Network analysis for identifying potential anti-virulence targets from whole transcriptome of Pseudomonas aeruginosa and Staphylococcus aureus exposed to certain anti-pathogenic polyherbal formulations

ABSTRACT

Introduction:

Antimicrobial resistance (AMR) is a serious global threat. Identification of novel antibacterial targets is urgently warranted to help antimicrobial drug discovery programs. This study attempted identification of potential targets in two important pathogens Pseudomonas aeruginosa and Staphylococcus aureus.

Methods:

Transcriptomes of P. aeruginosa and S. aureus exposed to two different quorum-modulatory polyherbal formulations were subjected to network analysis to identify the most highly networked differentially expressed genes (hubs) as potential anti-virulence targets.

Results:

Genes associated with denitrification and sulfur metabolism emerged as the most important targets in P. aeruginosa. Increased buildup of nitrite (NO₂) in P. aeruginosa culture exposed to the polyherbal formulation Panchvalkal was confirmed through in vitro assay too. Generation of nitrosative stress and inducing sulfur starvation seemed to be effective anti-pathogenic strategies against this notorious gram-negative pathogen. Important targets identified in S. aureus were the transcriptional regulator sarA, immunoglobulin-binding protein Sbi, serine protease SplA, the saeR/S response regulator system, and gamma-hemolysin components hlgB and hlgC.

Conclusion:

Further validation of the potential targets identified in this study is warranted through appropriate in vitro and in vivo assays in model hosts. Such validated targets can prove vital to many antibacterial drug discovery programs globally.

Introduction

Despite wide recognition of antimicrobial resistance (AMR) as a major global health threat, the progress on discovery and development of new antibiotics in the last three to four decades clearly has fallen short from being satis­factory. For a variety of reasons, for example, lack of interest among major pharmaceutical firms, rapid emergence and spread of resistance among pathogenic bacterial populations, dearth of new validated cellular and molecular targets, the list of effective antimicrobials available for treatment of resistant infections remains short. The status of antibiotic discovery research has been reviewed thoroughly (1,2,3,4). Since most currently available antibiotics target a narrow range of bacterial traits, that is, cell envelope synthesis, protein or nucleic acid synthesis, or folic acid synthesis, a truly new class of antibiotics will be discovered only if we have a longer list of validated targets. Development of new bactericidal antibiotics is not the only way of tackling the slow pandemic of AMR infections; discovery of resistance modifiers and non-antibiotic virulence-attenuating agents can also be of great value (5,6). Hence identification of new potential targets for both bactericidal antibiotics as well as antibiotic adjuvants is useful. There is a clear need for antibiotics with previously unexploited new targets and wide target diversity in the discovery pipeline. One of the major challenges in antibacterial discovery is associated with the proper target selection, for example, the requirement of pursuing molecular targets that are not prone to rapid resistance development (7).

Various public health agencies like CDC (Centers for Disease Control and Prevention, USA), WHO (World Health Organization), and DBT (Department of Biotechnology, India) have published lists of priority pathogens against which novel antimicrobials need to be discovered urgently. Antibiotic-resistant strains of Pseudomonas aeruginosa and Staphylococcus aureus commonly appear on all such lists. As per CDC’s Vital Signs report (https://www.cdc.gov/vitalsigns/index.html) more than 33% of the bloodstream infections in patients on dialysis in the United States in 2020 were caused by S. aureus. This gram-positive human commensal has been recognized as an important opportunistic pathogen responsible for a wide range of infections (8). P. aeruginosa is the primary cause of gram-negative nosocomial infections. Its ability to adapt to a wide range of environmental niches combined with its nutritional versatility and genome plasticity, along with a multitude of intrinsic and acquired resistance mechanisms make it one of the most notorious pathogens of critical clinical importance. Efforts for finding perturbants capable of targeting the P. aeruginosa pathogenicity and antibiotic resistance are highly desired (9).

We had previously studied the anti-virulence effect of certain polyherbal formulations against S. aureus or P. aeruginosa at the whole transcriptome level of the target pathogen, wherein we gained some insight into the molecular mechanisms associated with the virulence-attenuating potential of the test formulations, which was largely independent of any growth-inhibitory effect. Pathogens exposed to the test formulations were compromised in their ability to kill the model host Caenorhabditis elegans. The current study attempted network analysis of the differentially expressed genes (DEG) of P. aeruginosa and S. aureus exposed to the anti-pathogenic polyherbal formulations Panchvalkal (10) and Herboheal (11), respectively, reported in the previous studies, with an aim to identify highly networked genes as potential anti-virulence targets. Panchvalkal is a mixture of bark extracts of five different plants – Ficus benghalensis, Ficus religiosa, Ficus racemosa, Ficus lacor, and Albizia lebbeck. Herboheal comprised of extracts of six different plants. Its full composition can be seen at: https://downloads.hindawi.com/journals/aps/2019/1739868.f1.pdf

Methods

Network analysis

We accessed the list of DEG for Panchvalkal (Pentaphyte­P-5^®)-exposed P. aeruginosa (NCBI Bioproject ID 386078) and Herboheal-exposed S. aureus (NCBI Bioproject ID 427073). The P. aeruginosa used was a multidrug-resistant strain. Network analysis for both the studies was carried out independently, wherein only the DEG fulfilling the dual filter criteria of log fold change ≥2 and False Discovery Rate (FDR) ≤0.01 were selected for further analyses. The list of such DEG was fed into the database STRING (v. 11.5) (12) for generating the PPI (Protein-Protein Interaction) network. Then the genes were arranged in decreasing order of ‘node degree’ (a measure of connectivity with other genes or proteins), and those above a certain threshold value were subjected to ranking by cytoHubba (v. 3.9.1) (13). Since cytoHubba uses 12 different ranking methods, we considered the DEG being top-ranked by more than six different methods (i.e., 50% of the total ranking methods) for further analysis. These top-ranked shortlisted proteins were further subjected to network cluster analysis through STRING and those which were part of multiple clusters were considered ‘hubs’ which can be taken up for further validation of their targetability. Here ‘hub’ refers to a gene or protein interacting with many other genes/proteins. Hubs thus identified were further subjected to co-occurrence analysis to see whether an anti-virulence agent targeting them is likely to satisfy the criterion of selective toxicity (i.e., targeting the pathogen without harming the host). This sequence of analysis allowed us to end with a limited number of proteins which satisfied various statistical and biological significance criteria simultaneously, that is, (i) log fold change ≥2; (ii) FDR ≤0.01; (iii) relatively higher node degree; (iv) top-ranking by at least six cytoHubba methods; (v) (preferably) member of more than one local network cluster; and (vi) high probability of the target being absent from the host. A schematic presentation of the methodology employed for network analysis is presented in Figure 1.

Fig. 1 -.

A schematic of methodology for network analysis and hub identification.

Nitrite estimation

Nitrite estimation in P. aeruginosa culture supernatant was done through Griess assay (14). P. aeruginosa strain studied by us is a multidrug-resistant strain, which is resistant to ampicillin (10 µg), augmentin (30 µg), nitrofurantoin (300 µg), clindamycin (2 µg), chloramphenicol (30 µg), cefixime (5 µg), and vancomycin (30 µg). This bacterium was grown in Pseudomonas broth (HiMedia, Mumbai) with or without Panchvalkal (547 μg/mL; dried extract powder without any bulking agent was procured from Dr. Palep’s Medical Education and Research Foundation Pvt. Ltd., Mumbai, India, and dissolved in dimethylsulfoxide (DMSO) for assay purpose) at 35°C for 21±1 hour. Following incubation, cell density was quantified at 764 nm (15), and then the bacterial culture suspension was centrifuged at 13,600 g for 10 minutes. Resulting supernatant was mixed with Griess reagent (Sigma-Aldrich) in 1:1 ratio and incubated for 15 minutes in the dark at room temperature. Absorbance of the resulting pink color was quantified at 540 nm (Agilent Technology Cary 60 UV-Vis). These optical density (OD) values were plotted on standard curve prepared using NaNO₂ to calculate the nitrite concentration. To nullify any effect of variation in cell density between control and experimental culture, nitrite unit (i.e., nitrite produced per unit of growth) was calculated by dividing the nitrite concentration values by cell density. Sodium nitroprusside (Astron chemicals, Ahmedabad) being a chemical known to be capable of generating nitrosative stress in bacteria (16,17,18) was used as a positive control. Appropriate vehicle control (i.e., bacteria grown in the presence of 0.5% v/v DMSO (Merck)), negative control (deionized water), and abiotic control (Panchvalkal-supplemented Pseudomonas broth) were included in the experiment. Griess reagent was added in all these controls in the same proportion as that in extract-exposed or not-exposed bacterial culture samples.

Results

Network analysis of DEG in Panchvalkal-exposed P. aeruginosa

Our original experimental study exposed P. aeruginosa to Panchvalkal at 567 μg/mL, wherein the extract-exposed pathogen could kill 90% lesser host worms than its extract-not-exposed counterpart. Whole transcriptome study revealed that approximately 14% of the P. aeruginosa genome was expressed differently under the influence of Panchvalkal. The total number of DEG satisfying the dual criteria of log fold change ≥2 and FDR ≤0.01 was 228, of which 105 were downregulated (Tab. S1) and 123 were upregulated (Tab. S4). We created PPI network for up- and downregulated genes separately (Figs. 5 and 2, respectively). PPI network for downregulated genes generated through STRING is presented in Figure 2, which shows 101 nodes connected (105 genes were fed to string, out of which 101 were shown in the PPI network) through 86 edges with an average node degree of 1.7. Since the number of edges (86) in this PPI network is 3.18-fold higher than expected (27) with a PPI enrichment p value <1.0e-16, this network can be said to possess significantly more interactions among the member proteins than what can be expected for a random set of proteins of identical sample size and degree distribution. Such an enrichment can be taken as an indication of the member proteins being at least partially biologically connected. When we arranged the 105 downregulated genes in decreasing order of node degree, 52 nodes were found to have a nonzero score (Tab. S2), and we selected top 13 genes with a node degree ≥6 for further ranking by different cytoHubba methods. Then we looked for genes which appeared among the top-10 ranked candidates by ≥6 cytoHubba methods, and 10 such shortlisted genes (Tab. S3) were further checked for interactions among themselves followed by cluster analysis (Fig. 3), which showed them to be strongly networked as the average node degree score was 8. This network possessed 40 edges as against expected (zero) for any such random set of proteins (PPI enrichment p value <1.0e-16). The PPI network generated through STRING showed these 10 important genes to be distributed among three different local network clusters. Five (norB, norC, norD, nirS, and nirQ) of the predicted hubs were part of each of the three clusters, and they have a role in denitrification (19). Of the remaining five predicted hub proteins, one more (norE) is also associated with nitrogen metabolism, and two (nosL and nosY) have a role in denitrification as well as copper homeostasis. These three proteins were members of two out of three clusters. The eight proteins (Tab. I) found to be members of minimum two clusters can be said to be potential hubs, whose downregulation can be hypothesized to attenuate P. aeruginosa virulence.

Fig. 2 -.

Protein-Protein Interaction (PPI) network of downregulated genes in Panchvalkal-exposed Pseudomonas aeruginosa. Edges represent protein-protein associations that are meant to be specific and meaningful, that is, proteins jointly contribute to a shared function; this does not necessarily mean they are physically binding to each other. Network nodes represent proteins. Splice isoforms or post-translational modifications are collapsed, that is, each node represents all the proteins produced by a single, protein-coding gene locus.

Fig. 3 -.

Protein-Protein Interaction (PPI) network of top-ranked genes revealed through cytoHubba among downregulated differentially expressed genes (DEG) in Panchvalkal-exposed Pseudomonas aeruginosa.

Table I -.

Hubs identified as potential targets from among the downregulated genes in Panchvalkal-exposed Pseudomonas aeruginosa

No.	Gene ID	Gene name	Functional role
1	PA0520	nirQ	Denitrification regulatory protein NirQ
2	PA0519	nirS	Heme d1 biosynthesis protein, which is important for denitrification (20)
3	PA0524	norB	Nitric oxide reductase subunit B
4	PA0523	norC	Nitric oxide reductase subunit C
5	PA0525	NorD	Nitric oxide reductase NorD protein
6	PA0521	NorE	Nitric oxide reductase NorE protein
7	PA3395	nosY	Nitrous oxide reductase; a Cu-processing system permease protein having role in denitrification pathway (21)
8	PA3396	nosL	A lipoprotein attached to the outer membrane described as a copper-binding protein. Regulator of nos operon, NosR also associates with NosL. This protein is probably responsible for the insertion and coordination of the multicopper center within NosZ (22).

Since all the targets mentioned in Table I are known to play an important role in P. aeruginosa with respect to detoxification of reactive nitrogen species, we hypothesized that Panchvalkal-treated P. aeruginosa’s ability to detoxify reactive nitrogen species is compromised. To check this hypothesis, we quantified nitrite concentration in extract-treated P. aeruginosa culture, wherein it was found to have 31% higher nitrite concentration in supernatant as compared to control (Fig. 4). This higher accumulation of nitrite can be taken as an indication of compromised denitrification efficiency as nitrite is an intermediate of denitrification pathway (22).

Fig. 4 -.

Panchvalkal-treated Pseudomonas aeruginosa culture has higher extracellular accumulation of nitrite. While nitrite concentration in vehicle control (P. aeruginosa incubated in media supplemented with 0.5% v/v dimethylsulfoxide (DMSO)) was at par to that without DMSO, Panchvalkal caused nitrite concentration in P. aeruginosa culture supernatant to rise (A). Sodium nitroprusside used as positive control caused a dose-dependent 2.37 to 52.29-fold higher nitrite buildup in P. aeruginosa culture (B). Nitrite unit (i.e., nitrite concentration:cell density ratio) was calculated to nullify any effect of cell density on nitrite production. ***p<0.001.

PPI network for upregulated genes in Panchvalkal-exposed P. aeruginosa generated through STRING is presented in Figure 5, which shows 121 nodes connected through 70 edges with an average node degree of 1.16. Though empirically the centrality of the upregulated genes appeared to be lesser than those downregulated in Panchvalkal-exposed P. aeruginosa, since the number of edges (70) in this PPI network is 1.89-fold higher than expected (37) with a PPI enrichment p value of 1.27e-06, this network can be said to possess significantly more interactions among the member proteins than what can be expected for a random set of proteins of this much sample size and degree distribution. Such an enrichment can be taken as an indication of the member proteins being at least partially biologically connected. When we arranged the 121 upregulated genes in decreasing order of node degree, 62 nodes were found to have a nonzero score, and we selected the top 26 genes with a node degree ≥3 (Tab. S5) for further ranking by different cytoHubba methods. Then we looked for genes which appeared among top-ranked candidates by ≥6 cytoHubba methods, and 14 such genes (Tab. S6) were identified for further cluster analysis. Interaction map of these 14 important genes (Fig. 6) showed them to be networked with the average node degree score of 2.29. Number of edges possessed by this network was 16 as against expected 1 for any such random set of proteins. These 14 genes were found to be distributed among five different local network clusters. Strength score for each of these clusters was >1.5. While three of the proteins (atsB, msuE, and ssuB1) were common members of three different clusters, one gene (tauA) appeared in two clusters. All these four highly networked upregulated genes (Tab. II) are involved in sulfur metabolism in P. aeruginosa (23). Hence it may be speculated that Panchvalkal has induced sulfur starvation in P. aeruginosa, to overcome which the pathogen is forced to upregulate genes involved in sulfur transport and metabolism.

Fig. 5 -.

Protein-Protein Interaction (PPI) network of up-regulated genes in Panchvalkal-exposed P. aeruginosa.

Fig. 6 -.

PPI network of top-ranked genes revealed through cytoHubba among up-regulated DEG in Panchvalkal-exposed P. aeruginosa.

Table II -.

Hubs identified as potential targets from among the upregulated genes in Panchvalkal-exposed Pseudomonas aeruginosa

No.	Gene ID/name	Codes for	Remarks
1	PA2357/msuE (slfA)	FMN reductase	Involved in riboflavin metabolism and sulfur metabolism pathways
2	PA3442/ssub1	Aliphatic sulfonates import ATP-binding protein SsuB 1	Aliphatic sulfonates import ATP-binding protein SsuB 1; part of the ABC transporter complex SsuABC involved in aliphatic sulfonate import. Responsible for energy coupling to the transport system
3	PA0185/atsB	Serine-modifying enzyme (24); probable permease of ABC transporter	atsB is a member of a cys regulon in P. aeruginosa, which constitutes a general sulfate ester transport system (25)
4	PA3938/tauA	TauA (sulfonate transport system ATP-binding protein)	This probable periplasmic taurine-binding protein precursor is part of tau operon involved in sulfur metabolism

Network analysis of DEG in Herboheal-exposed S. aureus

Herboheal is a folk-inspired wound-healing formulation, and we had earlier demonstrated its anti-virulence potential against multiple bacterial pathogens including S. aureus. Pretreatment of S. aureus with Herboheal (0.1% v/v) could attenuate its virulence toward the surrogate host C. elegans by 55%. This concentration had a moderate growth-inhibitory effect (32%) on S. aureus, while heavily inhibiting staphyloxanthin production (79%). Whole transcriptome study revealed that approximately 17% of the S. aureus genome was expressed differently under the influence of Herboheal. The total number of DEG satisfying the dual criteria of log fold change ≥2 and FDR ≤0.01 was 113, of which 57 were upregulated and 56 were downregulated (Tab. S7). Since the number of genes amenable to mapping by STRING turned out to be only 28 of these 113, we went for a combined PPI network (Fig. 7) of all these DEG instead of preparing separate PPI map of upregulated or downregulated genes. The said PPI network had 28 nodes connected through 36 edges with an average node degree of 2.57. Since the number of edges (36) in this PPI network is threefold higher than expected (12) with a PPI enrichment p value of 1.02e-08, this network can be said to possess significantly more interactions among the member proteins than what can be expected for a random set of proteins having identical sample size and degree distribution. Such an enrichment is suggestive of the member proteins being at least partially biologically connected.

Fig. 7 -.

Protein-Protein Interaction (PPI) network of upregulated and downregulated genes in Herboheal-exposed Staphylococcus aureus.

When we arranged all the 28 nodes in decreasing order of node degree, 23 nodes were found to have a nonzero score, and we selected the top 13 genes with a node degree ≥3 (Tab. S8) for further ranking by different cytoHubba methods. Then we looked for genes which appeared among top-ranked candidates by ≥6 cytoHubba methods. Of such 12 genes, 8 (Tab. S9) which were ranked among top 10 by ≥11 cytoHubba methods were taken for further cluster analysis. Interaction map of these eight important genes (Fig. 8) showed them to be networked with the average node degree score of 4. Number of edges possessed by this network was 16 as against expected 1 for any such random set of proteins. These eight genes were found to be distributed among three different local network clusters. Strength score for each of these clusters was >1.46. While three of the proteins (sarA, sbi, and splA) were common members of two different clusters, four proteins were part of any one cluster, while pnp was not shown to be connected to the remaining seven genes. Since in case of S. aureus, we analyzed up- and downregulated genes together, instead of considering only the multi-cluster proteins as hubs, we took all of those which appeared to be part of PPI network as shown in Figure 7. Functions of these seven potential hubs are listed in Table III.

Fig. 8 -.

Protein-Protein Interaction (PPI) network of top-ranked genes revealed through cytoHubba among differentially expressed genes (DEG) in Herboheal-exposed Staphylococcus aureus.

Table III -.

Hubs identified as potential targets from among the up- and down-regulated genes in Herboheal-exposed Staphylococcus aureus

No.	Gene ID	Gene name	Codes for	Function
1	SAXN108_0683	sarA	Transcriptional regulator SarA	Probably activates the development of biofilm by both enhancing the ica operon transcription and suppressing the transcription of either a protein involved in the turnover of PIA/PNAG or a repressor of its synthesis, whose expression would be sigma-B-dependent
2	SAXN108_2673	sbi	Immunoglobulin-binding protein Sbi	Plays a role in the inhibition of both the innate and adaptive immune responses
3	SAXN108_1846	splA	Serine protease SplA	Poorly characterized secreted protein probably involved in virulence
4	SAXN108_0774	saeR	Response regulator transcription factor SaeR	The saeR/S system plays a role in regulating such virulence factors which decrease neutrophil hydrogen peroxide and hypochlorous acid production following S. aureus phagocytosis
5	SAXN108_0773	saeS	Histidine kinase
6	SAXN108_2677	hlgB	Gamma-hemolysin component B precursor	Toxins that seem to act by forming pores in the membrane of the cell; has a hemolytic and a leukotoxic activity
7	SAXN108_2676	hlgC	Gamma-hemolysin component C precursor

PIA = polysaccharide intercellular adhesin; PNAG = poly-N-acetyl-β-(1-6)-glucosamine.

Discussion

Panchvalkal-exposed P. aeruginosa appears to suffer from sulfur starvation and nitrosative stress. Compromised nitric oxide (NO) detoxification can render bacteria more susceptible to the NO produced by the host immune system (19). Mutant P. aeruginosa deficient in NO reductase was shown to register a reduced survival rate in NO-producing macrophages (26). NO has a strategic role in the metabolism of microorganisms in natural environments and also during host-pathogen interactions. NO as a signaling molecule is able to influence group behavior in microorganisms. Downregulation of the denitrification pathway can disturb the homeostasis of the bacterial biofilms. NO levels can also affect motility, attachment, and group behavior in bacteria by affecting various signaling pathways involved in the metabolism of 3ʹ,5ʹ-cyclic diguanylic acid (c-di-GMP). Suppressing bacterial detoxification of NO can be an effective anti-pathogenic strategy, as NO is known to modulate several aspects of bacterial physiology, including protection from oxidative stress and antimicrobials, homeostasis of the bacterial biofilm, etc. (27,28,29). From this in silico exercise, nitric oxide reductase (NOR) has emerged as the most important target of Panchvalkal in P. aeruginosa. NOR is one of the important detoxifying enzymes of this pathogen, which is crucial to its ability to withstand nitrosative stress, and has also been reported to be important for virulence expression of this pathogen, and thus can be a plausible potential target for novel anti-virulence agents (19). NOR inhibitors can be expected to compromise the pathogen’s ability to detoxify nitric oxide (NO), not allowing its virulence traits (e.g., biofilm formation, as NO has been indicated to act as a biofilm-dispersal signal) to be expressed fully. NOR inhibitors can be expected to be effective not only against P. aeruginosa but against multiple other pathogens too, as NO is reported to be perceived as a dispersal signal by various gram-negative and gram-positive bacteria (30). This is to say, NOR inhibitors may be expected to have broad-spectrum activity against multiple pathogens. Major function of NOR is to detoxify NO generated by nitrite reductase (NIR). NO is a toxic byproduct of anaerobic respiration in P. aeruginosa. NO-derived nitrosative species can damage DNA and compromise protein function. Intracellular accumulation of NO is likely to be lethal for the pathogen. It can be logically anticipated that P. aeruginosa’s ability to detoxify NO will be compromised under the influence of potent NOR inhibitors like Panchvalkal. Since NO seems to have a broad-spectrum anti-biofilm effect, NOR activity is essential for effective biofilm formation by the pathogens. NOR activity and NO concentration can modulate cellular levels of c-di-GMP, which is a secondary messenger molecule recognized as a key bacterial regulator of multiple processes such as virulence, differentiation, and biofilm formation (31). In the mammalian pathogens, the host’s macrophages are a likely source of NO. NOR expressed by the pathogen provides protection against the host defense mechanism (26). Since NOR activity is known to be important in multiple pathogenic bacteria (e.g., P. aeruginosa, S. aureus, Serratia marcescens) for biofilm formation, virulence expression, combating nitrosative stress, and evading hose defense, NOR seems to be an important target for novel broad-spectrum anti-pathogenic agents. A potential NOR inhibitor besides troubling the pathogen directly may also boost its clearance by the host macrophages (32).

Based on the analysis of differently expressed upregulated genes, sulfur-starved culture of P. aeruginosa can be expected to experience compromised virulence. Upregulation of organic sulfur transport and metabolism genes has been reported in P. aeruginosa facing sodium hypochlorite-induced oxidative stress (33). Two of the upregulated hubs mentioned in Table II are part of tau or ssu gene clusters, which are reported in gram-negative bacteria like Escherichia coli too for being necessary for the utilization of taurine and alkane sulfonates as sulfur sources. Since these genes are exclusively expressed under conditions of sulfate or cysteine starvation (34), one of the multiple effects exerted by Panchvalkal on P. aeruginosa can be said to be sulfur starvation. Upregulation of n-alkane sulfonates or taurine (sources of carbon and organic sulfur) utilization genes in P. aeruginosa suggests that the sulfur in these compounds was used to counter Panchvalkal-induced sulfur starvation, and that the neutrophilic amines and alpha-amino acids formed by catabolization of n-alkane sulfonates may guard the cell against oxidative stress (35). Thus, depriving P. aeruginosa of sulfur can be viewed as a potential anti-virulence strategy.

Among the potential targets identified in S. aureus in this study, first we discuss two such downregulated genes which are common members of two different clusters. Of them, splA is a serine protease, exclusively specific to S. aureus, and thought to have a role in the second invasive stage of the infection (36). Another potential hub sbi is an IgG-binding protein, which has a role in the inhibition of the innate as well as adaptive immune responses. Its secreted form acts as a potent complement inhibitor of the alternative pathway-mediated lysis. sbi helps mediate bacterial evasion of complement via a mechanism called futile fluid-phase consumption (37). Among the remaining potential hubs listed in Table III, SaeR/S two-component system is recognized as a major contributor to S. aureus pathogenesis and neutrophil evasion. SaeR/S also plays a role in regulating such virulence factors which decrease neutrophil hydrogen peroxide and hypochlorous acid production following S. aureus phagocytosis (38). S. aureus escapes from the antimicrobial protein’s neutrophil extracellular traps (NETs), which is dependent on its secreting nuclease (nuc), and the latter in turn is regulated by SaeR/S. The SaeR/S system also modulates neutrophil fate by inhibiting interleukin (IL)-8 production and nuclear factor (NF)-κB activation. SaeR/S deletion mutant of S. aureus was shown to be inferior than its wild-type counterpart in causing programmed neutrophil death (39). The SaeR/S system regulates expression of many important virulence factors in S. aureus, and some of them do appear in our list of important targets such as sbi, hlgB, and hlgC. Thus, inhibiting SaeR/S from sensing its environment can be expected to prevent expression of a multitude of S. aureus virulence factors in response to host signals. hlgB and hlgC are hemolytic proteins, and such proteins are used by many pathogens to fulfill their iron requirement as the concentration of free iron in human serum is much lesser than that required by the bacteria (40). Downregulation of bacterial hemolytic machinery may push them toward iron starvation, thus compromising their fitness for in-host survival. This corroborates well with our earlier report (11) describing reduced hemolytic potential of S. aureus under the influence of Herboheal. Among all the potential hubs identified in Herboheal-exposed S. aureus, only one (sarA) was upregulated, and its upregulation seems to be a response from S. aureus to compensate the Herboheal-induced downregulation of many important virulence traits. For example, sarA regulates expression of ica operon, which is required for biofilm formation in S. aureus. It can be said that S. aureus’s ability to adhere to surfaces and biofilm formation was compromised in the presence of Herboheal as suggested by downregulation of adhesion/biofilm-relevant genes (SaeR/S and sarA), and as an adaptation to such challenge the pathogen is trying to upregulate SarA. This corroborates well with our previous report describing 56% reduced biofilm formation by S. aureus in the presence of Herboheal (11).

This study has identified certain potential hubs in P. aeruginosa (Tabs. I and II) and S. aureus (Tab. III) which should further be investigated for their candidature as potential anti-pathogenic targets. The most suitable targets in bacterial pathogens would be the ones which are absent from their host, as this will allow the criteria of selective toxicity to be satisfied for a newly discovered drug. We did a gene co-occurrence pattern analysis of gene families across genomes (through STRING) with respect to the major hubs identified in each of the pathogens (Tab. IV). Of the 19 hubs identified in either of the pathogen, none was shown to be present in Homo sapiens, and hence drugs causing dysregulation of one or more of these genes in pathogens are less likely to be toxic to humans.

Table IV -.

Co-occurrence analysis of genes coding for potential targets in Pseudomonas aeruginosa and Staphylococcus aureus

The darker the shade of the squares, higher is the homology between the genes being compared.

If any target gene is present among multiple pathogens, then it can be considered suitable for a broad-spectrum antibacterial. We analyzed the co-occurrence of identified hubs among some of the important pathogens listed by CDC and WHO. From among those listed in Table IV, atsB, msuE, ssub1, norE, and norB seemed to be present in multiple gram-negative as well as gram-positive pathogens, and thus suitable to be targeted by a broad-spectrum anti-pathogenic discovery program. On the other hand, tauA and nirQ seemed to be present only among gram-negative pathogens. They can prove to be important targets in light of the fact that discovery of novel antimicrobials against gram-negative bacteria is relatively more challenging (41).

One of the issues with conventional antibiotics is that they cannot differentiate between the ‘good’ (symbionts in human microbiome) and ‘bad’ (pathogens) bacteria, and hence their consumption may lead to gut dysbiosis. An ideal antimicrobial agent should target pathogens exclusively without causing gut dysbiosis. In this respect, a target in pathogenic bacteria absent from symbionts of human microbiome will be the most suitable candidate for antibiotic discovery programs. To gain some insight on this front regarding the targets identified by us, we run a gene co-occurrence analysis with some representative ‘good’ bacteria reported to be part of healthy human microbiome. Bifidobacterium species showed presence of no other target except SaeR/S. SaeR/S being widely distributed among bacteria can be considered a valid target; however, an antibacterial agent targeting it may lead to gut dysbiosis too. All downregulated targets in P. aeruginosa were absent from the selected symbionts, which further adds value to their potential candidature as anti-virulence targets. However, atsB and ssub1 appeared to be present in Lactobacillus casei.

Conclusion

This study has identified certain potential targets in two important pathogens. Such in silico studies being predictive in nature, further work is warranted on wet-lab validation of the identified targets. Deletion mutants of the identified hub genes should be assessed for their expected attenuated virulence in appropriate host models. Next-generation pathoblockers targeting any one of these genes may not always be effective as stand-alone therapeutic, and simultaneous targeting of more than one of these genes may be required for an effective therapy. They can also prove to be useful adjuvants to conventional antibiotics allowing use of bactericidal antibiotics at lower concentrations.

Besides indicating generation of nitrosative stress, inducing sulfur starvation, and disturbing regulation of bacterial virulence as potentially effective anti-pathogenic strategies, this study also demonstrates the relevance of the polyherbalism concept of the Traditional Medicine systems, and utility of the network analysis approach in elucidating the multiple modes of anti-pathogenic action exerted by the multicomponent natural extracts.

Abbreviations

AMR =

antimicrobial resistance;

DEG =

differentially expressed genes;

NO =

nitric oxide;

NOR =

nitric oxide reductase;

PPI =

protein-protein interaction

Supplementary File

Network analysis for identifying potential anti-virulence targets from whole transcriptome of Pseudomonas aeruginosa and Staphylococcus aureus exposed to certain anti-pathogenic polyherbal formulations

Feny Ruparel, Siddhi Shah, Jhanvi Patel, Nidhi Thakkar, Gemini Gajera, Vijay Kothari

Institute of Science, Nirma University, Ahmedabad, India

Correspondence: vijay.kothari@nirmauni.ac.in

Table S1. List of down regulated genes in Panchvalkal exposed P. aeruginosa satisfying the dual criteria of log fold-change ≥2 and FDR≤0.01.

No.	Feature ID/ Gene	Codes for	log fold change	FDR
1	PA0521	Nitric oxide reductase NorE protein	8.76	2.34E-10
2	PA4962	Inner membrane protein	8.40	0.0001
3	PA2182	Hypothetical protein	7.75	0.001
4	PA2607	tRNA 2-thiouridine synthesizing protein B	6.75	0.003
5	PA2980	Hypothetical protein	5.88	7.77E-05
6	norB	Nitric oxide reductase subunit B	5.44	0
7	norC	Nitric oxide reductase subunit B	5.04	0
8	PA1827	3-oxoacyl-[acyl-carrier protein] reductase	4.52	3.77E-06
9	PA1013.1	tRNA-Ser	4.50	0.002
10	atuE	Isohexenylglutaconyl-CoA hydratase	4.50	0.009
11	PA1492	Hypothetical protein	4.20	0.001
12	PA2085	Ring-hydroxylating dioxygenase small subunit	4.16	0.01
13	nosL	Copper chaperone NosL	4.15	1.82E-05
14	PA0525	Nitric oxide reductase NorD protein	4.14	0
15	PA5071	16S ribosomal RNA methyltransferase RsmE	3.57	0.001
16	PA2146	Hypothetical protein	3.53	0.001
17	nosY	Cu-processing system permease protein	3.46	0.002
18	PA3377	Alpha-D-ribose 1-methylphosphonate 5-phosphate C-P lyase	3.45	0.0002
19	PA1211	Hypothetical protein	3.40	0.01
20	PA0818	Hypothetical protein	3.30	0.004
21	PA3033	Hypothetical protein	3.25	0.0005
22	kynB	Arylformamidase (kynurenine formamidase)	3.15	0.0001
23	PA5196	Hypothetical protein	3.14	0.005
24	PA5181.1	P34	3.11	0.001
25	nuoI	NADH-quinone oxidoreductase subunit I	3.08	2.73E-08
26	PA4702	Hypothetical protein	3.04	1.11E-09
27	algE	Alginate production protein	3.00	0.003
28	PA0526	Hypothetical protein	2.95	0
29	PA2180	Hypothetical protein	2.94	0.004
30	nirQ	Nitric oxide reductase NorQ protein	2.91	0
31	PA3493	Electron transport complex protein RnfG |	2.80	3.64E-05
32	nirS	Heme d1 biosynthesis protein	2.78	0
33	infA	Translation initiation factor IF-1	2.73	4.90E-06
34	PA1879	Hypothetical protein	2.71	1.94E-05
35	PA0270	Hypothetical protein	2.70	0.002
36	PA4466	Phosphoryl carrier protein	2.68	9.20E-06
37	PA0806	Hypothetical protein	2.68	0.006
38	rluA	Ribosomal large subunit pseudouridine synthase A	2.66	0.001
39	PA2506	Hypothetical protein	2.66	0.01
40	lldD	L-lactate dehydrogenase	2.65	4.74E-05
41	PA2570.1	tRNA-Leu	2.63	0.008
42	PA2433	Hypothetical protein	2.62	1.35E-09
43	pcaC	4-carboxymuconolactone decarboxylase	2.62	0.001
44	pilE	Type IV pilus assembly protein	2.62	0.001
45	PA2754a	Hypothetical protein	2.59	0.005
46	PA0682	HxcX atypical pseudopilin	2.55	0.001
47	pscL	Type III secretion protein L	2.55	0.01
48	PA2602	Hypothetical protein	2.54	0.006
49	pcaH	Protocatechuate 3,4-dioxygenase	2.54	0.006
50	PA3580	Cys-tRNA(Pro)/Cys-tRNA(Cys) deacylase	2.48	5.39E-09
51	PA5535	Hypothetical protein	2.44	3.13E-08
52	PA0179	Two-component system, chemotaxis family, response regulator CheY	2.43	5.44E-06
53	PA1312	Transcriptional regulator	2.42	0.0005
54	PA0431	Hypothetical protein	2.42	0.005
55	PA2039	Hypothetical protein	2.42	0.01
56	PA5115	Hypothetical protein	2.40	0.005
57	gntR	Transcriptional regulator GntR	2.37	4.44E-16
58	PA2162	(1->4)-alpha-D-glucan 1-alpha-D-glucosylmutase	2.37	1.51E-05
59	PA3274	Hypothetical protein	2.36	9.66E-05
60	PA3275	Small multidrug resistance family-3 protein	2.36	0.001
61	PA2150	DNA end-binding protein Ku	2.35	0.0006
62	narH	Respiratory nitrate reductase beta chain	2.34	2.12E-05
63	PA2136	Hypothetical protein	2.33	0.0003
64	nadE	NH3-dependent NAD synthetase	2.32	0.004
65	PA4171	Protease I	2.31	0.003
66	PA0924	Hypothetical protein	2.29	1.81E-09
67	PA3880	Hypothetical protein	2.28	0.0007
68	PA0544	Hypothetical protein	2.27	2.71E-08
69	PA3450	Antioxidant protein	2.25	0.006
70	PA0522	Hypothetical protein	2.25	0.01
71	PA0952	Hypothetical protein	2.20	7.23E-05
72	PA5155	Polar amino acid transport system permease protein	2.20	0.01
73	PA0830	Hypothetical protein	2.19	2.18E-14
74	PA1093	Flagellar protein FlaG	2.19	0.008551
75	PA4064	Putative ABC transport system ATP-binding protein	2.18	0.007
76	cueR	Copper efflux regulator	2.15	0.0002
77	PA0942	Transcriptional regulator	2.14	0
78	PA0515	Heme d1 biosynthesis protein NirD	2.14	8.23E-05
79	PA1020	Acyl-CoA dehydrogenase	2.14	0.007
80	PA4921	Hypothetical protein	2.12	0.0001
81	PA1763	Hypothetical protein	2.12	0.001
82	glpD	Glycerol-3-phosphate dehydrogenase	2.10	1.14E-12
83	PA0121	Hypothetical protein	2.10	2.00E-05
84	PA3172	Phosphoglycolate phosphatase	2.09	6.52E-05
85	PA3859	Phospholipase/carboxylesterase	2.09	0.0005
86	moeA1	Molybdopterin molybdotransferase	2.08	1.48E-06
87	braZ	Branched-chain amino acid:cation transporter	2.08	0.0002
88	PA0218	Transcriptional regulator	2.08	0.0003
89	PA3016	Hypothetical protein	2.08	0.0009
90	PA4357	Ferrous iron transport protein C	2.06	1.11E-12
91	PA3224	Hypothetical protein	2.06	6.11E-07
92	PA1221	Hypothetical protein	2.06	0.002
93	PA3459	Asparagine synthase	2.05	0
94	PA0665	Iron-sulfur cluster insertion protein	2.05	9.64E-12
95	PA0443	Nucleobase:cation symporter-1, NCS1 family	2.05	0.01
96	PA0828	Transcriptional regulator	2.04	0.0009
97	PA1015	Transcriptional regulator	2.04	0.004
98	PA3913	Putative protease	2.03	1.03E-05
99	PA1057	Multicomponent K+:H+ antiporter subunit E	2.03	0.01
100	ohrR	Transcriptional regulator	2.03	0.01
101	PA0177	Purine-binding chemotaxis protein CheW	2.03	0.01
102	PA3847	Hypothetical protein	2.03	0.01
103	PA1470	3-oxoacyl-[acyl-carrier protein] reductase	2.02	0.003
104	PA0911	Hypothetical protein	2.02	0.01
105	PA3070	MoxR-like ATPase	2.01	5.10E-07

Genes are arranged in decreasing order of Fold Change.

Table S2. Node degree score of the genes mentioned in Table S1.

No.	Gene ID/ Symbol	Identifier	Node degree
1	nirS	208964.PA0519	11
2	norB	208964.PA0524	11
3	PA0525	208964.PA0525	10
4	nirQ	208964.PA0520	10
5	norC	208964.PA0523	10
6	nosL	208964.PA3396	10
7	PA0521	208964.PA0521	9
8	nosY	208964.PA3395	9
9	PA3913	208964.PA3913	8
10	PA0515	208964.PA0515	6
11	PA0522	208964.PA0522	6
12	PA2146	208964.PA2146	6
13	narH	208964.PA3874	6
14	anvM	208964.PA3880	5
15	PA0177	208964.PA0177	3
16	PA1763	208964.PA1763	3
17	PA2180	208964.PA2180	3
18	PA3274	208964.PA3274	3
19	ku	208964.PA2150	3
20	nadE	208964.PA4920	3
21	PA0828	208964.PA0828	2
22	algE	208964.PA3544	2
23	moeA1	208964.PA3914	2
24	nuoI	208964.PA2644	2
25	pcaH	208964.PA0153	2
26	PA0179	208964.PA0179	1
27	PA0544	208964.PA0544	1
28	PA0682	208964.PA0682	1
29	PA0830	208964.PA0830	1
30	PA0924	208964.PA0924	1
31	PA0952	208964.PA0952	1
32	PA1015	208964.PA1015	1
33	PA1020	208964.PA1020	1
34	PA1211	208964.PA1211	1
35	PA1221	208964.PA1221	1
36	PA1470	208964.PA1470	1
37	PA1827	208964.PA1827	1
38	PA2136	208964.PA2136	1
39	PA2433	208964.PA2433	1
40	PA3016	208964.PA3016	1
41	PA3224	208964.PA3224	1
42	PA3450	208964.PA3450	1
43	PA3859	208964.PA3859	1
44	PA5071	208964.PA5071	1
45	choE	208964.PA4921	1
46	erpA	208964.PA0665	1
47	glpD	208964.PA3584	1
48	infA	208964.PA2619	1
49	kynB	208964.PA2081	1
50	pcaC	208964.PA0232	1
51	pscL	208964.PA1725	1
52	rluA	208964.PA3246	1

Rest 48 genes with node degree score ‘zero’ are not listed.

Table S3.

Top ten cytoHubba ranked genes from among the top-13 in Table S2

No.	Gene ID	Gene Name	Number of methods ranking this protein among top 10	Names of 12 ranking methods of CytoHubba and rank score provided by them
				Degree	MNC	DMNC	MCC	Bottleneck	EcCentricity	Closeness	Radiality	Betweenness	Stress	CC	EPC
1	PA0525	norD	12	10	10	0.71829	7200	2	0.325	11	1.35417	3.28571	18	0.8	5.793
2	PA0520	nirQ	12	10	10	0.71829	7200	1	0.325	11	1.35417	3.28571	18	0.8	5.68
3	PA3395	nosY	12	9	9	0.69213	5772	1	0.325	10.5	1.3	3.18571	14	0.80556	5.559
4	PA3396	nosL	11	10	10	0.63848	5784	3	0.325	11	1.35417	7.01905	26	-	5.69
5	PA0521	norE	11	9	9	0.73986	6480	-	0.325	10.5	1.3	1.66667	10	0.86111	5.492
6	PA0519	nirS	10	11	11	-	6498	2	0.325	11.5	1.48033	13.4	38	-	5.84
7	PA0524	norB	10	11	11	0.64479	7206	-	0.325	11.5	1.48033	9.11905	34	-	5.917
8	PA0523	norC	9	10	10	0.71829	7200	-	-	11	1.35417	3.28571	18	-	5.572
9	PA0522	Hypothe- tical protein	10	6	6	0.713237	720	1	0.325	9	1.1375			1	4.708
10	PA3913	UbiU	10	7	7	0.621988	726	-	-	9.5	1.191667	1.9	8	0.809524	4.88

ʺ-ʺ: This method did not rank the shown protein among top 12.

MNC: Maximum Neighborhood Component; DMNC: Density of Maximum Neighborhood Component; MCC: Maximal Clique Centrality; CC: Clustering Co-efficient; EPC: Edge Percolated Component

Table S4. List of up regulated genes in Panchvalkal exposed P. aeruginosa satisfying the dual criteria of log fold-change ≥2 and FDR≤0.01.

No.	Feature ID/Gene	Codes for	log fold change	FDR
1	mexC	Membrane fusion protein, multidrug efflux system	16.82	0
2	PA2139	Pseudogene	15	0.01
3	PA3441	Molybdopterin-binding protein	10	0.006
4	PA5328	Mono-heme cytochrome C	8	0.01
5	PA2161	Hypothetical protein	7.22	4.16E-06
6	oprJ	Outer membrane protein, multidrug efflux system	6.91	0
7	PA3383	Phosphonate transport system substrate-binding protein	6.33	0.0006
8	PA0700	Hypothetical protein	5.8	0.003
9	PA2565	Hypothetical protein	5.74	0
10	PA0909	Hypothetical protein	5.4	0.005
11	napB	Cytochrome c-type protein	5.26	1.12E-13
12	PA2090	Hypothetical protein	5.11	0.0004
13	hpcD	5-carboxymethyl-2-hydroxymuconate isomerase	4.8	0.01
14	PA2285	Hypothetical protein	4.8	1.55E-05
15	PA3566	Hypothetical protein	4.75	0.001
16	PA0695	Hypothetical protein	4.42	0.005
17	PA1107	Diguanylate cyclase	4.21	0
18	mexD	Multidrug efflux pump	4.15	0
19	PA2364	Type VI secretion system protein	4.03	1.29E-12
20	coaB	Phage coat protein B	4	0.01
21	PA3442	Sulfonate transport system ATP-binding protein	4	0.004
22	PA4866	Phosphinothricin acetyltransferase	4	0.002
23	PA1231	Hypothetical protein	3.93	0.0002
24	PA4172	Exodeoxyribonuclease III	3.93	5.00E-07
25	PA0640	Bacteriophage protein	3.55	1.84E-07
26	PA2499	Deaminase	3.53	0.002
27	PA1352	Hypothetical protein	3.51	1.34E-05
28	ospR	Transcriptional regulator	3.43	0
29	cdhA	Carnitine 3-dehydrogenase	3.42	0.002
30	PA4908	Ornithine cyclodeaminase	3.31	1.18E-06
31	PA5391	Hypothetical protein	3.21	0.0008
32	PA2307	NitT/TauT family transport system permease protein	3.18	8.69E-05
33	PA5135	Hypothetical protein	2.97	9.42E-06
34	PA2933	large subunit ribosomal protein L6 (rplF; 50S ribosomal protein L6)	2.96	0.0004
35	PA3235	Hypothetical protein	2.96	1.62E-10
36	PA4290	Methyl-accepting chemotaxis protein	2.96	0
37	PA0384	Hypothetical protein	2.92	0.01
38	PA3287	Hypothetical protein	2.92	5.20E-14
39	PA0848	Peroxiredoxin (alkyl hydroperoxide reductase subunit C)	2.9	2.08E-09
40	PA1021	enoyl-CoA hydratase	2.88	0.0007
41	PA2916	Hypothetical protein	2.86	0.009
42	PA0638	Bacteriophage protein	2.84	8.11E-06
43	PA0633	Hypothetical protein	2.84	1.29E-10
44	msuE	FMN reductase	2.75	0.01
45	PA0623	Bacteriophage protein	2.74	9.84E-08
46	soxG	Sarcosine oxidase	2.73	0.002
47	PA0814	Hypothetical protein	2.71	0.01
48	PA2666	6-pyruvoyltetrahydropterin/6-carboxytetrahydropterin synthase	2.7	0.009
49	PA3431	Hypothetical protein	2.7	0.009
50	PA5431	GntR family transcriptional regulator	2.67	1.41E-08
51	PA0941	Hypothetical protein	2.66	0.01
52	PA3757	GntR family transcriptional regulator	2.64	0.01
53	PA2679	Hypothetical protein	2.61	0
54	PA1343	Bacteriophage protein	2.59	1.03E-08
55	mraY	Phospho-N-acetylmuramoyl-pentapeptide-transferase	2.59	1.92E-10
56	PA0622	Bacteriophage protein	2.59	3.12E-11
57	PA0185	Sulfonate transport system permease protein	2.55	1.45E-06
58	PA1260	Polar amino acid transport system substrate-binding protein	2.5	0.01
59	PA4596	Transcriptional regulator	2.5	3.21E-09
60	PA3606	DTW domain-containing protein	2.47	0.0001
61	PA1977	Hypothetical protein	2.47	1.73E-05
62	hutU	Urocanate hydratase	2.46	0
63	mdcC	Malonate decarboxylase delta subunit	2.45	0.009
64	hasD	ATP-binding cassette, subfamily C, bacterial exporter for protease/lipase	2.45	5.11E-08
65	gloA2	Lactoylglutathione lyase	2.44	0.01
66	betT1	Choline/glycine/proline betaine transport protein	2.44	0.0009
67	pslK	Polysaccharide biosynthesis protein PslK	2.44	2.46E-05
68	PA2122	Hypothetical protein	2.43	0.0002
69	ahpC	Peroxiredoxin (alkyl hydroperoxide reductase subunit C)	2.43	0
70	PA3412	Hypothetical protein	2.42	0.01
71	PA3938	Taurine transport system substrate-binding protein	2.41	0.002
72	PA1038	Hypothetical protein	2.4	0.005
73	PA1958	Nicotinamide mononucleotide transporter	2.39	0.0004
74	PA5377	Glycine betaine/proline transport system permease protein	2.39	0.0002
75	PA4093	Hypothetical protein	2.37	0.009
76	PA1518	5-hydroxyisourate hydrolase	2.36	0.01
77	ureD	Urease accessory protein	2.35	0.003
78	PA0118	Hypothetical protein	2.33	0.01
79	mtlD	Mannitol 2-dehydrogenase	2.32	0.01
80	PA5033	Hypothetical protein	2.32	0.001
81	PA3453	Hypothetical protein	2.32	1.43E-07
82	PA2352	Glycerophosphoryl diester phosphodiesterase	2.3	0.0008
83	PA3534	Oxidoreductase	2.3	1.04E-06
84	PA0962	Starvation-inducible DNA-binding protein	2.28	4.83E-09
85	hcnA	Hydrogen cyanide synthase	2.27	0.01
86	mobA	Molybdenum cofactor guanylyltransferase	2.27	0.01
87	PA2111	Hypothetical protein	2.26	6.66E-16
88	PA1922	Outer membrane receptor for ferrienterochelin and colicins	2.25	0.01
89	PA4790	S-adenosylmethionine-dependent methyltransferase	2.25	0.006
90	PA0647	Hypothetical protein	2.25	0.001
91	PA4578	Hypothetical protein	2.25	6.67E-08
92	PA4280.5	16S ribosomal RNA	2.25	0
93	acsA	Acetyl-CoA synthetase	2.24	0
94	PA4508	Lrp/AsnC family transcriptional regulator, leucine-responsive regulatory protein	2.22	0.002
95	PA0098	3-oxoacyl-[acyl-carrier-protein] synthase I	2.21	0.003
96	PA4651	Fimbrial chaperone protein	2.19	0.001
97	xcpT	Type II secretion system protein G	2.17	0.002
98	PA2555	Acetyl-CoA synthetase	2.17	8.88E-16
99	PA0817	Hypothetical protein	2.16	0.01
100	PA2375	Hypothetical protein	2.15	0.003
101	masA	Enolase-phosphatase E1	2.15	2.30E-11
102	PA2826	Glutathione peroxidase	2.14	1.89E-07
103	PA5445	Succinyl-CoA:acetate CoA-transferas	2.13	4.02E-09
104	PA0630	Hypothetical protein	2.11	0.009
105	panD	Aspartate 1-decarboxylase	2.1	0.006
106	PA0557	Hypothetical protein	2.1	0.0009
107	cyoA	Cytochrome o ubiquinol oxidase subunit II	2.09	0.003
108	PA0306	Transcriptional regulator	2.07	3.45E-05
109	oprH	oprH; PhoP/Q and low Mg2+ inducible outer membrane protein H1	2.06	0
110	PA2293	Hypothetical protein	2.05	0.007
111	PA3694	Hypothetical protein	2.05	0.007
112	PA3294	Type VI secretion system secreted protein VgrG	2.05	0.0002
113	rpmD	Large subunit ribosomal protein L30	2.05	4.80E-09
114	PA3289	Hypothetical protein	2.04	0.0009
115	PA5539	GTP cyclohydrolase I	2.03	0.01
116	PA0864	Transcriptional regulator	2.03	0.01
117	PA3420	Transcriptional regulator	2.03	2.15E-06
118	PA3568	Propionyl-CoA synthetase	2.03	2.01E-09
119	ampDh3	N-acetylmuramoyl-L-alanine amidase	2.02	0.008
120	PA3332	Hypothetical protein	2	0.01
121	PA3882	Hypothetical protein	2	0.006
122	PA4824	Hypothetical protein	2	0.002
123	PA4612	Hypothetical protein	2	4.55E-07

Genes are arranged in decreasing order of Fold Change.

Table S5. Node degree score of the genes mentioned in Table S4.

No.	Gene ID / Symbol	Identifier	Node degree
1	PA0185	208964.PA0185	5
2	PA0630	208964.PA0630	5
3	PA3938	208964.PA3938	5
4	ssuB1	208964.PA3442	5
5	PA2090	208964.PA2090	4
6	PA3287	208964.PA3287	4
7	acsA	208964.PA0887	4
8	ahpC	208964.PA0139	4
9	ampDh3	208964.PA0807	4
10	msuE	208964.PA2357	4
11	oprJ	208964.PA4597	4
12	PA0622	208964.PA0622	3
13	PA0695	208964.PA0695	3
14	PA0817	208964.PA0817	3
15	PA0848	208964.PA0848	3
16	PA0909	208964.PA0909	3
17	PA2555	208964.PA2555	3
18	PA3383	208964.PA3383	3
19	PA3568	208964.PA3568	3
20	PA4596	208964.PA4596	3
21	PA4612	208964.PA4612	3
22	PA4824	208964.PA4824	3
23	PA5445	208964.PA5445	3
24	mexC	208964.PA4599	3
25	mexD	208964.PA4598	3
26	oprH	208964.PA1178	3
27	PA0557	208964.PA0557	2
28	PA0623	208964.PA0623	2
29	PA1107	208964.PA1107	2
30	PA1260	208964.PA1260	2
31	PA2307	208964.PA2307	2
32	PA2826	208964.PA2826	2
33	PA5539	208964.PA5539	2
34	cdhA	208964.PA5386	2
35	hcnA	208964.PA2193	2
36	mdcC	208964.PA0210	2
37	napB	208964.PA1173	2
38	PA0098	208964.PA0098	1
39	PA0633	208964.PA0633	1
40	PA0640	208964.PA0640	1
41	PA0700	208964.PA0700	1
42	PA1021	208964.PA1021	1
43	PA1343	208964.PA1343	1
44	PA1922	208964.PA1922	1
45	PA2139	208964.PA2139	1
46	PA2161	208964.PA2161	1
47	PA2285	208964.PA2285	1
48	PA2364	208964.PA2364	1
49	PA2375	208964.PA2375	1
50	PA2666	208964.PA2666	1
51	PA2679	208964.PA2679	1
52	PA3235	208964.PA3235	1
53	PA3420	208964.PA3420	1
54	PA3441	208964.PA3441	1
55	PA3694	208964.PA3694	1
56	PA3882	208964.PA3882	1
57	PA5328	208964.PA5328	1
58	hutU	208964.PA5100	1
59	ospR	208964.PA2825	1
60	pitA	208964.PA4866	1
61	pslK	208964.PA2241	1
62	ureD	208964.PA4864	1

Rest 58 genes with node degree score ‘zero’ are not listed.

Table S6.

Top fourteen cytoHubba ranked genes from among the top-26 in Table S5

No.	Gene ID	Gene Name	Number of methods ranking this protein among top 10	Names of 12 ranking methods of CytoHubba and rank score provided by them
				Degree	MNC	DMNC	MCC	Bottleneck	EcCentricity	Closeness	Radiality	Betweenness	Stress	CC	EPC
1	PA2357	msuE, slfA	11	4	3	0.46346	7	6	0.10256	5.333333	1.27473	9	18	-	5.113
2	PA3442	ssub1	10	4	4	0.47366	12	1	-	5.083333	1.18681	0.5	2	-	5.131
3	PA0185	atsB	9	4	4	0.47366	12	-	-	5.083333	1.18681	0.5	2	-	5.124
4	PA3938	tauA	8	4	-	-	7	2	-	5.333333	1.27473	9	18	-	5.101
5	PA4597	oprJ	11	4	3	0.46346	7	2	0.19231	4	0.52885	6	6	-	3.575
6	PA4599	mexC	11	3	3	0.46346	6	1	-	3.5	0.48077	0	0	1	3.436
7	PA0630		10	4	4	-	8	1	0.19231	4	0.52885	2	4	-	3.827
8	PA0807	ampDh3	10	4	4	-	8	2	0.19231	4	0.52885	2	4	-	3.835
9	PA2090		9	4	4	0.47366	12	-	-	5.083333	1.18681	0.5	2	-	5.094
10	PA3287		9	3		0.46346	6	1	-	-	-	0	0	1	3.456
11	PA4596		9	3	3	0.46346	6	1	-	3.5	0.48077	-	-	1	3.456
12	PA5445		9	3	3	0.46346	6	1	0.15385	-	-	0	0	1	-
13	PA2555		7	3	3	0.46346	6	1	0.15385	-	-	-	-	1	-
14	PA3383		7	-	-	-	-	3	0.153846	5	1.27473	20.5	34	-	4.629

ʺ-ʺ: This method did not rank the shown protein among top 12.

MNC: Maximum Neighborhood Component; DMNC: Density of Maximum Neighborhood Component; MCC: Maximal Clique Centrality; CC: Clustering Co-efficient; EPC: Edge Percolated Component

Table S7. List of DEG in Herboheal-exposed S. aureus satisfying the dual criteria of log fold-change ≥2 and FDR≤0.01.

No.	Feature ID/ Gene	Codes for	log fold change	FDR	Up- or down- regulation
1	sarT	HTH-type transcriptional regulator SarT	18.00	0.005	↑
2	SAFDA_1030	Alpha-hemolysin	17.35	0	↓
3	SAFDA_1326	Hypothetical protein	11.00	0.003	↓
4	SAFDA_0523	Hypothetical protein	10.00	0.006	↓
5	SAFDA_0033	Hypothetical protein	8.50	0.01	↑
6	hlgA	Gamma-hemolysin component A precursor	7.16	2.45E-14	↓
7	SAFDA_1218	Sensor histidine kinase	6.91	8.32E-07	↓
8	SAFDA_1829	Truncated beta-hemolysin	6.75	0.003	↓
9	SAFDA_0271	Pyrimidine nucleoside transporter (nupC)	6.16	2.27E-06	↑
10	SAFDA_1022	Fibrinogen binding-related protein	6.00	6.10E-05	↓
11	SAFDA_1441	Competence protein ComGA	6.00	0.007	↑
12	SAFDA_2337	Hypothetical protein	5.87	0	↓
13	hlgB	Gamma-hemolysin component B precursor	5.83	4.33E-15	↓
14	SAFDA_0231	Hypothetical protein	5.50	0.01	↑
15	SAFDA_1217	ABC transporter permease	5.44	0.0002	↓
16	acuA	Acetoin utilization protein	4.80	0.01	↓
17	icaR	Intercellular Adhesin Locus Regulator	4.80	0.01	↓
18	ureA	Urea catabolic process	4.80	0.01	↑
19	SAFDA_0372	Hypothetical protein	4.67	0.001	↓
20	SAFDA_1187	Hypothetical protein	4.60	0.007	↑
21	saeP	Auxillary protein	4.26	1.77E-07	↓
22	SAFDA_0127	Hypothetical protein	4.25	0.004	↑
23	SAFDA_2543	Hypothetical protein	4.13	2.77E-06	↑
24	saeR	two-component system, OmpR family, response regulator	4.10	1.68E-07	↓
25	SAFDA_0843	HAD superfamily hydrolase	4.00	0.0006	↓
26	glpQ	Glycerophosphoryldiesterphosphodiesterase	3.85	0.0009	↓
27	dapB	4-hydroxy-tetrahydrodipicolinate reductase	3.71	0.01	↓
28	ureD	urease accessory protein	3.66	6.33E-05	↑
29	SAFDA_1182	Phage repressor	3.64	0.004	↓
30	hlgC	Gamma-hemolysin component C precu	3.63	4.79E-08	↓
31	modC	molybdenum transport protein	3.57	1.41E-05	↑
32	SAFDA_1138	50S ribosomal protein L7	3.54	0.002	↓
33	saeS	Two-component system, OmpR family, sensor histidine kinase	3.47	3.31E-10	↓
34	secG	Preproteintranslocase subunit	3.44	0.001	↓
35	splA	Serine protease	3.44	0.001	↓
36	sbi	Immunoglobulin G-binding protein Sbi	3.43	1.02E-09	↓
37	SAFDA_0853	Hypothetical protein	3.38	0.003	↓
38	SAFDA_0003	S4 region YaaA family protein	3.33	0.002	↓
39	SAFDA_1229	Hypothetical protein	3.33	0.01	↓
40	trpA	Tryptophan synthase alpha chain	3.33	0.01	↑
41	SAFDA_1219	Two-component response regulator	3.26	6.13E-06	↓
42	SAFDA_0085	Hypothetical protein	3.22	0.001	↑
43	SAFDA_0794	Hypothetical protein	3.22	0.001	↑
44	SAFDA_0277	Hypothetical protein	3.18	0.002	↑
45	SAFDA_1494	HAD superfamily hydrolase	3.14	0.005	↓
46	SAFDA_1538	Hypothetical protein	3.13	0.004	↑
47	SAFDA_0193	Hypothetical protein	3.05	0	↓
48	SAFDA_0562	Hydrolase	2.93	0.007	↓
49	SAFDA_1410	Hypothetical protein	2.93	2.62E-07	↓
50	SAFDA_2187	Phosphosugar-binding transcriptional regulator	2.92	0.01	↑
51	SAFDA_t0025	tRNA-Cys	2.88	0.007	↓
52	spsB	signal peptidase I	2.88	0.007	↓
53	SAFDA_0228	Choloylglycine hydrolase	2.87	0.007	↑
54	glpP	Glycerol uptake operon antiterminator regulatory protein	2.85	0.01	↑
55	lukG	leukocidin/hemolysin toxin family protein	2.80	9.14E-06	↓
56	SAFDA_2043	Hypothetical protein	2.78	6.55E-10	↓
57	SAFDA_r0007	5S ribosomal RNA	2.71	0	↑
58	coaE	dephospho-CoA kinase	2.71	0.0008	↑
59	SAFDA_0232	Hypothetical protein	2.71	0.01	↑
60	pnp	Polyribonucleotide nucleotidyltransferase	2.71	4.39E-08	↑
61	SAFDA_2297	Hypothetical protein	2.66	0.01	↑
62	SAFDA_2405	MmpL efflux pump	2.66	9.41E-09	↑
63	SAFDA_0565	Alpha/beta fold family hydrolase	2.66	6.40E-11	↑
64	SAFDA_2223	ABC transporter permease	2.64	0.01	↑
65	SAFDA_0423	Orn Lys Arg decarboxylase family protein	2.63	1.62E-08	↑
66	SAFDA_2221	Hypothetical protein	2.63	0.008	↑
67	tagX	glycosyltransferase	2.58	0.002	↓
68	sraP	Serine-rich adhesin for platelets	2.55	7.48E-10	↑
69	SAFDA_2160	Transcription regulator	2.55	0.0003	↑
70	SAFDA_0932	Hypothetical protein	2.53	1.10E-05	↓
71	SAFDA_1828	Truncated cell surface protein map-w	2.52	7.00E-07	↓
72	saeQ	transmembrane protein	2.47	0.001	↓
73	drm	Phosphopentomutase	2.45	0	↑
74	SAFDA_0392	Cobalamin synthesis protein	2.45	0.01	↑
75	SAFDA_2310	Amino acid transporter	2.43	0.008	↑
76	SAFDA_2453	2-dehydropantoate 2-reductase	2.43	0.002	↑
77	sarA	Transcriptional regulator SarA	2.43	2.11E-07	↑
78	SAFDA_1135	Hypothetical protein	2.43	0.003	↓
79	nreA	NreA protein; GAF domain containing protein	2.41	0.005	↓
80	gcvH	Glycine cleavage system H protein	2.33	0.0004	↓
81	sdaAB	L-serine dehydratase	2.32	0.006	↓
82	recQ_1	ATP-dependent DNA helicase RecQ	2.32	4.68E-06	↑
83	SAFDA_2273	Polar amino acid ABC transporter ATPase	2.31	0.003	↓
84	icaA	intercellular adhesion (ica) locus	2.30	0.009	↑
85	SAFDA_0225	Ribose transcriptional repressor RbsR	2.28	0.001	↑
86	SAFDA_2537	Lipoprotein, putative	2.26	0.01	↑
87	rpsO	Small subunit ribosomal protein S15	2.26	9.03E-06	↓
88	SAFDA_1759	Sugar ABC transporter ATPase	2.26	2.72E-07	↓
89	SAFDA_0987	Hypothetical protein	2.25	0.009	↓
90	SAFDA_2261	Transcriptional regulator NirR	2.25	0.009	↓
91	SAFDA_0998	Iron-regulated heme-iron binding protein	2.25	0.001	↑
92	SAFDA_1045	HAD superfamily hydrolase	2.25	0.009	↑
93	xerC	Integrase/recombinase	2.24	0.005	↓
94	SAFDA_2230	Glycosylglycerophosphatetransferase involvedin teichoic acid biosynthesis	2.22	0.003	↑
95	pheT	Phenylalanine--tRNA ligase beta subunit	2.22	1.75E-07	↑
96	SAFDA_2057	Alcohol dehydrogenase	2.21	5.04E-08	↑
97	SAFDA_0091	Major facilitator transporter	2.21	6.48E-14	↑
98	tcaB	teicoplanin-associated operon	2.20	0.001	↑
99	dra	Deoxyribose phosphate aldolase	2.19	5.62E-11	↑
100	SAFDA_1716	Hypothetical protein	2.18	0.006	↓
101	Dps	General stress protein 20U	2.17	0	↓
102	SAFDA_2462	Hypothetical protein	2.14	0.007	↑
103	SAFDA_0189	ABC transporter substrate-binding protein	2.11	0.009	↑
104	Geh	Glycerol ester hydrolase	2.11	6.59E-06	↓
105	rpoE	Probable DNA-directed RNA polymerase subunit delta	2.10	0.001	↑
106	SAFDA_1657	Aesenical pump membrane protein	2.10	0.005	↑
107	rpsB	Small subunit ribosomal protein S2	2.09	3.60E-11	↓
108	SAFDA_2315	M42 glutamylaminopeptidase, cellulose	2.04	4.16E-10	↑
109	ureC	Urease subunit alpha	2.03	0.0008	↑
110	mviM	putative oxidoreductase	2.02	0.01	↑
111	SAFDA_1331	Major facilitator superfamily permease	2.02	0.01	↑
112	dapA	4-hydroxy-tetrahydrodipicolinate synthase	2.01	0.001	↑
113	SAFDA_2229	L-lactate permease	2.00	3.68E-09	↓

Genes are arranged in decreasing order of Fold Change.

Table S8. Node degree score of up-down regulated genes mentioned in Table S7.

No.	Gene symbol	Identifier	Node degree
1	sbi	1280.SAXN108_2673	7
2	saeR	1280.SAXN108_0774	6
3	hlgB	1280.SAXN108_2677	5
4	hlgC	1280.SAXN108_2676	5
5	pheT	1280.SAXN108_1134	5
6	saeS	1280.SAXN108_0773	5
7	sarA	1280.SAXN108_0683	5
8	icaA	1280.SAXN108_2939	4
9	splA	1280.SAXN108_1846	4
10	lip2	1280.SAXN108_0305	3
11	pnp	1280.SAXN108_1278	3
12	rpsB	1280.SAXN108_1258	3
13	rpsO	1280.SAXN108_1277	3
14	icaR	1280.SAXN108_2938	2
15	ureA	1280.SAXN108_2536	2
16	ureC	1280.SAXN108_2538	2
17	ureD	1280.SAXN108_2542	2
18	coaE	1280.SAXN108_1714	1
19	dapA	1280.SAXN108_1411	1
20	dapB	1280.SAXN108_1412	1
21	sarT	1280.SAXN108_2745	1
22	tagX	1280.SAXN108_0708	1
23	trpA	1280.SAXN108_1389	1

Rest 5 genes with node degree score ‘zero’ are not listed.

Table S9. Top twelve cytoHubba ranked genes from among the top-13 in Table S8.

No.	Gene ID	Gene Name	Number of methods ranking this protein among top 10	Names of 12 ranking methods of CytoHubba and rank score provided by them
				Degree	MNC	DMNC	MCC	Bottleneck	EcCentricity	Closeness	Radiality	Betweenness	Stress	CC	EPC
1	SAXN108_0683	sarA	11	4	3	0.46346306	7	1	0.346154	6	2.076923	7.4	18	-	4.534
2	SAXN108_2673	sbi	12	7	7	0.40246305	38	4	0.346154	7.5	2.336538	13.266667	28	0.52381	5.374
3	SAXN108_1846	splA	11	4	4	-	8	1	0.346154	6	2.076923	2.8	8	0.666667	4.527
4	SAXN108_0774	saeR	11	5	5	0.51861011	30	-	0.346154	6.5	2.163462	2.1333333	8	0.8	4.973
5	SAXN108_0773	saeS	11	5	5	0.51861011	30	-	0.346154	6.5	2.163462	2.1333333	8	0.8	4.943
6	SAXN108_2677	hlgB	11	5	5	0.51861011	30	1	-	6.333333	2.076923	1.3333333	4	0.8	4.876
7	SAXN108_2676	hlgC	11	5	5	0.51861011	30	1	-	6.333333	2.076923	1.3333333	4	0.8	4
8	SAXN108_1278	pnp	11	3	3	0.46346306	6	1	0.307692	3	0.512821	0	0	1	-
9	SAXN108_0305	lip2	8	3	-	-	-	2	0.346154	5.5	1.990385	4.6	12	-	3.953
10	SAXN108_1134	pheT	7	3	3	0.46346306	6	1	0.307692	-	-	-	-	1	-
11	SAXN108_1277	rpsO	7	-	3	0.46346306	6	1	0.307692	-	-	-	-	1	2.315
12	SAXN108_2939	icaA	6	-	-	-	-	1	-	4.666667	1.730769	1	2	-	3.092

ʺ-ʺ: This method did not rank the shown protein among top 12.

NC: Maximum Neighborhood Component; DMNC: Density of Maximum Neighborhood Component; MCC: Maximal Clique Centrality; CC: Clustering Co-efficient; EPC: Edge Percolated Component