Whole-transcriptome analysis reveals mechanisms underlying antibacterial activity and biofilm inhibition by a malic acid combination (MAC) in Pseudomonas aeruginosa

Malic acid combination (MAC) formulation

All organic acids were procured from Psaitong Beijing, China, in a solid form. The initial concentration of the MAC was used and designated as “C”. The composition of the MAC mixture at concentration C is 12 mg/mL malic acid, 2 mg/mL citric acid, 1 mg/mL glycine, and 3 mg/mL hippuric acid. The stock solution was prepared by the two-fold dilution method in eight different concentrations of 4C, 2C, C, 0.5C, 0.25C, 0.125C, 0.0625C, and 0.03125C.

Bacterial strain, growth media, and culture conditions

The antibacterial efficacy of the MAC was tested using four types of bacteria strains, including Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, and Serratia marcescens BNCC 107931. The bacterial strains utilized in this study were sourced from Dr. Aloysius Wong and Dr. Bo Zhang at Wenzhou-Kean University, China. These strains were initially procured from the public culture collection center. All bacterial cell lines were cultured and propagated in two mL Luria-Bertani broth (LB; Qingdao Hope Bio-Technology Co., Ltd., Qingdao, China) and incubated at 37 °C with 200 rpm.

Assessment of the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC)

The minimum inhibitory concentration (MIC) of the MAC was assessed using the sterile 96-well polystyrene microtiter plate according to the previously described method (Wiegand, Hilpert & Hancock, 2008). The logarithmic growth phase of the bacterial broth was adjusted to a concentration of 0.5 McFarland turbidity standard (1 × 10⁸–2  × 10⁸ CFU/mL). Approximately 5  × 10⁴ CFU in 3 µL bacterial suspension was added to 180 µL LB broth medium. Subsequently, 20 µL serial two-fold dilutions of the MAC in concentrations in the order of 4C to 0.03125C were added to columns 3–10. A volume of 200 µL of LB broth medium was introduced into column 1 to serve as the negative control. The positive control, designated as column 2, solely consisted of LB broth subsequently introduced with bacteria. The optical density (OD) was determined at 600 nm using a Varioskan Flash microplate reader (Thermo Fisher Scientific) after 3 h, 6 h, 9 h, 12 h, and 24 h of incubation at 37 °C with 200 rpm. The OD for each replicate well at negative control was subtracted from the OD of the same replicate well at different times. The MIC endpoint is determined as the minimum concentration of the MAC at which there is no observable growth in the test tubes. The growth inhibition at each MAC dilution was measured using the formula: % growth = (OD test well−OD negative control well/OD of corresponding positive control well−OD negative control well/)×100%. In addition, the BIOMYC-3 antibiotic solution (antibiotic ciprofloxacin) (Biological Industries, Kibbutz Beit-Haemek, Israel) was used as a positive control at a concentration of 0.01 mg/mL, and the same volume as MAC under its MIC was incubated with P. aeruginosa at 37 °C and 200 rpm for 24 h.The OD value was measured at 600 nm using a Varioskan Flash microplate reader (Thermo Fisher Scientific, Waltham, MA, USA).

The minimum bactericidal concentration (MBC) refers to the lowest concentration of antibacterial agent that kills the test bacteria. After determination of the MIC of the MAC, 50 µL aliquots of all tubes with no visible bacterial growth were inoculated onto LB agar plates (Qingdao Hope Bio-Technology Co., Ltd., Qingdao, China) and incubated at 37 °C for 24 h. The MBC endpoint was identified as the lowest concentration of the MAC at which no visible colonies were observed. All experiments were repeated in triplicate.

Assessing bacterial ultrastructural alterations through TEM

To explore the bactericidal mechanism of the MAC, TEM was employed to assess ultrastructural damage and membrane integrity in P. aeruginosa (Bouhdid et al., 2010). The bacterial species were subjected to the MIC (0.2C) concentration, and four groups were formed including two treatment groups and two control groups. Initially, bacterial suspensions were treated with 2.5% glutaraldehyde and fixed for 24 h at 4 °C. The fixed cells were harvested by centrifugation at 4,000× g for five minutes and washed with 0.1M PBS (pH = 7.2) thrice. Gradually increasing concentrations of ethanol ranging from 30%, 50%, 70%, 80%, 90% to 95% were used for dehydration of the samples for 15 min, followed by 100% ethanol for 20 min. All samples were fixed with pure acetone for 20 min. Samples were subjected to a mixture of embedding medium and acetone (V/V = 1/1) for 1 h; samples were then treated with a mixture of embedding medium and acetone (V/V = 3/1) for 3 h; samples were treated overnight with the pure embedding medium. The infiltration-treated samples were embedded and heated overnight at a temperature of 70 °C to acquire the embedded samples. The samples were sectioned using an ultramicrotome (HT7800; Leica, Ltd., Wetzlar, German) to obtain sections ranging from 70–90 nm, which were then stained with lead citrate solution and 2% uranyl acetate solutions for a period of 10 min each. The samples were dried and examined using a transmission electron microscope (HT7800; Hitachi, Ltd., Tokyo, Japan). The images were captured using an accelerating voltage of 10.0 kV under high vacuum conditions.

Biofilm inhibition assay and SEM analysis

The biofilm inhibition assay of the MAC was conducted in the sterile 96-well polystyrene microtiter plate as described previously (Macia, Rojo-Molinero & Oliver, 2014). Different concentrations (4C to 0.125C) of the MAC were loaded into wells containing 180 µL of Tryptic Soy Broth (TSB; Qingdao Hope Bio-Technology Co., Ltd, Qingdao, China). Then, 3 µL of bacterial suspension containing approximately 5  × 10⁴ CFU within the logarithmic growth phase was added to each well, and the plate was incubated at 37 °C for 24 h. Distilled water was used as a control for cells treated. After 24 h, the plate was inverted, and the contents of each well were decanted without disturbing the biofilm. The excess planktonic cells were removed using 0.1 M phosphate-buffered saline (PBS, pH = 7.3). The biofilm adhering to each well was fixed with 99% methanol for 20 min, washed once with PBS, and stained with 1% aqueous crystal violet (CV) solution for 20 min. The unbound crystal violet stain was then gently rinsed with PBS. The plate was air-dried at room temperature for 1 h, and OD was measured at 570 nm using a Varioskan Flash microplate reader (Thermo Scientific, China). The experiments were performed in 3 biological replicates, each with 4 technical replicates.

To evaluate the biofilm biomass and observe alterations in biofilm structure following treatment with the MAC, SEM analysis was performed on P. aeruginosa. Briefly, P. aeruginosa cultures were incubated untreated for 6 h to allow biofilm formation on the tube wall. The cells were then treated with the MIC (0.2C) of the MAC and subsequently incubated at 37 °C for 24 h, in comparison to control samples. The content was decanted slightly to prevent damage to the biofilm, and bacterial cells were fixed with 2.5% glutaraldehyde and dehydrated by filtration in an ethanol solution. The samples were then fixed with a 1% osmic acid solution for 2 h, and the osmic acid waste solution was carefully removed. Samples were rinsed three times, for 15 min each time, with 0.1M PBS (pH = 7.3). The samples were dehydrated in a gradient of 30%, 50%, 70%, 80%, 90%, and 95% ethanol for 15 min each, followed by two 20-minute washes in 100% ethanol. The biofilms were then critical point dried (K850; Quorum Technologies Ltd., Lewes, UK) and sputter-coated with gold using an ion sputtering apparatus (MC1000; Hitachi, Ltd., Tokyo, Japan). SEM micrographs were acquired using a Field emission scanning electron microscope (SU8010; Hitachi, Ltd., Tokyo, Japan). The images were obtained by subjecting them to high vacuum conditions with an accelerating voltage of 3.0 kV.

RNA extraction, library construction and sequencing

Analysis was conducted using three MAC-treated P. aeruginosa cells samples (treatment group) and three untreated P. aeruginosa cell samples as controls. P. aeruginosa was exposed to using sub-MIC (0.1 C) of the MAC for 24 h. Total RNA from each sample was performed using the Trizol Reagent (Qiagen, Hilden, Germany). Each sample group has three biological replicates. The concentration and purity of the obtained total RNA were determined using the Nanodrop 2000 (Thermo Fisher Scientific Inc., Waltham, MA, USA), while the RNA integrity was evaluated through the use of the Agilent 2200 Bioanalyzer (Agilent Technologies, USA).

RNA with high quality was used for library construction. The QIAseq FastSelect − 5S/16S/23S Kit (Qiagen, Valencia, CA, USA) was employed to eliminate ribosomal RNA from the total RNA. The resulting RNA was fragmented and reverse-transcribed to generate the first strand of cDNA. The synthesis of the first strand cDNA involved the use of random primers and Actinomycin D. Subsequently, the double-stranded cDNA was purified and subjected to end repair and dA-tailing in a single reaction, followed by T-A ligation to add adaptors to both ends. Size selection of the adaptor-ligated DNA was performed using beads to isolate fragments of approximately 400 bp. Polymerase Chain Reaction (PCR) amplification was carried out for each sample using P5 and P7 primers, followed by bead-based clean-up. Validation was performed using a Qsep100 (Bioptic, Taiwan, China), and quantification was conducted using a Qubit3.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). All libraries were sequenced on the Illumina Novaseq 6000 platform using a 2  × 150 paired-end strategy.

Data processing, differential gene expression and functional enrichment analysis

All raw data generated by sequencing were filtered by Cutadapt (v 1.9.1) (Martin, 2011) to remove contamination and low-quality data such as adapter sequence and quality of base less than 20%. To examine the quality of our sequencing data, all clean readswere remapped onto the reference genome of P. aeruginosa ATCC 27853 (NCBI reference sequence, GCF_001687285.1) by using Bowtie2 (version: 2.2.6) using default parameters (Langmead & Salzberg, 2012). Transcripts in FASTA format were derived from a pre-existing GFF annotation file and appropriately indexed. The indexed file was used as a reference gene file for HTSeq (version: 0.6.1) (Anders, Pyl & Huber, 2015) to estimate gene expression levels from the clean pair-end data. Differential expression analysis was performed using the DESeq2 Bioconductor package (Love, Huber & Anders, 2014), which compared gene expression levels between the control group and the treatment group to identify differentially expressed genes (DEGs). Genes with —log2 (Fold Change)— ≥ 1 and Padj <0.05 were considered as DEGs. DEGs with a log2(fold change) ≥ 1 were considered upregulated genes, whereas DEGs with a log2(fold change) ≤ −1 were considered downregulated genes. The Gene Ontology (GO) enrichment analysis was conducted using GOSeq software (version: 1.34.1) (Young et al., 2010) with Gene Ontology Resource (Ashburner et al., 2000). GO terms with Padj < 0.05 were considered significant. Pathway enrichment analysis was performed using KOBAS software (version: 2.0) (Xie et al., 2011), treating Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa & Goto, 2000) pathways as individual units. Pathway with Padj < 0.05 was considered significant.

Real-time polymerase chain reaction (RT-PCR)

To validate the list of DEGs, RT-PCR analysis was performed on a random set of DEGs and functionally relevant genes. The primer pairs used for this analysis are listed in Table S1. In order to normalize the mRNA levels, the rpoD housekeeping gene was used as the control (Savli et al., 2003). Total RNA extraction was performed using Trizol reagent, followed by reverse transcription into cDNA using the PrimeScript RT Master Mix reagent kit from Takara (Japan). RT-PCR was performed using the SYBR Green Master Mix (Yeasen Bio-Technology Co., Ltd, Shanghai, China) and the Applied Biosystems QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems, Waltham, MA, USA).

Statistical analysis

Statistical results of the antibacterial rate and their growth curves were quantitatively expressed as mean ± SEM (Standard Error of the Mean) based on three independent biological replicates. The differences between the groups’ treatment and control were operated by unpaired t-test and were considered statistically significant at P < 0.05. Statistical analysis of the antibacterial rate, the growth curve, and biofilm inhibition of P. aeruginosa were performed using GraphPad Prism 9.0 (GraphPad Software Inc., La Jolla, CA, USA).

The MAC exhibits antibacterial activity and alters the morphology of bacteria

To evaluate the efficacy of the MAC against four prevalent pathogenic bacteria (E. coli, S. aureus, P. aeruginosa, and S. marcescens), minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were performed. The findings of the investigation revealed that all the tested bacterial strains exhibited comparable MIC and MBC values (Table 1), which were determined to be 0.2C–0.4C (the variable denoted by “C” was established by the initial concentration of the MAC). The results demonstrated the antimicrobial efficacy of the MAC, which was found to inhibit the growth of all four bacterial species tested at various incubation periods (Figs. 1A–1D). Notably, a reduction in bacterial growth was observed after 24 h of exposure to the MAC (Figs. 1E–1H). In addition, in order to investigate the difference in the effects of MAC and common antibiotics on P. aeruginosa, 0.01 mg/mL of ciprofloxacin was used as a control. After 24 h of incubation, the antibacterial effect of 0.2C MAC and ciprofloxacin was found to be comparable significantly inhibiting bacterial growth (Fig. S1). The ultrastructural changes of P. aeruginosa cells were evaluated using TEM. The untreated cells displayed well-formed, clearly defined cell walls and membranes, as well as uniformly dense cytoplasm (Figs. 1I–1J). Conversely, bacterial cells subjected to the MAC demonstrated significant cell wall and membrane impairments, accompanied by substantial intracellular material leakage (Figs. 1L–1N). These impairments caused alterations in the morphology of bacterial cells, which might lead to cell death.

The MAC can reduce biofilm formation

To evaluate the inhibitory potential of the MAC on biofilm formation in P. aeruginosa, we performed biofilm formation assays and SEM analysis. Our assays showed that all concentrations inhibited P. aeruginosa biofilm formation compared to the untreated control (Fig. 2A). The biofilm formation was significantly inhibited after the MAC treatment using 0.4C and 0.2C of the MAC. Our SEM analysis exposed a decrease in biofilm density after the 0.2C treatment (Fig. 2B). As shown in Fig. 2B, bacteria in control groups have wire-like structures, which potentially an extracellular DNA (eDNA) or matrix fibers (Alhede et al., 2014), that are the components of a biofilm matrix and useful for providing space for bacterial living and moving (Panlilio & Rice, 2021). In the treatment groups, the MAC drug cocktail can reduce the connections of those structures in P. aeruginosa, indicating that the MAC may be potential to directly act on the eDNA leading to denaturation and deactivation and affecting the synthesis of matrix fibers to reduce their connections. These findings also suggest that the MAC exhibits a considerable ability to effectively mitigate biofilm formation and potentially disrupt bacteria-bacteria interactions in P. aeruginosa.

Whole-transcriptome sequencing and analysis

Illumina sequencing platform generated 103.3 million paired-end raw reads in three control group samples, and 102.9 million paired-end raw reads in three MAC-treated sample groups (Table S2). After preprocessing the raw reads (the removal of low-quality reads), we had 14.9 GB (or 102.7 million of paired-end clean reads) and 15 GB (102.6 million paired-end clean reads) of data in the control and treatment groups, respectively (Table S3). The overall Q20 percentage was above 96%, the overall Q30 percentage was above 91%, and GC content was approximately 58% both in two sample groups. We also remapped all reads onto the reference genome sequence of P. aeruginosa ATCC 27853. At least 97.8% of total reads mapped to the reference genome for each sample from two groups with unique read mapping rate of at least 95% above (Table S4). These results indicate the high quality of the clean reads and are suitable for downstream analyses.

Identification of differentially expressed genes (DEGs)

Before comparing transcriptomic profiles of the MAC treatment and the control groups. Principal component analysis (PCA) and sample normalization were performed for all samples (Fig. S2). PCA analysis result indicates a significant difference in gene expression levels between the two groups, providing evidence that the MAC treatment had an impact on the gene expression of P. aeruginosa. The normalized data indicates that the normalization of six samples worked well, and the data was suitable for comparison and differential expression analysis. By comparing the transcriptomic profiles of the two groups, we identified 1,093 differentially expressed genes (DEGs) consisting of 659 upregulated genes and 434 downregulated genes (Fig. 3A and Table S5). Among the 434 downregulated genes in the MAC treatment group, we found 62 genes displaying fold changes greater than four folds. Notably, a diverse range of enzyme-related activities was found among the highly downregulated genes, encompassing aldol/keto reductases (ACG06_RS17845, fold change (FC) = 9.25), putative hydro-lyases (ACG06_RS15915, FC = 7.96), ribose-phosphate pyrophosphokinase (ACG06_RS12730, FC = 7.35), betaine-aldehyde dehydrogenase (ACG06_RS30685, FC = 7.28), FMN-dependent NADH-azoreductase AzoR2 (ACG06_RS16675, FC = 7.27), uroporphyrinogen-III C-methyltransferase (ACG06_RS02660, FC = 6.38), tryptophan synthase subunit beta (ACG06_RS00215, FC = 6.37), MBL fold metallo-hydrolase (ACG06_RS12740, FC = 6.36), NADP-dependent glyceraldehyde-3-phosphate dehydrogenase (ACG06_RS14485, FC = 6.32), and various others. Moreover, an assortment of genes associated with transporters and translocators were also identified, such as LysE family translocators (ACG06_RS10390, FC = 7.13), multidrug resistance MFS transporters (ACG06_RS04790, FC = 10.68), and components of the type III secretion system (ACG06_RS18070, 18090, 18005, 17955, 18045, 18035, 18055; fold change are all greater than four times). Conversely, upregulated genes encompassed peroxiredoxins, known for their involvement in safeguarding cells against oxidative stress, along with several tRNA molecules crucial for protein synthesis (ACG06_RS28405, 05490, 17535, 26955, 27425, 08615, 22155; fold change are all greater than four times), namely tRNA-Leu, tRNA-Met, tRNA-Asp, and tRNA-Glu. Furthermore, the results revealed that the gene expression patterns among the samples following treatment with the MAC were consistent (Fig. 3B), indicating that the treatment might target specific biological processes or pathways within P. aeruginosa.

Gene ontology (GO) enrichment analysis

To gain insights into the functions of the DEGs to elucidate the antibacterial mechanisms of the MAC, we performed gene ontology (GO) term enrichment analyses separately on the sets of upregulated and downregulated differentially expressed genes (DEGs). The 30 most significantly enriched upregulated and downregulated GO terms were plotted (Figs. 4A, 4B). The upregulated genes enriched in the identified DEGs related to bacterial production capacity, specifically translation and ribosomes, suggesting increased protein synthesis activity. Additionally, the upregulation of genes involved in the catabolic processes of tyrosine and L-phenylalanine, tricarboxylic acid cycle, and oxidative phosphorylation indicated an increased energy production and metabolism, which likely contributed to the bacterial adaptation and survival strategies. On the contrary, the downregulated DEGs exhibited distinct functional patterns. Among the biological processes, the most significantly downregulated pathway was associated with biosynthetic and catabolic processes, including urea catabolic process, heme biosynthetic process, porphyrin-containing compound biosynthetic process, coenzyme biosynthetic process, and arginine catabolic process to succinate, with 58% of the genes assigned to these processes. Moreover, approximately 50% of the downregulated genes were associated with protein secretion, predominantly involving genes related to the type III secretion system, probably impacting bacterial pathogenesis and virulence. Regarding the molecular function, the most highly downregulated genes were mainly assigned to energy metabolism, such as cobalamin—transporting ATPase activity, GTPase activator activity, electron carrier activity, oxidoreductase activity, Oxidoreductase activity, potassium—transporting ATPase activity, suggesting potential disruptions in these vital activities. Additionally, to better understand the enriched GO terms identified in the GO analysis, we conducted directed acyclic graph (DAG) analyses to generate DAG diagrams representing the molecular function, cellular component, and biological process (Figs. S3–S8).

KEGG pathway enrichment analysis

To investigate the biological pathways regulated by the MAC, KEGG pathway enrichment analyses on the sets of upregulated and downregulated genes were performed (Figs. 4C, 4D). The exposure to the MAC had a compensatory effect on bacterial production capacity, likely aimed at preserving cellular homeostasis. Notably, a subset of genes associated with ribosome formation and aminoacyl-tRNA biosynthesis displayed significant upregulation (Fig. S9). Ribosomes are integral to protein synthesis, and their upregulation enables bacteria to generate an increased quantity of proteins. This augmented protein synthesis capability is pivotal for repairing damaged cellular components, synthesizing stress-related proteins, and sustaining essential cellular functions in demanding conditions (Cheng-Guang & Gualerzi, 2021). The intensified biosynthesis of aminoacyl-tRNAs, which ensures accurate attachment of amino acids to their corresponding tRNA molecules, aids bacteria in maintaining fidelity in protein synthesis and adapting to the fluctuating environment (Laursen et al., 2005). On the contrary, our results suggested that the MAC inhibited the growth of P. aeruginosa by suppressing various bacterial activities involved in metabolism (metabolic pathways, riboflavin metabolism, purine metabolism, pentose phosphate pathway, nitrogen metabolism, nicotinate and nicotinamide metabolism, microbial metabolism in diverse environments, galactose metabolism, fructose and mannose metabolism, ether lipid metabolism, arginine and proline metabolism, and arginine and proline metabolism), biosynthesis (streptomycin biosynthesis, polyketide sugar unit biosynthesis, lysine biosynthesis, glycolysis/gluconeogenesis, carbapenem biosynthesis, and arginine biosynthesis), biodegradation (nitrotoluene degradation, naphthalene degradation, and atrazine degradation), and transportation (bacterial secretion system and phosphotransferase system). Notably, the MAC downregulated the expression of almost all genes associated with the Type III secretion system, porphyrin metabolism, and two-component system (Fig. S10). These findings highlight the comprehensive inhibitory effects of the MAC on various cellular processes that are essential for bacterial growth, survival, and pathogenesis.

Validation of DEGs by real-time RT-PCR

To validate the list of DEGs identified in our RNA-Seq analysis, RT-PCR was utilized to assess the expression levels of nine randomly selected DEGs. Our RT-PCR results showed that the expression level of ahpC, feoA, gatC, cobU, and pfeR were upregulated, while the expression levels of pscF, pscP, glcF, cobA, and azoR2 were downregulated. The qRT-PCR and RNA-Seq data exhibit a high degree of concordance (R² = 0.82), suggesting that they are correlated and providing support to the list of DEGs identified in our RNA-Seq analysis (Fig. 5).