Metabolite Profiles in Sepsis: Developing Prognostic Tools Based on the Type of Infection. Crit Care Med. 2016;44:1649-1662. [PMID: 27097292 DOI: 10.1097/ccm.0000000000001740]

Features of Metabolites and Biomarkers in Inflammatory and Infectious Complications of Childhood Cancers

Background: In the treatment of oncological diseases in children, the search for opportunities for the earlier detection of complications to improve treatment results is very important. Metabolomic studies are actively conducted to stratify different groups of patients in order to identify the most promising markers. Methods: Three groups of patients participated in this study: healthy children as a control group (n = 18), children with various malignant oncological diseases (leukemia, lymphoma, nephroblastoma, ependymoma, etc.) as patients (n = 40) without complications, and patients (n = 31) with complications (inflammatory and infectious). The mitochondrial metabolites (succinic and fumaric acids); biomarkers related to inflammation such as C-reactive protein (CRP), procalcitonin (PCT), and presepsin (PSP); and sepsis-associated aromatic metabolites, such as phenyllactic (PhLA), hydroxyphenyllactic (p-HPhLA), and hydroxyphenylacetic acids (p-HPhAA), were identified. Results: It was found that children with malignant oncological diseases had profound metabolic dysfunction compared to healthy children, regardless of the presence of systemic inflammatory response syndrome (SIRS) or sepsis. The prognostic ability of procalcitonin and presepsin for detecting sepsis was high: AUROC = 0.875, cut-off value (Youden index) = 0.913 ng/mL, and AUROC = 0.774, with cut-off value (Youden index) of 526 pg/mL, respectively. Conclusions: A significant increase in aromatic microbial metabolites and biomarkers in non-survivor patients that is registered already in the first days of the development of complications indicates the appropriateness of assessing metabolic dysfunction for its timely targeted correction.

Modulation of Metabolomic Profile in Sepsis According to the State of Immune Activation

OBJECTIVE

To investigate the metabolomic profiles associated with different immune activation states in sepsis patients.

DESIGN

Subgroup analysis of the PROVIDE (a Personalized Randomized trial of Validation and restoration of Immune Dysfunction in severe infections and Sepsis) prospective clinical study.

SETTING

Results of the PROVIDE study showed that patients with sepsis may be classified into three states of immune activation: 1) macrophage-activation-like syndrome (MALS) characterized by hyperinflammation, sepsis-induced immunoparalysis, and 3) unclassified or intermediate patients without severe immune dysregulation.

PATIENTS OR SUBJECTS

Two hundred ten patients from 14 clinical sites in Greece meeting the Sepsis-3 definitions with lung infection, acute cholangitis, or primary bacteremia.

INTERVENTIONS

During our comparison, we did not perform any intervention.

MEASUREMENTS AND MAIN RESULTS

Untargeted metabolomics analysis was performed on plasma samples from 210 patients (a total of 1394 products). Differential abundance analysis identified 221 significantly different metabolites across the immune states. Metabolites were enriched in pathways related to ubiquinone biosynthesis, tyrosine metabolism, and tryptophan metabolism when comparing MALS to immunoparalysis and unclassified patients. When comparing MALS to unclassified, 312 significantly different metabolites were found, and pathway analysis indicated enrichment in multiple pathways. Comparing immunoparalysis to unclassified patients revealed only two differentially regulated metabolites.

CONCLUSIONS

Findings suggest distinct metabolic dysregulation patterns associated with different immune dysfunctions in sepsis: the strongest metabolic dysregulation is associated with MALS.

Advancements in molecular disassembly of optical probes: a paradigm shift in sensing, bioimaging, and therapeutics

The majority of self-assembled fluorescent dyes suffer from aggregation-caused quenching (ACQ), which detrimentally affects their diagnostic and therapeutic effectiveness. While aggregation-induced emission (AIE) active dyes offer a promising solution to overcome this limitation, they may face significant challenges as the intracellular environment often prevents aggregation, leading to disassembly and posing challenges for AIE fluorogens. Recent progress in signal amplification through the disassembly of ACQ dyes has opened new avenues for creating ultrasensitive optical sensors and enhancing phototherapeutic outcomes. These advances are well-aligned with cutting-edge technologies such as single-molecule microscopy and targeted molecular therapies. This work explores the concept of disaggregation-induced emission (DIE), showcasing the revolutionary capabilities of DIE-based dyes from their design to their application in sensing, bioimaging, disease monitoring, and treatment in both cellular and animal models. Our objective is to provide an in-depth comparison of aggregation versus disaggregation mechanisms, aiming to stimulate further advancements in the design and utilization of ACQ fluorescent dyes through DIE technology. This initiative is poised to catalyze scientific progress across a broad spectrum of disciplines.

The nasal microbiota is a potential diagnostic biomarker for sepsis in critical care units

This study aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A total of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital. Nasal swabs and fecal specimens were collected from septic and non-septic patients in the intensive care unit (ICU) and Department of Respiratory and Critical Care Medicine. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using Illumina technology. Bioinformatics analysis, statistical processing, and machine learning techniques were employed to differentiate between septic and non-septic patients. The nasal microbiota of septic patients exhibited significantly lower community richness (P = 0.002) and distinct compositions (P = 0.001) compared to non-septic patients. Corynebacterium, Staphylococcus, Acinetobacter, and Pseudomonas were identified as enriched genera in the nasal microbiota of septic patients. The constructed machine learning model achieved an area under the curve (AUC) of 89.08, indicating its efficacy in differentiating septic and non-septic patients. Importantly, model validation demonstrated the effectiveness of the nasal microecological diagnosis prediction model with an AUC of 84.79, while the gut microecological diagnosis prediction model had poor predictive performance (AUC = 49.24). The nasal microbiota of ICU patients effectively distinguishes sepsis from non-septic cases and outperforms the gut microbiota. These findings have implications for the development of diagnostic strategies and advancements in critical care medicine.IMPORTANCEThe important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected.

Mendelian randomization analysis reveals causal associations of serum metabolites with sepsis and 28-day mortality

Metabolic disorder has been found to be an important factor in the pathogenesis and progression of sepsis. However, the causation of such an association between serum metabolites and sepsis has not been established. We conducted a two-sample Mendelian randomization (MR) study. A genome-wide association study of 486 human serum metabolites was used as the exposure, whereas sepsis and sepsis mortality within 28 days were set as the outcomes. In MR analysis, 6 serum metabolites were identified to be associated with an increased risk of sepsis, and 6 serum metabolites were found to be related to a reduced risk of sepsis. Furthermore, there were 9 metabolites positively associated with sepsis-related mortality, and 8 metabolites were negatively correlated with sepsis mortality. In addition, "glycolysis/gluconeogenesis" (p = 0.001), and "pyruvate metabolism" (p = 0.042) two metabolic pathways were associated with the incidence of sepsis. This MR study suggested that serum metabolites played significant roles in the pathogenesis of sepsis, which may provide helpful biomarkers for early disease diagnosis, therapeutic interventions, and prognostic assessments for sepsis.

Biomarkers in Sepsis: A Current Review of New Technologies

Sepsis syndromes have been recognized since antiquity yet still pose significant challenges to modern medicine. One of the biggest challenges lies in the heterogeneity of triggers and its protean clinical manifestations, as well as its rapidly progressive and lethal nature. Thus, there is a critical need for biomarkers that can quickly and accurately detect sepsis onset and predict treatment response. In this review, we will briefly describe the current consensus definitions of sepsis and the ideal features of a biomarker. We will then delve into currently available and in-development markers of pathogens, hosts, and their interactions that together comprise the sepsis syndrome.

Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes

Delayed diagnosis of patients with sepsis or septic shock is associated with increased mortality and morbidity. UPLC-MS and NMR spectroscopy were used to measure panels of lipoproteins, lipids, biogenic amines, amino acids, and tryptophan pathway metabolites in blood plasma samples collected from 152 patients within 48 h of admission into the Intensive Care Unit (ICU) where 62 patients had no sepsis, 71 patients had sepsis, and 19 patients had septic shock. Patients with sepsis or septic shock had higher concentrations of neopterin and lower levels of HDL cholesterol and phospholipid particles in comparison to nonsepsis patients. Septic shock could be differentiated from sepsis patients based on different concentrations of 10 lipids, including significantly lower concentrations of five phosphatidylcholine species, three cholesterol esters, one dihydroceramide, and one phosphatidylethanolamine. The Supramolecular Phospholipid Composite (SPC) was reduced in all ICU patients, while the composite markers of acute phase glycoproteins were increased in the sepsis and septic shock patients within 48 h admission into ICU. We show that the plasma metabolic phenotype obtained within 48 h of ICU admission is diagnostic for the presence of sepsis and that septic shock can be differentiated from sepsis based on the lipid profile.

Electrochemical analysis of total phospholipids in human serum for severe sepsis diagnosis

Electrochemical analysis of total phospholipids was performed for the diagnosis of sepsis. The influence of electrode materials on the analysis of the chromogenic substrate was analyzed using Au, graphite, and pyrolyzed carbon electrodes. The total phospholipid analysis based on electrochemical analysis with pyrolyzed carbon was used for diagnosis of sepsis using sera from healthy volunteers, systemic inflammatory response syndrome (SIRS), and severe sepsis patients. The analysis results using the optical measurement and the electrochemical analysis were compared for the serum samples from sepsis patients and healthy controls. Additionally, the interference of human serum on the optical measurement and electrochemical analysis was estimated by signal-to-noise (S/N) calculation. The assay results of the levels of other biomarkers for sepsis (C-reactive protein and procalcitonin) and the total phospholipid levels obtained using the optical measurement and electrochemical analysis methods were statistically similar. Finally, the mortality of patients, indicated by the results of the total phospholipid assay performed using the electrochemical analysis of the patient samples collected daily (1, 3, and 7 day(s) after admission to hospital), was compared with the patient mortality assessed via conventional severity indexes, such as the SOFA and APACHE Ⅱ scores. The 28-day survival rate was estimated by Kaplan-Meier survival analysis based on the total phospholipid level of patient samples that were obtained after 1, 3, and 7 day(s) from hospital admission.

Flow-Rate-Insensitive Plasma Extraction by the Stabilization and Acceleration of Secondary Flow in the Ultralow Aspect Ratio Spiral Channel

Although microfluidic devices have made remarkable strides in blood cell separation, there is still a need for further development and improvement in this area. Herein, we present a novel ultralow aspect ratio (H/W = 1:36) spiral channel microfluidic device with ordered micro-obstacles for sheathless and flow-rate-insensitive blood cell separation. By introducing ordered micro-obstacles into the spiral microchannels, reduced magnitude fluctuations in secondary flow across different loops can be obtained through geometric confinement. As a result, the unique Dean-like secondary flow can effectively enhance the separation efficiency of particles in different sizes ranging from 3 to 15 μm. Compared to most existing microfluidic devices, our system offers several advantages of easy manufacturing, convenient operation, long-term stability, highly efficient performance (up to 99.70% rejection efficiency, including platelets), and most importantly, insensitivity to cell sizes as well as flow rates (allowing for efficient separation of different-sized blood cells in a wide flow rate from 1.00 to 2.50 mL/min). The unique characteristics, such as ultralow aspect ratio, sequential micro-obstacles, and controlled secondary flow, make our device a promising solution for practical plasma extraction in biomedical research and clinical applications.

High-Throughput Blood Plasma Extraction in a Dimension-Confined Double-Spiral Channel

Microfluidic technologies enabling the control of secondary flow are essential for the successful separation of blood cells, a process that is beneficial for a wide range of medical research and clinical diagnostics. Herein, we introduce a dimension-confined microfluidic device featuring a double-spiral channel designed to regulate secondary flows, thereby enabling high-throughput isolation of blood for plasma extraction. By integrating a sequence of micro-obstacles within the double-spiral microchannels, the stable and enhanced Dean-like secondary flow across each loop can be generated. This setup consequently prompts particles of varying diameters (3, 7, 10, and 15 μm) to form different focusing states. Crucially, this system is capable of effectively separating blood cells of different sizes with a cell throughput of (2.63-3.36) × 108 cells/min. The concentration of blood cells in outlet 2 increased 3-fold, from 1.46 × 108 to 4.37 × 108, while the number of cells, including platelets, exported from outlets 1 and 3 decreased by a factor of 608. The engineering approach manipulating secondary flow for plasma extraction points to simplicity in fabrication, ease of operation, insensitivity to cell size, high throughput, and separation efficiency, which has potential utility in propelling the development of miniaturized diagnostic devices in the field of biomedical science.

PATHWAYS ASSOCIATED WITH POSITIVE SEPSIS SURVIVAL OUTCOMES IN AFRICAN AMERICAN/BLACK AND NON-HISPANIC WHITE PATIENTS WITH URINARY TRACT INFECTION

ABSTRACT

Urinary tract infections (UTIs) are a common cause of sepsis worldwide. Annually, more than 60,000 US deaths can be attributed to sepsis secondary to UTIs, and African American/Black adults have higher incidence and case-fatality rates than non-Hispanic White adults. Molecular-level factors that may help partially explain differences in sepsis survival outcomes between African American/Black and Non-Hispanic White adults are not clear. In this study, patient samples (N = 166) from the Protocolized Care for Early Septic Shock cohort were analyzed using discovery-based plasma proteomics. Patients had sepsis secondary to UTIs and were stratified according to self-identified racial background and sepsis survival outcomes. Proteomics results suggest patient heterogeneity across mechanisms driving survival from sepsis secondary to UTIs. Differentially expressed proteins (n = 122, false discovery rate-adjusted P < 0.05) in Non-Hispanic White sepsis survivors were primarily in immune system pathways, while differentially expressed proteins (n = 47, false discovery rate-adjusted P < 0.05) in African American/Black patients were mostly in metabolic pathways. However, in all patients, regardless of racial background, there were 16 differentially expressed proteins in sepsis survivors involved in translation initiation and shutdown pathways. These pathways are potential targets for prognostic intervention. Overall, this study provides information about molecular factors that may help explain disparities in sepsis survival outcomes among African American/Black and Non-Hispanic White patients with primary UTIs.

Blood component separation in straight microfluidic channels

Separation of blood components is required in many diagnostic applications and blood processes. In laboratories, blood is usually fractionated by manual operation involving a bulk centrifugation equipment, which significantly increases logistic burden. Blood sample processing in the field and resource-limited settings cannot be readily implemented without the use of microfluidic technology. In this study, we developed a small footprint, rapid, and passive microfluidic channel device that relied on margination and inertial focusing effects for blood component separation. No blood dilution, lysis, or labeling step was needed as to preserve sample integrity. One main innovation of this work was the insertion of fluidic restrictors at outlet ports to divert the separation interface into designated outlet channels. Thus, separation efficiency was significantly improved in comparison to previous works. We demonstrated different operation modes ranging from platelet or plasma extraction from human whole blood to platelet concentration from platelet-rich plasma through the manipulation of outlet port fluidic resistance. Using straight microfluidic channels with a high aspect ratio rectangular cross section, we demonstrated 95.4% platelet purity extracted from human whole blood. In plasma extraction, 99.9% RBC removal rate was achieved. We also demonstrated 2.6× concentration of platelet-rich plasma solution to produce platelet concentrate. The extraction efficiency and throughput rate are scalable with continuous and clog-free recirculation operation, in contrast to other blood fractionation approaches using filtration membranes or affinity-based purification methods. Our microfluidic blood separation method is highly tunable and versatile, and easy to be integrated into multi-step blood processing and advanced sample preparation workflows.

Migration Study of Dielectrophoretically Manipulated Red Blood Cells in Tapered Aluminium Microelectrode Array: A Pilot Study

Dielectrophoresis (DEP) is one of the microfluid-based techniques that can manipulate the red blood cells (RBC) for blood plasma separation, which is used in many medical screening/diagnosis applications. The tapered aluminium microelectrode array (TAMA) is fabricated for potential sensitivity enhancement of RBC manipulation in lateral and vertical directions. In this paper, the migration properties of dielectrophoretically manipulated RBC in TAMA platform are studied at different peak-to-peak voltage (Vpp) and duration supplied onto the microelectrodes. Positive DEP manipulation is conducted at 440 kHz with the RBC of 4.00 ± 0.2 µm average radius attracted to the higher electric field intensity regions, which are the microelectrodes. High percentage of RBC migration occurred at longer manipulation time and high electrode voltage. During DEP manipulation, the RBC are postulated to levitate upwards, experience the electro-orientation mechanism and form the pearl chains before migrating to the electrodes. The presence of external forces other than the dielectrophoretic force may also affect the migration response of RBC. The safe operating limit of 10 Vpp and manipulation duration of ≤50 s prevent RBC rupture while providing high migration percentage. It is crucial to define the safe working region for TAMA devices that manipulate small RBC volume (~10 µL).

Effect of Silane Monolayers and Nanoporous Silicon Surfaces on the Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Detection of Sepsis Metabolites Biomarkers Mixed in Solution

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) is a promising strategy for clinical diagnosis based on metabolite detection. However, several bottlenecks (such as the lack of reproducibility in analysis, the presence of an important background in low-mass range, and the lack of organic matrix for some molecules) prevent its transfer to clinical cases. These limitations can be addressed by using nanoporous silicon surfaces chemically functionalized with silane monolayers. In the present study, sepsis metabolite biomarkers were used to investigate the effects of silane monolayers and porous silicon substrates on MALDI-ToF MS analysis (signal-to-noise value (S/N), relative standard deviation of the S/N of triplicate samples (STDmean), and intra-substrates uniformity). Also, the impact of the physicochemical properties of metabolites, with different isoelectric points and hydrophobic-hydrophilic balances, was assessed. Four different silane molecules, with various alkyl chain lengths and head-group charges, were self-assembled in monolayers on plane and porous silicon surfaces. Their surface coverage and conformity were investigated by X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The seven metabolites detected on the stainless-steel target plate (lysophosphatidylcholine, caffeine, phenylalanine, creatinine, valine, arginine, and glycerophosphocholine) are also detected on the silanized and bare, plane and porous silicon surfaces. Moreover, two metabolites, glycine and alanine, which are not detected on the stainless-steel target plate, are detected on all silanized surfaces, except glycine which is not detected on CH3 short-modified porous silicon and on the bare plane silicon substrate. In addition, whatever the metabolites (except phenylalanine and valine), at least one of the silicon surfaces allows to increase the S/N value in comparison with the stainless-steel target plate. Also, the heterogeneity of matrix crystallization features is linked to the STDmean which is poor on the NH3+ monolayer on plane substrate and better on the NH3+ monolayer on porous substrate, for most of the metabolites. Nevertheless, matrix crystallization features are not sufficient to systematically get high STDmean and uniformity in MALDI-ToF MS analysis. Indeed, the physicochemical properties of metabolites and surfaces, limitations in metabolite extraction from the pores, and improvement in metabolite desorption due to the pores are shown to significantly impact MS analysis. In particular, in the case of the most hydrophobic metabolites studied, the highest S/N values and the best STDmean and uniformity (the lowest values) are reached by using porous substrates, while in the case of the most hydrophilic metabolites studied, plane substrates demonstrated the highest S/N and the lowest STDmean. No clear trend of surface chemistry was evidenced.

A targeted metabolomics approach for sepsis-induced ARDS and its subphenotypes

BACKGROUND

Acute respiratory distress syndrome (ARDS) is etiologically and clinically a heterogeneous disease. Its diagnostic characteristics and subtype classification, and the application of these features to treatment, have been of considerable interest. Metabolomics is becoming important for identifying ARDS biology and distinguishing its subtypes. This study aimed to identify metabolites that could distinguish sepsis-induced ARDS patients from non-ARDS controls, using a targeted metabolomics approach, and to identify whether sepsis-induced direct and sepsis-induced indirect ARDS are metabolically distinct groups, and if so, confirm their metabolites and associated pathways.

METHODS

This study retrospectively analyzed 54 samples of ARDS patients from a sepsis registry that was prospectively collected from March 2011 to February 2018, along with 30 non-ARDS controls. The cohort was divided into direct and indirect ARDS. Metabolite concentrations of five analyte classes (energy metabolism, free fatty acids, amino acids, phospholipids, sphingolipids) were measured using liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry by targeted metabolomics.

RESULTS

In total, 186 metabolites were detected. Among them, 102 metabolites could differentiate sepsis-induced ARDS patients from the non-ARDS controls, while 14 metabolites could discriminate sepsis-induced ARDS subphenotypes. Using partial least-squares discriminant analysis, we showed that sepsis-induced ARDS patients were metabolically distinct from the non-ARDS controls. The main distinguishing metabolites were lysophosphatidylethanolamine (lysoPE) plasmalogen, PE plasmalogens, and phosphatidylcholines (PCs). Sepsis-induced direct and indirect ARDS were also metabolically distinct subgroups, with differences in lysoPCs. Glycerophospholipid and sphingolipid metabolism were the most significant metabolic pathways involved in sepsis-induced ARDS biology and in sepsis-induced direct/indirect ARDS, respectively.

CONCLUSION

Our study demonstrated a marked difference in metabolic patterns between sepsis-induced ARDS patients and non-ARDS controls, and between sepsis-induced direct and indirect ARDS subpheonotypes. The identified metabolites and pathways can provide clues relevant to the diagnosis and treatment of individuals with ARDS.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

A Nomogram for Early Diagnosis of Community-Acquired Pneumonia Based on Bronchoalveolar Lavage Fluid Metabolomics

Purpose

There is a high disease burden associated with community-acquired pneumonia (CAP) around the world. A timely and correct diagnosis of CAP can facilitate early treatment and prevent illness progression. The present study aimed to find some novel biomarkers of CAP by metabolic analysis and construct a nomogram model for precise diagnosis and individualized treatment of CAP patients.

Patients and Methods

42 CAP patients and 20 controls were enrolled in this study. The metabolic profiles of bronchoalveolar lavage fluid (BALF) samples were identified by untargeted LC-MS/MS analysis. With a VIP score ≥ 1 in OPLS-DA analysis and P < 0.05, the significantly dysregulated metabolites were estimated as potential biomarkers of CAP, which were further included in the construction of the diagnostic prediction model along with laboratory inflammatory indexes via stepwise backward regression analysis. Discrimination, calibration, and clinical applicability of the nomogram were evaluated by the C-index, the calibration curve, and the decision curve analysis (DCA) estimated by bootstrap resampling.

Results

The metabolic profiles differed obviously between CAP patients and healthy controls, as shown by PCA and OPLS-DA plots. Seven metabolites significantly dysregulated in CAP were established: dimethyl disulfide, oleic acid (d5), N-acetyl-a-neuraminic acid, pyrimidine, choline, LPC (12:0/0:0) and PA (20:4/2:0). Multivariate logistic regression revealed that the expression levels of PA (20:4/2:0), N-acetyl-a-neuraminic acid, and CRP were associated with CAP. After being validated by bootstrap resampling, this model showed satisfactory diagnostic performance.

Conclusion

A novel nomogram prediction model containing metabolic potential biomarkers in BALF that was developed for the early diagnosis of CAP offers insights into the pathogenesis and host response in CAP.

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

Using a multiomics approach to unravel a septic shock specific signature in skeletal muscle

Sepsis is defined as a dysregulated host response to infection leading to organs failure. Among them, sepsis induces skeletal muscle (SM) alterations that contribute to acquired-weakness in critically ill patients. Proteomics and metabolomics could unravel biological mechanisms in sepsis-related organ dysfunction. Our objective was to characterize a distinctive signature of septic shock in human SM by using an integrative multi-omics approach. Muscle biopsies were obtained as part of a multicenter non-interventional prospective study. Study population included patients in septic shock (S group, with intra-abdominal source of sepsis) and two critically ill control populations: cardiogenic shock (C group) and brain dead (BD group). The proteins and metabolites were extracted and analyzed by High-Performance Liquid Chromatography-coupled to tandem Mass Spectrometry, respectively. Fifty patients were included, 19 for the S group (53% male, 64 ± 17 years, SAPS II 45 ± 14), 12 for the C group (75% male, 63 ± 4 years, SAPS II 43 ± 15), 19 for the BD group (63% male, 58 ± 10 years, SAPS II 58 ± 9). Biopsies were performed in median 3 days [interquartile range 1-4]) after intensive care unit admission. Respectively 31 patients and 40 patients were included in the proteomics and metabolomics analyses of 2264 proteins and 259 annotated metabolites. Enrichment analysis revealed that mitochondrial pathways were significantly decreased in the S group at protein level: oxidative phosphorylation (adjusted p = 0.008); branched chained amino acids degradation (adjusted p = 0.005); citrate cycle (adjusted p = 0.005); ketone body metabolism (adjusted p = 0.003) or fatty acid degradation (adjusted p = 0.008). Metabolic reprogramming was also suggested (i) by the differential abundance of the peroxisome proliferator-activated receptors signaling pathway (adjusted p = 0.007), and (ii) by the accumulation of fatty acids like octanedioic acid dimethyl or hydroxydecanoic. Increased polyamines and depletion of mitochondrial thioredoxin or mitochondrial peroxiredoxin indicated a high level of oxidative stress in the S group. Coordinated alterations in the proteomic and metabolomic profiles reveal a septic shock signature in SM, highlighting a global impairment of mitochondria-related metabolic pathways, the depletion of antioxidant capacities, and a metabolic shift towards lipid accumulation.ClinicalTrial registration: NCT02789995. Date of first registration 03/06/2016.

Circulating N-formylmethionine and metabolic shift in critical illness: a multicohort metabolomics study

BACKGROUND

Cell stress promotes degradation of mitochondria which release danger-associated molecular patterns that are catabolized to N-formylmethionine. We hypothesized that in critically ill adults, the response to N-formylmethionine is associated with increases in metabolomic shift-related metabolites and increases in 28-day mortality.

METHODS

We performed metabolomics analyses on plasma from the 428-subject Correction of Vitamin D Deficiency in Critically Ill Patients trial (VITdAL-ICU) cohort and the 90-subject Brigham and Women's Hospital Registry of Critical Illness (RoCI) cohort. In the VITdAL-ICU cohort, we analyzed 983 metabolites at Intensive Care Unit (ICU) admission, day 3, and 7. In the RoCI cohort, we analyzed 411 metabolites at ICU admission. The association between N-formylmethionine and mortality was determined by adjusted logistic regression. The relationship between individual metabolites and N-formylmethionine abundance was assessed with false discovery rate correction via linear regression, linear mixed-effects, and Gaussian graphical models.

RESULTS

Patients with the top quartile of N-formylmethionine abundance at ICU admission had a significantly higher adjusted odds of 28-day mortality in the VITdAL-ICU (OR, 2.4; 95%CI 1.5-4.0; P = 0.001) and RoCI cohorts (OR, 5.1; 95%CI 1.4-18.7; P = 0.015). Adjusted linear regression shows that with increases in N-formylmethionine abundance at ICU admission, 55 metabolites have significant differences common to both the VITdAL-ICU and RoCI cohorts. With increased N-formylmethionine abundance, both cohorts had elevations in individual short-chain acylcarnitine, branched chain amino acid, kynurenine pathway, and pentose phosphate pathway metabolites.

CONCLUSIONS

The results indicate that circulating N-formylmethionine promotes a metabolic shift with heightened mortality that involves incomplete mitochondrial fatty acid oxidation, increased branched chain amino acid metabolism, and activation of the pentose phosphate pathway.

The role of proteomics and metabolomics in severe infections

PURPOSE OF REVIEW

Severe infections are a common cause of ICU admission, with a high morbidity and mortality. Omics, namely proteomics and metabolomics, aim to identify, characterize, and quantify biological molecules to achieve a systems-level understanding of disease. The aim of this review is to provide a clear overview of the current evidence of the role of proteomics and metabolomics in severe infections.

RECENT FINDINGS

Proteomics and metabolomics are technologies that are being used to explore new markers of diagnosis and prognosis, clarify mechanisms of disease, and consequently discover potential targets of therapy and finally of a better disease phenotyping. These technologies are starting to be used but not yet in clinical use.

SUMMARY

Our traditional way of approaching the disease as sepsis is believing that a process can be broken into its parts and that the whole can be explained by the sum of each part. This approach is highly reductionist and does not take the system complexity nor the nonlinear dynamics of the processes. Proteomics and metabolomics allow the analysis of several proteins and metabolites simultaneously, thereby generating diagnostic and prognostic signatures. An exciting future prospect for proteomics and metabolomics is their employment towards precision medicine.

Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques

Background: Sepsis-induced myocardial dysfunction (SIMD) is the most common and severe sepsis-related organ dysfunction. We aimed to investigate the metabolic changes occurring in the hearts of patients suffering from SIMD.

Methods: An animal SIMD model was constructed by injecting lipopolysaccharide (LPS) into mice intraperitoneally. Metabolites and transcripts present in the cardiac tissues of mice in the experimental and control groups were extracted, and the samples were studied following the untargeted metabolomics–transcriptomics high-throughput sequencing method. SIMD-related metabolites were screened following univariate and multi-dimensional analyses methods. Additionally, differential analysis of gene expression was performed using the DESeq package. Finally, metabolites and their associated transcripts were mapped to the relevant metabolic pathways after extracting transcripts corresponding to relevant enzymes. The process was conducted based on the metabolite information present in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results: One hundred and eighteen significant differentially expressed metabolites (DEMs) (58 under the cationic mode and 60 under the anionic mode) were identified by studying the SIMD and control groups. Additionally, 3,081 significantly differentially expressed genes (DEGs) (1,364 were down-regulated and 1717 were up-regulated DEGs) were identified in the transcriptomes. The comparison was made between the two groups. The metabolomics–transcriptomics combination analysis of metabolites and their associated transcripts helped identify five metabolites (d-mannose, d-glucosamine 6-phosphate, maltose, alpha-linolenic acid, and adenosine 5′-diphosphate). Moreover, irregular and unusual events were observed during the processes of mannose metabolism, amino sugar metabolism, starch metabolism, unsaturated fatty acid biosynthesis, platelet activation, and purine metabolism. The AMP-activated protein kinase (AMPK) signaling pathways were also accompanied by aberrant events.

Conclusion: Severe metabolic disturbances occur in the cardiac tissues of model mice with SIMD. This can potentially help in developing the SIMD treatment methods.

Identification of biomarkers and the mechanisms of multiple trauma complicated with sepsis using metabolomics

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.

Metabolic profile of exhaled breath condensate from the pneumonia patients

PURPOSE OF THE STUDY

Exhaled breath condensate (EBC) is increasingly being used for disease diagnosis and environmental exposure assessment as a noninvasive method reducing the risk of exposure. The purpose of this study was to investigate the application of a new sample type of EBC in pneumonia by metabolomics and to explore differential metabolites and potential metabolic pathways.

MATERIALS AND METHODS

A case-control study was performed at the Peking University Third Hospital from August to December 2020. C-MS/MS analyses were performed on EBC samples using a UHPLC system.

RESULTS

Totally 22 patients with pneumonia and 24 healthy controls were recruited. Using untargeted metabolomics based on LC-MS/MS analysis, 25 kinds of differential metabolites were found. Through a comprehensive analysis of the pathways in which the differential metabolites were located, the key pathway with the highest correlation with the difference of metabolites was taurine and hypotaurine metabolism.

CONCLUSIONS

The study implicates that the hypotaurine/taurine metabolic pathway may play a role on the development of pneumonia through metabolism analysis on EBC and the 3-Sulfinoalanine may be used as a biomarker in the diagnosis of pneumonia.

Metabolomic Profiling in Patients with Heart Failure and Exercise Intolerance: Kynurenine as a Potential Biomarker

Aims: Metabolic and structural perturbations in skeletal muscle have been found in patients with heart failure (HF) both with preserved (HFpEF) and reduced (HFrEF) ejection fraction in association with reduced muscle endurance (RME). We aimed in the current study to create phenotypes for patients with RME and HFpEF compared to RME HFrEF according to their metabolomic profiles and to test the potential of Kynurenine (Kyn) as a marker for RME. Methods: Altogether, 18 HFrEF, 17 HFpEF, and 20 healthy controls (HC) were prospectively included in the current study. The following tests were performed on all participants: isokinetic muscle function tests, echocardiography, spiroergometry, and varied blood tests. Liquid chromatography tandem mass spectrometry was used to quantify metabolites in serum. Results: Except for aromatic and branched amino acids (AA), patients with HF showed reduced AAs compared to HC. Further perturbations were elevated concentrations of Kyn and acylcarnitines (ACs) in HFpEF and HFrEF patients (p < 0.05). While patients with HFpEF and RME presented with reduced concentrations of ACs (long- and medium-chains), those with HFrEF and RME had distorted AAs metabolism (p < 0.05). With an area under the curve (AUC) of 0.83, Kyn shows potential as a marker in HF and RME (specificity 70%, sensitivity 83%). In a multiple regression model consisting of short-chain-ACs, spermine, ornithine, glutamate, and Kyn, the latest was an independent predictor for RME (95% CI: −13.01, −3.30, B: −8.2 per 1 µM increase, p = 0.001). Conclusions: RME in patients with HFpEF vs. HFrEF proved to have different metabolomic profiles suggesting varied pathophysiology. Kyn might be a promising biomarker for patients with HF and RME.

Metabolic preference assay for rapid diagnosis of bloodstream infections

Bloodstream infections (BSIs) cause >500,000 infections and >80,000 deaths per year in North America. The length of time between the onset of symptoms and administration of appropriate antimicrobials is directly linked to mortality rates. It currently takes 2–5 days to identify BSI pathogens and measure their susceptibility to antimicrobials – a timeline that directly contributes to preventable deaths. To address this, we demonstrate a rapid metabolic preference assay (MPA) that uses the pattern of metabolic fluxes observed in ex-vivo microbial cultures to identify common pathogens and determine their antimicrobial susceptibility profiles. In a head-to-head race with a leading platform (VITEK 2, BioMérieux) used in diagnostic laboratories, MPA decreases testing timelines from 40 hours to under 20. If put into practice, this assay could reduce septic shock mortality and reduce the use of broad spectrum antibiotics.

It is currently slow to identify bloodstream infection pathogens. Here the authors report a rapid metabolic preference assay that uses the pattern of metabolic fluxes observed in ex-vivo microbial cultures to identify common pathogens and determine their antimicrobial susceptibility profiles.

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.

Patients with hypothermic sepsis have a unique gene expression profile compared to patients with fever and sepsis

The pathophysiology of hypothermia during sepsis is unclear. Using genomic profiling of blood leukocytes, we aimed to determine if hypothermia is associated with a different gene expression profile compared to fever during sepsis. Patients with sepsis and either hypothermia or fever within 24 hours after ICU admission were included in the study (n = 168). Hypothermia was defined as body temperature below 36 °C. Fever was defined as body temperature equal to or above 38.3°C. We compared blood gene expression (whole-genome transcriptome in leukocytes) in hypothermic septic compared to febrile septic patients in an unmatched analysis and matched for APACHE IV score and the presence of shock. In total, 67 septic patients were hypothermic and 101 patients were febrile. Hypothermia was associated with a distinct gene expression profile in both unmatched and matched analyses. There were significant differences related to the up- and downregulation of canonical signalling pathways. In the matched analysis, the top upregulated gene was cold-inducible mRNA binding protein (CIRBP) which plays a role in cold-induced suppression of cell proliferation. In addition, we found three signalling pathways significantly upregulated in hypothermic patients compared to febrile patients; tryptophan degradation X, phenylalanine degradation IV and putrescine degradation III. In conclusion, there are distinct signalling pathways and genes associated with hypothermia, including tryptophan degradation and CIRBP expression, providing a possible link to the modulation of body temperature and early immunosuppression. Future studies may focus on the canonical signalling pathways presented in this paper to further investigate spontaneous hypothermia in sepsis.

Antiviral metabolite 3'-deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19

BACKGROUND

There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.

METHODS

Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections (including COVID-19), bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.

FINDINGS

3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most significantly differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were among the five genes most highly correlated with ddhC abundance.

CONCLUSIONS

The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.

FUNDING

NIHR Imperial BRC; UKRI.

Molecular Probes, Chemosensors, and Nanosensors for Optical Detection of Biorelevant Molecules and Ions in Aqueous Media and Biofluids

Synthetic molecular probes, chemosensors, and nanosensors used in combination with innovative assay protocols hold great potential for the development of robust, low-cost, and fast-responding sensors that are applicable in biofluids (urine, blood, and saliva). Particularly, the development of sensors for metabolites, neurotransmitters, drugs, and inorganic ions is highly desirable due to a lack of suitable biosensors. In addition, the monitoring and analysis of metabolic and signaling networks in cells and organisms by optical probes and chemosensors is becoming increasingly important in molecular biology and medicine. Thus, new perspectives for personalized diagnostics, theranostics, and biochemical/medical research will be unlocked when standing limitations of artificial binders and receptors are overcome. In this review, we survey synthetic sensing systems that have promising (future) application potential for the detection of small molecules, cations, and anions in aqueous media and biofluids. Special attention was given to sensing systems that provide a readily measurable optical signal through dynamic covalent chemistry, supramolecular host-guest interactions, or nanoparticles featuring plasmonic effects. This review shall also enable the reader to evaluate the current performance of molecular probes, chemosensors, and nanosensors in terms of sensitivity and selectivity with respect to practical requirement, and thereby inspiring new ideas for the development of further advanced systems.

Precision medicine in sepsis and septic shock: From omics to clinical tools

Sepsis is a heterogeneous disease with variable clinical course and several clinical phenotypes. As it is associated with an increased risk of death, patients with this condition are candidates for receipt of a very well-structured and protocolized treatment. All patients should receive the fundamental pillars of sepsis management, which are infection control, initial resuscitation, and multiorgan support. However, specific subgroups of patients may benefit from a personalized approach with interventions targeted towards specific pathophysiological mechanisms. Herein, we will review the framework for identifying subpopulations of patients with sepsis, septic shock, and multiorgan dysfunction who may benefit from specific therapies. Some of these approaches are still in the early stages of research, while others are already in routine use in clinical practice, but together will help in the effective generation and safe implementation of precision medicine in sepsis.

UPLC-Q-TOF/MS-Based Plasma and Urine Metabolomics Contribute to the Diagnosis of Sepsis

In this study, we aimed to identify potential metabolic biomarkers that can improve the diagnostic accuracy of sepsis. Sixty-six patients including 30 septic and 36 nonsepsis patients from an intensive care unit were recruited. The global plasma and urine metabolomic profiles were determined by ultraperformance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry-based methodology. The risk factors, including both traditional physiological indicators and metabolic biomarkers, were investigated by binary logistic regression analysis and used to build a least absolute shrinkage and selection operator (Lasso) regression model to evaluate the ability of diagnosis. Fifty-five metabolites in plasma and 11 metabolites in urine were identified through orthogonal projections to latent structures discriminant analysis (OPLS-DA). Among them, ten (PE (20:4(5Z, 8Z, 11Z, 14Z)/P-18:0), harderoporphyrinogen, chloropanaxydiol, (Z)-2-octenal, N¹,N⁸-diacetylspermidine, 1-nitroheptane, venoterpine, α-CEHC, LysoPE (20:0/0:0), corticrocin) metabolites were identified as risk factors. The Lasso regression model incorporating these ten metabolic biomarkers and five traditional physiological indicators displayed better differentiation than the traditional model, represented by the elevated area under receiver operating characteristic curve (AUROC) from 96.80 to 100.0%. Furthermore, patients with septic shock presented a significantly lower level of PE-Cer (d16:1(4E)/19:0). This study suggests that metabolomic profiling could be an effective tool for sepsis diagnosis.

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

A disruption of several metabolic pathways in critically ill patients with sepsis indicates that metabolomics might be used as a more precise tool for sepsis and septic shock when compared with the conventional biomarkers. This article provides information regarding metabolomics studies in sepsis and septic shock patients. It has been shown that a variety of metabolomic pathways are altered in sepsis and septic shock, including amino acid metabolism, fatty acid oxidation, phospholipid metabolism, glycolysis, and tricarboxylic acid cycle. Based upon this comprehensive review, here, we demonstrate that metabolomics is about to change the world of sepsis biomarkers, not only for its utilization in sepsis diagnosis, but also for prognosticating and monitoring the therapeutic response. Additionally, the future direction regarding the establishment of studies integrating metabolomics with other molecular modalities and studies identifying the relationships between metabolomic profiles and clinical characteristics to address clinical application are discussed in this article. All of the information from this review indicates the important impact of metabolomics as a tool for diagnosis, monitoring therapeutic response, and prognostic assessment of sepsis and septic shock. These findings also encourage further clinical investigations to warrant its use in routine clinical settings.

Gender-specific association of oxidative stress and immune response in septic shock mortality using NMR-based metabolomics

Background: Sepsis and septic shock are still associated with a high mortality rate. The early-stage prediction of septic shock outcomes would be helpful to clinicians for designing their treatment protocol. In addition, it would aid clinicians in patient management by understanding gender disparity in terms of clinical outcomes of septic shock by identifying whether there are sex-based differences in sepsis-associated mortality. Objective: This study aimed to test the hypothesis that gender-based metabolic heterogeneity is associated with sepsis survival and identify the biomarkers of mortality for septic shock in an Indian cohort. Method: The study was performed in an Indian population cohort diagnosed with sepsis/septic shock within 24 hours of admission. The study group was 50 patients admitted to intensive care, comprising 23 females and 27 males. Univariate and multivariate analysis were performed to identify the biomarkers for septic shock mortality and the gender-specific metabolic fingerprint in septic shock-associated mortality. Results: The energy-related metabolites, ketone bodies, choline, and NAG were found to be primarily responsible for differentiating survivors and non-survivors. The gender-based mortality stratification identified a female-specific association of the anti-inflammatory response, innate immune response, and β oxidation, and a male-specific association of the pro-inflammatory response to septic shock. Conclusion: The identified mortality biomarkers may help clinicians estimate the severity of a case, as well as predict the outcome and treatment efficacy. The study underlines that gender is one of the most significant biological factors influencing septic shock metabolomic profiles. This understanding can be utilized to identify novel gender-specific biomarkers and innovative targets relevant for gender medicine.

Identification of transcriptomics biomarkers for the early prediction of the prognosis of septic shock from pneumopathies

Background

Identifying the biological subclasses of septic shock might provide specific targeted therapies for the treatment and prognosis of septic shock. It might be possible to find biological markers for the early prediction of septic shock prognosis.

Methods

The data were obtained from the Gene Expression Omnibus databases (GEO) in NCBI. GO enrichment and KEGG pathway analyses were performed to investigate the functional annotation of up- and downregulated DEGs. ROC curves were drawn, and their areas under the curves (AUCs) were determined to evaluate the predictive value of the key genes.

Results

117 DEGs were obtained, including 36 up- and 81 downregulated DEGs. The AUC for the MME gene was 0.879, as a key gene with the most obvious upregulation in septic shock. The AUC for the THBS1 gene was 0.889, as a key downregulated gene with the most obvious downregulation in septic shock.

Conclusions

The upregulation of MME via the renin-angiotensin system pathway and the downregulation of THBS1 through the PI3K–Akt signaling pathway might have implications for the early prediction of prognosis of septic shock in patients with pneumopathies.

Protective effect of isosteviol sodium against LPS-induced multiple organ injury by regulating of glycerophospholipid metabolism and reducing macrophage-driven inflammation

Sepsis is a severe inflammatory disorder that can lead to multiple organ injury. Isosteviol sodium (STV-Na) is a terpenoid derived from stevioside that exerts anti-inflammatory, antioxidant and antiapoptotic activities. However, the influence of STV-Na on sepsis remains unknown. Here, we assessed the potential effects of STV-Na on sepsis and multiple organ injury induced by lipopolysaccharide (LPS). We found that STV-Na increased the survival rate of mice treat with LPS, significantly improved the functions of the heart, lung, liver, and kidney, reduced the production of inflammatory cytokines and decreased macrophage infiltration. Moreover, Multiorgan metabolomics analysis demonstrated that glutathione metabolism, purine metabolism, glycerophospholipid metabolism and pantothenate and CoA biosynthesis, were significantly altered by STV-Na. This study provides novel insights into the metabolite changes of multiple organ injury in septic mice, which may help characterize the underlying mechanism and provide an improved understanding of the therapeutic effects of STV-Na on sepsis.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).

Serum Sphingolipids Aiding the Diagnosis of Adult HIV-Negative Patients with Talaromyces marneffei Infection

Increasing attention has been directed to Talaromyces marneffei (T. marneffei) infection in HIV-negative patients due to its high mortality rate. However, nonspecific symptoms and biological characteristics similar to those of other common pathogenic fungi complicate the rapid and accurate diagnosis of T. marneffei infection. Sphingolipids (SPLs) are bioactive lipids involved in the regulation of various physiological and pathological processes and have been identified as serum biomarkers for several diseases. This study employed a sphingolipidomic approach established in our previous work to explore the use of serum SPLs in the diagnosis of HIV-negative patients with T. marneffei infection. Additional clinical cohorts of patients infected with other microorganisms were also recruited. We found that sphinganine (Sa) (d16:0) exhibited obvious depletion after infection; moreover, its level in patients with T. marneffei infection was significantly lower than that in patients infected with other microorganisms. Therefore, Sa (d16:0) was considered a specific diagnostic biomarker for T. marneffei infection, and 302.71 nM was selected as the optimal cutoff value with a diagnostic sensitivity of 87.5% and specificity of 100%. These results suggested that determination of serum Sa (d16:0) levels can be used as a new alternative tool for the rapid diagnosis of T. marneffei infection in HIV-negative patients.

The lipid biology of sepsis

Sepsis, defined as the dysregulated immune response to an infection leading to organ dysfunction, is one of the leading causes of mortality around the globe. Despite the significant progress in delineating the underlying mechanisms of sepsis pathogenesis, there are currently no effective treatments or specific diagnostic biomarkers in the clinical setting. The perturbation of cell signaling mechanisms, inadequate inflammation resolution, and energy imbalance, all of which are altered during sepsis, are also known to lead to defective lipid metabolism. The use of lipids as biomarkers with high specificity and sensitivity may aid in early diagnosis and guide clinical decision making. In addition, identifying the link between specific lipid signatures and their role in sepsis pathology may lead to novel therapeutics. In this review, we discuss the recent evidence on dysregulated lipid metabolism both in experimental and human sepsis focused on bioactive lipids, fatty acids, and cholesterol as well as the enzymes regulating their levels during sepsis. We highlight not only their potential roles in sepsis pathogenesis but also the possibility of using these respective lipid compounds as diagnostic and prognostic biomarkers of sepsis.

Metabolite profiles associated with disease progression in influenza infection

BACKGROUND

We performed metabolomic profiling to identify metabolites that correlate with disease progression and death.

METHODS

We performed a study of adults hospitalized with Influenza A(H1N1)pdm09. Cases (n = 32) were defined by a composite outcome of death or transfer to the intensive care unit during the 60-day follow-up period. Controls (n = 64) were survivors who did not require transfer to the ICU. Four hundred and eight metabolites from eight families were measured on plasma sample at enrollment using a mass spectrometry based Biocrates platform. Conditional logistic regression was used to summarize the association of the individual metabolites and families with the composite outcome and its major two components.

RESULTS

The ten metabolites with the strongest association with disease progression belonged to five different metabolite families with sphingolipids being the most common. The acylcarnitines, glycerides, sphingolipids and biogenic metabolite families had the largest odds ratios based on the composite endpoint. The tryptophan odds ratio for the composite is largely associated with death (OR 17.33: 95% CI, 1.60-187.76).

CONCLUSIONS

Individuals that develop disease progression when infected with Influenza H1N1 have a metabolite signature that differs from survivors. Low levels of tryptophan had a strong association with death.

REGISTRY

ClinicalTrials.gov Identifier: NCT01056185.

A Novel Microfluidic Device for Blood Plasma Filtration

The use of whole blood and some biological specimens, such as urine, saliva, and seminal fluid are limited in clinical laboratory analysis due to the interference of proteins with other small molecules in the matrix and blood cells with optical detection methods. Previously, we developed a microfluidic device featuring an electrokinetic size and mobility trap (SMT) for on-chip extract, concentrate, and separate small molecules from a biological sample like whole blood. The device was used to on-chip filtrate the whole blood from the blood cells and plasma proteins and then on-chip extract and separate the aminoglycoside antibiotic drugs within 3 min. Herein, a novel microfluidic device featuring a nano-junction similar to those reported in the previous work formed by dielectric breakdown was developed for on-chip filtration and out-chip collection of blood plasma with a high extraction yield of 62% within less than 5 min. The filtered plasma was analyzed using our previous device to show the ability of this new device to remove blood cells and plasma proteins. The filtration device shows a high yield of plasma allowing it to detect a low concentration of analytes from the whole blood.

Sphingolipidomics in Translational Sepsis Research-Biomedical Considerations and Perspectives

Scientific Background: Sphingolipids are a highly diverse group of lipids with respect to physicochemical properties controlling either structure, distribution, or function, all of them regulating cellular response in health and disease. Mass spectrometry, on the other hand, is an analytical technique characterizing ionized molecules or fragments thereof by mass-to-charge ratios, which has been prosperingly developed for rapid and reliable qualitative and quantitative identification of lipid species. Parallel to best performance of in-depth chromatographical separation of lipid classes, preconditions of precise quantitation of unique molecular species by preprocessing of biological samples have to be fulfilled. As a consequence, “lipid profiles” across model systems and human individuals, esp. complex (clinical) samples, have become eminent over the last couple of years due to sensitivity, specificity, and discriminatory capability. Therefore, it is significance to consider the entire experimental strategy from sample collection and preparation, data acquisition, analysis, and interpretation.

Areas Covered: In this review, we outline considerations with clinical (i.e., human) samples with special emphasis on sample handling, specific physicochemical properties, target measurements, and resulting profiling of sphingolipids in biomedicine and translational research to maximize sensitivity and specificity as well as to provide robust and reproducible results. A brief commentary is also provided regarding new insights of “clinical sphingolipidomics” in translational sepsis research.

Expert Opinion: The role of mass spectrometry of sphingolipids and related species (“sphingolipidomics”) to investigate cellular and compartment-specific response to stress, e.g., in generalized infection and sepsis, is on the rise and the ability to integrate multiple datasets from diverse classes of biomolecules by mass spectrometry measurements and metabolomics will be crucial to fostering our understanding of human health as well as response to disease and treatment.

Diagnosis of severe sepsis using phospholipids enzymatic assay based on cyclic voltammetry

In this work phospholipid quantification was carried out using an enzymatic assay based on cyclic voltammetry of the condensation product of N-ethyl-N-(2-hydroxy-3-sulfopropyl)-3,5-dimethoxyaniline sodium salt (DAOS) and 4-aminoantipyrine (4-AP) with a graphite electrode. For the optimization of electrochemical measurement for the product, electrochemical properties such as the electrochemical window, double layer capacitance (Cdl) and electron transfer rate (kapp) were analyzed for a graphite-electrode and Au-electrode. The phospholipid enzymatic assay based the on electrochemical measurement using the graphite electrode was applied to the diagnosis of sepsis for sera from healthy volunteers (n = 16), patients with systemic inflammatory response syndrome (SIRS, n = 16) and severe sepsis patients (n = 24). Finally, the phospholipid quantification results from the electrochemical measurement were statistically compared with the conventional method based on optical density measurement.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Ultrahigh throughput beehive-like device for blood plasma separation

We report here a low-cost, rapid-prototyping, and beehive-like multilayer polymer microfluidic device for ultrahigh-throughput blood plasma separation. To understand the device physics and optimize the device structure, the effect of cross-sectional dimension and operational parameter on particle focusing behavior was explored using a single spiral microchannel device. Then, the blood plasma separation performance of the determined channel structure was validated using the blood samples with different hematocrits (HCTs). It was found that a high separation efficiency of 99% could be achieved using the blood sample with an HCT of 0.5% at a high throughput of 1 mL/min. Finally, a multilayer microfluidic device with a novel beehive-like multiplexing channel arrangement was developed for ultrahigh-throughput blood plasma separation. The prototype device could be fabricated within ∼1 hour utilizing the laser cutting and thermal lamination methods. The total processing throughput could reach up to 72 mL/min for 0.5% HCT sample with a plasma separation ratio close to 90%. Our device may hold potentials for the ultrahigh-throughput separation of blood plasma from large volume blood samples for downstream disease diagnosis.

Title NMR-based metabolic profiling provides diagnostic and prognostic information in critically ill children with suspected infection

Sepsis, defined as life-threatening organ dysfunction caused by infection is difficult to distinguish clinically from infection or post-operative inflammation. We hypothesized that in a heterogeneous group of critically ill children, there would be different metabolic profiles between post-operative inflammation, bacterial and viral infection and infection with or without organ dysfunction. 1D ¹H nuclear magnetic resonance spectra were acquired in plasma samples from critically ill children. We included children with bacterial (n = 25) and viral infection (n = 30) and controls (n = 58) (elective cardiac surgery without infection). Principal component analysis was used for data exploration and partial least squares discriminant analysis models for the differences between groups. Area under receiver operating characteristic curve (AUC) values were used to evaluate the models. Univariate analysis demonstrated differences between controls and bacterial and viral infection. There was excellent discrimination between bacterial and control (AUC = 0.94), and viral and control (AUC = 0.83), with slightly more modest discrimination between bacterial and viral (AUC = 0.78). There was modest discrimination (AUC = 0.73) between sepsis with organ dysfunction and infection with no organ dysfunction. In critically ill children, NMR metabolomics differentiates well between those with a post-operative inflammation but no infection, and those with infection (bacterial and viral), and between sepsis and infection.

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome

Systemic inflammatory response syndrome (SIRS) and sepsis are two conditions which are difficult to differentiate clinically and which are strongly impacted for prompt intervention. This study identified potential lipid signatures that are able to differentiate SIRS from sepsis and to predict prognosis. Forty-two patients, including 21 patients with sepsis and 21 patients with SIRS, were involved in the study. Liquid chromatography coupled to mass spectrometry and multivariate statistical methods were used to determine lipids present in patient plasma. The obtained lipid signatures revealed 355 features for the negative ion mode and 297 for the positive ion mode, which were relevant for differential diagnosis of sepsis and SIRS. These lipids were also tested as prognosis predictors. Lastly, L-octanoylcarnitine was found to be the most promising lipid signature for both the diagnosis and prognosis of critically ill patients, with accuracies of 75% for both purposes. In short, we presented the determination of lipid signatures as a potential tool for differential diagnosis of sepsis and SIRS and prognosis of these patients.

Metabolomic Profiling Reveals Distinct and Mutual Effects of Diet and Inflammation in Shaping Systemic Metabolism in Ldlr-/- Mice

Changes in modern dietary habits such as consumption of Western-type diets affect physiology on several levels, including metabolism and inflammation. It is currently unclear whether changes in systemic metabolism due to dietary interventions are long-lasting and affect acute inflammatory processes. Here, we investigated how high-fat diet (HFD) feeding altered systemic metabolism and the metabolomic response to inflammatory stimuli. We conducted metabolomic profiling of sera collected from Ldlr^−/− mice on either regular chow diet (CD) or HFD, and after an additional low-dose lipopolysaccharide (LPS) challenge. HFD feeding, as well as LPS treatment, elicited pronounced metabolic changes. HFD qualitatively altered the systemic metabolic response to LPS; particularly, serum concentrations of fatty acids and their metabolites varied between LPS-challenged mice on HFD or CD, respectively. To investigate whether systemic metabolic changes were sustained long-term, mice fed HFD were shifted back to CD after four weeks (HFD > CD). When shifted back to CD, serum metabolites returned to baseline levels, and so did the response to LPS. Our results imply that systemic metabolism rapidly adapts to dietary changes. The profound systemic metabolic rewiring observed in response to diet might affect immune cell reprogramming and inflammatory responses.

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection. We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis. Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital. Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients. The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission. Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94 ± 0.054 and 0.80 ± 0.085, respectively. Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure. The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis.

Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP)

Introduction

Pneumonia is the most common cause of mortality from infectious diseases, the second leading cause of nosocomial infection, and the leading cause of mortality among hospitalized adults. To improve clinical management, metabolomics has been increasingly applied to find specific metabolic biopatterns (profiling) for the diagnosis and prognosis of various infectious diseases, including pneumonia.

Methods

One hundred fifty bacterial community-acquired pneumonia (CAP) patients whose plasma samples were drawn within the first 24 h of hospital admission were enrolled in this study and separated into two age- and sex-matched cohorts: non-survivors (died ≤ 90 days) and survivors (survived > 90 days). Three analytical tools, ¹H-NMR spectroscopy, GC-MS, and targeted DI-MS/MS, were used to prognosticate non-survivors from survivors by means of metabolic profiles.

Results

We show that quantitative lipid profiling using DI-MS/MS can predict the 90-day mortality and in-hospital mortality among patients with bacterial CAP compared to ¹H-NMR- and GC-MS-based metabolomics. This study showed that the decreased lysophosphatidylcholines and increased acylcarnitines are significantly associated with increased mortality in bacterial CAP. Additionally, we found that decreased lysophosphatidylcholines and phosphatidylcholines (> 36 carbons) and increased acylcarnitines may be used to predict the prognosis of in-hospital mortality for bacterial CAP as well as the need for ICU admission and severity of bacterial CAP.

Discussion

This study demonstrates that lipid-based plasma metabolites can be used for the prognosis of 90-day mortality among patients with bacterial CAP. Moreover, lipid profiling can be utilized to identify patients with bacterial CAP who are at the highest risk of dying in hospital and who need ICU admission as well as the severity assessment of CAP.