Abstract
Background
Aging human skin undergoes significant morphological and functional
changes such as wrinkle formation, reduced wound healing capacity, and
altered epidermal barrier function. Besides known age-related
alterations like DNA-methylation changes, metabolic adaptations have
been recently linked to impaired skin function in elder humans.
Understanding of these metabolic adaptations in aged skin is of special
interest to devise topical treatments that potentially reverse or
alleviate age-dependent skin deterioration and the occurrence of skin
disorders.
Results
We investigated the global metabolic adaptions in human skin during
aging with a combined transcriptomic and metabolomic approach applied
to epidermal tissue samples of young and old human volunteers. Our
analysis confirmed known age-dependent metabolic alterations, e.g.
reduction of coenzyme Q10 levels, and also revealed novel age effects
that are seemingly important for skin maintenance. Integration of
donor-matched transcriptome and metabolome data highlighted
transcriptionally-driven alterations of metabolism during aging such as
altered activity in upper glycolysis and glycerolipid biosynthesis or
decreased protein and polyamine biosynthesis. Together, we identified
several age-dependent metabolic alterations that might affect cellular
signaling, epidermal barrier function, and skin structure and
morphology.
Conclusions
Our study provides a global resource on the metabolic adaptations and
its transcriptional regulation during aging of human skin. Thus, it
represents a first step towards an understanding of the impact of
metabolism on impaired skin function in aged humans and therefore will
potentially lead to improved treatments of age related skin disorders.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-017-3547-3)
contains supplementary material, which is available to authorized
users.
Keywords: Skin, Aging, Metabolism, Metabolomics, Transcriptomics,
Systems biology
Background
Tissue aging is caused by intrinsic and extrinsic factors that induce
complex molecular changes and, in turn, a deterioration of cellular
structures and function. These changes are major causes of age-related
diseases like cancer or cardiovascular disorders [[41]1, [42]2]. The
main molecular adaptations occurring during aging are loss of genomic
stability due to reduced DNA repair capacities [[43]3], loss of
proliferative potential caused by increased senescence [[44]1, [45]4],
and age-related alterations in the DNA-methylation patterns that affect
cellular plasticity [[46]5, [47]6]. Metabolic adaptations are also
considered to play a major role in aging [[48]7–[49]10]. For instance,
the metabolic function of mitochondria is progressively impaired during
aging in different tissues [[50]8, [51]11]. This can result in
increased generation of reactive oxygen species that foster genomic
instability [[52]8, [53]12]. Moreover, several studies reported that
caloric restrictions and diet adaptations, such as supplementation of
food with branched chain amino acids [[54]13, [55]14], can
significantly increase lifespan [[56]15]. This suggests that metabolic
activity as well as nutrient sensing pathways are highly relevant for
cellular aging processes (reviewed in [[57]10]). Accordingly,
interference with the insulin/IGF1 and the mammalian target of
rapamycin (mTOR) pathways increased lifespan in different model
organisms [[58]7, [59]16–[60]18].
While the underlying molecular mechanisms that cause cellular aging and
influence lifespan of model organisms are well described, the
mechanistic details of age-related alterations in human tissues in vivo
are barely explored. This is due to the low availability of healthy
human tissue samples from internal organs of donors of different age
[[61]19]. Skin is an exception because it’s simply accessible and thus
constitutes a good model to study aging in humans [[62]20]. Skin aging
is caused by both intrinsic factors including age-dependent changes in
hormonal levels and extrinsic factors, such as smoking and UV exposure.
Both intrinsic and extrinsic factors induce significant morphological
changes such as wrinkles, reduced elasticity, increased pigmentation
and thinning of the epidermis [[63]2, [64]20–[65]24]. Moreover,
metabolic studies suggested that aged epidermal keratinocytes shift
their energy generation from aerobic respiration in mitochondria to
anaerobic glycolysis. This was attributed to a reduction of coenzyme
Q10 levels in the respiratory chain [[66]25–[67]27]. Notably,
metabolites such as coenzyme Q10 or vitamins are widely used in
anti-aging treatment in skin care products [[68]25, [69]28–[70]31].
These examples highlight the relevance of metabolic changes in human
skin aging, both as drivers of functional deterioration as well as a
target for anti-aging treatments.
Besides the reduction in respiratory chain activity, however, very
little is known about metabolic alterations in aged skin. Due to the
fact that metabolism is crucial to support further skin functions, e.g.
the epidermal water loss barrier or epidermal differentiation, we
analyzed the global metabolic adaptations occurring in human epidermal
skin during aging. We applied an integrative metabolomics and
transcriptomics approach on healthy epidermal tissue from young and old
human donors. The analysis revealed age-dependent metabolic adaptations
of metabolites already reported to be involved in skin aging and
metabolites with potential impact on skin function, such as osmolytes.
Moreover, the integration of transcriptome and metabolome data revealed
a transcriptionally regulated reduction in protein as well as polyamine
biosynthesis and adaptation in upper glycolysis and glycerolipid
biosynthesis in aged skin.
Results
Differences in the epidermal skin metabolome of young and old human
volunteers
To chart metabolic adaptations in human skin during aging in vivo, we
performed non-targeted metabolomics analysis of epidermal skin tissue
samples obtained from the inner side of the forearm of 28 young (20 to
25 years) and 54 old (55 to 66 years) female human donors. Polar
metabolite extracts were analyzed by flow injection time-of-flight mass
spectrometry as described before (Additional file [71]1, Additional
file [72]2) [[73]32]. In total we detected 4585 ions of which 829 could
be putatively assigned to 2530 metabolites listed in the Human
Metabolome Database v3.0 (HMDB) [[74]33] on the basis of accurate mass,
isotopologue abundance, and cross-correlation [[75]32]. To account for
differences in the amount of epidermal tissue, we normalized the
intensities using quantile normalization [[76]34]. To find age related
differences in metabolism, we performed two different analyses: On the
one hand we correlated metabolite intensities with donor age
(Fig. [77]1a) and on the other hand we performed a univariate analysis
to compare metabolite levels in skin of young and old donors
(Additional file [78]3 A). In both analyses, less than 10% of the
metabolites indicated significant age-dependent alterations. In the
correlation analysis, 34 metabolites negatively correlated and 46
positively correlated with age (Fig. [79]1a). Comparably, the
univariate analysis indicated that the levels of 10 metabolites
decreased in old compared to young donors while 46 metabolites
increased (Additional file [80]3 A).
Fig. 1.
Fig. 1
[81]Open in a new tab
Metabolome differences between young and old human skin. a Correlation
analysis of metabolites and donor age. Correlations with |rho| > 0.25
and q < 0.01 are considered significant. b-c Example of metabolites
that either decrease (in b) or increase (in c) significantly during
aging (q < 0.05; differential analysis old vs young). d Metabolic
pathway enrichment analysis on significantly changing ions obtained
selecting metabolites with (|log[2](fold-change)| > 0.1 and q < 0.05)
and the pathways as defined by the Human Metabolome DB (HMDB). Only
enriched pathways are listed
Next, we focused on metabolites with potential relevance for skin
function that decreased with advancing age. Consistent with previous
studies, coenzyme Q10 levels were lower in epidermis of elderly donors
(Fig. [82]1b). This reduction in coenzyme Q10 is thought to play a
major role in impaired mitochondrial function during aging [[83]26,
[84]27]. Moreover, we found metabolites with age-dependent level
reduction that are known to feedback to important cellular signaling
processes. For instance, retinoic acid was found lower in aging skin
and is involved in the regulation of keratinocyte proliferation and
differentiation during epidermal homeostasis (Fig. [85]1b) [[86]35].
Additionally, we found an age-dependent decrease of the hormone
dehydroepiandrosterone (DHEA) sulfate (Fig. [87]1b). It is known that
the blood levels of DHEA and its conjugate DHEA sulfate decrease with
age [[88]36]. Our study suggests that this age-dependent reduction of
the systemic DHEA availability translates to the in vivo concentration
in human epidermis. Furthermore, we observed an age-dependent change in
the concentration of organic osmolytes, which convey protection against
environmental stresses, for instance ultraviolet radiation, in human
skin [[89]37–[90]39]. We measured a reduction of the organic osmolyte
proline betaine and increased levels of taurine, which are involved
osmoprotection of human skin cells (Fig. [91]1b) [[92]39–[93]42]. With
an average 1.8-fold increase, taurine was the largest change in the
metabolome (Additional file [94]3 A). Besides taurine, other
metabolites with potential relevance for skin function were found to be
increased such as for example the aging biomarker candidates cresol and
cresol sulfate [[95]43] and the vitamin E metabolite α-CEHC
(Fig. [96]1c). Vitamin E metabolites carry important anti-oxidative
functions in skin and protect against oxidative damage caused by UV
irradiation [[97]44]. Besides these alterations glucose levels were
also increased in aged skin (Fig. [98]1c). Previous studies showed that
glucose uptake is elevated in vitro in cultured keratinocytes from old
compared to those from young donors [[99]25]. It is thought that the
major part of the additionally taken up glucose is converted to lactate
potentially to compensate energy deficits due to defects in
mitochondrial respiration. Thus, the increased glucose levels in aged
skin might indicate that the increased glucose uptake is also relevant
in human epidermis in vivo.
To elucidate if aging induced milder but accumulated metabolic
adaptations in specific metabolic pathways, we performed a pathway
enrichment analysis on the basis of significantly changing metabolite
ions (Additional file [100]3 B). We found an enrichment for amino sugar
metabolism, ammonia recycling, glutathione metabolism, mitochondrial
electron transport chain, urea cycle and different amino acid
metabolism pathways including arginine and proline metabolism, glycine
and serine metabolism, methionine metabolism and
transcription/translation (Fig. [101]1d). In agreement, the metabolite
levels of most amino acids increased with age (Figs. [102]1a and
[103]2). The general accumulation of amino acids might be the mere
consequence of decreased protein biosynthesis associated to the reduced
proliferation in aged skin. Alternatively, amino acids are natural
moisturizing factors and their increase might reflect an adaptive
response to prevent skin dryness in the epidermis of elder humans
[[104]45].
Fig. 2.
Fig. 2
[105]Open in a new tab
Differences in amino acid metabolite levels comparing young and old
skin. The number in the title reports the measured m/z. The q-values
are FDR-corrected p-values obtained from unpaired, heteroscedastic
t-test
Age-dependent adaptations of gene expression in epidermal skin
We performed a complementary transcriptome analysis using Agilent Whole
Human Genome Oligo Microarrays 8x60K V2 on epidermal tissue samples
from 24 young and 24 old donors, of which 23 donors of each group were
also included in the metabolomics analysis (Additional file [106]1). In
total, 1053 transcripts indicated significant decreased and 932
transcripts significant increased levels (Fig. [107]3a). We compared
the identified age-dependent genes of our study with genes predicted to
be involved in aging in humans in multiple tissues [[108]46] and with
genes classified to show age-dependent changes in gene expression in
skin [[109]19, [110]47]. The identified genes with age-dependent
expression of our study had no significant overlap with any of the
age-dependent gene groups from the other studies (p > 0.05,
hypergeometric-test, Additional file [111]4). However, also a
comparison amongst the reported genes with age-dependent expression of
the different studies demonstrated no significant overlap (Additional
file [112]4). This indicated that age-dependent gene expression
strongly depends on the originating tissue. Furthermore, age-dependent
gene expression in skin likely depends on the anatomic location of the
sample and on the composition of the skin tissue, e.g. epidermis,
dermis, or subcutaneous tissue [[113]48].
Fig. 3.
Fig. 3
[114]Open in a new tab
Transcriptional differences between young and old human skin. a-b
Significant changes in gene expression of either all (a) or only
metabolic enzyme coding genes (b). c Examples of metabolic enzymes with
significantly changing gene expression. Adjusted p-values (adj.p) in
a-c) are false discovery corrected p-values using the from a
differential analysis. d Pathway enrichment analysis of significantly
changing transcripts. Q-values are FDR-corrected p-values of a
hypergeometric test
To investigate the potential role of altered expression of genes
encoding for metabolic enzymes in mediating metabolic adaptations
during aging, we focused on the 1140 transcripts that could be mapped
to genes involved in human metabolism according to the KEGG database
(Fig. [115]3b) [[116]49]. Notably, similar to the metabolic
alterations, the transcript level changes of metabolic enzymes are only
mild and with one exception do not exceed a two-fold increase or
decrease in expression (Fig. [117]3b). In total, 66 metabolic enzyme
encoding genes demonstrated a reduced expression and 90 an elevated
expression (Benjamini-Hochberg adjusted p < 0.01, |log[2](fold
change)| > 0.25, Fig. [118]3b). Among the genes with the strongest
reduction in expression we found hexokinase 2 (HK2) and glutaminase
(GLS), which were both reported to be essential for energy generation
to support proliferation in different cancer types [[119]50, [120]51].
Therefore, the age-dependent decrease of those enzymes could be related
to reduced proliferation of epidermal cells during aging [[121]52]. In
addition, glutaminase converts glutamine into glutamate, which is
involved in the homeostasis of the epidermal barrier [[122]53]
(Fig. [123]3c). Moreover, we identified enzymes with larger changes in
expression that are involved in keratinocyte differentiation. For
example, the glycerol-3-phosphate acyltransferase 3 (AGPAT9) showed
almost 50% reduction in gene expression during aging (Fig. [124]3c).
This enzyme is involved in the synthesis of glycerolipids that are
essential for the formation of the epidermal barrier [[125]54,
[126]55]. If this age-dependent decrease of the enzyme is functional
and results in reduced glycerolipid biosynthesis, it might be involved
in impaired epidermal barrier formation in the stratum corneum of aged
skin [[127]28]. The expression of inositol-1(or 4)-monophosphatase 2
(IMPA2) that is involved in inositol phosphate metabolism and
hydroxysteroid (11-beta) dehydrogenase 2 (HSD11B2), which is involved
in cortisol homeostasis, were elevated in the skin of old donors
(Fig. [128]3c). Previous studies showed that both metabolic systems
adapt during differentiation and were involved in regulation of
epidermal homeostasis [[129]56–[130]59].
To elucidate the metabolic pathways that are effected by accumulated
adaptations in gene expression, we performed a pathway enrichment
analysis on transcriptome data using the KEGG pathway definition
[[131]49]. Several metabolic pathways involved in keratinocyte
differentiation showed a significant enrichment (Fig. [132]3d), for
instance inositol phosphate metabolism with generally elevated gene
expression (Additional file [133]5 B) and retinol metabolism with a mix
of increased and decreased gene expression (Additional file [134]5 E).
We additionally identified increased expression of enzymes in different
pathways including glycosaminoglycan biosynthesis, steroid hormone
biosynthesis or pantothenate and CoA metabolism (Fig. [135]3d). In
contrast, pathways involved in central carbon metabolism, in amino acid
metabolism (e.g. arginine and proline metabolism), tRNA biosynthesis or
amino- and nucleotidesugar metabolism were enriched for enzymes with
decreasing gene expression during aging (Fig. [136]3d, Additional file
[137]5 ACD). In summary, we identified age-dependent changes in gene
expression in different metabolic pathways that have been associated
with epidermal homeostasis and therefore might be important to sustain
epidermal function.
Integrated analysis of transcriptome and metabolome data
Since the age-dependent adaptations of metabolite and transcript levels
are only mild, we set out to identify metabolic enzymes that featured
an age-dependent and functional change in activity driven by altered
gene expression. We hypothesized that functional changes in expression
of enzyme encoding genes should induce alterations of the levels of
proximal metabolites. We applied a previously developed locality
scoring approach [[138]60] on the matched transcriptome and metabolome
data of 23 young and 23 old donors (Additional file [139]1). The
algorithm assumes that a functional change in enzyme levels should
induce (anti)correlating adaptations in the substrates and products of
the catalyzed reaction. It scores each enzyme by a weighted sum of the
correlation of the enzyme’s gene expression and the intensities of
surrounding metabolites.
We found 61 enzymes with significant locality scores suggesting that
altered gene expression had a functional impact on metabolic activity
(Additional file [140]6). To infer which of these functional hits
mediate age-dependent metabolic alterations, we focused on the 21
predicted enzymes with age-dependent gene expression (Table [141]1).
Amongst the top hits were the aldehyde dehydrogenase 4 family, member
A1 (ALDH4A1, Additional file [142]7) and the branched chain keto acid
dehydrogenase (BCKDHA, Additional file [143]8). Moreover, interleukin 4
induced 1 (IL4I1), which is a lysosomal amino-acid oxidase that
decreased in aged skin, had a significant locality score (Additional
file [144]9). The lower expression of this amino acid oxidase
potentially explains at least partially the increased levels of amino
acids, like phenylalanine or tyrosine, and the reduced levels of their
oxidation products, like 2-hydroxypheylacetate or homogentisate, in
aged epidermis (Additional file [145]9).
Table 1.
Results of locality analysis of genes with changing transcript levels
over time
Gene Symbol Gene Name Locality Correlation Gene/Age Diff. Analysis
Old/young
Score p r p log2 (FC) adj.p
ALDH4A1 aldehyde dehydrogenase 4 family, member A1 0.25 1E-04 −0.33
0.026 −0.26 0.006
ODC1 ornithine decarboxylase 1 0.26 2E-04 −0.26 0.076 −0.30 0.085
GALNT6 polypeptide N-acetylgalactosaminyltransferase 6 0.22 4E-04 0.38
0.01 0.30 0.112
TARS2 threonyl-tRNA synthetase 2, mitochondrial (putative) 0.31 7E-04
0.55 7E-05 0.45 7E-05
BCKDHA branched chain keto acid dehydrogenase E1, alpha polypeptide
0.21 0.002 0.34 0.022 0.18 0.026
ALDOA aldolase A, fructose-bisphosphate 0.24 0.003 0.57 3E-05 0.24
0.002
TARS threonyl-tRNA synthetase 0.29 0.003 −0.54 1E-04 −0.75 6E-06
YARS2 tyrosyl-tRNA synthetase 2, mitochondrial 0.28 0.006 −0.39 0.007
−0.27 0.011
CYP51A1 cytochrome P450, family 51, subfamily A, polypeptide 1 0.19
0.008 −0.40 0.005 −0.20 0.012
ALAD aminolevulinate dehydratase 0.24 0.008 0.19 0.204 0.22 0.061
IL4I1 interleukin 4 induced 1 0.21 0.014 −0.16 0.297 −0.58 0.052
FBP1 fructose-1,6-bisphosphatase 1 0.27 0.016 0.34 0.022 0.22 0.094
GATM glycine amidinotransferase (L-arginine:glycine amidinotransferase)
0.26 0.017 0.37 0.012 0.29 0.019
YARS tyrosyl-tRNA synthetase 0.27 0.02 −0.48 8E-04 −0.54 6E-04
GART phosphoribosylglycinamide formyltransferase,
phosphoribosylglycinamide synthetase, phosphoribosylaminoimidazole
synthetase 0.28 0.021 −0.35 0.019 −0.39 0.006
PRDX6 peroxiredoxin 6 0.24 0.021 0.36 0.015 0.14 0.056
AGA aspartylglucosaminidase 0.18 0.032 0.25 0.101 0.15 0.077
TST thiosulfate sulfurtransferase (rhodanese) 0.24 0.034 0.31 0.039
0.31 0.041
ACP5 acid phosphatase 5, tartrate resistant 0.26 0.038 0.31 0.036 0.36
0.007
IARS isoleucyl-tRNA synthetase 0.28 0.039 −0.40 0.006 −0.44 0.002
NDUFV2 NADH dehydrogenase (ubiquinone) flavoprotein 2, 24kDa 0.22 0.041
0.26 0.077 0.13 0.062
[146]Open in a new tab
All genes with p < 0.05 for the locality analysis and p < 0.1 for the
gene-age correlation or the differential analysis are listed. The full
results are summarized in Additional file [147]6
Next we concentrated on three cases that might be relevant for
age-dependent skin defects: i) amino acid tRNA synthetases, ii)
polyamine biosynthesis, and iii) switch between glycolysis and
glycerolipid metabolism. Several amino acid tRNA synthetases indicated
a significant locality score and age-dependent gene expression
(Table [148]1). Both the increase of amino acid metabolite levels and
change in tRNA synthetase expression with age emerged already in the
individual analysis of metabolome and transcriptome (Fig. [149]2,
Additional file [150]5 C). We identified five amino acid - tRNA
synthetase pairs with significantly decreased gene expression of the
tRNA synthetases and elevated levels of the corresponding amino acids
in aged skin (Fig. [151]4). This suggests that protein biosynthesis is
- like in other organisms and tissues [[152]61] - reduced in old skin,
which could be linked to a lower proliferation in the epidermis
[[153]52].
Fig. 4.
Fig. 4
[154]Open in a new tab
Change of tRNA synthetases and amino acid levels during aging. Q-values
are FDR-corrected p-values of a t-test comparing metabolite intensities
of old and young skin samples. Stars (*) mark genes with significant
changes in gene expression comparing skin from old and young donors
(|log[2](old/young)| > 0.25, adj.p < 0.01)
The ornithine decarboxylase 1 (ODC1) indicated a decreased gene
expression in aged skin and was amongst the top hits of the locality
scoring (Table [155]1, Fig. [156]5). ODC1 catalyzes the conversion of
ornithine to putrescine, the committing step in the biosynthesis of
polyamines. Polyamines are essential to support cell growth and
proliferation in normal and cancerous cells [[157]62]. In skin it has
been shown that ODC1 gets activated upon UV exposure [[158]63] and is
crucially involved in the development of both squamous and basal cell
carcinomas [[159]64–[160]66]. Polyamines were reported to decrease
during aging in different organism and supplementation of them to an
organism’s diet increased live span [[161]67]. Moreover, in rat skin it
was shown that ornithine decarboxylase activity decreases with age
[[162]68]. Our study suggests that ornithine decarboxylase activity
declines also in epidermal skin tissue of humans during aging. However,
whether the probable reduction in polyamine biosynthesis is involved in
the reduced proliferation in the epidermis still remains open
[[163]52].
Fig. 5.
Fig. 5
[164]Open in a new tab
Age-dependent changes in ornithine utilization. Upper panel: Locality
scores for and metabolite-gene correlation. Lower panel: Metabolite-age
and gene-age correlation. Node size indicates statistical significance
Finally, we elaborate on a last case of age-dependent metabolic
adaptions at the interface of upper glycolysis and glycerolipid
metabolism. The locality analysis identified the fructose
bisphosphatase 1 (FBP1) and aldolase A (ALDOA) as age-dependent enzymes
with correlating metabolite changes (Table [165]1). Moreover, in the
transcriptomics analysis AGPAT9, HK2 and glycerol kinase (GK) were
amongst the enzymes with the highest magnitude changes (Fig. [166]3bc).
A detailed investigation within the context of the metabolic network
indicated that the decreased expression of HK2 might explain the
increased glucose (hexose) metabolite pool and the decreased levels of
pentose phosphates metabolites including sedoheptulose phosphate and
the pentose phosphates in aged skin (Fig. [167]6). Additionally, the
slightly elevated transcript levels of FBP1 as well as ALDOA and the
reduced levels of phosphofructokinase (PFKP) could suggest that
glycolytic flux is reduced and gluconeogenesis activated (Fig. [168]6).
However, this is contradicted by previous studies reporting that old
keratinocytes increase glycolytic flux [[169]25] and also by the
slightly increased expression of glyceraldehyde 3-phosphate
dehydrogenase (GAPDH, Fig. [170]6). Another potential explanation could
be the link of glycolysis to glycerolipid metabolism. The expression of
AGPAT9 and GK, both involved in glycerolipid biosynthesis, was
significantly lowered during aging suggesting that glycerolipid
biosynthesis is reduced in old skin (Fig. [171]6). Recent studies
suggested that increased glycerolipid biosynthesis during keratinocyte
differentiation mediated by elevated AGPAT9 expression is essential for
epidermal barrier formation [[172]54, [173]55]. The reduction in
glycerolipid biosynthesis could lead to a reduced necessity of carbons
from upper glycolysis in the epidermis of old human donors which would
also explain the transcriptional and metabolic adaptations we observed.
Fig. 6.
Fig. 6
[174]Open in a new tab
Changes in upper glycolysis, glycerolipid metabolism and pentose
phosphate pathway during aging. Q-values are false discovery corrected
p-values of a t-test comparing metabolite intensities of old and young
skin samples. Adjusted p-values (adj.p) in A-C) are FDR-corrected
p-values from a differential analysis
Discussion
Human skin undergoes significant morphological and functional changes
during aging, including wrinkle formation, thinning of the epidermis
and altered epidermal barrier function [[175]20, [176]23, [177]24,
[178]28]. Besides these high-level adaptations, different studies
demonstrated that metabolic activity is altered in aged skin as well
[[179]25, [180]26]. In this study we aimed at expanding the knowledge
on age-dependent metabolic adaptions in human skin using a combined
transcriptomic and metabolomics approach applied on epidermal skin
tissue samples of young and old human donors. It should be noted, that
we used exclusively skin samples from female volunteers. Although there
is no evidence that gender-related genes are affected, we cannot
completely rule out this possibility.
Both the metabolomics and transcriptomics analysis revealed that less
than 10% of the detectable metabolites and transcripts adapted
significantly during aging. Importantly, in comparison to many other
studies the magnitudes of the age-dependent metabolite and transcript
changes were only minor. From our perspective, this is not surprising,
because in contrast to other biological perturbations such as cancerous
transformations - that are associated with massive cellular alterations
- epidermal cells need to maintain in general the functionality of
human skin whether they are young or old. Nevertheless, the minor
metabolite as well as expression changes that we have identified in
this study could contribute to the morphological and molecular
alterations that are associated with skin aging.
Due to the mild adaptions, the identification of functionally altered
metabolic activity in aged skin interpretation of significant
metabolite and transcript changes of small magnitude is especially
challenging. Therefore, we employed the previously presented locality
scoring approach [[181]60] to identify age-dependent transcriptional
alterations of enzymes that functionally effect proximal metabolic
activity and thus metabolite levels. This integrated analysis revealed
age-dependent, concerted metabolite and transcript changes that are
potentially relevant for skin and in particular epidermal function.
Additionally, in the individual analysis of the two datasets we
identified other adaptations of metabolites and transcripts of high
magnitude that are most likely relevant for altered skin function in
aged skin. Together, we categorize those alterations into adaptations
that potentially effect cellular signaling, epidermal barrier, and skin
structure (Fig. [182]7).
Fig. 7.
Fig. 7
[183]Open in a new tab
Overview of age-dependent metabolic changes and their potential
functional implication in human skin
Feedback of metabolic alterations in aged skin to cellular signaling
The first category of metabolic adaptations includes altered
hydrocortisone homeostasis and decrease of retinoic acid metabolite
levels during aging. Both are involved in the regulation of
proliferation and differentiation in epidermal keratinocytes, which is
important for continuous epidermal maintenance (Fig. [184]7) [[185]35,
[186]57, [187]58]. Interestingly, recent studies demonstrated that
topical treatment with retinoids increased epidermal thickening and
also reduced the effects of photoaging [[188]69, [189]70]. Therefore,
the reduction in retinoid levels is potentially involved in the
decrease of keratinocyte proliferation and reduction of epidermal
thickness during aging [[190]52, [191]71].
Age-dependent metabolic adaptations and epidermal barrier function
The second group contains age-dependent metabolic adaptations that
might affect epidermal barrier function. These comprise adaptations of
the levels of amino acids functioning as natural moisturizing factors
[[192]45], of α-CEHC functioning as antioxidant [[193]44, [194]72] and
of proline betaine as well as taurine serving as organic osmolytes
[[195]37–[196]39]. Considering epidermal skin function, the latter are
of special interest. Skin cells are frequently exposed to environmental
stresses, such as UV irradiation or climatic changes, that cause highly
varying osmotic pressures [[197]45]. For instance UV radiation induces
oxidative stress [[198]73] that causes cell hydration changes and thus
hyperosmotic stress [[199]39, [200]45]. Under these stress conditions,
skin cells actively take up organic osmolytes, such as taurine or
betaine, to regulate intracellular water levels and counteract cell
hydration changes [[201]37–[202]39, [203]45]. Therefore organic
osmolytes are suggested to play a major role in maintaining skin
hydration [[204]45]. Additionally, organic osmolytes act as
antioxidants that prevent oxidative damage induced by environmental
stressors [[205]45]. Furthermore, taurine exhibits anti-apoptotic
activity, prevents cell membrane disruption upon UV exposure [[206]45,
[207]74], and stimulates the synthesis of lipids for the epidermal
barrier [[208]45, [209]75]. Interestingly, proline betaine was reduced
in aged skin while taurine levels were upregulated. Thus, we
hypothesize that the regulation of organic osmolytes appears to be more
complex and a balanced interplay of these molecules has to be present
to induce positive effects. In aged skin we observed an altered balance
comparing proline betaine and taurine levels suggesting that increased
taurine levels alone are not sufficient to prevent body dehydration
through transepidermal water loss and to protect against environmental
stresses like UV irradiation in the thinned aged skin.
Moreover, using our integrative approach we identified age-dependent
adaptations at the interface of upper glycolysis and glycerolipid
metabolism with potential impact on epidermal barrier function
(Fig. [210]7). On a first sight the metabolite and transcript
differences between young and old skin suggest that glycolytic flux is
reduced and gluconeogenetic flux increased during aging. However, this
contradicts with a previous study in young and old keratinocytes in
vitro, which reported that old keratinocytes have increased glucose
uptake and lactate secretion indicative of increased glycolysis
[[211]25]. Since important enzymes for the rerouting of glycolytic flux
into glycerolipid metabolism showed a decreased expression during
aging, another potential explanation could be that less carbon is
needed to fuel glycerolipid metabolism in the epidermis of old donors.
This reduction in glycerolipid metabolism could have severe consequence
for the lipid layer of the epidermal barrier in the stratum corneum and
might lead to impaired barrier function in aged skin [[212]54,
[213]76–[214]78].
Influence of altered metabolism on skin structure in old humans
During aging skin undergoes different structural adaptations including
thinning of the epidermis due to reduced proliferation of epidermal
keratinocytes [[215]52]. We identified different age-dependent
metabolic adaptations that might be involved in this phenotype. For
example, in the integrated transcriptome and metabolome analysis, we
observed a reduction in tRNA synthetase levels linked to an increase in
the levels of their corresponding amino acids (Fig. [216]7). This
adaptation might be due to a reduced protein synthesis in the epidermis
of old donors. Multiple studies reported that protein turnover rate,
i.e. protein synthesis and protein degradation, is diminished during
aging in various organisms and that an artificial reduction of protein
synthesis prolongs lifespan (Reviewed in [[217]61]). We argue that
protein biosynthesis decreases during aging in human epidermis as well.
This might be cause or consequence of a reduction in proliferation of
epidermal keratinocytes [[218]52]. Additionally, the integrated
analysis revealed a second age-dependent adaptation in polyamine
biosynthesis mediated by ODC1 that also could influence proliferation
in the epidermis (Fig. [219]7) [[220]67, [221]68]. Polyamines are
necessary for cell growth and proliferation [[222]62] and therefore we
suggest that the reduction in ODC1 transcript levels during aging
causes a reduced polyamine biosynthesis which could also cause the
reduced proliferation in the aged skin [[223]52]. Besides epidermal
thinning, aged skin is also less elastic and forms wrinkles. In the
metabolomics analysis we identified decreased DHEA sulfate levels in
epidermal samples from old donors, which potentially has an influence
on these structural properties of the skin. DHEA sulfate is a human
hormone with reported age-dependent reduction of its levels in the
blood [[224]36]. Our study suggests that this age-dependent decrease in
DHEA availability is translated to the in vivo concentration in the
epidermis. DHEA and DHEA sulfate regulate collagen synthesis and matrix
metalloproteinase (MMP) production in the dermis [[225]79–[226]81],
which are both causing mechanical defects in aged skin including
wrinkling and loss of elasticity [[227]22, [228]81, [229]82]. Though we
did measure the DHEA sulfate levels only in the epidermis, we propose
that DHEA sulfate levels decrease in the whole skin tissue during aging
in vivo. Probably, they are mediating alterations in the collagen
network in the dermis that account for the changed mechanical
properties of aged skin. Indeed, different studies demonstrated that
topical treatment with DHEA induced collagen synthesis as well as
decreased MMP levels in aged skin and oral treatment improved skin
status of old humans [[230]36, [231]81].
Conclusion
The integrated metabolome and transcriptome analyses on human epidermal
tissue samples provide an overview of the global metabolic adaptations
in epidermal skin during aging and their potential impact on skin
function. Considering that different metabolites, including coenzyme
Q10 [[232]83], α-CEHC [[233]72] or DHEA sulfate [[234]36, [235]81], are
able to reverse age related changes in human skin and are therefore
included in anti-aging skin care products, this knowledge will be
valuable to improve skin care and treatments of age-related skin
disorders like xerosis [[236]84].
Methods
Collection of skin tissue samples
Suction blistering is a technic that can be used to separate epidermis
from dermis by purely mechanical forces avoiding chemical or thermal
damage [[237]85]. Epidermis samples (suction blister roofs) were
obtained as described previously from the inner forearms of 28 young
(20 to 25 years) and 54 old (aged between 55 and 66 years) healthy
female volunteers [[238]86]. Epidermis samples were taken and
immediately stored at -80°C. For metabolomics analysis epidermis
samples were lysed in 350 μL isopropanol utilizing a Precellys 24
homogenizer (Peqlab). For transcriptomics analysis the epidermis
samples were homogenized using a ball mill (MM301, Retsch and
TissueLyser Adapter Set, Qiagen).
Non-targeted metabolomics analysis
For mass spectrometric analysis intracellular samples were analyzed in
undiluted or in a 1-10 dilution in ddH[2]O, and extracellular samples
were analyzed with a 1-20 dilution in ddH[2]O by flow injection
analysis on an Agilent Q-TOF 6550 QTOF instrument (Agilent) in negative
mode 4 GHz, high resolution in a m/z range of 50-1000 [[239]32]. A
60:40 mixture of isopropanol:water supplemented with NH[4]F at pH 9.0,
as well as 10 nM hexakis(1H, 1H, 3H-tetrafluoropropoxy)phosphazine and
80 nM taurocholic acid for online mass calibration. Ions were annotated
to metabolites based on exact mass considering [M-H+] and [M + F-] and
0.001 Da mass accuracy using the HMDB v3.0 database [[240]33]. To
account for mass differences of the skin samples, metabolite
intensities were normalized using quantile normalization [[241]34]. All
data analysis was done using Matlab 2014b (The Mathworks).
For correlation analysis of metabolite levels with age, we calculated
the Pearson’s correlation between ion intensities and donor’s age
[[242]87]. The p-values were corrected for false discovery rate using
Storey’s method (q-values) [[243]88]. Ions with |rho| > 0.25 and
q < 0.01 were considered significant.
Significantly changing ions between the old and young conditions were
identified with a univariate analysis using a two-sample t-test.
Multiple testing correction was performed by correcting p-values for
false discovery rate as described before (q-values) [[244]88]. Ions
with |log[2](fold-change)| > 0.1 and q < 0.05 were considered
significant. To identify significantly changing metabolic pathways we
performed an enrichment analysis on the univariate analysis results
using HMDB metabolic pathway definitions [[245]33]. Significantly
changing ions (|log[2](FC)| > 0.1, q < 0.05) were sorted lowest to
highest q-values. The p-values for the enrichment were calculated using
a hypergeometric test defined as
[MATH:
pPtwPtwHits|TotalAllDetected,PtwAllDetected,TotalHits=PtwAllDetectedPtwHitsTotalAllDetected−PtwAllDetectedTotalHits−PtwHitsTotalAllDetectedTotalHits :MATH]
where Total [Hits] is the total number of ions in the hit subset, Total
[AllDetected] is the total number of detected ions (background), Ptw
[Hits] is the intersect of all ions in the hit subset and ions involved
in a given pathway and Ptw [AllDetected] is the intersect of all ions
and ions involved in a given pathway. The hit subset for each pathway
and each comparison between two conditions was defined recursively by
first considering only the most significant ion, and then increasing
the hit subset with the next best significant ion at each iteration
until all significant ions were in the hit subset. Enrichment analysis
was performed on each of those hit subsets and the best p-value was
used as p-value for the enrichment of a given comparison and a given
pathway. We corrected the p-values as described before for multiple
testing by Storey's method [[246]88].
Transcriptomic analysis
Microarray analysis and data extraction was performed using Agilent
Whole Human Genome Oligo Microarrays 8x60K V2 and Agilent Feature
Extraction Software (Agilent Technologies, Waldbronn, Germany) by
Genomics Services from Miltenyi Biotec (Bergisch Gladbach, Germany).
Raw data was preprocessed and analyzed using the limma package from
bioconductor for R [[247]89]. We removed features that had in at least
50% of the cases either a saturated signal or a signal not
distinguishable of the background noise. To account for illumination
differences of the different microarrays, the feature intensities of
each microarray were normalized using quantile normalization [[248]34].
Differential gene expression was determined using linear models with
young and old groups as variables [[249]89]. The p-values were
corrected for false-discovery rate using the Benjamini-Hochberg
approach [[250]90]. Transcripts with Benjamini-Hochberg adjusted
p < 0.01 and |log[2](FC)| > 0.25 were considered significant.
To identify metabolic pathways with significantly overrepresented
transcript changes we performed an enrichment analysis on the
differentially expressed genes using KEGG metabolic pathway definitions
specific for homo sapiens (hsa) [[251]49]. Significantly changing
transcripts (Benjamini-Hochberg adjusted p < 0.01, |log[2](FC)| > 0.25)
were sorted lowest to highest p-values. The p-values for the enrichment
were calculated using a hypergeometric test defined as
[MATH:
pPtwPtwHits|TotalAllDetected,PtwAllDetected,TotalHits=PtwAllDetectedPtwHitsTotalAllDetected−PtwAllDetectedTotalHits−PtwHitsTotalAllDetectedTotalHits :MATH]
where Total [Hits] is the total number of genes in the hit subset,
Total [AllDetected] is the total number of detected genes (background),
Ptw [Hits] is the intersect of all genes in the hit subset and genes
involved in a given pathway and Ptw [AllDetected] is the intersect of
all genes and genes involved in a given pathway. The hit subset for
each pathway and each comparison between two conditions was defined
recursively by first considering only the most significant genes, and
then increasing the hit subset with the next best significant gene at
each iteration until all significant genes were in the hit subset.
Enrichment analysis was performed on each of those hit subsets and the
best p-value was used as p-value for the enrichment of a given
comparison and a given pathway. We corrected the p-values as described
before for multiple testing by Storey's method (q-values) [[252]88].
Pathways with q-values < 0.01 were considered significantly enriched.
Integration of transcriptome and metabolome data using locality analysis
The integration of transcriptomics and metabolomics data we performed
using the previously described locality analysis on matched
transcriptome and metabolome data of 23 young and 23 old human donors
[[253]60]. The algorithm scores enzymes according to the weighted sum
of the spearman correlation of their gene expression with the levels of
surrounding metabolites. Thereby the correlations are weighted
according to the distance of the metabolite to the enzyme within the
metabolic network. We used the KEGG main reaction pair network specific
for homo sapiens generated with a modified version of the
MetaboNetworks toolbox [[254]91] as metabolic model for the algorithm.
Moreover, we reannotated the ions from the metabolomics dataset to
metabolites defined in the KEGG hsa database to fit the metabolic model
[[255]49]. The locality scores S(t [i]) for a given metabolic enzyme
coding transcript t [i] are calculated with
[MATH: Sti=∑m=1MDi,m<
mrow>−2⋅1−pCi,m<
mo>⋅Ci,m∑m=1MDi,m<
mrow>−2⋅1−pCi,m<
/mrow>, :MATH]
where i is the index for the transcripts t, m is the index for
metabolites, C [i,m] is the spearman correlation between the
transcripts and the metabolite levels, D [i,m] is the network distance
between metabolite m and transcript t [i] and
[MATH:
pCi,m :MATH]
is the p-value for the spearman correlation between the transcript and
metabolite levels. The significance of the final score was determined
by comparing the real score to the score of 10000 random locality
scores S ^k[rand](t [i]) calculated using randomly permuted versions of
the distance matrix D (D [rand]). In detail the randomly permuted
scores are calculated with
[MATH: Srandkti=∑m=1MDrandi,
m−2⋅1−pCi,m<
mo>⋅Ci,m∑m=1MDrandi,
m−2⋅1−pCi,m<
/mrow>, :MATH]
where k is the index of the permutation. The p-value p(S(t [i])) for
the locality score of a given transcript S(t [i]) gets calculated with
[MATH: pSti=∑kSrandkti≥StiK, :MATH]
where K is the total number of permutations (10000 in this study).
Locality scores with p < 0.05 were considered significant. Genes with
significant locality scores were reduced to filtering them for
transcripts with age-dependent changes (p < 0.1 of a Spearman’s
correlation comparing gene expression and donor age or
Benjamini-Hochberg corrected p < 0.1 of a univariate analysis comparing
transcripts from young and old donors).
Acknowledgements