Determination and Application of Nineteen Monoamines in the Gut Microbiota Targeting Phenylalanine, Tryptophan, and Glutamic Acid Metabolic Pathways

The development and optimization of this method for the nineteen monoamine neurotransmitters and related metabolites (in Figure 1) included three parts: (1) chromatographic conditions, (2) mass spectrometric conditions, and (3) sample preparation. In this study, considering that most of the metabolites were highly polarized, they could hardly be retained on conventional C₁₈ columns, and matrix interference may have occurred. Therefore, a HSS PFP column was used to achieve the separation, the stationary phase of which was high-strength bonded silica with pentafluorophenyl ligand. The separation principle was composed of multiple factors, including hydrogen bonds, dipole–dipole interactions, and aromatic (π−π) and hydrophobic interactions. Then, polar metabolites with similar structures could be successfully separated without interference between peaks, eliminating the complex steps of derivatization treatment. Moreover, this column is favorable for nonprofessional researchers to operate when compared with hydrophilic interaction liquid chromatography columns, which have higher durability like frequently used C₁₈ columns. The column temperature was maintained at 35 °C, as suggested.

In terms of the selection of mobile phase, because almost all the neurotransmitters contained amino and hydroxyl groups, the mass spectrometric response of these compounds would increase under acidic conditions, and therefore, the pH value of the mobile phase was adjusted to acidity. Finally, a 0.2% concentration of formic acid was chosen because of its preferable peak shape and acceptable response by comparing various levels of formic acid, acetic acid (0.05%, 0.1%, 0.2%, 0.5%), or the combination of an acid with ammonium formate/ammonium acetate (1 mM, 2 mM, 5 mM, 10 mM). In addition, the combination of methanol and acetonitrile (v:v = 1:1) was selected for better response and proper retention time.

The mass spectrometric conditions, including gas flow, gas pressure, and interface temperature, were adjusted according to the response of the metabolites. The collision energy (CE) of detection for each metabolite was optimized separately (as shown in Table 1). In the pretreatment process, the peak shape and stability of the compounds were taken into consideration. Protein precipitation by acetonitrile showed better performance than that by methanol, while some metabolites, such as 5-HT, 5-HIAA, and IAA etc., were unstable upon analysis. Formic acid or ascorbic acid has been reported to increase the stability of neurotransmitters in brain samples [39,40]. However, addition of ascorbic acid led to an increase in the response of 5-HIAA and 5-HT over time, and lowered the response of 5-HTP in the matrix of the gut microbiota. Finally, 2% formic acid was added (to acetonitrile) to stabilize these metabolites before analysis. The drying and resolvation procedures were constructed to obtain a good peak shape and preferable sensitivity.

Currently, there is no unified guideline for the methodical validation of endogenous substances, although this is indispensable in biological analysis. Some strategies have been reported, including (1) adding authentic analytes into the real matrix, (2) adding surrogate analytes via stable isotope-labeled internal standards into the real matrix, and (3) using a surrogate matrix by adding authentic analytes [41]. The second strategy is accepted as the most accurate and convenient strategy for analyzing endogenous substances, while it is relatively expensive and difficult to acquire all nineteen stable isotope-labeled compounds. Preliminary experiments showed that these endogenous analytes could not be completely removed by conventional procedures, including the adsorption of activated carbon or hydrolysis under alkaline conditions. Furthermore, any surrogate matrix, whether water, buffer, synthetic matrix, or treated matrix, may have different matrix effects compared with the untreated authentic matrix, leading to a possible system error in the true level of these compounds in practical samples. Herein, authentic analytes were added to the real matrix for method validation.

The newly established method was used to investigate differences under mental illnesses. Then, a rat model of chronic unpredictable stress depression was established. After an eight-week unpredictable stimulation, the depression model was successfully established. Then, fresh fecal samples from both the model and control groups were collected for analysis. The variations in behavior were monitored and recorded by the sucrose preference test. Rats with depression showed a significantly lower trend to drink sucrose water (in Figure 3a), suggesting anhedonia in the rats.

Table 4 shows the presence of the neurotransmitters in the gut microbiota. Apart from the abundant presence of amino acids (Phe, Tyr, Trp, and Glu), the concentrations of transmitters varied a lot in the rat fecal samples, including Dopa (1660 ± 253 ng/g), DA (263.8 ± 76.3 ng/g), MLT (1.48 ± 1.88 ng/g), 5-HT (806.5 ± 34.1 ng/g), GABA (11.89 ± 0.51 μg/g), and Ach (8.04 ± 6.96 ng/g). The level of MT, derived from Phe, was 11.19 ± 1.36 ng/g. However, the other metabolites in this pathway, such as adrenaline and norepinephrine etc., were undetectable, which is different from the in vivo results. In terms of the Trp metabolites, the levels of 5-HIAA and 5-HTP (which are derived from 5-HT) in feces were 17.76 ± 2.18 μg/g and 13.82 ± 5.14 ng/g, respectively; KN and KYNA (which are derived from Trp) were 139.3 ± 10.5 ng/g and 1051 ± 116 ng/g, respectively; and the levels of TA, IAA, ILA, and IPA ranged from 1067 ± 111 ng/g to 8833 ± 1531 ng/g. While, if the animals were depressed, then differences in the concentrations of these metabolites were observed, and the results are summarized in Figure 3b,c. The average concentrations of all these amino acids showed a decreasing trend of −36% for Phe, −49% for Tyr (P < 0.05), −29% for Trp, and −34% for Glu (p < 0.01). The level of Dopa showed a significant increase in the model group, with an average of +105% (p < 0.001). Regarding the Trp metabolic pathway, the levels of 5-HT and 5-HIAA were downregulated by −20% (p < 0.001) and −30% (p < 0.01), respectively, in the depression model. The levels of KN and KYNA decreased as well (63.0 vs. 139.3 ng/g and 668 vs. 1051 ng/g, respectively, p < 0.001). In addition, the production of IAA, IPA, and ILA was significantly decreased (−33%, −36%, and −34%, respectively; p < 0.01, p < 0.05, and p < 0.001). These data suggest the variations in the gut microbiota or in the function of the gut under disease. Apart from the above results, the ratio of KYNA/KN levels was elevated in the depression model (in Figure 3d). This result was consistent with the hypothesis that the KYNA/KN ratio is a potential marker for the degree of depression [42].

Several previous studies have shown that abnormal behaviors are not only manifested in changes in brain neurotransmitter levels but are also accompanied by intestinal dysfunction and imbalance of the gut flora [43,44,45]. This phenomenon could be partly explained by the “gut–brain axis” theory, which proposes that a series of signaling pathways mediate the communication between the intestinal bacteria and brain. Interestingly, under depression, changes in the metabolic pathways in the gut microbiota seemed to be concentrated on Trp. This phenomenon is similar to the well-known observation that the levels of 5-HT, 5-HIAA, or their corresponding receptors in brain are disordered under depression [46,47]. Meanwhile, the downregulation of “Trp metabolic” bacteria, namely, Clostridium spp., Bifidobacterium spp., Escherichia spp., Ruminococcus spp., and Lactobacilli spp., etc., have been reported by several independent investigations in depression patients [48,49,50]. However, whether the change in the composition of the gut microbiota is the reason for, or the result of, the development of mental illness requires further and deeper exploration.

Several guideline suggested first-line drugs that treat NVS diseases have been screened for their regulatory potential of gut microbiota [51]. Sertraline and fluoxetine are selective serotonin reuptake inhibitors (SSRIs), and imipramine is a nerve terminal reuptake inhibitor; the indications of these three drugs are depression in adults. Levodopa is a typical anti-Parkinson’s disease drug, using direct transmitter supplementation. Clozapine is a nonclassical neuroblocker for schizophrenia, and chlorpromazine is an antagonist of central dopamine receptors in alleviating schizophrenia symptoms. In Figure 4a, all the samples are within the 95% confidence level, and among them, sertraline, imipramine, and chlorpromazine exhibited preferable regulatory potential for neurotransmitters and metabolites. The transmitters or metabolites that showed variations in level are shown in the heatmap (Figure 4b). Both groups of the SSRIs showed a significant increase in the levels of GABA (p < 0.001) and Dopa (p < 0.01 and p < 0.001), an increase in DA plus KN, and a decrease in Glu in the gut microbiota. High-dose imipramine (100 μM) showed an obvious downregulation of the KYNA/KN ratio (p < 0.001). Levodopa did not have an obvious effect on the metabolites in the Trp or Glu pathways, and similar results were observed in the clozapine groups. In addition, the high dose of chlorpromazine induced the production of DA (p < 0.05) and KN (p < 0.01), and simultaneously decreased the Glu level (p < 0.05) in the gut microbiota.

Melatonin (MLT), acetylcholine (Ach), and gamma-aminobutyric acid (GABA) were purchased from Solarbio Scientific Ltd. (Beijing, China). l-dopa (Dopa), dopamine (DA), hydroxytryptophan (5-HTP), 5-hydroxytryptamine (5-HT), and 4-hydroxybenzylamine (internal standard, IS) were obtained from J&K Scientific Ltd. (Beijing, China). Phenylalanine (Phe), tyrosine (Tyr), glutamic acid (Glu), and tryptophan (Trp) were obtained from the National Institutes for Food and Drug Control (Beijing, China). 3-Methoxytyramine (MT), 5-hydroxyindole-3-acetic acid (5-HIAA), kynurenine (KN), kynurenic acid (KYNA), and tryptamine (TA) were obtained from Shanghai Haohong Biological Medicine Technology Co., LTD (Shanghai, China). Indole-3-lactic acid (ILA), indole-3-acetic acid (IAA), and indolyl-3-propionic acid (IPA) were purchased from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China). The purities of all the reference standards were greater than 98%. Formic acid (100%) was purchased from Merck (Darmstadt, Germany). Acetonitrile and methanol were obtained from Fisher Scientific (HPLC grade, Fair Lawn, NJ, USA). Deionized distilled water was purchased from Hangzhou Wahaha Group Co. Ltd. (Hangzhou, China). All the other chemical reagents were obtained from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China) at the purest degree available. The structural formulas of the nineteen monoamine compounds and IS are shown in Figure 1.

Sprague-Dawley rats (180–220 g, 8 weeks, male) were supplied by Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). All the animals had free access to food and water. The temperature was maintained at 22–24 °C with a 12 h light/dark cycle, and the relative humidity was 40–60%. Fresh fecal samples were collected in sterile nitrogen-filled self-sealing bags and kept at −70 °C. The research complied with the Institutional Guidelines and Ethics, and Laboratory Institutional Animal Care and Use Committees of the Chinese Academy of Medical Sciences and Peking Union Medical College (No. 00003053).

Liquid chromatography with tandem mass spectrometry LC-MS/MS 8060 (Shimadzu Corporation, Kyoto, Japan) with an electrospray ionization (ESI) source was conducted for analysis. An xSelect HSS PFP column (with fluoro-phenyl bonded) was used for separation (100 mm × 2.0 mm × 1.8 μm, Waters, Milford, USA). The flow rate was 0.3 mL/min, and the column temperature was maintained at 35 °C. The mobile phase was formic acid:water (0.2:100, v/v) as phase A, and methanol:acetonitrile (1:1) with 0.2% formic acid as phase B. The binary gradient elution conditions were as follows (A:B): 0.01 min, 98:2; 3.50 min, 98:2; 4.00 min, 10:90; 4.50 min, 2:98; 6.00 min, 2:98; 6.50 min, 98:2; 8.50 min, and controller stop. Multiple reaction monitoring (MRM) in the positive mode was performed for detection, and the optimized MRM parameters for each compound are shown in Table 1. The mass condition parameters were set as follows: nebulizer gas, 2.7 L/min; drying gas, 10.0 L/min; heating gas, 10.0 L/min; interface temperature, 300 °C; collision-induced dissociation (CID) gas, 230 kPa; DL temperature, 250 °C; heat block temperature, 400 °C; and interface voltage, −4.5 kV. The autosampler was maintained at 4 °C.

The neurotransmitter standards, including Dopa, DA, MT, Trp, 5-HTP, 5-HT, 5-HIAA, MLT, KN, TA, ILA, IAA, IPA, GABA, and Ach, were dissolved in a 0.2% formic acid–water solution to obtain a concentration of 1.00 mg/mL. Phe, Tyr, Glu, and KYNA were dissolved in a 0.2% formic acid–methanol solution to a final concentration of 1.00 mg/mL. The stock solution was prepared by thoroughly mixing the standards according to the proportion, with Phe, Tyr, Trp, and Glu 50,000 ng/mL; 5-HIAA, ILA, IAA, IPA, and GABA 2000 ng/mL; Dopa, DA, 5-HTP, 5-HT, KN, TA, and Ach 500 ng/mL; MT, 200 ng/mL; KYNA, 100 ng/mL; and MLT, 10 ng/mL. Eight calibration curve standards (working solutions) were prepared by series dilution, and the final concentrations of Dopa in the mixed standards were as follows: 500 ng/mL, 200 ng/mL, 100 ng/mL, 50 ng/mL, 10 ng/mL, 5 ng/mL, 2 ng/mL, and 0.5 ng/mL. The levels of other substances in the working solutions achieved the corresponding values. The concentrations of the QC for each compound, namely, the low-concentration quality control (LQC), middle-concentration quality control (MQC), and high-concentration quality control (HQC), are listed.

Fresh fecal samples were prepared by diluting with 5-fold (w:v = 1:5) formic acid–water (v:v = 0.2:100). After thoroughly mixing by vortexing and centrifuging at 12,000× g for 5 min, the diluted fecal sample (50 μL) was immediately added to 150 μL cold acetonitrile (with 2% formic acid and 100 ng/mL 4-hydroxybenzylamine as IS). The resulting solution was centrifuged at 12,000× g for 5 min at 4 °C, and the supernatant was collected into another 1.5 mL tube. Then, the solvent of the collected solution was removed under a flow of nitrogen. The residue was redissolved in 50 μL of the initial mobile phase (with 2% formic acid added) and centrifuged at 12,000× g for 5 min at 4 °C. Five microliters of the supernatant was injected for analysis. For QC samples, fecal samples from six rats were first mixed and diluted with a 5-fold (weight:volume = 1:5) of formic acid–water (v:v = 0.2:100). Then, 50 μL of the working solutions with different levels of standards were added to an aliquot of the mixed fecal sample (50 μL). Then, 150 μL of cold acetonitrile (with 2% formic acid and 100 ng/mL IS) was added and treated as described above.

Twelve Sprague-Dawley rats (male, 8 weeks) were randomly separated into two groups to establish a chronic unpredictable stress depression model. Six rats were set as the normal control group (Group 1), and the other six rats were set as the depression model group (Group 2). Animals in group 2 were exposed to two different kinds of unpredictable stresses every day: light/dark reversed, food and water deprivation, ice water swimming, electric shock, behavioral constraints, cold stimulation, clip tail, wet bedding, and slope [58,59]. After continuous modeling for 8 weeks, the sucrose preference test was performed to examine the model after 8 weeks [58,59]. Then, fecal samples were collected from each group for the analysis of the nineteen neurotransmitters and metabolites by LC-MS/MS.

Six postlisting drugs, including sertraline hydrochloride (Shanghai Macklin Biochemical Co., Ltd., Shanghai, China), clozapine (Shanghai Yuanye Biological Technology Co., LTD, Shanghai, China), levodopa (J&K Technology Co. LTD, Beijing, China), chlorpromazine hydrochloride (Macklin, Shanghai, China), imipramine hydrochloride (Macklin, Shanghai, China), and fluoxertine hydrochloride (Yuanye, Shanghai, China), were used for drug screening. Five grams of the colon contents from six Sprague-Dawley rats (male, eight weeks) was collected under sterile conditions to isolate intestinal bacteria. After adding 100 mL of sterile anaerobic culture medium (Solarbio Biotechnology Co., LTD, Beijing, China) under a nitrogen atmosphere, the resulting mixture was filtered through gauze to remove the food debris. The intestinal bacterial culture was preincubated for 30 min. Then, 10 μL of standards (in methanol, 5 mM and 10 mM) was added to 990 μL of the intestinal bacterial culture and incubated for 12 h under anaerobic conditions [60]. Finally, the resulting culture was immediately mixed with cold acetonitrile (with 2% formic acid and IS) and treated as described above.

Statistical analysis was performed by GraphPad Prism version 5.72 (GraphPad Software, La Jolla, CA, USA) and SIMCA version 14 (MKS Umetrics AB, Umea, Sweden). Data are expressed as the mean ± standard deviation (SD) and were analyzed by two-tailed Student’s t test. A P value less than 0.05 was regarded as statistically significant for all the tests. The heatmap was constructed to express the compound levels in different colors, of which blue represents a lower level and red represents a higher level. The drug screening data were first normalized to the sum of total intensity, and then subjected to principal component analysis (PCA). The PCA score plot was built based on the 35 samples (n = 5 in each group, including the control group and six groups with 100 μM drug treatment) and those targeted features measured in the samples. The total variance retained in the four PCs was 0.833 with Q² = 0.436.