The Metabolomics Innovation Centre

Mitochondrial electron transport flavoprotein (ETF) insufficiency causes metabolic diseases known as a multiple acyl-CoA dehydrogenase deficiency (MADD). In contrast to muscle, ETFDH is a non-essential gene in acute lymphoblastic leukemia NALM6 cells, and its expression is reduced across human cancers. In various human cancer cell lines and mouse models, ETF insufficiency caused by decreased ETFDH expression limits flexibility of OXPHOS fuel utilisation but paradoxically increases bioenergetics and accelerates neoplastic growth via activation of the mTORC1/BCL-6/4E-BP1 axis. Collectively, these findings reveal that while ETF insufficiency is rare and has detrimental effects in non-malignant tissues, it is common in neoplasia, where ETFDH downregulation leads to bioenergetic and signaling reprogramming that accelerates neoplastic growth.

Donor kidney tissue based transcriptomics may represent new dimension for prediction of kidney transplant outcomes. In this prospective, single-center study, 276 kidneys from 174 deceased brain-death donors were assessed by microarrays to identify phenotypes of procurement biopsies. Molecular classifiers (extreme gradient boosting, logistic and Poisson regression) with 10-fold cross-validation were employed to categorize donors based on clinical variables (age, BMI, hypertension, ECD kidney) and histological scores (vascular fibrous intimal thickening, interstitial fibrosis, tubular atrophy, arteriolar hyaline thickening). Archetypal analysis and linear mixed model were applied to determine molecular phenotypes and their association with posttransplant 1-year eGFR in 234 donor kidneys. Three molecular archetypes were identified. The "ideal" archetype (median donor age 42 years, low KDRI, minimal chronic histological changes) was associated with the highest 1-year eGFR, while the "marginal" archetype (68 years, extensive chronic changes, high KDRI) with the lowest one. The "intermediate" archetype yielded better 1-year eGFR despite donor profiles similar to the marginal group. While KDRI predicted 1-year eGFR, adding molecular archetypes improved model performance (AIC 80.0 vs. 83.7;p<0.05). External validation in an independent dataset (n=174, GSE147451) confirmed predictive value of the model. Molecular profiling of procurement biopsies may help to identify donor kidneys with higher posttransplant eGFR.

Mitochondrial electron transport flavoprotein (ETF) insufficiency causes metabolic diseases known as a multiple acyl-CoA dehydrogenase deficiency (MADD). In contrast to muscle, ETFDH is a non-essential gene in acute lymphoblastic leukemia NALM-6 cells, and its expression is reduced across human cancers. ETF insufficiency caused by decreased ETFDH expression limits flexibility of OXPHOS fuel utilization but paradoxically increases cancer cell bioenergetics and accelerates neoplastic growth by activation of the mTORC1/BCL-6/4E-BP1 axis. Collectively, these findings reveal that while ETF insufficiency is rare and has detrimental effects in non-malignant tissues, it is common in neoplasia, where ETFDH downregulation leads to bioenergetic and signaling reprogramming that accelerate neoplastic growth.

Integration of machine learning (ML) and artificial intelligence (AI) into point-of-care (POC) sensor systems represents a transformative advancement in healthcare. This integration enables sophisticated data analysis and real-time decision-making in emergency and intensive care settings. AI and ML algorithms can process complex biomedical data, improve diagnostic accuracy, and enable early disease detection for better patient outcomes. Predictive analytics in POC devices supports proactive healthcare by analyzing data to forecast health issues and facilitating early intervention and personalized treatment. This review covers the key areas of ML and AI integration in POC devices, including data analysis, pattern recognition, real-time decision support, predictive analytics, personalization, automation, and workflow optimization. Examples of current POC devices that use ML and AI include AI-powered blood glucose monitors, portable imaging devices, wearable cardiac monitors, AI-enhanced infectious disease detection, and smart wound care sensors are also discussed. The review further explores new directions for POC sensors and ML integration, including mental health monitoring, nutritional monitoring, metabolic health tracking, and decentralized clinical trials (DCTs). We also examined the impact of integrating ML and AI into POC devices on healthcare accessibility, efficiency, and patient outcomes.

Blackleg disease poses a major threat to global canola production. The resistance gene Rlm1, corresponding to the avirulence gene AvrLm1 in the pathogen Leptosphaeria maculans, has been widely used to mitigate the impact of the disease. To investigate the biochemical basis of Rlm1-mediated resistance against blackleg, we conducted an LC-MS–based analysis of a susceptible Topas double haploid (DH) line and its isogenic Rlm1-carrying resistant counterpart for metabolomic profiles during the infection process. Samples were labeled with 12C- and 13C for LC-MS analyses to enhance both chemical and physical properties of metabolites for improved quantification and detection sensitivity. Resistant plants showed early and sustained accumulation of several defense metabolites, notably pipecolic acid (PA, up to 326-fold), salicylic acid (SA), and gentisic acid (GA) in L. maculans-inoculated Topas–Rlm1 plants compared to mock-inoculated Topas–Rlm1 controls (adjusted p < 0.05), indicating activation of lysine degradation and hormonal defense pathways. Elevated glucosinolates (GLS), γ-aminobutyric acid (GABA), and melatonin precursors may further contribute to antimicrobial defense and cell-wall reinforcement. In contrast, flavonoid and phenylpropanoid pathways were down-regulated, suggesting metabolic reallocation during resistance. Exogenous application of PA, SA, GA, ferulic acid, and piperonylic acid (a known inhibitor of the phenylpropanoid pathway in plants) significantly reduced infection in susceptible canola varieties, validating their defense roles against blackleg. These results offer new insights into Rlm1-mediated resistance and support metabolic targets for breeding durable blackleg resistance in canola.

Early detection of lung cancer significantly improves survival, yet current screening methods have limitations. This study aimed to identify a robust panel of plasma metabolites for early-stage non-small cell lung cancer (NSCLC) diagnosis using a large, clinically diverse patient cohort. A total of 680 archived plasma samples from biopsy-confirmed NSCLC patients and controls (including healthy individuals and patients with non-cancerous lung diseases) were analyzed using targeted, quantitative mass spectrometry-based metabolomics and used as the discovery cohort. An independent set of 216 plasma samples served as the validation cohort. Logistic regression (LR) models developed from the discovery set using ten metabolites achieved area under the receiver-operating characteristic curve (AUROC) values of 93.63%, 93.74%, and 93.91% for distinguishing all-stage, stage I-II, and stage I NSCLC patients from controls, respectively. Incorporating smoking history further improved model performance. The validation cohort confirmed the model's robustness, demonstrating high sensitivity and specificity for early-stage detection. These results support the potential of metabolomic biomarkers as a minimally invasive, accurate tool for early NSCLC diagnosis. This approach may complement current screening methods, enabling earlier intervention and improved patient outcomes. Further studies are warranted to validate these findings in more diverse populations and real-world clinical settings.

This publication reports the controlled cultivation of Zanthoxylum chiloperone var. angustifolium Engl. (Rutaceae) in several growth substrates under controlled greenhouse conditions. This plant is well-known for its anti-Chagas (trypanocidal) activity, related to the presence of several β-carboline alkaloids. The metabolomic study of Z. chiloperone seedlings over two years of growth (2018-2020) was performed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). The canthin-6-one alkaloids, canthin-6-one and 5-methoxy-canthin-6-one, were putatively identified in Z. chiloperone extracts. Finally, in vitro and in silico studies of trypanocidal activity were performed, suggesting that canthin-6-one alkaloids could interact with the main pharmacological targets against Trypanosoma cruzi, cruzain protease, dihydroorotate dehydrogenase, lanosterol 14-alpha-demethylase, farnesyl diphosphate, and squalene synthases.

Background and Objective: Nutrimetabolomics may reveal novel insights into early metabolic alterations and the role of dietary exposures on prostate cancer (PCa) risk. We aimed to prospectively investigate the associations between plasma metabolite concentrations and PCa risk, including clinically relevant tumor subtypes. Methods: We used a targeted and large-scale metabolomics approach to analyze plasma samples of 851 matched PCa case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Associations between metabolite concentrations and PCa risk were estimated by multivariate conditional logistic regression analysis. False discovery rate (FDR) was used to control for multiple testing correction. Results: Thirty-one metabolites (predominately derivatives of food intake and microbial metabolism) were associated with overall PCa risk and its clinical subtypes (p < 0.05), but none of the associations exceeded the FDR threshold. The strongest positive and negative associations were for dimethylglycine (OR = 2.13; 95% CI 1.16-3.91) with advanced PCa risk (n = 157) and indole-3-lactic acid (OR = 0.28; 95% CI 0.09-0.87) with fatal PCa risk (n = 57), respectively; however, these associations did not survive correction for multiple testing. Conclusions: The results from the current nutrimetabolomics study suggest that apart from early metabolic deregulations, some biomarkers of food intake might be related to PCa risk, especially advanced and fatal PCa. Further independent and larger studies are needed to validate our results.

A severe consequence of SARS-CoV-2 infection that manifests as systemic inflammation and multi-organ involvement is called Multisystem Inflammatory Syndrome in Children (MIS-C). This review examines the possible relationship between gut barrier integrity, the microbiome, dysregulation of interleukin 6 (IL-6) signaling, and MIS-C. Clinical and biochemical features of MIS-C are comparable to those of other hyper-inflammatory syndromes, suggesting a dysregulated immune response. One possible explanation for the systemic inflammation seen in MIS-C patients is the SARS-CoV-2-induced dysregulation of the IL-6 signaling pathway. In addition, new data suggest a reciprocal link between gut barrier integrity and IL-6. SARS-CoV-2 exhibits bacteriophage-like behavior, highlighting the role of bacteria as a reservoir for the virus and emphasizing the importance of understanding the bacteriophagic mechanism of the virus in fecal–oral transmission. The increased translocation of viral products and bacterial toxins may result from disrupting the intestinal barrier and cause systemic inflammation. On the other hand, systemic inflammation can weaken the integrity of the intestinal barrier, which feeds back into the loop of immunological dysregulation. In the context of MIS-C, understanding the interaction between SARS-CoV-2 infection, IL-6, and gut barrier integrity may shed light on the etiology of the disease and guide treatment options. Since children with gut dysbiosis may be more susceptible to MIS-C, it is critical to reinforce their microbiome through probiotics supplementation, and plant-fiber-rich diets (prebiotics). Early antibiotic treatment and the use of zonulin antagonists should also be considered.

A subset of individuals (10-20%) experience post-COVID condition (PCC) subsequent to initial SARS-CoV-2 infection, which lacks effective treatment. PCC carries a substantial global burden associated with negative economic and health impacts. This study aims to evaluate the association between plasma taurine levels with self-reported symptoms and adverse clinical outcomes in patients with PCC. We analyzed the plasma proteome and metabolome of 117 individuals during their acute COVID-19 hospitalization and at the convalescence phase six-month post infection. Findings were compared with 28 age and sex-matched healthy controls. Plasma taurine levels were negatively associated with PCC symptoms and correlated with markers of inflammation, tryptophan metabolism, and gut dysbiosis. Stratifying patients based on the trajectories of plasma taurine levels during six-month follow-up revealed a significant association with adverse clinical events. Increase in taurine levels during the transition to convalescence were associated with a reduction in adverse events independent of comorbidities and acute COVID-19 severity. In a multivariate analysis, increased plasma taurine level between acute and convalescence phase was associated with marked protection from adverse clinical events with a hazard ratio of 0.13 (95% CI: 0.05-0.35; p<0.001). Taurine emerges as a promising predictive biomarker and potential therapeutic target in PCC. Taurine supplementation has already demonstrated clinical benefits in various diseases and warrants exploration in large-scale clinical trials for alleviating PCC.