Abstract

   Flavonoids are the largest class of plant polyphenols, with common
   structure of diphenylpropanes, consisting of two aromatic rings linked
   through three carbons and are abundant in both daily diets and
   medicinal plants. Fueled by the recognition of consuming flavonoids to
   get better health, researchers became interested in deciphering how
   flavonoids alter the functions of human body. Here, systematic studies
   were performed on 679 flavonoid compounds and 481 corresponding targets
   through bioinformatics analysis. Multiple human diseases related
   pathways including cancers, neuro-disease, diabetes, and infectious
   diseases were significantly regulated by flavonoids. Specific functions
   of each flavonoid subclass were further analyzed in both target and
   pathway level. Flavones and isoflavones were significantly enriched in
   multi-cancer related pathways, flavan-3-ols were found focusing on
   cellular processing and lymphocyte regulation, flavones preferred to
   act on cardiovascular related activities and isoflavones were closely
   related with cell multisystem disorders. Relationship between chemical
   constitution fragment and biological effects indicated that different
   side chain could significantly affect the biological functions of
   flavonoids subclasses. Results will highlight the common and preference
   functions of flavonoids and their subclasses, which concerning their
   pharmacological and biological properties.

   Keywords: flavonoids, mechanism of action, pathway analysis,
   protein–protein interaction network, structure activity relationship

Introduction

   Flavonoids are a family of phenolic substances sharing the same
   backbone structure of 2-pheny1-1,4-benzopyronemay, which are very
   abundant in nature, being accumulated in regular human diets including
   flowers ([35]Zhang and Ma, 2018), fruits ([36]Chang et al., 2018),
   vegetables, tea, wine ([37]Matveeva et al., 2018), and so on
   ([38]Szmitko and Verma, 2005). With the basic core scaffold, flavonoids
   have been demonstrated to exhibit relevant biological properties
   involving strong activity for anti-oxidant ([39]Pietta, 2000),
   anti-allergy ([40]Kawai et al., 2007; [41]Castell et al., 2014),
   anti-inflammatory ([42]Nijveldt et al., 2001; [43]Serafini et al.,
   2010; [44]Matias et al., 2014), anti-microbial ([45]Cushnie and Lamb,
   2005), and anti-obesity ([46]Hughes et al., 2008) effects. Also,
   flavonoids have been reported to have effect on reducing the risk of
   cardiovascular disease ([47]Hooper et al., 2008; [48]Mulvihill and
   Huff, 2010; [49]Feliciano et al., 2015) and cancers ([50]Yao et al.,
   2011; [51]Batra and Sharma, 2013), ameliorating cognition ([52]Spencer
   et al., 2009; [53]Williams and Spencer, 2012) and neuro-protection in
   Alzheimer’s disease ([54]Bakhtiari et al., 2017; [55]Mohebali et al.,
   2018). Moreover, it is also found that flavonoids act as agonist or
   antagonist depending on the estrogen concentrations to regulate
   estrogenic-like activity ([56]Breinholt et al., 1999; [57]Hwang et al.,
   2006).

   On the basis of common core scaffold, various combinations of
   substituent chemical groups on different positions may lead to
   structure diversity of flavonoids. This diversity can be further
   increased with possible variations of different functional groups, such
   as hydroxyl, methoxyl, carbonyl, and olefinic groups ([58]Gontijo et
   al., 2017). According to the structure variations, flavonoids can be
   generally assigned into six main subclasses: flavones, flavonols,
   flavanones, flavanols, flavan-3-ols, and isoflavones ([59]Ross and
   Kasum, 2002), for which the chemical properties depend on their
   structural classes, degrees of hydroxylation, substitutions,
   conjugation, and degree of polymerization ([60]Kumar and Pandey, 2013).
   However, the functional similarities and differences, as well as the
   structure basis of different functions for flavonoids subclasses are
   not fully revealed yet.

   In this study, a comprehensive bioinformatics analysis was performed
   based on a large-scale dataset including 679 flavonoids and 481
   corresponding targets to decipher the mechanism of action (MOA) of
   flavonoids with a new perspective. Results illustrated the structure
   activity relationship of different flavonoids subclasses, which hint
   the protective roles of flavonoids subclasses in different human
   diseases. With the accumulation of flavonoids and corresponding
   targets, it is possible to comprehensively investigate the MOA of
   flavonoids in a systematic level and interpret the therapeutic
   mechanism to guide the drug discovery from natural flavonoid products.

Materials and Methods

Dataset

Flavonoids and Corresponding Targets

   A total number of 5,006 chemical structures of natural plant products
   were derived from Natural Product Activity and Species Source Database
   (NPASS) ([61]Zeng et al., 2018). Among them, main types of flavonoids
   including flavones, flavonols, flavanones, flavanonol, isoflavones, and
   flavan-3-ols were categorized according to the scaffold structures
   derived by cheminformatics software-RDKit ([62]Landrum, 2010), which
   were illustrated in Figure [63]1A. Further, corresponding direct
   targets of flavonoids were selected from 5,337 targets of natural plant
   products in NPASS. After that, 679 flavonoids and 481 corresponding
   targets were selected and listed in Supplementary Table [64]1. Number
   of targets for different flavonoid subclasses were illustrated in
   Figure [65]1B.

FIGURE 1.

   [66]FIGURE 1
   [67]Open in a new tab

   Structures and targets information of flavonoids. (A) Core scaffold
   structures of six flavonoid subclasses. (B) Target number of different
   flavonoid subclasses.

Enrichment Analysis of Flavonoids’ Targets

Diversity Analysis of Natural Flavonoid Products’ Targets

   Targets of natural flavonoid products were mapped into Kyoto
   Encyclopedic of Genes and Genomes (KEGGs) ([68]Kanehisa et al., 2012)
   and Gene Ontology (GO) ([69]Ashburner et al., 2000) through Metascape
   ([70]Tripathi et al., 2015) to analyze their enrichment pathways. Then,
   the enrichment pathways were generated for six flavonoid subclasses.

Specific Pathway Enrichment Analysis of Natural Flavonoids Products

   To distinguish the specific pathway of flavonoids from other natural
   plant products, permutation test was implemented 1,000 times to
   identify the specific pathway of flavonoids’ targets by setting the
   4,327 other natural plant products as background.

Pharmacology Network Analysis

   Protein–protein interaction (PPI) networks of flavonoids’ targets were
   generated and modularized through Metascape ([71]Tripathi et al.,
   2015). Further, the bio-functional similarity and difference between
   networks of six subclasses were compared based on the main functional
   modules. Then, PPI enrichment analysis was carried out with the
   following databases including BioGrid ([72]Chatr-Aryamontri et al.,
   2017), InWeb_IM ([73]Li et al., 2017), and OmniPath ([74]Turei et al.,
   2016). The densely connected network components was identified by
   Molecular Complex Detection (MCODE) algorithm ([75]Bader and Hogue,
   2003) and viewed by Cytoscape ([76]Shannon et al., 2003).

Structure–Activity Relationship Analysis

   In order to analyze the structure–activity relationship, basic
   physicochemical properties including molecular mass (weight), lipid
   water distribution coefficient (LogP), hydrogen bond receptor
   (NumHAcceptors), hydrogen bond donor (NumHDonors), rotatable bond
   (NumRotatableBonds), topological molecular polarity surface area (TPSA)
   and Lipinski’s Rule of five were calculated for different natural
   flavonoid products through RDKit ([77]Landrum, 2010).

   Also, the core scaffold and side chains of each natural flavonoid
   products were derived according to their chemical structures. Since
   flavones, flavonols, flavanones, flavanonol, and flavan-3-ols share the
   same core scaffold, the structure–activity relationships of above five
   subclasses were analyzed. Then, according to GO ([78]Ashburner et al.,
   2000), the bio-functional annotation of each structure segment can be
   obtained. Further, to identify the association between chemical
   structure of flavonoid subclasses and biological function,
   structure–activity relationship was further analyzed through Apriori
   algorithm ([79]Agrawal and Srikant, 1994). Here, the minimum support
   parameter was set as 0.01 and the minimum confidence was set as 0.5 for
   calculation.

Results

Pathway Enrichment Analysis of Flavonoids’ Targets

   The biological function of flavonoids’ target was deciphered through
   pathway enrichment analysis based on the background pathway dataset
   (Figure [80]2 and Supplementary Table [81]2). Results showed that, the
   targets of flavonoids were enriched in multiple essential pathways
   including metabolism, genetic information processing, environmental
   information processing, cellular process, organismal systems, and
   multiple pathways which were related to human diseases such as
   infectious diseases and cancer. For instance, in environmental
   information processing, flavonoids were enriched in multiple cell
   signaling pathways including MAPK signaling pathway, PI3K-Akt signaling
   pathway, FoxO signaling pathway and cAMP signaling pathway. In cellular
   processes, flavonoids can significantly regulate pathways such as
   apoptosis, focal adhesion, cell cycle, and autophagy. Further, it can
   be found that flavonoids’ targets were significantly enriched in
   several organismal systems including immune system, endocrine system
   and nervous system. Especially for immune system related pathways,
   flavonoids were enriched in Th17 cell differentiations, IL-17 signaling
   pathway, Toll-like, and NOD-like signaling pathways. Besides, multiple
   flavonoids’ targets can be found in the endocrine system pathways, such
   as progesterone-medicated oocyte maturation, GnRH signaling, oxytocin
   signaling and thyroid hormone signaling pathways. Also, nervous
   system-related pathways such as serotonergic synapse, and neurotrophin
   signaling pathways were enriched by corresponding targets. Moreover,
   flavonoids’ targets existed in pathways of essential human diseases
   such as multi-cancer, insulin resistance and infectious diseases
   including HTLV-1 infection, Epstein–Barr virus infection and Hepatitis
   B.

FIGURE 2.

   [82]FIGURE 2
   [83]Open in a new tab

   KEGG pathway enrichment analysis of natural flavonoid products’
   targets. Here, X-axis represents the enriched pathways (p-value <
   0.05), which were categorized according to KEGG classification. Y-axis
   represents target proportion of flavonoids in each pathway (number of
   flavonoids’ target in pathway/total number of flavonoids’ targets), the
   size of each nodes represents the significance of enrichment level
   (–LogP). Flavonoids and all six subclasses were marked in different
   colors.

   Besides the common enrichment pathways, different flavonoid subclasses
   illustrated different preference. For instance, targets of flavanonol
   and flavan-3-ols were more significantly enriched in nitrogen
   metabolism pathways than other subclasses. Targets of isoflavones,
   flavanones, and flavonols were enriched in metabolism pathways such as
   lipid, retinol, and drug metabolism pathway. Flavones’ targets were
   significantly enriched in MAPK signaling pathway and neurotrophin
   signaling pathway, which means natural flavone products may have
   therapeutic effects on neurological-related diseases. Pervious
   researches indicated that flavones such as apigenin and luteolin could
   activate Nrf2-antioxidant response element (ARE)-mediated gene
   expression and induce anti-inflammatory activities through the PI3K and
   MAPK signaling pathways ([84]Paredes-Gonzalez et al., 2015). Also, both
   compounds could significantly increase the endogenous mRNA and protein
   level of Nrf2 and Nrf2 targeting genes with important effects on hemo
   oxygenase-1 (HO-1) expression, thus, led to cytoprotective effects and
   neurite outgrowth ([85]Lin et al., 2010; [86]Zhao et al., 2013;
   [87]Zhang et al., 2015). In addition, corresponding targets of
   flavan-3-ols and flavanonol were enriched in cancer-related pathways.
   Natural flavan-3-ol products such as (-)-epigallocatechin gallate
   (EGCG), (-)-epicatechin gallate (ECG), (-)-epigallocatechin (EGC), and
   (-)-epicatechin (EC) were discovered flavan-3-ols from green tea, which
   could provide possible prevention of cancers ([88]Henning et al., 2013;
   [89]Yang C.S. et al., 2014). Although the flavonoids contain similar
   biological function based on the same core scaffold, above results
   indicated the different biological functions of flavonoid subclasses
   with different chemical structures. Thus, the therapeutic selection and
   clinical application for flavonoid subclasses were different from each
   other.

Functional Difference Between Flavonoids and Other Natural Plant Products

   To further discover the functional difference between flavonoids and
   other natural plant products, the specific enrichment pathway of
   flavonoids’ targets were analyzed by setting other natural plant
   products as background. Results showed that, flavonoids were enriched
   in cancer-related pathways compared with other natural products (Figure
   [90]3 and Supplementary Table [91]3). Among them, isoflavones and
   flavones were enriched in multi-cancer related pathways, flavan-3-ols
   can regulate the pathway of microRNA in cancer and isoflavones
   significantly enriched in the pathway of breast cancer, indicating the
   potential anti-cancer preferences of flavonoid subclasses. It can be