Use of a combined antibacterial synergy approach and the ANNOgesic tool to identify novel targets within the gene networks of multidrug-resistant Klebsiella pneumoniae

ABSTRACT Since the 1980s, the development of new drug classes for the treatment of multidrug-resistant Klebsiella pneumoniae has become limited, highlighting the urgent need for novel antibiotics. To address this challenge, this study aimed to explore the synergistic interactions between chemical compounds and representative antibiotics, such as carbapenem and colistin. The primary objective of this study was not only to mitigate the adverse impact of multidrug-resistant K. pneumoniae on public health but also to establish a sustainable balance among humans, animals, and the environment. Phenotypical measurements were conducted using the broth microdilution technique to determine the drug sensitivity of bacterial strains. Additionally, a genotypical approach was employed, involving traditional RNA sequencing analysis to identify differentially expressed genes and the computational ANNOgesic tool to detect noncoding RNAs. This study revealed the existence of various pathways and regulatory RNA elements that form a functional network. These pathways, characterized by the expression of specific genes, contribute to the combined treatment effect and bacterial survival strategies. The connections between pathways are facilitated by regulatory RNA elements that respond to environmental changes. These findings suggest an adaptive response of bacteria to harsh environmental conditions. IMPORTANCE Noncoding RNAs were identified as key players in post-transcriptional regulation. Moreover, this study predicted the presence of novel small regulatory RNAs that interact with target genes, as well as the involvement of riboswitches and RNA thermometers in conjunction with associated genes. These findings will contribute to the discovery of potential antimicrobial therapeutic candidates. Overall, this study offers valuable insights into the synergistic effects of chemical compounds and antibiotics, highlighting the role of regulatory RNA elements in bacterial response, and survival strategies. The identification of novel noncoding RNAs and their interactions with target genes, riboswitches, and RNA thermometers holds promise for the development of antimicrobial therapies.

Among these, Sequence Type 11 K56 is a multidrug-resistant strain that lacks carbapene mase production (3), and no colistin-related resistance genes have been detected at the DNA level (Table S1) (4).K56 shares a vertical gene transfer relationship with K26 and is susceptible to both meropenem and colistin.K56 also exhibits adaptive resistance under stress exposure, such as harsh environmental conditions (5).
Meropenem, a broad-spectrum β-lactam antibiotic, covalently binds to penicillinbinding proteins and disrupts cell wall synthesis.Colistin, a polymyxin E antibiotic, interacts with lipopolysaccharides and phospholipids, leading to outer membrane disruption, increased cell permeability, and subsequent cell lysis and death (6).However, colistin monotherapy often causes adverse effects, such as nephrotoxicity and neurotox icity (7).The combination of colistin with existing antibiotics presents a potential solution against multidrug-resistant Gram-negative bacteria (8).
Previous studies have investigated the properties and mechanisms of K56 in response to meropenem treatment while also exploring colistin-related resistance mechanisms (3).Through transcriptome analysis, complementation, and knockout techniques, these studies have identified both agonists (downregulated genes) and antagonists (upregu lated genes) associated with meropenem treatment.However, certain barriers have hindered the ability to create new drugs and selectively knock out genes for therapeutic use.
Combination therapy is emerging as a strategy to minimize the detrimental impact of diseases on global public health by preventing transmission among humans, animals, and the environment.Repurposing or partially repurposing existing drugs offers a novel approach with a broad spectrum of action, reduced likelihood of development of resistance compared with monotherapy, and effectiveness against diverse strains (9).Moreover, the use of low therapeutic doses further minimizes side effects while maintaining practical efficacy.
In addition to the phenotypic approach, a genotypic approach can be employed to unveil the hidden aspects of the K56 response.Transcriptome analysis has enabled the discovery of differentially expressed genes (DEGs) involved in antibiotic resistance and therapeutic mechanisms.For extensive investigation, ANNOgesic was used to analyze the characteristics of K56 after exposure to combination treatment.Many of the genome annotation tools are sequence-based, such as Prokka and EuGene-PP, but only using a reference genome cannot reflect the real situations of different experimental conditions.ANNOgesic is a software that can identify more than 20 bacterial genomic features based on RNA-Seq (10).This modular tool also optimizes parameters and removes false positives for high-quality results (11).Affected genes can be identified based on ANNOgesic analysis of a variety of regulatory RNAs, including RNA thermometers, small regulatory RNAs (sRNAs), and riboswitches.These regulatory RNAs have exhibi ted diverse roles in physiological functions, developmental metabolism, and virulent pathogenesis (12), including enhancing the efficacy of combination treatment and reducing fitness under limited conditions to combat antibiotic resistance (13).

K56's survival strategy against chemical #3 single treatment
Among the 6,696 chemicals tested, six compounds showed synergistic with meropenem, but only chemical #3 remained an additive at a low concentration.When the merope nem was replaced by colistin, chemical #3 became synergistic.This chemical #3 group had highly similar gene expression to that of the control group, with Pearson correlation coefficients of 0.996 (Fig. 1C).In contrast to the combination treatment, the single treatment exhibited a considerably smaller number of DEGs (Fig. 1D).
As a metabolic linking mechanism of chemical #3 only treatment, the inositol catabolic process occurs.Inositol is found in the phospholipids of cell membranes (14).Inositol-1,2,3,4,5 phosphates are downregulated (Fig. S1), but there are no gene expres sion changes in myo-inositols.On the other hand, D-chiro-inositols are significantly decreased.The reduced inositol metabolism causes decreased carbon availability due to the nutrient-poor condition without changes in phospholipids for cell permeability.The other key pathway is the phosphoenolpyruvate-dependent sugar phosphotransferase system (PTS), which catalyzes transport and phosphorylation in bacteria (15).The MtlA (KPHS_26190 phosphoenolpyruvate-dependent PTS family enzymes IIA component) with downregulated gene expression (Fig. S1B) forms the PTS phosphorylation cascade.Thus, reduced sugar transport and phosphorylation cause an energy decline.

Combination treatment (colistin and chemical #3) of K56 significantly affects its gene expression
Combination treatment of K56 with colistin and chemical #3 (K56_Co_Che) exhibited a substantial impact on gene expression (Fig. S2), as indicated by the principal compo nent analysis.The K56_Co_Che group demonstrated a marked difference compared to the other treatment groups and the control group (Fig. 1; Fig. S3), which was further supported by correlation analysis (Fig. 1).To identify the factors that influence gene expression, a differential expression analysis was conducted to investigate significantly DEGs (Table S2).The volcano plot revealed 242 upregulated and 580 downregulated genes with a P-value of 0.01.Almost no overlap in DEGs was observed among the three treatment groups (Fig. S4).

Global downregulation of phenylalanine metabolism
Decreased expression of the KPHS_23690 (phenylacetate-CoA oxygenase subunit PaaA) and KPHS_23710 (phenylacetic acid degradation protein) genes leads to reduced succinyl-CoA and acetyl-CoA levels (16).This decrease in gene expression negatively affects the citrate cycle, as observed in the metabolic pathway analysis results (Fig. 2A).Furthermore, these two genes were functionally linked to the downregulation of KPHS_48720 (bacterioferritin).The iron ion transport system was related to the porphyrin metabolism pathway after exposure to K56_Co_Che (Fig. 3B).As shown in Table 1, the RNA thermometer associated with KPHS_31750 (6-phosphofructokinase) displays regulatory characteristics as an attenuator (17).KPHS_31750 is also connected to the gene network (Fig. 3A), potentially contributing to the overall negative gene expression trend.

Regulation of cobalamin in porphyrin metabolism
Determination of the involvement of regulatory noncoding RNAs is essential to explore the interactions among pathways, even though the individual metabolic pathways have been described in the graphical representations (Fig. 4; Fig. S5).Notably, some of these connections represent regulatory RNAs.For instance, the absence of noncoding RNAs disrupts the circuit, indicating their important role.The ANNOgesic tool predicted the presence of a cobalamin riboswitch located between positions 117353 and 117540 (Table 2).This riboswitch is positioned upstream of the TonB-dependent gene KPHS_01080.KPHS_01080 functions as a cobalamin transporter and serves as a receptor for bacteriocins and bacteriophages (18)(19)(20).This cis-regulatory riboswitch controls the expression of associated genes in a negative feedback loop (21).Based on this informa tion, other genes involved in cobalamin biosynthesis indicate significant upregulation, except for the associated gene KPHS_01080 (Fig. 3B and 5B).

Enhancement of nutrient acquisition through the type II secretion system
The type II secretion system (T2SS) plays a crucial role in nutrient acquisition from the extracellular environment by releasing substrates that act as local and long-range effectors (22).It is a multi-protein complex that helps the transport of extracellular factors from the periplasm to the cell surface (23), and several T2SS components were upregulated (Fig. 2C; Fig. S6).KPHS_08870 (pullulanase D protein) relies on a sec transporter for its function (24) and utilizes a signal sequence-dependent pathway and a species-specific accessory mechanism to export proteins across cell membranes (25)(26)(27).KPHS_08820 (pullulanase I protein) is involved in the secretion of proteins as part of the pseudopilin complex (28).Both KPHS_08860 (pullulanase E protein) and KPHS_08790 (pullulanase L protein) may work in the opening and closing of the secretion pore (29) as an ATPase (30) and inner membrane element, respectively, to secrete toxins and hydrolytic enzymes (31).

Similarities in the tyrosine metabolism and phenylalanine pathways
Another metabolic pathway that interacts with phenylalanine is tyrosine metabolism.Both pathways show an overall downregulated pattern, indicating a close relationship between aromatic amino acids.KPHS_06330 is involved in the degradation of 4hydroxyphenyl acetic acid (32), and is functionally related to KPHS_23680 (phenylacetic acid degradation protein PaaN) (33) under combination treatment (Fig. 3D).

Involvement of RNA thermometers in regulatory processes
The phenylalanine leader peptide attenuator is linked to KPHS_31750, which acts as a 6phosphofructokinase in the carbohydrate metabolic process, specifically in the carbon metabolism and glucose or gluconeogenesis pathways.According to the Database for Annotation, Visualization, and Integrated Discovery (DAVID), KPHS_31180 (pyruvate kinase), KPHS_49540 (gluconate kinase), and KPHS_31750 (6-phosphofructokinase) are functionally related (34,35).KPHS_31180 is associated with pyruvate conversion during glycolysis (36), and KPHS_51160 is involved in the glucose catabolic process.Upregula tion of KPHS_49540 (gluconate kinase) is involved in the carbohydrate metabolic process, which includes thermoresistant and thermosensitive isozymes (37).KPHS_49540 catalyzes the phosphoryl transfer to produce gluconate-6-phosphate, a precursor of gluconate metabolism (38,39).Consequently, an RNA thermometer regulates carbon metabolism, glycolysis, and gluconeogenesis (Fig. 3E).Furthermore, KPHS_51710 (triosephosphate isomerase) converts dihydroxyacetone phosphate to glyceraldehyde-3phosphate dehydrogenase (GAPDH) (40), contributing significantly to energy production for survival.In the present study, KPHS_31520 (phosphoenolpyruvate synthase) was revealed to be responsible for phosphorylation.Fig. S7 shows that the RNA thermome ter-associated gene KPHS_31750 and the lysine riboswitch-associated gene KPHS_02470 are indirectly linked through KPHS_21050 (glutamate dehydrogenase).Although no significant changes were observed in the expression of the KPHS_21050 gene itself, the gene may functionally interact with these regulators.Glutamate and phenylalanine exhibit structural and functional similarities due to the presence of conserved lysine residues that are important for catalytic activity (41).However, unlike KPHS_31750, a functional network between KPHS_33570 (cold shock protein) and significantly expressed genes was not observed in the KlebNet results.This finding contradicts the results of the ANNOgesic analysis (Table 1).One possible explanation is that our ANNO gesic analysis was not based on differential RNA sequencing (dRNA-seq) or term-seq.RNA-seq-based ANNOgesic analysis has limitations, such as the inability to predict bacterial transcription start sites, processing sites, and promoters.Therefore, further investigation is needed to clarify this discrepancy.metabolism genes were highly expressed, except for KPHS_11300 (protoheme IX farnesyltransferase). KPHS_42470 (adenosylcobinamide kinase/adenosylco binamide-phosphate guanylyltransferase) and KPHS_42460 (nicotinate-nucleotide-dimethylbenzimidazole phosphoribosyltransferase) were strongly increased.

Gene expression in glycolysis and gluconeogenesis for energy acquisition and survival
Gluconeogenesis, the reverse process of glycolysis, is responsible for synthesizing glucose as a carbon source (42,43).Reduced KPHS_03180 (acetyl-CoA synthetase) expression may result in decreased acetyl-CoA levels.In response to combination treatment, downregulation of aldehyde dehydrogenase leads to decreased production of acetate from ethanol (44).Reduced GAPDH (KPHS_26520) expression is associated with decreased bacterial pathogenesis (45).Additionally, upregulation of the glycoside hydrolase enzyme KPHS_36400 works in starch and sucrose metabolism (Fig. 2F).KPHS_29390 (6-phospho-beta-glucosidase), KPHS_43980 (6-phospho-beta-glucosidase), and KPHS_38950 (6-phospho-beta-glucosidase) participate in the cellulose catabolic and carbohydrate metabolic processes by hydrolyzing glycosidic bonds.

Disruption of fatty acid membranes by lysine degradation
The presence of a functional network involving carbon metabolism and lysine degra dation (Fig. 3G) indicates the involvement of lysine in the catalytic mechanism.The highly expressed protein KPHS_13090 (lysine decarboxylase 1) acts as an enzyme, catalyzing the decarboxylation of lysine for amino acid metabolism (46).Contrarily, the downregulation of KPHS_09840 (glutarate 2-hydroxylase/carbon starvation-induced protein) participates in the L-lysine degradation pathway by catalyzing glutamate hydroxylation to produce L-2-hydroxyglutarate (47).Another contributor to the pathway is the downregulated protein KPHS_43350 (acetyl-CoA acetyltransferase), which affects fatty acid degradation.Additionally, KPHS_23740 (enoyl-CoA hydratase-isomerase) is (cobalamin biosynthesis protein).These genes exhibit positive expression in the porphyrin metabolism pathway.(C) The differentially expressed genes are functionally connected with KPHS_01080, which is associated with the cobalamin riboswitch.(D) KPHS_06330, KPHS_23680 (bifunctional aldehyde dehy drogenase/enoyl-CoA hydratase), KPHS_06340 (4-hydroxyphenlacetate degradation bifunctional isomerase/decarboxylase C-terminal subunit), KPHS_06310 (4-hydroxyphenlacetate catabolism), and KPHS_06350 (4-hydroxyphenlacetate catabolism) are linked in a functional network.(E) According to the Database for Annotation, Visualization, and Integrated Discovery (DAVID), the downregulation of KPHS_31750 (6-phosphofructokinase) is functionally related to the downregulation of KPHS_50720 (putative kinase) and KPHS_38680 (transaldolase kinase) and the upregulation of KPHS_49540 (gluconate kinase) and KPHS_31180 (pyruvate kinase).KPHS_38680 is an enzyme involved in the pentose phosphate pathway and forms an integrative network with KPHS_02470 (glucose-6-phosphate), KPHS_09830 (hypothetical protein), KPHS_09840 (hypothetical protein), and KPHS_46340 (putrescine).Notably, a lysine riboswitch is associated with KPHS_02470.(F) Decreased levels of KPHS_31520 (phosphoenolpyruvate synthase) and KPHS_31750 (6-phosphofructokinase), along with increased levels of KPHS_51710 (triosephosphate isomerase), KPHS_31180 (pyruvate kinase), and KPHS_51160 (phosphoglyceromutase), form a functionally related integrative network.(G) KPHS_23680 (enoyl-CoA hydratase), which is involved in phenylalanine metabolism, is linked to KPHS_23740.Downregulation of KPHS_46340 (putrescine aminotransferases) and KPHS_43350 (acetyl-CoA acetyltransferase) results in a connection with the target gene KPHS_28070, which is a novel noncoding sRNA.KPHS_02470 (glucose-6-phosphate isomerase) is a positively expressed lysine riboswitch-associated gene.KPHS_02470 reduced KPHS_09840 levels, and KPHS_46340 form a network indirectly through KPHS_13980 (carboxylate-amine ligase).(H) The genes shown are linked to slight downregulation of both KPHS_34130 (alpha, alpha-trehalose-phosphate synthase) and KPHS_34140 (trehalose-6-phosphate phosphatase).KPHS_34130 is linked to the downregulation of KPHS_31750 and the upregulation of KPHS_31180.In particular, KPHS_34140 is directly associated with KPHS_28070 (general stress protein), the target gene of a novel sRNA predicted by the ANNOgesic analysis.significantly downregulated in both phenylalanine metabolism and lysine degradation.It represents catalytic activity as an enoyl-CoA delta isomerase for the degradation of unsaturated fatty acids (48).This enzyme also plays a role in membrane disruption by altering phospholipid membranes and fatty acid synthesis (49).
Nevertheless, transmission electron microscopy results did not reveal significant morphological changes in the structure (Fig. S8), presumably due to a twofold decrease from the minimum inhibitory concentration (MIC).According to the ANNOgesic analysis, a novel sRNA can target the KPHS_28070 (stress-induced bacterial acidophils repeat motif ) gene (Fig. 6).This negatively expressed gene is related to acid stress and can cause bacteriostasis (50).The lysine riboswitch-associated KPHS_02470 (glucose-6-phos phate isomerase) gene was indirectly linked to the functionally associated KPHS_13980 (carboxylate-amine ligase) gene (Fig. 3G).Within the lysine degradation pathway, this gene may function under lysine riboswitch regulation.

Impact of starch and sucrose metabolism on bacterial durability
In the last starch and sucrose metabolism pathway (Fig. 2H), KPHS_34140 (treha lose-6-phosphate phosphatase) downregulation controls the dephosphorylation of trehalose-6-phosphate to produce trehalose and orthophosphate under stressful conditions (51).Oppositely, the upregulation of KPHS_04990 (trehalose(maltose)-spe cific PTS system component IIBC) is associated with the degradation of trehalose-6phosphate to glucose and glucose 6-phosphate in the carbohydrate transport system.To achieve a maximum growth rate, bacteria reduce carbon source utilization and increase glucose production (52).Increased KPHS_49390 (glycogen phosphor ylase) expression is related to the formation of glycosidic bonds.Inversely, the downregulation of KPHS_28030 (putative glycosidase) expression plays a role in hydrolyzing the glycosidic bond and is involved in carbohydrate metabolism.The overall trend indicates an increase in glycosidic bond formation.The highly upregulated KPHS_04980 (trehalose 6-P hydrolase) helps protect against hyperos motic and thermal stress (53).KPHS_52250 (putative PTS, EIIC), part of the phosphoenolpyruvate-dependent sugar PTS, induces metabolism and transcription (54).This system regulates sugar transport and the phosphorylation process (55).Its downregulation negatively affects sugar uptake.The upregulation of KPHS_04990 also works in a similar area.KPHS_52250, along with downregulation of KPHS_50400 (cellulose synthase catalytic subunit), inhibits starch saccharification and nutrient acquisition (56).KPHS_52250 also interacts with upregula a Three different RNA thermometers with eight associated genes were identified in the K56_Co Che condition.According to the initial ANNOgesic prediction, KPHS_31750 contains the associated coding sequence of a phenylalanine leader peptide RNA thermometer.

DISCUSSION
Gram-negative bacteria have a wavy double-layered membrane structure with a thin peptidoglycan layer (57), and some lipopolysaccharides produced by these bacteria can act as endotoxins.Therefore, invasion of the cell envelope of Gram-negative bacteria is a necessary step for infection.The physiochemical property of chemical #3 in this study may lead to strong interaction with colistin.The counteractive interplay between cell surface stress and metabolic rewiring plays a vital role in the balance between the effect of the combination treatment and bacterial homeostasis.The present study provides evidence supporting the perspective that lysine degradation, starch and sucrose metabolism, glycolysis, and gluconeogenesis contribute to the vulnerability of bacterial surface protection against the combined effects of colistin and chemical #3.
Carbohydrates are important for bacterial energy storage and cell envelope integrity (58), and are a substantial component of peptidoglycans.Reduced carbohydrate biosynthesis affects glycosyltransferase activity and glycosidic bond formation.Furthermore, altered fatty acid composition leads to changes in phospholipid membrane properties, and phenylalanine and tyrosine metabolism contribute to energy deficiency.Under antimicrobial pressure, carbon metabolism, as well as starch and sucrose metabolism, participate in energy production as part of the survival strategy in strain K56.Bacterial infections involve the secretion of proteins into the extracellular environment via the T2SS as a virulence mechanism (59).It is worth noting that cobalt ions promote pullulanase activity during T2SS secretion (60).Porphyrin metabolism also enhances cobalamin biosynthesis, which serves as a cofactor regulating gene expression (61).KPHS_42460 is involved in the intermediate step of decreasing biosynthesis of the cobalamin ligand (62).Both KPHS_42480 and KPHS_42640 catalyze amidation during cobalamin biosynthesis (63) and show enhanced expression under aerobic conditions in the step of cobalt insertion and under anaerobic conditions in the first step of cobalt insertion in the cobalamin biosynthetic process (64).Decreased KPHS_11300 expression leads to a reduction in the heme biosynthetic process.Throughout this process, regulatory RNA elements modulate the expression of functionally related genes, forming a systemic network that fine-tunes gene expression for regulation.
The RNA thermometer, specifically the phenylalanine leader peptide, is implicated in nutritional and energy control, although additional investigation is required to establish its precise role.This structure relies on temperature or starvation conditions to modu late gene expression (65) by adjusting the secondary structure and affecting ribosome binding site exposure (66), which in turn influences translation in response to heat shock or cold shock.
Another strategy involves exploring the functionality of novel sRNAs and their interaction with the target gene KPHS_28070 (Fig. 6).Antisense sRNAs can inhibit sRNA function by binding to sRNAs before they can interact with target mRNAs (67), which relieves translational repression and promotes gene expression (68).Utilization of novel sRNAs targeting KPHS_28070, a general stress protein, holds promise as a future therapeutic approach for regulating metabolism based on specific nucleotide sequences (69).
Riboswitches, regulatory segments of mRNA, play a critical role in controlling gene expression by binding to small molecules at their aptamer domains.This binding can switch gene expression on or off depending on the presence or absence of the ligand.The lysine riboswitch, for example, regulates lysine biosynthesis by inversely modulating citrate synthase activity in the tricarboxylic acid cycle (70).Targeting the lysine riboswitch could be an effective approach for remotely controlling bacterial cell growth.Similarly, the cobalamin riboswitch holds promise for various applications due to its specificity to bacteria and safe host interaction.Association with KPHS_01080 (cobalamin transporter) is crucial for utilizing the riboswitch as a delivery system for treatments.
The current study highlights the potential of combination treatment with colistin and chemical #3 as a starting point for addressing multidrug-resistant K. pneumo niae.Through a comprehensive analysis, this study elucidates important directions by unraveling the intricate functional network between the mode of action of the novel combination treatment and the bacterial survival mechanisms.This systematic interpretation will allow the identification of specific pathways and noncoding RNAs that play significant roles in multidrug resistance.However, further biotechnology, pharma cology, and synthetic biology studies are required to validate the effectiveness of the identified candidates.Nonetheless, this study serves as a fundamental basis for future investigations in drug discovery and therapeutic approaches aimed at combating antibiotic resistance.

Chemical library screening and compounds
To evaluate the effect of chemical compounds on K56, the strain was exposed to sub-MIC meropenem (8 µg/mL) and a chemical compound library (6,696 compounds) was obtained from the Korea Chemical Bank.Synergistic screening was conducted using the broth microdilution technique, following the guidelines of the Clinical and Laboratory Standards Institute.Additionally, 1,142 similar compounds from the same source were included in the screening.The experimental procedure was identical for all compounds (Fig. S8 and S9).

Checkerboard studies
A single colony of the K56 strain was selected and adjusted to a 0.  lowest concentration with no visible growth.The fractional inhibitory concentration index (FICI) was calculated using the following formula: FICI = FICA + FICB = MICAB/ MICA + MICBA/MICB, where MICAB represents the MIC of compound A in combination with compound B, MICA represents the concentration of compound A alone, MICBA represents the concentration of compound B with compound A, and MICB represents the MIC of compound B alone (Fig. S10).The FICI interpretation range was as follows: FICI ≤ 0.5 indicated synergy, 0.5 < FICI ≤ 1 indicated an additive effect, 1 < FICI ≤ 2 indicated no interaction, and FICI > 2 indicated antagonism (Table S3).

Bacterial growth curve evaluation
A 96-well clear, flat-bottomed microplate (SPL Life Sciences, Kyeonggido, South Korea) and optical adhesive films (Thermo Fisher Scientific, MA, USA) were used for growth curve measurements in a microplate reader (Molecular Devices, CA, USA).The microplate reader was set to measure the optical density at 600 nm every 10 minutes for 24 hours.The midpoint of the logarithmic phase of bacterial growth was determined.Error bars represent the standard deviation.Statistical analysis was performed using student's t-test with GraphPad Prism v9.3.0 (Fig. S11).

RNA extraction
The treatment conditions included 0.5 µg/mL colistin and 1 µg/mL chemical #3 diluted with Luria broth in round-bottom tubes.After 14 hours and 50 minutes of incubation, the cells were harvested and subjected to RNA extraction using the RNeasy mini kit and RNase-free DNase set (Qiagen, Hilden, Germany) according to the manufacturer's instructions.RNA quality was assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific).

Transcriptome analysis
The Illumina total RNA-seq assay was performed using a stranded total RNA library prep kit.Library sequencing was carried out using the Nextseq550 sequencing platform at the Yonsei University Genome Center.The RNA-seq data were analyzed by the Bioinfor matics Collaboration Unit at Yonsei University College of Medicine.In the processing and quality control steps, bcl format files were converted to fastq files using bcl2fastq2 (v2.20.0.422).Adapter and quality trimming were performed using Cutadapt (v1.18) (71) and Trimmomatic (v0.32) (72).Quality checks were conducted before and after trimming using FastQC (v0.11.7) (Table S4) (73).Alignment and transcriptome assembly were performed on the reference genome of K. pneumoniae HS strain 11286 using HISAT2 (v2.1.0)(74) and StringTie (v2.0.6) (75).Differential expression analysis was performed using the statistical algorithm LPEseq (76).Normalized count values, calculated P-values, and q-values were provided without replicates.

FIG 1
FIG 1 The effect of combination treatment (K56_Co_Che) is shown in correlation and differential expression plots.Principal component analysis was performed.Red, orange, yellow, and green squares represent the K56_Co_Che, K56_Che, K56_Co, and K56 (control) groups, respectively.Panel (A) shows the plot based on components 1 and 2, and panel (B) displays the results of components 1 and 3.The graphs indicate the relatively greater significance of K56_Co_Che.Multiple scatter plots with correlations are shown in panel (C).The plots illustrate the positive association and linear relationships between the values.The dispersion of the values reveals patterns and relationships between variables.The combination treatment frequently exhibited a wider numerical distribution than those of the two single treatments and the control.The correlation coefficient values ranged from 0.923 to 0.996, with higher values indicating closer proximity between the groups in terms of linear relationships.Volcano plots of (D) K56 vs K56_Co_Che, (E) K56 vs K56_Che, and (F) K56 vs K56_Co groups are shown.The X-axis represents the log2 fold change (log2FC).The Y-axis represents the −log10(P-value) based on the LPEseq method.Genes on the left side of the plots are downregulated, while those on the right side are upregulated.The dotted line represents the cutoff with a P-value of 0.01 for identifying DEGs.Magenta dots represent DEGs and blue dots represent non-DEGs.Plot (D) shows a higher number of expressed genes in the combination treatment condition than those in plots (E) and (F).

FIG 6
FIG6 Identification of a novel sRNA by ANNOgesic.The sRNA is an antisense RNA with a length of 389 nucleotides that regulates gene expression via negative binding energy.This cis-acting antisense sRNA is transcribed in the opposite strand of the target gene, KPHS_28070.This gene is a general stress protein, related to the stress-induced acidophilic repeat motif-containing protein.
Institute of Chemical Technology.We also acknowledge Dr. Young Lag Cho from LegoChemBio for providing chemical compound #3.The Bioinformatics Collaboration Unit of Yonsei Biomedical Research Institute of Yonsei University College of Medicine provided support through provision of a transcriptome analysis service.All figures were created or edited using BioRender.com.This study was supported by the Ministry of Science and Technology, Taiwan (Grant No. MOST 110-2222-E-110-008-MY3) and scholarships from the Health Fellowship Foundation in 2022 and the Brain Korea 21 PLUS Project for Medical Science, Yonsei University.D.Y. supervised and designed the experimental study.H.L. carried out the laboratory work.J.S. performed the transcriptomic analysis.S.Y. and H.L. conceived the study and analyzed and interpreted the data.S.Y. and H.L. wrote the manuscript with input from all authors.

TABLE 1
Genomic RNA thermometer activity a

TABLE 2
Genomic riboswitch activity a