Abstract
The liver plays a vital role in maintaining whole-body metabolic
homeostasis, compound detoxification and has the unique ability to
regenerate itself post-injury. Ageing leads to functional impairment of
the liver and predisposes the liver to non-alcoholic fatty liver
disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the
molecular changes of the liver with ageing may help to understand the
crosstalk of ageing with different liver diseases. A systems-level
analysis of the ageing-induced liver changes and its crosstalk with
liver-associated conditions is lacking. In the present study, we
performed network-level analyses of the ageing liver using mouse
transcriptomic data and a protein–protein interaction (PPI) network. A
sample-wise analysis using network entropy measure was performed, which
showed an increasing trend with ageing and helped to identify ageing
genes based on local entropy changes. To gain further insights, we also
integrated the differentially expressed genes (DEGs) between young and
different age groups with the PPI network and identified core modules
and nodes associated with ageing. Finally, we computed the network
proximity of the ageing network with different networks of liver
diseases and regeneration to quantify the effect of ageing. Our
analysis revealed the complex interplay of immune, cancer signalling,
and metabolic genes in the ageing liver. We found significant network
proximities between ageing and NAFLD, HCC, liver damage conditions, and
the early phase of liver regeneration with common nodes including
NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. Overall, our study maps
the network-level changes of ageing and their interconnections with the
physiology and pathology of the liver.
Subject terms: Cellular signalling networks, Data mining, Gene
regulatory networks, Microarrays, Network topology
Introduction
Ageing is an inevitable complex process altering a multitude of
cellular processes. Several studies employing animal models across
different organs have outlined the general hallmarks of ageing related
to epigenetic modifications, cellular senescence, altered intercellular
communication, telomere shortening, nutrient sensing deregulation,
mitochondrial dysfunction, stem cell exhaustion, loss of proteostasis,
genomic instability, which culminate in the loss of tissue
homeostasis^[26]1. The complexity of ageing process is further
heightened by the interconnected feature of some of these
processes^[27]2. Different factors are suggested to cause or contribute
to ageing, including DNA damage, free radical (ROS) accumulation and
metabolic dysfunction^[28]3. The oxidative theory of ageing proposes
macromolecular damage by the products of metabolism and inefficient
repair.
Molecular pathways involving IGF1/GH and mTOR have been implicated in
the ageing process^[29]4,[30]5. Caloric restriction and mTOR inhibition
by rapamycin slow down many age-dependent processes and extends
lifespan^[31]6,[32]7. With the advent of high-throughput techniques,
biological processes underlying the initiation and progression of
ageing can be unfolded at the systems level. However, most studies
focused on identifying DEGs and patterns of gene expression in ageing
to characterize the transcriptomic changes^[33]8–[34]10. The
upregulation of inflammatory, immune and stress response genes has been
reported in different microarray and RNA-Seq experiments of ageing in
mice^[35]11,[36]12. The inflammaging theory postulates ageing accrues
inflammation^[37]13. Tissue-wise transcriptomics study across multiple
age groups in mice shows distinct gene expression signatures in
different organs, with the liver undergoing extensive changes over time
compared to other tissues^[38]9. The liver is an important metabolic
organ that plays a vital role in synthesizing plasma proteins, clotting
factors, triglycerides, cholesterol, glycogen, and
detoxification^[39]14,[40]15. Therefore, understanding how ageing
rewires the regulatory network of the liver is crucial.
The impairment of structure and function of liver tissue with ageing
exacerbates the risk of liver diseases and affects its regeneration
potential after damage^[41]16. Non-alcoholic fatty liver disease
(NAFLD) is the commonly seen pathological condition of the liver that
evolves into non-alcoholic steatohepatitis (NASH), cirrhosis and
hepatocellular carcinoma (HCC). The progression of NAFLD to NASH and
further to HCC is favoured by increased inflammation in old age^[42]13.
Interwinding nature of liver ageing and age-related diseases may create
a futile cycle of each fuelling the other, leading to a transition from
chronological ageing to pathological ageing. In addition to increasing
the disease risks, ageing also delays regeneration after partial
hepatectomy (PH)^[43]16. Most of the studies designed to understand
liver diseases were dealt independently of each other and without
involving the intrinsic process of ageing^[44]17. Delineating the
shared mechanisms inherent to the ageing process and age-related
disease shows a road ahead, thereby suggesting therapeutics for liver
diseases that are influenced by age.
Network-based approaches can be applied to understand the dynamic
changes in gene expression patterns with lifespan and to study the
crosstalk between ageing and ageing-related diseases. This provides a
systems-level understanding and helps to map dynamical changes. The PPI
network provides a scaffold to integrate gene expression data and study
the statistical and topological properties of the network in the
context of liver ageing and its related diseases. The usage of the PPI
network helps to distinguish direct and indirect control compared to
the correlation-based co-expression network.
In this work, we studied how the statistical properties of the liver
network change with ageing by integrating the PPI network and mRNA
expression profiles of mouse liver samples across ten different age
groups available from Tabula Muris Consortium^[45]10. Network entropy
quantifying randomness offers a new perspective for studying ageing and
diseases. We show that entropy of the liver network increases with
ageing, indicating the increase in randomness due to network disruption
by genomic alterations. We computed the local entropy measure to
identify genes and pathways associated with ageing. The genomic
alterations in ageing may either increase or decrease the randomness of
the local connectivity patterns (change the probability of
interactions)^[46]18–[47]20. A decrease in entropy signifies specific
signaling interactions with higher weights, while an increase in
entropy signifies the unpredictable nature of interactions. To gain
further insights, we integrated the DEGs between young and different
age groups with the PPI network to identify core modules and nodes that
show changes in local and global topological network measures with
ageing. Finally, we computed the network proximity of the ageing
network with different networks of liver diseases and regeneration to
study the effect of ageing. The workflow of the study is shown in
Fig. [48]1.
Figure 1.
[49]Figure 1
[50]Open in a new tab
The workflow to study the network-level changes of ageing and its
association with tissue regeneration and diseases in the mouse liver.
Methods
Network entropy-based approach to analyze liver ageing:
Transcriptomics data (bulk RNA-Seq) of mouse liver tissue with age
groups 3, 6, 9, 12, 15, 18, 21, 24, and 27 months was obtained from GEO
with accession number [51]GSE132040 (Tabula Muris Consortium). Each age
group has 3–4 replicate samples. The raw count data was normalized
using variance stabilizing transformation (VST)^[52]21. An
entropy-based approach was used to integrate the gene expression data
with the PPI network. Mouse-specific STRING PPI network (10,596 nodes
and 86,074 edges) with interaction confidence-score cut-off ≥ 0.9 was
used as the initial PPI network. A network characterised by a specific
number of nodes, edges and edge weights is considered an instance in an
ensemble of large number of networks with similar features. This system
has two sets of observables related to degree sequence and distribution
of edge weights. The entropy metric of a network is given by
calculating the maximum entropy of the ensemble satisfying the given
constraints (with the identical topological and spatial structure of
the network) rather than the original network (see supplementary
methods)^[53]20. For integration of gene expression and PPI network,
nodes in the PPI network are assigned with their corresponding gene
expression values specific to a particular sample. The edges connecting
nodes are weighted as the distance between gene expression values. The
edge weights are converted to a distribution by partitioning them into
number of bins equal to the square root of number of nodes in the
network. While building the network, nodes with zero gene expression
value are removed from the network. Hence, the final network which is
subjected to entropy maximization differs from the original PPI network
and is sample-specific. Therefore, the static PPI network evolves when
it is integrated with sample-specific gene expression.
Further, the network entropy of a sample can be used to derive the
entropy associated with a single node that takes the form of Shannon
entropy (local entropy) (see Supplementary Methods). The Wilcoxon rank
sum test was applied to identify nodes showing significant differences
(FDR < 0.05) in entropy between groups of samples at the single-node
level. This analysis was performed by considering samples of 3–6 months
old mice as the younger age group and samples of 24–27 months old mice
as the older age group. The pathway enrichment analysis of nodes that
display significant differences in single-node was performed using
Enrichr^[54]22 to obtain significantly affected pathways (adjusted
p-value < 0.05).
Graph theoretical analysis of ageing PPI network
Unlike the previous approach, which integrates sample-specific gene
expression with the PPI network for entropy calculation, we
alternatively constructed the age group-wise networks to compare the
local and global network measure changes with ageing. For this, DEGs
comparing 3 months old mice with 18, 24 and 27 months old were used for
building individual PPI networks. DEGs identified using the DESeq2
pipeline were integrated with STRING PPI (confidence-score
cut-off ≥ 0.9) to build individual networks for comparison. Each PPI
network was further expanded by including the first neighbours of DEGs,
and this network was considered for all the downstream analyses.
Each PPI network was analyzed using the CytoHubba plugin in Cytoscape
3.9.0^[55]23. A PPI network is assumed to be an undirected network
G = (V, E) with V as set of nodes and E as set of edges connecting the
nodes. CytoHubba identifies essential hub nodes and subnetworks within
the PPI network using various local and global metrics^[56]24. Each of
these metrics is associated with a function F which assigns every node
v a numeric value F(v). A node u is awarded a higher rank compared to
another node v if F(u) > F(v). A local ranking method only considers
the relationship between the node and its direct neighbours to
calculate the score. On the other hand, a global ranking method assigns
a score to a node based on its relationship with the entire network.
For local measure, we used Maximal Clique Centrality (MCC), which is
based on the concept of a clique that emphasizes the highly connected
clusters within a network. A clique C in a network is a subset of nodes
(C ⊆ V) such that every pair of nodes is connected. Further, if such a
clique cannot be extended by adding one or more other nodes (for any x
∈ V\C, C ∪ {x} is not a clique), it becomes a maximal clique. MCC score
for a node v is given as
[MATH: MCCv=∑CϵS(v)C-1! :MATH]
where S(v) is the collection of maximal cliques C which contains v, and
(|C| − 1)! is the product of all positive integers less than |C|.
Therefore, a node with a higher MCC score implies that it is part of
larger cliques or many smaller cliques or both.
In addition to the connectivity of a node, its spatial position in the
network also influences the communication among other nodes. To capture
the nodes that regulate the information flow within the network, we
used two shortest path-based global measures, Bottleneck centrality
(BN) and Betweenness centrality (BW), for each node. Bottlenecks are
considered to act as bridges holding crucial functional and dynamic
properties in the network^[57]25. While the BN(v) score of node v is
based on the shortest path trees of all other nodes in the network,
BW(v) is based on the number of shortest paths between every pair of
nodes traversing the node v. Scoring of BN(v) for a node v begins with
the construction of tree T[s] of shortest paths from a node s to all
other nodes in the network, followed by enumeration of the number of
these shortest paths going through node v. This process is iterated for
all s ∈ V. A node v in the shortest path tree T[s] is considered as a
bottleneck if more than
[MATH: |VTs|4 :MATH]
of the paths in the tree cross it^[58]26, where |V(T[s])| is the number
of nodes in the tree. Finally, BN(v) of node v is scored as the number
of such shortest path trees where it is considered as a bottleneck and
is given by
[MATH: BNv=∑sϵVp<
mi>s(v), :MATH]
where
[MATH: psv=1,ifnumberofpathsfromstoVTs\vthroughv>|VTs|40,otherwise
. :MATH]
Betweenness centrality BW(v) of node v in the connected component C(v)
containing v is the sum of fraction of shortest paths between every
pair of nodes s and t traversing through v, σ[st](v), to the total
number of shortest paths between every pair of nodes s and t, σ[st,]
and is given by
[MATH: BWv=∑s
≠t≠vϵC(v)σst(v)σst.
:MATH]
Further, the densely connected components of the network that are
likely to form molecular complexes were identified using the Molecular
Complex Detection (MCODE)^[59]27 program’s default settings in
Cytoscape. MCODE clusters with scores ≥ 5 were further analysed by
using Enrichr^[60]22 for pathway enrichment.
Network-based proximity analysis
Gaining insights into the interconnectedness of disease genes with
ageing within the PPI network helps to understand the risk of ageing.
If disease modules in an interactome overlap or are significantly
closer to ageing modules, then perturbations due to ageing may affect
pathways in disease or drive its progression. Proximity analysis was
performed to study the associations between the ageing liver and each
of the perturbed liver conditions (liver regeneration post-PH, NAFLD,
HCC, acute liver damage by CCl[4] and chronic liver damage by
CCl[4])[.]The association between two conditions was quantified using a
network proximity metric^[61]28:
[MATH: <dABC>=<
/mo>1||A||+||B||∑a∈A<
/munder>minb∈Bd(a,b)+∑b∈Bmina∈Ad(a,b), :MATH]
where d(a, b) represents the shortest path length between gene a from
condition A and gene b from condition B in the interactome.
The significance of this distance metric was evaluated using the
Z-score of permutation test by randomly selecting nodes from the whole
network with degree distributions similar to that of the nodes in the
two sets. Z-scores were calculated by permutation tests of 1,000
repetitions as follows:
[MATH: ZdAB=dAB-dmσm, :MATH]
where d[m] and σ[m] are the mean and standard deviation of the
permutation test.
Candidate gene lists for ageing and other conditions were selected from
different studies with similar mouse strains (Table [62]1). DEGs
between 3 and 27 months old mice were considered as signatures of
ageing for proximity analysis. We also performed proximity analysis
using DEGs from different age groups, including 12, 18 and 21 months,
for comparison. Candidates for different phases of liver regeneration
were considered by taking the union of DEGs of early-phase (1, 4, 10 h
post-PH compared to pre-PH), mid-phase (36, 44, 48 h post-PH compared
to pre-PH), and late-phase (1- and 4-weeks post-PH compared to pre-PH).
We also included DEGs of sham-operated control samples at different
phases, i.e., early-phase (1, 4, 10 h post sham surgery) and mid-phase
(48 h post sham surgery). Candidate genes for NAFLD and HCC (DEGs
between healthy control and disease) were pooled from their respective
studies (Table [63]1). The proximity analysis was performed using the
high confidence mouse-specific STRING PPI network (confidence
score ≥ 0.9). Two conditions with Z-score < − 1.5 and FDR < 0.05 were
considered significantly proximal. To infer the biological significance
of proximity of ageing signatures with other conditions, the shortest
path connecting each pair of DEG sets was identified as depicted in
Fig. [64]2.
Table 1.
Datasets used to define the list of candidate genes for different liver
associated conditions.
Liver condition Accession no. Experimental mouse model Age of mice
during sample collection Strain
Aging [65]GSE132040 Age group spanning 3–27 months 3, 6, 9, 12, 15, 18,
21, 24, 27 months old C57/BL6J
Regeneration and sham-operated control [66]GSE95135 12–14 weeks old
mice 3 months old C57/BL6J
NAFLD [67]GSE148080 Normal diet beginning at 8 weeks followed by
8–16 weeks of normal diet/high sucrose diet 8 months old C57/BL6J
[68]GSE184019 Normal diet at 8 weeks followed by 3 weeks of normal
diet/high sucrose diet. Samples collected at 11 weeks 3 months old
C57/BL6J
HCC [69]GSE132728 Single dose of DEN at 2 weeks followed by weekly
twice dose of CCl[4] from 8 to 24 weeks 6 months old C57/BL6J
[70]GSE89689 Single dose of DEN at 2 weeks followed by first dose of
CCl[4] dose 4 weeks later. Further weekly dose of CCL[4] for 15 weeks.
Final samples were collected 10 weeks after the last dose of CCl[4]
8 months old C57/BL6J
Acute damage (CCl[4]) [71]GSE167033 8–10 weeks old mice were
administered with CCl[4.] Samples were collected 2 and 8 h post
treatment, 1, 2, 4, 8, 16 days post treatment 2–3 months old C57/BL6/N
Chronic damage (CCl[4]) [72]GSE167216 8–10 weeks old mice were treated
with CCl[4] twice a week for 2, 6, 12 months 4, 8, 12 months old
C57/BL6/N
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Figure 2.
[74]Figure 2
[75]Open in a new tab
The flowchart to identify proximity genes between two conditions A and
B. The shortest path connecting each pair of genes was identified. The
nodes (A1, A2…An and B1, B2…Bm) can be directly connected or through an
intermediate node. C represents common nodes between two conditions.
Results
Alteration in network entropy with ageing in the mouse liver
We used the network entropy measure to study ageing. The sample-wise
gene expression was integrated with the PPI network. The estimation of
network entropy from the liver gene expression data shows that entropy
increases in old age (18–21 months) compared to the young (3–6 months)
mice (Fig. [76]3A). This is in agreement with other studies that used a
similar approach to study the progression of ageing in the context of
skeletal muscle and T-lymphocytes^[77]20,[78]29. The 18–21 months is a
tipping point after which entropy slightly decreases in the oldest age
group (24–27 months). This reveals that the liver tissue undergoes
network disorganization with ageing, increasing the disorderness or
randomness. We also performed local differential entropy analysis
between young and old age groups to identify nodes showing significant
increase in randomness. We identified 684 nodes with significantly
differing single node entropies (Wilcoxon Rank sum test q-value < 0.05,
absolute difference in median > 0.03) between young (3m–6m age) and
oldest age (24m–27m) groups. The pathway enrichment of these genes
revealed that Complement and coagulation cascades, Cytokine–cytokine
receptor interaction, Xenobiotics metabolism, steroid hormone
biosynthesis, NFKβ signalling pathway, PI3-AKT signalling pathway, and
MAPK signalling are significantly affected (Table [79]2). The
entropy-based approach captures relevant pathways associated with
ageing.
Figure 3.
[80]Figure 3
[81]Open in a new tab
Network entropy-based analysis of liver ageing network. (A) Boxplot
showing the change in network entropy across different age groups.
Sample-wise entropy is calculated and is normalized by number of nodes
in its corresponding network. (B) Network of top 50 nodes with
significant change in local entropy and their neighbours. Top nodes are
colored in red and the edges connecting them are shown with red dashed
lines. Edges between neighbours are not shown.
Table 2.
Pathway enrichment of nodes that showing significant change in entropy
(FDR < 0.05) between young (3–6 months) and old (24–27 months) mice
with absolute mean difference greater than 0.03.
S. no. KEGG pathway Overlap p-value Adj p-value
1 Complement and coagulation cascades 18/88 8.46E−10 2.42E−07
2 Cytokine–cytokine receptor interaction 30/292 8.91E−08 1.27E−05
3 Primary immunodeficiency 10/36 2.33E−07 2.15E−05
4 Metabolism of xenobiotics by cytochrome P450 13/66 3.01E−07 2.15E−05
5 Chemical carcinogenesis 15/94 6.66E−07 3.81E−05
6 Steroid hormone biosynthesis 14/89 1.87E−06 8.88E−05
7 PI3K-Akt signaling pathway 31/357 2.17E−06 8.88E−05
8 Pathways in cancer 39/535 8.15E−06 0.000291
9 Pentose and glucuronate interconversions 8/34 1.48E−05 0.000471
10 MAPK signaling pathway 25/294 2.93E−05 0.000796
11 Cholinergic synapse 14/113 3.16E−05 0.000796
12 Drug metabolism 14/114 3.49E−05 0.000796
13 Th1 and Th2 cell differentiation 12/87 3.94E−05 0.000796
14 T cell receptor signaling pathway 13/101 4.01E−05 0.000796
15 Fatty acid degradation 9/50 4.33E−05 0.000796
[82]Open in a new tab
The top-ranking nodes based on increase in entropy belong to the
cytochrome P450 superfamily (CYP7B1, CYP2D9, CYP2F2, CYP2C29) and
UDP-glucuronosyltransferases (UGT2B5, UGT2B36 and UGT2B1), which are
linked to drug metabolism and steroid hormone synthesis (Fig. [83]3B).
The entropy increase is observed with FGG, FGB and VTN, which are
associated with ECM and wound healing. VTN encodes for a secreted
protein vitronectin that inhibits the membrane-damaging effect of the
terminal cytolytic complement pathway (endothelial cells)^[84]30. TDO2
shows an increase in entropy and is linked to changes in tryptophan and
kynurenine (Kyn). Tryptophan metabolism controls the
inflammation-associated decline in age-related tissue homeostasis
(inflammaging)^[85]31.
Fatty acid oxidation genes ACSL1, ACADVL, ETFDH, ACOX2, HADHA, HSD17B4
and fatty acid transport gene SLC27A2 show an increase in entropy. The
involvement of mitochondrial and peroxisome genes linked to fatty acid
oxidation suggests an interplay between peroxisome-mitochondria in
liver ageing^[86]32. CREB3L3, which cooperates with PPARA to regulate
the expression of genes involved in fatty acid metabolism, also shows
an increase in entropy (Fig. [87]3B). On the other hand, the entropy of
lipid synthesis genes FASN, SREBF1, FADS1 and AACS and lipid transport
gene LDLR decrease with ageing. Interestingly, the entropy of PGRMC1
and INSIG2 that regulate hepatic de novo lipogenesis via SREBF1
increases. Similarly, PLIN2, a gene associated with the metabolism of
intracellular lipid droplets (LDs), also shows an increase with ageing.
Further, genes of glutathione metabolism show a change in entropy with
ageing. Glutathione S-transferase (GSTs) GSTP1 shows an increase in
entropy, while GSTM1 shows a decrease in entropy. GSTs are the Phase-II
enzymes that protect the cells against damage induced by electrophiles
and products of oxidative stress. They are shown to have anti-ageing
effect^[88]33. GPTX1, which catalyzes the reduction of hydrogen
peroxide (H[2]O[2]) by GSH, also shows an increase in entropy along
with GCLC, an essential gene for GSH synthesis. RARRES2, which encodes
a chemoattractant protein (Chemerin) secreted by the liver, shows a
decrease in entropy with ageing. Chemerin is a modulator of immune
response by promoting the chemotaxis of numerous immune cell types and
it has a role in pathophysiological conditions including HCC and
NAFLD^[89]34.
The overlap of entropy-based genes with DEGs between 3- and 27-months
old mice shows only a few overlaps indicating that genes identified
based on statistical properties of the underlying network are unique
(Supplementary Fig. [90]S1A). We also compared the entropy-based
candidate genes with the curated mouse immune genes^[91]35
(Supplementary Fig. [92]S1B). The entropy-based analysis also
identifies distinct immune-ageing genes compared to DEG analysis with a
small overlap. This suggests that ageing is characterized by global
changes in the immune system. Non-overlapping 454 genes also include
genes related to neurodegeneration (DNAHs) and protein digestion and
absorption (Collagens). Immune markers unique to entropy-based analysis
include genes VTN, FGB and FGG.
Core gene expression modules associated with ageing
We also alternatively explored the ageing gene expression changes at
the network level by integrating DEGs and PPI network. We expanded the
network to include the first neighbours of DEGs. The PPI network built
from DEGs comparing extreme age groups (3 and 27 months) and their
first neighbours resulted in 38,764 edges connecting 3770 nodes.
Similarly, we also constructed an ageing network for other age groups
(18, 21 and 24 months) for comparative analysis. We first clustered
genes based on network topology to identify densely connected regions
using MCODE.
The modular analysis of the liver ageing network (3 and 27 months)
shows that genes corresponding to pathways such as Ribosome, Proteasome
and Oxidative phosphorylation are associated with top-scoring clusters
(Fig. [93]4). These pathways are also found in the 18- and 24-months
old mice ageing network (Supplementary Tables [94]S1 and [95]S2).
Signalling pathways (mainly Wnts) regulating pluripotency of stem cells
emerged as a significant pathway in the oldest 27-month age group. The
clusters from 18 to 24-month networks are also associated with cell
cycle, DNA repair, p53 signalling pathway and senescence. The
enrichment of top clusters shows the relationship to NAFLD, basal cell
carcinoma, neurodegenerative diseases, and viral infection.
Figure 4.
[96]Figure 4
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Top five MCODE clusters of the ageing network obtained using the DEGs
between 3 and 27 month old mice and their first neighbours. Pathway
enrichment of clusters are shown along with the gene information. Hub
nodes/genes are highlighted with green border.
We also identified critical nodes based on local and global network
measures. Topological analysis of the ageing network based on local
(MCC) and global (Bottleneck and Betweenness) metrics show that RPS27A
and TRP53 are critical nodes in the network (Table [98]3). Other nodes
of global importance in the network include AKT1, SRC, CTNNB1, and
EGFR, while genes associated with proteosome (PSMB2, PSMA6, PSMB4,
PSMA1, PSMB1, PSMA3, PSMD12, PSMC1, PSMD3, PSMA4, PSMD4) are locally
important. It is also observed that RPS27A and TRP53 are not only the
nodes of global and local importance nodes in the extreme age group but
also form the early signs of ageing (Table [99]3).
Table 3.
Hub nodes of liver ageing network based on local and global network
measures.
Network measure 3 vs 18 months 3 vs 24 months 3 vs 27 months
MCC NDUFB7, NDUFB9, NDUFAB1, NDUFB8, NDUFA5, NDUFA6, NDUFV2, NDUFB10,
NDUFA12, NDUFB5, NDUFA8, NDUFS8, NDUFS7, NDUFA9, NDUFA10, NDUFV1,
NDUFS1, NDUFS3, UQCRFS1, NDUFS2, UQCRC1, UQCRC2, COPS3, COPS4, COPS2,
COPS5 PSMD1, PSMC3, PSMC6, PSMD11, PSMC5, PSMD12, PSMC1, PSMD3, PSMB7,
PSMA5, PSMB5, PSMA2, PSMD6, PSMA1, PSMB3, PSMB2, PSMB10, PSMA3, PSMA6,
PSMA4, PSMA7, PSMB4, PSMB6, PSMB1, PSMD4, PSMB8, PSMA8, PSMB9, CDC6
RELA, CCND1, UBA52, UBC, UBB, RPS27A, TRP53, CCNB1, CDK1 PSMB2, PSMA6,
PSMB4, PSMA1, PSMA3, PSMD12, PSMC1, PSMD3, PSMA4, PSMD4, PTEN, RELA,
UBB, UBC, UBA52, RPS27A, CDK1, TRP53
Bottleneck AKT1, SRC, EGFR, CTNNB1, TRP53, RAC1, JUN TRP53, ESR1, AKT1,
CTNNB1 PTEN, UBA52, RPS27A, TRP53, AKT1, SRC, CTNNB1, ESR1
Betweenness AKT1, SRC, EGFR, CTNNB1, TRP53, ESR1, RAC1 RPS27A, TRP53,
ESR1, AKT1, CTNNB1, KRAS, SRC, RHOA RPS27A, TRP53, AKT1, SRC, CTNNB1,
EGFR, ESR1
[100]Open in a new tab
Relationship between ageing and pathways associated with liver diseases and
regeneration
Ageing can increase the susceptibility to liver diseases like HCC and
NAFLD and affect the ability of the liver to regenerate after damage.
We hypothesized that this might arise due to shared or related pathways
associated with liver diseases and regeneration. We performed network
proximity analysis using condition-specific DEGs to study the
relationship between ageing and perturbations that influence liver
function. The network distance was quantified using the mouse PPI
network. We found significant proximities between ageing and
liver-related pathologies such as NAFLD, HCC and acute and chronic
damage by CCl[4] by integrating DEGs and mouse PPI network
(Fig. [101]5). The proximity distance decreases with an increase in the
age of mice.
Figure 5.
Figure 5
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Network proximity of ageing with different liver-associated conditions:
early (Regen_early), mid (Regen_mid) and late (Regen_late) phases of
liver regeneration, early and mid-phases of sham-operated control,
NAFLD, HCC, Acute and chronic liver damages. The proximity is explored
for different age groups (12, 18, 21 and 27 months). Text in the tiles
represents proximity distance. *Indicates FDR < 0.05 and
Z-score < − 1.5.
Proximity analysis between ageing and the early phase of liver
regeneration (1, 4, and 10 h post-PH) shows that older age groups are
significantly proximal to the liver regeneration module. This proximity
may influence the liver regeneration process in ageing. Ageing is shown
to delay liver regeneration post-PH. However, the mid and late phases
of liver regeneration associated with cell cycle and termination
phases, respectively, are not significantly proximal to the ageing
module (Fig. [103]5). Therefore, proximity analysis captures and
quantifies the impact imposed by ageing on regeneration at the network
level. The early phase of sham-operated control is also proximal to the
older age compared to the young age. This is consistent with the
observation that the early phase of sham-operated control and liver
regeneration is similar^[104]36. Further, the proximity of the
mid-phase of sham-operated control to the ageing network increases
compared to the early phase.
To probe the qualitative picture of proximity analysis, we identified
nodes falling in the shortest path between every ageing gene and all
candidate genes of other conditions (Fig. [105]2). This resulted in
2101, 2112, 1791, 2075 and 2322 nodes in the pairwise comparisons:
ageing and regeneration, ageing and NAFLD, ageing and HCC, ageing and
acute damage, ageing and chronic damage, respectively. Nodes from each
comparison were collectively projected onto the PPI network
(Fig. [106]6A). The connectivity pattern suggests that ageing is
connected to different conditions through intermediate nodes between
condition-specific DEGs. We observed a common theme of 926 proximal
molecular players connecting ageing with different liver conditions
emerges (Fig. [107]6B). This converges on important KEGG pathways such
as pathways in cancer, proteoglycans in cancer, Epstein-Barr virus
infection, PI3K-Akt signalling pathway and MAPK signalling pathway
(Fig. [108]6C). GRB2, SOS, RAS, RAF and ERK1/2, are the important
molecular players present in the top pathways associated with the
common theme. GSK3B is another interesting candidate gene common across
ageing, NAFLD and HCC (Fig. [109]7). It is upregulated in NAFLD and
downregulated in HCC. GSK3B connects different conditions via CTNNB1.
TRP53 signalling pathway also connects ageing to liver-associated
conditions. This may control cell cycle entry by regulating genes such
as CCND1, CDKN1A and GADD45A (Fig. [110]8). Both GSK3B and TRP53
interaction is also part of the common theme. The overlap of 926 genes
with curated mouse-specific immune genes shows that 366 genes are
common (Supplementary Fig. [111]S2), with NFKβ as a key transcriptional
factor. NFKβ regulates innate and adaptive immunity and is the master
regulator of inflammatory responses^[112]37. We also identified NLRP12
as a common candidate gene that plays the role of a mitigator of
inflammation. It is upregulated in the early phase of liver
regeneration while downregulated in ageing, NAFLD, and acute and
chronic liver damage.
Figure 6.
[113]Figure 6
[114]Open in a new tab
Overlap of proximity nodes obtained in the pairwise comparison of
ageing and different liver associated conditions. (A) Crosstalk
(interactions) between nodes of different liver-associated conditions
are shown using the PPI network. The common theme comprises of nodes
that are present in all comparisons. Nodes that are neither part of
common theme nor specific to a condition are shown in grey. A node that
is a DEG in at least one condition is shown by triangle and first
neighbour (FN) of DEG is shown by circle. (B) Venn diagram showing the
number of nodes overlapping between different pairwise comparisons. (C)
The pathway enrichment of 926 genes in the common theme is shown.
Figure 7.
[115]Figure 7
[116]Open in a new tab
Interacting partners of candidate gene, GSK3β, present in the common
theme.
Figure 8.
[117]Figure 8
[118]Open in a new tab
Interacting partners of candidate gene, TRP53, present in the common
theme.
In addition to the immune system, we also explored other common
relationships between ageing and different liver conditions. Lipid
(FASN, HMGCR, SREBF1) and bile acid synthesis (CYP27A1) genes are
differentially expressed in ageing and liver regeneration. FASN, HMGCR
and SREBF1 are upregulated in ageing. Mitochondrial fatty acid
β-oxidation (FADS1, HADHA, HADHB, ACSL1, ACADVL, CPT2, ECI2) also shows
this differential pattern. CREB3L3 is differentially expressed across
conditions. It is upregulated in liver regeneration and downregulated
in ageing. PCSK9, which plays a role in cholesterol homeostasis, is
downregulated in the early phase of regeneration and upregulated in
ageing and NAFLD. It protects the liver against steatosis and liver
injury. On the other hand, ANGPTL4, which facilitates the accumulation
of TAG by inhibiting LPL, is downregulated in ageing while it is
upregulated in liver regeneration.
Ageing also influences amino acid metabolism. Genes of one carbon
metabolism (DHFR, MTHFD1, BHMT, SHMT1/2, MAT1A, MTR, TYMS) are affected
across conditions. S-adenosyl-methionine (SAM) metabolism controlled by
MAT1 is significantly upregulated in liver regeneration compared to
ageing and is downregulated in HCC. MAT1 regulates the production of
SAM from methionine, which is required for methylation reactions inside
the cell. In NAD metabolism, NAMPT is upregulated in ageing, while NNMT
is upregulated in regeneration and downregulated in NAFLD and HCC.
Genes involved in BCAA catabolism, glutamine catabolism (GLS2),
aspartate synthesis (ASS1), and Tryptophan metabolism (TDO2) are also
affected across different liver conditions.
Discussion
Ageing can lead to functional impairment of liver and predisposes the
liver to NAFLD and HCC. The liver has a unique ability to regenerate
itself post-injury and help in whole-body metabolic homeostasis and
compound detoxification. Mapping the molecular changes of the liver
with ageing may help to understand how ageing influences liver function
and predisposes the liver to different pathological conditions. A
systems-level analysis of the ageing-induced liver changes and its
crosstalk with the pathology of liver diseases is lacking. In the
present study, we performed a network-level analysis of liver ageing
using transcriptomic data of ageing and the PPI network. We used
network entropy measure to identify nodes and pathways that show
significant entropy changes in ageing. Further, we also performed the
topological analysis of the ageing network by considering the nodes
differentially expressed in ageing and their first neighbours to
identify the core modules of ageing. This framework was also used to
study the proximity of the ageing network with liver regeneration and
disease networks. We showed proximity measure provides insights into
the interconnection between ageing and liver-associated conditions.
We observed an increase in entropy with ageing liver with the subtle
difference between old and oldest groups (Fig. [119]3). The
entropy-based approach captured the relevant pathway-level changes
linked to ageing and helped identify novel candidate genes. The entropy
change is driven by the selected group of genes belonging to the
immune, complement and coagulation cascade, lipid metabolism,
cytochrome P450 and UDP-glucuronosyltransferases. Immune and lipid
metabolism-related changes have been reported in
ageing^[120]12,[121]16. The candidate genes were filtered based on
entropy changes. We provide experimental evidence available from the
literature for the involvement of candidate genes in ageing or its
related liver diseases. Top novel candidate genes with high entropy
values include VTN, FGB and FGG, which are associated with changes
observed in fibrosis under chronic liver damage condition^[122]38. We
hypothesize that transcriptional remodelling of the liver during ageing
can affect the integrity of the membrane and increase the
susceptibility to fibrosis. Ageing is shown to increase the
susceptibility to fibrosis in response to high-fat diet
feeding^[123]39. We also found genes of the complement system (C6, C8,
C8A, C8B) that are part of the membrane attack complex changing with
ageing (Fig. [124]3B). There is increasing evidence that complement
systems may play a role in ageing^[125]40.
Our analysis also revealed the PGRMC1-INSIGs-SREBF1 axis in controlling
the lipid levels in ageing (Fig. [126]3B). PGRMC1 knockout leads to the
buildup of fatty acids and predisposes mice to NAFLD^[127]41. PGRMC1
forms complex with INSIG1 and is associated with cleavage of SREBF1 via
SCAP^[128]41,[129]42. Deletion of INSIG2 also results in the activation
of SREBF1 and de novo lipid synthesis^[130]43. Age induced hepatic
steatosis is alleviated in INSIG2 elevated condition^[131]44. Another
candidate gene, PLIN2, also controls the activation of SREBP-1 and
SREBP-2^[132]45. Its expression is shown to be altered in age-related
diseases, including fatty liver^[133]46,[134]47. Fat accumulation is
negatively correlated with the decrease in mitochondrial mass with
ageing^[135]48. Further, ageing is shown to affect lipid homeostasis by
controlling the phosphorylation of CEBPα/β^[136]49 and changing the
nucleosome occupancy at the foci of PPAR targets^[137]50. We found that
PPARA can also be affected through CREB3L3, the knockout of which
results in severe fatty liver^[138]51. CEBPβ is implicated in the
activation of SREBF1 transcription in liver^[139]52. RARRES2 (Chemerin)
is another candidate ageing gene, which is also induced in NAFLD and
Hepatitis B-related HCC. These observations suggest that ageing may
increase susceptibility to liver diseases.
The network topology-based analysis of the ageing network revealed the
involvement of ribosomes and proteasomes, which reflects the changes in
the proteostasis capacity of cells with ageing^[140]53. The module
associated with oxidative phosphorylation in the ageing network
(Fig. [141]4) reflects the change in mitochondrial metabolism with
ageing^[142]3. We found Wnt pathway as an ageing module, which controls
cell renewal, tissue regeneration and the development of HCC^[143]54.
Further, TRP53 was identified as a critical node based on local and
global graph theoretical measures. It has relevance in ageing as it can
promote repair, survival, or elimination of damaged cells^[144]55.
TRP53 optimally balances tumor suppression and longevity^[145]56. The
decline in the function of TRP53 is observed in various tissues of the
mouse with ageing, which can contribute to increased mutation frequency
and tumorigenesis^[146]57. Other critical nodes include AKT1, SRC,
CTNNB1 and EGFR, which are related to cancer signalling. CTNNB1 encodes
a β-catenin protein responsible for controlling gene expression in the
Wnt signalling pathway. EGFR also shows an increase in entropy, and its
expression is correlated with liver steatosis in mice and
human^[147]58.
The PPI network analysis of ageing and different liver conditions also
shows the proximity of ageing genes to different liver conditions,
including NAFLD, HCC, liver damage and repair (Fig. [148]5). The common
theme shared between conditions maps to immune-related pathways,
pathways in cancer and metabolic changes. MAPK, PI3K-AKT, Ras, Wnt and
NFκB signalling are common pathways across conditions (Fig. [149]6C).
Studies on extended lifespan by pharmacological intervention suggested
that anti-ageing effects are mediated by targeting the canonical MAPK
pathway^[150]59. With ageing, there is an upregulation of MEK1, which
triggers translation by phosphorylating its downstream target
eIF4E^[151]59. Increased activity of eIF4E has been shown to promote
tumorigenesis, thus implicating ageing effects on cancer^[152]60. GSK3β
is a common node across conditions. Ageing is shown to inhibit GSK3β
function^[153]61 and this, in turn, affects the liver regeneration
potential^[154]62. Inhibition of GSK3β acts as a protective role
against lipid accumulation in NAFLD. GSK3β can regulate cell
proliferation by controlling the growth-inhibitory activity of CEBPα
and negatively regulates many oncogenic signalling pathways, such as
the Wnt/β-catenin pathway^[155]63. We found GSK3B-CTNBB1 interaction as
part of the common theme (Fig. [156]7), which is linked to HCC
development and NAFLD. There is also a mechanistic link between
inflammation and the development of HCC mediated by NFKβ
signalling^[157]64. NASH condition exhibits morphological conditions
related to infiltration of lymphocytes and neutrophils, hepatocyte
death and activation of liver resident macrophages Kupffer cells,
creating an environment favourable for compensatory hepatocyte
proliferation that further drives hepatocarcinogenesis^[158]65.
Further, the priming phase of liver regeneration after PH depends on
the activation of NFKβ^[159]66.
We observed lipid metabolism as a common theme across ageing and
liver-associated conditions. Induced alteration in lipid metabolic
genes in ageing may increase susceptibility to NAFLD and affect liver
regeneration. Both lipid overloading and deficiency can affect the
liver regeneration ability. Fine-tuning lipid levels by transport,
biosynthesis and oxidation is crucial for liver regeneration^[160]67. A
high fat diet impairs liver regeneration through IKKβ overexpression
and subsequent NFKβ inhibition^[161]68. The aberrant activation of FASN
plays a major role in the development of HCC and its level is also
shown to increase during the induction of senescence^[162]69.
In amino acid metabolism, one-carbon metabolism is altered across
conditions, and it plays a crucial role in maintaining tissue
homeostasis and longevity^[163]70,[164]71. It generates various
metabolites that are building blocks of nucleotide synthesis,
methylation, and redox reactions. Oncogenic signalling hijacks the
one-carbon metabolism to support proliferation and survival^[165]72.
Genetic disruption of MAT1 inhibits liver regeneration^[166]73. MAT1
expression is reduced in different liver pathologies, including NAFLD
and HCC. Hepatic methionine is depleted in mice that developed NAFLD,
and administration of methionine and choline-deficient diet led to
alterations in the expression of lipid metabolism
genes^[167]74,[168]75. Metabolomics analysis of ageing shows the levels
of serine and methionine decrease in liver^[169]76. These highlight the
importance of one-carbon metabolism in liver function and pathology.
Further, BCAA is altered across conditions, and loss of BCAA catabolism
promotes HCC development and progression^[170]77. However, this is not
suppressed in liver regeneration^[171]73. BCAA metabolites are also
altered in aged liver^[172]3.
In summary, our study maps the network-level changes of ageing and
dissects the crosstalk between different conditions, including
regeneration and diseases in the mouse liver. We uncovered the local
and global changes in immune response, cancer signalling and metabolism
with ageing and identified novel candidate genes. We showed the
proximity of the liver ageing network to liver-condition-specific
networks and identified the interconnections through common pathways.
This explains how ageing increases susceptibility to different disease
conditions and affects the capacity of the liver to regenerate.
As an initial study, we used the bulk sequencing data to generate a
liver tissue-specific PPI network in different contexts for comparison.
The bulk changes can be due to cell composition changes or alterations
in the gene expression of each cell in the population. The single-cell
data will further help to refine the interactions in a cell-type
specific manner. Nevertheless, our study provides the initial framework
for single-cell network analysis of liver ageing and its related
diseases. We will also extend our analysis pipeline to human liver
aging, transplantation, and associated pathologies.
Supplementary Information
[173]Supplementary Information.^ (304KB, pdf)
Author contributions
Conceptualization: P.K.V.; methodology: M.P.; formal analysis and
investigation: M.P. Writing—original draft preparation: M.P.;
writing—review and editing: M.P., P.K.V.; funding acquisition: P.K.V.;
supervision: P.K.V.
Funding
PKV acknowledges financial support from the Department of Biotechnology
(BT/PR31936/BID/7/861/2019) and IHub-Data, IIIT Hyderabad.
Data availability
All the datasets are freely available and can be downloaded from
[174]https://www.ncbi.nlm.nih.gov/geo/ using the given accession
numbers in Table [175]1.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Supplementary Information
The online version contains supplementary material available at
10.1038/s41598-023-31315-2.
References