Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 May 20:12:e17414.
doi: 10.7717/peerj.17414. eCollection 2024.

Nucleolin myocardial-specific knockout exacerbates glucose metabolism disorder in endotoxemia-induced myocardial injury

Yuting Tang #  1   2   3 , Leijing Yin #  1   2   3 , Ludong Yuan  1   2   3 , Xiaofang Lin  1   2   3 , Bimei Jiang  1   2   3
Affiliations

Nucleolin myocardial-specific knockout exacerbates glucose metabolism disorder in endotoxemia-induced myocardial injury

Yuting Tang et al. PeerJ. .

Abstract

Background: Sepsis-induced myocardial injury, as one of the important complications of sepsis, can significantly increase the mortality of septic patients. Our previous study found that nucleolin affected mitochondrial function in energy synthesis and had a protective effect on septic cardiomyopathy in mice. During sepsis, glucose metabolism disorders aggravated myocardial injury and had a negative effect on septic patients.

Objectives: We investigated whether nucleolin could regulate glucose metabolism during endotoxemia-induced myocardial injury.

Methods: The study tested whether the nucleolin cardiac-specific knockout in the mice could affect glucose metabolism through untargeted metabolomics, and the results of metabolomics were verified experimentally in H9C2 cells. The ATP content, lactate production, and oxygen consumption rate (OCR) were evaluated.

Results: The metabolomics results suggested that glycolytic products were increased in endotoxemia-induced myocardial injury, and that nucleolin myocardial-specific knockout altered oxidative phosphorylation-related pathways. The experiment data showed that TNF-α combined with LPS stimulation could increase the lactate content and the OCR values by about 25%, and decrease the ATP content by about 25%. However, interference with nucleolin expression could further decrease ATP content and OCR values by about 10-20% and partially increase the lactate level in the presence of TNF-α and LPS. However, nucleolin overexpression had the opposite protective effect, which partially reversed the decrease in ATP content and the increase in lactate level.

Conclusion: Down-regulation of nucleolin can exacerbate glucose metabolism disorders in endotoxemia-induced myocardial injury. Improving glucose metabolism by regulating nucleolin was expected to provide new therapeutic ideas for patients with septic cardiomyopathy.

Keywords: Endotoxemia; Glycolysis; Metabolomics; Myocardial injury; Nucleolin; Sepsis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Quality control and PCA analysis of metabolomics samples.
(A and B) POS (Figure A) and NEG (Figure B) data were analyzed by PCA for their quality control results. (C and D) PCA analysis of myocardial metabolomics POS (Figure E) and NEG (Figure F) data in the WT group versus the WT _LPS group. (E and F) PCA analysis of myocardial metabolomics POS (Figure E) and NEG (Figure F) data in WT_LPS group versus KO_ LPS group. The tight distribution of samples in each of the two groups suggested intergroup variability in metabolites and low intragroup variability.
Figure 2
Figure 2. PLS-DA analysis of metabolomics samples.
(A and B) PLS-DA analysis of myocardial metabolomics POS (Figure A) and NEG (Figure B) data in the WT group versus the WT_LPS group. The Q2 values obtained for both PLS-DA of POS and NEG were greater than 0.9, indicating a significant difference between the two groups of metabolites. (C and D) PLS-DA analysis of myocardial metabolomics POS (Figure C) and NEG (Figure D) data in WT_ LPS group versus KO_LPS group. The Q2 values of the results in the POS and NEG groups were 0.806 and 0.796, respectively, showing the variability between the metabolomics data of the two groups.
Figure 3
Figure 3. Identification of specific differential metabolites between metabolomics samples.
(A and B) Volcano plot of metabolite differences between WT group and WT_LPS group in POS and NEG mode. The up-regulated and down-regulated metabolites and the corresponding VIP values were shown, respectively. (C and D) Volcano plot of metabolite differences between WT_LPS group and KO_LPS group in POS and NEG mode. (E and F) The number of metabolite differences between groups in POS mode and NEG mode.
Figure 4
Figure 4. Metabolic pathway analysis of myocardial differential metabolites.
(A) KEGG annotation of myocardial differential metabolites. (B) KEGG enrichment bubble plots of differential metabolite between the WT group and WT_LPS group. The 20 metabolic pathways with the highest difference values were plotted as vertical coordinates and the degree of enrichment as horizontal coordinates. (C) KEGG enrichment bubble plots of differential metabolite between WT_LPS group and KO_LPS group.
Figure 5
Figure 5. Metabolite abundance was analyzed by MSEA analysis.
(A and B) Metabolite abundance in MSEA analysis of differential metabolites between WT group and WT_LPS group in POS mode and NEG mode. (C and D) Metabolite abundance in MSEA analysis of differential metabolites between WT_LPS group and KO_LPS group in POS mode and NEG mode. The vertical coordinate of the MSEA analysis result was the name of the metabolic set, and the horizontal coordinate indicated the degree of enrichment, and the top 25 of the degree of enrichment were selected for analysis in this study.
Figure 6
Figure 6. The alteration of glucose metabolism during TNF-α combined with LPS stimulation in H9C2 cells.
(A) ATP content of different groups were detected by the ATP assay kit. The concentration of ATP was converted to nmol per mg protein. (B) The lactate levels in cell supernatants were detected by the Lactic Acid assay kit. (C) Continuous dynamic changes of OCR after TNF-α combined with LPS stimulation in H9C2 cells. The error bars are sometimes smaller than the symbol. (D–F) The basal OCR value, spare respiratory capacity, and ATP production after TNF-α combined with LPS stimulation in H9C2 cells. *p < 0.05, **p < 0.01, *** p < 0.005; n ≥ 3 in each group.
Figure 7
Figure 7. The effect of nucleolin on glucose metabolism during TNF-α combined with LPS stimulation in H9C2 cells.
(A and B) ATP content of different groups were detected by the ATP assay kit after nucleolin overexpression (LvNCL) or nucleolin interference (siNCL) in the H9C2 cell. The concentration of ATP was converted to nmol per mg protein. (C and D) The lactate levels in cell supernatants were detected by the Lactic Acid assay kit after nucleolin overexpression (LvNCL) or nucleolin interference (siNCL) in the H9C2 cell. (E) Continuous dynamic changes of OCR after TNF-α combined with LPS stimulation in the nucleolin interference (siNCL) groups or control groups. (F–I) The basal OCR value, spare respiratory capacity, and ATP production after TNF-α combined with LPS stimulation of the nucleolin interference (siNCL) groups or control groups. *p < 0.05, ** p < 0.01, *** p < 0.005; n ≥ 3 in each group.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Bakker J, Nijsten MW, Jansen TC. Clinical use of lactate monitoring in critically ill patients. Annals of Intensive Care. 2013;3(1):12. doi: 10.1186/2110-5820-3-12. - DOI - PMC - PubMed
    1. Bertero E, Maack C. Metabolic remodelling in heart failure. Nature Reviews Cardiology. 2018;15(8):457–470. doi: 10.1038/s41569-018-0044-6. - DOI - PubMed
    1. Brown DA, Perry JB, Allen ME, Sabbah HN, Stauffer BL, Shaikh SR, Cleland JG, Colucci WS, Butler J, Voors AA, Anker SD, Pitt B, Pieske B, Filippatos G, Greene SJ, Gheorghiade M. Expert consensus document: mitochondrial function as a therapeutic target in heart failure. Nature Reviews Cardiology. 2017;14(4):238–250. doi: 10.1038/nrcardio.2016.203. - DOI - PMC - PubMed
    1. Cao Y, Liu Z, Ma W, Fang C, Pei Y, Jing Y, Huang J, Han X, Xiao W. Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction. Frontiers in Endocrinology. 2023;14:1060470. doi: 10.3389/fendo.2023.1060470. - DOI - PMC - PubMed
    1. Chen C, Liu M, Tang Y, Sun H, Lin X, Liang P, Jiang B. LncRNA H19 is involved in myocardial ischemic preconditioning via increasing the stability of nucleolin protein. Journal of Cellular Physiology. 2020;235(9):5985–5994. doi: 10.1002/jcp.29524. - DOI - PubMed

MeSH terms

Grants and funding

This work was supported by the National Natural Science Foundation of China (Grant number 81971820). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

LinkOut - more resources