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Impaired peripheral mononuclear cell metabolism in patients at risk of developing sepsis: A cohort study Cover

Impaired peripheral mononuclear cell metabolism in patients at risk of developing sepsis: A cohort study

The Journal of Critical Care Medicine
Volume 12 (2026): Issue 1 (January 2026)
By: Velma Herwanto,  Ya Wang,  Maryam Shojaei,  Alamgir Khan,  Kevin Lai,  Amith Shetty,  Stephen Huang,  Tracy Chew,  Sally Teoh,  Marek Nalos,  Mandira Chakraborty,  Anthony S McLean and  Benjamin M P Tang  
Open Access
|Jan 2026

Figures & Tables

Fig. 1.

illustrates the workflow of experiment.
illustrates the workflow of experiment.

Fig. 2.

Cytokine levels in healthy control/HC (n = 12), uncomplicated infection/I (n = 20) and sepsis/S (n = 31). Sepsis demonstrated the highest cytokine levels in IL-6, IL-10 and TNF-α. Comparison between groups were performed by Kruskal-Wallis test followed by Dunn’s multiple comparison test.
Cytokine levels in healthy control/HC (n = 12), uncomplicated infection/I (n = 20) and sepsis/S (n = 31). Sepsis demonstrated the highest cytokine levels in IL-6, IL-10 and TNF-α. Comparison between groups were performed by Kruskal-Wallis test followed by Dunn’s multiple comparison test.

Fig. 3A.

Heat map demonstrated 29 differentially expressed genes in log2-counts. The samples were grouped using semi-supervised clustering. Each sample is labelled by colour according to the clinical condition (performed on a subset of 10 healthy controls, 14 uncomplicated infection and 15 sepsis subjects).
Heat map demonstrated 29 differentially expressed genes in log2-counts. The samples were grouped using semi-supervised clustering. Each sample is labelled by colour according to the clinical condition (performed on a subset of 10 healthy controls, 14 uncomplicated infection and 15 sepsis subjects).

Fig. 3B.

The differentially expressed genes (FDR <0.05) in uncomplicated infection (n = 14) and sepsis patients (n = 15) relative to healthy controls’ (n = 10). Function of each gene is indicated by colour (e.g. regulations on mitochondrial function, ROS production and apoptosis cell death). Twenty genes were expressed differently in sepsis. Comparison between groups were performed by one-way ANOVA followed by Benjamini-Yakutieli False Discovery Rate method.
The differentially expressed genes (FDR <0.05) in uncomplicated infection (n = 14) and sepsis patients (n = 15) relative to healthy controls’ (n = 10). Function of each gene is indicated by colour (e.g. regulations on mitochondrial function, ROS production and apoptosis cell death). Twenty genes were expressed differently in sepsis. Comparison between groups were performed by one-way ANOVA followed by Benjamini-Yakutieli False Discovery Rate method.

Fig. 4

Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) on subjects with gene expression data: 4A. basal respiration, 4B. maximal respiration, 4C. spare capacity, 4D. ATP production, 4E. basal ECAR in healthy control/HC (n = 10), uncomplicated infection/I (n = 14) and sepsis/S (n = 15) group. Comparison between groups were performed by one-way ANOVA followed by Tukey’s multiple comparison test.
Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) on subjects with gene expression data: 4A. basal respiration, 4B. maximal respiration, 4C. spare capacity, 4D. ATP production, 4E. basal ECAR in healthy control/HC (n = 10), uncomplicated infection/I (n = 14) and sepsis/S (n = 15) group. Comparison between groups were performed by one-way ANOVA followed by Tukey’s multiple comparison test.

Figure 5

MitoSOX (5A) and DCFDA (5B) staining in healthy control (HC), uncomplicated infection (I) and sepsis (S). N for MitoSOX = HC (20), I (24), S (36), respectively; n for DCFDA = HC (20), I (26), S (37), respectively. For MitoSOX dataset, comparison between groups was performed with Kruskal-Wallis test followed by Dunn’s multiple comparison. For DCFDA dataset, comparison between groups was made using one-way ANOVA followed by Tukey’s multiple comparison.
MitoSOX (5A) and DCFDA (5B) staining in healthy control (HC), uncomplicated infection (I) and sepsis (S). N for MitoSOX = HC (20), I (24), S (36), respectively; n for DCFDA = HC (20), I (26), S (37), respectively. For MitoSOX dataset, comparison between groups was performed with Kruskal-Wallis test followed by Dunn’s multiple comparison. For DCFDA dataset, comparison between groups was made using one-way ANOVA followed by Tukey’s multiple comparison.

Fig. 6.

Correlation between mitochondrial superoxide level (measured with MitoSOX) with cellular metabolism parameters (n = 80) (A. Basal respiration, B. Maximal respiration, C. Spare respiratory capacity, D. ATP production, E. ECAR). Analysis was performed with Pearson correlation. r = correlation coefficient.
Correlation between mitochondrial superoxide level (measured with MitoSOX) with cellular metabolism parameters (n = 80) (A. Basal respiration, B. Maximal respiration, C. Spare respiratory capacity, D. ATP production, E. ECAR). Analysis was performed with Pearson correlation. r = correlation coefficient.

Patients Demographic and Clinical Characteristics (n = 67)

CharacteristicsUncomplicated InfectionSepsisP value
N (%)27 (40.3)40 (59.7)
Age – yr56.4 ± 18.1269.1 ± 13.830.002
Male sex – no. (total no., %)19 (70.4)20 (50)0.099
Source of infection
Respiratory tract (%)12 (44.4)20 (50)0.6551
Urinary tract (%)4 (14.8)12 (30)0.1554
Abdominal, liver and biliary tract (%)5 (18.5)5 (12.5)0.5022
Skin and soft tissue (%)4 (14.8)5 (12.5)0.7881
Cardiovascular (%)0 (0)1 (2.5)0.4113
Bone and joint (%)1 (3.7)1 (2.5)0.7787
Unknown2 (7.4)1 (2.5)0.3450
Comorbidities (%)21 (77.8)39 (97.5)0.0150
Cardiovascular disease (%)15 (55.6)30 (75)0.0997
Respiratory disease (%)7 (25.9)13 (32.5)0.5654
Diabetes mellitus (%)6 (22.2)15 (37.5)0.1887
Malignancy (%)4 (14.8)13 (32.5)0.1050
Chronic kidney disease (%)3 (11.1)6 (15)0.6485
Septic shock (%)NA10 (25.0)NA
ICU admission (%)0 (0)9 (22.5)0.0086
Hospital readmission – 28 day (%)2 (7.4)3 (7.5)0.9879
Length of stay (day)5 (1–68)8 (1–106)0.073
In-hospital mortality (%)0 (0)3 (7.5)0.1484
Improving SOFA score on 3–5 days (%)8/9 (88.9)15/17 (88.2)0.9585
Leukocyte count (×109/L)12.5 ± 3.7214.6 ± 8.620.188
Neutrophil count (×109/L)10.0 ± 3.4412.3 ± 8.170.124
Lymphocyte count (×109/L)1.3 (0.5–5.1)0.9 (0.2–4.2)0.053
Monocyte count (×109/L)0.7 (0.2–2.5)0.9 (0.0–4.0)0.453
Platelet (×109/L)222 (136–701)187 (43–414)0.038
CRP (mg/L)43 (3–295)118 (3–390)0.026
Lactate (mmol/L)1.45 (0.8–2.9)2 (0.4–6.5)0.017
Positive culture
From source of infection (%)11/24 (45.8)20/37 (54.1)0.5299
From blood (%)6/21 (28.6)13/32 (40.6)0.3775
DOI: https://doi.org/10.2478/jccm-2026-0010 | Journal eISSN: 2393-1817 | Journal ISSN: 2393-1809
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Language: English
Page range: 64 - 77
Submitted on: Feb 23, 2025
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Accepted on: Jan 15, 2026
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Published on: Jan 30, 2026
Published by: University of Medicine, Pharmacy, Science and Technology of Targu Mures
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
cellular metabolism,
oxidative stress,
peripheral blood mononuclear cells,
sepsis,
uncomplicated infection
Related subjects:
Medicine,
Clinical medicine,
Internal medicine,
Internal medicine, other,
Surgery,
Surgery, other,
Anaesthesiology,
Emergency medicine and intensive-care medicine

© 2026 Velma Herwanto, Ya Wang, Maryam Shojaei, Alamgir Khan, Kevin Lai, Amith Shetty, Stephen Huang, Tracy Chew, Sally Teoh, Marek Nalos, Mandira Chakraborty, Anthony S McLean, Benjamin M P Tang, published by University of Medicine, Pharmacy, Science and Technology of Targu Mures
This work is licensed under the Creative Commons Attribution 4.0 License.

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