Multimodal biomarker discovery for active Onchocerca volvulus infection

The neglected tropical disease onchocerciasis, or river blindness, is caused by infection with the filarial nematode Onchocerca volvulus. Current estimates indicate that 17 million people are infected worldwide, the majority of them living in Africa. Today there are no non-invasive tests available that can detect ongoing infection, and that can be used for effective monitoring of elimination programs. In addition, to enable pharmacodynamic studies with novel macrofilaricide drug candidates, surrogate endpoints and efficacy biomarkers are needed but are non-existent. We describe the use of a multimodal untargeted mass spectrometry-based approach (metabolomics and lipidomics) to identify onchocerciasis-associated metabolites in urine and plasma, and of specific lipid features in plasma of infected individuals (O. volvulus infected cases: 68 individuals with palpable nodules; lymphatic filariasis cases: 8 individuals; non-endemic controls: 20 individuals). This work resulted in the identification of elevated concentrations of the plasma metabolites inosine and hypoxanthine as biomarkers for filarial infection, and of the urine metabolite cis-cinnamoylglycine (CCG) as biomarker for O. volvulus. During the targeted validation study, metabolite-specific cutoffs were determined (inosine: 34.2 ng/ml; hypoxanthine: 1380 ng/ml; CCG: 29.7 ng/ml) and sensitivity and specificity profiles were established. Subsequent evaluation of these biomarkers in a non-endemic population from a different geographical region invalidated the urine metabolite CCG as biomarker for O. volvulus. The plasma metabolites inosine and hypoxanthine were confirmed as biomarkers for filarial infection. With the availability of targeted LC-MS procedures, the full potential of these 2 biomarkers in macrofilaricide clinical trials, MDA efficacy surveys, and epidemiological transmission studies can be investigated.

A Physiological-Based Model for Simulating the Bioavailability and Kinetics of Sulforaphane from Broccoli Products

There are no known physiological-based digestion models that depict glucoraphanin (GR) to sulforaphane (SR) conversion and subsequent absorption. The aim of this research was to make a physiological-based digestion model that includes SR formation, both by endogenous myrosinase and gut bacterial enzymes, and to simulate the SR bioavailability. An 18-compartment model (mouth, two stomach, seven small intestine, seven large intestine, and blood compartments) describing transit, reactions and absorption was made. The model, consisting of differential equations, was fit to data from a human intervention study using Mathwork’s Simulink and Matlab software. SR urine metabolite data from participants who consumed different broccoli products were used to estimate several model parameters and validate the model. The products had high, medium, low, and zero myrosinase content. The model’s predicted values fit the experimental values very well. Parity plots showed that the predicted values closely matched experimental values for the high (r2 = 0.95), and low (r2 = 0.93) products, but less so for the medium (r2 = 0.85) and zero (r2 = 0.78) myrosinase products. This is the first physiological-based model to depict the unique bioconversion processes of bioactive SR from broccoli. This model represents a preliminary step in creating a predictive model for the biological effect of SR, which can be used in the growing field of personalized nutrition.

Network of Interactions Between Gut Microbiome, Host Biomarkers, and Urine Metabolome in Carotid Atherosclerosis

Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.

Kidney Allograft Function Is a Confounder of Urine Metabolite Profiles in Kidney Allograft Recipients

Noninvasive biomarkers of kidney allograft status can help minimize the need for standard of care kidney allograft biopsies. Metabolites that are measured in the urine may inform about kidney function and health status, and potentially identify rejection events. To test these hypotheses, we conducted a metabolomics study of biopsy-matched urine cell-free supernatants from kidney allograft recipients who were diagnosed with two major types of acute rejections and no-rejection controls. Non-targeted metabolomics data for 674 metabolites and 577 unidentified molecules, for 192 biopsy-matched urine samples, were analyzed. Univariate and multivariate analyses identified metabolite signatures for kidney allograft rejection. The replicability of a previously developed urine metabolite signature was examined. Our study showed that metabolite profiles can serve as biomarkers for discriminating rejection biopsies from biopsies without rejection features, but also revealed a role of estimated Glomerular Filtration Rate (eGFR) as a major confounder of the metabolite signal.

MO482URINE METABOLITE LEVELS OF CKD PATIENTS ARE ASSOCIATED WITH ADVERSE KIDNEY OUTCOMES AND MORTALITY*

Background and Aims Chronic kidney disease (CKD) affects >10% of the adult population worldwide and is associated with increased risk of kidney failure (KF) and mortality. Mechanisms underlying the variable course of disease progression are incompletely understood. This study aimed at identifying novel metabolite biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiological mechanisms. Method Using measurements of 1,487 metabolites in urine of 5,087 CKD patients enrolled in the German Chronic Kidney Disease study, we evaluated the association of urine metabolite levels with adverse events. Main endpoints include KF, a combined endpoint of KF and acute kidney injury (KF+AKI), and overall mortality. Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable adjusted Cox regression models. We performed cause-specific hazard regression as well as subdistribution hazard analyses with death of other causes as a competing event for the endpoints KF and KF+AKI. Statistical significance was defined using a Bonferroni correction for the number of tested metabolites per stage. An association was considered replicated if effect estimates from both stages were significant and direction-consistent. Results Median follow-up time was 4 years. At time of analysis, 362 patients died, 241 experienced KF, and 382 KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly and reproducibly associated with adverse kidney outcomes and/or mortality. Cause-specific and subdistribution hazard analyses showed almost identical results. Higher levels of the amino acid C-glycosyltryptophan in urine were associated with higher risk for all three endpoints (KF: hazard ratio 1.43, 95% confidence interval [1.27;1.61], KF+AKI: 1.40 [1.27;1.55], mortality: 1.47 [1.33;1.63]). The cumulative incidence function of KF was higher for each quartile of urine C-glycosyltryptophan levels and the effect were most pronounced in the highest quartile (see Figure). The replicated metabolites belong to different biochemical classes, and those belonging to the phosphatidylcholines pathway showed enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established kidney failure risk equation by Tangri. Conclusion This comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identified and replicated 55 urine metabolites associated with adverse kidney events, potentially providing new insights into the mechanisms of kidney disease progression. The study represents a valuable resource for future experimental studies of biomarkers of CKD progression.