Age-dependent changes in protein incorporation into collagen-rich tissues of mice by in vivo pulsed SILAC labelling
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Evaluation Summary:
Capturing the rate and degree of protein turnover in tissues rich in collagen, proteoglycans and glycoproteins over the life span provides valuable information about how these tissues age and can increase our understanding of age-related disease. Using a Stable Isotope Labeling (SILAC) method to examine protein retention, new protein incorporation and protein turnover in three high collage content tissues, the authors show that turnover is low in older mice in these tissues, but the depth of the data generated provide a detailed examination of what low turnover means at a level we have not previously had. This paper would be of interest to a broad range of scientists studying connective tissues in the context of development and ageing.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Collagen-rich tissues have poor reparative capacity that predisposes to common age-related disorders such as osteoporosis and osteoarthritis. We used in vivo pulsed SILAC labelling to quantify new protein incorporation into cartilage, bone, and skin of mice across the healthy life course. We report dynamic turnover of the matrisome, the proteins of the extracellular matrix, in bone and cartilage during skeletal maturation, which was markedly reduced after skeletal maturity. Comparing young adult with older adult mice, new protein incorporation was reduced in all tissues. STRING clustering revealed changes in epigenetic modulators across all tissues, a decline in chondroprotective growth factors such as FGF2 and TGFβ in cartilage, and clusters indicating mitochondrial dysregulation and reduced collagen synthesis in bone. Several pathways were implicated in age-related disease. Fewer changes were observed for skin. This methodology provides dynamic protein data at a tissue level, uncovering age-related molecular changes that may predispose to disease.
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Author Response:
Reviewer #1 (Public Review):
In this study, the authors sought to characterize protein turn over in young/growing and skeletally mature mice. These authors were most interested in protein turnover in three tissues rich in collagen, proteoglycans and glycoproteins. These tissues were articular cartilage, bone and skin. They also examined protein turn over in peripheral blood as a comparison/control tissue. To accomplish this, male C57BL/6 mice were feed a heavy isotope diet for 3 weeks at an immature (4-7 week of age), young adult (12 to 15 weeks of age) and older adult (42 to 45 weeks of age) ages. Tibial bone, articular cartilage and skin were collected, and mass spectrometry was used to identify labelled (heavier) and therefore newer proteins relative to lighter/ residual proteins. They observed that turnover …
Author Response:
Reviewer #1 (Public Review):
In this study, the authors sought to characterize protein turn over in young/growing and skeletally mature mice. These authors were most interested in protein turnover in three tissues rich in collagen, proteoglycans and glycoproteins. These tissues were articular cartilage, bone and skin. They also examined protein turn over in peripheral blood as a comparison/control tissue. To accomplish this, male C57BL/6 mice were feed a heavy isotope diet for 3 weeks at an immature (4-7 week of age), young adult (12 to 15 weeks of age) and older adult (42 to 45 weeks of age) ages. Tibial bone, articular cartilage and skin were collected, and mass spectrometry was used to identify labelled (heavier) and therefore newer proteins relative to lighter/ residual proteins. They observed that turnover decreased with age and there was far less protein turn over in bone and cartilage relative to skin. The study design is appropriate, and the ages of the mice are justified, but to be clear, the oldest group of mice used were not old and do not reflect a comparable period of old age/elderly in humans. Rather this oldest group of mice in this study reflect mature adulthood. The results of this paper are not overly surprising given previous work in the field, but what sets this work apart is the level of detail that this method afforded. This work provides detailed information about what collagens and other cellular proteins turn over with aging in these matrix rich tissues, providing information that is complimentary to what is collected with other omics methods such as RNA seq. A limitation of this work is that only male mice were used there are known differences in bone turn over and aging as a function of sex.
We thank the reviewer for their comments. The initial purpose of our study was to consider how matrix rich connective tissues alter their synthetic activity with age and to see whether this related to how prone these tissues are to developing age-related disease. We thought very carefully about which ages of mice to choose for our analysis; we correctly hypothesised that the period of rapid skeletal growth would provide a good positive control for synthetic activity within articular cartilage and bone. Once skeletal maturity had occurred these tissues, as expected, reduced their synthetic activity, particular for stable proteins of the matrisome such as the fibrillar collagens. These tissues also had a more evident adult ‘ageing’ phenotype, compared with skin and plasma although it is worth saying that all tissues still maintained quite high synthetic rates up to 45 weeks of age. We didn’t go beyond this age as mice will spontaneously develop osteoarthritis and osteoporosis, which we felt would confound our analysis. We also only looked at male mice initially, but are very keen to consider comparing male and female profiles now that we have seen the data. Whilst our data are able to confirm previous findings from other labs using different methods to measure protein turnover ((Heinemeier et al., 2016), (Verzijl et al., 2000) ) the breadth of our analysis allowed us to look at much larger numbers of regulated proteins at a given time and to look for clusters of proteins sharing common pathways. In doing so we identified both common and distinct ageing protein signatures among the 3 collagen-rich tissues.
Reviewer #2 (Public Review):
The manuscript entitled "Age-dependent changes in protein incorporation into collagen-rich tissues of mice by in vivo pulsed SILAC labelling" by Ariosa-Morejon and co-workers describes the incorporation of the stable amino acid Lys6 into different tissues in living mice. The authors used different time points during development and the adult stage and measured Lys6 incorporation rates using state-of-the-art mass spectrometry. Although protein turnover is an important issue for assessing protein stability and activity, the authors compared different tissues that differ greatly in their cellular composition and proliferation. It is known from previous studies that dividing tissues can incorporate labelled amino acids into their proteome compared to post-mitotic cells. However, this does not represent protein turnover but rather tissue turnover. A weakness of this paper is the scant attention paid to this critical point.
Thank you for this important comment which we did not address adequately in the first draft. The reviewer is correct that these tissues are very different in their composition. Articular cartilage contains just chondrocytes which are largely regarded as post-mitotic cells in healthy adult tissue. Bone cells contain a mixture of post mitotic (osteocyte), renewable (from blood monocytes) cells (osteoclasts), and proliferating cells (osteoblasts). Skin fibroblasts are mitotic cells. We have stressed this in the revised manuscript and indicate that this may in part account for some of the changes we note as the animal ages. It is perhaps surprising that, when one considers global synthetic activity, this is maintained at quite high levels in all tissues indicating that, even in cartilage, the non-collagenous tissue still turns over even though there is no recognised ‘shedding’ as seen with skin. Our analysis really highlights that it is specific groups (clusters) of proteins that change in an age-dependent and tissue dependent manner rather than proteins generally. This has been emphasised in the revised manuscript. The other important point to make is that connective tissue cells, in particular, rely on their native matrix to maintain their phenotype and that is why it is important to do these sorts of analyses in the native tissue (whatever its cellular makeup), rather than trying to extrapolate from studies in isolated populations of cells in vitro.
Reviewer #3 (Public Review):
The authors have conducted an elegant study to monitor proteostasis in collagen rich tissues from immature, adult and ageing mice using SILAC labelling combined with mass spectrometry analysis. Resulting data demonstrate rapid turnover of extracellular matrix proteins in immature tissues, which declines with ageing, particularly in bone and cartilage, with network analysis revealing alterations in regulatory elements which may be driving this process. The methods used in this study are highly appropriate, and the data analysis is sound. The main conclusions are supported by the data presented and the study description is clear. Establishing how proteostasis is altered with ageing at the level of the proteome provides information crucial to developing strategies to prevent age-related diseases and promote healthy ageing.
A weakness of this work is the comprehensive analysis of the signaling pathways and upstream regulators involved in the age-related decline in protein turnover observed, which would provide potential targets for age-related diseases that are common in these tissues. Establishing any alterations in the abundance of specific proteins with ageing, as well as alterations in their turnover rate would identify proteins most impacted by ageing, and are therefore likely to play a role in age-associated diseases.
Thank you for this important comment. As part of our original analysis we performed a number of bioinformatic analyses for pathway enrichment, enrichment for ageing and relevant age-related diseases (osteoarthritis, osteoporosis, wound healing) and STRING clustering. We used a variety of software packages, including IPA, DAVID, the recently developed Clinical Knowledge Graph (CKG) and supplemented this with manual searches in Pubmed. As expected, we found that we were underpowered for most of the bioinformatic pathway analyses (more often used for larger transcriptomic datasets). In the revised manuscript we have included supplementary data showing these results which identify pathways of potential interest, albeit not reaching statistical significance after correction. Both the STRING protein clusters and cluster enrichment using DAVID showed similar results that appeared robust and biologically sensible.
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Evaluation Summary:
Capturing the rate and degree of protein turnover in tissues rich in collagen, proteoglycans and glycoproteins over the life span provides valuable information about how these tissues age and can increase our understanding of age-related disease. Using a Stable Isotope Labeling (SILAC) method to examine protein retention, new protein incorporation and protein turnover in three high collage content tissues, the authors show that turnover is low in older mice in these tissues, but the depth of the data generated provide a detailed examination of what low turnover means at a level we have not previously had. This paper would be of interest to a broad range of scientists studying connective tissues in the context of development and ageing.
(This preprint has been reviewed by eLife. We include the public reviews from the …
Evaluation Summary:
Capturing the rate and degree of protein turnover in tissues rich in collagen, proteoglycans and glycoproteins over the life span provides valuable information about how these tissues age and can increase our understanding of age-related disease. Using a Stable Isotope Labeling (SILAC) method to examine protein retention, new protein incorporation and protein turnover in three high collage content tissues, the authors show that turnover is low in older mice in these tissues, but the depth of the data generated provide a detailed examination of what low turnover means at a level we have not previously had. This paper would be of interest to a broad range of scientists studying connective tissues in the context of development and ageing.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
In this study, the authors sought to characterize protein turn over in young/growing and skeletally mature mice. These authors were most interested in protein turnover in three tissues rich in collagen, proteoglycans and glycoproteins. These tissues were articular cartilage, bone and skin. They also examined protein turn over in peripheral blood as a comparison/control tissue. To accomplish this, male C57BL/6 mice were feed a heavy isotope diet for 3 weeks at an immature (4-7 week of age), young adult (12 to 15 weeks of age) and older adult (42 to 45 weeks of age) ages. Tibial bone, articular cartilage and skin were collected, and mass spectrometry was used to identify labelled (heavier) and therefore newer proteins relative to lighter/ residual proteins. They observed that turnover decreased with age and …
Reviewer #1 (Public Review):
In this study, the authors sought to characterize protein turn over in young/growing and skeletally mature mice. These authors were most interested in protein turnover in three tissues rich in collagen, proteoglycans and glycoproteins. These tissues were articular cartilage, bone and skin. They also examined protein turn over in peripheral blood as a comparison/control tissue. To accomplish this, male C57BL/6 mice were feed a heavy isotope diet for 3 weeks at an immature (4-7 week of age), young adult (12 to 15 weeks of age) and older adult (42 to 45 weeks of age) ages. Tibial bone, articular cartilage and skin were collected, and mass spectrometry was used to identify labelled (heavier) and therefore newer proteins relative to lighter/ residual proteins. They observed that turnover decreased with age and there was far less protein turn over in bone and cartilage relative to skin. The study design is appropriate, and the ages of the mice are justified, but to be clear, the oldest group of mice used were not old and do not reflect a comparable period of old age/elderly in humans. Rather this oldest group of mice in this study reflect mature adulthood. The results of this paper are not overly surprising given previous work in the field, but what sets this work apart is the level of detail that this method afforded. This work provides detailed information about what collagens and other cellular proteins turn over with aging in these matrix rich tissues, providing information that is complimentary to what is collected with other omics methods such as RNA seq. A limitation of this work is that only male mice were used there are known differences in bone turn over and aging as a function of sex.
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Reviewer #2 (Public Review):
The manuscript entitled "Age-dependent changes in protein incorporation into collagen-rich tissues of mice by in vivo pulsed SILAC labelling" by Ariosa-Morejon and co-workers describes the incorporation of the stable amino acid Lys6 into different tissues in living mice. The authors used different time points during development and the adult stage and measured Lys6 incorporation rates using state-of-the-art mass spectrometry. Although protein turnover is an important issue for assessing protein stability and activity, the authors compared different tissues that differ greatly in their cellular composition and proliferation. It is known from previous studies that dividing tissues can incorporate labelled amino acids into their proteome compared to post-mitotic cells. However, this does not represent protein …
Reviewer #2 (Public Review):
The manuscript entitled "Age-dependent changes in protein incorporation into collagen-rich tissues of mice by in vivo pulsed SILAC labelling" by Ariosa-Morejon and co-workers describes the incorporation of the stable amino acid Lys6 into different tissues in living mice. The authors used different time points during development and the adult stage and measured Lys6 incorporation rates using state-of-the-art mass spectrometry. Although protein turnover is an important issue for assessing protein stability and activity, the authors compared different tissues that differ greatly in their cellular composition and proliferation. It is known from previous studies that dividing tissues can incorporate labelled amino acids into their proteome compared to post-mitotic cells. However, this does not represent protein turnover but rather tissue turnover. A weakness of this paper is the scant attention paid to this critical point.
-
Reviewer #3 (Public Review):
The authors have conducted an elegant study to monitor proteostasis in collagen rich tissues from immature, adult and ageing mice using SILAC labelling combined with mass spectrometry analysis. Resulting data demonstrate rapid turnover of extracellular matrix proteins in immature tissues, which declines with ageing, particularly in bone and cartilage, with network analysis revealing alterations in regulatory elements which may be driving this process. The methods used in this study are highly appropriate, and the data analysis is sound. The main conclusions are supported by the data presented and the study description is clear. Establishing how proteostasis is altered with ageing at the level of the proteome provides information crucial to developing strategies to prevent age-related diseases and promote …
Reviewer #3 (Public Review):
The authors have conducted an elegant study to monitor proteostasis in collagen rich tissues from immature, adult and ageing mice using SILAC labelling combined with mass spectrometry analysis. Resulting data demonstrate rapid turnover of extracellular matrix proteins in immature tissues, which declines with ageing, particularly in bone and cartilage, with network analysis revealing alterations in regulatory elements which may be driving this process. The methods used in this study are highly appropriate, and the data analysis is sound. The main conclusions are supported by the data presented and the study description is clear. Establishing how proteostasis is altered with ageing at the level of the proteome provides information crucial to developing strategies to prevent age-related diseases and promote healthy ageing.
A weakness of this work is the comprehensive analysis of the signaling pathways and upstream regulators involved in the age-related decline in protein turnover observed, which would provide potential targets for age-related diseases that are common in these tissues. Establishing any alterations in the abundance of specific proteins with ageing, as well as alterations in their turnover rate would identify proteins most impacted by ageing, and are therefore likely to play a role in age-associated diseases.
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