The phytochemicals present in each constituent herb were compiled from six different database sources: CMAUP (29), Duke (30), IMPPAT (31), NPASS (32), PCIDB and TCM-mesh (33). The phytochemicals were checked for their chemical information from the PubChem database (http://www.ncbi.nlm.nih.gov/), and duplicate entries were removed to obtain a unique set of phytochemicals. Secondly, ADME parameters of the phytochemicals were acquired using FAFdrugs4 (http://fafdrugs4.mti.univ-paris-diderot.fr) (34). FAFdrugs4 is a free ADME-Tox filtering tool that is conductive in screening our compounds. The phytochemicals were screened on the basis of a “drug-like soft” filter and referred to as potentially-active compounds (PACs) of TC (Figure 1).

The potential human protein targets of phytochemicals were retrieved from the STITCH database (35). For the selection, only those compound protein targets within the score ≥700 were compiled and used for constructing the interaction network to study their relationship. To facilitate subsequent analysis, the target names were converted to UniProt IDs using the UniProt database (https://www.uniprot.org), a universal protein database.

Liver disease-associated genes were collected from DisGeNET database (36). Herein, 38 liver disease terms were used and annotated using Medical Subject Headings (MeSH) or UMLS vocabularies (Supplementary Table S1). Only datasets corresponding to the curated gene-disease pairs were used in the present study. The target genes were labeled by using their Uniprot IDs.

The network construction, visualization, and investigation were performed using an open-source software of Cytoscape (version 3.0) (37). For the network analysis, the Network analyzer utility of the Cytoscape was used.

Protein-protein interactions (PPIs) are central to the proper functioning of the most basic molecular mechanisms underlying cellular life and are often disturbed in disease states. To elucidate the regulatory effect of TC on PPIs associated with ALD, a protein interaction network (PIN) was derived from STRING, within the confidence, score range ≥900 (38). The Molecular Complex Detection (MCODE) algorithm was used to cluster the PIN and organize proteins into various modules to examine module-regulation of TC as per the mentioned approach (39). The MCODE algorithm detects densely connected regions among the protein interaction networks and offers a directed mode for fine-tuning of clusters among the PINs. It also provides a controlled mode for fine-tuning of the clusters to examine their interconnectivity in detail (40). Gene ontology (GO) based annotation as well as protein-class annotation of the protein targets among the networks were carried out using PANTHER (41). KEGG-pathway enrichment analysis was done using Database for Annotation, Visualization, and Integrated Discovery (DAVID) (42).

We used silymarin, ethanol, saturated picric acid, and staining reagent hematoxylin and eosin (H&E) that were of the standard protocol and analytical grade (Sigma, USA). Biochemical parameters were analyzed by using an automated analyzer and diagnostic kits (ERBA, Czech Republic).

The crude herbs such as raw tuber/rhizome of Z. officinale and fruits of P. longum and P. nigrum were procured from the local market of Jammu, Jammu, and Kashmir, India and authenticated from the botanical institute of Savitribai Phule Pune University, Pune, India with specimen voucher number DNM/19. The samples were identified and authenticated using floras, taxonomic literature, macroscopic (external structure of the plant)/microscopic (palisade ratio, stomatal index, cell structure, epidermis, vascular bundle arrangement) characters. Their nomenclature is done as per IPNI (theplantlist.com).

The herbs were ground separately (#80) and blended in equal proportions to produce a homogeneous mixture. The prepared formulation was weighed and packed in an air-tight container under aseptic conditions (43). Physicochemical analysis of each herb and TC was done according to the Ayurvedic Pharmacopeia of India (API) guidelines. Physicochemical tests were performed to determine the foreign matter, moisture content, total ash value, acid insoluble ash, alcohol soluble extractive, and water-soluble extractive value (44). High-performance thin-layer chromatography (HPTLC) was performed to standardize and fingerprint TC. Based on the existing knowledge, chromatographic separation was carried out on HPTLC plates precoated with silica gel 60 F254 (E. Merck) of 0.2 mm thickness supported with aluminum sheet support. Plates were developed in a glass twin trough chamber (CAMAG automatic TLC sampler 4) that was pre-saturated with the mobile phase. The experimental condition was maintained at 20 ± 2°C. After derivatizing plates with anisaldehyde sulphuric acid reagent and photo documentation with CAMAG Reprostar 3 photo documentation platform at 550 nm, piperine could be detected. The solvent system consisting of toluene-ethyl acetate-glacial acetic acid (8:2:0.1, v/v/v) was used to resolve piperine from the matrix of TC (45–47). Densitometric determinations were performed in fluorescence mode by CAMAG TLC scanner.

For the present study, the doses of TC were calculated by extrapolating the maximum tolerated dose from existing scientific evidence on the toxicity of TC. TC at 2,000 mg/kg body weight once orally and 5, 50, and 300 mg/kg body weight once daily for 28 days were well tolerated by the Charles Foster rats (both male and female) (48). The safety of consuming the herb was also a great concern despite its potential, so all these evidence are crucial in justifying its safety. Also, a study on an ethanol extract of TC at an oral dose of 150 mg/kg exhibited a significant protective effect by lowering serum levels of glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, alkaline phosphatase, and total bilirubin (24). By considering all these data from the previous studies, the two different dose groups of 200 and 400 mg/kg orally were considered for evaluation of the hepatoprotective activity.

An animal study was conducted at Agharkar research institute, Pune, India, according to the recommendations and approval of the Institutional Animal Ethical Committee with approval number ARI/IAEC/2019/11. A total of 24 male Wistar rats 6–8 weeks old (180–200 g) were procured and housed under standard environmental conditions (normal daily light cycle, 40–60% relative humidity and 25°C temperature). Animals were fed on a standard chow diet and provided with purified water ad libitum. In addition, animals were marked with a saturated picric acid solution in water for proper identification.

Hepatotoxicity was induced by oral administration of ethanol once daily for 28 days. As tolerance developed, ethanol dosage was gradually increased in order to maintain blood alcohol levels between 150 and 300 mg/dl. The starting dose was 8 g/kg per day for 15 days and then increased gradually by 1 g/kg per day for the next 7 days and then final dose for the next 7 days and the rest of the study was 16 g/kg per day. Following 28 days, six rats in total were randomly selected and 2 ml of blood was collected under light anesthesia from the retro-orbital space of the eye for biochemical analysis: serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), serum alkaline phosphate (ALP), total bilirubin, cholesterol, triglyceride, total protein, albumin, and globulin levels to confirm hepatotoxicity. After confirmation of toxicity, animals were randomly divided into four groups (each group containing 6 rats): control group (no treatment, only ethanol), Test drug 1 group (TC 200 mg/kg + ethanol), Test drug 2 group (TC 400 mg/kg + ethanol), Standard group (Silymarin 100 mg/kg + ethanol). Rats were weighed and given following treatment along with a standard diet for 28 days. A suspension of TC was made with distilled water for oral administration to the animals. Even after induction of hepatotoxicity, in the dosing schedule, ethanol is not stopped and is given to the control group and treatment groups along with the treatment to maintain the state of hepatotoxicity as it is known that liver has tendency to regenerate by itself also. Total duration of study is for 56 days (28 days for induction of hepatotoxicity and 28 days for treatment schedule).

On the 29th day of treatment, under light anesthesia, 2 ml of the blood was conveniently collected from the retro-orbital space of each rat and sent for biochemical investigations. The serum from the blood was separated by centrifuging it at 3,000 rpm, 4°C for 10 min and used for measurement of the various biochemical markers SGOT, SGPT, ALP, total bilirubin, cholesterol, triglycerides, total protein, albumin, and globulin. Furthermore, rats of each group were sacrificed by giving a 50 mg/kg dose of pentobarbital intraperitoneally, and then the dissection was done. The slices of the left liver lobe and left kidney (from six animals of each group) were removed and fixed in 10% formalin for 24 h. After 24 h, tissues were embedded in paraffin and 5–6 μm sections were routinely stained with hematoxylin and eosin (H&E) and assessed in a light microscope. All alterations from the normal structure were registered.

Results were presented as mean ± SEM, the difference between the groups was statistically determined by one-way analysis of variance (ANOVA) followed by the Tukey-Kramer Multiple Comparisons Test. The comparison of weight before treatment but after induction of disease (BT) and after treatment at end of the study (AT) was statistically determined by paired t-test with a level of significance at P < 0.05. The level of significance was noted and interpreted accordingly.

A phytochemical search among the databases (mentioned in the Material and methods section) resulted in the compilation of 1,127 PCs in TC (Figure 3A).

The information of each herb of TC and their corresponding PCs was used to construct herb-phytochemical (H-PC) network (Figure 3B). The herb-phytochemical association along with the chemical information of each PC is given in Supplementary Table S1.

ADME analysis of the PCs using FAFdrugs4 leads to the selection of 418 PCs (referred to as PACs) when screened on the basis of a “drug-like soft” filter (Supplementary Table S1).

Human target proteins of TC were compiled from the STITCH database, which were further used to construct TC-protein target (T-PT) network. For the STITCH confidence score ≥700, 2,765 protein targets were obtained for 198 PCs, resulting in the T-PT network of size 2,963 nodes and 7,055 edges (Supplementary Figure S1; Supplementary Table S2).

A sub-network specific to PACs was derived from the T-PT network, to highlight the regulatory PACs and their specific human protein targets. This resulted in a sub-network of size of 1,100 nodes and 1,967 edges containing 95 PACs and their 1,005 protein targets (Figure 4A; Supplementary Table S3).

The protein targets of PACs majorly belonged to the class of metabolite interconversion enzyme, transmembrane signal receptor, and protein modifying enzymes (Figure 4B).

To decipher the biological classification of protein targets of PACs, gene ontology analysis was also carried out. As shown in Figures 4C,D, GO analysis indicated that the enriched molecular function corresponds to binding (GO:0005488) and catalytic activity (GO:0003824). For the biological process, the proteins were involved in the cellular process (GO:0009987), biological regulation (GO:0065007), response to stimulus (GO:0050896), metabolic process (GO:0008152), signaling (GO:0023052), and immune system processes (GO:0002376). GO-based annotation and the protein class association are detailed in Supplementary Table S3.

Based on 38 liver disease terms and their associated curated gene-disease DisGeNET data, 516 unique proteins were harvested. The proteins and their associated liver disease term were used for obtaining the liver-specific disease gene (LD-G) network. In curated gene-disease pairs, only 24 of 38 liver disease terms could be obtained, which resulted in an LD-G network of size 539 nodes (515 proteins and 24 disease terms) and 1,308 edges (Supplementary Figure S2; Supplementary Table S4).

To explore ALD in detail, all the protein targets linked with ALD were derived from the LD-G network. The proteins were checked for their regulatory phytochemicals in the T-PT network and a sub-network specific to ALD was derived, which is referred to as ALD-PT network. The network consisted of 51 nodes and 63 edges, demonstrating the information of 11 ALD proteins (Figure 5A) and 40 PCs of TC (Figure 5B; Supplementary Table S5).

In addition to managing ALD, these 40 PCs also have the ability to regulate other liver diseases. This can be seen in Figure 5C, where 453 protein targets of these 40 PCs are linked with 24 liver disease terms. When checked for the pathway association of these proteins, the proteins were playing a major role in the pathways associated with cancer and signaling processes like advanced glycation end products (AGE-RAGE) signaling, Hypoxia-inducible factors (HIF-1) signaling pathway, interlukin-17 (IL-17) signaling, chemokine, and cytokine signaling, apoptosis signaling, Phosphatidylinositol 3-kinase—protein kinase B (PI3k-Akt) signaling pathway, etc (Figure 5D; Supplementary Table S6). Pathway analysis also highlighted the direct role of 26 proteins in the ALD disease pathway. The mapping of these proteins onto the ALD-disease pathway is given in Figure 6.

Among the disease association network, liver cirrhosis and liver carcinoma were identified as the major disease targets. It is interesting to note that 15 of these 40 PCs belong to PACs, further highlighting their role as lead molecules for future drug-discovery procedures against liver complications. These 15 PACs and their regulators can be checked in the ALD-PT network. The network topology attributes of the ALD-PT network were analyzed for their degree centrality measure, betweenness and closeness centrality. Among the network P01375 and P04040 were highlighted as nodes with high-degree centrality value of 19 each, reflecting high neighborhood connectivity of these nodes as degree centrality is a measure of number of connections a node of interest possesses. P01375 is a tumor necrosis factor encoded by TNFA gene, where TNF shows multifunctional properties for the liver heath including liver regeneration and inflammation, hepatocyte apoptosis and necroptosis, etc. (49). The other node among the network with highest degree value is P04040, a catalase protein encoded by CAT gene which is commonly expressed in liver. When checked for the betweenness centrality, a measure that quantifies the role of node to control the flow of information among the network, Q03181 and P01375 were showing high betweenness of 1 and 0.49 respectively (50, 51). Q03181 is a Peroxisome proliferator-activated receptor delta and play a significant role in liver health by acting as a major class of metabolic regulators (52).

Closeness centrality takes into account the average distance of all shortest path between a node to all other nodes among the network, where high closeness indicates high proximity to all other nodes. Among the ALD-PT network, Q03181 and C_0013 (1-butanol) were showing high closeness centrality values of 1.0 and 0.66, respectively (53). The degree, betweenness and closeness centrality values of each node among the ALD-PT network is provided in Supplementary Table S5. The information on multi-targeting PCs, including PACs in managing ALD among the ALD-PT network, obtained from network analysis has been shown in Table 1.

Toward elucidating the global effects of the TC in managing ALD, a PIN network of ALD-specific protein targets of TC was constructed and analyzed in detail. For this, high-confidence protein-protein interaction data of 11 proteins among the ALD-PT network was obtained from STRING via restricting the confidence score ≥900. This resulted in the compilation of 569 protein interactors of these 11 proteins leading to the network of size 580 nodes and 7,947 edges (Figure 7, TRH-PIN). The protein-protein interaction data of TRH-PIN along with the confidence score can be checked in Supplementary Table S7.

The information of topologically important nodes was also inferred for the TRH-PIN. Network analysis highlighted the role of P0CG48 and Q04206 as hub nodes among the PIN, owing to their high connectivity (or high degree value) with ability to regulate 207 and 194 other proteins, respectively. P0CG48 is a human Polyubiquitin-C protein encoded by UBC gene which is highly expressed in the liver and dysregulation of this gene is linked to defective proliferation of hepatocytes (54). The other hub-protein, Q04206 is a Transcription factor p65 encoded by RELA (NFKB3) gene responsible for master regulation of inflammatory and cell death processes with central link to the hepatocellular injury and liver fibrosis (51).

As already discussed, betweenness centrality measures the information-flow among the network. The nodes with high betweenness represent critical points of the network and are referred to as bottlenecks (55). Among the TRH-PIN, P0CG48 appeared as bottleneck with high betweenness centrality value of 0.10. Thus suggests the high relevance of this node in terms of its connectivity and control over the communication among the TRH-PIN. The degree and betweenness centrality values of each node among the TRH-PIN can be checked in Supplementary Table S7.

To understand the modular architecture of the TRH-PIN of ALD, the network was subjected to module identification using MCODE. MCODE returned six highly organized and connected topological clusters (Figure 8; Table 2). To access the biological role of clusters, GO-based annotation of each cluster was carried out. The GO-based analysis highlighted the major association of proteins in molecular processes like binding (GO:0005488) and transcription regulator activity (GO:0140110). While the major biological processes were cellular processes (GO:0009987) and cellular anatomical entities (GO:0110165). The cluster proteins were showing their involvement among the pathways like PI3K-Akt signaling (hsa04151), Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappa B) signaling (hsa04064), and pathways in cancer (hsa05200) especially hepatocellular carcinoma (hsa05225). A detailed description of the protein composition of each cluster and their associated GO annotation is given in Supplementary Table S8.

The regulatory phytochemicals of each cluster were also checked via searching for cluster proteins among the T-PT network. As a result, the regulatory phytochemicals in each cluster can be checked in Table 2. The multi-module regulatory potential of the PACs was also identified by searching the participation of protein targets of PACs among the clusters. This led to the identification of PACs like C_0032, C_0598 and C_0028 toward directly regulating the proteins of all the identified six-clusters, thus highlighting their high multi-module regulatory potential.

During powder preparation, the average mass yield% of Z. officinale, P. longum, and P. nigrum was 64.50, 63.20, and 88.57%, respectively. Organoleptic and physicochemical analyses of Z. officinale, P. longum, P. nigrum powder, and TC are recorded in Tables 3, 4, respectively. Results of physicochemical analysis of TC come under permissible values of pharmacopeial standards. Fingerprinting and monographs of HPTLC data of TC in triplicates were stipulated in Figure 9 and Table 5. Results indicate the presence of complex compounds. Piperine was found in TC.

Induction of hepatotoxicity was confirmed, as all the biochemical parameters were raised from their normal range (Table 6).

Changes that took place in the weights of rats BT and AT from each group were statistically determined by Paired t-test, which are presented in Table 7. Mean and SEM represents the mean and standard error of the mean difference in animals of a group. It was found that treatment with TC and silymarin resulted in a significant weight increase in rats. However, on applying ANOVA (Table 8), no significant difference in the body weight of rats was found.

The biochemical parameters of the different groups are expressed as mean ± SEM in Table 8 along with the statistically determined significance level of one-way analysis of variance (ANOVA). Further, the Tukey Krammer Multiple Comparisons test to compare treatment efficacy between the groups' results is performed and recorded in Table 9. TC at both doses shows highly significant potential in reducing SGOT, SGPT, ALP, total bilirubin, cholesterol, triglycerides, total protein, albumin, and globulin compared to the control group. But only, the higher dose of 400 mg/kg has shown a significant difference in reducing SGOT, SGPT, total bilirubin, cholesterol, total protein, albumin, and globulin parameters compared to the standard drug, which was less shown by TC (200 mg/kg) dose.

Histopathology changes in the liver and kidney of all groups are illustrated in Figure 10, and data of different groups are summarized in Table 10. Fatty accumulation, inflammatory cellular infiltration, degeneration changes, and necrosis of hepatic tissues caused due to alcohol intoxication were improved in the treatment group of both doses of TC. But higher dose, 400 mg/kg of TC, has shown tremendous potential in reversing hepatic lesions. There is no fatty cell accumulation, inflammatory cells, and only minimal degenerative changes comparable to that of Silymarin.

Liver diseases cause insufficient absorption and metabolism of nutrients resulting in malnutrition and decreased body weight. This study evidenced a significant increase in rats' weights among TD2, TD1, and standard groups which is not evident in the control group directed toward the reversal of liver injury by TC (61).

Alcohol consumption causes substantial diseases, and the liver is the most adversely affected organ. Liver function test (LFT), is a well-known biochemical test that is used to determine the health and function of the liver through the measurement of liver enzymes (SGOT, SGPT, alkaline phosphate), bilirubin, and protein in the blood (62). During intoxication of alcohol, there are structural changes, leading to an increase in the permeability of the cell membrane. As there is an increase in permeability of the membrane, it further produces SGOT and SGPT leakage into blood circulation as shown by abnormally high levels of serum hepatic markers. Elevated LFT from the normal values is the indication of liver injury (62). TC shows a reduction in SGOT and SGPT levels in these respective studies of alcohol-mediated hepatotoxicity which may be attributed to its cell membrane stabilizing ability, hepatic enzymes leakage prevention, and translocation into the serum (63). Liver steatosis is the earliest stage of liver disease in chronic ethanol consumption, with the characteristic of fat accumulation (64). In this study, alcohol-induced hepatic steatosis was demonstrated by increased cholesterol, triglycerides values, and fatty accumulation in liver histology (64). Data from the present study the first show that TC can reverse the ethanol-induced hepatotoxicity. Oral treatment of TC by both doses significantly reduced the elevated biochemical markers compared with the control group. The highest suppressive effect on decreasing SGOT, SGPT, total bilirubin, and cholesterol was induced by the high dose of TC 400 mg, respectively, comparable to that of silymarin, and decreased triglyceride level more than that of silymarin.

The total protein content consists of globulin and albumin protein. Albumin is a very common protein found in the blood with various functions, produced by the liver only. The blood levels of these plasma proteins are decreased when there is extensive liver damage. Hypoalbuminaemia may occur in liver disease, with significant destruction of hepatocytes and hyperglobulinaemia in chronic inflammatory diseases such as cirrhosis and chronic hepatitis (64, 65). Usually, in liver diseases, synthesis of this protein decreases, but in this study after administration of ethanol, it is noted that the values of total protein, globulin, and albumin neither increased nor decreased, the values just are in the normal range. But after treatment, it was noticed that there is a difference in the means of each treatment. In all the three treatment groups-total protein, globulin, and albumin values are found significantly less compared to that of the control group also reflecting toward the improved functional capacity of the liver by TC (64).

Pretreatment with ethanol resulted in damage to the liver as infiltration of mononuclear cells, distortion of the normal structure of the hepatocytes cells, loss of nucleus, presence of necrotic cells, and accumulation of fatty cells were detected in many regions of the liver sections (66). Treatment with TC at both doses and silymarin has helped in modulating degenerative changes, hepatic lesions, inflammatory cellular infiltration, and fatty accumulation in hepatocytes which is evident in the control group. The higher dose of 400 mg/kg of TC showed a remarkable liver regeneration by improving hepatic lesions, inflammation, and the only occasional presence of fatty changes (66).

Histopathology of the kidney shows mild pathological changes in the control group, demonstrating harmful effects of chronic consumption of ethanol on the kidney. TC, however, also demonstrates improvement in pathological lesions of the kidney compared to that of silymarin indicating its renal protective ability (67).