Predicting lupus membranous nephritis using reduced picolinic acid to tryptophan ratio as a urinary biomarker

Tryptophan metabolism as a 'reflex' feature of neuroimmune communication: Sensor and effector functions for the indoleamine-2, 3-dioxygenase kynurenine pathway

Although the central nervous system (CNS) and immune system were regarded as independent entities, it is now clear that immune system cells can influence the CNS, and neuroglial activity influences the immune system. Despite the many clinical implications for this 'neuroimmune interface', its detailed operation at the molecular level remains unclear. This narrative review focuses on the metabolism of tryptophan along the kynurenine pathway, since its products have critical actions in both the nervous and immune systems, placing it in a unique position to influence neuroimmune communication. In particular, since the kynurenine pathway is activated by pro-inflammatory mediators, it is proposed that physical and psychological stressors are the stimuli of an organismal protective reflex, with kynurenine metabolites as the effector arm co-ordinating protective neural and immune system responses. After a brief review of the neuroimmune interface, the general perception of tryptophan metabolism along the kynurenine pathway is expanded to emphasize this environmentally driven perspective. The initial enzymes in the kynurenine pathway include indoleamine-2,3-dioxygenase (IDO1), which is induced by tissue damage, inflammatory mediators or microbial products, and tryptophan-2,3-dioxygenase (TDO), which is induced by stress-induced glucocorticoids. In the immune system, kynurenic acid modulates leucocyte differentiation, inflammatory balance and immune tolerance by activating aryl hydrocarbon receptors and modulates pain via the GPR35 protein. In the CNS, quinolinic acid activates N-methyl-D-aspartate (NMDA)-sensitive glutamate receptors, whereas kynurenic acid is an antagonist: the balance between glutamate, quinolinic acid and kynurenic acid is a significant regulator of CNS function and plasticity. The concept of kynurenine and its metabolites as mediators of a reflex coordinated protection against stress helps to understand the variety and breadth of their activity. It should also help to understand the pathological origin of some psychiatric and neurodegenerative diseases involving the immune system and CNS, facilitating the development of new pharmacological strategies for treatment.

Investigating the value of urinary biomarkers in relation to lupus nephritis histopathology: present insights and future prospects

Lupus nephritis (LN), a leading cause of death in Systemic Lupus Erythematosus (SLE) patients, presents significant diagnostic and prognostic challenges. Although renal pathology offers critical insights regarding the diagnosis, classification, and therapy for LN, its clinical utility is constrained by the invasive nature and limited reproducibility of renal biopsies. Moreover, the continuous monitoring of renal pathological changes through repeated biopsies is impractical. Consequently, there is a growing interest in exploring urine as a non-invasive, easily accessible, and dynamic "liquid biopsy" alternative to guide clinical management. This paper examines novel urinary biomarkers from a renal pathology perspective, encompassing cellular components, cytokines, adhesion molecules, auto-antibodies, soluble leukocyte markers, light chain fragments, proteins, small-molecule peptides, metabolomics, urinary exosomes, and ribonucleic acids. We also discuss the application of combined models comprising multiple biomarkers in assessing lupus activity. These innovative biomarkers and models offer insights into LN disease activity, acute and chronic renal indices, fibrosis, thrombotic microangiopathy, podocyte injury, and other pathological changes, potentially improving the diagnosis, management, and prognosis of LN. These urinary biomarkers or combined models may serve as viable alternatives to traditional renal pathology, potentially revolutionizing the method for future LN diagnosis and observation.

Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples

The collision cross-sections (CCS) measurement using ion mobility spectrometry (IMS) in combination with mass spectrometry (MS) offers a great opportunity to increase confidence in metabolite identification. However, owing to the lack of sensitivity and resolution, IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites (VLMs ≤ 250 Da). Here, we describe an analytical method using ultrahigh-performance liquid chromatography (UPLC) coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples. The experimental CCS values, along with mass spectral properties, were reported for the 174 metabolites. The experimental data included the mass-to-charge ratio (m/z), retention time (RT), tandem MS (MS/MS) spectra, and CCS values. Among the studied metabolites, 263 traveling wave ion mobility spectrometry (TWIMS)-derived CCS values (TWCCSN2) were reported for the first time, and more than 70% of these were CCS values of VLMs. The TWCCSN2 values were highly repeatable, with inter-day variations of <1% relative standard deviation (RSD). The developed method revealed excellent TWCCSN2 accuracy with a CCS difference (ΔCCS) within ±2% of the reported drift tube IMS (DTIMS) and TWIMS CCS values. The complexity of the urine matrix did not affect the precision of the method, as evidenced by ΔCCS within ±1.92%. According to the Metabolomics Standards Initiative, 55 urinary metabolites were identified with a confidence level of 1. Among these 55 metabolites, 53 (96%) were VLMs. The larger number of confirmed compounds found in this study was a result of the addition of TWCCSN2 values, which clearly increased metabolite identification confidence.

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Urinary Biomarkers for Lupus Nephritis: A Systems Biology Approach

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disorder. Kidney involvement, termed lupus nephritis (LN), is seen in 40-60% of patients with systemic lupus erythematosus (SLE). After the diagnosis, serial measurement of proteinuria is the most common method of monitoring treatment response and progression. However, present treatments for LN-corticosteroids and immunosuppressants-target inflammation, not proteinuria. Furthermore, subclinical renal inflammation can persist despite improving proteinuria. Serial kidney biopsies-the gold standard for disease monitoring-are also not feasible due to their inherent risk of complications. Biomarkers that reflect the underlying renal inflammatory process and better predict LN progression and treatment response are urgently needed. Urinary biomarkers are particularly relevant as they can be measured non-invasively and may better reflect the compartmentalized renal response in LN, unlike serum studies that are non-specific to the kidney. The past decade has overseen a boom in applying cutting-edge technologies to dissect the pathogenesis of diseases at the molecular and cellular levels. Using these technologies in LN is beginning to reveal novel disease biomarkers and therapeutic targets for LN, potentially improving patient outcomes if successfully translated to clinical practice.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Gbp3 is associated with the progression of lupus nephritis by regulating cell proliferation, inflammation and pyroptosis

Lupus nephritis (LN) is a major cause death in patients with systemic lupus erythematosus. We aimed to find the differentially expressed genes (DEGs) in LN and confirm the regulatory mechanism on LN. The mouse model of LN was constructed by subcutaneous injection of pristane. RNA-seq screened 392 up-regulated and 447 down-regulated DEGs in LN mouse model, and KEGG analysis found that the top 20 DEGs were enriched in arachidonic acid metabolism, tryptophan metabolism, etc. The hub genes, Kynu, Spidr, Gbp3, Cbr1, Cyp4b1, and Cndp2 were identified, in which Gbp3 was selected for following study. Afterwards, the function of Gbp3 on the proliferation, inflammation, and pyroptosis of LN was verified by CCK-8, ELISA, and WB in vitro. The results demonstrated that si-Gbp3 promoted cell proliferation and inhibited the levels of inflammatory factors (IL-1β, TNF-α and IL-8) and pyroptosis-related proteins (GSDMD, Caspase-1 and NLRP3) in a cell model of LN. In constrast, Gbp3 overexpression played an opposite role. In summary, Gbp3 promoted the progression of LN via inhibiting cell proliferation and facilitating inflammation and pyroptosis.

Immunometabolomics provides a new perspective for studying systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic multi-organ autoimmune disease characterized by clinical heterogeneity, unpredictable progression, and flare ups. Due to the heterogeneous nature of lupus, it has been challenging to identify sensitive and specific biomarkers for its diagnosis and monitoring. Despite the fact that the mechanism of SLE remains unknown, impressive progress has been made over the last decade towards understanding how different immune cells contribute to its pathogenesis. Research suggests that cellular metabolic programs could affect the immune response by regulating the activation, proliferation, and differentiation of innate and adaptive immune cells. Many studies have shown that the dysregulation of the immune system is associated with changes to metabolite profiles. The study of metabolite profiling may provide a means for mechanism exploration and novel biomarker discovery for disease diagnostic, classification, and monitoring. Here we review the latest advancements in understanding the role of immunometabolism in SLE, as well as the systemic metabolite profiling of this disease along with possible clinical application.

An integrated cytokine and kynurenine network as the basis of neuroimmune communication

Two of the molecular families closely associated with mediating communication between the brain and immune system are cytokines and the kynurenine metabolites of tryptophan. Both groups regulate neuron and glial activity in the central nervous system (CNS) and leukocyte function in the immune system, although neither group alone completely explains neuroimmune function, disease occurrence or severity. This essay suggests that the two families perform complementary functions generating an integrated network. The kynurenine pathway determines overall neuronal excitability and plasticity by modulating glutamate receptors and GPR35 activity across the CNS, and regulates general features of immune cell status, surveillance and tolerance which often involves the Aryl Hydrocarbon Receptor (AHR). Equally, cytokines and chemokines define and regulate specific populations of neurons, glia or immune system leukocytes, generating more specific responses within restricted CNS regions or leukocyte populations. In addition, as there is a much larger variety of these compounds, their homing properties enable the superimposition of dynamic variations of cell activity upon local, spatially limited, cell populations. This would in principle allow the targeting of potential treatments to restricted regions of the CNS. The proposed synergistic interface of 'tonic' kynurenine pathway affecting baseline activity and the superimposed 'phasic' cytokine system would constitute an integrated network explaining some features of neuroimmune communication. The concept would broaden the scope for the development of new treatments for disorders involving both the CNS and immune systems, with safer and more effective agents targeted to specific CNS regions.

Validation of a liquid chromatography coupled to tandem mass spectrometry method for simultaneous quantification of tryptophan and 10 key metabolites of the kynurenine pathway in plasma and urine: Application to a cohort of acute kidney injury patients

A sensitive and rapid liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the simultaneous determination of tryptophan (Trp) and ten metabolites of kynurenine pathway, including kynurenine (Kyn), 3-hydroxy-kynurenine (3-HK), kynurenic acid (KA), xanthurenic acid (XA), 3-Hydroxy-anthranilic acid (3-HANA), quinolinic acid (QA), nicotinic acid mononucleotide (NaMN), picolinic acid (Pic), nicotinamide (NAM) and nicotinic acid (NA) in both plasma and urine. This LC-MS/MS method was used to predict the occurrence of acute kidney injury (AKI) in a cohort of patients with cardiac surgery under cardiopulmonary bypass (CPB). Urinary concentrations of Pic, as well as Pic to Trp and Pic to 3-HANA ratios were highly predictive of an AKI episode the week after CPB, indicating that Pic could be a predictive biomarker of AKI. Thus, monitoring the kynurenine pathway activity with this LC-MS/MS method is a clinically relevant tool to identify new biomarkers of kidney injury.