Abstract

   Shuang-huang-lian injection (SHLI) is a famous Chinese patent medicine,
   which has been wildly used in clinic for the treatment of acute
   respiratory tract infection, pneumonia, influenza, etc. The existing
   randomized controlled trial (RCT) studies suggested that SHLI could
   afford a certain anti-febrile action. However, seldom does research
   concern the pharmacological mechanisms of SHLI. In the current study,
   we explored plasma metabolomic profiling technique and selected
   potential metabolic markers to reveal the antipyretic mechanism of SHLI
   on yeast-induced pyrexia rat model using UPLC-Q-TOF/MS coupled with
   multivariate statistical analysis and pattern recognition techniques.
   We discovered a significant perturbance of metabolic profile in the
   plasma of fever rats and obvious reversion in SHLI-administered rats.
   Eight potential biomarkers, i.e. 1) 3-hydeoxybutyric acid, 2) leucine,
   3) 16∶0 LPC, 4) allocholic acid, 5) vitamin B[2], 6) Cys-Lys-His, 7)
   18∶2 LPC, and 8) 3-hydroxychola-7, 22-dien-24-oic acid, were screened
   out by OPLS-DA approach. Five potential perturbed metabolic pathways,
   i.e. 1) valine, leucine, and isoleucine biosynthesis, 2)
   glycerophospholipid metabolism, 3) ketone bodies synthesis and
   degradation, 4) bile acid biosynthesis, and 5) riboflavin metabolism,
   were revealed to relate to the antipyretic mechanisms of SHLI. Overall,
   we investigated antipyretic mechanisms of SHLI at metabolomic level for
   the first time, and the obtained results highlights the necessity of
   adopting metabolomics as a reliable tool for understanding the holism
   and synergism of Chinese patent drug.

Introduction

   Fever, also called pyrexia, was defined as a state of elevated core
   temperature, which is often, but not necessary, a part of the defensive
   responses of organisms (host) to the invasion of micro-organisms or
   inanimate matter recognized as pathogenic or alien [41][1]. It is also
   a cornerstone diagnostic sign in clinical practice that helps to carry
   out appropriate therapy in the early stage. Even though the febrile
   response seems useful in the adaptive reaction to a stressful
   situation, it could induce several detrimental effects on clinical
   outcomes, e.g. metabolic disorder, neurological injury [42][2],
   myocardial injury [43][3], pulmonary injury [44][4], and even worsens
   pre-existing disease. In general, febrile patients are frequently
   treated with kinds of antipyretic methods, including direct cooling,
   non-steroidal anti-inflammatory drugs (NSAIDs), and paracetamol, etc.
   Despite a lack of experimental and clinical data, these treatments may
   cause certain undesirable side effects, such as bleeding, hypotension,
   hepatic and renal injury [45][5]. Hence, it is complex for physicians
   and/or researchers to balance the benefits and harms of various
   therapeutic means.

   Over the past thousands of years, traditional Chinese medicine (TCM)
   has been used to treat fever by practitioners according to their
   experience and heritage in China. Nowadays, TCM is attracting
   considerable attentions worldwide due to its low toxicity yet effective
   therapeutic performance [46][6]. In recent years, some modern formulae
   (e.g. Qing-kai-ling injection [47][7], [48][8], Nao-re-qing oral liquid
   [49][9], Tan-re-qing injection [50][10], [51][11], etc.) showed great
   anti-febrile action in clinic. Among them, Shuang-huang-lian injection
   (SHLI), which derived from three herbal drugs (Lonicera Japonica,
   Scutellariae Radix, and Forsythiae Fructus), is a commonly used TCM
   preparation with officially recorded in the Chinese Pharmacopoeia
   (2005). Although considerable numbers of chemical compounds are
   contained, three effective ingredients, caffeotannic acid, baicalin and
   phillyroside, are officially recorded as quality control standards.
   Since 1973, SHLI has been used extensively in clinic to treat
   infectious diseases, such as pneumonia, influenza, acute tonsillitis,
   and acute faucitis [52][12], [53][13]. The existing clinical randomized
   controlled trials (RCT) suggest that all SHL preparations (tablet, oral
   liquid, injection, and so on) could show certain anti-febrile action
   (in Chinese) [54][14], [55][15]. However, there are seldom researches
   focusing on the pharmacological mechanisms of antipyretic effect of SHL
   preparations, and even most of the results were extracted from some
   routine tests on conventional animal models, such as resisting free
   radicals damage, raising immune response, inhibiting inflammatory
   factor (TNF-α, IL-1, IL-6, etc.), and influencing PGE2 related pathways
   [56][8], [57][16]. Therefore, it is necessary to introduce in-depth
   study for the antipyretic effect of SHLI using some holistic techniques
   owing to the ill-defined underlying mechanisms of this famous formula.

   In comparison with conventional research tools, metabolomics could
   address some novel features for studying the pathophysiological
   characteristics and pathogenesis of diseases by comprehensively
   monitoring low-molecular-weight endogenous substances using some
   advanced data acquiring methods, such as LC-MS, NMR, GC, and so on
   [58][17]–[59][20]. Among these techniques, ultra performance liquid
   chromatography coupled with quadrupole time-of-flight mass spectrometry
   (UPLC-Q-TOF/MS) has widely used for metabolomic analysis [60][21],
   [61][22]. Owing that UPLC possesses great peak capacity and
   chromatographic resolution due to its special column with stationary
   phase of 1.8 µm particles and Q-TOF/MS can provide the chromatographic
   peak area, accurate mass value of m/z, and abundant structural
   information for the eluate from LC domain.

   In this study, pyrexia rat model was developed by injecting aqueous
   suspension of yeast and plasma samples were collected from normal,
   model and SHLI-treated model groups, while UPLC-Q-TOF/MS was introduced
   for the metabolite analysis. At the meanwhile, multivariate statistical
   analysis in combination with pattern recognition techniques and
   metabolic pathway analysis were adopted to gain potential plasma
   markers for yeast-induced pyrexia. Moreover, SHLI was administrated to
   the model animals in parallel aiming to gain mechanistic insights for
   their antipyretic effects.

Results

Temperature Changes Measurement Results

   To investigate the antifebrile action of SHLI, the rectal temperatures
   of the rats at different time points were recorded ([62]Fig. 1). The
   results showed that body temperature significantly increased 5 hrs
   after yeast injecting (p<0.01 vs. node control group). On the other
   side, SHLI could markedly decrease the body temperature at the checking
   points of 6 hr and 8 hr (p<0.01 vs. model group), suggesting a definite
   antifebrile action for SHLI. As a consequence, the time point of 6 hr
   was selected for subsequent metabolomic analysis.

Figure 1. The changing trend of rectal temperatures in NC, M and SHLI
treatment group.

   [63]Figure 1
   [64]Open in a new tab

   Data was expressed as mean ± SD. ^### P<0.001 vs., NC group; **P<0.01
   vs., M group.

Multivariate Data Analysis of UPLC-ESI-Q-TOF/MS Data

   Typical UPLC-Q-TOF/MS base peak intensity (BPI) chromatograms of plasma
   samples from the normal control (NC), model (M) and SHLI-treated groups
   were illustrated in [65]Fig. 2. Some obvious drug-related components
   could be observed in [66]Fig. 2C (marked with blue arrows). As well
   known, drug-related components in plasma might interfere with the
   profile of unsupervised multivariate data analysis. In order to
   overcome the interference, the drug-related components with high
   contents in plasma were identified and deducted (See [67]Table S1). An
   unsupervised principle component analysis (PCA) method was used to
   intuitively characterize the disturbance among NC, M and SHLI-treated
   group ([68]Fig. 3A and 3B). As shown in [69]Fig. 3 (R^2X = 0.744,
   Q^2 = 0.444 in positive ionization mode; R^2X = 0.543, Q^2 = 0.327 in
   negative ionization mode), obvious separation was achieved with the
   stable cumulative modeled variation and good prediction capability by
   the first two principle components under two kinds of ionization modes.
   The results suggested that the perturbation of plasma metabolic profile
   occurred in rats after yeast administrated and SHLI treated.

Figure 2. Typical base peak intensity (BPI) chromatograms ofplasma samples
from each groups.

   [70]Figure 2
   [71]Open in a new tab

   (A) NC at positive ion mode. (B) M at positive ion mode (C) SHLI
   treatment at positive mode (Blue arrows show drug induced components).

Figure 3. The results of multiple pattern recognition of plasma metabolites
(PCA scores).

   [72]Figure 3
   [73]Open in a new tab

   (A) At positive ion mode. (B) At negative ion mode. Note: NC (▴), M (•)
   and SHLI (▪).

Identification of Metabolites

   A supervised orthogonal partial least squares discriminant analysis
   (OPLS-DA) technique was implemented to search distinct metabolites
   between M and NC groups. The parameter R^2Y (0.966 in positive ion
   mode; 0.982 in negative ion mode) indicated that the established model
   was capable of differentiating NC group from M group. The parameter Q^2
   (0.885 in positive ion mode; 0.939 in negative ion mode) showed that
   the established model owned strong predictability. The OPLS-DA S-plots
   were shown in [74]Fig. 4, and potential markers were extracted based on
   their contribution to the variations and correlation within the
   dataset. Based on VIP >1 and the perturbanced degrees of the
   metabolites evaluated by Student’s t-test and Mann-Whitney U test, 15
   metabolites were identified as potential biomarkers and listed in
   [75]Table 1. Among them, 5 metabolites in the M group, including
   Cys-Lys-His (P<0.001), allocholic acid (P<0.01),
   3-hydroxychola-7,22-dien-24-oic acid (P<0.01), vitamine B2 (P<0.01),
   and LysoPC (18∶2) (P<0.001) decreased obviously comparing with the NC
   group, while 3 ones in the M group, including 3-hydroxybutyricacid
   (P<0.05), leucine (P<0.05), and LysoPC (16∶0) (P<0.05), increased
   significantly comparing with the NC group. Following SHLI treatment,
   the changes of 8 out of the 15 metabolites was significantly reversed
   ([76]Fig. 5). All the results above suggested that SHLI treatment could
   effectively regulate some metabolic networks associated with their
   related metabolites in the yeast induced pyrexia rats.

Figure 4. The results of S-plots of OPLS-DA models.

   [77]Figure 4
   [78]Open in a new tab

   (A) At positive ion mode. (B) At negative ion mode. Note: NC (▴), M (•)
   and SHLI (▪).

Table 1. Metabolites selected by OPLS-DA with VIP >1 and significant test
P<0.05 between the pyretic model group and the normal control group.

   No. t[R]-m/z VIP Quasi-molecular ion Formula Metabolites M vs.C SHLI
   vs.M MS/MS Loss Related Pathway
   1 1.2403–136.0763 1.58 [M+H]^+ C[8]H[9]NO 2-Aminoacetophenone ↓^# ↑
   119.0490; 91.0678 −NH[3]; −C[2]H[3]O Unknown
   2 1.2996–132.1027 1.09 [M+H]^+ C[6]H[13]NO[2] Leucine^a ↑^# ↓* 86.0968;
   69.0701; 55.0398 −CH[2]O[2]; −CH[5]O[2]N; −C[2]H[7]O[2]N Valine,
   leucine and isoleucine degradation/biosynthesis; Glucosinolate
   biosynthesis; Aminoacyl-tRNA biosynthesi
   3 1.6469–120.0815 1.91 [M+H]^+ C[8]H[9]N Indoline ↑^# # ↓ 104.0626
   −NH[2] Unknown
   4 5.9690–373.2733 3.19 [M+H]^+ C[24]H[36]O[3] 3-Hydroxychola-7,
   22-dien-24-oic acid ↓^# # ↑* 355.2595; 227.1801; 201.1626 −H[2]O;
   −C[7]H[14]O[3]; −C[9]H[16]O[3] Secondary bile acid biosynthesis
   5 6.0673–387.1802 4.41 [M+H]^+ C[15]H[26]N[6]O[4]S Cys-Lys-His ↓^# # #
   ↑** 231.1104; 105.0709 −C[5]H[8]N[4]S; −C[12]H[18]O[2]N[4]S Unknown
   6 6.1132–357.2787 2.31 [M+H]^+ C[24]H[36]O[2] 5beta-Chola-7, 9 (11)
   -dien-24-oic acid ↓^# # ↑ 339.2680; 247.1692; 111.1143 −H[2]O;
   −C[8]H[14]; −C[16]H[22]O[2] Secondary bile acid biosynthesis
   7 7.3612–520.3396 5.01 [M+H]^+ C[26]H[50]NO[7]P LysoPC (18∶2) ↓^# # #
   ↑*** 502.3279; 240.1013; 184.0727 −H[2]O; −C[18]H[32]O[2];
   −C[21]H[36]O[3] Glycerophospholipid metabolism
   8 1.1853–103.0396 4.54 [M-H]^− C[4]H[8]O[3] 3-Hydroxybutyric acid
   (3-HB)^a ↑^# ↓** 85.0301; 59.0141 −H[2]O; −C[2]H[4]O Synthesis and
   degradation of ketone bodies; Butanoate metabolism; Metabolic pathways
   9 3.6313–453.2850 1.80 [M+FA-H]^− C[24]H[40]O[5] Allocholic acid^a ↓^#
   # ↑* 407.2804; 343.2635; 289.2235; 251.2093 −CH[2]O[2]; −C[2]H[6]O[5];
   −C[6]H[12]O[5]; −C[9]H[14]O[5] Secondary bile acid biosynthesis
   10 3.7077–421.1409 1.04 [M+FA-H]^− C[17]H[20]N[4]O[6] Vitamin B2^a ↓^#
   # ↑*** 255.0880; 241.0738; 212.0816 −C[5]H[10]O[6]; −C[6]H[12]O[6];
   −C[12]H[10]N[4]O[3] Riboflavin metabolism; Vitamin digestion and
   absorption
   11 3.7717–448.3059 1.84 [M-H]^− C[26]H[43]NO[5] Deoxycholic acid
   glycine conjugate ↓^# # ↓ 294.1783; 257.1782 −C[10]H[18]O;
   −C[9]H[19]O[4] Secondary bile acid biosynthesis
   12 4.3496–391.2858 1.31 [M-H]^− C[24]H[40]O[4] Deoxycholic acid^a ↓^# #
   ↑ 345.2807; 329.2882; 327.2711; 311.2400 −CH[2]O[2]; −CH[2]O[3];
   −CH[4]O[3]; −CH[4]O[4] Secondary bile acid biosynthesis
   13 4.3993–564.3294 2.57 [M+FA-H]^− C[26]H[50]NO[7]P LysoPC (18∶2)^a ↓^#
   ↑ 504.3081; 279.2325; 224.0688 −C[2]H[5]O[2]; −C[9]H[21]O[7]NP;
   −C[20]H[43]O[5] Glycerophospholipid metabolism
   14 4.6068–540.3299 2.48 [M+FA-H]^− C[24]H[50]NO[7]P LysoPC (16∶0)^a ↑^#
   ↓* 480.3089; 255.2323 −C[2]H[5]O[2]; −C[9]H[21]O[7]NP
   Glycerophospholipid metabolism
   15 4.7769–566.3451 1.43 [M+FA-H]− C[26]H[52]NO[7]P LysoPC (18∶1) ↓^# ↑
   506.3269; 281.2488 −C[2]H[5]O[2]; −C[9]H[21]O[7]NP Glycerophospholipid
   metabolism
   [79]Open in a new tab

   Note: ^aMetabolites were identified based on database information in
   METLIN, Lipid MAPs or HMDB; ↑showed up-regulated metabolites and
   ↓showed down-regulated metabolites; ^# p<0.05, ^# # p<0.01, ^# # #
   p<0.001 Model vs. normal control; *p<0.05, **p<0.01, ***p<0.001 SHLI
   vs. Model.

Figure 5. The results of relative integral levels of metabolites among NC, M
and SHLI treatment groups.

   [80]Figure 5
   [81]Open in a new tab

   (A) At positive ion mode. (B) At negative ion mode. ^a P<0.05 or P<0.01
   among NC, M, and SHLI treatment group.

Metabolic Pathway Analysis of Drug Target Candidates

   To further reveal the correlation among the distinct candidates,
   bioinformatics analysis was performed using MetPA, a web-based free
   tool that could combine results from powerful pathway enrichment
   analysis with the pathway topology analysis. As shown in [82]Fig. 6,
   the selected metabolites were involved in valine/leucine/isoleucine
   biosynthesis/degradation, glycerophospholipid metabolism, synthesis and
   degradation of ketone bodies, riboflavin metabolism, butanoate
   metabolism, secondary bile acid biosynthesis and aminoacyl-tRNA
   biosynthesis ([83]Table S2), suggesting that the target pathways might
   be the diagnostic perturbations during pyrexia and might be the targets
   for the treatment of SHLI.

Figure 6. Summary of pathway analysis with MetPA.

   [84]Figure 6
   [85]Open in a new tab

   Note: 1. Valine, leucine and isoleucine biosynthesis. 2.
   Glycerophospholipid metabolism. 3. Synthesis and degradation of ketone
   bodies. 4. Riboflavin metabolism. 5. Butanoate metabolism. 6. Valine,
   leucine and isoleucine degradation. 7. Aminoacyl-tRNA biosynthesis.

Discussion

   In this study, we firstly evaluated the antipyretic effect of
   Shuang-huang-lian injection (SHLI) using UPLC-Q-TOF/MS based
   metabolomic study. SHLI is a famous Chinese patent medicine and has
   been developed in a freeze-dried powder form by Harbin Pharmaceutical
   Group in 1990s. In recent years, SHLI has drawn widespread attention
   and the market demand increase dramatically owing to its wide
   distribution and potential biological effects. In 2009, SHLI was
   shortlisted by the ministry of health of China as one of the
   recommended treatments against influenza A H1N1 virus [86][23].

   Although SHLI is one of the major drugs to treat pyrexia (fever) in
   clinic, there is still little information concerning its antipyretic
   mechanism [87][24]. As we known, fever is a type of nonspecific and
   phylogenetic response to various infectious or non-infectious stimuli.
   Yeast-induced fever (also called pathogenic fever), which is usually
   accompanied by certain radical and inflammatory reaction caused by
   capsular polysaccharides and/or proteins of yeast, is a classical model
   for pharmacological study of antipyretic medicines [88][25]. GC/MS
   combined NMR has been adopted to analyze the metabolic profiling in the
   plasma in fever rat induced by baker’s yeast and the antipyretic effect
   of aspirin was also evaluated by metabolomic methods, while those
   results indicated that the perturbation of amino acid metabolism
   coupled with energy metabolism, lipid metabolism, and glycometabolism
   were involved in the yeast induced fever model [89][26]. Moreover, we
   have investigated the metabolic profiling and potential biomarkers in
   the urine from yeast induced pyrexia rats using UPLC/MS technique, and
   our previous results were in great agreement with that amino acid
   metabolism played a pivotal role on the pathogenesis of fever [90][27].
   Furthermore, we also studied the antipyretic effects of Qing-kai-ling
   injection using UPLC/MS based metabolomic study [91][7]. Honestly,
   pharmacological mechanisms of tradition Chinese preparation are
   unpredictable and poorly understood due to not only the complexity of
   multiple chemical ingredients, but also the multiple targeting sites
   involved during its in vivo process. Therefore, it is important to
   adopt comprehensive techniques to address this shortcoming.

   At present study, we explored the plasma metabolic profiling and
   potential metabolic markers to investigate the antipyretic effects of
   SHLI using the well established research strategy [92][7], [93][27].
   The results indicated that the metabolic alteration including increment
   of 3 metabolites in plasma (3-hydeoxybutyric acid, leucine, and 16∶0
   LPC), and decrement of 5 metabolites in plasma (allocholic acid,
   vitamin B[2], Cys-Lys-His, 18∶2 LPC, and 3-hydroxychola-7,
   22-dien-24-oic acid) occurred after yeast treatment. It is noteworthy
   that all the changes of these metabolites could be reversed according
   to SHLI treatment, suggesting the possible pharmacological mechanisms
   of antipyretic effects of SHLI. In other words, SHLI might contribute
   to repair these metabolites involved pathways, i.e. lipid metabolism
   (glycerophospholipid metabolism, ketone bodies synthesis and
   degradation, bile acid biosynthesis), amino acid metabolism (valine,
   leucine and isoleucine biosynthesis/degradation) and energy metabolism.
   Regarding these, a published similar study arouses our intense
   interest. Guang-yan Yan et al. has adopted NMR-based metabolomic
   approaches to investigate the toxicological effects of SHLI in Beagle
   dog [94][28]. They showed that high-dose SHLI could lead to hemolytic
   anemia and hepatic hemosiderosis due to membrane impairment and
   hemolytic effects. And their metabolomic investigations suggested that
   the toxicological profiling of high-dose SHLI might link disordered
   energy and lipid metabolism processes [95][28]. Even more remarkably,
   most of the toxic related metabolic biomarkers of SHLI, e.g.
   3-hydeoxybutyric acid, choline, phosphocholine, valine, and leucine,
   were also detected on the antipyretic action in SHLI treatment group.
   These findings highlight the pharmacological characteristics and safety
   evaluation of SHLI [96][29], [97][30]. Furthermore, the further
   experiments should be taken fully into account a dose and time
   dependent manners of SHLI. And the limitation of this study is that we
   only focused on the pharmacological mechanisms without the adverse drug
   reaction (ADR) profiles of SHLI.

   According to the current knowledge of metabolic pathways, we mapped the
   relationship network of these metabolites. As shown in [98]Fig. 7, the
   accelerated energy metabolism involving oxidation-reduction reactions,
   branched-chain amino acid metabolism, cholesterol metabolism, and
   ketone bodies metabolism were found to link with the significantly
   modified metabolites. Therefore, the antipyretic mechanism of SHLI was
   performed by correcting the perturbed metabolism of energy by a variety
   of metabolic pathways based on the results obtained from plasma
   metabolomic profiling.

Figure 7. The profile of metabolic network.

   [99]Figure 7
   [100]Open in a new tab

   The map was gained by analyzing the known metabolic pathways.

   3-Hydroxybutyric acid (3-HB), also called β-hydroxybutyric acid or
   β-hydroxybutyate, is a kind of carboxylic acid (ketone body) which
   synthesized in the liver from acetyl-CoA and can be used as an energy
   source by the brain when blood glucose is insufficient
   [101][31]–[102][34]. 3-Hydroxybutyric acid concentration increased in
   the plasma of the fever rats, indicating that fatty acid disintegration
   was enhanced to furnish energy. Leucine, a branched chain amino acid,
   is an essential amino acid which cannot be synthesized by animals.
   Since dietary leucine intake during the study period was likely zero
   (imposition of fasting), the increased leucine in plasma possibly stems
   from protein decomposition, indicating a negative nitrogen balance
   state in the modeling body. This result was in agreement with the
   similar researching reports [103][26]. It was reported that leucine
   metabolism could be affected in tumor necrosis factor α (TNF-α) and
   endotoxin (such as LPS) treated rats [104][35], suggesting that TNF-α
   or LPS can increase leucine oxidation and clearance of whole body which
   might subsequently affect the production of L-enkephalin in the
   hypothalamus. In addition, branched-chain amino acids that participate
   in valine/leucine/isoleucine biosynthesis and degradation might change
   the metabolic environment inside the blood-brain barrier [105][36].

   LysoPC (lysophosphatidylcholine, LPCs), also called lysolecithins, is a
   class of phospholipids derived from phosphatidylcholines, which is
   formed by phosphatidylcholine hydrolysis by the enzyme of phospholipase
   A[2], as a part of the de-acylation/re-acylation cycle that controls
   its overall molecular species composition. The other LPC formation
   pathway catalyzes by a specific enzyme system, i.e.
   lecithin-cholesterol acyltransferase (LCAT), which is an enzyme that
   could convert free cholesterol into cholesteryl ester. Since LPCs has
   cytolytic and membrane perturbing properties, the level of them must be
   strictly controlled. The increased level of LPCs in the plasma was
   considered as a marker for cell membrane injury. Researchers have found
   that LPC increases under reactive oxygen species (ROS) and inflammatory
   conditions, such as patients suffering from liver injury, lung
   infection, and diabetes, etc [106][37], [107][38]. Interestingly, the
   level of the saturated LPC (16∶0) increased, while the content of
   unsaturated LPC (18∶2) decreased in the plasma of fever rats. It was
   known that LPC species exhibited different capacities in the induction
   of cyclooxygenase-2 (COX-2) and prostaglandins (PGs) expression due to
   the length and the saturation degree of LPC acyl chains, i.e. 16∶0 LPC
   possessed more pronounced capacity than 18∶2 LPC [108][39]–[109][41].
   Therefore, the increase of 16∶0 LPC could elevate PGE2 levels in the
   plasma of fever rats which could give rise to pyrexia finally. However,
   the relationships between plasma levels of LPCs and fever were not
   known, which calls for further evidences and much more explorations
   in-depth.

   As an essential dietary, vitamin B2 (or riboflavin) is the main source
   of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD),
   and is therefore required by all favoproteins. As we known, vitamin B2
   plays a key role in energy metabolism, and for the metabolism of fats,
   ketone bodies, carbohydrates, and proteins [110][42]–[111][44].
   Moreover, as coenzymes, FMN and FAD play a wide role on
   oxidation-reduction reactions in metabolic transformations by aiding
   one or two electrons [112][45]–[113][47].

   Since the limitations of the analysis based on MetPA, some biomarkers
   such as allocholic acid and 3-hydroxychola-7, 22-dien-24-oic acid were
   not matched in the pathway analysis. In order to find the contribution
   of pyrexia, we searched them manually in documented data and
   biochemical databases. Allocholic acid and 3-hydroxychola-7,
   22-dien-24-oic acid, both of which was in the plasma of pyrexia rats,
   belong to bile acids and their derivatives that are the metabolites of
   cholesterol. Previous reports have documented that neutropenic patients
   with fever showed a significant decrement for the level of serum
   cholesterol [114][48], indicating that the decrease bile acids in
   pyrexia rats might be related to the cholesterol decrease.

Conclusion

   In summary, we discovered a significant perturbance of metabolic
   profile in the plasma of yeast induced fever rats, which could be
   obviously reversed by SHLI treatment. The metabolic profiles alteration
   was characterized not only by the increase of plasma 3-hydeoxybutyric
   acid, leucine, 16∶0 LPC, but also by the decrease of plasma allocholic
   acid, vitamin B[2], Cys-Lys-His, 18∶2 LPC, and 3-hydroxychola-7,
   22-dien-24-oic acid following yeast administration. Based on metabolic
   pathway analysis and particular explanations, we concluded that the
   antipyretic mechanism of SHLI lay on correcting perturbed energy
   metabolism according to a variety of metabolic pathways. Taken
   together, the findings obtained in current study can be used to predict
   some possible targets for fever and SHLI treatment. Furthermore, our
   study also highlights the importance of metabolomics as a potential
   tool for uncovering metabolic pathways to discover targets for
   understanding the holism and synergism of Chinese medicine formulas.

Materials and Methods

Chemicals and Materials

   Yeast was purchased from Mauri Food Co., Ltd. (Hebei, China). HPLC
   grade formic acid was obtained from Sigma Chemical Co., Ltd (St. Louis,
   MO, USA). Methanol and acetonitrile (HPLC grade) was acquired from
   Fisher Corporation (Michigan, USA). Ultra high purity water was
   prepared by Millipore-Q SAS 67120MOLS HEIM (France). Shuang-huang-lian
   injection was achieved from the second Chinese Medicine Factory of
   Harbin Pharm Group CO., Ltd. (Registered No. Z10960058).

Animals and Handle

   The protocol of the study was approved by the Ethics Committee of
   Beijing University of Chinese Medicine. A total of 96 male
   Sprague-Dawley rats (200±20 g) were commercially obtained from the
   institute of experimental animals in the Weitonglihua Laboratory Animal
   Technology Co., Ltd. (Beijing, China) (Rodent license No. SYXK
   11-00-0039), housed in an environmentally controlled room at a constant
   temperature (23±2°C) and humidity (60±5%) on a 12 h light/dark cycle
   and provided with standard diet and water ad libitum for a one-week
   acclimation period. The rats’ rectal temperatures were measured two
   times per day using a digital thermometer for monitoring the regular
   rhythm of body temperatures at the last three days. The rats with a
   temperature difference that was greater than 0.5°C were excluded. Then
   rats were randomly divided into 3 groups: the normal control group (NC,
   n = 24), the pyretic model group (M, n = 24), and the model rats
   treated with SHLI group (SHLI, n = 24). Besides, the rest of the rats
   were taken as parallel control to detect exogenous compounds produced
   with SHLI metabolism.

   The rats’ rectal temperatures were measured before modeling. The M and
   SHLI groups were subcutaneously injected with a 40% aqueous suspension
   of yeast (7.5 mL/kg) in the back below the nape of the rats (NC groups
   were given an equal volume of 0.9% saline). The rats in SHLI groups
   were then injected 600 mg/kg SHLI (dissolved in 0.9% saline) from the
   intraperitoneal (NC and M groups were given an equal volume of 0.9%
   saline instead). Six rats in each group were anesthetized with 10%
   chloral hydrate and blood samples containing heparin sodium
   anticoagulant were collected from the abdominal aortic at 5 hrs, 6 hrs,
   8 hrs, and 10 hrs after SHLI or 0.9% saline was administrated.

Preparation of Plasma Samples and UPLC-ESI-Q-TOF/MS Analysis

   Plasma sample preparation was carried out by protein precipitation with
   methanol using Ostro 96-well plates with pressure valves (Ostro plates,
   Waters). Briefly, 400 µL of methanol was added to 100 µL plasma in an
   Ostro 96-well plate. The mixture was then quickly mixed by aspirating
   the samples 10 times using the micropipette and kept for 10 min. Vacuum
   was then applied to the Ostro plate to collect the mixture. Added 400
   µL of methanol into the plate and repeated the steps once again. The
   prepared plasma samples were removed to the injection vial for
   UPLC-Q-TOF/MS analysis.

   Metabolomics analysis was performed on an Waters Acquity UPLC system
   coupled to a Xevo G2-Q-TOF (Waters MS Technologies, Manchester, UK)
   equipped with an electrospray ionization source operating in positive
   and negative ion modes. An Acquity UPLC HSS T3 column (2.1×100 mm, 1.8
   µm, UK) equipped with a binary solvent delivery system and an
   autosampler. The column was maintained at 45°C and eluted at a flow
   rate of 0.45 mL/min. The gradient mobile phase was a mixture of 0.1%
   formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The
   proportion of mobile phase B was optimized as follows: 0∼10 min, 1∼99%,
   maintained 1 min with 99%, then returned to 1% at the next 1 min and
   held for 3 min for positive ion mode; 0∼5 min, 1∼99%, maintained 1 min
   with 99%, then returned to 1% at the next 1 min and held for 2 min for
   negative ion mode. The autosampler was maintained at 4°C and the
   injection volume was 5 µL. Data were collected from m/z 50 to m/z 1200.
   For positive ion mode, the capillary and cone voltage were set at 3 kV
   and 30 V. For negative ion mode, the capillary and cone voltage were
   set at 2.5 kV and 45 V. The desolvation gas was set at 800 L/h at a
   temperature of 450°C. The source temperature was set at 120°C. The cone
   gas was set at 15 L/h for positive ion mode and 30 L/h for negative ion
   mode. Leucine-enkephalin was used as the lock mass solution to ensure
   the accuracy and reproducibility.

Data Processing and Multivariate Data Analysis

   UPLC-Q-TOF/MS data of the plasma samples were processed using Waters
   Markerlynx XS software to transform the raw data into a single matrix
   containing aligned peaks with the m/z/retention time pair along with
   normalized peak intensities and a sample name. The matrix was then
   introduced to EZinfo 2.0 software for principal component analysis
   (PCA) and orthogonal partial least-squared discriminant analysis
   (OPLS-DA) analyses. All variables were Pareto scaled before analysis.
   In order to gain an overview of the rat plasma metabolic profiling; an
   unsupervised PCA method was used to give the comprehensive view of the
   clustering trends for the three groups. A supervised OPLS-DA analysis
   technique was used to search biomarkers based on the variable
   importance in the projection (VIP >1) between NC and M. The quality of
   these models can be explained byR^2 and Q^2 values. R^2 indicates the
   goodness of fit displaying the variance explained in the model and Q^2
   indicates predictability displaying the variance in the data
   predictable by the model. The values of these parameters close to 1.0
   indicating a reasonably good prediction for the constructed
   mathematical model.

Identification of Plasma Biomarkers

   For the identification of potential biomarkers, some available
   biochemical databases, such as HMDB ([115]http://www.hmdb.ca/), KEGG
   ([116]http://www.genome.jp/kegg/), METLIN
   ([117]http://metlin.scripps.edu/), LIPIDMAPS
   ([118]http://www.lipidmaps.org/) and Chemspider
   ([119]http://www.chemspider.com) were used by comparing the accurate
   mass and fragments information obtained from UPLC-Q-TOF/MS.

Comparative and Analysis of Plasma Metabolites

   The statistical analysis of the relative intensity of biomarkers was
   performed by SPSS 17.0. The integration areas of the detected
   metabolites with high VIP values were first tested for the normality of
   the distribution. If the distribution followed the normality
   assumption, a parametric Student’s t-test was applied; otherwise, a
   nonparametric Mann-Whitney U test was performed to detect statistically
   significant metabolites that were increased or decreased between
   groups. Differences were considered significant at a value of P<0.05.
   Statistic analysis of One-way ANOVA was also performed for the rectal
   temperature results.

   Metabolomic pathway analysis was performed with MetPA (metabolomics
   pathway analysis) based on potential metabolite biomarkers. MetPA is a
   user-friendly, web-based tool for pathway analysis and visualization of
   metabolomic data within the biological context of metabolic pathways
   [120][49]. For the pathway analysis algorithms, Hypergeometric Test was
   used for over representation analysis, and Relative-betweeness
   Centrality was used for pathway topology analysis.

Supporting Information

   Table S1

   Drug-induced components and their metabolites in the plasma of
   Shuang-huang-lian injection ( SHLI) treated rats.

   (DOC)
   [121]Click here for additional data file.^ (207KB, doc)
   Table S2

   Summary of pathway analysis with MetPA.

   (DOCX)
   [122]Click here for additional data file.^ (17.7KB, docx)

Funding Statement

   This work was supported in part by National Science and Technology
   Major Projects for “Major new drugs innovation and development” (Grant
   No. 2011ZX09201-201-15), and National Science Foundation of China
   (Grant No. 81173562). The funders had no role in study design, data
   collection and analysis, decision to publish, or preparation of the
   manuscript.

References