Past Experiences for Future Applications of Metabolomics in Critically Ill Patients with Sepsis and Septic Shocks

Tryptophan catabolism via the kynurenine pathway regulates infection and inflammation: from mechanisms to biomarkers and therapies

BACKGROUND

L-Tryptophan (L-Trp), an essential amino acid, is the only amino acid whose level is regulated specifically by immune signals. Most proportions of Trp are catabolized via the kynurenine (Kyn) pathway (KP) which has evolved to align the food availability and environmental stimulation with the host pathophysiology and behavior. Especially, the KP plays an indispensable role in balancing the immune activation and tolerance in response to pathogens.

SCOPE OF REVIEW

In this review, we elucidate the underlying immunological regulatory network of Trp and its KP-dependent catabolites in the pathophysiological conditions by participating in multiple signaling pathways. Furthermore, the KP-based regulatory roles, biomarkers, and therapeutic strategies in pathologically immune disorders are summarized covering from acute to chronic infection and inflammation.

MAJOR CONCLUSIONS

The immunosuppressive effects dominate the functions of KP induced-Trp depletion and KP-produced metabolites during infection and inflammation. However, the extending minor branches from the KP are not confined to the immune tolerance, instead they go forward to various functions according to the specific condition. Nevertheless, persistent efforts should be made before the clinical use of KP-based strategies to monitor and cure infectious and inflammatory diseases.

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.

Exploring the Muscle Metabolomics in the Mouse Model of Sepsis-Induced Acquired Weakness

Background/Aim

We aimed to identify the differentially expressing metabolites (DEMs) in the muscles of the mouse model of sepsis-induced acquired weakness (sepsis-AW) using liquid chromatography-mass spectrometry (LC-MS).

Materials and Methods

Sepsis by cecal ligation puncture (CLP) with lower limb immobilization was used to produce a sepsis-AW model. After this, the grip strength of the C57BL/6 male mice was investigated. The transmission electron microscopy was utilized to determine the pathological model. LC-MS was used to detect the metabolic profiles within the mouse muscles. Additionally, a statistically diversified analysis was carried out.

Results

Compared to the sepsis group, 30 DEMs, including 17 upregulated and 13 down-regulated metabolites, were found in the sepsis-AW group. The enriched metabolic pathways including purine metabolism, valine/leucine/isoleucine biosynthesis, cGMP-PKG pathway, mTOR pathway, FoxO pathway, and PI3K-Akt pathway were found to differ between the two groups. The targeted metabolomics analysis explored significant differences between four amino acid metabolites (leucine, cysteine, tyrosine, and serine) and two energy metabolites (AMP and cAMP) in the muscles of the sepsis-AW experimental model group, which was comparable to the sepsis group.

Conclusion

The present work identified DEMs and metabolism-related pathways within the muscles of the sepsis-AW mice, which offered valuable experimental data for diagnosis and identification of the pathogenic mechanism underlying sepsis-AW.

Multivariate Prognostic Model for Predicting the Outcome of Critically Ill Patients Using the Aromatic Metabolites Detected by Gas Chromatography-Mass Spectrometry

A number of aromatic metabolites of tyrosine and phenylalanine have been investigated as new perspective markers of infectious complications in the critically ill patients of intensive care units (ICUs). The goal of our research was to build a multivariate model for predicting the outcome of critically ill patients regardless of the main pathology on the day of admission to the ICU. Eight aromatic metabolites were detected in serum using gas chromatography-mass spectrometry. The samples were obtained from the critically ill patients (n = 79), including survivors (n = 44) and non-survivors (n = 35), and healthy volunteers (n = 52). The concentrations of aromatic metabolites were statistically different in the critically ill patients and healthy volunteers. A univariate model for predicting the outcome of the critically ill patients was based on 3-(4-hydroxyphenyl)lactic acid (p-HPhLA). Two multivariate classification models were built based on aromatic metabolites using SIMCA method. The predictive models were compared with the clinical APACHE II scale using ROC analysis. For all of the predictive models the areas under the ROC curve were close to one. The aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcome of critically ill patients on the day of admission to the ICU.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.