Plasma ceramide and lysophosphatidylcholine inversely correlate with mortality in sepsis patients

Comparative analysis of lysophospholipid metabolism profiles and clinical characteristics in patients with high vs. low C-reactive protein levels in acute exacerbations of chronic obstructive pulmonary disease

BACKGROUND

The precise role of lysophospholipids (LysoPLs) in the pathogenesis of acute exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) remains unclear. In this study, we sought to elucidate the differences in serum LysoPL metabolite profiles and their correlation with clinical features between patients with low versus high CRP levels.

METHODS

A total of 58 patients with AECOPD were enrolled in the study. Patients were classified into two groups: low CRP group (CRP < 20 mg/L, n = 34) and high CRP group (CRP ≥ 20 mg/L, n = 24). Clinical data were collected, and the LysoPL metabolite profiles were analyzed using Liquid Chromatography-Mass Spectrometry (LC-MS) and identified by matching with the LipidBlast library.

RESULTS

Nineteen differential LysoPLs were initially identified through Student's t-test (p < 0.05 and VIP > 1). Subsequently, four LysoPLs, LPC(16:0), LPE(18:2), LPC(22:0), and LPC(24:0), were identified by FDR adjustment (adjusted p < 0.05). These four lysoPLs had a significant negative correlation with CRP. Integrative analysis revealed that LPC (16:0) and LPC (22:0) correlated with less hypercapnic respiratory failure and ICU admission.

CONCLUSION

AECOPD patients with high CRP levels demonstrated a distinctive LysoPL metabolism profile, with LPC (16:0), LPE(18:2), LPC(22:0), and LPC(24:0) being the most significantly altered lipid molecules. These alterations were associated with poorer clinical outcomes.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS

BACKGROUND

The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified.

METHODS

We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis.

RESULTS

In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P  = 0.008).

INTERPRETATION

Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

A metabolomics pipeline highlights microbial metabolism in bloodstream infections

The growth of antimicrobial resistance (AMR) highlights an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe infections profoundly alter host metabolism, prior studies have largely ignored microbial metabolism in this context. Here, we describe an iterative, comparative metabolomics pipeline to uncover microbial metabolic features in the complex setting of a host and apply it to investigate gram-negative bloodstream infection (BSI) in patients. We find elevated levels of bacterially derived acetylated polyamines during BSI and discover the enzyme responsible for their production (SpeG). Blocking SpeG activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity also enhances bacterial membrane permeability and increases intracellular antibiotic accumulation, allowing us to overcome AMR in culture and in vivo. This study highlights how tools to study pathogen metabolism in the natural context of infection can reveal and prioritize therapeutic strategies for addressing challenging infections.

Subcutaneous lysophosphatidylcholine administration promotes a febrile and immune response in Holstein heifer calves

Lysophosphatidylcholine (LPC) is immunomodulatory in nonruminants; however, the actions of LPC on immunity in cattle are undefined. Our objective was to study the effects of LPC administration on measures of immunity, liver health, and growth in calves. Healthy Holstein heifer calves (n = 46; age 7 ± 3 d) were randomly assigned to 1 of 4 treatments (n = 10 to 11 calves/treatment): a milk replacer diet unsupplemented with lecithin in the absence (CON) or presence of subcutaneously (s.c.) administered mixed (mLPC; 69% LPC-16:0, 25% LPC-18:0, 6% other) or pure LPC (pLPC; 99% LPC-18:0), or a milk replacer diet supplemented with 3% lecithin enriched in lysophospholipids containing LPC in the absence of s.c.-administered LPC (LYSO) for 5 wk. Calves received 5 s.c. injections of vehicle (10 mL of phosphate-buffered saline containing 20 mg of bovine serum albumin/mL; CON and LYSO) or vehicle containing mLPC or pLPC to provide 10 mg of total LPC per kilogram of BW per injection every 12 h during wk 2 of life. Calves were fed a milk replacer containing 27% crude protein and 24% fat at 1.75% of BW per day (dry matter basis) until wk 6 of life (start of weaning). Starter grain and water were provided ad libitum. Body measurements were recorded weekly, and clinical observations were recorded daily. Blood samples were collected weekly before morning feeding and at 0, 5, and 10 h, relative to the final s.c. injection of vehicle or LPC. Data were analyzed using a mixed model, with repeated measures including fixed effects of treatment, time, and their interaction. Dunnett's test was used to compare treatments to CON. Peak rectal temperatures were higher in mLPC or pLPC, relative to CON. Plasma LPC concentrations were greater in mLPC and LYSO calves 5 h and 10 h after the final injection, relative to CON. Calves receiving mLPC and pLPC also had higher circulating serum amyloid A concentrations, relative to CON. Calves receiving mLPC had greater serum aspartate aminotransferase, γ-glutamyltransferase, and glutamate dehydrogenase concentrations, relative to CON. Calves provided mLPC experienced lower average daily gain (ADG) after weaning, relative to CON. The LYSO treatment did not modify rectal temperatures, ADG, or measures of liver health, relative to CON. We conclude that LPC administered as s.c. injections induced an acute febrile response, modified measures of liver and immune function, and impaired growth in calves.

Association of atherogenic indices with myocardial damage and mortality in COVID-19

BACKGROUND

Lipoproteins in cell membranes are related to membrane stability and play a role against microorganisms. Patients with COVID-19 often experience myocyte membrane damage.

OBJECTIVE

This study aimed to search the relationship of atherogenic indices with myocardial damage and mortality in COVID-19.

METHODS

This was an observational, single-center, retrospective study. The study population was grouped according to in-hospital mortality. C-reactive protein (CRP), CRP to albumin ratio (CAR), monocyte to high density lipoprotein cholesterol ratio (MHR), levels of total cholesterol (TC), triglycerides, high-density lipoprotein cholesterol (HDLc), and low-density lipoprotein cholesterol (LDLc) and cardiac troponin I (cTnI) were recorded. Atherogenic indices (plasma atherogenic index [AIP], atherogenic coefficient [AC], Castelli's risk indices I and II [CRI I and II], triglyceride to HDLc ratio (THR) were calculated.

RESULTS

A total of 783 patients were included. The mortality rate was 15.45% (n = 121). The median age of non-survivor group (NSG) was higher than survivor group (SG) [66.0 years (Q1 -Q3: 55.0-77.5) vs 54.0 years (Q1 -Q3: 43.0-63.0)] (p < 0.001). Study parameters which were measured significantly higher in the NSG were CRP, cTnI, triglyceride, CRI-I, CRI-II, AC, AIP, ferritin, CAR, MHR and THR. LDLc, HDLc, TC and albumin were significantly lower in NSG (p<0.001).

CONCLUSION

THR is positively correlated with myocardial damage and strongly predicts in-hospital mortality in COVID-19.

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.

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.

Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Low levels of small HDL particles predict but do not influence risk of sepsis

BACKGROUND

Low levels of high-density lipoprotein (HDL) cholesterol have been associated with higher rates and severity of infection. Alterations in inflammatory mediators and infection are associated with alterations in HDL cholesterol. It is unknown whether the association between HDL and infection is present for all particle sizes, and whether the observed associations are confounded by IL-6 signalling.

METHODS

In the UK Biobank, ~ 270,000 individuals have data on HDL subclasses derived from nuclear magnetic resonance analysis. We estimated the association of particle count of total HDL and HDL subclasses (small, medium, large, and extra-large HDL) with sepsis, sepsis-related death, and critical care admission in a Cox regression model. We subsequently utilised genetic data from UK Biobank and FinnGen to perform Mendelian randomisation (MR) of each HDL subclass and sepsis to test for a causal relationship. Finally, we explored the role of IL-6 signalling as a potential causal driver of changes in HDL subclasses.

RESULTS

In observational analyses, higher particle count of small HDL was associated with protection from sepsis (Hazard ratio, HR 0.80; 95% CI 0.74-0.86, p = 4 × 10-9 comparing Quartile 4, highest quartile of HDL to Quartile 1, lowest quartile of HDL), sepsis-related death (HR 0.80; 95% CI 0.74-0.86, p = 2 × 10-4), and critical care admission with sepsis (HR 0.72 95% CI 0.60-0.85, p = 2 × 10-4). Parallel associations with other HDL subclasses were likely driven by changes in the small HDL compartment. MR analyses did not strongly support causality of small HDL particle count on sepsis incidence (Odds ratio, OR 0.98; 95% CI 0.89-1.07, p = 0.6) or death (OR 0.94, 95% CI 0.75-1.17, p = 0.56), although the estimate on critical care admission with sepsis supported protection (OR 0.73, 95% CI 0.57-0.95, p = 0.02). Bidirectional MR analyses suggested that increased IL-6 signalling was associated with reductions in both small (beta on small HDL particle count - 0.16, 95% CI - 0.10 to - 0.21 per natural log change in SD-scaled CRP, p = 9 × 10-8).and total HDL particle count (beta - 0.13, 95% CI - 0.09 to - 0.17, p = 7 × 10-10), but that the reverse effect of HDL on IL-6 signalling was largely null.

CONCLUSIONS

Low number of small HDL particles are associated with increased hazard of sepsis, sepsis-related death, and sepsis-related critical care admission. However, genetic analyses did not strongly support this as causal. Instead, we demonstrate that increased IL-6 signalling, which is known to alter infection risk, could confound associations with reduced HDL particle count, and suggest this may explain part of the observed association between (small) HDL particle count and sepsis.

Plasma lipidomic analysis to investigate putative biomarkers of P-glycoprotein activity in healthy volunteers

P-glycoprotein (P-gp) is an efflux transporter involved in the bioavailability of many drugs currently on the market. P-gp is responsible for several drug-drug interactions encountered in clinical practice leading to iatrogenic hospital admissions, especially in polypharmacy situations. ABCB1 genotyping only reflects an indirect estimate of P-gp activity. Therefore, it would be useful to identify endogenous biomarkers to determine the P-gp phenotype to predict in vivo activity prior to the initiation of treatment and to assess the effects of drugs on P-gp activity. The objective of this study was to assess changes in plasma lipidome composition among healthy volunteers selected on the basis of their ABCB1 genotype and who received clarithromycin, a known inhibitor of P-gp. Untargeted lipidomic analysis based on liquid chromatography-tandem mass spectrometry was performed before and after clarithromycin administration. Our results revealed changes in plasma levels of some ceramides (Cers) {Cer(d18:1/22:0), Cer(d18:1/22:1), and Cer(d18:1/20:0) by ~38% (p < 0.0001), 13% (p < 0.0001), and 13% (p < 0.0001), respectively} and phosphatidylcholines (PCs) {PC(17:0/14:1), PC(16:0/18:3), and PC(14:0/18:3) by ~24% (p < 0.001), 10% (p < 0.001), and 23.6% (p < 0.001)} associated with both ABCB1 genotype and clarithromycin intake. Through the examination of plasma lipids, our results highlight the relevance of untargeted lipidomics for studying in vivo P-gp activity and, more generally, to safely phenotyping transporters.

Serum metabolite profiling reveals metabolic characteristics of sepsis patients using LC/MS-based metabolic profiles: a cross-sectional study

BACKGROUND

Individuals with sepsis exhibited a higher likelihood of benefiting from early initiation of specialized treatment to enhance the prognosis of the condition. The objective of this study is to identify potential biomarkers of sepsis by means of serum metabolomics.

MATERIALS AND METHODS

The screening of putative biomarkers of sepsis was conducted using serum samples from patients with sepsis and a control group of healthy individuals. The pathogenesis of sepsis was determined through the utilization of liquid chromatography-mass spectrometry-based metabolic profiles and bioinformatic techniques, which in turn provided a foundation for timely diagnosis and intervention.

RESULTS

Individuals with sepsis had significantly different metabolic characteristics compared to those with normal health. The concentrations of phosphatidylcholines (PCs), phosphatidylserine (PS), lysophosphatidylethanolamine (LysoPEs), and lysophosphatidylcholine (LysoPCs) exhibited a decrease, while the levels of creatinine, C17-Sphinganine, and PS(22:0/22:1(11Z)) demonstrated an increase in the serum of sepsis patients when compared to the control group. Additionally, ROC curves were generated to assess the discriminatory ability of the differentially expressed metabolites. The area under the ROC curve for PS (22:0/22:1(11Z)) and C17-Sphinganine were determined to be 0.976 and 0.913, respectively. These metabolites may potentially serve as diagnostic markers for sepsis. Additionally, the pathogenesis of sepsis is associated with mTOR signaling, NF-κB signaling pathway, calcium signaling, calcium transport, and tRNA charging pathway.

CONCLUSION

The identification of differential expression of these metabolites in sepsis serum samples could aid in the timely diagnosis and intervention of sepsis, as well as enhance our understanding of its pathogenesis.

Identification and targeting of microbial putrescine acetylation in bloodstream infections

The growth of antimicrobial resistance (AMR) has highlighted an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe bacterial infections profoundly alter host metabolism, prior studies have largely ignored alterations in microbial metabolism in this context. Performing metabolomics on patient and mouse plasma samples, we identify elevated levels of bacterially-derived N-acetylputrescine during gram-negative bloodstream infections (BSI), with higher levels associated with worse clinical outcomes. We discover that SpeG is the bacterial enzyme responsible for acetylating putrescine and show that blocking its activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity enhances bacterial membrane permeability and results in increased intracellular accumulation of antibiotics, allowing us to overcome AMR of clinical isolates both in culture and in vivo. This study highlights how studying pathogen metabolism in the natural context of infection can reveal new therapeutic strategies for addressing challenging infections.

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.

The plasma metabolome of long COVID patients two years after infection

One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as "long COVID") which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

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.

Effects of Anemoside B4 on Plasma Metabolites in Cows with Clinical Mastitis

Anemoside B4 has a good curative effect on cows with CM; however, its impact on their metabolic profiles is unclear. Based on similar somatic cell counts and clinical symptoms, nine healthy dairy cows and nine cows with CM were selected, respectively. Blood samples were collected from cows with mastitis on the day of diagnosis. Cows with mastitis were injected with anemoside B4 (0.05 mL/kg, once daily) for three consecutive days, and healthy cows were injected with the same volume of normal saline. Subsequently, blood samples were collected. The plasma metabolic profiles were analyzed using untargeted mass spectrometry, and the concentrations of interleukin (IL)-1β, IL-6, and tumor necrosis factor-α (TNF-α) in serum were evaluated via ELISA. The cows with CM showed increased concentrations of IL-1β, IL-6, and TNF-α (p < 0.05). After treatment with anemoside B4, the concentrations of IL-1β, IL-6, and TNF-α were significantly decreased (p < 0.01). Untargeted metabolomics analysis showed that choline, glycocholic acid, PC (18:0/18:1), 20-HETE, PGF3α, and oleic acid were upregulated in cows with CM. After treatment with anemoside B4, the concentrations of PC (16:0/16:0), PC (18:0/18:1), linoleic acid, eicosapentaenoic acid, phosphorylcholine, and glycerophosphocholine were downregulated, while the LysoPC (14:0), LysoPC (18:0), LysoPC (18:1), and cis-9-palmitoleic acid were upregulated. This study indicated that anemoside B4 alleviated the inflammatory response in cows with CM mainly by regulating lipid metabolism.

Association between Changes in Plasma Metabolism and Clinical Outcomes of Sepsis

Current prognostic biomarkers for sepsis have limited sensitivity and specificity. This study aimed to investigate dynamic lipid metabolomics and their association with septic immune response and clinical outcomes of sepsis. This prospective cohort study included patients with sepsis who met the Sepsis 3.0 criteria. On hospitalization days 1 (D1) and 7 (D7), plasma samples were collected, and patients underwent liquid chromatography with tandem mass spectrometry. A total of 40 patients were enrolled in the study, 24 (60%) of whom were men. The median age of the enrolled patients was 81 (68-84) years. Thirty-one (77.5%) patients had a primary infection site of the lung. Participants were allocated to the survivor (25 cases) and nonsurvivor (15 cases) groups based on their 28-day survival status. Ultimately, a total of 113 lipids were detected in plasma samples on D 1 and D 7, of which 42 lipids were most abundant in plasma samples. The nonsurvival group had significantly lower lipid expression levels in lysophosphatidylcholine (LysoPC) (16 : 0, 17 : 0,18 : 0) and 18 : 1 SM than those in the survival group (p <  0.05) on D7-D1. The correlation analysis showed that D7-D1 16 : 0 LysoPC (r = 0.367, p = 0.036),17 : 0 LysoPC (r = 0.389, p = 0.025) and 18 : 0 LysoPC(r = 0.472, p = 0.006) levels were positively correlated with the percentage of CD3⁺ T cell in the D7-D1. Plasma LysoPC and SM changes may serve as prognostic biomarkers for sepsis, and lipid metabolism may play a role in septic immune disturbances.

The human malaria parasite Plasmodium falciparum can sense environmental changes and respond by antigenic switching

The primary antigenic and virulence determinant of the human malaria parasite Plasmodium falciparum is a variant surface protein called PfEMP1. Different forms of PfEMP1 are encoded by a multicopy gene family called var, and switching between active genes enables the parasites to evade the antibody response of their human hosts. var gene switching is key for the maintenance of chronic infections; however, what controls switching is unknown, although it has been suggested to occur at a constant frequency with little or no environmental influence. var gene transcription is controlled epigenetically through the activity of histone methyltransferases (HMTs). Studies in model systems have shown that metabolism and epigenetic control of gene expression are linked through the availability of intracellular S-adenosylmethionine (SAM), the principal methyl donor in biological methylation modifications, which can fluctuate based on nutrient availability. To determine whether environmental conditions and changes in metabolism can influence var gene expression, P. falciparum was cultured in media with altered concentrations of nutrients involved in SAM metabolism. We found that conditions that influence lipid metabolism induce var gene switching, indicating that parasites can respond to changes in their environment by altering var gene expression patterns. Genetic modifications that directly modified expression of the enzymes that control SAM levels similarly led to profound changes in var gene expression, confirming that changes in SAM availability modulate var gene switching. These observations directly challenge the paradigm that antigenic variation in P. falciparum follows an intrinsic, programed switching rate, which operates independently of any external stimuli.

Effects of Silane Monolayers on Lysophosphatidylcholine (LysoPC) Detection by Desorption Ionization on Silicon Mass Spectrometry (DIOS-MS) in Solution and Plasma

Desorption ionization on silicon mass spectrometry (DIOS-MS) enables high throughput analysis of low-molecular-weight biomolecules. However, detection of metabolite biomarkers in complex fluids such as plasma requires sample pretreatment, limiting clinical application. Here, we show that porous silicon, chemically modified using monolayers of n-propyldimethylmethoxysilane molecules, is a good candidate for fingerprinting lysophosphatidylcholine (lysoPC) in plasma, without sample pretreatment, for DIOS-MS-based diagnosis (e.g., sepsis). Results were correlated to lysoPC molecule location inside/outside the pores, determined by time-of-flight secondary ion mass spectrometry profiling, and to physicochemical properties.

Determination of lysophosphatidylcholine using peroxidase-mimic PVP/PtRu nanozyme

Lysophosphatidylcholine (LPC) can be used as a biomarker for diseases such as cancer, diabetes, atherosclerosis, and sepsis. In this study, we demonstrated the ability of nanozymes to displace the natural derived enzyme in enzyme-based assays for the measurement of LPC. Synthesized polyvinylpyrrolidone-stabilized platinum-ruthenium nanozymes (PVP/PtRu NZs) had a uniform size of 2.48 ± 0.24 nm and superb peroxidase-mimicking activity. We demonstrated that the nanozymes had high activity over a wide pH and temperature range and high stability after long-term storage. The LPC concentration could be accurately analyzed through the absorbance and fluorescence signals generated by the peroxidation reaction using the synthesized nanozyme with substrates such as 3,3',5,5'-tetramethylbenzidine (TMB) and 10-acetyl-3,7-dihydroxyphenoxazine (Ampliflu™ Red). LPC at a concentration of 0-400 µM was used for the analysis, and the coefficient of determination (R²) was 0.977, and the limit of detection (LOD) was 23.1 µM by colorimetric assay. In the fluorometric assay, the R² was 0.999, and the LOD was 8.97 µM. The spiked recovery values for the determination of LPC concentration in human serum samples were 102-115%. Based on these results, we declared that PVP/PtRu NZs had an ability comparable to that of the native enzyme horseradish peroxidase (HRP) in the enzyme-based LPC detection method.

Identification of Lipid Biomarkers for Chronic Joint Pain Associated with Different Joint Diseases

Lipids, especially lysophosphatidylcholine LPC16:0, have been shown to be involved in chronic joint pain through the activation of acid-sensing ion channels (ASIC3). The aim of the present study was to investigate the lipid contents of the synovial fluids from controls and patients suffering from chronic joint pain in order to identify characteristic lipid signatures associated with specific joint diseases. For this purpose, lipids were extracted from the synovial fluids and analyzed by mass spectrometry. Lipidomic analyses identified certain choline-containing lipid classes and molecular species as biomarkers of chronic joint pain, regardless of the pathology, with significantly higher levels detected in the patient samples. Moreover, correlations were observed between certain lipid levels and the type of joint pathologies. Interestingly, LPC16:0 levels appeared to correlate with the metabolic status of patients while other choline-containing lipids were more specifically associated with the inflammatory state. Overall, these data point at selective lipid species in synovial fluid as being strong predictors of specific joint pathologies which could help in the selection of the most adapted treatment.

Discrimination of serum metabolomics profiles in infants with sepsis, based on liquid chromatography-mass spectrometer

Sepsis is one of the most important problems to be addressed in pediatrics, characterized by insidious onset, rapid progression, and high rates of severe infection and even mortality. Biomarkers with high sensitivity and robustness are urgently required for the early diagnosis of infant sepsis. Serum metabolomic approaches based on liquid chromatography-mass spectrometry were used to analyze the samples from 30 infants with sepsis at an early stage and 30 infants with noninfectious diseases. Multivariate statistical analysis was used to screen for differential metabolites and ROC curves were generated to find potential biomarkers. Six metabolites, including phosphatidic acid (PA (8:0/14:0)), phosphatidyl ethanolamine (PE (16:0/18:2(9Z,12Z))), cytidine 5'-diphosphocholine (CDP-CHO), sphingomyelin (SM (d18:0/16:1(9Z))), prolylhydroxyproline and phosphorylcholine (P-CHO), were identified between the two groups. ROC curve analysis showed that prolylhydroxyproline (AUC = 0.832) had potential diagnostic values for infant sepsis. The AUC value was 0.859 (CI: 0.764, 0.954) in the combined model. Prolylhydroxyproline were found to be correlated with CRP and PCT levels, while PE and CDP-CHO associated with PCT levels. Pathway analysis indicated that glycerophospholipid metabolism, aminoacyl-tRNA biosynthesis and necroptosis pathways played important roles in infant sepsis. Network analysis showed that the differential metabolites were linked to ERK/ MAPK, NF-κB, AMPK, mTOR, and other classical inflammatory and metabolic signaling pathways. This study identified serum metabolite profiles and three metabolites as potential biomarkers in infants with sepsis. The findings will help improve the early diagnosis of sepsis in infants.

Biological functions of bacterial lysophospholipids

Lysophospholipids (LPLs) are lipid-derived metabolic intermediates in the cell membrane. The biological functions of LPLs are distinct from their corresponding phospholipids. In eukaryotic cells LPLs are important bioactive signaling molecules that regulate many important biological processes, but in bacteria the function of LPLs is still not fully defined. Bacterial LPLs are usually present in cells in very small amounts, but can strongly increase under certain environmental conditions. In addition to their basic function as precursors in membrane lipid metabolism, the formation of distinct LPLs contributes to the proliferation of bacteria under harsh circumstances or may act as signaling molecules in bacterial pathogenesis. This review provides an overview of the current knowledge of the biological functions of bacterial LPLs including lysoPE, lysoPA, lysoPC, lysoPG, lysoPS and lysoPI in bacterial adaptation, survival, and host-microbe interactions.

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.

Myristoyl lysophosphatidylcholine is a biomarker and potential therapeutic target for community-acquired pneumonia

There is no gold standard for evaluating the severity of community-acquired pneumonia (CAP), and it is still based on a score. This study aimed to use the metabolomics method to find promised biomarkers in assessing disease severity and potential therapeutic targets for CAP. The result found that the metabolites in the plasma samples of CAP patients had significantly different between the acute phase and the remission phase, especially lysophosphatidylcholine (LPCs) in glycerophospholipids, whose levels are negatively linked to the severity of the disease. Subsequently, the two key metabolites of myristoyl lysophosphatidylcholine (LPC 14:0) and LPC 16:1 were screened. We analyzed the predictive performance of the two metabolites using Spearman-related analysis and ROC curves, and LPC14:0 showed more satisfactory diagnostic performance than LPC16:1. Then we explored the protective role and mechanism of LPC 14:0 in animal and cell models. The results showed that LPC 14:0 could inhibit the LPS-induced secretion of IL-1β, IL-6, and TNF-α, lower the ROS and MDA levels, and decreased the depletion of SOD and GSH, thereby reducing lung tissue and cell damage, such as down-regulating the protein level in BALF, lung W/D ratio, MPO activity, and apoptosis. We found that LPC 14:0 inhibited LPS-induced inflammatory response and oxidative stress, and the above protection was achieved by inhibiting LPS-induced activation of the NLRP3 inflammasome. LPC 14:0 may serve as a novel biomarker for predicting the severity of CAP. In addition, our exploration of the role of LPC 14:0 in animal and cellular models has reinforced its promise as a therapeutic target to improve the clinical efficacy for CAP.

The Sphingosine Kinase 2 Inhibitor Opaganib Protects Against Acute Kidney Injury in Mice

Introduction

Acute kidney injury (AKI) is a common multifactorial adverse effect of surgery, circulatory obstruction, sepsis or drug/toxin exposure that often results in morbidity and mortality. Sphingolipid metabolism is a critical regulator of cell survival and pathologic inflammation processes involved in AKI. Opaganib (also known as ABC294640) is a first-in-class experimental drug targeting sphingolipid metabolism that reduces the production and activity of inflammatory cytokines and, therefore, may be effective to prevent and treat AKI.

Methods

Murine models of AKI were used to assess the in vivo efficacy of opaganib including ischemia-reperfusion (IR) injury induced by either transient bilateral occlusion of renal blood flow (a moderate model) or nephrectomy followed immediately by occlusion of the contralateral kidney (a severe model) and lipopolysaccharide (LPS)-induced sepsis. Biochemical and histologic assays were used to quantify the effects of oral opaganib treatment on renal damage in these models.

Results

Opaganib suppressed the elevations of creatinine and blood urea nitrogen (BUN), as well as granulocyte infiltration into the kidneys, of mice that experienced moderate IR from transient bilateral ligation. Opaganib also markedly decreased these parameters and completely prevented mortality in the severe renal IR model. Additionally, opaganib blunted the elevations of BUN, creatinine and inflammatory cytokines following exposure to LPS.

Conclusion

The data support the hypotheses that sphingolipid metabolism is a key mediator of renal inflammatory damage following IR injury and sepsis, and that this can be suppressed by opaganib. Because opaganib has already undergone clinical testing in other diseases (cancer and Covid-19), the present studies support conducting clinical trials with this drug with surgical or septic patients at risk for AKI.

High-coverage targeted lipidomics could reveal lipid alterations and evaluate therapeutic efficacy of membranous nephropathy

Background

Membrane nephropathy (MN) often presents as nephrotic syndrome with characteristic lipid metabolism that could not be explained by lipid indicators commonly used in clinical practice. Studies have shown that invigorating spleen and qi, activating blood and detoxication in the treatment of MN is an effective method proved by randomized controlled clinical trial. However, the alterations of lipid profile before and after traditional Chinese medicine (TCM) treatment and the related lipid markers that affect the therapeutic effect have not been fully clarified.

Methods

We analyzed plasma lipid profiles of 92 patients with MN before and after TCM treatment by high-coverage targeted lipidomics.

Results

675 lipids were identified, of which 368 stably expressed lipids (coefficient of variation less than 30% and deletion value less than 10%) were eventually included for statistical analysis. 105 lipids were altered mainly including spingolipids, glycerides, glycerophosholipid, fatty acyl and steroids, among which, the abundance of ceramides (Cers), sphingomyelins (SMs), diacylglycerols (DGs), phosphatidylcholines (PCs) were lower than those before treatment with statistically significant difference. The WGCNA network to analyze the correlation between the collective effect and the therapeutic effect showed that the triglyceride (TG) molecules were most relevant to the therapeutic effect. Analysis of 162 triglyceride molecules showed that 11 TGs were significantly down-regulated in the effective group which were concentrated in carbon atom number of 52–56 and double bond number of 0–4. TGs molecules including TG56:2-FA20:0, TG56:2-FA20:1, TG56:3-FA20:0 and TG56:5-FA20:2 were most closely related to the therapeutic effect of TCM after adjusting the influence of clinical factors. ROC curve analysis showed that these four lipids could further improve the predictive efficacy of treatment based on clinical indicators.

Conclusion

Our work demonstrated that the therapeutic effect of invigorating spleen and qi, activating blood and detoxication in the treatment of MN may be exerted by regulating lipid metabolism. High-coverage targeted lipidomics provided a non-invasive tool for discovery of lipid markers to improve the predictive efficacy of TCM therapy.

Effects of acute intravenous lipopolysaccharide administration on the plasma lipidome and metabolome in lactating Holstein cows experiencing hyperlipidemia

INTRODUCTION

The effects of lipopolysaccharides (i.e., endotoxin; LPS) on metabolism are poorly defined in lactating dairy cattle experiencing hyperlipidemia.

OBJECTIVES

Our objective was to explore the effects of acute intravenous LPS administration on metabolism in late-lactation Holstein cows experiencing hyperlipidemia induced by intravenous triglyceride infusion and feed restriction.

METHODS

Ten non-pregnant lactating Holstein cows (273 ± 35 d in milk) were administered a single bolus of saline (3 mL of saline; n [Formula: see text] 5) or LPS (0.375 [Formula: see text]g of LPS/kg of body weight; n [Formula: see text] 5). Simultaneously, cows were intravenously infused a triglyceride emulsion and feed restricted for 16 h to induce hyperlipidemia in an attempt to model the periparturient period. Blood was sampled at routine intervals. Changes in circulating total fatty acid concentrations and inflammatory parameters were measured. Plasma samples were analyzed using untargeted lipidomics and metabolomics.

RESULTS

Endotoxin increased circulating serum amyloid A, LPS-binding protein, and cortisol concentrations. Endotoxin administration decreased plasma lysophosphatidylcholine (LPC) concentrations and increased select plasma ceramide concentrations. These outcomes suggest modulation of the immune response and insulin action. Lipopolysaccharide decreased the ratio of phosphatidylcholine to phosphatidylethanomanine, which potentially indicate a decrease in the hepatic activation of phosphatidylethanolamine N-methyltransferase and triglyceride export. Endotoxin administration also increased plasma concentrations of pyruvic and lactic acids, and decreased plasma citric acid concentrations, which implicate the upregulation of glycolysis and downregulation of the citric acid cycle (i.e., the Warburg effect), potentially in leukocytes.

CONCLUSION

Acute intravenous LPS administration decreased circulating LPC concentrations, modified ceramide and glycerophospholipid concentrations, and influenced intermediary metabolism in dairy cows experiencing hyperlipidemia.

A Prospective, Case-Control Study of Serum Metabolomics in Neonates with Late-Onset Sepsis and Necrotizing Enterocolitis

Late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) are major causes of neonatal morbidity and mortality. In this prospective, case-control study, we evaluated the metabolic profile of neonates with LOS and NEC. Blood samples were collected from 15 septic neonates and 17 neonates with NEC at the clinical suspicion of the specific diseases. Sixteen gestational and postnatal age-matched neonates without sepsis/NEC served as controls. Serum metabolic profiles were assessed using liquid chromatography–quadrupole time-of-flight mass spectrometry. Metabolomic analysis revealed significant differences in the metabolic profile of neonates with LOS or NEC compared to controls. More specifically, a number of molecules possibly identified as phosphatidylcholines or lysophosphatidylcholines were found to be significantly reduced both in neonates with LOS and those with NEC compared to controls. Additionally, L-carnitine could efficiently discriminate NEC cases from controls. The results of the current study suggest that certain phospholipids and their derivatives could possibly be used as biomarkers for the early detection of LOS and NEC.

Identifying potential biomarkers and therapeutic targets for dogs with sepsis using metabolomics and lipidomics analyses

Sepsis is a diagnostic and therapeutic challenge and is associated with morbidity and a high risk of death. Metabolomic and lipidomic profiling in sepsis can identify alterations in metabolism and might provide useful insights into the dysregulated host response to infection, but investigations in dogs are limited. We aimed to use untargeted metabolomics and lipidomics to characterize metabolic pathways in dogs with sepsis to identify therapeutic targets and potential diagnostic and prognostic biomarkers. In this prospective observational cohort study, we examined the plasma metabolomes and lipidomes of 20 healthy control dogs and compared them with those of 21 client-owned dogs with sepsis. Patient data including signalment, physical exam findings, clinicopathologic data and clinical outcome were recorded. Metabolites were identified using an untargeted mass spectrometry approach and pathway analysis identified multiple enriched metabolic pathways including pyruvaldehyde degradation; ketone body metabolism; the glucose-alanine cycle; vitamin-K metabolism; arginine and betaine metabolism; the biosynthesis of various amino acid classes including the aromatic amino acids; branched chain amino acids; and metabolism of glutamine/glutamate and the glycerophospholipid phosphatidylethanolamine. Metabolites were identified with high discriminant abilities between groups which could serve as potential biomarkers of sepsis including 13,14-Dihydro-15-keto Prostaglandin A₂; 12(13)-DiHOME (12,13-dihydroxy-9Z-octadecenoic acid); and 9-HpODE (9-Hydroxyoctadecadienoic acid). Metabolites with higher abundance in samples from nonsurvivors than survivors included 3-(2-hydroxyethyl) indole, indoxyl sulfate and xanthurenic acid. Untargeted lipidomic profiling revealed multiple sphingomyelin species (SM(d34:0)+H; SM(d36:0)+H; SM(d34:0)+HCOO; and SM(d34:1D3)+HCOO); lysophosphatidylcholine molecules (LPC(18:2)+H) and lipophosphoserine molecules (LPS(20:4)+H) that were discriminating for dogs with sepsis. These biomarkers could aid in the diagnosis of dogs with sepsis, provide prognostic information, or act as potential therapeutic targets.

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism's physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this study, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen's secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host-pathogen interactions underlying Burkholderia infection.

Impact of Extensively Hydrolyzed Infant Formula on Circulating Lipids During Early Life

Background

Current evidence suggests that the composition of infant formula (IF) affects the gut microbiome, intestinal function, and immune responses during infancy. However, the impact of IF on circulating lipid profiles in infants is still poorly understood. The objectives of this study were to (1) investigate how extensively hydrolyzed IF impacts serum lipidome compared to conventional formula and (2) to associate changes in circulatory lipids with gastrointestinal biomarkers including intestinal permeability.

Methods

In a randomized, double-blind controlled nutritional intervention study (n = 73), we applied mass spectrometry-based lipidomics to analyze serum lipids in infants who were fed extensively hydrolyzed formula (HF) or conventional, regular formula (RF). Serum samples were collected at 3, 9, and 12 months of age. Child’s growth (weight and length) and intestinal functional markers, including lactulose mannitol (LM) ratio, fecal calprotectin, and fecal beta-defensin, were also measured at given time points. At 3 months of age, stool samples were analyzed by shotgun metagenomics.

Results

Concentrations of sphingomyelins were higher in the HF group as compared to the RF group. Triacylglycerols (TGs) containing saturated and monounsaturated fatty acyl chains were found in higher levels in the HF group at 3 months, but downregulated at 9 and 12 months of age. LM ratio was lower in the HF group at 9 months of age. In the RF group, the LM ratio was positively associated with ether-linked lipids. Such an association was, however, not observed in the HF group.

Conclusion

Our study suggests that HF intervention changes the circulating lipidome, including those lipids previously found to be associated with progression to islet autoimmunity or overt T1D.

Clinical Trial Registration

[Clinicaltrials.gov], identifier [NCT01735123].

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients

The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.

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.

The clinical population pharmacokinetics, metabolomics and therapeutic analysis of alkaloids from Alstonia scholaris leaves in acute bronchitis patients

BACKGROUND

Capsule of alkaloids from leaf of Alstonia scholaris (CALAS) is a new investigational botanical drug (No. 2011L01436) for respiratory disease. Clinical population pharmacokinetics (PK), metabolomics and therapeutic data are essential to guide dosing in patients. Previous research has demonstrated the potential therapeutic effect of CALAS on acute bronchitis. Further clinical trial data are needed to verify its clinical efficacy, pharmacokinetics behavior, and influence of dosage and other factors.

PURPOSE

To verify the clinical efficacy and explore the potential biomarkers related to CALAS treatment for acute bronchitis.

MATERIALS AND METHODS

Oral CALAS was assessed in a randomized, double-blind, placebo-controlled trial. Fifty-five eligible patients were randomly assigned to four cohorts to receive 20, 40 or 80 mg, of CALAS three times daily for seven days, or placebo. Each CALAS cohort included 15 subjects, and the placebo group included 10 subjects. A population PK model of CALAS was developed using plasma with four major alkaloid components. Metabolomics analysis was performed to identify biomarkers correlated with the therapeutic effect of CALAS, and efficacy and safety were assessed based on clinical symptoms and adverse events.

RESULTS

The symptoms of acute bronchitis were alleviated by CALAS treatment without serious adverse events or clinically significant changes in vital signs, electrocardiography or upper abdominal Doppler ultrasonography. Moreover, one compartment model with first-order absorption showed that an increase in aspartate transaminase will reduce the clearance (CL) of scholaricine, and picrinine CL was inversely proportional to body mass index, while 19-epischolaricine and vallesamine CL increased with aging. The serum samples from acute bronchitis patients at different time points were analyzed using UPLC-QTOF in combination with the orthogonal projection to latent structures-discriminant analysis, which indicated higher levels of lysophosphatidylcholines, lysophosphatidylethanolamines and amino acids with CALAS treatment than with placebo.

CONCLUSION

This is the first study to evaluate the clinical efficacy and explored the potential biomarkers related to CALAS therapeutic mechanism of acute bronchitis by means of clinical trial combined the metabolomics study. This exploratory study provides a basis for further research on clinical efficacy and optimal dosing regimens based on pharmacokinetics behavior. Additional acute bronchitis patients and CALAS PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

Qinggan Huoxue Recipe Alleviates Alcoholic Liver Injury by Suppressing Endoplasmic Reticulum Stress Through LXR-LPCAT3

Endoplasmic reticulum stress (ERS) plays a key role in alcohol liver injury (ALI). Lysophosphatidylcholine acyltransferase 3 (LPCAT3) is a potential modifier of ERS. It was examined whether the protective effect of Qinggan Huoxue Recipe (QGHXR) against ALI was associated with LPCAT3 by suppressing ERS from in vivo and in vitro experiment. Male C57BL/6 mice were randomly divided into five groups (n = 10, each) and treated for 8 weeks as follows: the control diet-fed group (pair-fed), ethanol diet-fed group (EtOH-fed), QGHXR group (EtOH-fed + QGHXR), Qinggan recipe group (EtOH-fed + QGR), and Huoxue recipe group (EtOH-fed + HXR). QGHXR, QGR, and HXR groups attenuated liver injury mainly manifested in reducing serum ALT, AST, and liver TG and reducing the severity of liver cell necrosis and steatosis in ALI mouse models. QGHXR mainly inhibited the mRNA levels of Lxrα, Perk, Eif2α, and Atf4 and activated the mRNA levels of Lpcat3 and Ire1α, while inhibiting the protein levels of LPCAT3, eIF2α, IRE1α, and XBP1u and activating the protein levels of GRP78 to improve ALI. QGR was more inclined to improve ALI by inhibiting the mRNA levels of Lxrα, Perk, Eif2α, Atif4, and Chop and activating the mRNA levels of Lpcat3 and Ire1α while inhibiting the protein levels of LPCAT3, PERK, eIF2α, IRE1α, and XBP1u. HXR was more inclined to improve ALI by inhibiting the mRNA levels of Perk, Eif2α, Atf4, and Chop mRNA while inhibiting the protein levels of LPCAT3, PERK, eIF2α, IRE1α, and XBP1u and activating the protein levels of GRP78. Ethanol (100 mM) was used to intervene HepG2 and AML12 to establish an ALI cell model and treated by QGHXR-, QGR-, and HXR-medicated serum (100 mg/L). QGHXR, QGR, and HXR groups mainly reduced the serum TG level and the expression of inflammatory factors such as IL-6 and TNF-α in the liver induced by ethanol. In AML12 cells, QGHXR and its disassembly mainly activated Grp78 mRNA expression together with inhibiting Lxrα, Lpcat3, Eif2α, Atf4, and Xbp1 mRNA expression. The protein expression of eIF2α and XBP1u was inhibited, and the expression of PERK and GRP78 was activated to alleviate ALI. In HepG2 cells, QGHXR mainly alleviated ALI by inhibiting the mRNA expression of LPCAT3, CHOP, IRE1α, XBP1, eIF2α, CHOP, and IRE1α protein. QGR was more inclined to inhibit the protein expression of PERK, and HXR was more likely to inhibit the protein expression of ATF4.

Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition

Children admitted to hospital with an acute illness and concurrent severe malnutrition [complicated severe malnutrition (CSM)] have a high risk of dying. The biological processes underlying their mortality are poorly understood. In this case-control study nested within a multicenter randomized controlled trial among children with CSM in Kenya and Malawi, we found that blood metabolomic and proteomic profiles robustly differentiated children who died (n = 92) from those who survived (n = 92). Fatalities were characterized by increased energetic substrates (tricarboxylic acid cycle metabolites), microbial metabolites (e.g., propionate and isobutyrate), acute phase proteins (e.g., calprotectin and C-reactive protein), and inflammatory markers (e.g., interleukin-8 and tumor necrosis factor-α). These perturbations indicated disruptions in mitochondria-related bioenergetic pathways and sepsis-like responses. This study identified specific biomolecular disturbances associated with CSM mortality, revealing that systemic inflammation and bioenergetic deficits are targetable pathophysiological processes for improving survival of this vulnerable population.

Postmortem Metabolomics Reveal Acylcarnitines as Potential Biomarkers for Fatal Oxycodone-Related Intoxication

Postmortem metabolomics has recently been suggested as a potential tool for discovering new biological markers able to assist in death investigations. Interpretation of oxycodone concentrations in postmortem cases is complicated, as oxycodone tolerance leads to overlapping concentrations for oxycodone intoxications versus non-intoxications. The primary aim of this study was to use postmortem metabolomics to identify potential endogenous biomarkers that discriminate between oxycodone-related intoxications and non-intoxications. Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry data from 934 postmortem femoral blood samples, including oxycodone intoxications and controls positive and negative for oxycodone, were used in this study. Data were processed and evaluated with XCMS and SIMCA. A clear trend in group separation was observed between intoxications and controls, with a model sensitivity and specificity of 80% and 76%. Approximately halved levels of short-, medium-, and long-chain acylcarnitines were observed for oxycodone intoxications in comparison with controls (p < 0.001). These biochemical changes seem to relate to the toxicological effects of oxycodone and potentially acylcarnitines constituting a biologically relevant biomarker for opioid poisonings. More studies are needed in order to elucidate the potential of acylcarnitines as biomarker for oxycodone toxicity and their relation to CNS-depressant effects.

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.

A new update of MALDI-TOF mass spectrometry in lipid research

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

RECOGNITION AND AVOIDANCE OF ION SOURCE-GENERATED ARTIFACTS IN LIPIDOMICS ANALYSIS

Lipid research is attracting more and more attention as various key roles and novel biological functions of lipids have been demonstrated and discovered in the organism. Mass spectrometry (MS)-based lipidomics approaches are the most powerful and effective tools for analysis of cellular lipidomes with very high sensitivity and specificity. However, the artifacts generated from in-source fragmentation are always present in all kinds of ion sources, even soft ionization techniques (i.e., electrospray ionization and matrix-assisted laser desorption/ionization [MALDI]). These artifacts can cause many problems for lipidomics, especially when the fragment ions correspond to/are isomeric species of other endogenous lipid species in complex biological samples. These commonly observed artifacts could lead to misannotation, false identification, and consequently, incorrect attribution of phenotypes, and will have negative impact on any MS-based lipidomics research including but not limited to biomarker discovery, drug development, etc. Liquid chromatography-MS, shotgun lipidomics, and MALDI-MS imaging are three representative lipidomics approaches in which ion source-generated artifacts are all manifested and are comprehensively summarized in this article. The strategies on how to avoid/reduce the artifacts of in-source fragmentation on lipidomics analysis are also discussed in detail. We believe that with the recognition and avoidance of ion source-generated artifacts, MS-based lipidomics approaches will provide better accuracy on comprehensive analysis of biological samples and will make greater contribution to the research on metabolism and translational/precision medicine (collectively termed functional lipidomics). © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

The acid sphingomyelinase/ceramide system in COVID-19

Acid sphingomyelinase (ASM) cleaves sphingomyelin into the highly lipophilic ceramide, which forms large gel-like rafts/platforms in the plasma membrane. We showed that SARS-CoV-2 uses these platforms for cell entry. Lowering the amount of ceramide or ceramide blockade due to inhibitors of ASM, genetic downregulation of ASM, anti-ceramide antibodies or degradation by neutral ceramidase protected against infection with SARS-CoV-2. The addition of ceramide restored infection with SARS-CoV-2. Many clinically approved medications functionally inhibit ASM and are called FIASMAs (functional inhibitors of acid sphingomyelinase). The FIASMA fluvoxamine showed beneficial effects on COVID-19 in a randomized prospective study and a prospective open-label real-world study. Retrospective and observational studies showed favorable effects of FIASMA antidepressants including fluoxetine, and the FIASMA hydroxyzine on the course of COVID-19. The ASM/ceramide system provides a framework for a better understanding of the infection of cells by SARS-CoV-2 and the clinical, antiviral, and anti-inflammatory effects of functional inhibitors of ASM. This framework also supports the development of new drugs or the repurposing of "old" drugs against COVID-19.

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.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Development of a novel analytical method for inflammation and immunity-related metabolites in serum based on liquid chromatography tandem mass spectrometry

Some metabolites have been found to play key roles in inflammation and immunity events that are associated with diseases such as cancer, diabetes and cytokine release syndrome. Characterization upon the inflammation and immunity-related metabolites (IIMs) will be helpful to the assessment of related pathological states. Although these metabolites have been partially reported in previous studies, the methods for specific measurement of them remain lacking. In the present study, a liquid chromatography - mass spectrometry based method was developed for the targeted analyses of 45 IIMs including amino acids, organic acids, phosphatidylcholines (PCs), polyunsaturated fatty acids and hormones selected based on the literature knowledge. Direct extraction with dansyl-chloride in acetonitrile was proved to be the most efficient and time-saving strategy, in which precipitation, extraction and derivatization were integrated. IIMs derivatized for 4 min and quenched for 2 min revealed the most comprehensive abundance. Based on the defined conditions, all the IIMs had a low limit of detection smaller than 1 ng/mL with the linear range greater than three orders of magnitude. The relative standard derivations of intra-day and inter-day precisions were ranged from 2.2% to 13.4% and 1.7% to 19.5%, respectively. The recovery rates and accuracy in low concentration were 98.9% ± 5.6% and 106.7% ± 11.6%, in medium concentration were 97.1% ± 6.8% and 106.9% ± 9.5%, and in high concentration were 98.4% ± 8.9% and 98.1% ± 8.1%, respectively. Matrix effect and stability were ranged from -37.8% to 35.6% and 2.9% to 14.2%, respectively. To show the usefulness of the method, serum IIMs in hepatitis B virus (HBV) infected patients and healthy subjects were determined and compared. Bile acids, lipoxygenase-mediated lipid mediators and non-enzymatic products showed global increases, whereas most of LysoPCs and cyclooxygenase-mediated prostaglandin D2 decreased in HBV serum samples. This study provided a robust approach for the characterization of IIMs.

Lysophosphatidic acid species are associated with exacerbation in chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) exacerbations are heterogenous and profoundly impact the disease trajectory. Bioactive lipid lysophosphatidic acid (LPA) has been implicated in airway inflammation but the significance of LPA in COPD exacerbation is not known. The aim of the study was to investigate the utility of serum LPA species (LPA16:0, 18:0, 18:1, 18:2, 20:4) as biomarkers of COPD exacerbation.

PATIENTS AND METHODS

LPA species were measured in the baseline placebo sera of a COPD randomized controlled trial. Tertile levels of each LPA were used to assign patients into biomarker high, medium, and low subgroups. Exacerbation rate and risk were compared among the LPA subgroups.

RESULTS

The levels of LPA species were intercorrelated (rho 0.29-0.91). Patients with low and medium levels of LPA (LPA16:0, 20:4) had significantly higher exacerbation rate compared to the respective LPA-high patients [estimated rate per patient per year (95% CI)]: LPA16:0-low = 1.2 (0.8-1.9) (p = 0.019), LPA16:0-medium = 1.3 (0.8-2.0) (p = 0.013), LPA16:0-high = 0.5 (0.2-0.9); LPA20:4-low = 1.4 (0.9-2.1) (p = 0.0033), LPA20:4-medium = 1.2 (0.8-1.8) (p = 0.0089), LPA20:4-high = 0.4 (0.2-0.8). These patients also had earlier time to first exacerbation (hazard ratio (95% CI): LPA16:0-low = 2.6 (1.1-6.0) (p = 0.028), LPA16:0-medium = 2.7 (1.2-6.3) (p = 0.020); LPA20.4-low = 2.8 (1.2-6.6) (p = 0.017), LPA20:4-medium = 2.7 (1.2-6.4) (p = 0.021). Accordingly, these patients had a significant increased exacerbation risk compared to the respective LPA-high subgroups [odd ratio (95% CI)]: LPA16:0-low = 3.1 (1.1-8.8) (p = 0.030), LPA16:0-medium = 3.0 (1.1-8.3) (p = 0.031); LPA20:4-low = 3.8 (1.3-10.9) (p = 0.012), LPA20:4-medium = 3.3 (1.2-9.5) (p = 0.025). For the other LPA species (LPA18:0, 18:1, 18:2), the results were mixed; patients with low and medium levels of LPA18:0 and 18:2 had increased exacerbation rate, but only LPA18:0-low patients had significant increase in exacerbation risk and earlier time to first exacerbation compared to the LPA18:0-high subgroup.

CONCLUSIONS

The study provided evidence of association between systemic LPA levels and exacerbation in COPD. Patients with low and medium levels of specific LPA species (LPA16:0, 20:4) had increased exacerbation rate, risk, and earlier time to first exacerbation. These non-invasive biomarkers may aid in identifying high risk patients with dysregulated LPA pathway to inform risk management and drug development.

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).