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Open Access

Peer-reviewed

Research Article

Evaluating metabolic changes in H9c2 cardiomyoblasts exposed to LPS: Towards understanding sepsis mechanisms

Abstract

Sepsis is a major public health concern associated with high mortality rates, particularly due to sepsis-induced myocardial dysfunction (SIMD), which affects about 50% of septic patients. This study investigates how mitochondrial dysfunction contributes to SIMD by examining metabolic changes in H9c2 cardiomyoblasts exposed to varying concentrations of lipopolysaccharide (LPS), a bacterial endotoxin, to enhance our understanding of the relationship between infection severity and metabolic responses. H9c2 cells were treated with LPS at concentrations of 0.5, 1, 2.5, and 5 µg/mL for 24 or 48 hours. Cell viability was measured using the MTT assay, and gene expression related to inflammation and mitochondrial function was analyzed through Real-Time PCR. Mitochondrial respiration and energy metabolism were assessed using the Seahorse MitoStress kit. Results showed that while 2.5 and 5 µg/mL of LPS for 24 hours did not significantly impact cell viability, exposure to 5 µg/mL for 48 hours led to a 77.7% decrease in survival. Gene analysis indicated significant overexpression of IL-6 and SOD2, with consistent underexpression of mt-ND1. Mitochondrial respiration increased at lower LPS concentrations but decreased at 5 µg/mL. Glycolytic metabolism also increased at lower LPS levels but decreased at higher concentrations. Inhibition of metabolic pathways affected mitochondrial function, especially at higher LPS concentrations. Our findings suggest that LPS induces metabolic disturbances in H9c2 cells, with adaptive responses at lower concentrations. However, excessive exposure results in mitochondrial and cellular damage, decreasing overall metabolism.

Citation: Szczesnowski A, Pichavant-Rafini K, Jezequel M-D, Théron M, Léon K (2025) Evaluating metabolic changes in H9c2 cardiomyoblasts exposed to LPS: Towards understanding sepsis mechanisms. PLoS One 20(10): e0334234. https://doi.org/10.1371/journal.pone.0334234

Editor: Rami Salim Najjar, Emory University School of Medicine, UNITED STATES OF AMERICA

Received: May 6, 2025; Accepted: September 24, 2025; Published: October 9, 2025

Copyright: © 2025 Szczesnowski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Sepsis, defined since 2016 as organ dysfunction resulting from an inappropriate response to infection [1], is a critical public health issue. In 2017, there were approximately 49 million cases, leading to 11 million deaths, which accounted for 20% of global mortality [2]. Among its complications, sepsis-induced myocardial dysfunction (SIMD) is particularly concerning, as it affects about 50% of septic patients and is associated with increased mortality [3]. While there is no universal definition for SIMD, it encompasses various cardiac dysfunctions, including reduced left ventricular ejection fraction and impaired diastolic function [4]. These impairments often coincide with shifts in energy substrate utilization, further diminishing the heart’s ATP-generating capacity.

The pathophysiology of SIMD involves complex interactions among extracellular and intracellular mechanisms, including calcium dysregulation, autonomic dysfunction, oxidative stress, altered metabolism, and mitochondrial dysfunctions [5–10]. Mitochondrial dysfunctions are central in SIMD, characterized by structural changes, altered electron transport chain activity, and increased reactive oxygen species (ROS) production [6]. Given mitochondria’s essential role in energy metabolism, understanding cardiac metabolism in SIMD is vital. The heart, with its high energy demands, primarily generates ATP through oxidative phosphorylation (95%) and to a lesser extent from glycolysis (5%) [11]. Under normal conditions, 70% to 90% of cardiac ATP is produced via fatty acid oxidation, while glucose oxidation contributes 10% to 30%, facilitated by glucose transporters (GLUT) [12]. Disruptions in these processes can significantly impair myocardial contractility [13].

Sepsis triggers an uncontrolled systemic inflammatory response, characterized by phases of hyperinflammation [14] and microvascular dysfunction, leading to tissue hypoxia [15], which can disrupt mitochondrial metabolism. This complexity complicates in vivo studies aimed at pinpointing the origins of mitochondrial dysfunction in SIMD. To explore the metabolic changes associated with SIMD due to pathogen exposure, we utilized the H9c2 cardiomyoblast model, exposing the cells to various concentrations of LPS to simulate clinical conditions encountered in sepsis. This approach enhances the representativeness of our findings and deepens our understanding of the underlying mechanisms, considering the variability in disease severity.

Our objectives are to (1) investigate mitochondrial gene expression and the balance between oxidative and glycolytic metabolism in H9c2 cells during inflammation, and (2) determine the primary energy substrate utilized by these cells in an inflammatory environment. This research aims to enhance the intricate relationship between mitochondrial dysfunction and energy metabolism in the context of sepsis.

2 Materials and methods

2.1 Cell culture

The H9c2 cell line (CRL-1446, ATCC), derived from the ventricular tissue of embryonic rat heart, was cultured in modified Eagle’s medium Dulbecco – Ham F12 (DMEM-F12; L0093-500, BioWest) containing 10% (v:v) fetal bovine serum (FBS; S1810-500 BioWest) and 1% (v:v) penicillin and streptomycin (P/S; DE17-602E, LONZA). The cells were maintained at 37°C in a humidified atmosphere of 95% air and 5% CO2. The medium was changed every 2–3 days, and the cells were passaged when a confluence of 80–90% was reached. Manipulations were performed on cells with fewer than 20 passages.

2.2 Exposure to LPS

After seeding, the cells were allowed to adhere for 24 hours. The medium was replaced with DMEM-F12 supplemented with 1% (v:v) FBS and 1% P/S for a duration of 24 hours. Based on literature data [16–19] and considering excessively high concentrations are likely to lead to significant cell mortality, hindering the study of metabolism, the cells were treated for 24 or 48 hours without LPS or with 0.5, 1, 2.5, and 5 µg/mL of LPS diluted in DMEM-F12 1% P/S without FBS, and incubated at 37°C, 95% air, 5% CO2 in a humidified environment.

2.3 Cell viability test

Cell viability was assessed using the MTT assay (M5655, Sigma Aldrich). Cells were seeded at 6,000 cells/well in a 96-well plate and treated without LPS or with LPS (2.5, 5 µg/mL) for 24 or 48 hours. The medium was then removed and replaced with 100 µL per well of a solution of MTT reconstituted at 0.05% in DMEM-F12 1% P/S, 0% FBS. The cells were incubated for 2 hours at 37°C and 5% CO2 in the dark. The supernatant was removed, and dimethyl sulfoxide (DMSO, D4540, Sigma Aldrich) was added for 15 minutes in each well to lyse the cell membranes and dissolve the formed formazan crystals. Absorbance was measured at 570 nm with background noise at 690 nm using a plate reader (Xenius XM Spectrofluorometer, SAFAS). Optical density (OD) measurements were reported relative to the control group, which was set at 100%.

2.4 Gene expression

2.4.1 RNA isolation for RT-PCR.

Total RNA was isolated using the NucleoZOL RNA Kit NucleoSpin™ (740406.50, Macherey-Nagel). Briefly, a cell pellet of 2 × 105 H9c2 cells was recovered in 500 µL of NucleoZOL using a 20G needle. Then, 200 μL of DNase/RNase-free water was added to the lysate. The mixture was incubated for 15 minutes at room temperature and then centrifuged (15 min, 12,000 G, 4°C). 600 µL of supernatant was collected, and an equivalent volume of mix buffer was added. This mix was transferred to a NucleoSpin® column and centrifuged (1 min, 8,000 G, room temperature). After this step, the RNA was bound to the silica membrane. Subsequently, two washes were performed with the wash buffer (RA3) provided in the kit. RNA was eluted in 30 µL of DNase/RNase-free water and recovered after centrifugation (1 min, 11,000 G, room temperature). It was then stored at −80°C. The concentration of messenger RNA was determined using the SimpliNano™ spectrophotometer (29-0617-12, GE Healthcare Life Sciences). The purity of the sample was evaluated using the optical density (OD) ratios 260 nm/OD 280 nm, with threshold values ranging from 1.8 to 2.1. The integrity of the RNA was verified by electrophoresis on a 1.5% (m: v) agarose gel (A9539, Sigma Aldrich) containing ethidium bromide.

2.4.2 Quantification of gene expression by Real-Time Reverse Transcriptase-PCR (RT-PCR).

Reverse transcription was performed following the manufacturer’s recommendations for the qScript™ cDNA synthesis kit (733−1174, QuantaBio), using 1 ng of RNA. The reverse transcription was carried out according to the following program: 1 cycle at 22°C for 5 min, 1 cycle at 42°C for 30 min, 1 cycle at 85°C for 5 min, end at 4°C. The obtained cDNA was diluted 1:10 in DNase/RNase-free water and stored at −20°C until analysis.

Analyses were performed using the 7500 Fast Real-Time PCR system (Applied Biosystems, Thermo Fisher Scientific). Amplification of target genes was conducted using specific primers (Table 1), and quantification was performed by incorporating SYBR® Green (PB20.11−05, Eurobio). A denaturation step at 95°C was conducted for 2 minutes, followed by 50 amplification cycles (denaturation: 5 seconds at 95 °C; hybridization/extension: 30 seconds at 60°C). Each gene (IL-6, SOD2, mt-ND1, Sirt3, Fis1, OPA1, Mfn1, Mfn2, Drp1, mt-Cox2, mt-Cox4i1, mt-ATP6, β-actin) was amplified in duplicate. IL-6 was used for inflammatory stress and activation of the NFkB pathway; SOD2, Sirt3, Fis1, and Drp1 for oxidative stress response and mitochondrial dynamics, with Fis1 and Drp1 indicating cellular stress; OPA1, Mfn1, Mfn2 for mitochondrial fusion genes and mt-ND1, mt-Cox2, mt-Cox4i1, mt-ATP6 for subunits of the electron transport chain. Data were normalized using β-actin as a reference gene (Table 1). This choice was supported by the absence of significant differences in β-actin RNA levels between the different experimental groups (p-value > 0.05). Data analysis was performed by comparing the relative expression of mRNA using the 2-ΔΔCt method [20].

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Table 1. Primer sequences used for Real-Time RT-PCR analysis.

https://doi.org/10.1371/journal.pone.0334234.t001

2.5 Mitochondrial respiration and energetic metabolism

Mitochondrial respiration was evaluated using the Seahorse MitoStress kit (Agilent, Cat. No. 103010−100) following the manufacturer’s instructions. H9c2 cells were seeded in Seahorse assay microplates with 180 μL of growth medium and incubated overnight. The day before the assay, an XF cartridge was hydrated with XF calibrant (Agilent, Cat. No. 103059−000) and incubated overnight at 37 °C in a non-CO2 incubator. Prior to the assay, the culture media was changed to 180 μL/well of XF DMEM supplemented with 10 mM glucose, 2 mM glutamine, and 1 mM pyruvate (Agilent, Cat. Nos. 103575−100, 103577−100, 103579−100, 103578−100) and incubated for 1 hour at 37 °C (without CO2). The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured using the XF HS Seahorse Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Obtained values were normalized to 1,000 cells by staining the nuclei with 4 µg/mL of Hoechst, incubated for 15 minutes in the dark at 37 °C. Counting was performed using the cell imaging software of the Cytation (Cytation 1, Agilent Technologies). The normalized OCR and ECAR data were reported relative to the control group, which was set at 100%. The final concentrations of injected compounds were 1 μM oligomycin, 5 μM carbonyl cyanide-4(trifluoromethoxy)phenylhydrazone (FCCP), and 1 μM rotenone + antimycin A. From the values obtained, mitochondrial respiration and energetic metabolism were measured and calculated on H9c2 cells and more particularly Basal OCR, OCR related to ATP production, Maximal OCR, Coupling efficiency, Proton leak, Reserve capacity, Basal extracellular acidification, Glycolytic capacity, Glycolytic reserve and mitochondrial ATP/Glycolytic ATP ratio. Data were reported relative to the control group, which was set at 100%.

The utilization of metabolic substrates by the mitochondria was determined using the Seahorse XF Substrate Oxidation Stress Test Kits (Agilent, Cat. Nos. 103672−100, 103673−100, 103674−100). Briefly, cells were seeded and exposed to LPS as described above (paragraph 2.2). On the day of the assay, the culture media was changed to XF DMEM supplemented with 10 mM glucose, 2 mM glutamine, and 1 mM pyruvate (Agilent, Cat. Nos. 103575−100, 103577−100, 103579−100, 103578−100) without FBS. Various metabolic pathway inhibitors were used (final concentrations): Etomoxir (4 µM; irreversibly blocks CPT1a, thereby inhibiting the fatty acid pathway), UK5099 (2 µM; inhibits the mitochondrial pyruvate transporter and prevents glycolysis) and BPTES (3 µM; inhibits glutaminase 1 and blocks the amino acid pathway) prior to exposure to the mitochondrial inhibitors oligomycin, FCCP, rotenone and antimycin A.

The concentrations of these inhibitors were determined by the manufacturer based on their tests and the literature [21–23].

2.6 Statistical analysis

Statistical analyses were performed using Graphpad Prism 9.0.2 software. Results are presented as mean ± SEM or median and interquartile range.

After checking for normality using the Shapiro-Wilk test and verifying homogeneity of variances, a non-parametric Kruskal-Wallis test followed by Dunn’s post hoc test was applied for comparisons among more than two conditions. For comparisons between two conditions, the Mann-Whitney test was conducted. Differences were considered significant for a p-value less than 0.05.

Each experimental condition included 3–8 biological replicates, each performed with 2–5 technical replicates.

3 Results

3.1 Cell viability

Exposure of cells for 24 hours to 2.5 or 5 µg/mL of LPS did not lead to significant changes in cell viability compared to control cells (No LPS). However, after 48 hours of exposure to 5 µg/mL of LPS, a significant decrease of 77.7 ± 3.8% in cell survival (p < 0.0001) was observed compared to the control group (Fig 1).

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Fig 1. Viability of H9c2 cells by MTT assay, after 24 hours of exposure to 0 (n = 8); 2.5 (n = 4); 5 (n = 8) µg/mL of LPS or 48 hours with 0 (n = 5); 2.5 (n = 3); 5 (n = 5) µg/mL of LPS.

Values are represented as medians, 1st quartile, and 3rd quartile. The Kruskal-Wallis test followed by Dunn’s post hoc test was used to compare the effects of each LPS concentration with the respective control at 24 or 48 hours. * indicates a significant difference compared to the control condition (p-value < 0.05).

https://doi.org/10.1371/journal.pone.0334234.g001

3.2 Gene expression

An exposure to 5 µg/mL of LPS for 24 or 48 hours led to a significant overexpression of IL-6 transcripts compared to the control (Fig 2A). A 24-hour exposure to 5 µg/mL of LPS resulted in an increase of 4 ± 1.8 times (p = 0.01) compared to the control. Moreover, a 24-hour exposure to 5 µg/mL of LPS led to a significant overexpression (5.5 ± 2.5 times; p = 0.03) of SOD2 transcripts compared to control cells (Fig 2B). Conversely, a significant underexpression of mt-ND1 transcripts was observed compared to the control group (Fig 2C). For all experimental conditions, no significant changes in the expression of Fis1 (Fig 2D), OPA1 (Fig 2E), Mfn1 (Fig 2F), Drp1 (Fig 2G), mt-Cox2 (Fig 2H), mt-Cox4i1 (Fig 2I), and mt-ATP6 (Fig 2J) transcripts were observed compared to the control condition. Overall, a high variability of data is observed after 24 hours of exposure to 5 µg/mL of LPS and 48 hours at 2.5 µg/mL of LPS.

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Fig 2. Relative expression of mRNA in H9c2 cells exposed to 2.5 and 5 µg/mL of LPS for 24 and 48 hours.

Expression was normalized to β-actin mRNA. The 24 h and 48 h control groups were pooled due to the absence of significant differences between them. The analysis was performed using the ΔΔCT method for the groups: no LPS (n = 5); LPS 2.5 µg/mL (n = 3); LPS 5 µg/mL (n = 3 or 5). Results are presented as medians and individual values. The non parametric Kruskal-Wallis test followed by Dunn’s post hoc test was used to compare the effects of each LPS concentration with the control condition.* indicates p-value < 0.05 compared to the control condition.

https://doi.org/10.1371/journal.pone.0334234.g002

3.3 Mitochondrial respiration and energetic metabolism

3.3.1 Oxidative metabolism.

After 24 hours of exposure to LPS 0.5, a significant increase in basal OCR of H9c2 was observed compared to control cells. For example, an increase of 7.9 ± 2.4% in basal OCR was measured after 24 hours of exposure to 0.5 µg/mL of LPS (p = 0.021). This effect was no longer observed at higher concentrations of LPS (2.5 and 5 µg/mL). In contrast, after 48 hours of exposure to LPS, a decrease in basal OCR of 12.4 ± 4,0% and 55.8 ± 6.4% was observed at 2.5 and 5 µg/mL of LPS respectively (Fig 3A).

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Fig 3. Mitochondrial respiration and energetic metabolism measured and calculated on H9c2 cells exposed without LPS for 24 hours (n = 6) or at 0.5 (n = 3); 1 (n = 3); 2.5 (n = 3); 5 (n = 4) µg/mL of LPS and for 48 hours at 0 (n = 3); 2.5 (n = 3); 5 (n = 3) µg/mL.

Basal OCR (A), OCR related to ATP production (B), Maximal OCR (C), Coupling efficiency (D), Proton leak (E), Reserve capacity (3F). Values are represented as medians, 1st quartile, and 3rd quartile. The Kruskal-Wallis test followed by Dunn’s post hoc test was used to compare the effects of each LPS concentration with the respective control at 24 or 48 hours. * indicates p-value < 0.05 compared to the 24-hour control condition; # indicates p-value < 0.05 compared to the 48-hour control condition).

https://doi.org/10.1371/journal.pone.0334234.g003

Exposure to 5 µg/mL of LPS for 24 hours, as well as exposure to 2.5 and 5 µg/mL LPS for 48 hours, significantly decreased OCR-linked ATP production and maximal OCR. For example, a decrease of 31.1 ± 9.0% (p = 0.013) and 28.5 ± 9.0% (p = 0.02) was respectively measured in OCR-linked ATP production and maximal OCR after 24 hours exposure to 5 µg/mL of LPS (Fig 3B and 3C). Whereas, a trend toward increased OCR-linked ATP production and maximal OCR was observed at 0.5 and 1 µg/mL of LPS.

Furthermore, a significant decrease in coupling efficiency was observed compared to the control group after 24 hours of exposure to 0.5 and 1 µg/mL of LPS (Fig 3D). For example, after 24 hours of exposure at 1 µg/mL of LPS, a decrease of 5.2 ± 0.7% is noted (p = 0.002).

After 24 hours of exposure, at 0.5 and 1 µg/mL of LPS a significant increase in proton leak was described compared to control cells. Indeed, exposure to 1 µg/mL of LPS resulted in an increase of 26.4 ± 4.3% (p = 0.002) in proton leak. Whereas after 48 hours of exposure to 5 µg/mL of LPS, a significant decrease was observed compared to control (Fig 3E).

After 24 and 48 hours of exposure to and 5 µg/mL of LPS, reserve capacity was significantly reduced compared to the control group (Fig 3F). After 24 hours of exposure, regardless of the LPS concentration, non-mitochondrial OCR was not significantly altered (data not shown).

3.3.2 Glycolytic metabolism.

After 24 hours of exposure to 1 µg/mL of LPS, a significant increase (16.0 ± 4.8%; p = 0.0038) in extracellular acidification compared to the control group was observed. This effect was not observed at 2.5 and 5 µg/mL of LPS. However, after 48 hours of treatment with 2.5 and 5 µg/mL of LPS, extracellular acidification was significantly decreased. For example, by 51.0 ± 7.8% (p < 0.0001) after 48 hours exposure to 2.5 µg/mL of LPS compared to control (Fig 4A).

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Fig 4. Parameters of glycolytic metabolism measured and calculated on H9c2 cells exposed without LPS for 24 hours (n = 6) or at 0.5 (n = 3); 1 (n = 3); 2.5 (n = 3); 5 (n = 4) µg/mL and for 48 hours with no LPS (n = 3) and at 2.5 (n = 3); 5 (n = 3) µg/mL.

Basal extracellular acidification (4A), Glycolytic capacity (4B), Glycolytic reserve (4C); mitochondrial ATP/Glycolytic ATP ratio (4D). Values are represented as medians, 1st quartile, and 3rd quartile. The Kruskal-Wallis test followed by Dunn’s post hoc test was used to compare the effects of each LPS concentration with the respective control at 24 or 48 hours.* indicates p-value < 0.05 compared to the 24-hour control condition; # indicates p-value < 0.05 compared to the 48-hour control condition.

https://doi.org/10.1371/journal.pone.0334234.g004

After 48 hours of treatment with 2.5 µg/mL of LPS, a significant decrease in glycolytic capacity was measured as well as after exposure to 5 µg/mL of LPS for 24 hours and 48 hours compared to the control group. Indeed, 24 hours of exposure to 5 µg/mL resulted in a significant decrease of 27.9 ± 8.2% (p = 0.019) (Fig 4B).

After 24 hours of exposure to LPS, all experimental conditions led to a significant decrease in glycolytic reserve compared to control (Fig 4C). A decrease of 24.4 ± 6.0% (p = 0.041) is observed at 1 µg/mL of LPS. This decrease was of 55.5 ± 8.2% (p < 0.0001) was observed at 5 µg/mL of LPS. After 48 hours of exposure to LPS at 2.5 µg/mL, glycolytic capacity was significantly decreased by 47.1 ± 13.8% (p = 0.021) compared to control. The results for glycolytic reserve after 48 hours of exposure to 5 µg/mL of LPS were not usable.

At 1 µg/mL of 24 h LPS exposure, the ratio between the mitochondrial ATP and the glycolytic ATP was significantly decreased by 20.6% (p = 0.026), respectively, compared to the control group. This effect was no longer observed at 2.5 and 5 µg/mL of LPS (Fig 4D).

3.3.3 Substrate utilization.

The inhibition of the amino acid pathway by BPTES had no significant effect on the variations of mitochondrial OCR, maximal OCR, and reserve capacity induced by exposure to 0.5, 1, and 2.5 µg/mL of LPS. At 5 µg/mL of LPS, the inhibition of the amino acid pathway compensates for the decrease in mitochondrial OCR, maximal OCR, and reserve capacity caused by LPS (Fig 5A, 5B and 5C).

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Fig 5. Parameters of OCR measured on H9c2 cells exposed to LPS for 24 hours in the presence of an amino acid pathway inhibitor: Mitochondrial OCR (A), Maximal OCR (B), Reserve capacity (C) or in the presence of an fatty acid pathway inhibitor (Etomoxir): Mitochondrial OCR (D), Maximal OCR (E), Reserve capacity (F) or in the presence of a glycolysis pathway inhibitor (UK 5099).

Values are represented as medians, 1st quartile, and 3rd quartile. A Mann-Whitney test was used to compare each LPS concentration with inhibitor to the corresponding condition without inhibitor. * indicates p-value < 0.05 compared to the same condition without the inhibitor.

https://doi.org/10.1371/journal.pone.0334234.g005

In the absence of LPS, the inhibition of the fatty acid pathway by etomoxir did not alter mitochondrial OCR, maximal OCR, or reserve capacity. Regardless of the experimental condition, the inhibition of the fatty acid pathway by etomoxir did not compensate for the changes in mitochondrial OCR, maximal OCR, and reserve capacity induced by LPS, as described in part 3.3.1 (Fig 5D, 5E and 5F).

For all experimental conditions except 5 µg/mL of LPS, the inhibition of the glycolysis pathway by UK5099 had no significant effect on the previously described variations in mitochondrial OCR (part 3.3.1). At 5 µg/mL of LPS, the inhibition of glycolysis compensates for the decrease in mitochondrial OCR caused by LPS (p = 0.041) (Fig 5G). In the absence of LPS and at 0.5 and 1 µg/mL of LPS, the inhibition of glycolysis led to a significant decrease in maximal OCR. For example, in the absence of LPS, the inhibition of glycolysis resulted in a significant decrease of 8.7 ± 3.0% (p = 0.014). This effect of inhibition was not observed at 2.5 and 5 µg/mL of LPS (Fig 5H). For all experimental conditions, except 5 µg/mL of LPS, the inhibition of glycolysis induced a significant decrease in reserve capacity. In the absence of LPS, a decrease of 26.0 ± 3.8% (p < 0.0001) in reserve capacity was noted in the presence of UK5099 while the inhibition of glycolysis at 1 µg/mL of LPS decreased the reserve capacity by 28.1 ± 2.1% (p = 0.0001)) (Fig 5I).

4 Discussion

In the context of sepsis, when an uncontrolled systemic inflammatory response to infection can alter the activities of tissues and organs, LPS was used to induce a direct activation of inflammatory intracellular pathways observed in SIMD to investigate the induced mitochondrial dysfunction. Our results showed that while 2.5 and 5 µg/mL of LPS for 24 hours did not induced cytotoxicity, the exposure to 5 µg/mL of LPS for 48 hours led to 77.7% decrease in cell viability. Gene expression revealed an overexpression of IL-6 and SOD2 transcripts with consistent underexpression of mt-ND1. Our inflammatory model was validated by those results. The study of mitochondrial respiration revealed an increase at lower LPS concentrations but a decrease at higher LPS concentrations. Glycolytic metabolism also increased at lower LPS levels while decreasing at higher concentrations. Our results revealed two distinct metabolic profiles in response to LPS.

The reduction of survival rate in H9c2 cells after 48 h of exposure to LPS at 5 µg/mL is in accordance with the literature, despite the wide range of responses to LPS varying from 0.1 to 10 µg/mL

In our experiment, overexpression of Il-6 transcripts, was observed. This overexpression supports validation of our experimental model. In our model, as in other studies [24,25], LPS triggered inflammatory signalling pathways and cytokine secretion. Our results suggest an inflammatory response in H9c2 cells, likely leading to oxidative stress due to an imbalance between radical defence systems and ROS production. The increase in SOD2 mRNA expression after 24 hours of LPS exposure at 2.5 µg/mL supports this hypothesis and the establishment of oxidative stress, likely due to inflammation and secreted cytokines like IL-6. The same results (overexpression of IL-6 and SOD2) were observed in a neuroinflammation model (BV2 cells) exposed to 200 ng/mL of LPS for 5.5 hours [26]. SOD2 mRNA overexpression was also demonstrated in H9c2 and AC16 cardiomyocytes exposed to 10 µg/mL of LPS for 24 hours [27]. When redox balance is not restored, inflammation leads to cellular damage, including mitochondrial dysfunction. To maintain functional integrity and energy needs, damaged mitochondria are removed through quality control mechanisms, including mitochondrial fission and fusion. Thus, we have studied the gene expression of fission genes (Fis1 and Drp1) and fusion genes (OPA1, Mfn1, and Mfn2). Even though, we did not observed significant change in expression of OPA1, Mfn1, Drp1 or Fis1 after LPS exposure, a trend of Mfn2 underexpression after 24 hours of exposure to 2.5 µg/mL of LPS was observed., Literature indicates that LPS can increase mitochondrial fission mechanisms to contain mitochondrial damage and decrease fusion to maintain equilibrium between these processes. For example, in a murine cardiac cell model (HL-1), LPS (10 mg/mL) significantly increased fission gene expression (Drp1 and Fis1) and decreased fusion gene expression (Mfn2 and OPA1) [28]. Further experiments are required to confirm this trend. In our study, the gene expression of subunits of various mitochondrial transport chain complexes was also investigated. An underexpression of mt-ND1 transcripts at 24 and 48 hours of LPS exposure was observed whereas no change in expression of mt-Cox2, mt-Cox4i1 and mt-ATP6 was shown. As in our study, in an in vitro model of acute kidney injury induced by 4 µg/mL of LPS (HK-2 cells), a decrease in mt-ND1 mRNA expression was demonstrated The downregulation of mt-ND1, the gene encoding complex I suggest metabolic alterations and mitochondrial dysfunction in our study. In summary, based on our results, LPS is primarily recognized by the TLR4 receptor activating both signalling pathways leading to the activation of the NFkB transcription factor. This factor promotes the secretion of pro-inflammatory cytokines, such as IL-6, disrupting the redox balance and triggering oxidative stress. This stress is partially managed by SOD2. At high concentrations of LPS (5 µg/mL) and prolonged exposure times of 48 hours, inflammation and oxidative stress exceed the adaptive capacities of the cell, resulting in deleterious cellular damage, particularly mitochondrial, which disrupts cellular and mitochondrial function.

In our study, an increase in basal OCR for cells exposed to low concentration of LPS was observed. This suggests that LPS leads to an increase in the basal metabolic needs necessary to maintain essential cellular functions. The trend toward increased maximal OCR at low LPS concentrations may indicate a better efficiency of the electron transport chain and a greater oxidative metabolic efficiency. Moreover, the trend toward enhanced reserve capacity at low LPS concentrations also suggests effective mitochondria that are potentially capable of adapting to metabolic disturbances. Similar results have been observed in a model of synovial fibroblast cells from rheumatoid arthritis patients exposed to 1 µg/mL of LPS for 24 hours [29] and in a model of primary neutrophils extracted from healthy volunteers and exposed to 0.1 µg/mL of LPS for 4 or 8 hours [30]. These observations may reflect a phenomenon of mitohormesis, whereby moderate exposure to mitochondrial stress induces the establishment of protective adaptive responses beneficial to the cell. Nevertheless, in our study, these adaptation to LPS seem to be exceeded at high concentrations of LPS, potentially causing cellular and mitochondrial damage. Indeed, from 2.5 µg/mL of LPS and when the cells are faced with a simulated high energy demand (FCCP), our results showed a decrease in maximal OCR and reserve capacity. The same results were observed in a model of porcine intestinal epithelial cells (IPEC-J2) exposed for 48 hours to 40 ng/mL of LPS [31] or in microglial cells (BV2) by [32]. These results may be partly explained by a decrease in the efficiency of the electron transport chain (ETC), particularly in the enzymatic activity of the different ETC complexes in response to LPS [33].

Our study reveals that 24 hours of LPS exposure leads to increased proton leak and decreased coupling efficiency at low concentrations. Similarly, an increase in proton leak have been observed in a rat model of inflammatory pathology [34]. It may be indicative of mitochondrial damage as it is associated with a decrease in ATP concentration and mitochondrial membrane potential. However, by decreasing mitochondrial membrane potential, it can also be beneficial and regulate ROS production. Indeed, proton leak can be induced by UCPs 1–3, which are directly activated by superoxide dismutase (SOD) to induce mitochondrial membrane depolarization and reduce ROS production [35]. However, in our model, further investigations are needed to determine if the increase is beneficial or deleterious. The uncoupling observed may be linked to the partial opening of the mitochondrial permeability transition pore, resulting in mitochondrial membrane potential loss [36,37]. Ultimately, this uncoupling can lead to reduced ATP levels and cell death [38].

Our results suggest that LPS induces metabolic reprogramming. Indeed, we observed a decrease at low LPS concentrations of the ratio determining the contribution of oxidative phosphorylation and glycolysis to ATP and pointing towards a glycolytic phenotype in H9c2 cells [39]. [39] found similar results in macrophages exposed to 100 ng/mL of LPS for 12 hours, showing significant increases in glycolysis and a reduced oxidative/glycolytic metabolism ratio, confirming metabolic reprogramming towards a glycolytic phenotype. Several studies demonstrated that this metabolic reprogramming involves multiple pathways [40,41]. Moreover, in our study, a decrease in glycolytic metabolism was observed at high concentrations of LPS (2.5 and 5 µg/mL), similar to oxidative metabolism. This shows that high concentrations of LPS and/or prolonged exposure likely reduced metabolic flexibility due to LPS exposure and cause deleterious cellular damage or glycolytic disturbances involving mediators. Similar findings were reported in primary cardiomyocytes from TBMIM6 mice injected with LPS 24 hours prior to experimentation [42].

Our results mainly show that H9c2 use glucose as the primary substrate in cases of high metabolic demand. Indeed, when glycolysis is inhibited, the control group cells and those exposed to low concentrations of LPS can no longer meet the high metabolic needs. Thus, glycolysis, which provides reduced equivalents for oxidative phosphorylation, seems essential during significant metabolic needs, and its inhibition does not appear to be compensated by amino acid or fatty acid pathways. Van Wyngene et al explain that glycolysis allows for rapid ATP generation, thus providing immediate energy through the action of glycolytic enzymes, which can be crucial in response to stress, unlike oxidative phosphorylation, which is a slower and more complex process [43]. The absence of compensation by the amino acid pathway is consistent, as they contribute only in very small proportion to ATP synthesis. In a context of normal energy demand (without LPS), the inhibition of the glutamine, fatty acid, or glycolytic pathways does not lead to changes in mitochondrial O₂ consumption, suggesting compensation by the remaining pathways. In contrast, this compensation is limited to mitochondrial O₂ consumption when glycolysis is inhibited and is not observed at all when the fatty acid pathway is blocked.

In conclusion, in our study, we have described the direct metabolic (mitochondrial and glycolytic) effects of LPS on activity in H9c2 cells that vary based on exposure time and concentration. A phenomenon of mitohormesis occurs in response to LPS exposure, where H9c2 cells develop adaptive mechanisms to combat the stress caused by LPS. This is reflected in increased oxidative and glycolytic metabolism, as well as a metabolic reprogramming towards a glycolytic phenotype. However, when these adaptive mechanisms are overwhelmed, mitochondrial and cellular damage ensues, leading to a decrease in metabolism. In the context of SIMD, it would be interesting to go further in the analysis of cardiac metabolic disturbances coupling LPS direct effects with proinflammatory cytokines simulating a systemic inflammation in order to understand the combined direct effects of LPS with a simulated systemic inflammation.

There are also some limitations to this study. One notable limitation is the use of immature cardiomyocytes. Although H9c2 cardiomyoblasts are among the most commonly used cell lines in the literature for studying various cardiac syndromes, they, like any in vitro model, have inherent limitations. As immortalized cells, they have lost some physiological characteristics, such as contractile function. Alternative models, such as primary cardiomyocytes or induced pluripotent stem cell (iPSC)-derived cardiomyocytes, can offer more physiologically relevant data. However, these models are more complex to maintain in culture and are less cost-effective. In addition, the use of primary cardiomyocytes requires animal sacrifice, which poses ethical concerns.

It is also important to note that none of these in vitro models, including H9c2 cells, can fully reproduce the complexity of a whole organism. Nevertheless, to support the validity of our findings, our results were confirmed using a primary cardiomyocyte model extracted from mice by [42].

Supporting information

S1 Data. The supplementary file contains the raw data from the different assays: viability assay (optical density, Sheet 1), gene expression analysis (threshold cycle values, Sheet 2), oxygen consumption measurements over time (OCR, Sheets 3–6), and extracellular acidification rate measurements over time (ECAR, Sheets 7–9).

https://doi.org/10.1371/journal.pone.0334234.s001

(XLSX)

References

  1. 1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. pmid:26903338
  2. 2. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11. pmid:31954465
  3. 3. Lv X, Wang H. Pathophysiology of sepsis-induced myocardial dysfunction. Mil Med Res. 2016;3:30. pmid:27708836
  4. 4. Zaky A, Deem S, Bendjelid K, Treggiari MM. Characterization of cardiac dysfunction in sepsis: an ongoing challenge. Shock. 2014;41(1):12–24. pmid:24351526
  5. 5. Hao Y, Liu R, Wang H, Rui T, Guo J. Research Progress on Mechanisms and Treatment of Sepsis-Induced Myocardial Dysfunction. Int J Gen Med. 2024;17:3387–93. pmid:39130486
  6. 6. Lin Y, Xu Y, Zhang Z. Sepsis-Induced Myocardial Dysfunction (SIMD): the Pathophysiological Mechanisms and Therapeutic Strategies Targeting Mitochondria. Inflammation. 2020;43(4):1184–200. pmid:32333359
  7. 7. Liu Y-C, Yu M-M, Shou S-T, Chai Y-F. Sepsis-Induced Cardiomyopathy: Mechanisms and Treatments. Front Immunol. 2017;8:1021. pmid:28970829
  8. 8. Martin L, Derwall M, Al Zoubi S, Zechendorf E, Reuter DA, Thiemermann C, et al. The Septic Heart. Chest. 2019;155(2):427–37.
  9. 9. Song J, Fang X, Zhou K, Bao H, Li L. Sepsis‑induced cardiac dysfunction and pathogenetic mechanisms (Review). Mol Med Rep. 2023;28(6).
  10. 10. Walley KR. Sepsis-induced myocardial dysfunction. Curr Opin Crit Care. 2018;24(4):292–9. pmid:29846206
  11. 11. Doenst T, Nguyen TD, Abel ED. Cardiac metabolism in heart failure: implications beyond ATP production. Circ Res. 2013;113(6):709–24. pmid:23989714
  12. 12. Aerni-Flessner L, Abi-Jaoude M, Koenig A, Payne M, Hruz PW. GLUT4, GLUT1, and GLUT8 are the dominant GLUT transcripts expressed in the murine left ventricle. Cardiovasc Diabetol. 2012;11:63. pmid:22681646
  13. 13. van der Vusse GJ, van Bilsen M, Glatz JF. Cardiac fatty acid uptake and transport in health and disease. Cardiovasc Res. 2000;45(2):279–93. pmid:10728348
  14. 14. Zhang X, Zhang Y, Yuan S, Zhang J. The potential immunological mechanisms of sepsis. Front Immunol. 2024;15:1434688. pmid:39040114
  15. 15. Ruan H, Zhang Q, Zhang Y-P, Li S-S, Ran X. Unraveling the role of HIF-1α in sepsis: from pathophysiology to potential therapeutics-a narrative review. Crit Care. 2024;28(1):100. pmid:38539163
  16. 16. Qiu Z, He Y, Ming H, Lei S, Leng Y, Xia Z-Y. Lipopolysaccharide (LPS) Aggravates High Glucose- and Hypoxia/Reoxygenation-Induced Injury through Activating ROS-Dependent NLRP3 Inflammasome-Mediated Pyroptosis in H9C2 Cardiomyocytes. J Diabetes Res. 2019;2019:8151836. pmid:30911553
  17. 17. Xie Z, Yang C, Xu T. Hesperetin attenuates LPS-induced the inflammatory response and apoptosis of H9c2 by activating the AMPK/P53 signaling pathway. Immun Inflamm Dis. 2023;11(8):e973. pmid:37584301
  18. 18. Castel T, Pichavant‐Rafini K, Théron M, Gandubert C, Léon K. Effects of selenium, selenium‐enriched spirulina and phycocyanin on myocarditis stress parameters on LPS‐induced injury in H9c2 ventricular cardiomyoblasts. Physiological Reports. 2025;13(10).
  19. 19. Xue J, Liu J, Xu B, Yu J, Zhang A, Qin L, et al. miR-21-5p inhibits inflammation injuries in LPS-treated H9c2 cells by regulating PDCD4. Am J Transl Res. 2021;13(10):11450–60. pmid:34786071
  20. 20. Livak KJ, Schmittgen TD. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method. Methods. 2001;25(4):402–8.
  21. 21. Divakaruni AS, Hsieh WY, Minarrieta L, Duong TN, Kim KKO, Desousa BR, et al. Etomoxir Inhibits Macrophage Polarization by Disrupting CoA Homeostasis. Cell Metab. 2018;28(3):490–503.e7. pmid:30043752
  22. 22. Divakaruni AS, Paradyse A, Ferrick DA, Murphy AN, Jastroch M. Analysis and Interpretation of Microplate-Based Oxygen Consumption and pH Data. Methods in Enzymology. Elsevier. 2014:309–54.
  23. 23. Hildyard JCW, Ammälä C, Dukes ID, Thomson SA, Halestrap AP. Identification and characterisation of a new class of highly specific and potent inhibitors of the mitochondrial pyruvate carrier. Biochim Biophys Acta. 2005;1707(2–3):221–30. pmid:15863100
  24. 24. Andreassen SM, Berg LC, Nielsen SS, Kristensen AT, Jacobsen S. mRNA expression of genes involved in inflammation and haemostasis in equine fibroblast-like synoviocytes following exposure to lipopolysaccharide, fibrinogen and thrombin. BMC Vet Res. 2015;11:141. pmid:26116380
  25. 25. Luo Q, Yang A, Cao Q, Guan H. 3,3’-Diindolylmethane protects cardiomyocytes from LPS-induced inflammatory response and apoptosis. BMC Pharmacol Toxicol. 2018;19(1):71. pmid:30413180
  26. 26. Park J-H, Hwang J-W, Lee H-J, Jang GM, Jeong YJ, Cho J, et al. Lomerizine inhibits LPS-mediated neuroinflammation and tau hyperphosphorylation by modulating NLRP3, DYRK1A, and GSK3α/β. Front Immunol. 2023;14:1150940. pmid:37435081
  27. 27. Zhu H, Wu J, Li C, Zeng Z, He T, Liu X, et al. Transcriptome analysis reveals the mechanism of pyroptosis-related genes in septic cardiomyopathy. PeerJ. 2023;11:e16214. pmid:37872948
  28. 28. Zhao Y, Pan Y, Chen M, Tan Y, Chang X, Li H, et al. PKM2 interacts with and phosphorylates PHB2 to sustain mitochondrial quality control against septic cerebral-cardiac injury. Int J Med Sci. 2024;21(4):633–43. pmid:38464826
  29. 29. Bai J, Xie N, Hou Y, Chen X, Hu Y, Zhang Y, et al. The enhanced mitochondrial dysfunction by cantleyoside confines inflammatory response and promotes apoptosis of human HFLS-RA cell line via AMPK/Sirt 1/NF-κB pathway activation. Biomedicine & Pharmacotherapy. 2022;149:112847.
  30. 30. Pan T, Sun S, Chen Y, Tian R, Chen E, Tan R, et al. Immune effects of PI3K/Akt/HIF-1α-regulated glycolysis in polymorphonuclear neutrophils during sepsis. Crit Care. 2022;26(1).
  31. 31. Hussain T, Yuan D, Tan B, Murtaza G, Rahu N, Kalhoro MS, et al. Eucommia ulmoides flavones (EUF) abrogated enterocyte damage induced by LPS involved in NF-κB signaling pathway. Toxicology in Vitro. 2020;62:104674.
  32. 32. Zhu J, Lee MJ, An JH, Oh E, Chung W, Heo JY. ATF5 Attenuates the Secretion of Pro-Inflammatory Cytokines in Activated Microglia. IJMS. 2023;24(4):3322.
  33. 33. Suliman H, Weltywolf K, Carraway M, Tatro L, Piantadosi C. Lipopolysaccharide induces oxidative cardiac mitochondrial damage and biogenesis. Cardiovascular Research. 2004;64(2):279–88.
  34. 34. Aguer C, Pasqua M, Thrush AB, Moffat C, McBurney M, Jardine K, et al. Increased proton leak and SOD2 expression in myotubes from obese non-diabetic subjects with a family history of type 2 diabetes. Biochim Biophys Acta. 2013;1832(10):1624–33. pmid:23685312
  35. 35. Mailloux RJ, Harper M-E. Uncoupling proteins and the control of mitochondrial reactive oxygen species production. Free Radic Biol Med. 2011;51(6):1106–15. pmid:21762777
  36. 36. Halestrap AP. What is the mitochondrial permeability transition pore? J Mol Cell Cardiol. 2009;46(6):821–31. pmid:19265700
  37. 37. Halestrap AP, McStay GP, Clarke SJ. The permeability transition pore complex: another view. Biochimie. 2002;84(2–3):153–66. pmid:12022946
  38. 38. Ruiz-Ramírez A, López-Acosta O, Barrios-Maya MA, El-Hafidi M. Cell Death and Heart Failure in Obesity: Role of Uncoupling Proteins. Oxid Med Cell Longev. 2016;2016:9340654. pmid:27642497
  39. 39. Wang S, Liu F, Tan KS, Ser H, Tan LT, Lee L, et al. Effect of (R)‐salbutamol on the switch of phenotype and metabolic pattern in LPS‐induced macrophage cells. J Cellular Molecular Medi. 2019;24(1):722–36.
  40. 40. Kelly B, O’Neill LAJ. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 2015;25(7):771–84. pmid:26045163
  41. 41. Krawczyk CM, Holowka T, Sun J, Blagih J, Amiel E, DeBerardinis RJ, et al. Toll-like receptor-induced changes in glycolytic metabolism regulate dendritic cell activation. Blood. 2010;115(23):4742–9. pmid:20351312
  42. 42. Zhou H, Dai Z, Li J, Wang J, Zhu H, Chang X, et al. TMBIM6 prevents VDAC1 multimerization and improves mitochondrial quality control to reduce sepsis-related myocardial injury. Metabolism. 2023;140:155383. pmid:36603706
  43. 43. Van Wyngene L, Vandewalle J, Libert C. Reprogramming of basic metabolic pathways in microbial sepsis: therapeutic targets at last?. EMBO Mol Med. 2018;10(8):e8712. pmid:29976786