Untargeted Metabolomic Profiling of the Correlation Between Prognosis Differences and PD-1 Expression in Sepsis: A Preliminary Study

Persistence and Sexual Dimorphism of Gut Dysbiosis and Pathobiome after Sepsis and Trauma

OBJECTIVE

To evaluate the persistence of intestinal microbiome dysbiosis and gut-plasma metabolomic perturbations following severe trauma or sepsis weeks after admission in patients experiencing chronic critical illness (CCI).

SUMMARY

Trauma and sepsis can lead to gut dysbiosis and alterations in the plasma and fecal metabolome. However, the impact of these perturbations and correlations between gut dysbiosis and the plasma metabolome in chronic critical illness have not been studied.

METHODS

A prospective observational cohort study was performed with healthy subjects, severe trauma patients, and patients with sepsis residing in an intensive care unit for 2 to 3 weeks. A high-throughput multi-omics approach was utilized to evaluate the gut microbial and gut-plasma metabolite responses in critically ill trauma and sepsis patients 14 to 21 days after intensive care unit admission.

RESULTS

Patients in the sepsis and trauma cohorts demonstrated strikingly depleted gut microbiome diversity, with significant alterations and specific pathobiome patterns in the microbiota composition compared to healthy subjects. Further subgroup analyses based on sex revealed resistance to changes in microbiome diversity among female trauma patients compared to healthy counterparts. Sex--specific changes in fecal metabolites were also observed after trauma and sepsis, while plasma metabolite changes were similar in both males and females.

CONCLUSIONS

Dysbiosis induced by trauma and sepsis persists up to 14 to 21 days after onset and is sex-specific, underscoring the implication of pathobiome and entero-septic microbial-metabolite perturbations in post-sepsis and posttrauma chronic critical illness. This indicates resilience to infection or injury in females' microbiome and should inform and facilitate future precision/personalized medicine strategies in the intensive care unit.

LASSO regression shows histidine and sphingosine 1 phosphate are linked to both sepsis mortality and endothelial damage

Sepsis is a major cause of death worldwide, with a mortality rate that has remained stubbornly high. The current gold standard of risk stratifying sepsis patients provides limited mechanistic insight for therapeutic targeting. An improved ability to predict sepsis mortality and to understand the risk factors would allow better treatment targeting. Sepsis causes metabolic dysregulation in patients; therefore, metabolomics offers a promising tool to study sepsis. It is also known that that in sepsis endothelial cells affecting their function regarding blood clotting and vascular permeability. We integrated metabolomics data from patients admitted to an intensive care unit for sepsis, with commonly collected clinical features of their cases and two measures of endothelial function relevant to blood vessel function, platelet endothelial cell adhesion molecule and soluble thrombomodulin concentrations in plasma. We used least absolute shrinkage and selection operator penalized regression, and pathway enrichment analysis to identify features most able to predict 30-day survival. The features important to sepsis survival include carnitines, and amino acids. Endothelial proteins in plasma also predict 30-day mortality and the levels of these proteins also correlate with a somewhat overlapping set of metabolites. Overall metabolic dysregulation, particularly in endothelial cells, may be a contributory factor to sepsis response. By exploring sepsis metabolomics data in conjunction with clinical features and endothelial proteins we have gained a better understanding of sepsis risk factors.

The implication of targeting PD-1:PD-L1 pathway in treating sepsis through immunostimulatory and anti-inflammatory pathways

Sepsis currently remains a major contributor to mortality in the intensive care unit (ICU), with 48.9 million cases reported globally and a mortality rate of 22.5% in 2017, accounting for almost 20% of all-cause mortality worldwide. This highlights the urgent need to improve the understanding and treatment of this condition. Sepsis is now recognized as a dysregulation of the host immune response to infection, characterized by an excessive inflammatory response and immune paralysis. This dysregulation leads to secondary infections, multiple organ dysfunction syndrome (MODS), and ultimately death. PD-L1, a co-inhibitory molecule expressed in immune cells, has emerged as a critical factor in sepsis. Numerous studies have found a significant association between the expression of PD-1/PD-L1 and sepsis, with a particular focus on PD-L1 expressed on neutrophils recently. This review explores the role of PD-1/PD-L1 in immunostimulatory and anti-inflammatory pathways, illustrates the intricate link between PD-1/PD-L1 and sepsis, and summarizes current therapeutic approaches against PD-1/PD-L1 in the treatment and prognosis of sepsis in preclinical and clinical studies.

Association of Plasma Selenium and Its Untargeted Metabolomic Profiling with Cervical Cancer Prognosis

Selenium is an essential trace element that shows beneficial or adverse health effects depending on the dose. However, its role in the prognosis of cervical cancer (CC) has been less reported. We aimed to explore the association between selenium status and prognosis in CC patients with different prognoses and to elucidate the underlying mechanism of selenium in CC prognosis. This cross-sectional observational study had a case-control design at the Harbin Medical University Cancer Hospital and was conducted using 29 CC cases with poor prognosis and 29 CC cases with good prognosis. Plasma selenium levels were measured using an atomic fluorescence spectrometer. Untargeted metabolomics was used to identify metabolites. Plasma selenium levels of the poor prognosis group (49.90 ± 13.81 µg/L) were lower than that of the good prognosis group (59.38 ± 13.00 µg/L, t = 2.69, P = 0.009). In the logistic regression analysis, plasma selenium levels were associated with lower poor prognosis risk [odds ratio (OR) = 0.952, 95% CI: 0.909-0.998]. Receiver operating characteristic curve analysis revealed an optimal cut-off point of plasma selenium levels ≤ 47.68 µg/L for poor prognosis of CC. Based on the cut-off selenium levels, patients with different prognoses were divided into high and low selenium groups. Metabolomic analysis revealed six differential metabolites among different prognoses with low and high selenium levels, and the glycerophospholipid (GPL) metabolism was enriched. Plasma selenium levels were positively correlated with metabolite levels. Our findings provided evidence that low plasma selenium levels may associate with a poor prognosis of CC. Low plasma selenium levels might suppress GPL metabolism and influence the prognosis of CC. This finding requires confirmation in future prospective cohort studies.

Regulatory role of the programmed cell death 1 signaling pathway in sepsis induced immunosuppression

Sepsis is a multiple organ dysfunction syndrome caused by the host's immune response to infection, with extremely high incidence and mortality. Immunosuppression is an essential pathophysiological alteration that influences the clinical treatment and prognosis of sepsis. Recent studies have suggested that the programmed cell death 1 signaling pathway is involved in the formation of immunosuppression in sepsis. In this review, we systematically present the mechanisms of immune dysregulation in sepsis and elucidate the expression and regulatory effects of the programmed cell death 1 signaling pathway on immune cells associated with sepsis. We then specify current research developments and prospects for the application of the programmed cell death 1 signaling pathway in immunomodulatory therapy for sepsis. Several open questions and future research are discussed at the end.

Alterations in levels of cytokine following treatment to predict outcome of sepsis: A meta-analysis

BACKGROUND

The mortality rate of patients with sepsis has been increasing in recent years. Alterations of biomarkers levels during treatment are important in evaluating treatment efficacy and predicting outcomes in sepsis. This meta-analysis investigated the relationship between changes in cytokine levels after treatment compared with those on hospital admission, and their relationship with the prognosis of patients with sepsis.

METHODS

From conception until August 4, 2021, a complete literature search of the PubMed, Web of Science, and Cochrane Library electronic databases was done. Observational studies where the outcomes of sepsis patients were divided into non-survivors and survivors and which reported cytokine levels at least before treatment in ICU were included in the current study. Standardized mean difference (SMD) with 95% confidence intervals (CI) values from individual studies were pooled using a random-effects model. Quality assessment, subgroup analysis, publication bias, and sensitivity analyses were all carried out.

RESULTS

A total of 2570 patients with sepsis from 25 eligible studies were included, and 14 of them measured the cytokine levels before and after treatment in ICU. Among IL-6, TNF-α, IL-1β and IL-10 levels, those of IL-6 were significantly lower after treatment in ICU than at baseline in patients with sepsis in the survival group (SMD = -0.69, P < 0.0001), but were comparable in the non-survival group (SMD = -0.99, P = 0.0575). Similarly, post-treatment TNF-α levels were significantly lower than those at baseline only in patients with sepsis in the survival group (SMD = -0.44, P < 0.0001), but not in the non-survival group (SMD =-0.17, P = 0.0842).

CONCLUSION

This meta-analysis shows that reduced IL-6 and TNF-α levels after sepsis treatment in ICU may be indicators of better prognosis and survival of patients with sepsis.

Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction

OBJECTIVE

Sepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction.

METHOD

Plasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, P < 0.05, and fold change (FC) > 1.5 or < 0.7. Pathway enrichment analysis further revealed associated metabolic pathways. In addition, we constructed a subgroup metabolic analysis between the survivors and non-survivors according to 28-day mortality in the cardiac dysfunction group.

RESULTS

Two metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism.

CONCLUSION

Metabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.

Expression of PD-1 on Memory T Lymphocytes Predicts 28-Day Mortality of Patients with Sepsis: A Prospective Observational Study

Background

Methods

Results

Conclusion

ELEVATED PD-1/CD28 RATIO RATHER THAN PD-1 EXPRESSION IN CD8+ T CELLS PREDICTS NOSOCOMIAL INFECTION IN SEPSIS PATIENTS: A PROSPECTIVE, OBSERVATIONAL COHORT STUDY

ABSTRACTBACKGROUND

The expression of programmed cell death 1 receptor (PD-1) and CD28 on CD8+ T cells is considered to be related to immune function and prognosis markers in patients with sepsis. However, the relationship between the ratio of PD-1/CD28 and nosocomial infection has not been elucidated. Methods: A prospective, observational cohort study was conducted in a general intensive care unit. Patients were enrolled according to the sepsis-3 criteria and peripheral blood samples were collected within 24 hours of enrollment. Programmed cell death 1 receptor and CD28 expression on CD8+ T cells was assayed on day 1. Patients were followed up until 28 days. Multivariate regression analysis was used to assess independent risk factors for nosocomial infection. The accuracy of biomarkers for nosocomial infection and mortality was determined by the area under the receiver operating characteristic curve analysis. The association between biomarkers and 28-day mortality was assessed by Cox regression survival analysis. Results: A total of 181 patients were recruited, and 68 patients were finally included for analysis. Of these, 19 patients (27.9%) died during 28 days and 22 patients (32.4%) acquired nosocomial infection. The PD-1/CD28 ratio of patients with nosocomial infection was significantly higher than those without (0.27 [0.10-0.55] vs. 0.15 [0.08-0.28], P = 0.025). The PD-1/CD28 ratio in CD8+ T cells (odds ratio, 53.33; 95% confidence interval, 2.39-1188.22, P = 0.012) and duration of mechanical ventilation (odds ratio, 1.14; 95% confidence interval, 1.06-1.24; P = 0.001) were independently associated with nosocomial infection. The area under the receiver operating characteristic curve of PD-1/CD28 ratio in CD8+ T cells was 0.67 (0.52-0.82). The PD-1/CD28 ratio in CD8+ T cells of the nonsurvivors was significantly higher than the survivors (0.23 [0.15-0.52] vs. 0.14 [0.07-0.32]); Cox regression analysis showed that the survival time of patients with PD-1/CD28 ratio in CD8+ T cells of 0.13 or greater was shorter compared with patients with lower levels (hazard ratio, 4.42 [1.29-15.20], χ2 = 6.675; P = 0.010). Conclusions: PD-1/CD28 ratio in CD8+ T cells at admission may serve as a novel prognostic biomarker for predicting nosocomial infection and mortality.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.