Abstract
Aging that refers the accumulation of genetic and physiology changes in
cells and tissues over a lifetime has been shown a high risk of
developing various complex diseases, such as neurodegenerative disease,
cardiovascular disease and cancer. Over the past several decades,
natural products have been demonstrated as anti-aging interveners via
extending lifespan and preventing aging-associated disorders. In this
study, we developed an integrated systems pharmacology infrastructure
to uncover new indications for aging-associated disorders by natural
products. Specifically, we incorporated 411 high-quality
aging-associated human genes or human-orthologous genes from mus
musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans
(CE), and drosophila melanogaster (DM). We constructed a global
drug-target network of natural products by integrating both
experimental and computationally predicted drug-target interactions
(DTI). We further built the statistical network models for
identification of new anti-aging indications of natural products
through integration of the curated aging-associated genes and
drug-target network of natural products. High accuracy was achieved on
the network models. We showcased several network-predicted anti-aging
indications of four typical natural products (caffeic acid, metformin,
myricetin, and resveratrol) with new mechanism-of-actions. In summary,
this study offers a powerful systems pharmacology infrastructure to
identify natural products for treatment of aging-associated disorders.
Keywords: quantitative and systems pharmacology, natural products,
target identification, aging, network-based
Introduction
Aging is a complex biological process accompanied by accumulation of
degenerative damages as well as the decline of various physiological
function, leading to the death of an organism ultimately (Fontana et
al., [37]2010; Lopez-Otin et al., [38]2013; Vaiserman et al.,
[39]2016). As an inevitable outcome of life, aging is a primary risk
factor for various complex diseases, including cancer, cardiovascular
diseases, and neurodegenerative disease (Kaeberlein et al., [40]2015;
Vaiserman and Marotta, [41]2016). Thus, development of novel agents for
delaying or preventing aging-associated disorders plays essential roles
during drug discovery and development.
Natural products have been demonstrated preclinical or clinical
efficiency for developing anti-aging interveners with few side effects
(Ding et al., [42]2017). Over the past few decades, several natural
products have been reported as anti-aging agents to extend lifespan and
prevent aging-associated diseases in various organism and animal models
(Pan et al., [43]2012; Correa et al., [44]2016). Currently, over
300,000 natural products have been available for drug discovery and
development (Banerjee et al., [45]2015). Among of them, 547 natural
products and derivatives have been approved by U.S. Food and Drug
Administration (FDA) for treating or preventing various disorders by
the end of 2013 (Patridge et al., [46]2016). There is pressing need of
novel approaches or tools for systematic identification of natural
products with novel pharmacotherapeutic mechanism-of-action for
treatment of aging-associated disorders.
Traditional drug target identification includes ligand-based and
structure-based approaches, such as machine learning and molecular
docking (Fang et al., [47]2013, [48]2017b). However, machine learning
is limited by high quality of negative samples as well as overfitting
issues on small training sets, while molecular docking is constrained
by lack of available crystallographic three-dimensional (3D) structures
of proteins. To overcome the pitfalls of traditional approaches,
several network-based approaches for prediction of drug-target
interaction (DTI) have been proposed recently (Cheng et al.,
[49]2012a,[50]b; Wu et al., [51]2016, [52]2017). These approaches have
showed a great promise in drug discovery and development, since they do
not rely on either 3D structures of proteins or negative DTIs.
Quantitative and systems pharmacology refers to a multidisciplinary
approach for the emerging development of efficacious drugs with novel
mechanisms via integration of experimental assays and computational
strategies (Vicini and van der Graaf, [53]2013; Fang et al.,
[54]2017b,[55]c). In the past decade, systems pharmacology-based
approaches have demonstrated advance in drug discovery and development
(Lu et al., [56]2015; Cheng et al., [57]2016; Fang et al., [58]2017a).
For example, a recent study has reported a systems pharmacology
approach for identifying new anticancer indications via integrating
drug-gene signatures from the connectivity map into the cancer driver
genes derived from tumor-normal matched whole-exome sequencing data
(Cheng et al., [59]2016). They identified several new anticancer
indications of resveratrol with new molecular mechanisms. Recently, the
same group further proposed a system pharmacology approach that
facilitated to identify new anticancer indications of natural products
through integration of known DTI network into significantly mutated
genes in cancer (Fang et al., [60]2017a). The high-confidence
anticancer indications were identified computationally and further
validated by various literatures on four natural products, including
resveratrol, quercetin, fisetin, and genistein. They showed that
integration of the computationally predicted DTIs could significantly
enhance the success rate of identifying new anticancer indications of
natural products via reducing the incompleteness of known drug-target
networks. The aforementioned examples have shed light on the systems
pharmacology-based approaches for drug discovery through exploiting the
polypharmacology of natural products with pleiotropic effects for
treatment of various complex diseases (Fang et al., [61]2017b).
In this study, we further proposed an integrated systems pharmacology
framework (Figure [62]1) to identify new targets of natural products
for potential treatment of aging-associated disorders. Specifically, we
manually collected high-quality aging-associated human genes or
human-orthologous genes covering four species: caenorhabditis elegans
(CE), drosophila melanogaster (DM), mus musculus (MM), and
saccharomyces cerevisiae (SC). We reconstructed a global DTI network of
natural products by integrating both experimentally reported and
computationally predicted DTIs from our previous predictive network
models (Wu et al., [63]2016; Fang et al., [64]2017c). Finally, we built
the statistical network models with high accuracy to prioritize new
anti-aging indications of natural products through integration of the
curated aging-associated genes and drug-target network of natural
products. We computationally identified anti-aging indications of
multiple natural products with novel molecular mechanisms, providing
potential promising candidates for further treatment of
aging-associated diseases. Taken together, this study offers a powerful
systems pharmacology infrastructure for identification of natural
products with new mechanism-of-action for potential treatment of
aging-associated disorders.
Figure 1.
[65]Figure 1
[66]Open in a new tab
Schematic diagram of the systems pharmacology infrastructure for
identification of aging-associated indications by natural products. (A)
Construction of drug-target network of natural products. (B) Manual
curation of aging-associated genes. (C) Discovery of new anti-aging
indications for natural products via network-based prediction. (D)
Identification of new anti-aging mechanism-of-action via network
analysis.
Materials and methods
Manual curation of aging-associated genes
Aging-associated genes (AAGs) were collected from two comprehensive
databases: the JenAge Ageing Factor Database (AgeFactDB) (Huhne et al.,
[67]2014) and Human Ageing Genomic Resources database (HAGR) (Tacutu et
al., [68]2013). AgeFactDB collects and integrates aging phenotype data
with both experimental and computational evidence, while HAGR only
contains AAGs from experiments. In this study, we only extracted AAGs
from AgeFactDB and HAGR with well-known experimental evidences across
five organisms: Homo sapiens (HS), CE, DM, MM, and SC. After removing
the duplicated genes between two databases, we obtained 309 (HS), 194
(DM), 1,012 (SC), 1,149 (CE), and 143 (MM) AAGs, respectively. We then
obtained high-quality human-orthologous AAGs via mapping
human-orthologous genes across four species (CE, DM, MM, and SC) from
ensemble database ([69]http://www.ensembl.org/index.html). Finally, 169
(DM), 555 (SC), 331 (CE), and 96 (MM) human-orthologous AAGs were
collected (Table [70]S1).
Construction of a known drug-target network of natural products
We firstly integrated comprehensive natural products from six
publically available natural product-related data sources: traditional
Chinese medicine database (TCMDb) (He et al., [71]2001), Chinese
natural product database (CNPD) (Shen et al., [72]2003), traditional
Chinese medicine integrated database (TCMID) (Xue et al., [73]2013),
traditional Chinese medicine systems pharmacology (TCMSP) (Ru et al.,
[74]2014), traditional Chinese medicine database@Taiwan (TCM@Taiwan)
(Chen, [75]2011), and universal natural product database (UNPD) (Gu et
al., [76]2013). For each data source, we converted its initial
structure format (e.g., mol2) into unified SDF format. Secondly, we
merged the unified SDF files from the six data sources into single SDF
file, and removed the duplicated natural products according to InChIKey
by Open Babel (v2.3.2) (O'Boyle et al., [77]2011). Finally, 259,547
unique natural products were collected. The details are provided in our
previous study (Fang et al., [78]2017a,[79]c).
To construct a global drug-target network of natural products, we
pooled DTIs from two commonly used databases: ChEMBL (v21) (Bento et
al., [80]2014) and BindingDB (v19, accessed in June 2016) (Gilson et
al., [81]2016). All chemical structures were carefully standardized via
removing salt ions and standardizing dative bonds using Open Babel
toolkit (v2.3.2). We further filtered DTIs with the following five
criteria: (i) Ki, Kd, IC[50], or EC[50] ≤ 10 μM; (ii) the target
organism should be homo sapiens; (iii) the target has a unique UniProt
accession number; (iv) compound can be transformed to canonical SMILES
format; and (v) compound has at least one carbon atom. Subsequently, we
extracted experimentally validated DTIs for 2,349 natural products
after mapping 259,547 unique natural products into the global DTIs
using the “InChIKey.”
Prediction of new drug-target interactions of natural products
In a recent study, we have developed predictive network models to
predict targets of natural products via a balanced
substructure-drug-target network-based inference (bSDTNBI) (Fang et
al., [82]2017c; Wu et al., [83]2017) approach. The bSDTNBI utilizes
resource-diffusion processes to prioritize potential targets for both
known drugs and new chemical entities (NCEs) via
substructure-drug-target network (Wu et al., [84]2016). The
substructure-drug (or NCE)-target network was built via integrating the
known DTI network, drug-substructure associations and NCE-substructure
associations. Two parameters were introduced to balance the initial
resource allocation of different node types (α) and the weighted values
of different edge types (β), respectively. The third parameter γ was
imported to balance the influence of hub nodes in resource-diffusion
processes. The fourth parameter k denotes for the number of resource
diffusion processes. Herein, four parameters (α = β = 0.1, γ = −0.5,
and k = 2) in bSDTNBI were adopted based on our previous study (Wu et
al., [85]2016). Here, the predictive model based on KR molecular
fingerprint (bSDTNBI_KR) with the best performance was used to predict
the new targets of natural products and top 20 predicted candidates
were used (Wu et al., [86]2016, [87]2017).
Identification of new anti-aging indications for natural products
Here, we further proposed an integrated statistical network model to
prioritize new anti-aging indications of natural products by
incorporating DTI network of natural products and the manually curated
AAGs. We asserted that a natural product with polypharmacological
profiles exhibits a high possibility to treat an aging-associated
disorder if its targets are more likely to be aging-associated proteins
(AAPs). Then we utilized a permutation testing to estimate the
statistical significance of a natural product to be prioritized for
anti-aging indications. The null hypothesis asserts that targets of a
natural product randomly locate at AAPs across the human proteome. The
permutation testing was performed as below:
[MATH: P = # {<
mi>Sm(p)>Sm}#<
/mi> {total permutations} :MATH]
(1)
A nominal P was computed for each natural product by counting the
number of observed AAPs greater [S[m] (p)] than the permutations (Sm).
Here we repeated 100,000 permutations by randomly selecting 441
proteins (the same number of AAPs) from protein products at the
genome-wide scale, 20,462 human protein-coding genes from the National
Center for Biotechnology Information (NCBI) database (Coordinators,
[88]2017; Table [89]S2). Subsequently, the nominal P-values from the
permutation tests were corrected as adjusted P-values (q) based on
Benjamini-Hochberg approach (Benjamini and Hochberg, [90]1995) using R
package (v3.01). In addition, a Z-score was calculated for each natural
product to be prioritized for anti-aging indications during permutation
testing:
[MATH: Z = x-μσ<
/mfrac> :MATH]
(2)
where x is the real number of AAPs targeted by a given natural product,
μ is the mean number of AAPs targeted by a given natural product during
100,000 permutations, and σ is the standard deviation.
Network and statistical analysis
The statistical analysis in this study was carried out using the Python
(v3.2, [91]http://www.python.org/) and R platforms (v3.01,
[92]http://www.r-project.org/). Networks were visualized by Cytoscape
(v3.2.0, [93]http://www.cytoscape.org/).
Results
A catalogue of aging-associated genes
We collected the high-quality human-orthologous AAGs from four species:
CE, DM, MM, and SC. In total, 1,006 human-orthologous AAGs identified
in at least one species with literature-reported experimental evidences
were collected after removing the duplicated AAGs (Figure [94]2). Among
1,006 genes, 130 human-orthologous AAGs are reported in at least two
non-human organisms (CE, DM, MM and SC) simultaneously. Meanwhile, 12
human-orthologous AAGs (e.g., AKT1, CAT, GABARAP, MAPK8, MAPK9, MAPK10,
MTOR, PRDX1, PRDX2, RPS6KB1, SIRT1, and SOD2) were included in at least
three non-human organisms. To improve the quality of gene set, we only
selected the 130 human-orthologous AAGs identified in at least two
non-human organisms. We found that 130 human-orthologous AAGs are
significantly enriched with 309 human AAGs (28 overlapping genes, P =
1.7 × 10^−24, Fisher's exact test). Finally, we pooled 130
human-orthologous AAGs and 309 human AAGs and generated 411 AAGs (Table
[95]S3) for building the statistical network models.
Figure 2.
Figure 2
[96]Open in a new tab
Overlaps among four gene sets of human-orthologous aging-associated
genes (AAGs) from 4 non-human organisms: Caenorhabditis elegans (CE),
Drosophila melanogaster (DM), mus musculus (MM), and saccharomyces
cerevisiae (SC). The detailed AAGs are provided in Table [97]S1.
Reconstruction of anti-aging drug-target network for natural products
We constructed a global drug-target network of natural products by
integrating 7,314 high-quality experimental DTIs as well as 11,940 new
computational predicted DTIs as described in our recent study (Fang et
al., [98]2017c). The global DTI network (Table [99]1) was consisted of
17,223 DTIs connecting 2,349 unique natural products and 732 targets.
The average experimental target degree (connectivity) of a natural
product is 2.97, which is significantly stronger than the average
degree 2.22 of non-natural product drugs in DrugBank database (P = 6.81
× 10^−72, one-side Wilcoxon test). The detailed DTI pairs are provided
in Table [100]S4. We further built a specific drug-target network by
focusing on FDA-approved or clinically investigational natural products
(Table [101]S4). Figure [102]3 displays a bipartite drug-target network
of 2,408 DTIs connecting 224 FDA-approved or clinically investigational
natural products and 494 targets encoded by 70 AAGs and 424 non-AAGs.
Network analysis shows that the average connectivity of experimentally
known targets for each natural product in this network is 6.26, which
is significantly stronger than that (average degree = 2.22) of
non-natural products drugs in DrugBank (P = 4.34 × 10^−50, one-side
Wilcoxon test, Table [103]S5). Among 224 FDA-approved or clinically
investigational natural products, eight natural products have
connectivity (K) > 25: quercetin (K = 73), ellagic acid (K = 56),
apigenin (K = 43), haloperidol (K = 32), myricetin (K = 32),
resveratrol (K = 30), genistein (K = 26), and dopamine (K = 25).
Meanwhile, among 70 targets encoded by AAGs, 6 are targeted by over 15
natural products (D): LMNA (D = 79), MAPT (D = 33), BLM (D = 22), HIF1A
(D = 22), TP53 (D = 20), and NFKB1 (D = 16), based on current available
experimental data. The targets encoded by these AAGs play essential
roles in aging-associated diseases. For example, products encoded by
LMNA are primarily lamin A and C. Alterations in lamin A and C were
reported to accelerate physiological aging via nuclear envelope budding
(Li Y. et al., [104]2016). A recent study also showed that nuclear
factor-kappa B (NF-kB) inhibition could delay the onset of aging
symptoms in mice via reducing DNA damage (Tilstra et al., [105]2012).
Table 1.
The statistics of global drug-target interactions (DTI) network and
local DTI network for natural products.
Data set N[D] N[T](N[AT]) N[DTI] Sparsity (%)
Global DTI network 2,349 732 (101) 17,223 1.00
Local DTI network 224 494 (70) 2408 2.17
[106]Open in a new tab
Local DTI network: a specific drug-target network by focusing on
FDA-approved or clinically investigational natural products, N[D], the
number of natural products; N[T], the number of targets; N[AT], the
number of aging-associated targets; N[DTI], the number of DTIs;
Sparsity, the ratio of N[DTI] to the number of all possible DTIs.
Figure 3.
[107]Figure 3
[108]Open in a new tab
A bipartite drug–target interaction network for FDA-approved or
clinically investigational natural products. This network contains
2,408 interactions connecting 224 natural products to 494 target
proteins, including proteins encoded by 70 aging-associated genes
(AAGs) and 424 non-AAGs. The label font size and node size are
proportional to degree (connectivity).
Chemical diversity analysis of natural products targeting aging-associated
proteins
We extracted 1,877 natural products targeting AAP via mapping 411
high-quality human or human-orthologous AAGs into the global
drug-target network of natural products. Clustering analysis was
performed to examine chemical scaffolds of 1,877 natural products by
measuring the root-men-square value of the Tanimoto distance based on
FCFP_6 fingerprint implemented in Discovery Studio 4.0 (version 4.0,
Accelrys Inc.). The 1,887 natural products are clustered into 10 groups
with cluster centers: 1,2-propanediol, luteolin, tetrahydroalstoine,
ZINC03870415, chryseriol, benzamide, p-toluidine, L-His,
cis-10-octadecenoic acid, and 3-epioleanolic acid, respectively (Figure
[109]4A). The structures of each cluster center are shown in Figure
[110]4B. Among them, cluster 5 (Cluster center: Chryseriol) and cluster
2 (Cluster center: Luteolin) are grouped as flavonoids, with the
largest number of natural products. The structures in cluster 3 and
cluster 9 are represented as alkaloids, while the structures in cluster
8 are represented as unsaturated aliphatic hydrocarbon or unsaturated
fatty acid. Overall, 1887 natural products share diverse chemical
scaffolds (Figure [111]4), providing a valuable resource for systems
pharmacology-based anti-aging drug discovery.
Figure 4.
[112]Figure 4
[113]Open in a new tab
Chemical diversity analysis of natural products targeting
aging-associated proteins. (A) Chemical structure clustering of 1,877
natural products via FCFP_6 fingerprint; (B) The representative
structures of 10 cluster centers during chemical structural clustering
analysis.
Mechanism-of-action of anti-aging indications by natural products
To investigate the anti-aging mechanism-of-action (MOA) of natural
products, we performed KEGG pathway, molecular function, and biological
process enrichment analysis using ClueGO (Bindea et al., [114]2009).
Here, we focused on 54 AAPs with connectivity larger than 10 in the
global drug-target network of natural products (Table [115]S6). Figure
[116]S1 showed that 54 anti-aging targets are significantly enriched in
several aging-associated pathways: longevity regulating pathway
(adjusted-P = 1.9 × 10^−5), MAPK signaling pathway (adjusted-P = 1.6 ×
10^−5), ERBB signaling pathway (adjusted-P = 7.8 × 10^−7), estrogen
signaling pathway (adjusted-P = 3.3 × 10^−4), and insulin signaling
pathway (adjusted-P = 2.1 × 10^−3) (Hall et al., [117]2017). Similar
trends were observed for molecular function and biological process
enrichment analyses (Table [118]S7). To further showcase the
aging-associated mechanisms, we selected four typical natural products:
caffeic acid, hesperetin, myricetin and resveratrol.
Caffeic acid
Caffeic acid is a natural phenol found in fruits, tea and wine (Magnani
et al., [119]2014), with a wide range of aging-associated
pharmacological activities, such as antioxidant (Deshmukh et al.,
[120]2016), anti-inflammatory (da Cunha et al., [121]2004), and
neuroprotective (Pereira et al., [122]2006). For example, caffeic acid
phenethylester (CAPE) was reported to extend lifespan in CE via
regulation of the insulin-like DAF-16 signaling pathway (Havermann et
al., [123]2014). The detailed molecular mechanisms of anti-aging
effects by caffeic acid remain unclear. Figure [124]5 shows that
caffeic acid interacts with 5 AAPs (LMNA, MAPT, NFKB1, PTPN1 and MAPK1)
and 22 non-AAPs, consisting of 23 experimentally validated and 4
computationally predicted ones. Protein tyrosine phosphatase 1B
(PTP1B), encoded by PTPN1, is a potential target for treatment of
type-2 diabetes (Gonzalez-Rodriguez et al., [125]2012) and Alzheimer's
disease (Vieira et al., [126]2017). A recent study showed that caffeic
acid is a moderate inhibitor of PTP1B with an IC[50] value of 3.06 μM
(He et al., [127]2009).
Figure 5.
[128]Figure 5
[129]Open in a new tab
A bipartite drug-target network for 4 typical natural products. This
network includes 90 experimentally validated and 16 computationally
predicted drug-target interactions connecting 4 natural products
(caffeic acid, hesperetin, myricetin and resveratrol) and 70 targets
(21 aging-associated proteins and 49 non-aging proteins).
Hesperetin
Hesperetin is flavanone abundant in citrus fruits with a wide range of
biological activities. Recent studies revealed the potential
antioxidant, neuroprotective, and anti-inflammatory properties (Parhiz
et al., [130]2015; Miler et al., [131]2016), by hesperetin.
Furthermore, a recent clinical trial ([132]NCT02095873) has reported
that hesperetin in combination with trans-resveratrol can prevent and
alleviate early-stage of aging-associated disorders (Xue et al.,
[133]2016). Network analysis reveals that hesperetin binds with 9
targets (6 AAPs and 3 non-AAPs), including 4 computationally predicted
targets and 5 experimentally reported ones. Interestingly, 4 predicted
anti-aging targets (MAPT, LMNA, TP53, and NFKB1) suggest potential
underlying anti-aging mechanisms by hesperetin. For example, a previous
study revealed that hesperetin modulated the aging-associated NF-κB
pathway in the kidney of rats (Kim et al., [134]2006).
Myricetin
Myricetin, a common plant-derived flavonoid, displays several
pharmacological activities against aging-associated indications, such
as anti-aging (Aliper et al., [135]2016), antioxidant (Wang et al.,
[136]2010), anti-inflammatory (Lee et al., [137]2007), and
immunomodulatory (Fu et al., [138]2013) effects. Figure [139]5 shows
that myricetin binds with 11 AAPs and 25 non-AAPs, consisting of 4
computationally predicted targets (TP53, LMNA, ELAVL1, and KMT2A) and
32 experimentally reported ones. A recent study has suggested that
myricetin can extend lifespan in Caenorhabditis elegans via modulating
aging-related transcription factors (Buchter et al., [140]2013).
Resveratrol
Resveratrol, a non-flavonoid polyphenol abundant in the skin of grapes,
displays a broad spectrum of anti-aging effects (Gines et al.,
[141]2017). Currently, over 20 clinical trials
([142]http://clinicaltrials.gov/) are being conducted or completed to
treat aging or aging-associated disorders by resveratrol, such as
anti-aging ([143]NCT02523274 and [144]NCT02909699), aging-associated
macular degeneration ([145]NCT02625376), and Alzheimer's disease
([146]NCT01504854). Figure [147]5 indicates that resveratrol interacts
with 12 AAPs: ESR1, HIF1A, HTT, LMNA, MAPT, MTOR, NFKB1, PIK3CA,
PIK3CB, PTGS2, RELA, and TP53, suggesting new potential anti-aging
mechanisms of resveratrol. For example, two AAPs: estrogen receptor
alpha (ER-alpha) and cyclooxygenase-2 (COX-2), play crucial role on the
pathogenesis of several aging-associated diseases, such as Alzheimer's
disease and osteoporosis (Kermath et al., [148]2014; Kim et al.,
[149]2016). Resveratrol was reported to bind to ER-alpha with a Ki
value of 0.78 μM (de Medina et al., [150]2005) and inhibit COX-2 with
an IC[50] value of 0.99 μM (Kang et al., [151]2009).
Taken together, aforementioned examples demonstrated that network
analysis could assist to identify new potential anti-aging mechanisms
of natural products. Systems pharmacology-based integration of
drug-target networks and known AAPs would enable to identify new
natural products for treatment of aging-associated diseases.
Discovery of potential anti-aging indications for natural products with novel
mechanism-of-action
We further built statistical network models for comprehensive
identification of new anti-aging indications of natural products
through integrating both experimentally reported and computationally
predicted drug-target network into the curated APPs (see section
Materials and Methods). Here, we focused on 224 FDA-approved or
clinical investigational natural products annotated in DrugBank
database (Law et al., [152]2014). Table [153]2 summarizes number of the
predicted anti-aging indications for the experimentally reported
drug-target network only and the pooled data from both experimentally
reported and computationally predicted drug-network, respectively. We
only identified 56 natural products with significantly predicted
anti-aging indications (q < 0.05) using the experimentally reported
drug-target network, while we identified 143 natural products with
significantly predicted anti-aging indications (q < 0.05) via
integration of both experimentally reported and computationally
predicted drug-target networks (Table [154]S8). Interestingly, among
143 natural products, 92 natural products cannot be identified to have
significant anti-aging indications using experimentally reported
drug-target network only, including some well-known anti-aging natural
products (e.g., metformin, vitamin E, and huperzine A). We
systematically retrieved previously anti-aging reported data from
PubMed for 73 FDA-approved natural products out of 143 ones. The
detailed experimental evidences are provided in Table [155]S9. Then we
found 23 natural products [with a success rate of 31.5% (23/73)] with
reported experimental data. This suggests a reliable accuracy of our
proposed network model. The remaining 50 natural products without
experimental data provide potential anti-aging candidates that deserve
to be validated by various experimental assays in the future.
Table 2.
Summary of the newly predicted anti-aging indications of natural
products based on the experimentally reported drug-target network only
(ExpNet) and the combination of the experimentally reported and
computationally predicted (ExpNet&ComNet) drug-target networks,
respectively.
Data source Number of DTIs (number of targets, number of drugs) #
N[saI](q < 0.05) # N[saI] (q < 1/10^−5)
ExpNet 1,163 (361,113) 56 28
ExpNet&ComNet 2,408 (494, 224) 143 87
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N[saI] denotes to the number of natural products with significantly
predicted anti-aging indication.
In summary, we showed that integration of computationally predicted
drug-target network could improve the chance to identify new anti-aging
indications of natural products via increasing completeness of current
drug-target network. We next chose three typical natural products
(metformin, vitamin E, and huperzine A) as case studies to illustrate
the predicted anti-aging indications with new mechanism-of-actions.
Metformin
Metformin, originating from Galega officinalis, is a biguanide drug
widely used in clinical practice for treating type-2 diabetes.
Nowadays, metformin is currently being tested as an anti-aging drug in
several clinical trials, such as [157]NCT02432287 and [158]NCT02308228
(Barzilai et al., [159]2016). Figure [160]6 shows that metformin binds
with 3 AAPs (BLM, HTT, and LMNA) and 3 non-APPs. In our network model,
metformin was predicted to have significant anti-aging indication (Z =
8.42, q < 10^−5) via integration of one experimentally validated target
and five predicted ones. There is no significant anti-aging indication
for metformin based on experimentally validated DTI only. Previous
studies have shown that metformin extended lifespan in several model
organisms (Anisimov, [161]2013; Cabreiro et al., [162]2013;
Martin-Montalvo et al., [163]2013). Figure [164]6 shows several
potential anti-aging mechanisms of metformin, including inhibition of
the inflammatory pathway, activation of AMP-activated kinase (AMPK),
and inducing autophagy (Moiseeva et al., [165]2013; Foretz et al.,
[166]2014; Song et al., [167]2015).
Figure 6.
[168]Figure 6
[169]Open in a new tab
A discovered anti-aging drug-target network for 3 typical natural
products. This network displays the predicted anti-aging indications as
well as the known and predicted drug targets for three typical natural
products: metformin, vitamin E, and huperzine A. The thickness of blue
line between natural products and anti-aging indication is proportional
to the predicted Z-score (Equation 2, see section Materials and
Methods).
Vitamin E
Vitamin E, the most potent antioxidant, protects cells from damage
related to oxidative stress (La Fata et al., [170]2014). Vitamin E
supplementation has been reported to delay or prevent aging and
inflammatory aging-associated diseases via prolonging the life span in
several model organisms (Navarro et al., [171]2005; Mocchegiani et al.,
[172]2014). However, mechanism-of-action of anti-aging effects by
vitamin E remains unclear. Figure [173]6 shows that vitamin E interacts
with 2 known and 4 predicted targets, consisted of 3 AAPs (TP53, LMNA,
and MAPT) as well as 3 non-AAPs. In our network model, vitamin E was
predicted to show potential for anti-aging indication (Z = 8.32, q <
10^−5) based on the pooled data of experimentally validated and
computationally predicted DTIs. However, there is no significance based
on experimentally validated DTIs only. Among 4 predicted targets, TP53,
a well-known AAP, regulates cell cycle progression, apoptosis, and
cellular senescence. A recent study reported that vitamin E
significantly down-regulated TP53 expression in senescent cells,
indicating a potential anti-aging mechanism of vitamin E (Durani et
al., [174]2015).
Huperzine A
Huperzine A (HupA), a natural acetylcholinesterase (ACHE) inhibitor
derived from Huperzia serrate, is a licensed anti-Alzheimer drug in
China (Qian and Ke, [175]2014). HupA was reported to show various
anti-inflammatory, neuroprotective, and anti-aging properties (Ruan et
al., [176]2013; Damar et al., [177]2016). Figure [178]6 reveals that
HupA binds with one experimentally reported target (ACHE) and 5
computationally predicted targets (BCHE, GDA, LMNA, TOP1, and ADORA2A).
Among five predicted targets, LMNA and TOP1 are experimentally reported
AAPs (Li Y. et al., [179]2016). Here, HupA was predicted to have
significant anti-aging indication (Z = 5.47, q = 0.031) via the
integration of both experimentally reported and computationally
predicted targets, while no significance using the experimentally
reported targets alone. Oxidative stress accelerates the chronic
inflammatory process during aging and aging-associated diseases
(Cannizzo et al., [180]2011). A previous study showed that HupA
alleviated oxidative stress-induced inflammatory damage in aging rat
(Ruan et al., [181]2014).
Put together, we have suggested that our network model provided a
useful tool for systematic identification of natural products for
treatment of aging-associated disorders with novel molecular
mechanisms. Some newly predicted anti-aging indications of natural
products and the according mechanisms are suggested to be
experimentally validated before clinical uses, which we hope to be
promoted by findings shown here.
Discussion
Natural products are valuable pharmaceutical wealth and show great
promise for developing anti-aging agents (Ding et al., [182]2017). In
this study, we developed an integrated systems pharmacology
infrastructure to identify new targets of natural products for
treatment of aging-associated diseases. This computational
infrastructure is consisted of three key components: (i) reconstructing
DTI networks of natural products via integrating known and
computationally predicted DTIs; (ii) curation of high-quality
aging-associated human or orthologous genes from various aging-related
bioinformatics sources; (iii) building statistical network models to
prioritize new aging-associated indications of natural products through
integrating data from aforementioned two steps. Overall, this framework
has several advantages. First, we found that assembling computationally
predicted drug-target network could identify more significant
anti-aging indications for natural products by increasing completeness
of currently known drug-target network. Second, our systems
pharmacology-based approach is independent of three-dimensional (3D)
structure of targets, which can be applied in human targets without
known 3D structures (e.g., membrane proteins).
There are still several potential limitations in the current systems
pharmacology model. First, antagonistic or agonistic effects of
drug–target pairs have not been considered. Drug-induced gene
expression database, such as the Connectivity Map (CMap), has provided
specific biological functions (upregulation or downregulation) (Lamb et
al., [183]2006). Integration of large-scale gene expression profiles of
natural products may help improve performance of our network model
(Cheng et al., [184]2016). In addition, current approach can only
predict the potential aging-associated indications of natural products
targeting known or predicted AAPs. Integrating systems biology
resources may assist on identifying the growing potential AAPs by
indirectly targeting their neighbors in the human protein-protein
interaction network, gene regulatory network, or biological pathways
(Li et al., [185]2014; Li J. et al., [186]2016). Finally, we only
focused on three well-known natural products (metformin, vitamin E, and
huperzine A) with more available literature-reported data for
validation. Further in vitro or in vivo experimental assays should be
performed to validate the predicted DTIs and anti-aging effects of
natural products before preclinical and clinical studies.
Author contributions
JF and FC conceived the project. FC and QWa provided supervision. JF
and LG performed the research. JF, HM, QWu, TW, JW, and FC analyzed the
data. JF and FC wrote the article.
Conflict of interest statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Acknowledgments