Peer review of: Multi-layered proteomic analyses decode compositional and functional effects of cancer (2020)

Reviewers' comments: 

 

Reviewer #1 (Remarks to the Author): 

 

Mehnert et al. use a series of proteomics approaches (AP-MS, BioID-MS, crosslinking-MS, 
proteomics, and phosphoproteomics) to investigate the impact of mutations of the DYRK2 kinase on 
its interactome, topology, and downstream signaling. They describe this as a multi-layered workflow. 
DYRK2 was chosen because of its known participation in a multi-protein complex and possible role in 
cancer progression. The authors perform AP-MS and BioID-MS experiments to map the interactome 
of WT DYRK2 and five mutant versions and find mutation-specific changes in the interactome. They 
compared the phosphorylation state of WT and mutant DYRK2 and again found that different 
mutants affected phosphorylation and topology to different degrees, which correlated with the 
effects of the mutations on the interactome. Next, they investigate the topology of WT and a subset 
of mutants by cross-linking mass spectrometry and found some increased and decreased 
interactions compared to WT. It is unclear how informative these results are. Finally, the authors 
conduct proteomics and phosphoproteomics studies. While the proteome of cells expressing the 
different mutants displayed little differences to WT, the phosphoproteome was differentially 
affected by different mutations. To do so, the authors knock out endogenous DYRK2 and reintroduce 
WT and mutants. The authors employ SWATH-MS for the phosphoproteomics analysis and identify 
only 2888 unique phosphopeptides across all conditions which limits the impact of this study. 

This is a carefully conducted study. The proteomics analyses are of high quality. The authors stress 
that the advantage of their approach is that multi-layered proteomics information identifies how 
different mutations affect signaling and helps to pinpoint underlying mechanisms that thereby 
discriminate mutants, even those that have a similar predicted “damage score”. 

The major critique of this study is that the authors do not demonstrate the significance of their 
findings. They observe changes, some of which are mutation-specific but do not show how they 
affect cellular function. This limits the impact of this study. The authors generate a knockout and 
overexpression system but do not describe any cellular effects of these mutants. 

They conclude that mutations that strongly impact kinase function result in differences on several 
proteomics levels (interactome, topology, phosphoproteome). The authors should perform in vitro 
kinase assays of the mutants to see if their observation directly correlates with kinase activity as 
assumed for the kinase-dead mutant. Their multi-layered approach would be more justified if kinase 
activity would not be sufficient to discriminate mutations with similar damage score. 

The authors identify interactions of DYRK2 with components of the nuclear pore complex as well as 
some altered sites on other nuclear pore complex proteins. They speculate that interaction with 
some components guides the phosphorylation of others. However, they do not show if nuclear 
transport is altered in cells expressing DYRK2 mutants. 

 

Minor comments: 

1. The authors should include in S1 a table indicating how often the respective mutations are 
observed. 

2. Line 139-140 the authors refer to supp figure 2b, I think they mean supp figure 1b. 

 

 

 

 

Reviewer #2 (Remarks to the Author): 

 

In the present manuscript Mehnert et al. showcase a highly detailed, multi-layered proteomics 
study, meticulously dissecting the molecular mechanisms involved in the pathogenicity of five 
disease-associated mutations in the Dyrk2 protein kinase. In an extensive series of expertly 
conducted experiments, comprising AP-MS and BioID-MS, cross-linking MS, quantitative 
phosphoproteomics, the authors generate a vast amount of new and important biological 
knowledge on disease-associated mutations in the Dyrk2 protein kinase. 

The -in some parts lengthy and verbose- manuscript is well written, the authors’ conclusions are all 
supported by the data presented and I endorse its publication in its present form. 

 

 

 

Reviewer #3 (Remarks to the Author): 

 

In this manuscript the authors study the effect of cancer related mutations in the Dyrk2 protein 
kinase using several proteomic approaches. The authors selected five cancer related mutations of 
Dyrk2 and created stable cells with these mutations to then compare wild type interactions with 
mutant interactions using affinity purification coupled to mass spectrometry and proximity labeling 
using BioID. Next the authors used cross linking approaches to analyze potential changes in the 
structure of Dyrk2 wild type and mutant proteins. This was followed by a phosphoproteome analysis 
of cancer mutations in cells. All of these methods are widely used in the research community. Lastly, 
the authors attempted to integrated this information into a network of changes induced by 
mutations in Dyrk2. Overall, this manuscript has a number of major issues at this time. 

 

The first major issue is while there are multiple proteomics experiments conducted here and an 
attempt is made to map these datasets into a network model, the manuscript does not provide 
novel and actionable insights into Dyrk2 biology, the function of this protein kinase, and how to use 
this information to better understand cancer biology. Figure 6 is emblematic of this issue where the 
authors attempt to combine the datasets into functional insights, but no validation of any insights 
are provided. It is also unclear how information was chosen to be included in this figure other than a 
qualitative analysis of other datasets. A more sophisticated analysis would include a mathematical 
and statistical justification for inclusion of data in this figure and a way to then use this figure to 
make data driven decisions for pursuing additional experiments. 

 

A second major issue somewhat related to the first issue is the statistical and mathematical decision 
making in the manuscript needs better explanation and justification. Each individual proteomic 
experiment must be supported by at least three biological replicates and the statistically supported 
data then described and used in each figure. As it is written, the statistical analysis of the AP-MS and 
BioID is provided, but this appears to be lacking for the phosphoprteomics and cross linking MS 
datasets. Given the cumulative nature of the data and the importance of assembling a statistically 
supported network, it is critical that each proteomic experiment be conducted and analyzed with 
sufficient rigor in order to have a rigorously supported final network. 

 

A third major issue is the cross linking experiment is of limited value. The authors conducted this 
experiment on purified proteins from insect cells, which only provides information on Dyrk2 itself. 
This does not provide any insights regarding how these cancer mutations are affecting interactions 
of Dyrk2. The authors should carry out a study where AP-MS isolated Dyrk2, Dyrk2 KR, and Dyrk2 RL 
are then treated with cross liking reagents to determine the likely changes in interaction interfaces 
induced by these mutants. This type of information would be far more valuable to integrate with the 
other datasets in the manuscript than the limited insights provided by cross linking of the Dyrk2 
proteins alone.