Abstract

Introduction

   Gejie Zhilao Pill (GJZLP), a traditional Chinese medicine formula is
   known for its unique therapeutic effects in treating pulmonary
   tuberculosis. The aim of this study is to further investigate its
   underlying mechanisms by utilizing network pharmacology and molecular
   docking techniques.

Methods

   Using TCMSP database the components, potential targets of GJZLP were
   identified. Animal-derived components were supplemented through the
   TCMID and BATMAN-TCM databases. Tuberculosis-related targets were
   collected from the TTD, OMIM, and GeneCards databases. The intersection
   target was imported into the String database to build the PPI network.
   The Metascape platform was employed to carry out Gene Ontology (GO) and
   Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.
   Heatmaps were generated through an online platform
   ([33]https://www.bioinformatics.com.cn). Molecular docking was
   conducted between the core targets and core compounds to explore their
   binding strengths and patterns at the molecular level.

Results

   61 active ingredients and 118 therapeutic targets were identified.
   Quercetin, Luteolin, epigallocatechin gallate, and beta-sitosterol
   showed relatively high degrees in the network. IL6, TNF, JUN, TP53,
   IL1B, STAT3, AKT1, RELA, IFNG, and MAPK3 are important core targets. GO
   and KEGG revealed that the effects of GJZLP on tuberculosis mainly
   involve reactions to bacterial molecules, lipopolysaccharides, and
   cytokine stimulation. Key signaling pathways include TNF, IL-17,
   Toll-like receptor and C-type lectin receptor signaling. Molecular
   docking analysis demonstrated a robust binding affinity between the
   core compounds and the core proteins. Stigmasterol exhibited the lowest
   binding energy with AKT1, indicating the most stable binding
   interaction.

Discussion

   This study has delved into the efficacious components and molecular
   mechanisms of GJZLP in treating tuberculosis, thereby highlighting its
   potential as a promising therapeutic candidate for the treatment of
   tuberculosis.

   Keywords: Gejie Zhilao Pill, tuberculosis, traditional Chinese
   medicine, network pharmacology, molecular docking

Graphical Abstract

   A flowchart of this study on treating tuberculosis with Gejie Zhilao
   pills.[34] graphic file with name fcimb-14-1405627-g010.jpg

1. Introduction

   Tuberculosis, a chronic infectious illness, results from the infection
   caused by Mycobacterium tuberculosis ([35]Taye et al., 2021). It
   primarily affects the lungs and is known as pulmonary tuberculosis, but
   it can also involve various organs throughout the body ([36]Ahmed
   et al., 2022). The clinical manifestations of tuberculosis include
   low-grade fever, night sweats, cough, sputum production, hemoptysis,
   anorexia, fatigue, weight loss, and more. Pulmonary tuberculosis is the
   most common type of tuberculosis accounting for the majority of total
   cases across various organs. Historically, tuberculosis has been
   widespread globally and once posed a significant threat to human
   health. However, with the continuous discovery of anti-tuberculous
   drugs, its prevalence has been partially controlled. Nevertheless, in
   recent years, due to factors such as neglect of tuberculosis by some
   countries, decreased financial investment, population growth,
   increasing migrant populations, and the spread of HIV, the decline in
   the prevalence of tuberculosis has been slow, and there has even been a
   resurgence in some countries and regions ([37]Koegelenberg et al.,
   2021).

   Currently, tuberculosis remains the primary cause of adult mortality
   due to infectious diseases globally ([38]Furin et al., 2019). According
   to the World Health Organization (WHO), nearly one-fourth of the
   world’s population harbors latent infection with Mycobacterium
   tuberculosis ([39]Suárez et al., 2019). Furthermore, over 10 million
   individuals are diagnosed with tuberculosis each year. Tuberculosis
   stands as the second leading killer of humans, trailing only behind
   HIV/AIDS. The disease is particularly prevalent among socially
   marginalized groups and communities with low socio-economic status
   ([40]Natarajan et al., 2020). The 2023 Global Tuberculosis Report
   reveals that the number of new tuberculosis cases globally reached 10.6
   million in 2022, marking a new historical peak. In such a context, the
   development of new anti-tuberculosis drugs is very important.

   In China, traditional Chinese medicine (TCM) is increasingly being
   utilized in the treatment of tuberculosis ([41]Wang et al., 2015).
   Studies have demonstrated that the therapeutic effect of combining TCM
   with Western medicine for tuberculosis treatment is superior to using
   Western medicine alone, which contributes to reducing the incidence of
   adverse reactions ([42]Li et al., 2020). As a member of the treasure
   trove of TCM, the Gejie Zhilao Pill embodies the rich medical wisdom
   and cultural heritage of the Chinese nation. Over the course of
   long-term medical practice, GJZLP has gradually gained widespread
   recognition and application, becoming one of the significant TCM
   preparations for the treatment of tuberculosis and other related
   diseases. It is primarily used to nourish the lungs, suppressing
   coughs, and treating tuberculosis, and it is suitable for treating
   symptoms such as pulmonary tuberculosis, hot flashes, sweating at
   night, cough and hemoptysis. Among its primary constituents are gecko,
   stemona tuberosa, fritillaria ussuriensis, ginkgo biloba, bletilla
   striata, prunus mume, cordyceps sinensis, which work synergistically to
   exert therapeutic effects. Up to now, it is still widely used in
   clinical practice and plays a positive role in the treatment of
   tuberculosis. The Gejie Zhilao Pill is a large, brownish-colored
   honeyed pill with a subtle aroma and a sweet but slightly bitter taste.
   It is composed of natural ingredients, exhibiting minimal side effects
   and rare instances of severe adverse reactions. The multifaceted
   components of GJZLP target various aspects of the disease,
   demonstrating a synergistic effect and enhancing therapeutic outcomes.
   Compared to monotherapy, GJZLP intervenes more comprehensively in the
   pathological process of tuberculosis, thereby improving treatment
   efficacy. Furthermore, through rational compatibility and processing,
   the toxic and adverse effects of its medicinal components can be
   mitigated. GJZLP possesses antimicrobial, anti-inflammatory,
   antioxidant, immune-boosting, liver-protecting, BCG-potentiating,
   treatment-shortening, and prognosis-improving properties, conferring
   significant advantages in the treatment of tuberculosis. However,
   research on its application in the treatment of tuberculosis is still
   in its infancy, necessitating larger-scale, multicenter, randomized
   controlled trials to confirm its effectiveness and safety. GJZLP holds
   considerable potential in the treatment of tuberculosis. With the
   deepening of modern medical research, it is poised to become a widely
   used therapeutic agent for tuberculosis, ultimately improving the
   prognosis and quality of life for patients. Simultaneously, this
   development offers new research avenues and opportunities for the
   global application of TCM in the treatment of tuberculosis.

   Overall, the Gejie Zhilao Pill is a traditional Chinese medicine
   compound that functions to nourish the lungs, suppress coughs, and
   treat tuberculosis, exhibiting good therapeutic effects on pulmonary
   tuberculosis and related symptoms. However, to date, there have been
   few studies on the mechanism of the Gejie Zhilao Pill in treating
   tuberculosis, and further research is urgently needed.

   Network pharmacology is an emerging research paradigm that has
   superseded the traditional drug discovery model, which was
   predominantly focused on the “one drug, one target, one disease”
   approach. Instead, It has introduced an innovative research paradigm
   that delves into the intricate network interconnections between
   multiple targets and diverse diseases ([43]Zhao et al., 2023). This
   methodology is particularly effective in discovering bioactive
   substances and elucidating the mechanistic actions of traditional
   Chinese medicine (TCM) prescriptions, thereby providing a novel
   research paradigm for the transition of TCM from empirical medicine to
   evidence-based medicine systems ([44]Nogales et al., 2022). Network
   pharmacology has not only accelerated the discovery of TCM therapeutics
   but also improved existing drug discovery strategies. For instance,
   through similarity searches and identification of potential components
   in Chinese medicinal herbs, combined with GO enrichment analysis,
   pathway enrichment analysis, and network pharmacological analysis, it
   is possible to reveal the mechanistic actions and theoretical essences
   of a particular TCM formula ([45]Jiashuo et al., 2022).

   Molecular docking technology holds a pivotal position in the realm of
   computer-assisted drug design. It simulates the mutual recognition and
   interaction process between receptors and ligands by leveraging the
   principles of geometric spatial structure matching and chemical energy
   interactions between molecules ([46]Pinzi and Rastelli, 2019). By
   predicting the three-dimensional specific structure and chemical
   characteristics of receptor molecules, as well as the interactions
   between receptor molecules and other ligand molecules, molecular
   docking technology can accurately predict their binding sites, modes of
   action, and affinities ([47]Sahu et al., 2024). Furthermore, it enables
   the screening of ligand molecules and conformations that exhibit
   optimal affinity for the receptor molecule. This technique is widely
   used in drug discovery and drug repositioning, and it holds significant
   importance in screening effective components that truly exert
   pharmacological effects and elucidating the mechanisms of action of TCM
   in treating diseases ([48]Stanzione et al., 2021).

   Overall, both network pharmacology and molecular docking technology
   serve as crucial tools in modern drug research, each possessing unique
   advantages and application domains that collectively drive the
   advancement of drug discovery and design ([49]Jiao et al., 2021). By
   integrating these two techniques, our aim is to gain a deeper
   understanding of the interactions between drugs and biological systems,
   thus accelerating the process of novel drug discovery and development.

   To delve deeply into the therapeutic mechanism of GJZLP in treating
   tuberculosis, this study first employed the TCSMP, TCMID, and
   BATMAN-TCM databases to gather the bioactive components of GJZLP,
   converting the target IDs to UniProt database format. Then, ADMETlab2.0
   was utilized for Predictive Toxicity Analysis of Drug Components.
   Tuberculosis-related targets were subsequently collected from the TTD,
   OMIM, and GeneCards databases. Next, utilizing Cytoscape 3.10.0, a
   compound-target network was constructed, while the ggplot2 package
   facilitated the creation of a Venn diagram. This analysis allowed for
   the further construction of an effective compound-therapeutic target
   network, enabling the screening of core compounds. The STRING database
   was leveraged to construct a protein-protein interaction (PPI) network
   and pinpoint core targets. The Metascape platform was utilized to
   conduct GO enrichment analysis and KEGG pathway enrichment analysis.
   Additionally, molecular docking simulations were conducted for the core
   targets and core compounds to validate their binding stability. The
   entire research process is illustrated in the [50]Graphical Abstract .

2. Materials and methods

2.1. Screening the active ingredients and targets of GJZLP

   The active components and their corresponding targets of GJZLP were
   retrieved from the TCMSP database ([51]http://tcmspw.com/tcmsp.php)
   ([52]Ru et al., 2014), which encompassed Chinese herbs such as stemona
   tuberosa, fritillaria ussuriensis, ginkgo biloba, bletilla striata,
   prunus mume and cordyceps sinensis. Due to the absence of information
   on the animal-derived Chinese medicine Gecko in the TCMSP database, we
   retrieved relevant data from the TCMID
   ([53]http://www.bidd.group/TCMID/) database ([54]Huang et al., 2018)
   and the BATMAN-TCM ([55]http://bionet.ncpsb.org/batman-tcm/) database
   ([56]Liu et al., 2016). To ensure consistent screening criteria, all
   the acquired effective components were inputted into the TCMSP database
   for screening based on the criteria of oral bioavailability (OB) ≥ 30%
   and drug-likeness (DL) ≥ 0.18 ([57]Xu et al., 2012). Subsequently, the
   corresponding targets of the filtered effective components of GJZLP
   were collected, and the target IDs were converted to the UniProt
   database format for further analysis. ADMETlab2.0
   ([58]https://admetmesh.scbdd.com) is an online platform for predicting
   pharmacokinetic and toxicity properties of drugs ([59]Xiong et al.,
   2021), encompassing the characteristics of absorption, distribution,
   metabolism, excretion, and toxicity of the compounds ([60]Tian et al.,
   2015). To analyze the major chemical components of GJZLP using
   ADMETlab2.0, we retrieved the SMILES identifiers of the primary
   chemical constituents from the PubChem database ([61]Wang et al., 2014)
   and imported them into ADMETlab2.0 to obtain toxicity-related
   information.

2.2. Construct a compound-target network

   Cytoscape is an open-source software project designed to integrate
   biomolecular interaction networks with high-throughput expression data
   and other molecular states into a unified conceptual framework
   ([62]Shannon et al., 2003). The obtained drug component data and target
   data were imported into Cytoscape3.10.0 to construct the
   GJZLP-compound-target network. In the network, each component or target
   is represented by a node, and the relationship between the component
   and the target is represented by a connecting line.

2.3. Identification of predicted targets of tuberculosis

   To gather tuberculosis-related targets, we searched the databases of
   GeneCards ([63]https://www.genecards.org/) ([64]Stelzer et al., 2016),
   OMIM ([65]https://omim.org/) ([66]Amberger et al., 2019), and TTD
   ([67]http://db.idrblab.net/ttd/) ([68]Zhou et al., 2024). We entered
   the keyword “tuberculosis” to retrieve disease-related targets from
   each database. After merging the relevant targets from the three
   databases, duplicate entries were removed.

2.4. Construction of PPI network

   We compiled a list of drug targets and disease-related targets and used
   the ggplot2 package to create a Venn diagram ([69]Jia et al., 2021).
   Overlapping targets were identified as therapeutic targets.
   Subsequently, a network diagram of effective component-therapeutic
   target was constructed. The information of therapeutic targets was
   imported into the STRING database ([70]https://string-db.org/)
   ([71]Szklarczyk et al., 2023), with the biological species set to “Homo
   sapiens” and the minimum interaction score set to “highest confidence
   (0.9)” ([72]Szklarczyk et al., 2019). Isolated targets were hidden to
   obtain protein-protein interaction (PPI) network information ([73]Han
   et al., 2020). The PPI data was then imported into Cytoscape3.10.0 for
   further visualization. Network analysis using Cytoscape was conducted
   to calculate the degree, BC, and CC of each node in the network
   diagram. These parameters allowed for a deep analysis of the relevance
   and importance of nodes within the interaction network. We ranked the
   targets based on their degree values and identified the top ten targets
   as potential core targets.

2.5. GO enrichment and KEGG pathway analysis

   To investigate the biological functions involved in tuberculosis with
   respect to therapeutic targets, these targets were imported into the
   Metascape platform for GO and KEGG analysis ([74]Zhou et al., 2019). GO
   analysis was utilized to screen biological processes (BP), cellular
   components (CC), and molecular functions (MF) ([75]Aleksander et al.,
   2023). Additionally, KEGG enrichment analysis was employed to identify
   crucial signaling pathways involved in the biological processes
   ([76]Kanehisa et al., 2017). The data obtained from GO enrichment
   analysis and KEGG pathway enrichment analysis were uploaded to the
   online platform for data analysis and visualization
   ([77]https://www.bioinformatics.com.cn) for further visualization
   processing.

2.6. Molecular docking

   The three-dimensional structures of the core components of GJZLP were
   downloaded from the PubChem database
   ([78]https://pubchem.ncbi.nlm.nih.gov/) and converted to the “PDB”
   format using PyMOL software. Each chemical component was appropriately
   modified to calculate charges, select torsions, and set as ligands.
   Core proteins corresponding to tuberculosis-related genes were searched
   in the Protein Data Bank ([79]https://www.rcsb.org/). After eliminating
   free water molecules by adding all hydrogen atoms and calculating
   charges, the proteins were set as macromolecules using AutoDockTools
   ([80]Goodsell et al., 2021). We selected a ligand and a macromolecule
   for each molecular docking, and generated “dlg” files with
   corresponding Grid Box settings based on protein size. Conformations
   were selected based on the optimal affinity and the most stable
   intermolecular forces, and a heatmap was plotted accordingly. The
   three-dimensional structures were exported as “PDBQT” files and
   visualized using PyMOL ([81]Seeliger and de Groot, 2010).

3. Results

3.1. Compound-target network and analysis

   In the GJZLP-compound-target network, there are 413 nodes and 1410
   edges, comprising 61 chemical components and 344 targets, as depicted
   in [82]Figure 1 . The effective components of the six Chinese herbal
   medicines in GJZLP were retrieved from the TCMSP database.
   Specifiaally, 28 components were sourced from Stemona tuberosa, 7 from
   Fritillaria ussuriensis, 15 from Ginkgo biloba, 8 from Bletilla
   striata, 8 from Prunus mume, and 4 from Cordyceps sinensis. Among them,
   7 components were present in multiple herbal medicines. Due to the lack
   of information on animal-derived Chinese medicines in the TCMSP
   database, we retrieved and supplemented the effective components of
   Gecko from the TCMID and BATMAN-TCM databases. To ensure consistent
   screening criteria, the effective components of gecko were screened
   from TCMSP database by uniform standards, thus identifying one active
   ingredient.

Figure 1.

   [83]Figure 1
   [84]Open in a new tab

   Herbal medicine-compound-target network of GJZLP (The circle represents
   the component, The V shape represents the traditional Chinese medicine,
   and the rectangle in the middle represents the target OF GJZLP).

3.2. Effective compounds-therapeutic target network and analysis

   After obtaining the tuberculosis-related targets, the Venn diagram
   revealed 118 common targets shared between the effective components of
   GJZLP and tuberculosis-related targets ([85] Figure 2 ). These targets
   have been identified as therapeutic targets. In the further constructed
   network of effective compounds-therapeutic targets based on these
   therapeutic targets, there were 165 nodes and 503 edges, comprising 47
   effective compounds and 118 therapeutic targets ([86] Figure 3 ). The
   top 10 compounds were selected as core compounds based on their Degree,
   BC, and CC values, as shown in [87]Table 1 .

Figure 2.

   [88]Figure 2
   [89]Open in a new tab

   Venn diagram of the target of GJZLP and the target of tuberculosis.

Figure 3.

   [90]Figure 3
   [91]Open in a new tab

   Effective compound-therapeutic target network (The circle represents
   the effective component, and the square in the middle represents the
   therapeutic target).

Table 1.

   Top ten compounds information of GJZLP network.
   MOL ID Compound Molecule structure Degree OB DL Average Shortest Path
   Length Betweenness Centrality Closeness Centrality Hepatotoxicity AMES
   Toxicity Herb
   MOL000098 quercetin graphic file with name fcimb-14-1405627-i001.jpg
   150 46.43 0.28 1.914634146 0.454008164 0.522292994 — + Ginkgo biloba
   Prunus mume
   MOL000422 luteolin graphic file with name fcimb-14-1405627-i002.jpg 60
   41.88 0.24 2.451219512 0.078896442 0.407960199 — + Ginkgo biloba
   Prunus mume
   MOL006821 Epigallocatechin Gallate graphic file with name
   fcimb-14-1405627-i003.jpg 57 55.09 0.77 2.146341463 0.333159226
   0.465909091 – – Ginkgo biloba
   MOL000358 beta-sitosterol graphic file with name
   fcimb-14-1405627-i004.jpg 52 36.91 0.75 2.658536585 0.023859362
   0.376146789 – — Stemona tuberosa
   Ginkgo biloba
   Prunus mume
   MOL000392 formononetin graphic file with name fcimb-14-1405627-i005.jpg
   24 69.67 0.21 2.719512195 0.035755506 0.367713004 — – Stemona tuberosa
   Ginkgo biloba
   MOL000449 Stigmasterol graphic file with name fcimb-14-1405627-i006.jpg
   18 43.83 0.76 2.682926829 0.038300743 0.372727273 — — Stemona tuberosa
   Ginkgo biloba
   Prunus mume
   MOL002773 beta-carotene graphic file with name
   fcimb-14-1405627-i007.jpg 17 37.18 0.58 2.634146341 0.049200908
   0.37962963 – – Ginkgo biloba
   MOL001439 arachidonic acid graphic file with name
   fcimb-14-1405627-i008.jpg 15 45.57 0.20 2.658536585 0.07091199
   0.376146789 — - Cordyceps sinensis
   MOL000354 isorhamnetin graphic file with name fcimb-14-1405627-i009.jpg
   10 49.6 0.31 2.719512195 0.010605981 0.367713004 — + Ginkgo biloba
   MOL009374 7-methoxy-3-methyl-2,5-dihydroxy-9,10-dihydrophenanthrene
   graphic file with name fcimb-14-1405627-i010.jpg 8 59.00 0.21
   2.743902439 0.013785809 0.364444444 – + Stemona tuberosa
   [92]Open in a new tab

   Tip: For the classification endpoints, the prediction probability
   values are transformed into six symbols: 0-0.1(—), 0.1-0.3(–),
   0.3-0.5(-), 0.5-0.7(+), 0.7-0.9(++), and 0.9-1.0(+++). OB, Oral
   Bioavailability; DL, Drug-Likeness; AMES, The bacterial reverse
   mutation test. AMES Toxicity will only be shown as "++" or "+++" if the
   toxicity of the chemical is high. The absence of "++" and "+++" in the
   table proves that the toxicity of the TCM ingredients is low.

3.3. PPI network construction and analysis

   Using the therapeutic targets, we constructed a PPI network in the
   STING database. After removing outlier targets, a total of 107 target
   proteins were included in the study and visualized using
   Cytoscape3.10.0. In the visualization, nodes with higher degree values
   appeared larger and redder, indicating a greater likelihood of them
   being core targets ([93] Figure 4 ). Based on the Degree, BC, and CC
   values, we selected the top 10 proteins as core targets, as shown in
   [94]Table 2 .

Figure 4.

   [95]Figure 4
   [96]Open in a new tab

   The PPI network of GJZLP and tuberculosis targets. Nodes represent
   proteins (The larger and redder a node is, the more important the
   protein it represents). Edge represents protein-protein association.

Table 2.

   Top ten targets information of PPI network.
   Name  Degree Betweenness Centrality Closeness Centrality
   IL6   33     0.192103259            0.502415459
   TNF   32     0.147881096            0.497607656
   JUN   27     0.1453186              0.479262673
   TP53  25     0.095893042            0.426229508
   IL1B  24     0.048365205            0.468468468
   STAT3 23     0.081376202            0.468468468
   AKT1  21     0.055590002            0.446351931
   RELA  20     0.038235293            0.433333333
   IFNG  18     0.008725639            0.393939394
   MAPK3 18     0.037181845            0.448275862
   [97]Open in a new tab

3.4. GO enrichment and KEGG pathway analysis

   In the GO analysis, for the purpose of visualization, we chose the ten
   most prominent biological processes, cellular components, and molecular
   functions, as depicted in [98]Figure 5 . These include biological
   processes such as response to molecules of bacterial origin, response
   to lipopolysaccharide and cellular response to cytokine stimulus. The
   major cellular components involved are vesicle lumen, secretory granule
   lumen, and cytoplasmic vesicle lumen. The primary molecular functions
   involved are cytokine receptor binding, cytokine activity, and
   signaling receptor activator activity. Utilizing KEGG enrichment
   analysis, we conducted a thorough investigation into the signaling
   pathway mechanisms of GJZLP in the treatment of tuberculosis, and
   visualized the top 30 ranked signaling pathways as depicted in
   [99]Figure 6 . The main signaling pathways implicated are Lipid and
   atherosclerosis, TNF, IL-17, Toll-like receptor and C-type lectin
   receptor signaling pathway.

Figure 5.

   [100]Figure 5
   [101]Open in a new tab

   Top 10 GO terms of hub genes.

Figure 6.

   [102]Figure 6
   [103]Open in a new tab

   Top 30 KEGG pathway of hub genes.

3.5. Molecular docking study

   Based on the results of the aforementioned network pharmacological
   analysis, we screened 10 core components of GJZLP and 10 core proteins
   associated with tuberculosis-related genes for molecular docking. This
   further corroborated the potential crucial role of these compounds in
   the treatment of tuberculosis. After docking using Autodocktools, we
   analyzed the docking results of proteins and ligands separately and
   plotted a heatmap based on the binding energies, as shown in
   [104]Figure 7 . Additionally, three representative models were selected
   for visualization, as depicted in [105]Figure 8 . A lower
   intermolecular binding energy indicates a higher binding affinity
   between molecules. Generally, a binding energy lower than -1.2 kcal/mol
   is considered to indicate good binding activity between molecules.

Figure 7.

   [106]Figure 7
   [107]Open in a new tab

   Molecular docking heat map (The redder the corresponding grid, the
   lower the binding energy and the more stable the binding).

Figure 8.

   [108]Figure 8
   [109]Open in a new tab

   Molecular docking results of main chemical components of GJZLP.

4. Discussion

   The objective of this study is to explore the potential therapeutic
   mechanism of GJZLP in treating tuberculosis, leveraging network
   pharmacology and molecular docking approaches. Using databases such as
   TCMSP, TCMID, and BATMAN-TCM, a total of 61 chemical components and 344
   targets were collected, and the connections between these components
   and targets were visualized through a network diagram. The relevant
   targets for tuberculosis were obtained from databases like TTD,
   GeneCards and OMIM. A Venn diagram revealed that there are 118
   potential therapeutic targets for GJZLP in the treatment of
   tuberculosis. We further constructed a network diagram of effective
   components and therapeutic targets, and analyzed the core components,
   which mainly included quercetin, luteolin, epigallocatechin gallate,
   beta-sitosterol, formononetin, stigmasterol, beta-carotene, arachidonic
   acid, and isorhamnetin. According to the prediction of ADMETlab 2.0,
   these ingredients have low toxicity. These major chemical components,
   along with other effective components, can act on multiple targets in
   the network to produce therapeutic effects. Among the predicted main
   components, quercetin, luteolin, epigallocatechin gallate, and
   beta-sitosterol, which rank highly, have been proven to have good
   therapeutic effects on tuberculosis.

   Quercetin can combat persistent tuberculosis infection by inhibiting
   isocitrate lyase of Mycobacterium tuberculosis ([110]Shukla et al.,
   2015). The combined administration of quercetin and
   polyvinylpyrrolidone with antituberculous drugs can rapidly promote the
   fibrosis of epithelioid cell tubercles, leading to the separation of
   tuberculous granulomas through connective tissue and the emergence of a
   significant number of Langerhans cells and lymphocytes ([111]Butova
   et al., 2016). Additionally, quercetin can prevent liver injury caused
   by antituberculous drugs through the activation of NRF2 and the
   blockade of NF-κB/TLR-4 ([112]Sanjay et al., 2021).

   Luteolin is a plant-derived liver protective immunomodulator that can
   promote anti-tuberculous immunity, shorten the treatment time of
   tuberculosis, and prevent disease recurrence when administered as a
   potential host-directed therapy together with isoniazid. Additionally,
   luteolin enhances the activity of natural killer cells and natural
   killer T cells, both of which possess anti-tuberculous properties
   ([113]Singh et al., 2021). Luteolin is a Kv1.3 K+ channel inhibitor,
   profoundly promoting central memory T cells by selectively suppressing
   effector memory T cells, thereby significantly enhancing the potency of
   Bacillus Calmette - Guerin vaccine ([114]Singh et al., 2020).

   Epigallocatechin Gallate is a drug with broad-spectrum antimicrobial
   activity. It can directly inhibit early-stage infection by interfering
   with the attachment of pathogens to host cells, suppressing viral
   replication, reducing bacterial biofilm formation, and preventing toxin
   release. Additionally, it can indirectly suppress infection by
   modulating immune inflammation and exerting antioxidant effects
   ([115]Zhao et al., 2021). A study reported that green tea and black tea
   containing epigallocatechin gallate may reduce the risk of tuberculosis
   infection in the subjects ([116]Maiolini et al., 2020).
   Epigallocatechin Gallate exhibits a killing effect on Mycobacterium
   tuberculosis within mouse macrophages through lysosomal acidification
   and autophagy-induced cellular mechanisms ([117]Sharma et al., 2020).

   Beta-sitosterol targets Th1 and Th2 cells, contributing to the
   normalization of their functions and subsequently enhancing the
   activity of T lymphocytes and natural killer cells. Additionally, it
   exhibits inhibitory effects on overactive antibody responses and
   normalizes cortisol ratios. The reconstitution of these immune
   parameters is beneficial for numerous disease processes associated with
   chronic immune-mediated abnormalities, including tuberculosis
   ([118]Bouic and Lamprecht, 1999). A study has demonstrated that
   tuberculosis patients treated with Beta-sitosterol exhibited
   significant improvements in body weight, lymphocyte count, and
   eosinophil count ([119]Donald et al., 1997).

   In summary, the therapeutic impact of GJZLP on tuberculosis exhibits a
   multifaceted nature, encompassing numerous molecules, targets, and
   pathways. Its main active components, quercetin, luteolin,
   epigallocatechin gallate, and beta-sitosterol, act on various targets
   within the network to produce therapeutic outcomes. These findings
   suggest that GJZLP has considerable potential for the treatment of
   tuberculosis and merits further exploration.

   Utilizing 118 therapeutic targets, we constructed a protein-protein
   interaction (PPI) network and subsequently eliminated outlier targets,
   resulting in 108 targets remaining for a deeper understanding of the
   core targets. Through the analysis of the PPI network, we identified
   IL6, TNF, JUN, TP53, IL1B, STAT3, AKT1, RELA, IFNG, and MAPK3 as
   potential core targets. Among them, IL6, TNF, JUN, TP53, and IL1B are
   particularly crucial. Previous studies have revealed that, IL-6 and TNF
   play crucial role in the immune response to tuberculosis ([120]Keane,
   2005; [121]Boni et al., 2022). The polymorphisms of IL6 and TNFα are
   closely linked to the development of tuberculosis, and TNFα serves as a
   protective factor against pulmonary tuberculosis ([122]Wu et al.,
   2018). Pre-treatment IL-6 is a biomarker for unfavorable TB treatment
   outcomes ([123]Gupte et al., 2022). The use of anti-TNF-α agents may
   cause new tuberculous infections or reactivate latent tuberculous
   infections ([124]Xie et al., 2014). JUN and TP53 may be involved in the
   pathogenesis of Mycobacterium tuberculosis infection and invasion by
   influencing the activity of extracellular domains and protein binding
   during the infection process ([125]Yuan et al., 2018; [126]Yang et al.,
   2023). Interleukin-1 (IL-1) is a key participant in the immune response
   to pathogens, playing a role in promoting inflammation and recruiting
   immune cells to the site of infection. In tuberculosis (TB), strictly
   regulating the IL-1 response is crucial to ensure the host’s resistance
   to infection. Several polymorphisms of the IL-1β gene are associated
   with increased susceptibility to tuberculosis ([127]Silvério et al.,
   2021).

   To gain a deeper understanding of the biological functions and
   potential signaling pathways involved in the treatment of tuberculosis
   with GJZLP, we conducted a comprehensive enrichment analysis of the
   relevant biological functions and pathways within the Gene Ontology
   (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene
   Ontology analysis revealed that the biological processes associated
   with this treatment primarily encompassed response to molecule of
   bacterial origin, response to lipopolysaccharide and cellular response
   to cytokine stimulus. The cellular components involved primarily
   encompassed vesicle lumen, secretory granule lumen and cytoplasmic
   vesicle lumen. The molecular functions involved primarily encompassed
   cytokine receptor binding, cytokine activity, and signaling receptor
   activator activity. KEGG pathway enrichment analysis revealed that
   GJZLP treatment for tuberculosis could target multiple signaling
   pathways, primarily including Lipid metabolism, Atherosclerosis, TNF,
   IL-17, Toll-like receptor and C-type lectin receptor signaling pathway.
   The TNF signaling pathway is highly enriched, leading us to speculate
   that it may be the primary signaling pathway mediating the
   anti-tuberculosis effects of GJZLP. Relevant studies have demonstrated
   that the activation of the TNF signaling pathway stimulates the
   expression of downstream inflammatory factors, thereby triggering a
   robust inflammatory response within cells. Excessive accumulation of
   TNF-α in macrophages induces necroptosis, suppressing the survival of
   Mycobacterium tuberculosis within macrophages ([128]Qian et al., 2022).
   These results provide a theoretical foundation for further
   understanding the mechanism of action of GJZLP in the treatment of
   tuberculosis.

   Based on these results, we identified ten key active components and ten
   core targets. We then performed molecular docking between these core
   compounds and targets to validate our network pharmacology predictions.
   The docking results revealed significant binding affinity between the
   core compounds and targets, with Stigmasterol and AKT1 exhibiting the
   lowest binding energy and the most stable binding. These findings
   suggest that the selected core compounds may interact with the targets,
   thereby influencing the treatment of tuberculosis. These discoveries
   provide novel insights into the potential molecular mechanisms
   underlying the therapeutic effects of GJZLP in the treatment of
   tuberculosis.

   GJZLP, as a traditional Chinese medicine formula, has demonstrated
   promising potential in the treatment of tuberculosis. With the
   development of precision medicine, GJZLP is expected to facilitate
   individualized therapy tailored to the specific conditions and
   genotypes of tuberculosis patients, thereby enhancing treatment
   outcomes. Elucidating the effective components and their underlying
   mechanisms of GJZLP is crucial for optimizing its prescription
   composition and improving its therapeutic efficacy. Furthermore, the
   synergistic effects of GJZLP in combination with other Western or TCM
   could enhance the treatment outcomes of tuberculosis. As the
   internationalization of TCM continues to progress, GJZLP is poised to
   gain increased recognition from the international medical community,
   offering more treatment options for patients with tuberculosis.

   Subsequently, wet analysis methods can be employed to validate the
   current research. For instance, quantitative analysis of the active
   ingredients in GJZLP can be conducted using High-Performance Liquid
   Chromatography (HPLC), Gas Chromatography (GC), Thin-Layer
   Chromatography (TLC), spectrophotometry, and chemical analysis. These
   methods will further corroborate the predictive results of the current
   analysis, ensuring the quality and efficacy of GJZLP. Cell models can
   be utilized to investigate the impact of GJZLP on specific signaling
   pathways such as TNF, IL-17, Toll-like receptor, and C-type lectin
   receptor. Animal models of tuberculosis can also be established to
   observe the effects of GJZLP on cell signaling in vivo. Additionally,
   transcriptomics and proteomics techniques can comprehensively analyze
   the regulatory role of GJZLP on cellular signaling networks. Clinical
   trials in tuberculosis patients can also be conducted to observe the
   effects of GJZLP on cell signaling and therapeutic outcomes.

   In summary, GJZLP holds significant potential in the treatment of
   tuberculosis. Its components can be validated through wet analysis
   methods, and further exploration of its mechanism on cell signaling
   under different disease conditions can be achieved through a
   combination of cell experiments, animal studies, omics technologies,
   and clinical research. This will provide additional scientific evidence
   to support its clinical utilization. Furthermore, the development
   potential of Gejie Zhilao Pill extends far beyond the treatment of
   tuberculosis. Given the widespread application of TCM in the treatment
   of various diseases, future research can explore the potential of this
   compound formula in treating other related diseases. Through in-depth
   studies of its biological mechanisms and modes of action, new
   applications of Gejie Zhilao Pill in the treatment of other diseases
   may be discovered.

   There are several limitations to this study that need to be taken into
   consideration. The accuracy and timeliness of data from various
   databases require scientific validation, and it is possible that the
   databases may not comprehensively cover all types of TCM and their
   components. Additionally, compounds or targets that have not been
   validated or recorded may not have been included in our research
   analysis. Nevertheless, we recommend leveraging modern bioinformatics
   techniques for research, as they can provide valuable insights into
   understanding the mechanisms of TCM. In future studies, researchers can
   further improve the accuracy and timeliness of database data by
   implementing measures such as regular updates and quality control.

   The bioactive components identified in this study, including quercetin,
   luteolin, epigallocatechin gallate, beta-sitosterol, formononetin, and
   stigmasterol do not fully represent the complex composition of GJZLP.
   These compounds may only contribute to the overall pharmacological
   effects of GJZLP in the treatment of tuberculosis. Therefore, further
   pharmacological and molecular experiments are needed to validate our
   findings. Additionally, the potential active components of GJZLP in the
   treatment of tuberculosis have not been extensively studied. There is
   much to be explored regarding the specific roles and mechanisms of
   these compounds in tuberculosis. Future research should delve deeper
   into their interactions with identified targets and investigate their
   impact on tuberculosis-related pathways and processes. Furthermore,
   studying the synergistic effects of these components in tuberculosis is
   also worthy of attention. Understanding these interactions could
   provide valuable insights into the unique pharmacological properties of
   this formula. Finally, clinical trials are needed to assess the
   effectiveness and safety of GJZLP in the treatment of tuberculosis.
   This will help validate its use in real-world settings and provide
   stronger evidence for its application in tuberculosis management.

5. Conclusion

   In this research, we employed network pharmacology and molecular
   docking techniques to initially explore the main chemical components,
   core targets, and potential mechanisms of GJZLP in the treatment of
   tuberculosis. Our research indicates that quercetin, luteolin,
   epigallocatechin gallate and beta-sitosterol may be the primary active
   components of GJZLP, while protein targets such as IL6, TNF, JUN, TP53
   and IL1B are potential targets for GJZLP in treating tuberculosis.
   These components may play a therapeutic role through TNF, IL-17,
   Toll-like receptor and C-type lectin receptor signaling pathway. As
   shown in Prediction of the mechanism ([129] Figure 9 ). These pathways
   play a role in tuberculosis-associated processes encompassing
   inflammation, immune response, cell survival and apoptosis.

Figure 9.

   [130]Figure 9
   [131]Open in a new tab

   The possible strategies for GJZLP intervention in tubercle progression.

   This study provides a reference for further exploring the mechanism of
   action of GJZLP in the treatment of pulmonary tuberculosis.
   Additionally, it offers new insights into the potential therapeutic
   mechanisms and applications of traditional Chinese medicine formulas,
   laying a foundation for future antituberculosis drugs development.

Data availability statement

   The original contributions presented in the study are included in the
   article/[132] Supplementary Material . Further inquiries can be
   directed to the corresponding authors.

Author contributions

   YG: Investigation, Project administration, Writing – original draft.
   BS: Data curation, Software, Writing – original draft. YH:
   Investigation, Writing – original draft. WD: Software, Writing –
   original draft. LW: Conceptualization, Investigation, Writing – review
   & editing. SS: Funding acquisition, Investigation, Resources,
   Supervision, Writing – review & editing.

Funding Statement

   The author(s) declare financial support was received for the research,
   authorship, and/or publication of this article. This study was
   supported by the Peak Climbing Program of the Second Affiliated
   Hospital of Dalian Medical University.

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary Material for this article can be found online at:
   [133]https://www.frontiersin.org/articles/10.3389/fcimb.2024.1405627/fu
   ll#supplementary-material
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References