I wonder if there might be a more straightforward way to probe this interaction. It seems difficult to study the interaction between RNF13 and IDS because there are so many different precursors or processed forms of IDS. Perhaps a strategic approach could be to either isolate the process (in cells) for which the interaction is most important, and then determine which processed form of IDS is involved. Or maybe purify different forms of IDS and determine using binding assays, since IPs are less capable of identifying 'direct interactions'.

This is a clever way to probe protein processing! It might be useful to do some western blotting with this treatment to look at the impact on processing that way as well.

This is a very neat result! I'm wondering if it might be interesting to repeat an IP with different fractions - like a lysosome-enriched fraction vs a whole lysate. There are some pretty crude lysosome preps that could give some nice connections between forms that are trafficked, the maturation data you have, and the localization data you have below.

What is your hypothesis for this interaction, then? Does RNF13 interact with both species, throughout the whole processing stage? Or predominantly with one species?

It's really awesome that you developed these tools and processes to enable protein production in plants!

I wonder if you could also integrate a DSF or nano-DSF protocol to confirm folding/thermal stability of your protein as well. It could be another good indicator of successful protein production.

That's great!

this is a cool feature!

Given this is a membrane protein, did you have issues with solubility? Do you have data showing the protein is well folded after purification?

How long is this stable for? Do you have to make it fresh for each purification you do?

Are you treating with TEV in whole plant lysate?

I am not sure that co-localization of those tags is the best way to confirm that the protein integrity contains both tags. It is unlikely that you could see single proteins based on your image resolution - I think the stronger claim is the western blot you have that shows the size change upon cleavage (fig 2E,D)- and also just the fact that your protein is of expected size with the two tags.

It is super helpful that you included so many tag designs already in the construct. It really adds to the usability right off the bat.

It's really nice that this protocol is pretty agnostic to the source and doesn't require any modification of the natural biology!

This data isn't linked anywhere.

How do you control for lysosomal quality here, or intactness from your isolation protocol?

This is a really nice result! Do you have a way to measure 'lysosome concentration' or do you just base everything off of normalized protein concentration? I'm wondering if there is a way to be quantitative about experiments like these from one batch to another.

For all of these assays, is the assumption that most of your lysosomes are intact? How do you then measure enzyme activity from the intact lysosomes? Do you assume that the substrates can all pass through the membranes?

Do you have details on what speed and for how long?

This is a little unclear. Which step was repeated two more times? Just homogenization?

I don't agree with this statement. You do not need a packed column or a chromatography instrument to perform affinity chromatography; you can just bind to beads in batch and elute. It seems like the major improvement might just be cost effectiveness.

It would be super useful to see an outline of this cost versus buying magnetic beads for batch binding. This could include how many times you could reuse the beads with similar efficiency, etc.

Do you think that your beads are truly saturated? If you add more beads, can you recover what is lost in the Pass through? Or is it more of a binding efficiency issue?

It would be nice to see how the purity of these samples compared to one another.

How do you know this? Do you have a way to strip protein off of beads? Or are you just basing this off of estimated total protein concentration recovered?

Why not compare directly to 6x Ni-NTA magnetic beads? You can do this at very small scale and it would be nice to see the differences in capture efficiency and specificity.

Wouldn't you want to capture 100% of your purified protein? Is this just a matter of how many nanoparticles you add? Do you have a rough idea of the binding capacity of the nanoparticles, or is it pretty variable from one prep to the next?

How long can this powder be stored at 4C? Is it moisture sensitive?

What are the advantages of the dot blot over a conventional sds-page analysis? SDS-PAGE likely takes the same amount of time as a dot blot, but potentially gives you more information about each sample.

This might just be an artifact of biorxiv, but I don't see a table description anywhere. What were the adverse reactions for the SDS-PAGE? Did the gel just not run straight or stain evenly? Also, what are the units for purification yield?

what volume of extraction buffer do you use here? Information on the ratio of 'dried pellet material' to extraction buffer would be helpful here.

It makes sense that you use the auto-induction for more high throughput purposes. After finding an optimal condition and scaling up, would you recommend switching to IPTG? Would you expect any changes?

If you are growing the cells in a single flask, at which step do you split up the pellet into a 96-well plate?

It would be very illuminating to study how these different isoforms may differentially impact Rac1 activity in vitro. If their expression levels are similar in certain cell types, does one directly activate rac1 while the other indirectly activates Rac1? If the Lpd isoform doesn't bind directly to Rac1, how does it enhance Rac1 activity?

It might be useful to comment on why your structural predictions above (of just the csRAPH domain) gave you a different hypothesis than predicting the structure of the full Lpds in complex with Rac1. What do you get for structural predictions when you just look at Lpds alone?

It would also be interesting to examine expression patterns of all of these isoforms.

This is a really nice pulldown! It would be cool to see binding curves for these interactions, too.

What was your rationale for this construct in particular? Why not just express the RA-PH, or why not the full-length construct?

I think this only suggests that Lpds doesn't heterodimerize, but Lpds could still associate with itself as a dimer to facilitate interaction with Rac1. It would be interesting if you could purify it and determine its tendency to dimerize that way.

I would be careful with conclusions here - just because something coIPs with a protein doesn't mean it binds directly. There could be other proteins in the mix that facilitate this interaction/complex.

Why do you think that there wasn't the clear distribution along the periphery in Fig 5g like you saw for your 2uM His-actinin control? Does this distribution have any impact on multiple bleb formation?

What relevance does this have to a physiological process? Do you think it is just that there are multiple mechanisms for blebs to form, or do you think they are initiated in certain ways dependent on context?

This is a very interesting idea. You nicely demonstrated that blebs can form from basic elements of the cytoskeleton. It seems that an idea that follows is that more 'control' or determination on whether or where to form the bleb comes from higher-level signaling and localization.

Why do the blebs fade or resolve at t31 or t26 in figs 3e and 3f, respectively? The actin network seems to become even more concentrated with this event.

But the distribution was significantly different at 2uM of each. Do you have concerns about the distribution at these lower concentrations?

This seems mostly due to the methylcellulose, correct? I'm wondering if there is a way to determine the actual number of anchor points in the liposome? Perhaps some staining against the His tag? It might be interesting to see where deformations lie in relation to clusters of anchor points.

This is a really clever approach! I love how you separated out variables like this.

Do you also have the Kd of untagged actinin for F-actin? It could be nice to know if the tag has any impact on binding. I'm also curious if the membrane tethered actinin has a different affinity for actin filaments compared to free-floating actinin.

This is a super beautiful figure! Very clear distinction.

What do you think is in the bottom right corner of the liposome in figure 1b? It looks like that's where there is a cluster of actin filaments - is that a premature bleb or something? It shows up right at the beginning, before myosin contractility.

How much do the anchor points actually move within the membrane? Is it enough to generate just local changes in force, or larger scale changes in the overall relationship between the cortex and the membrane?

I'm really curious how broadly this extends to general movements of the body. Like - if you have a cyclist or runner who mainly use their legs in movement patterns, are they going to be more 'efficient' at generic movements that require the legs, and closer to a non-expert at movements that require the arms? Or is being an 'expert' at some motor skill enough? How about other non-sport motor skill experts, like musicians?

This suggests that tremors negatively impact mastery of a movement. Are these inefficiencies true barriers to mastery, or is efficiency just a byproduct of mastering a movement?

Very interesting, do you have any hypothesis for what might be going on there?