Clinicobiochemical and GC-MS Based Serum Metabolomics for determination of Therapeutic Efficacy of Silymarin in Pneumonic Sheep

doi:10.21203/rs.3.rs-4344803/v1

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Clinicobiochemical and GC-MS Based Serum Metabolomics for determination of Therapeutic Efficacy of Silymarin in Pneumonic Sheep

https://doi.org/10.21203/rs.3.rs-4344803/v1

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Background: The goal of this study was to evaluate the therapeutic efficacy of silymarin against sheep pneumonia utilizing clinical, biochemical and metabolomics approaches.

Methods: Fifty adult male Barki sheep were divided into two groups based on their health status. Group 1 included healthy sheep (n = 10); Group 2 included sick sheep with clinical evidence of pneumonia (n = 40), which were further classified into four subgroups based on treatment protocols: subgroup 1 (SG1) was given traditional treatment; subgroup 2 (SG2) received traditional treatment plus daily 280 mg of silymarin orally; subgroup 3 (SG3) was administrated daily 280 mg of silymarin orally; and subgroup 4 (SG4) received daily 560 mg of silymarin orally. Evaluation of hepatic and renal function as well as serum lipid profile, glucose concentrations, malondialdehyde (MDA) concentrations, and total antioxidant activity (TAC) was carried out using commercial kits. Efficacy-directed distinction between therapeutic groups was accomplished based on GC-MS generated serum metabolite profiles supported by partial least squares regression analysis (PLS).

Results: PLS score plot showed a clear discrimination between the healthy and pneumonic sheep groups that exhibited lower concentrations of TAC, total cholesterol, HDL-cholesterol, and glucose, but elevated liver enzyme, urea, creatinine, MDA and LDL-cholesterol (P < 0.05). Through clinical evaluations, the rapid clinical responses were achieved by the oral administration of silymarin 560 mg and through selective analysis of metabolomics profile, pneumonic therapy with 280 mg of silymarin was the best therapeutic outcome relying on a SG3 was strongly correlated with the upregulation of TAC, glucose, and total and HDL-cholesterol values.

Conclusions: Pneumonic sheep treated with silymarin exhibited healing as well as greater clinical, metabolomic and biochemical improvement than treatment with traditional treatment alone.

Silybum marianum

Metabolites profile

Discrimination analyses

Pneumonia

Pneumonia is a complex disease that in small ruminants results from a combination of environmental, management, immune, and infectious (bacterial, viral, and mycotic agents) factors that results in major economic losses due to cost of treatment, reduced productivity, and high mortality rates [1, 2].

Fluoroquinolones and macrolides are broad-spectrum antibiotics commonly used to treat pneumonia in sheep [3]. In addition, anti-inflammatory drugs are often indicated to control disease severity. Currently, antimicrobial resistance is a global concern that requires multidisciplinary strategies to ensure efficient therapies for human and animal populations [4]. In recent years, herbal therapy has received attention as a novel and alternative therapeutic approach due to its safety and cultural acceptability [5, 6].

Silybum marianum (SM; milk thistle) has been used for centuries as a natural remedy to cure a variety of illnesses, particularly hepatic ones. It contains a flavonolignan complex termed silymarin mainly found in the seeds and fruits [7]. Silymarin, of which silibinin is the main active component, has antimicrobial, antimycotic, anti-inflammatory, antifibrotic, immunomodulating, and anthelmintic properties [8]. It also reduces the expression of sterol regulatory element binding protein 1 and fatty acid transport protein 5 in hepatic cells to prevent the fat accumulation caused by free fatty acids [9, 10]. Silymarin's ability to scavenge free radicals and boost endogenous antioxidant defenses, such as the glutathione system, is linked to its ability to reduce oxidative stress-induced hepatocellular damage [11]. It protects kidney cells in vitro from medication-induced nephrotoxicity as well as from cyclosporine nephrotoxicity [12]. Given the pleiotropic actions of silymarin and the clinical importance of respiratory disease in small ruminants, investigating silymarin in sheep with pneumonia could provide valuable information.

Metabolomics analysis is a contemporary approach in drug development, disease diagnosis, pathophysiology, and prognosis. It provides more biochemical insight and understanding than other systems biology omics techniques [13]. Furthermore, it enables the measurement of changes in endogenous small molecules within cells, tissues, and biofluids of the body in response to environmental changes or contaminants. Gas chromatography-mass spectrometry (GC-MS) is the most used metabolomics technology for quantitatively analyzing various metabolites in biological materials. It can effectively and rapidly separate and identify large pools of metabolites [14]. Recently, scientists have directed their attention towards the biological and pharmaceutical applications of metabolites obtained from edible plants and foods [15, 16].

The purpose of this study was to assess the therapeutic effectiveness of silymarin in pneumonic sheep using clinical examination, metabolomics profiling, and biochemical parameter measurements. We expected that treating pneumonic sheep with silymarin would cure and enhance their health.

Milk thistle extract (Silybum marianum)

Silymarin powder was provided by Medical Union Pharmaceuticals (MUP) Company, Egypt. The powder consists of 50% silymarin with a potency of 104.49%, with code number 0111304600.

Animals’ criteria and experimental design

A total of fifty male adult Barki sheep at private farm in Sadat City, Egypt, aged between 1 to 2 years with a mean body weight of 60 ± 2 kilograms were assigned to two groups based on their health condition. Group 1 (G1; n =10), included healthy sheep with no clinical or laboratory evidence of disease, were free of external and internal parasites, and served as a control group. Group 2 (G2; n = 40), consisted of sheep with evidence of respiratory disease, including copious nasal discharge, fever, cough, dyspnea, and abnormal respiratory sounds upon auscultation. This group was further divided into four subgroups according to the therapeutic protocol. SG1 (n =10) included pneumonic sheep that received the traditional antimicrobial treatment for pneumonia using florfenicol (20 mg/kg body weight/ IM injection), a non-steroidal anti-inflammatory drug as diclofenac sodium (2.5 mg/kg body weight/ IM injection) and anti-histaminic drug as diphenhydramine hydrochloride 20 mg (1 ml/45 kg bodyweight/ IM injection). SG2 (n =10) consisted of pneumonic sheep treated as SG1 plus daily oral administration of silymarin at a dose of 280 mg every 24 hours for seven consecutive days. SG3 (n =10) consisted of pneumonic sheep treated with oral administration of silymarin 280 mg every 24 hours for seven consecutive days [17]. SG4 (n =10) included pneumonic sheep treated by daily oral administration of silymarin 560 mg every 24 hours for seven consecutive days.

General clinical examination

Clinical history and physical examination follow-ups were done for all sheep involved in this study [18].

Sampling

Blood samples were collected at early morning by jugular venipuncture from healthy and pneumonic sheep before starting different treatment protocols (zero day) and seventh days post treatment (7^th DPT) in serum clot tubes and kept at room temperature until coagulation for at least 60 minutes. The clotted blood samples were centrifuged at 2000 x g for 10 minutes at 4°C and aliquoted into small tubes then stored in at -80°C until analysis.

Biochemical assays of hepatic, renal function tests, glucose, lipid profile, malondialdehyde and total antioxidant capacity

Colorimetric methods were used to determine serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), creatinine, urea, glucose, triglycerides, cholesterol, HDL-cholesterol, LDL-cholesterol, malondialdehyde (MDA), and total antioxidant capacity (TAC).

Preparation of Metabolomic Samples

Frozen serum samples were thawed on ice and silymarin metabolites extracted and silylated using xylitol as an internal standard, as previously described [15]. Briefly, 100 µL of serum was combined with cold acetonitrile (100%, 200 µL) and centrifuged at 7000 x g for 15 min. Nitrogen gas was used to evaporate the supernatant until it was completely dry. A 50 µL pyridine solution of methoxyamine (20 mg/mL) was first added to the dried residue, then incubated at 60°C for 1 h to derivatize the metabolites. A second derivatization phase was performed by adding 100 µL N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) containing trimethylsilyl (TMS; 1%) to the mixture and incubating this for 1 h at 60°C. The quality control (QC) sample was formed by mixing aliquots from all samples into a pooled sample to analyze changes in MS response. Additionally, a hydrocarbon mixture standard (C8–C40) was analyzed.

GC–MS based serum metabolite profiling

The serum metabolite profiling was determined using gas chromatography (Thermo Scientific Corp., USA) and a thermo-mass spectrometer detector (ISQ Single Quadrupole Mass Spectrometer). The chromatographic separation was performed as described by [19], with few modifications over TG-5MS columns (30mm x 0.25mm i.d, 0.25 μm film thickness) and helium (He) at a 1 mL/min flow rate as carrier gas, and a 1:10 split ratio and according to the temperature program: 2 min at 80°C, rising with 5°C/min up to 300°C and held for 5 min. Both the injector and detector were held at 280°C. The mass spectral data were acquired via the electron ionization (EI) at 70 eV, with the spectral ranging at m/z 35–500.

Processing of GC–MS Data, Molecular Networking and Multivariate Data Analyses

Serum metabolite identification was accomplished via comparisons of their retention indices (RI) relative to n-alkanes standards (C8–C40) alongside mass matching to NIST library database. Before mass spectral matching, AMDIS software (www.amdis.net) was used to deconvolute the peaks. Data regarding metabolite abundance were retrieved using MS Dial software with default settings and Pareto scaling in preparation for multivariate data analysis. The detection (LOD) and quantification (LOQ) limits of 32 metabolites were also determined and signal-to-noise ratios of 3:1 and 10:1, respectively, were considered.

Statistical analyses

A Shapiro-Wilk test was used to determine data normality. Data in this study was normally distributed and expressed as mean with standard deviation. One way ANOVA was carried out to determine the difference between groups Using IBM SPSS statistics 16 (IBM Corporation, Armonk, NY). Comparative analysis of serum metabolic profiles derived from GC-MS of the different therapeutic approaches was performed using both supervised pattern recognition methods, i.e. partial least-squares regression analysis (PLS) using the program SIMCA-P Version 14.0 (Umetrics, Umeå, Sweden). Further, GC-MS derived serum profiles of endogenous metabolites and biochemical parameters viz. MDA and TAC, lipid profile (i.e. triglycerides (TG), cholesterol, HDL- cholesterol, LDL- cholesterol) and blood glucose concentrations, liver enzyme activities (AST, ALT, GGT) and kidney function tests (creatinine and urea) were subjected to partial least-squares regression analysis (PLS) for the sake of efficacy discrimination between the therapeutic approaches and to determine the serum metabolites that are positively or negatively correlated to restoration of pneumonic conditions in sheep to the normal status as implied by the concentrations of the different biochemical markers. Serum metabolites responsible for the differentiation between the four therapeutic approaches were identified based on variable inﬂuence on projection (VIP) values of the constructed PLS model (VIP > 1.0). Statistical significance for all analyses in this study was set to P < 0.05. All variables were mean-centered and scaled to Pareto variance [20].

The effect of traditional treatment and/ or silymarin on clinical profile.

Group 2 showed copious nasal discharge, fever, cough, dyspnea, and abnormal respiratory sounds upon auscultation. G2 showed significantly higher body temperature, respiratory and pulse rates than healthy ones (P < 0.05; Table 1). At 7th DPT, SG1 had still significant higher body temperature, respiratory and pulse rates compared to healthy ones (P value < 0.05; Table 1) and showed respiratory manifestations, while SG 2, 3 and 4 were not significantly different than G1 (P > 0.05). The rapid clinical response to treatment was achieved early 3 days post treatment in SG 4 by the high dose of oral silymarin administration (560 mg), while the therapeutic responses of SG2 and 3 were achieved at 7th DPT.

Table 1

Effect of traditional and /or silymarin on clinical examination of sheep
Variables	Body temperature (c)	Respiratory rate (cycle/ min)	Pulse rate (beat/min)
G1 (n = 10)	39.01 ± 0.27 ^a	42.00 ± 2.58 ^a	70.9 ± 3.2 ^a
G 2 (n = 40)	41.46 ± 0.28 ^b	68.2 ± 2.5 ^b	103.5 ± 5. 4 ^b
SG 1 (n = 10); 7th DPT	39.9 ± 0.38^c	52.8 ± 1.98 ^c	84.9 ± 3.2 ^c
SG 2 (n = 10); 7th DPT	39.4 ± 0.35 ^a	44.6 ± 2.27 ^a	73.7 ± 3.77 ^a
SG 3 (n = 10); 7th DPT	39.2 ± 0.13 ^a	44.00 ± 1.6 ^a	72.8 ± 2.89 ^a
SG 4 (n = 10); 7th DPT	39.29 ± 0.38^a	42.8 ± 2.4 ^a	71.5 ± 2.3 ^a
n: number; DPT, days post treatment; Means with different letter superscripts in the same column are significantly different at (P < 0.05).

The effect of traditional treatment and/ or silymarin on liver and kidney function tests

Pneumonic sheep had significantly higher AST, ALT, GGT, creatinine, and urea concentrations than healthy sheep (P < 0.05; Table 2). At 7th DPT, SG 2, 3 and 4 were not significantly different than healthy sheep (P > 0.05; Table 2), with exception in SG2 which revealed significantly higher creatinine and urea concentrations than healthy sheep (P < 0.05; Table 2), while SG 1 showed increased AST, ALT, creatinine, and urea concentrations than healthy sheep (P < 0.05; Table 2).

Table 2

Effect of traditional and /or silymarin on hepatic and renal function tests
Variables	AST (U/L)	ALT (U/L)	GGT (U/L)	Creatinine (µmol/L)	Urea (mmol/l)
G1 (n = 10)	47. 3 ± 1.9 ^a	27.75 ± 1.12 ^a	32.24 ± 1.13 ^a	41.04 ± 1.68 ^a	1.12 ± 0.15 ^a
G 2 (n = 40)	68.12 ± 2.6 ^b	42.16 ± 2.08 ^b	46.53 ± 3. 8 ^b	53.55 ± 1.48 ^b	2.8 ± 0.25 ^b
SG 1 (n = 10); 7th DPT	52. 7 ± 1.8^c	31.65 ± 1.017 ^c	34.68 ± 1.4 ^c	43.22 ± 1.36 ^c	1.45 ± 0.11 ^c
SG 2 (n = 10); 7th DPT	48.8 ± 1.06 ^a	29.16 ± 0.84 ^a	32.36 ± 0.76 ^a	42.3 ± 0.8 ^c	1.34 ± 0.05 ^c
SG 3 (n = 10); 7th DPT	48.46 ± 1.5 ^a	28.8 ± 1.2 ^a	32.6 ± 0.63 ^a	40.93 ± 0.87 ^a	1.23 ± 0.08 ^a
SG 4 (n = 10); 7th DPT	47.06 ± 0.9^a	28.69 ± 0.9 ^a	32.17 ± 1.1 ^a	39.82 ± 1.4 ^a	1.26 ± 0.07 ^a
n: number; DPT, days post treatment; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase, GGT: Gamma-glutamyl transferase; Means with different letter superscripts in the same column are significantly different at (P < 0.05).

The effect of traditional treatment and/ or silymarin on glucose and lipid profile.

Pneumonic sheep had significantly higher triglyceride and LDL-cholesterol, and lower cholesterol, HDL-cholesterol, and glucose concentrations than healthy sheep (P < 0.05; Table 3), while, at 7th DPT, SG 1, 2,3 and 4 were not different than healthy ones (P > 0.05; Table 3).

Table 3

Effect of traditional and /or silymarin treatment on glucose and lipid profile
Variables	Triglycerides (mg/dl)	Cholesterol (mg/dl)	HDL-cholesterol (mg/dl)	LDL-Cholesterol (mg/dl)	Glucose (mg/dl)
G 1 (n = 10)	38.15 ± 1.2 ^a	68.77 ± 1.9 ^a	56.02 ± 1.79 ^a	25.54 ± 1.18 ^a	62.63 ± 1.12 ^a
G 2 (n = 40)	47.9 ± 1.33 ^b	43.17 ± 1.68 ^b	44.1 ± 2.01^b	34.14 ± 1.8 ^b	43.68 ± 1.79 ^b
SG 1 (n = 10); 7th DPT	39.68 ± 1.03^a	64.75 ± 1.3 ^a	52.5 ± 0.77 ^a	25.5 ± 1.12 ^a	60.84 ± 1.23 ^a
SG 2 (n = 10); 7th DPT	38.96 ± 0.87 ^a	67.26 ± 1.04 ^a	54.95 ± 1.1 ^a	25.7 ± 1.08 ^a	61.26 ± 1.12 ^a
SG 3 (n = 10); 7th DPT	38.92 ± 0.78 ^a	67.63 ± 1.8 ^a	55.72 ± 0.9 ^a	25.69 ± 10.6 ^a	60.85 ± 1.6 ^a
SG 4 (n = 10); 7th DPT	38.95 ± 0.86 ^a	68.11 ± 1.5 ^a	55.11 ± 0.94 ^a	25.19 ± 1.02 ^a	61.66 ± 1.47 ^a
n: number; DPT, days post treatment; HDL: High density lipoprotein; LDL: Low density lipoprotein; Means with different letter superscripts in the same column are significantly different at (P < 0.05).

The effect of traditional treatment and/ or silymarin on oxidant and antioxidant status

Pneumonic sheep had significantly higher MDA and lower TAC concentrations than healthy sheep (P < 0.05; Table 4). For MDA concentrations at 7th DPT, SG 2, 3 and 4 were not significantly different than healthy sheep (P > 0.05), while SG1 showed higher concentrations than healthy sheep (P < 0.05), and for TAC concentrations at 7th DPT, SG3 and SG 4 were not significantly different than healthy ones (P > 0.05), while SG1 and SG2 had lower concentrations of TAC compared to healthy sheep (P < 0.05; Table 4).

Table 4

Effect of traditional treatment and silymarin on oxidant and antioxidant status
Variables	MDA (nmol/ml)	TAC (mmol/l)
G 1 (n = 10)	2.82 ± 0.2 ^a	0.83 ± 0.06 ^a
G 2 (n = 40)	4.7 ± 0.45 ^b	0.33 ± 0.08 ^b
SG 1 (n = 10); 7th DPT	3.12 ± 0.15 ^c	0.67 ± 0.03 ^c
SG 2 (n = 10); 7th DPT	2.94 ± 0.23 ^a	0.77 ± 0.04 ^d
SG 3 (n = 10); 7th DPT	2.8 ± 0.18 ^a	0.8 ± 0.06 ^a
SG 4 (n = 10); 7th DPT	2.7 ± 0.18 ^a	0.82 ± 0.04 ^a
n: number; DPT, days post treatment; MDA: Malonaldehyde; TAC: Total antioxidant capacity; Means with different letter superscripts in the same column are significantly different at (P < 0.05).

Serum metabolites profiling via GC-MS analysis aided by chemometrics.

Serum GC/MS-derived metabolite profiles of 49 endogenous metabolites, including organic acids, amino acids, fatty acids, and sugars were identified in group 1, 2 and treated subgroups (1–4) (Table 5). Supervised pattern recognition method i.e, PLS was employed to model serum-based GC-MS derived metabolite profiles of G1 as X-variables versus G2 and other seven PLS models were modeled to correlate the GC-MS derived serum metabolite profiles of four therapeutic approaches (SG 1- SG 4) along with G2 to the 12 tested biochemical parameters.

Table 5

Serum metabolites percentile level based on GC/MS analysis
No.	RT (min)	RI	Identification	G 1	G 2	SG 1, 7th DPT	SG 2, 7th DPT	SG 3, 7th DPT	SG 4, 7th DPT
		Amino acids
1	4.15	1099	L-Alanine, 2TMS	0.08 ± 0.02	0.21 ± 0.09	0.15 ± 0.01	---	0.17 ± 0.02	---
2	4.71	1106	Cystathionine, di-TMS	---	0.05 ± 0.01	---	---	---	---
3	5.95	1165	Pyruvic acid, MEOX-TMS	0.06 ± 0.01	0.17 ± 0.05	0.13 ± 0.01	---	---	0.22 ± 0.08
4	6.84	1208	L-Valine, 2TMS	0.02 ± 0.01	0.29 ± 0.02	0.15 ± 0.03	---	0.23 ± 0.05	0.20 ± 0.05
5	7.4	1210	L-Alanine, 2TMS isomer	0.34 ± 0.08	0.50 ± 0.04	1.01 ± 0.23	0.64 ± 0.12	1.05 ± 0.02	0.84 ± 0.10
6	8.27	1305	L-Leucine, 2TMS	0.22 ± 0.10	0.32 ± 0.10	0.27 ± 0.06	---	---	---
7	8.79	1316	L-Isoleucine, 2TMS	---	0.15 ± 0.01	0.14 ± 0.01	---	---	---
8	10.7	1362	Serine, 3TMS	---	0.10 ± 0.04	---	---	---	---
9	11.62	1377	L-Threonine, 3TMS	---	0.15 ± 0.09	0.19 ± 0.07	---	---	---
10	11.70	1385	L-Proline, 2TMS	0.04 ± 0.00	0.07 ± 0.01	0.24 ± 0.06	0.07 ± 0.02	0.42 ± 0.08	0.26 ± 0.05
11	12.08	1402	Glycine, 2TMS	1.09 ± 0.16	1.08 ± 0.03	0.16 ± 0.03	1.19 ± 0.05	2.45 ± 0.03	1.53 ± 0.09
12	13.99	1470	L-Threonine, 3TMS isomer	0.05 ± 0.01	0.08 ± 0.01	0.13 ± 0.02	0.01 ± 0.02	0.02 ± 0.04	0.01 ± 0.03
13	16.16	1473	Pyroglutamic acid, 2TMS	0.17 ± 0.05	0.28 ± 0.17	0.15 ± 0.02	0.03 ± 0.08	0.30 ± 0.08	0.19 ± 0.05
14	18.49	1612	Phenylalanine, 2TMS	0.02 ± 0.01	0.01 ± 0.02	0.11 ± 0.01	---	---	---
15	18.57	1616	L-Glutamic acid, 3TMS	0.15 ± 0.03	0.19 ± 0.06	0.36 ± 0.04	0.03 ± 0.07	0.53 ± 0.18	0.29 ± 0.06
16	22.55	1788	L-Ornithine, 4TMS	0.10 ± 0.02	0.13 ± 0.03	0.21 ± 0.03	---	0.25 ± 0.06	---
			Total Amino acids	2.32	3.76	3.39	1.97	5.42	3.55
		Fatty acids
17	25.82	2031	Palmitic acid, TMS	0.95 ± 0.17	0.34 ± 0.21	0.62 ± 0.2	1.17 ± 0.03	0.77 ± 0.12	0.46 ± 0.15
18	28.27	2154	Linoleic acid, TMS	0.71 ± 0.21	0.33 ± 0.04	0.57 ± 0.03	0.81 ± 0.02	0.70 ± 0.08	0.50 ± 0.03
19	28.39	2166	Oleic acid, TMS	2.26 ± 0.09	0.69 ± 0.05	0.91 ± 0.04	2.28 ± 0.07	1.49 ± 0.07	1.01 ± 0.04
20	28.82	2183	Stearic acid, TMS	0.23 ± 0.15	0.17 ± 0.07	0.26 ± 0.08	0.85 ± 0.03	0.42 ± 0.06	0.27 ± 0.06
			Total Fatty acids	4.15	1.53	2.36	5.10	3.38	2.23
		Esters
21	33.69	2210	1-Monopalmitin, 4TMS	0.38 ± 0.03	0.17 ± 0.02	0.31 ± 0.04	0.52 ± 0.13	0.39 ± 0.05	0.39 ± 0.06
22	36.06	2235	1-Monostearin, 2TMS	0.24 ± 0.08	0.18 ± 0.03	0.35 ± 0.06	0.60 ± 0.012	0.49 ± 0.08	0.45 ± 0.05
			Total Esters	0.62	0.35	0.66	1.12	0.88	0.85
		Organic acids
23	5.41	1140	Propane-1,2-diol, di-TMS	0.73 ± 0.04	0.82 ± 0.05	---	1.99 ± 0.03	0.23 ± 0.11	0.67 ± 0.04
24	6.49	1060	Lactic Acid, 2TMS	58.15 ± 1.41	39.56 ± 0.17	27.49 ± 2.87	9.14 ± 0.08	11.51 ± 1.05	24.43 ± 1.48
25	8.07	1081	Glycolic acid, 2TMS	0.98 ± 0.20	1.35 ± 0.09	4.87 ± 0.07	2.22 ± 0.06	3.88 ± 0.06	3.00 ± 0.06
26	8.64	1156	4-Hydroxybutanoic acid, 2TMS	0.38 ± 0.07	1.61 ± 0.07	1.80 ± 0.22	1.75 ± 0.02	2.85 ± 0.05	1.30 ± 0.12
27	10.22	1325	Caproic acid, TMS	---	0.15 ± 0.01	---	---	---	---
28	10.58	1136	Oxalic acid, 2TMS	---	0.11 ± 0.01	0.18 ± 0.02	---	---	---
29	11.80	1178	α-Aminocaproic acid (tms)	0.06 ± 0.01	0.13 ± 0.02	1.10 ± 0.15	0.35 ± 0.10	0.26 ± 0.06	0.25 ± 0.05
30	16.44	1503	3-Methylglutaric acid, 2TMS	----	0.22 ± 0.09	0.14 ± 0.04	---	---	---
31	22.47	1778	Citric acid, (4TMS)	0.16 ± 0.04	---	---	---	0.45 ± 0.16	---
			Total Organic acids	60.46	43.94	35.580	15.45	19.19	29.657
		Inorganic acids
32	11.29	1306	Phosphoric acid, 3TMS	0.22 ± 0.07	0.89 ± 0.11	0.91 ± 0.04	1.59 ± 0.03	0.99 ± 0.02	0.73 ± 0.05
			Inorganic acids	0.22	0.89	0.910	1.59	0.99	0.732
		Nitrogenous compounds
33	4.61	1101	Bis(trimethylsilyl)carbodiimide	0.17 ± 0.05	0.16 ± 0.02	0.37 ± 0.08	0.88 ± 0.06	0.43 ± 0.08	0.50 ± 0.08
34	7.86	1105	Hydroxylamine, 3TMS	0.16 ± 0.05	0.17 ± 0.04	0.23 ± 0.08	0.28 ± 0.01	0.28 ± 0.08	0.41 ± 0.12
35	7.95	1113	3-Hydroxybutyric acid, 2TMS	---	0.11 ± 0.03	0.26 ± 0.07	0.32 ± 0.01	0.22 ± 0.05	---
36	10.09	1281	Urea, 2TMS	7.62 ± 0.16	13.56 ± 0.60	14.81 ± 1.17	20.01 ± 0.02	27.73 ± 1.95	23.67 ± 1.95
37	17.2	1486	Creatinine, 3TMS	0.10 ± 0.01	0.10 ± 0.03	0.18 ± 0.07	0.03 ± 0.03	0.32 ± 0.04	0.18 ± 0.04
			Total Nitrogenous compounds	8.04	14.00	15.85	21.53	28.98	24.77
		Sugars
38	11.53	1300	Glycerol, 3TMS	0.37 ± 0.1	0.64 ± 0.09	0.57 ± 0.13	0.75 ± 0.03	0.71 ± 0.12	0.66 ± 0.26
39	20.25	1666	D-Arabinose, 4TMS	0.02 ± 0.01	0.15 ± 0.02	0.17 ± 0.02	---		0.20 ± 0.04
40	24.10	1858	Glucose, 5TMS	0.09 ± 0.02	---	---	---		---
41	24.16	1860	β-Galactopyranose, 5TMS	0.25 ± 0.04	0.46 ± 0.06	0.97 ± 0.05	1.36 ± 0.04	0.92 ± 0.21	0.77 ± 0.12
42	24.33	1858	Glucose, methyloxime, 5TMS	20.39 ± 0.80	30.00 ± 0.40	33.28 ± 2.40	39.45 ± 2.06	32.72 ± 1.20	31.50 ± 2.11
43	24.42	1866	D-Mannose, 5TMS	---	---	0.13 ± 0.01	---	---	---
44	24.63	1885	D-(+)-Talose, 5TMS	2.49 ± 0.08	2.99 ± 0.06	4.47 ± 0.04	5.25 ± 0.20	5.40 ± 0.07	3.76 ± 0.09
45	24.95	1493	Lactulose, 6TMS	---	0.09 ± 0.02	0.16 ± 0.01	---	---	---
46	25.63	1515	Glucopyranose, 5TMS	0.10 ± 0.02	---	---	---	---	0.14 ± 0.04
47	26.38	1568	β-D-Glucopyranose, TMS	0.04 ± 0.01	0.16 ± 0.05	0.18 ± 0.02	---	---	0.21 ± 0.03
48	27.53	1586	Myoinositol TMS	0.15 ± 0.04	0.40 ± 0.11	0.64 ± 0.21	0.71±	0.59 ± 0.12	0.40 ± 0.06
			Total Sugars	23.89	34.88	40.57	47.52	40.34	37.64
		Unknown
49	28.48	2172	unknown1	---	---	---	0.53 ± 0.14	---	---
50	35.33	2212	unknown2	---	0.20 ± 0.02	---	---	---	---
51	37.10	2274	unknown3	---	---	---	1.04 ± 0.08	---	---
52	37.13	2278	unknow4	---	---	---	3.02 ± 0.04	---	---
53	37.42	2284	unknow5	---	---	---	0.37 ± 0.08	---	---
54	40.31	2389	unknow6	0.21 ± 0.05	0.25 ± 0.05	0.50 ± 0.10	0.73 ± 0.14	0.57 ± 0.10	0.43 ± 0.05
			Total unknown	0.21	0.45	0.50	5.69	0.57	0.43
				99.90	99.89	99.81	99.96	99.75	99.84

To explore the differential therapeutic efficacy of the four therapeutic approaches and their influence on restoring the biochemical parameters in pneumonic sheep to their normal status, seven PLS models were attempted to correlate the GC-MS derived serum metabolite profiles of four therapeutic approaches (SG 1 - SG 4) along with the pneumonic sheep group (G 2) to the twelve tested biochemical parameters (Fig. 1). Validation of the PLS model represented by the relationship between observed and predicted values and permutation plots are depicted in Fig. S2. The percent of variation that can be predicted by the models according to a leave-one-out cross-validation procedure are ranging from 90.7–71.9% (Q2X(cum)).

As observed in Fig. 1(A-H), PLS models exhibited strong correlations between GC-MS serum metabolite profiles of the pneumonic sheep treated with the four therapeutic approaches as well as the pneumonic sheep group (G 2) (R2 = 0.878 to 0.907) as observed for the regression lines and the significant spread of the samples along the reference lines. Hence, strong relationships between the defined and predicted values of the tested biochemical parameters exist.

The derived PLS score plots (Fig. 1A) provided a clear distinction between the modeled groups (SG 1- SG 4) with pneumonic sheep group (G 2) the most distant from them. Further, all the scores plots exhibited trends of either decreasing or increasing order of the samples potency in modulating the tested biochemical parameters to restore their values to the normal status. For example, in PLS scores plot depicted in Figs. 2A & G, the samples were spanned from the positive to the negative PC1 side in order of their increasing potency in lowering MDA concentrations and liver activity markers, i.e. AST, ALT & GGT as follows: SG 1˂ SG 4˂ SG 2˂ SG 3, than that observed in the pneumonic sheep group (G 2) located at the positive side of PC1 with the highest positive score. The strongest effect on increasing the TAC concentrations was demonstrated with SG 2, and a trend of increasing potency of the therapeutic approaches was also found along PC1 as spanning from negative to positive side (SG 1˂ SG 4˂ SG 2 ≤ SG 3) in PLS score plot depicted in Fig. 1B. The same order was noticed in Fig. 1C for lowering the TG and LDL-cholesterol but increasing the total and HDL-cholesterol concentrations (Fig. 1D) and decreasing creatinine and urea (Fig. 1E) in serum of the pneumonic sheep groups in response to the four therapeutic approaches.

The endogenous metabolites that were deemed to be crucial in distinguishing between the different therapeutic approaches respective to their therapeutic efficacy on restoring the values of the biochemical parameters level to the normal status were those possessing VIP values > 1 in the Variable Importance for Projection (VIP) plots (Fig. S3) namely lactic acid, glycolic acid, urea, glucose, α-aminocaproic acid, propane-1,2-diol, glycine, L-alanine, 4-hydroxybutanoic acid, D-(+)-talose and oleic acid. These metabolites were considered the most important for the model prediction, which were then used to create two other PLS models to compare the two therapeutic approaches, SG 2 & SG 3, that were proposed to be the most effective pneumonic treatments in the PLS model depicted in Fig. 1.

The correlations between the two therapeutic approaches (SG 2 & SG 3) to their respective biochemical parameters were explored via two PLS regression models (Figs. 2A & B). The elevated biochemical parameters due to pneumonic condition that were suppressed by the therapeutic approaches i.e., TAC, glucose, and total and HDL-cholesterol were regressed in one model (Fig. 2A), and the other parameters were found to be decreasing in pneumonic condition and shown to be elevated by the treatment are regressed in the second model (Fig. 2B) which are MDA, LDL-cholesterol, creatinine, urea, AST, ALT and GGT.

The two models were validated using 200 random permutations and showed a performance with goodness of model fit (R2 = 0.956 and 0.873) and predictive power of the model (Q2 = 0.781 and 0.241), respectively, with the second model showing a lower predictive power. Validation of the two PLS models was demonstrated from the regression analysis and permutation plots depicted in Fig. S4 & S5, respectively.

The derived biplot (an amalgamation of the information revealed by both the score and loading plots) (Fig. 2A) showed that therapeutic approach relying on a silymarin dose of 280 mg only (SG 3) was strongly correlated with the upregulation of TAC, glucose, and total and HDL-cholesterol values as being projected close to SG 3 samples on the right side of the biplot however, all of the SG 2 samples were remotely distributed on the left side of the biplot. This indicated that the subgroup 3 treatment was more effective in increasing the TAC, glucose, and total and HDL-cholesterol values than the SG 2 treatment. Hence, SG 2 was more efficacious in suppressing oxidative stress and restoring lipid profile and blood sugar to their normal status.

PLS model (Fig. S1) was initially established by modeling the GC-MS derived serum metabolite profiles of healthy control (G1) (as X-variables) versus pneumonic sheep (G2) to the twelve tested biochemical parameters (as Y-variables) to identify endogenous metabolite markers and the biochemical status of each group. The covered variance and prediction power of the model was assessed by R2 and Q2 values which were computed to be 0.942 and 0.82 indicating model validity. PLS score plot (Fig. S1A) showed a clear discrimination between the healthy and pneumonic sheep groups, and that discrimination was explained by the loading plot (Fig. S1B), which revealed that lactic acid was more elevated in the healthy sheep, whereas urea and creatinine were detected at higher concentrations in pneumonic sheep. Furthermore, it was shown that some biochemical parameters were found to be more elevated in the pneumonic sheep group (G 2) i.e., liver and kidney functions (AST, ALT, ALT, GGT, creatinine, urea) as well as oxidative stress occur as represented by high MDA values in addition to high LDL-cholesterol than the healthy sheep. However, TAC, total cholesterol, HDL-cholesterol, and glucose were found at higher concentrations in the healthy sheep compared to the pneumonic sheep.

Group 2 showed copious nasal discharge, fever, cough, dyspnea, abnormal respiratory sounds upon auscultation, higher body temperature, respiratory and pulse rates than healthy ones, the same results were recorded by [21]. SG1 had significant higher body temperature, respiratory and pulse rates compared to healthy ones, this could be attributed to the microbial resistance to antibiotics administration [22], while SG2, 3 and 4 did not significantly differ than G1. The rapid clinical response to treatment was achieved early 3 days post treatment in SG 4 by the high dose of oral silymarin administration (560 mg) that could be attributed to the broad spectrum activity of silymarin as anti-inflammatory, anti-oxidative and antimicrobial agents [11; 22].

In the present study, pneumonic sheep had significantly higher AST, ALT, GGT, creatinine, and urea concentrations than healthy sheep. The same results were recorded [23; 24]. The increase of these liver function tests may be due to hepatic cellular damage and degenerative modifications brought on by bacterial infection and its toxins [25], or due to severe exposure to oxidative stress resulting in damage of phospholipid structure of hepatic cell membrane [21], while higher creatinine and urea concentrations could be related to renal damage caused by excessive release of free radicals that exhibited during the inflammatory progression and enhanced the protein catabolism [26]. Interestingly, liver and kidney functions were improved in pneumonic sheep treated with silymarin than traditional treatment. Our findings were similar to previous studies [27]. It is plausible that silymarin has an anti-inflammatory and antioxidants property that protect against cellular injury [28–30].

Pneumonic sheep had significantly higher triglyceride and LDL-cholesterol, and lower cholesterol, HDL-cholesterol and glucose concentrations compared to healthy sheep. Similar findings has been reported in previous studies [31]. Inflammation can cause hypertriglyceridemia in both humans and animals [32]. Lower serum cholesterol concentrations in the sheep of this study could have be due to liver injury with subsequent changes to lipoprotein metabolism [33]. Pneumonic sheep that received silymarin had significantly lower triglyceride and LDL-cholesterol and higher cholesterol, HDL-cholesterol, and glucose concentrations. Potential explanation include that silymarin could decrease blood cholesterol concentration by slowing down liver cell cholesterol synthesis and speeding up the conversion of cholesterol to other molecules [34], also the silymarin normalize the binding of low- density LDL [35], while the significant increase of HDL- cholesterol may be due to reducing cholesterol absorption in pneumonic sheep received silymarin in the protocol of treatment [36].

In the current study pneumonic sheep had significantly higher MDA and lower TAC concentrations compared to healthy sheep, similar to the results recorded by [21]. The increase in serum MDA concentrations could be due to excessive lipid peroxidation, while reduction of TAC in pneumonic sheep may be due to its sequestration during the inflammatory process in lung tissue [37]. Pneumonic sheep received silymarin in the protocol of treatment showed a significantly decrease in MDA and an increase in TAC concentrations compared to sheep treated by traditional treatment. It’s possible that silymarin has antioxidant and neutralization effects either on free radicals or toxins [35].

Discriminatory analysis of metabolomics profile revealed that pneumonic sheep treated with 280 mg oral silymarin had an improved health status and metabolomics profile. Furthermore, pneumonic sheep treated with 560 mg oral silymarin had faster curative achievement. Silymarin administration either alone or in combination with traditional treatment exhibited greater therapeutic improvement than treatment with traditional treatment alone.

Authors’ contributions

Hany Hassan: Conceptualization, Project administration, Supervision, reviewed the manuscript editing. Ahmed Kamr: Writing - original draft- review& editing. Abdel Nasser El-Gendy: Metabolomic analysis, Validation resources, Writing - original draft- review & editing. Ramiro Toribio: Writing - original draft- review & editing. Amira R. Khattab: Metabolomic analysis, Writing - original draft- review & editing. Walid Mousa: Investigation, Methodology. Hadeer Khaled: Investigation, Methodology. Abdelsalam Elkholey: Investigation, Methodology, Mohamed Kasem: Investigation, Methodology. Ali Arbaga: Investigation, Methodology, Writing- Original draft- review& editing.

Funding

Postgraduate studies and research sector funded this study- University of Sadat City, Egypt; under grant number: 22 in (29-12-2021).

Data Availability

All data generated and/or analyzed during this study are included in this manuscript. The raw data are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

This study was approved by the Animal Ethics Committee at the Faculty of Veterinary Medicine, University of Sadat City, Egypt (Approval code VUSC-028-1-22). All methods were performed in accordance with the guidelines and regulations of this committee.

Consent to Participate declaration

Not applicable.

Competing Interests

The authors declared that they have no competing interests.

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No competing interests reported.

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Clinicobiochemical and GC-MS Based Serum Metabolomics for determination of Therapeutic Efficacy of Silymarin in Pneumonic Sheep

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The effect of traditional treatment and/ or silymarin on liver and kidney function tests

The effect of traditional treatment and/ or silymarin on oxidant and antioxidant status

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