Dynamics of compartment-specific proteomic landscapes of hepatotoxic and cholestatic models of liver fibrosis

Reviewer #1 (Public review):

Summary:

Jirouskova and colleagues in their study have carried out an in-depth proteomic characterization of the dynamics of the liver fibrotic response and the resulting resolution in two distinct models of liver injury: CCl4-induced model of hepatotoxicity and pericentral/bridging liver fibrosis and the DDC feeding model of obstructive cholestasis and periportal fibrosis. They focussed on both the insoluble extracellular matrix (ECM) components as well as the soluble secreted factors produced by hepatic stellate cells (HSCs) and/or portal fibroblasts (PFs). They identified compartment- and time-resolved proteomic signatures in the two models with disease-specific factors or matrisomes. Their study also identified phenotypic differences between the models such as that while the CCl4-induced model induced profound hepatotoxicity followed by resolution, the DDC model induced more lasting liver damage and proteomic changes that resembled advanced human liver fibrosis favouring hepatocarcinogenesis.

Overall, this comprehensive and very well-conducted study is rigorous and well-planned. The conclusions are supported by compelling studies and analyses. One caveat is the lack of mechanistic experiments to prove causality, but this can be carried out in follow-up studies.

Strengths:

(1) A major strength of the study is that the experiments are rigorous and very well conducted. For instance, the authors utilized two models of liver fibrosis to study different aspects of the pathology - hepatotoxicity vs cholestasis. In addition, 4 time points for each model were investigated - 2 for fibrosis development and 2 for fibrosis resolution. They have taken 3 components for proteomic analyses - total lysates, insoluble ECM components as well as the soluble secreted factors. Thus, the authors provide a comprehensive overview of the fibrosis and resolution process in these models.

(2) Another great strength of the study is that the methodology utilized was able to dissect unique pathways relevant to each model as well as common targets. For example, the authors identified known pathways such as mTOR signalling to be differentially regulated in the CCl4 vs DDC model. mTOR signalling was increased in the DDC model which is associated with hyperproliferation. Thus showing that the approach taken is specific enough to distinguish between the two similar (both induce fibrosis) but distinct mechanisms (hepatotoxicity vs cholestasis) is a strong point of the study.

Weaknesses:

(1) The authors themselves propose in their Introduction that the "ECM-associated changes are increasingly perceived as causative, rather than consequential"; however, they have not conducted mechanistic (gain of function/loss of function) studies either in vitro or in vivo from any of their identified targets to truly prove causality. This remains one of the limitations of this study. Thus, future studies should investigate this point in detail. For instance, it would have been intriguing to dissect if knocking out specific genes involved in one specific model or genes common to both would yield distinct phenotypic outcomes.

(2) The majority of the conclusions are derived primarily from the proteomic analyses. Although well conducted, it would strengthen the study to corroborate some of the major findings by other means such as IHC/IF with the corresponding quantifications and not only representative images.

Reviewer #2 (Public review):

Summary:

The authors suggest that ECM abundance and composition change depending on the aetiology of liver fibrosis. To understand this they have investigated the proteome in two models of animal fibrosis and resolution. They suggest their findings could provide a foundation for future anti-fibrotic therapies.

Strengths:

The animal models used are widely studied models of liver fibrosis from both parenchymal and biliary damage aspects. Both would allow analysis of resolution. The CCl4 model in particular fully reverts to a 'healthy' liver following cessation of the insult. I am less clear whether/how quickly the ductal plugs clear in DDC models and thus this may not provide the response they are looking for in terms of reversibility. I believe there have been several extensive studies using a transcriptomics approach in assessing genes and cells involved in the CCl4 model of resolution. Even more mutliomic models of general fibrosis progression in many of the mouse models of fibrosis. However, the proteomic approach they have used is robust and they have made some attempts to integrate with cell-type specific signatures from previously published data.

Although there is minimal data, hepatocyte elasticity is a very interesting part of their study. Additional data and focussed attention on the mechanisms underpinning this would be very insightful.

Weaknesses:

As it currently stands, the data, whilst extensive, is primarily focussed on the proteomic data which is fairly descriptive and I am not clear on the additional insight gained in their approach that is not already detailed from the extensive transcriptomic studies. The manuscript overall would benefit from some mechanistic functional insight to provide new additional modes of action relevant to fibrosis progression. Whilst there is some human data presented it is a minimal analysis without quantification that would imply relevance to disease state.

Although studying disease progression in animals is a fundamental aspect of understanding the full physiological response of fibrotic disease, without more human insight makes any analysis difficult to fulfil their suggestion that these targets identified will be of use to treat human disease.

Some of the terminology is incorrect while discussing these models of injury used and care should be taken. For example - both models are toxin-induced and I do not think these data have any support that the DDC model has a higher carcinogenic risk. An investigation into the tumour-induced risk would require significant additional models. These types of statements are incorrect and not supported by this study.