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. 2025 Mar 8;25:124. doi: 10.1186/s12866-025-03826-7

Involvement of Mycobacterium smegmatis small noncoding RNA B11 in triacylglycerol accumulation and altered cell wall permeability

Zhuhua Wu 1,#, Weilong Liu 3,#, Qiuchan Tan 4, Yuhui Chen 1, Xiaoyu Lai 1, Jianming Hong 2, Hongdi Liang 1, Huizhong Wu 1,✉, Jing Liang 2,✉, Xunxun Chen 1,✉
PMCID: PMC11889869  PMID: 40057673

Abstract

Background

Pathways involving triacylglycerol (TAG) accumulation are thought to play a crucial regulatory role in bacterial growth and metabolism. Despite this understanding, little is known about the biological functions and regulatory mechanisms of small RNAs in Mycobacterium. Mycobacterium smegmatis (M. smegmatis), a type of Mycobacterium, serves as a model organism to investigate the molecular, physiological, and drug resistance features of M. tuberculosis.

Results

In this study, we demonstrated that overexpression of B11 significantly affects bacterial growth and colony morphology, increases antibiotic sensitivity and sodium dodecyl sulfate (SDS) surface stress, decreases intracellular survival, and suppresses cytokine secretion in macrophages. Transcriptomic and lipidomic analyses revealed a metabolic downshift in the B11 overexpression strain, characterized by reduced levels of TAG. Furthermore, transmission electron microscopy showed that the B11 overexpression strain exhibited decreased cell wall thickness, leading to reduced biofilm formation and altered cell wall permeability. Additionally, we observed that B11 regulated certain target genes but did not directly bind to those proteins tested.

Conclusions

Taken together, these findings suggest that B11 plays important roles in Mycobacterium survival under antibiotic and SDS stresses, TAG accumulation, and contributes to antibiotic sensitivity through altered cell wall permeability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-025-03826-7.

Keywords: Mycobacterium smegmatis, B11, Triacylglycerol, Cell wall permeability, Biofilms

Background

The unique cell wall structure of mycobacterium contributes to its resilience against the host immune system. Multiple cell wall lipids enhance impermeability, intrinsic resistance, persistence within macrophages, virulence, and adaptability to the host environment during infection [1]. Understanding the implications of outer membrane construction for cellular fitness and survival can offer insights into drug development and TB treatment [2].

Mycobacterium can adapt to changing environments and persist within the host due to its capacity to remodel metabolism and resist antibiotics [3]. Triacylglycerol (TAG), a glycerol triester with fatty acids, serves as a major carbon and energy source for M. tuberculosis during prolonged infection [4]. TAG accumulates in two forms: synthesized and stored in the bacterial cytoplasm, and present in the mycobacterial cell wall [5]. Cytoplasmic TAG in intracytoplasmic lipid inclusions (ILIs) confer phenotypic drug tolerance and promote pathogen survival [6, 7]. Various factors, including hypervirulent M. tuberculosis strains, lipid-rich diets, and environmental stresses, induce ILI formation in mycobacteria [8–10]. Altered biofilms associated with reduced mycolic acid wax ester and long-chain TAG levels play crucial roles in adaptive immune pressure and non-replicating persistence [11].

Small RNAs (sRNAs), typically 50–300 nt in length, are essential molecules involved in regulatory processes [12]. While extensively studied in gram-negative bacteria, knowledge of sRNA functions and regulatory mechanisms in high G + C bacteria, such as M. tuberculosis and M. smegmatis, remains limited [13, 14]. As novel sRNAs are discovered and their biological functions elucidated, their role as key post-transcriptional regulators of gene expression becomes increasingly evident.

B11 of M. tuberculosis (ncRv13660c, MTS2822, 6 C) is approximately 95 nucleotides long and is situated in the intergenic region between Rv3660c and Rv3661, exhibiting a conserved secondary structure featuring two C-rich loops [14]. Its structure suggests a potential role as a structural or protein-binding RNA [15]. Initially identified in 2009 by Arnvig et al., B11 demonstrates increased expression in response to H2O2 [16]. While overexpression of B11 in M. tuberculosis proves lethal to the bacterium, its overexpression in Mycobacterium smegmatis (M. smegmatis) leads to slow growth [14]. Disruption of B11 in Mycobacterium kansasii correlates with alterations in colony morphology and biofilm formation [17], while its absence in M. abscessus results in heightened virulence and pro-inflammatory immune signaling [18]. In M. smegmatis, B11 of M. tuberculosis binds to panD and dnaB mRNA via C-rich loops, implicating it in various cellular processes [14], suggesting a potential role in cell division regulation. However, the biological functions of B11 and its regulatory processes, especially regarding the regulation of cell wall biosynthesis, still require further in-depth study.

M. smegmatis shares over 2000 homologous genes with M.tuberculosis [19]. Its unique cell wall structure is akin to that of M. tuberculosis and various other mycobacterial species [20]. Consequently, M. smegmatis has been employed as a model organism to investigate the molecular, physiological, and drug resistance mechanisms of M.tuberculosis. In this study, we conducted a comprehensive analysis of B11 from M.tuberculosis using M. smegmatis as a model strain. We found that B11 overexpression affects bacterial growth and colony morphology, increases antibiotic sensitivity and sodium dodecyl sulfate (SDS) surface stress, decreases intracellular survival, and suppresses cytokine secretion in macrophages. Through RNA-seq and lipidomics analysis, we characterized the regulatory and metabolic pathways modulated by B11, particularly affecting TAG accumulation. Additionally, transmission electron microscopy (TEM) revealed a reduction in cell wall thickness associated with B11 overexpression, along with diminished biofilm formation and altered cell wall permeability. Furthermore, we observed that while B11 regulates certain target genes, it does not directly bind to proteins.

Results

Mutant isolation and phenotype analysis

Previously [14], reported that the C-rich loops of B11 are required for growth inhibition. To investigate the functions of B11, the RNAfold program was employed to predict the secondary structure of M. tuberculosis B11 (Fig. 1A). To assess the criticality of these C-rich loops for function, mutations were introduced in this region of L2 and L3 (C-to-A conversion at C-rich sequences) (Fig. 1B). Results indicated that Ms_B11 exhibited a smooth phenotype on a solid medium (Fig. 1C). Furthermore, the Ms_B11 strains displayed significantly decreased sliding motility (Fig. 1D). Compared to the control strain (Ms_vc), Ms_B11 began to show reduced growth (Fig. 1E). SEM examination revealed that Ms_B11 strains were significantly elongated compared to the control cells (Fig. 1F). Additionally, Ms_B11 strains exhibited significantly increased cell lengths (2.01 ± 0.85 μm) compared to control strains (1.32 ± 0.56 μm) (Fig. 1G). We also observed that the cell lengths were partially recovered in the Ms_B11 L2 and Ms_B11 L3 strains (1.67 ± 0.73 μm, 1.64 ± 0.59 μm, respectively) (Fig. 1G).

Fig. 1.

Fig. 1

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Mutant isolation and phenotype analysis. (A) Prediction of the secondary structure of M. tuberculosis B11 using MFold. (B) Schematic representation of site-directed mutagenesis in L2 and L3 of B11, with red indicating the mutation sites. (C) Colony morphology, (D) Sliding motility, and (E) Growth curve of recombinant strains. (F) Morphology of recombinant strains observed under SEM. (G) Calculation of cell lengths from representative fields (approximately 100 cells) as visualized by SEM. The cell length was calculated using ImageJ. A total of 100 cells from multiple fields of view were randomly selected and measured. Lengths of the bacterial cells were calculated from the coordinates of both ends of the cell as measured from representative fields as visualized by SEM. *P < 0.05, **P < 0.01, ***P < 0.001. Ms_vc, M. smegmatis harboring empty vector as the control. Ms_B11, M. smegmatis expressing B11. Ms_B11 as, M. smegmatis expressing B11 antisense. Ms_B11 L2, M. smegmatis expressing B11 with site-directed mutagenesis in the L2 loop. Ms_B11 L3, M. smegmatis expressing B11 with site-directed mutagenesis in the L3 loop

Overexpression of B11 enhances antibiotic sensitivity and SDS surface stress

Subsequently, we examined the susceptibility to vancomycin and linezolid, which target bacterial cell wall assembly and protein synthesis, respectively. The B11 overexpression strain exhibited decreased survival rates when exposed to high concentrations of vancomycin (5 µg/mL) and linezolid (1 µg/mL) compared to the empty vector strain (P < 0.05; Fig. 2A, B). Furthermore, when subjected to SDS surface stress, the B11 overexpression strain displayed reduced survival rates after exposure to 0.05% SDS for 1 and 5 h (Fig. 2C) compared to the empty vector control. Similar trends were observed with recombinant strains Ms_B11 and Ms_vc incubated with 0.05% SDS at varying intervals: the bacterial count in all recombinant strains exposed to 0.05% SDS decreased rapidly, with the B11 overexpression strain showing heightened sensitivity to SDS compared to the empty vector control (Fig. 2D).

Fig. 2.

Fig. 2

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Overexpression of B11 increases antibiotic sensitivity and SDS surface stress. (A) Survival of recombinant strains under indicated concentrations of vancomycin. (A) Survival of recombinant strains under indicated concentrations of linezolid. (C, D) Susceptibility of recombinant strains to 0.05% SDS. (C) Recombinant strains treated with 0.05% SDS for either 1–5 h, with 10-fold serial dilutions of recombinant strains spotted on 7H10 medium. The images were taken from different plates. (D) Determination of colony-forming units (CFU) at 1, 3, and 5 h

Overexpression of B11 reduces intracellular survival and suppresses cytokine secretion in macrophages

To assess whether B11 overexpression affects M. smegmatis survival within host cells, we infected THP-1-derived macrophages with the B11 overexpression and control strains. The B11 overexpression strain exhibited decreased intracellular survival during THP-1-derived macrophage infection (Fig. 3A). To evaluate the impact of B11 overexpression on pro-inflammatory cytokines in macrophages, the levels of TNF-α, IL-1β, and IL-6 were measured by ELISA. The supernatant of THP-1-derived macrophages infected with the B11 overexpression strain displayed lower levels of TNF-α (P = 0.002) (Fig. 3B), IL-1β (P = 0.02) (Fig. 3C), and IL-6 (P = 0.03) (Fig. 3D) compared to those infected with the empty vector control.

Fig. 3.

Fig. 3

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Overexpression of B11 decreased intracellular survival and suppresses cytokine secretion in macrophages. (A) Intracellular survival of recombinant strains within PMA-differentiated macrophages THP-1. Macrophages were infected with recombinant strains at an MOI of 10, followed by washing, lysing, and plating on 7H10 medium to determine bacterial numbers. (B) Measurement of cytokines released by macrophages after 24 h of infection with recombinant strains. (*P < 0.05; **P < 0.01)

B11 modulates TAG accumulation in M. smegmatis

To elucidate the regulatory and metabolic alterations induced by B11 overexpression in M. smegmatis, we conducted RNA-seq and lipidomics analyses. A total of 1,300 DEGs were identified in the B11 overexpression strain, comprising 785 upregulated and 515 downregulated genes (Fig. 4A, Supplementary Table 2). Gene Ontology analysis revealed significantly upregulated genes involved in cellular aromatic compound metabolic processes (Biological Process, BP), integral membrane component (Cellular Component, CC), and NADH dehydrogenase (ubiquinone) activity (Molecular Function, MF) in the B11 overexpression strain (Fig. 4B). Conversely, significantly downregulated genes were associated with DNA replication (BP), cytoplasmic localization (CC), and ATP binding (MF) (Fig. 4C). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that glycerolipid metabolism was predominantly enriched by upregulated genes in Ms_B11 strains (Fig. 4D), while mismatch repair pathways were most enriched by downregulated proteins (Fig. 4E). Additionally, numerous metabolic enzymes involved in glycerolipid metabolism were accumulated (Fig. 4F), the enzymes involved in these pathways may compete for the same substrate, 1,2-Diacyl-sn-glycerol, which will affect the biosynthesis of TAG. Underscoring the importance of this pathway in non-replicating persistence crucial for M. smegmatis survival and re-growth.

Fig. 4.

Fig. 4

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Transcriptome analysis. (A) Volcano plots showing differentially expressed genes between B11-overexpressing and control M. smegmatis strains. (A, D) GO terms and KEGG pathways of upregulated genes. (C, E) GO terms and KEGG pathways of downregulated genes. (F) Identification of glycerolipid metabolism as the most significantly enriched pathway in KEGG analysis. CC, Cellular Component; BP, Biological Process; MF, Molecular Function

Furthermore, comparative lipidomic analyses were conducted to assess differences in lipid profiles between Ms_B11 and Ms_vc strains. Among 10,967 total detected events, 103 met stringent criteria (VIP > 1, P < 0.05) (Supplementary Table 4). Notably, a distinct pattern emerged, with 34 events exclusively associated with TAG species (Fig. 5A). Heat map visualization of normalized concentration revealed 103 differential abundances of metabolites (34 events) (Fig. 5B). Individual TAG species, differing in length and unsaturation, were detected at significantly reduced levels in both Ms_B11 and Ms_vc strains (Fig. 5C). Subsequently, we investigated cell envelope ultrastructure and lipid inclusions in recombinant strains. M. smegmatis cells containing the B11 antisense overexpression vector exhibited ILIs (Fig. 6A, C). The proportion of cells with ILI profiles was more than halved in Ms_B11 compared to control strains (Fig. 6D).

Fig. 5.

Fig. 5

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Lipidomics analysis reveals decreased TAG accumulation upon B11 overexpression. (A) Positive mode comparative lipidomics analysis of Ms_B11 and Ms_vc strains resulted in 10,967 total events (black, blue, and red), with 103 metabolites (red) meeting criteria of VIP ≥ 1 and P-value < 0.05. Among them, 34 TAG alkylforms (blue) showed a 1.5-fold increase. (B) Hierarchical clustering analysis of the 103 differential metabolites shown in A. (C) The relative intensities of 34 TAG isoforms in the lipidomics analysis indicate that their abundances have changed significantly. Error bars represent mean ± SD. ****P < 0.0001

Fig. 6.

Fig. 6

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Cellular ultrastructure analysis of recombinant strains. (A) TEM images of recombinant strains depicting ILIs. Scale bar = 2 μm. (B) TEM images illustrating detailed ultrastructure of cell envelope layers: outer layer (OL), electron-transparent layer (ETL), and peptidoglycan layer (PGL). Scale bar = 500 nm. (C) Quantification of the average number of ILIs per cell. (D) Percentage of cells containing ILI. E. Measurement of cell envelope ETL thickness between recombinant strains. Measurements were taken from 50 cells of each strain. I: Ms_vc, II: Ms_B11, III: Ms_B11 as, IV: Ms_B11 L2, V: Ms_B11 L3

Overexpression of B11 alters cell envelope structure and biofilm formation

To delve deeper into the impact of B11 on bacterial cell envelopes, we examined the ultrastructural features of control and recombinant strains using TEM. The cell envelope exhibited a triple-layered structure comprising the electron-dense outer layer, electron-transparent layer (ETL), and peptidoglycan layer (PGL) (Fig. 6B). Notably, the ETL thickness differed significantly between Ms_B11 (6.38 ± 2.89 nm) and Ms_vc (10.27 ± 5.05 nm) strains, with the B11 overexpression strain showing a marked increase (12.46 ± 3.07 nm) (Fig. 6E).

Given the association between biofilm formation and cellular lipid profiles reported in various studies [5], we explored whether B11 overexpression and reduced TAG accumulation impact biofilm formation. The Ms_B11 strain exhibited significantly diminished biofilm growth and less structured pellicle compared to the control strain, a trend also observed in the Ms_B11 L2 and Ms_B11 L3 strains (Fig. 7A). Consistently, crystal violet staining revealed a notable decrease in biofilm signal intensity in the B11 overexpression strain relative to control strains (Fig. 7B).

Fig. 7.

Fig. 7

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Overexpression reduces biofilm formation and alters cell wall permeability. (A) Effect of B11 on biofilm formation of M. smegmatis. Recombinant strains were cultured in Sauton’s medium without shaking. (B) Quantification of biofilm formation after crystal violet staining. Values are shown as mean ± SD (n = 12). (C) Mid-log-phase cultures of recombinant strains incubated in phosphate-Buffered Saline containing Tween 20 (PBST) containing 2 µg/ml ethidium bromide (EtBr). (D) Mid-log-phase cultures of recombinant strains incubated in PBST containing 20 µM Nile red. RFI, relative fluorescence intensity. *P < 0.05, **P < 0.01, ***P < 0.001

To assess whether B11 overexpression affected cell wall permeability in M. smegmatis, we measured the accumulation rates of fluorescence dyes, EtBr, and Nile Red in Ms_B11 and Ms_vc strains. EtBr accumulation was significantly higher in the Ms_B11 strain compared to the control strain (Fig. 7C). Moreover, Nile Red dye, a lipid stain, exhibited increased uptake in the B11 overexpression strain relative to the control strain (Fig. 7D).

B11 indirectly modulates target genes

The functional mechanisms of noncoding RNAs often involve direct and indirect interactions with RNA-binding proteins. Therefore, we conducted RNA pull-down assays in B11-overexpressing M. smegmatis strains using both control and B11 probes. Mass spectrometry identified 49 proteins (emPAI > 1) associated with B11, including accC, rpsB, hspX, SecA, and MSMEG_6518 (Fig. 8A, Supplementary Table 3). Combining transcriptome sequencing data, we observed significant upregulation and downregulation (P < 0.05) of a group of genes in the B11-overexpressed strain (Fig. 8B). To validate some of these expression changes, qRT-PCR was performed on RNA from the Ms_B11 strain. The relative expression levels of hspX were significantly upregulated compared to the control group, while the expression of rpsB, MSMEG_6092, and rplJ was significantly downregulated (Fig. 8C). Further confirming the direct interaction, three recombinant M. smegmatis strains, Ms_hspX, Ms_rplJ, and Ms_6092 strains, were successfully constructed (Fig. 8D). However, RNA pull-down results demonstrated that B11 did not directly bind to these three proteins (Fig. 8E).

Fig. 8.

Fig. 8

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B11 indirectly regulates target genes without direct binding to proteins. (A) Identification of B11-binding proteins. Left: silver staining of pulled-down proteins. Right: mass spectrometry showing the top 10 proteins pulled-down by the B11 probe. (B) Heat map illustrating differentially expressed genes in RNA-seq dataset. Red indicates highly expressed genes; blue, lowly expressed genes. (C) qRT-PCR analysis verifying the expression of genes corresponding to B11-binding proteins. *P < 0.05, **P < 0.01, ***P < 0.001. (D) Expression of hspX, rpiL, and MSMEG_6092 in M. smegmatis. Western blot images showing the expression of flag-tagged protein. (E) Biotinylated B11 or antisense RNA incubated with whole-cell lysates from hspX, rpiL, or MSMG_6092 overexpression strains. Membranes immunoblotted for hspX, rpiL, and MSMG_6092. Input, total protein group

Discussion

A pivotal aspect of TB pathogenesis is M. tuberculosis’s ability to withstand diverse environmental stressors. Regulatory mechanisms triggered by stress play crucial roles in bacterial survival processes, including colony morphology, physiological adaptations, virulence, and antibiotic resistance. In most bacteria, survival mechanisms, such as the unique cell wall structure of mycobacteria acting as a permeable barrier, are tightly regulated at multiple levels. However, only a few reports have elucidated the physiological role of sRNAs, especially concerning survival under stressful conditions. In this study, we conducted a comprehensive investigation of B11 in M. smegmatis. B11 influenced cell wall morphology and TAG accumulation, potentially accounting for the observed growth and colony morphology changes in Ms_B11. The distinctive cell wall structure serves as a protective barrier against environmental stresses such as surface stress SDS and antibiotics. As anticipated, the recombinant Ms_B11 phenotype exhibited greater sensitivity to SDS surface stress, vancomycin, and linezolid compared to Ms_vc, suggesting a role for B11 in stress response. In this study, we found that B11 overexpression affects bacterial growth and colony morphology, increases antibiotic sensitivity and sodium dodecyl sulfate (SDS) surface stress, decreases intracellular survival, and suppresses cytokine secretion in macrophages. Furthermore, the noticeable decrease in TAG buildup resulting from the overexpression of B11 is consistent with variations in the cell wall’s permeability and its increased thickness, which probably plays a role in the displayed resistance to vancomycin and linezolid, also leads to the alteration of the phenotype.

According to previous studies, B11 appears to play a role in growth and colony morphology in bacteria, including M. tuberculosis, M. smegmatis [16], and M. kansasii [17]. Here, we utilized M. smegmatis mc2155 as our model to gain insights into the cellular function of B11. Our findings are consistent with previous reports: the expression of B11 resulted in slow growth and altered colony morphology, along with changes in cell wall-associated properties [14, 16]. The transition from rough to smooth colony morphology is closely associated with bacterial biofilm formation [21]. Congo red, an amphiphilic dye that binds to lipoproteins, was employed to assess the overall hydrophobicity of the mycobacterial cell wall [22]. Cell wall hydrophobicity is intricately linked to bacterial aggregation and biofilm formation. In our study, Congo red staining revealed significant differences between the B11 overexpression strains and the control strains. The altered Congo red staining of Ms_B11 colonies could also account for the impaired biofilm formation, which was further confirmed in a liquid medium. The abnormal Congo red staining and reduced biofilm formation of Ms_B11 colonies suggest that overexpression of B11 may inhibit biofilm formation. The early stage of bacterial biofilm formation is the adhesion and aggregation of microbial cells [23]. However, the SEM results of Ms_B11 and mutants show significant aggregation compared to vector control and antisense strains. The formation of pellicles, biofilms, and cords is a complex and dynamic process that involves multiple factors, including cell-cell interactions, extracellular matrix components, and environmental conditions. At this specific phase, bacteria are capable of either loosely clustering together or splitting off into the planktonic state [24]. It is likely that B11 and its mutants have had an impact on the expression of particular essential genes responsible for governing biofilm synthesis. Additionally, our findings reveal that the B11-overexpressing strains possess cell wall imperfections and a decreased TAG synthesis. As a result, even while aggregation is in progress, the surface attributes of the cells may be modified correspondingly, which in turn has an adverse effect on the biofilm formation process. Upon conducting the functional analysis, we found that L2 and L3 exhibited phenotypes quite similar to Ms_B11, albeit not identical. This indicates that L2 and L3 are integral to the functioning of B11, although the exact mechanism involved calls for additional investigation.

Our further investigations into the cell lipid profiles revealed significant alterations in TAG accumulation in B11-overexpressed strains, indicating changes in ILI accumulation and cell wall integrity. TAG serves as the primary energy reservoir in Mycobacterium, accumulating in large quantities within cytoplasmic ILIs [5]. Here, we have confirmed that B11 can decrease TAG accumulation in M. smegmatis. Additionally, previous studies have reported substantial amounts of TAG in the outer membrane of M. smegmatis [25]. Our ultrastructural analysis revealed cell wall defects in B11-overexpressed strains, characterized by a significant reduction in the ETL, suggesting a potential impact on TAG accumulation within the cell wall. ETL is a distinct and significant component of the mycobacterial cell envelope structure. It is is chiefly constituted by lipids such as mycolic acids [26]. Studies have shown that oxidative stress, iron deficiency and exposure to isoniazid reduce the thickness of the ETL of the cell envelope [10]. Prior research has underscored the significance of TAG in antibiotic tolerance and survival within lipid-rich environments [5, 27]. Specifically, ILI accumulation has been associated with phenotypic drug tolerance [28]. Consistent with these findings, our results demonstrate increased susceptibility of B11-overexpressed M. smegmatis to vancomycin and linezolid, indicative of reduced TAG accumulation. Moreover, we observed decreased intracellular survival of B11-overexpressed strains during macrophage infection. We provided evidence indicating that B11 overexpression leads to suppression of cytokine secretion, including TNF-α, IL-1β, and IL-6, in macrophages. While cytoplasmic TAG within ILIs may promote pathogen survival during dormancy and reactivation [5], the function of TAG accumulating on the cell wall remains unclear. Further investigation is warranted to elucidate the potential relationship between TAG accumulation and resistance or survival mechanisms. In summary, our findings suggest that B11 overexpression impacts TAG accumulation in M. smegmatis, influencing antibiotic tolerance and survival during macrophage infection.

Furthermore, our study revealed that B11 impacts cell wall permeability. The uptake of EtBr and Nile red has been utilized to assess mycobacterial cell wall permeability [22]. Previous research has linked the uptake of EtBr and Nile red to the lipid composition of the cell wall [29]. Here, we demonstrated for the first time, to our knowledge, that B11 alters cell wall permeability, evidenced by increased uptake of EtBr and Nile red. SDS serves as a valuable tool to investigate perturbations in the cell membrane/cell wall [30]. Notably, the survival capacity of Ms_B11 under SDS exposure was significantly diminished compared to control strains, indicating a cell wall defect in Ms_B11. Studies have underscored the significant impact of cell membrane permeability on drug penetration [31]. Decreased permeability of the bacterial cell wall can hinder antibacterial drug penetration [32]. Our experimental findings revealed that the B11 overexpression strain exhibited reduced survival following exposure to high concentrations of vancomycin and linezolid compared to the empty vector strain. This observation, suggesting a role for B11 in resistance to these two drugs, is particularly notable. Consistently, previous reports have indicated that B11 deletion mutants led to increased resistance to linezolid in M. abscessus [18]. Collectively, our results indicate that B11 influences survival under various in vitro stresses, including surface stress SDS and exposure to multiple antibiotics. Consequently, bacterial stress responses may contribute to the development of antibiotic resistance in bacteria exposed to stressful conditions [33]. However, a more comprehensive understanding of the impact of mycobacterial stress on antibiotic resistance warrants further investigation.

Previous research findings have shown that B11 is a regulator of genes involved in multiple cellular functions, including DNA replication [14]. In our study, genes related to DNA metabolism and ribosomal proteins appear downregulated. Some ribosomal proteins also appear in the pull-down experiment. DNA replication is a fundamental process for bacterial proliferation [34]. The downregulation of DNA replication appears to be a crucial factor contributing to both the observed bacterial growth retardation and the reduction in membrane formation in our study, and further investigations are needed to fully elucidate the complex molecular mechanisms underlying these relationships. Additionally, our findings suggest that B11 negatively regulates certain genes but does not directly bind to proteins. It is established that sRNAs can modulate gene expression by directly binding to mRNA sites. Previous studies have reported that the C-rich loops of B11 base-pair with panD mRNA independently of RNA chaperones, although G-rich stretches were not identified in other validated targets (MSMEG 0408 and mce1F) [14]. Further research is required to elucidate whether novel classes of RNA chaperones modulate the activity of B11. Despite the significant findings in this study regarding the influence of B11 on M.smegmatis, several limitations must be acknowledged. Regarding the mechanism of action, the study’s exploration of how B11 modulates TAG accumulation remains somewhat superficial. Although connections between B11 overexpression and alterations in glycerolipid metabolism genes and enzymes have been established, the precise molecular interactions and regulatory cascades are yet to be fully deciphered.

Conclusions

In summary, our study highlights the impact of B11 overexpression on M. smegmatis, revealing alterations in bacterial growth, colony morphology, and the inhibition of biofilm formation. Additionally, the observed reduction in triglyceride accumulation due to B11 overexpression correlates with changes in cell wall permeability and thickness, likely contributing to the observed phenotypic tolerance to vancomycin and linezolid. However, the precise function of the accumulated TAG on the cell wall remains unclear, presenting a promising avenue for potential drug development targets.

Materials and methods

Bacterial strains and growth conditions

The research utilized M. smegmatis mc2155 and Escherichia coli DH5α strains. M. smegmatis mc2155, overexpressing strains, and mutant strains were cultured in Middlebrook 7H9 medium supplemented with 0.2% glycerol, 0.05% Tween 80, and 50 mg/mL of kanamycin when necessary. Middlebrook 7H10 medium supplemented with 2% glycerol was used for culturing M. smegmatis mc2155. E. coli DH5α strains were cultured in Luria–Bertani (LB) medium supplemented with 50 mg/mL of kanamycin when required. All cultures were incubated at 37 °C with shaking at 150 rpm.

Molecular cloning and plasmid construction

Supplementary Table 1 provides details of the bacterial strains, plasmids, and primer sequences used for cloning. To assess the criticality of these C-rich loops of B11 for its function, mutations were introduced in this region of L2 and L3 (C-to-A conversion at C-rich sequences). B11 of M.tuberculosis, B11 antisense and its mutants were cloned into pMV261 at the HindIII sites. To identify the B11-binding proteins, genes from M. smegmatis mc2155 were PCR-amplified, digested with BamHI and HindIII, and then cloned into pMV261 with an N-terminal 3 × FLAG tag. The resulting plasmids were electroporated into M. smegmatis mc2155 as previously described. DNA sequencing was conducted to confirm all constructs. The expression of proteins in recombinant strains was analyzed via western blotting.

qPCR analysis

Cells were collected and lysed using Fastprep-24 (MP Biomedicals) following the manufacturer’s instructions. RNA was harvested using Trizol-based methods as previously described [35]. Briefly, RNA was recovered by centrifugation, phenol-chloroform extraction, and isopropyl alcohol precipitation, and re-suspended in RNase-free H2O. Subsequently, total RNA was reverse-transcribed to cDNA using ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech). The mRNA expression of selected genes was analyzed using the same qPCR Master Mix. Mean ± standard error of mean values were calculated from three independent experiments, and significant differences were determined using Student’s unpaired t-test. The primer sequences utilized in this study are listed in Supplementary Table 1.

Western blot analysis

The expression of Flag-tagged HspX, RplJ, and MSMEG_6092 was verified using western blotting with a mouse anti-Flag-tag monoclonal antibody (Beyotime, China) as the primary antibody and an HRP-conjugated goat anti-mouse IgG antibody (Beyotime, China) as the secondary antibody. Lysates of recombinant strains were separated using 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis, and proteins were transferred on to polyvinylidene fluoride membranes. The membranes were blocked and then incubated with diluted primary antibodies. Subsequently, membranes were incubated with an HRP-labeled anti-rabbit IgG antibody (Beyotime, China), and the signal was developed using the Bio-Rad ChemiDoc XRS + instrument and image software.

Scanning electron microscopy

Scanning electron microscopy (SEM) was performed according to our published procedure [36]. Briefly, cells were collected and fixed with 2.5% glutaraldehyde overnight at 4 °C. Sequential ethanol dehydrations were performed in a graded series, followed by critical point drying using a Hitachi HCP-2 Critical Point Dryer (Hitachi High-Technologies Corp., Tokyo, Japan). After drying, the samples were sputter-coated, and ultrastructure examination was conducted using a Zeiss Ultra55 electron microscope.

TEM

TEM was performed according to our published procedure [36]. Cells were harvested, fixed, and dehydrated in a graded ethanol series, then embedded in Spurr resin. Thin sections were examined using a Tecnai Spirit (FEI) transmission electron microscope. Lipid inclusions in the intracytoplasmic space were regularly detected. Cell wall thickness was analyzed using ImageJ software.

Phenotypic analysis

Growth curve analysis, sliding motility assay, Congo Red assay, and biofilm assay were performed as previously described [36]. Bacterial cultures were grown to the exponential phase (OD = 0.2–0.4 at 600 nm). The optical density at 600 nm (OD600) was measured at intervals of 3 h to obtain growth curves. M. smegmatis mc2155 recombinant strains were cultured in LB medium supplemented with 100 mg/mL Congo Red (Sigma), Sauton liquid medium, and 7H9 medium supplemented with 0.3% agar, respectively, and phenotypes were observed.

Spot tests

Recombinant strains were cultured to exponential phase and adjusted to an OD600 of 0.2. Ten-fold serial dilutions of recombinant strains were spotted onto 7H10 agar containing the indicated concentrations of SDS (0.05%), vancomycin (5.0 µg/mL), and linezolid (µg/mL).

Survival curves

Survival curve analysis was conducted as previously described [37]. Briefly, cultures of recombinant strains at mid-exponential phase were first diluted in 7H9 medium and incubated at 37 °C. Subsequently, they were treated with diverse concentrations of antibiotics as specified. SDS was introduced to reach a final concentration of 0.05% in the recombinant strain cultures, which were further incubated at 37 °C. After indicated time, bacterial survival was quantified by colony formation on drug-free agar.

Macrophage infections and cytokine assays

The human leukemia monocytic cell line (THP-1) was utilized for this study. THP-1 macrophages were infected with recombinant strains at a multiplicity of infection (MOI) of 10:1. For the intracellular bacterial survival assay, cells were washed, lysed, serially diluted, and inoculated onto 7H10 agar. The supernatant of each culture was collected and analyzed for cytokine levels using an enzyme-linked immunosorbent assay (ELISA) kit (Beyotime).

RNA-Seq analysis

Total RNA was extracted from recombinant strains and used for sequencing library construction. The Ultra-™Directional RNA Library Prep Kit for Illumina (NEB) was employed according to the manufacturer’s instructions. The final library products were purified and assessed using an Illumina second-generation high-throughput sequencing platform at Allwegene Technology Inc., Beijing. These clean reads were then mapped to the reference genome sequence (Mycobacterium smegmatis MC2155) by Bowtie2 v2.2.6. Corrected P-value of 0.05 and absolute value of log2 (Fold change) greater than 1 were set as the threshold for significantly differential expression. Three biological replicates were used for each group.

RNA pull-down assays and mass spectrometry (MS) anaysis

The full-length sense RNA of B11 was biotin-labeled using the Pierce™ RNA 3′ End Desthiobiotinylation Kit (BersinBio). Pull-down assays were performed by incubating biotin-labeled RNA with cell lysates using the RNA pull-down Kit (BersinBio). This complex could bind to streptavidin-labeled magnetic beads to separate from the other components of incubation solution. To identify the interacting proteins, MS analyses were performed by BersinBio (Guangzhou, China). The Thermo Scientific Q-Exactive Quadrupole-Orbitrap Mass Spectrometer, alongside the Thermo Dionex Ultimate 3000 RSLCnano System, were employed for the analytical procedures. Data acquisition was conducted using Xcalibur software, version 3.0.63.3. Subsequent processing of the MS raw file for protein identification and quantification was carried out with MaxQuant software, version 1.5.2.8.

Lipidomics analysis

Lipidomics analysis was conducted following our previously published protocol [36]. In brief, lipid extraction was performed using a chloroform/methanol solution (2:1, v: v) and an ultrasonic cleaner. The supernatant was then dried using N2 and dissolved in chloroform/methanol solution (2:1, v: v). Lipid samples were processed and analyzed by Biotree Biotech (Shanghai, China). LC-MS/MS analyses were performed using a Dionex UltiMate 3000 (UHPLC)–Thermo Orbitrap Elite. Raw data were converted to the mz.data format using Agilent MassHunter Qualitative Analysis B.08.00 software (Agilent Technologies, United States), and further processed using the XCMS program. Multivariate analysis was performed using the SIMCA16.0.2 software package (Umetrics, Umea, Sweden).

Ethidium bromide and Nile red uptake assay

The ethidium bromide (EtBr) and Nile red uptake assay were conducted according to a previously established protocol [38]. Recombinant strains were cultured to exponential phase and adjusted to an OD600 of 0.8. Then, 200 µL of bacterial suspension was added in triplicate to a 96-well black fluoroplate and treated with EtBr and Nile red dye to a final concentration of 2.0 µg/mL and 20 µM, respectively. The fluorescence intensity of accumulated dyes was measured on a BioTek Synergy HIM microplate reader (excitation: 544 nm, emission: 590 nm).

Statistical analysis

Data from each group are presented as the mean ± standard deviation (SD). Statistical analysis employed the unpaired Student t test, statistical signifcance was set at P < 0.05.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (249.7KB, xlsx)
Supplementary Material 2 (296.3KB, pdf)

Acknowledgements

Not applicable.

Abbreviations

M. tuberculosis

Mycobacterium tuberculosis

TB

Tuberculosis

M. smegmatis

Mycobacterium smegmatis

TAG

Triacylglycerol

SDS

Sodium dodecyl sulfate

ILIs

Intracytoplasmic lipid inclusions

sRNAs

Small RNAs

TEM

Transmission electron microscopy

ETL

Electron-transparent layer

PGL

Peptidoglycan layer

Author contributions

ZW, XC, JL and HW designed the experiments. ZW, WL, QT, YC, XL JH and HL performed the experiments, analyzed the data, and interpreted the results. ZW and QT wrote the manuscript. The author(s) read and approved the fnal manuscript.

Funding

This study was supported by Dongguan Science and Technology of Social Development Program (No. 202050715005178), the Youth Innovative Talents Project in Colleges and Universities in Guangdong Province (No. 2024KQNCX209), Guangdong Provincial Clinical Research Center for Tuberculosis (No. 2020B1111170014), the Science and Technology Planning Project of Guangdong Province, China (No. 2021B1212030003), the Science and Technology Program of Guangzhou, China (No. 202201011764), and the Special Fund for Science and technology innovation strategy of Guangdong province (No. pdjh2024b632).

Data availability

The RNA - seq data has been submitted to the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA1097297) with the bioProject accession number PRJNA1097297. The lipidomics data reported in this paper have been deposited in the OMIX, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (https://ngdc.cncb.ac.cn/omix: accession no.OMIX006976).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Zhuhua Wu and Weillong Liu contributed equally to this work

Contributor Information

Huizhong Wu, Email: 1627639699@qq.com.

Jing Liang, Email: 13999641@qq.com.

Xunxun Chen, Email: grace_chen514@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (249.7KB, xlsx)
Supplementary Material 2 (296.3KB, pdf)

Data Availability Statement

The RNA - seq data has been submitted to the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA1097297) with the bioProject accession number PRJNA1097297. The lipidomics data reported in this paper have been deposited in the OMIX, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (https://ngdc.cncb.ac.cn/omix: accession no.OMIX006976).

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