Serum and Urine Metabolomic Profiling of Newly Diagnosed Treatment-Naïve Inflammatory Bowel Disease Patients

BACKGROUND AND AIMS

Inflammatory bowel disease (IBD) is a prevalent chronic noncurable disease associated with profound metabolic changes. The discovery of novel molecular indicators for unraveling IBD etiopathogenesis and the diagnosis and prognosis of IBD is therefore pivotal. We sought to determine the distinctive metabolic signatures from the different IBD subgroups before treatment initiation.

METHODS

Serum and urine samples from newly diagnosed treatment-naïve IBD patients and age and sex-matched healthy control (HC) individuals were investigated using proton nuclear magnetic resonance spectroscopy. Metabolic differences were identified based on univariate and multivariate statistical analyses.

RESULTS

A total of 137 Crohn's disease patients, 202 ulcerative colitis patients, and 338 HC individuals were included. In the IBD cohort, several distinguishable metabolites were detected within each subgroup comparison. Most of the differences revealed alterations in energy and amino acid metabolism in IBD patients, with an increased demand of the body for energy mainly through the ketone bodies. As compared with HC individuals, differences in metabolites were more marked and numerous in Crohn's disease than in ulcerative colitis patients, and in serum than in urine. In addition, clustering analysis revealed 3 distinct patient profiles with notable differences among them based on the analysis of their clinical, anthropometric, and metabolomic variables. However, relevant phenotypical differences were not found among these 3 clusters.

CONCLUSIONS

This study highlights the molecular alterations present within the different subgroups of newly diagnosed treatment-naïve IBD patients. The metabolomic profile of these patients may provide further understanding of pathogenic mechanisms of IBD subgroups. Serum metabotype seemed to be especially sensitive to the onset of IBD.

Systematic Review: The Gut Microbiome and Its Potential Clinical Application in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic relapsing-remitting systemic disease of the gastrointestinal tract. It is well established that the gut microbiome has a profound impact on IBD pathogenesis. Our aim was to systematically review the literature on the IBD gut microbiome and its usefulness to provide microbiome-based biomarkers. A systematic search of the online bibliographic database PubMed from inception to August 2020 with screening in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. One-hundred and forty-four papers were eligible for inclusion. There was a wide heterogeneity in microbiome analysis methods or experimental design. The IBD intestinal microbiome was generally characterized by reduced species richness and diversity, and lower temporal stability, while changes in the gut microbiome seemed to play a pivotal role in determining the onset of IBD. Multiple studies have identified certain microbial taxa that are enriched or depleted in IBD, including bacteria, fungi, viruses, and archaea. The two main features in this sense are the decrease in beneficial bacteria and the increase in pathogenic bacteria. Significant differences were also present between remission and relapse IBD status. Shifts in gut microbial community composition and abundance have proven to be valuable as diagnostic biomarkers. The gut microbiome plays a major role in IBD, yet studies need to go from casualty to causality. Longitudinal designs including newly diagnosed treatment-naïve patients are needed to provide insights into the role of microbes in the onset of intestinal inflammation. A better understanding of the human gut microbiome could provide innovative targets for diagnosis, prognosis, treatment and even cure of this relevant disease.

The Interplay between Immune System and Microbiota in Inflammatory Bowel Disease: A Narrative Review

The importance of the gut microbiota in human health is currently well established. It contributes to many vital functions such as development of the host immune system, digestion and metabolism, barrier against pathogens or brain–gut communication. Microbial colonization occurs during infancy in parallel with maturation of the host immune system; therefore, an adequate cross-talk between these processes is essential to generating tolerance to gut microbiota early in life, which is crucial to prevent allergic and immune-mediated diseases. Inflammatory bowel disease (IBD) is characterized by an exacerbated immune reaction against intestinal microbiota. Changes in abundance in the gut of certain microorganisms such as bacteria, fungi, viruses, and archaea have been associated with IBD. Microbes that are commonly found in high abundance in healthy gut microbiomes, such as F. prausnitzii or R. hominis, are reduced in IBD patients. E. coli, which is usually present in a healthy gut in very low concentrations, is increased in the gut of IBD patients. Microbial taxa influence the immune system, hence affecting the inflammatory status of the host. This review examines the IBD microbiome profile and presents IBD as a model of dysbiosis.

Assessment of intraspecies variability in fungal growth initiation of Aspergillus flavus and aflatoxin B1 production under static and changing temperature levels using different initial conidial inoculum levels

Intraspecies variability in fungal growth and mycotoxin production has important implications for food safety. Using the Bioscreen C we have examined spectrophotometrically intraspecies variability of A. flavus using 10 isolates under different environments, including temperature shifts, in terms of growth and aflatoxin B₁ (AFB₁) production. Five high and five low AFB₁ producers were examined. The study was conducted at 5 isothermal conditions (from 15 to 37 °C) and 4 dynamic scenarios (between 15 and 30 °C). The experiments were carried out in a semisolid YES medium at 0.92 a_w and two inoculum levels, 10² and 10³ spores/mL. The Time to Detection (TTD) of growth initiation was determined and modelled as a function of temperature through a polynomial equation and the model was used to predict TTD under temperature upshifts conditions using a novel approach. The results obtained in this study have shown that a model can be developed to describe the effect of temperature upshifts on the TTD for all the studied isolates and inoculum levels. Isolate variability increased as the growth conditions became more stressful and with a lower inoculum level. Inoculum level affected the intraspecies variability but not the repeatability of the experiments. In dynamic conditions, isolate responses depended both on the temperature shift and, predominantly, the final temperature level. AFB₁ production was highly variable among the isolates and greatly depended on temperature (optimum temperature at 30-35 °C) and inoculum levels, with often higher production with lower inoculum. This suggests that, from an ecological point of view, the potential isolate variability and interaction with dynamic conditions should be taken into account in developing strategies to control growth and predicting mycotoxin risks by mycotoxigenic fungi.

Probability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus

The probability of growth and aflatoxin B₁ (AFB₁) production of 20 isolates of Aspergillus flavus were studied using a full factorial design with eight water activity levels (0.84-0.98 a_w) and six temperature levels (15-40 °C). Binary data obtained from growth studies were modelled using linear logistic regression analysis as a function of temperature, water activity and time for each isolate. In parallel, AFB₁ was extracted at different times from newly formed colonies (up to 20 mm in diameter). Although a total of 950 AFB₁ values over time for all conditions studied were recorded, they were not considered to be enough to build probability models over time, and therefore, only models at 30 days were built. The confidence intervals of the regression coefficients of the probability of growth models showed some differences among the 20 growth models. Further, to assess the growth/no growth and AFB₁/no- AFB₁ production boundaries, 0.05 and 0.5 probabilities were plotted at 30 days for all of the isolates. The boundaries for growth and AFB₁ showed that, in general, the conditions for growth were wider than those for AFB₁ production. The probability of growth and AFB₁ production seemed to be less variable among isolates than AFB₁ accumulation. Apart from the AFB₁ production probability models, using growth probability models for AFB₁ probability predictions could be, although conservative, a suitable alternative. Predictive mycology should include a number of isolates to generate data to build predictive models and take into account the genetic diversity of the species and thus make predictions as similar as possible to real fungal food contamination.

Single vs multiple-spore inoculum effect on growth kinetic parameters and modeled probabilities of growth and aflatoxin B1 production of Aspergillus flavus on pistachio extract agar

The objective of the present study was to assess the differences in modeled growth/AFB1 production probability and kinetic growth parameters for Aspergillus flavus inoculated as single spores or in a concentrated inoculation point (~500 spores). The experiment was carried out at 25°C and at two water activities (0.85 and 0.87) on pistachio extract agar (3%). Binary data obtained from growth and AFB1 studies were modeled using linear logistic regression analysis. The radial growth curve for each colony was fitted to a linear model for the estimation of the lag phase for growth and the mycelial growth rate. In general, radial growth rate and lag phase for growth were not normally distributed and both of them were affected by the inoculation type, with the lag phase for growth being more affected. Changing from the multiple spore to the single spore inoculation led to a delay of approximately 3-5days on the lag phase and higher growth rates for the multiple spore experiment were found. The same trend was observed on the probability models, with lower predicted probabilities when colonies came up from single spores, for both growth and AFB1 production probabilities. Comparing both types of models, it was concluded that a clear overestimation of the lag phase for growth occurred using the linear model, but only in the multiple spore experiment. Multiple spore inoculum gave very similar estimated time to reach some set probabilities (t10, t50 and t100) for growth or AFB1 production due to the abruptness of the logistic curve developed. The observed differences suggest that inoculum concentration greatly affects the outcome of the predictive models, the estimated times to growth/AFB1 production being much earlier for the concentrated inoculum than for a single spore colony (up to 9days). Thus the number of spores used to generate data in predictive mycology experiments should be carefully controlled in order to predict as accurately as possible the fungal behavior in a foodstuff.

Time-course of germination, initiation of mycelium proliferation and probability of visible growth and detectable AFB1 production of an isolate of Aspergillus flavus on pistachio extract agar

The aim of this work was to assess the temporal relationship among quantified germination, mycelial growth and aflatoxin B₁ (AFB1) production from colonies coming from single spores, in order to find the best way to predict as accurately as possible the presence of AFB1 at the early stages of contamination. Germination, mycelial growth, probability of growth and probability of AFB1 production of an isolate of Aspergillus flavus were determined at 25 °C and two water activities (0.85 and 0.87) on 3% Pistachio Extract Agar (PEA). The percentage of germinated spores versus time was fitted to the modified Gompertz equation for the estimation of the germination parameters (geometrical germination time and germination rate). The radial growth curve for each colony was fitted to a linear model for the estimation of the apparent lag time for growth and the growth rate, and besides the time to visible growth was estimated. Binary data obtained from growth and AFB1 studies were modeled using logistic regression analysis. Both water activities led to a similar fungal growth and AFB1 production. In this study, given the suboptimal set conditions, it has been observed that germination is a stage far from the AFB1 production process. Once the probability of growth started to increase it took 6 days to produce AFB1, and when probability of growth was 100%, only a 40-57% probability of detection of AFB1 production was predicted. Moreover, colony sizes with a radius of 1-2 mm could be a helpful indicator of the possible AFB1 contamination in the commodity. Despite growth models may overestimate the presence of AFB1, their use would be a helpful tool for producers and manufacturers; from our data 5% probability of AFB1 production (initiation of production) would occur when a minimum of 60% probability of growth is observed. Legal restrictions are quite severe for these toxins, thus their control from the early stages of contamination throughout the food chain is of paramount importance.