A targeted LC-MRM3 proteomic approach for the diagnosis of SARS-CoV-2 infection in nasopharyngeal swabs.

Since its first appearance, SARS-CoV-2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein (NCAP). A highly selective multiple reaction monitoring MRM3 strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral NCAP peptides from nasopharyngeal swabs samples presenting a cycle threshold (Ct) value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immuno-purification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with Ct-values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted mass spectrometry.

A comparison between different human hepatocyte models reveals profound differences in net glucose production, lipid composition and metabolism in vitro.

Hepatocytes are responsible for maintaining a stable blood glucose concentration during periods of nutrient scarcity. The breakdown of glycogen and de novo synthesis of glucose are crucial metabolic pathways deeply interlinked with lipid metabolism. Alterations in these pathways are often associated with metabolic diseases with serious clinical implications. Studying energy metabolism in human cells is challenging. Primary hepatocytes are still considered the golden standard for in vitro studies and have been instrumental in elucidating key aspects of energy metabolism found in vivo. As a result of several limitations posed by using primary cells, a multitude of alternative hepatocyte cellular models emerged as potential substitutes. Yet, there remains a lack of clarity regarding the precise applications for which these models accurately reflect the metabolic competence of primary hepatocytes. In this study, we compared primary hepatocytes, stem cell-derived hepatocytes, adult donor-derived liver organoids, immortalized Upcyte-hepatocytes and the hepatoma cell line HepG2s in their response to a glucose production challenge. We observed the highest net glucose production in primary hepatocytes, followed by organoids, stem-cell derived hepatocytes, Upcyte-hepatocytes and HepG2s. Glucogenic gene induction was observed in all tested models, as indicated by an increase in G6PC and PCK1 expression. Lipidomic analysis revealed considerable differences across the models, with organoids showing the closest similarity to primary hepatocytes in the common lipidome, comprising 347 lipid species across 19 classes. Changes in lipid profiles as a result of the glucose production challenge showed a variety of, and in some cases opposite, trends when compared to primary hepatocytes.

Loss of temporal coherence in the circadian metabolome across multiple tissues during ageing in mice.

Circadian clock function declines with ageing, which can aggravate ageing-related diseases such as type 2 diabetes and neurodegenerative disorders. Understanding age-related changes in the circadian system at a systemic level can contribute to the development of strategies to promote healthy ageing. The goal of this study was to investigate the impact of ageing on 24-h rhythms in amine metabolites across four tissues in young (2 months of age) and old (22-25 months of age) mice using a targeted metabolomics approach. Liver, plasma, the suprachiasmatic nucleus (SCN; the location of the central circadian clock in the hypothalamus) and the paraventricular nucleus (PVN; a downstream target of the SCN) were collected from young and old mice every 4 h during a 24-h period (n = 6-7 mice per group). Differential rhythmicity analysis revealed that ageing impacts 24-h rhythms in the amine metabolome in a tissue-specific manner. Most profound changes were observed in the liver, in which rhythmicity was lost in 60% of the metabolites in aged mice. Furthermore, we found strong correlations in metabolite levels between the liver and plasma and between the SCN and the PVN in young mice. These correlations were almost completely abolished in old mice. These results indicate that ageing is accompanied by a severe loss of the circadian coordination between tissues and by disturbed rhythmicity of metabolic processes. The tissue-specific impact of ageing may help to differentiate mechanisms of ageing-related disorders in the brain versus peripheral tissues and thereby contribute to the development of potential therapies for these disorders.

Comparative Metabolomics and Microbiome Analysis of Ethanol versus OMNImet/gene•GUT Fecal Stabilization.

Metabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide the stability of the fecal metabolome and microbiome at room temperature (RT). Here, we investigated the stability of various metabolites and the microbiome (16S rRNA) in feces collected in 95% ethanol (EtOH) and commercially available sample collection kits with specific preservatives OMNImet•GUT/OMNIgene•GUT. To simulate field-collection scenarios, the samples were stored at different temperatures at varying durations (24 h + 4 °C, 24 h RT, 36 h RT, 48 h RT, and 7 days RT) and compared to aliquots immediately frozen at -80 °C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar metabolites, endocannabinoids, short-chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the nonstabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet•GUT. When comparing the metabolite levels between aliquots stored at room temperature and at +4 °C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing samples at RT and stabilizing them in 95% EtOH yielded metabolomic results comparable to those from flash freezing. We also found that the overall composition of the microbiome did not vary significantly between different storage types. However, notable differences were observed in the α diversity. Altogether, the stability of the metabolome and microbiome in 95% EtOH provided results similar to those of the validated commercial collection kits OMNImet•GUT and OMNIgene•GUT, respectively.

Cardinality optimization in constraint-based modelling: application to human metabolism.

MOTIVATION: Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution. RESULTS: By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction. AVAILABILITY AND IMPLEMENTATION: Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.

Advances in single-cell metabolomics to unravel cellular heterogeneity in plant biology.

Single-cell metabolomics is a powerful tool that can reveal cellular heterogeneity and can elucidate the mechanisms of biological phenomena in detail. It is a promising approach in studying plants, especially when cellular heterogeneity has an impact on different biological processes. In addition, metabolomics, which can be regarded as a detailed phenotypic analysis, is expected to answer previously unrequited questions which will lead to expansion of crop production, increased understanding of resistance to diseases, and in other applications as well. In this review, we will introduce the flow of sample acquisition and single-cell techniques to facilitate the adoption of single-cell metabolomics. Furthermore, the applications of single-cell metabolomics will be summarized and reviewed.

Cerebrospinal Fluid and Plasma Amine Profiles in Interictal Migraine.

OBJECTIVE: Impaired amine metabolism has been associated with the etiology of migraine, that is, why patients continue to get migraine attacks. However, evidence from cerebrospinal fluid (CSF) is lacking. Here, we evaluated individual amine levels, global amine profiles, and amine pathways in CSF and plasma of interictal migraine patients and healthy controls. METHODS: CSF and plasma were sampled between 8:30 am and 1:00 pm, randomly and interchangeably over the time span to avoid any diurnal and seasonal influences, from healthy volunteers and interictal migraine patients, matched for age, sex, and sampling time. The study was approved by the local medical ethics committee. Individual amines (n = 31), global amine profiles, and specific amine pathways were analyzed using a validated ultraperformance liquid chromatography mass spectrometry platform. RESULTS: We analyzed n = 99 participants with migraine with aura, n = 98 with migraine without aura, and n = 96 healthy volunteers. Univariate analysis with Bonferroni correction indicated that CSF L-arginine was reduced in migraine with aura (10.4%, p < 0.001) and without aura (5.0%, p = 0.03). False discovery rate-corrected CSF L-phenylalanine was also lower in migraine with aura (6.9%, p = 0.011) and without aura (8.1%, p = 0.001), p = 0.088 after Bonferroni correction. Multivariate analysis revealed that CSF global amine profiles were similar for both types of migraine (p = 0.64), but distinct from controls (p = 0.009). Global profile analyses were similar in plasma. The strongest associated pathways with migraine were related to L-arginine metabolism. INTERPRETATION: L-Arginine was decreased in the CSF (but not in plasma) of interictal patients with migraine with or without aura, and associated pathways were altered. This suggests that dysfunction of nitric oxide signaling is involved in susceptibility to getting migraine attacks. ANN NEUROL 2023;93:715-728.

Application of a capillary electrophoresis-mass spectrometry metabolomics workflow in zebrafish larvae reveals new effects of cortisol.

In contemporary biomedical research, the zebrafish (Danio rerio) is increasingly considered a model system, as zebrafish embryos and larvae can (potentially) fill the gap between cultured cells and mammalian animal models, because they can be obtained in large numbers, are small and can easily be manipulated genetically. Given that capillary electrophoresis-mass spectrometry (CE-MS) is a useful analytical separation technique for the analysis of polar ionogenic metabolites in biomass-limited samples, the aim of this study was to develop and assess a CE-MS-based analytical workflow for the profiling of (endogenous) metabolites in extracts from individual zebrafish larvae and pools of small numbers of larvae. The developed CE-MS workflow was used to profile metabolites in extracts from pools of 1, 2, 4, 8, 12, 16, 20, and 40 zebrafish larvae. For six selected endogenous metabolites, a linear response (R2  > 0.98) for peak areas was obtained in extracts from these pools. The repeatability was satisfactory, with inter-day relative standard deviation values for peak area of 9.4%-17.7% for biological replicates (n = 3 over 3 days). Furthermore, the method allowed the analysis of over 70 endogenous metabolites in a pool of 12 zebrafish larvae, and 29 endogenous metabolites in an extract from only 1 zebrafish larva. Finally, we applied the optimized CE-MS workflow to identify potential novel targets of the mineralocorticoid receptor in mediating the effects of cortisol.

Capillary electrophoresis-mass spectrometry for creatinine analysis in residual clinical plasma samples and comparison with gold standard assay.

When hospitalized, infants, particularly preterm, are often subjected to multiple painful needle procedures to collect sufficient blood for metabolic screening or diagnostic purposes using standard clinical tests. For example, at least 100 µL of whole blood is required to perform one creatinine plasma measurement with enzymatic colorimetric assays. As capillary electrophoresis-mass spectrometry (CE-MS) utilizing a sheathless porous tip interface only requires limited amounts of sample for in-depth metabolic profiling studies, the aim of this work was to assess the utility of this method for the determination of creatinine in low amounts of plasma using residual blood samples from adults and infants. By using a starting amount of 5 µL of plasma and an injection volume of only 6.7 nL, a detection limit (S/N = 3) of 30 nM could be obtained for creatinine, and intra- and interday precisions (for peak area ratios) were below 3.2%. To shorten the electrophoretic separation time, a multi-segment injection (MSI) strategy was employed to analyze up to seven samples in one electrophoretic run. The findings obtained by CE-MS for creatinine in pretreated plasma were compared with the values acquired by an enzymatic colorimetric assay typically used in clinical laboratories for this purpose. The comparison revealed that CE-MS could be used in a reliable way for the determination of creatinine in residual plasma samples from infants and adults. Nevertheless, to underscore the clinical efficacy of this method, a subsequent investigation employing an expanded pool of plasma samples is imperative. This will not only enhance the method's diagnostic utility but also contribute to minimizing both the amount and frequency of blood collection required for diagnostic purposes.

Longitudinal metabolite profiling of Streptococcus pneumoniae-associated community-acquired pneumonia.

INTRODUCTION: Longitudinal biomarkers in patients with community-acquired pneumonia (CAP) may help in monitoring of disease progression and treatment response. The metabolic host response could be a potential source of such biomarkers since it closely associates with the current health status of the patient. OBJECTIVES: In this study we performed longitudinal metabolite profiling in patients with CAP for a comprehensive range of metabolites to identify potential host response biomarkers. METHODS: Previously collected serum samples from CAP patients with confirmed Streptococcus pneumoniae infection (n = 25) were used. Samples were collected at multiple time points, up to 30 days after admission. A wide range of metabolites was measured, including amines, acylcarnitines, organic acids, and lipids. The associations between metabolites and C-reactive protein (CRP), procalcitonin, CURB disease severity score at admission, and total length of stay were evaluated. RESULTS: Distinct longitudinal profiles of metabolite profiles were identified, including cholesteryl esters, diacyl-phosphatidylethanolamine, diacylglycerols, lysophosphatidylcholines, sphingomyelin, and triglycerides. Positive correlations were found between CRP and phosphatidylcholine (34:1) (cor = 0.63) and negative correlations were found for CRP and nine lysophosphocholines (cor = - 0.57 to - 0.74). The CURB disease severity score was negatively associated with six metabolites, including acylcarnitines (tau = - 0.64 to - 0.58). Negative correlations were found between the length of stay and six triglycerides (TGs), especially TGs (60:3) and (58:2) (cor = - 0.63 and - 0.61). CONCLUSION: The identified metabolites may provide insight into biological mechanisms underlying disease severity and may be of interest for exploration as potential treatment response monitoring biomarker.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Reconstruction of Glutathione Metabolism in the Neuronal Model of Rotenone-Induced Neurodegeneration Using Mass Isotopologue Analysis with Hydrophilic Interaction Liquid Chromatography-Zeno High-Resolution Multiple Reaction Monitoring.

Accurate reconstruction of metabolic pathways is an important prerequisite for interpreting metabolomics changes and understanding the diverse biological processes in disease models. A tracer-based metabolomics strategy utilizes stable isotope-labeled precursors to resolve complex pathways by tracing the labeled atom(s) to downstream metabolites through enzymatic reactions. Isotope enrichment analysis is informative and achieved by counting total labeled atoms and acquiring the mass isotopologue distribution (MID) of the intact metabolite. However, quantitative analysis of labeled metabolite substructures/moieties (MS2 fragments) can offer more valuable insights into the reaction connections through measuring metabolite transformation. In order to acquire the isotopic labeling information at the intact metabolite and moiety level simultaneously, we developed a method that couples hydrophilic interaction liquid chromatography (HILIC) with Zeno trap-enabled high-resolution multiple reaction monitoring (MRMHR). The method enabled accurate and reproducible MID quantification for intact metabolites as well as their fragmented moieties, with notably high sensitivity in the MS2 fragmentation mode based on the measurement of 13C- or 15N-labeled cellular samples. The method was applied to human-induced pluripotent stem cell-derived neurons to trace the fate of 13C/15N atoms from D-13C6-glucose/L-15N2-glutamine added to the media. With the MID analysis of both intact metabolites and fragmented moieties, we validated the pathway reconstruction of de novo glutathione synthesis in mid-brain neurons. We discovered increased glutathione oxidization from both basal and newly synthesized glutathione pools under neuronal oxidative stress. Furthermore, the significantly decreased de novo glutathione synthesis was investigated and associated with altered activities of several key enzymes, as evidenced by suppressed glutamate supply via glucose metabolism and a diminished flux of glutathione synthetic reaction in the neuronal model of rotenone-induced neurodegeneration.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics.

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior.

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Microfluidics and Organoids, the Power Couple of Developmental Biology and Oncology Studies.

Organoids are an advanced cell model that hold the key to unlocking a deeper understanding of in vivo cellular processes. This model can be used in understanding organ development, disease progression, and treatment efficacy. As the scientific world embraces the model, it must also establish the best practices for cultivating organoids and utilizing them to the greatest potential in assays. Microfluidic devices are emerging as a solution to overcome the challenges of organoids and adapt assays. Unfortunately, the various applications of organoids often depend on specific features in a device. In this review, we discuss the options and considerations for features and materials depending on the application and development of the organoid.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Automated Segmented-Flow Analysis - NMR with a Novel Fluoropolymer Flow Cell for High-Throughput Screening.

High-throughput analysis in fields such as industrial biotechnology, combinatorial chemistry, and life sciences is becoming increasingly important. Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique providing exhaustive molecular information on complex samples. Flow NMR in particular is a cost- and time-efficient method for large screenings. In this study, we have developed a novel 3.0 mm inner diameter polychlorotrifluoroethylene (PCTFE) flow cell for a segmented-flow analysis (SFA) - NMR automated platform. The platform uses FC-72 fluorinated oil and fluoropolymer components to achieve a fully fluorinated flow path. Samples were repeatably transferred from 96-deepwell plates to the flow cell by displacing a fixed volume of oil, with a transfer time of 42 s. 1H spectra were acquired fully automated with 500 and 600 MHz NMR spectrometers. The spectral performance of the novel PCTFE cell was equal to that of commercial glass cells. Peak area repeatability was excellent with a relative standard deviation of 0.1-0.5% for standard samples, and carryover was below 0.2% without intermediate washing. The sample temperature was conditioned by using a thermostated transfer line in order to reduce the equilibration time in the probe and increase the throughput. Finally, analysis of urine samples demonstrated the applicability of this platform for screening complex matrices.

Cov2MS: An Automated and Quantitative Matrix-Independent Assay for Mass Spectrometric Measurement of SARS-CoV-2 Nucleocapsid Protein.

The pandemic readiness toolbox needs to be extended, targeting different biomolecules, using orthogonal experimental set-ups. Here, we build on our Cov-MS effort using LC-MS, adding SISCAPA technology to enrich proteotypic peptides of the SARS-CoV-2 nucleocapsid (N) protein from trypsin-digested patient samples. The Cov2MS assay is compatible with most matrices including nasopharyngeal swabs, saliva, and plasma and has increased sensitivity into the attomole range, a 1000-fold improvement compared to direct detection in a matrix. A strong positive correlation was observed with qPCR detection beyond a quantification cycle of 30-31, the level where no live virus can be cultured. The automatable sample preparation and reduced LC dependency allow analysis of up to 500 samples per day per instrument. Importantly, peptide enrichment allows detection of the N protein in pooled samples without sensitivity loss. Easily multiplexed, we detect variants and propose targets for Influenza A and B detection. Thus, the Cov2MS assay can be adapted to test for many different pathogens in pooled samples, providing longitudinal epidemiological monitoring of large numbers of pathogens within a population as an early warning system.

Single cell metabolism: current and future trends.

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Leptin mutation and mycobacterial infection lead non-synergistically to a similar metabolic syndrome.

INTRODUCTION: The leptin signaling pathway plays an important role as a key regulator of glucose homeostasis, metabolism control and systemic inflammatory responses. However, the metabolic effects of leptin on infectious diseases, for example tuberculosis (TB), are still little known. OBJECTIVES: In this study, we aim to investigate the role of leptin on metabolism in the absence and presence of mycobacterial infection in zebrafish larvae and mice. METHODS: Metabolites in entire zebrafish larvae and the blood of mice were studied using high-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and mass spectrometry, respectively. For transcriptome studies of zebrafish larvae, deep RNA sequencing was used. RESULTS: The results show that leptin mutation leads to a similar metabolic syndrome as caused by mycobacterial infection in the two species, characterized by the decrease of 11 amine metabolites. In both species, this metabolic syndrome was not aggravated further when the leptin mutant was infected by mycobacteria. Therefore, we conclude that leptin and mycobacterial infection are both impacting metabolism non-synergistically. In addition, we studied the transcriptomes of lepbibl54 mutant zebrafish larvae and wild type (WT) siblings after mycobacterial infection. These studies showed that mycobacteria induced a very distinct transcriptome signature in the lepbibl54 mutant zebrafish compared to WT sibling control larvae. Furthermore, lepbibl55 Tg (pck1:luc1) zebrafish line was constructed and confirmed this difference in transcriptional responses. CONCLUSIONS: Leptin mutation and TB lead non-synergistically to a similar metabolic syndrome. Moreover, different transcriptomic responses in the lepbibl54  mutant and TB can lead to the similar metabolic end states.