Metabolic profiling of yeast culture using gas chromatography coupled with orthogonal acceleration accurate mass time-of-flight mass spectrometry: Application to biomarker discovery

Mechanistic insights into rumen function promotion through yeast culture (Saccharomyces cerevisiae) metabolites using in vitro and in vivo models

Introduction

Yeast culture (YC) enhances ruminant performance, but its functional mechanism remains unclear because of the complex composition of YC and the uncertain substances affecting rumen fermentation. The objective of this study was to determine the composition of effective metabolites in YC by exploring its effects on rumen fermentation in vitro, growth and slaughter performance, serum index, rumen fermentation parameters, rumen microorganisms, and metabolites in lambs.

Methods

In Trial 1, various YCs were successfully produced, providing raw materials for identifying effective metabolites. The experiment was divided into 5 treatment groups with 5 replicates in each group: the control group (basal diet without additives) and YC groups were supplemented with 0.625‰ of four different yeast cultures, respectively (groups A, B, C, and D). Rumen fermentation parameters were determined at 3, 6, 12, and 24 h in vitro. A univariate regression model multiple factor associative effects index (MFAEI; y) was established to correlate the most influential factors on in vitro rumen fermentation with YC metabolites (x). This identified the metabolites promoting rumen fermentation and optimal YC substance levels. In Trial 2, metabolites in YC not positively correlated with MFAEI were excluded, and effective substances were combined with pure chemicals (M group). This experiment validated the effectiveness of YC metabolites in lamb production based on their impact on growth, slaughter performance, serum indices, rumen parameters, microorganisms, and metabolites. Thirty cross-generation rams (Small tail Han-yang ♀ × Australian white sheep ♂) with good body condition and similar body weight were divided into three treatment groups with 10 replicates in each group: control group, YC group, pure chemicals combination group (M group).

Results

Growth performance and serum index were measured on days 30 and 60, and slaughter performance, rumen fermentation parameters, microorganisms, and metabolites were measured on day 60. The M group significantly increased the dressing percentage, and significantly decreased the GR values of lambs (p  < 0.05). The concentration of growth hormone (GH), Cortisol, insulin (INS), and rumen VFA in the M group significantly increased (p < 0.05).

Discussion

These experiments confirmed that YC or its screened effective metabolites positively impact lamb slaughter performance, rumen fermentation, and microbial metabolism.

Lipidomics characterization of the lipid metabolism profiles in a cystinuria rat model: Precalculus damage in the kidney of cystinuria

Cystinuria is a genetic disorder of cystine transport, including defective protein b^0,+AT (encoded by SLC7A9), and/or rBAT (encoded by SLC3A1). Patients present hyperexcretion of cystine in the urine, recurrent cystine lithiasis, and progressive decline in kidney function. Moreover, heterodimer transport is defective. To date, little omics data are accessible regarding this metabolic disease caused by membrane proteins. Since membrane function is closely related to changes in the lipidome, we decided to explore the changes in kidney tissue of a self-established cystinuria rat model by performing lipidomic analysis by LC-MS/MS. Our results demonstrated that Slc7a9 deficiency changed the lipid profile of the renal cortex and induced vital modifications in the lipidome, including major alterations in ChE, LPA, and PA. Among those alterations, this lipidomic study highlights the lipid changes that participate in inflammatory responses during cystinuria. As a result, lipid research, perhaps has great potential, for it may lead to the identification of novel therapeutic targets for the prevention and treatment of cystinuria.

Development of variance rank initiated-unsupervised sample indexing for gas chromatography-mass spectrometry analysis

Traditional non-targeted chemometric workflows for gas chromatography-mass spectrometry (GC-MS) data rely on using supervised methods, which requires a priori knowledge of sample class membership. Herein, we propose a simple, unsupervised chemometric workflow known as variance rank initiated-unsupervised sample indexing (VRI-USI). VRI-USI discovers analyte peaks exhibiting high relative variance across all samples, followed by k-means clustering on the individual peaks. Based upon how the samples cluster for a given peak, a sample index assignment is provided. Using a probabilistic argument, if the same sample index assignment appears for several discovered peaks, then this outcome strongly suggests that the samples are properly classified by that particular sample index assignment. Thus, relevant chemical differences between the samples have been discovered in an unsupervised fashion. The VRI-USI workflow is demonstrated on three, increasingly difficult datasets: simulations, yeast metabolomics, and human cancer metabolomics. For simulated GC-MS datasets, VRI-USI discovered 85-90% of analytes modeled to vary between sample classes. Nineteen out of 53 peaks in the peak table developed for the yeast metabolome dataset had the same sample index assignments, indicating that those indices are most likely due to class-distinguishing chemical differences. A t-test revealed that 22 out of 53 peaks were statistically significant (p < 0.05) when using those sample index assignments. Likewise, for the human cancer metabolomics study, VRI-USI discovered 25 analytes that were statistically different (p < 0.05) using the sample index assignments determined to highlight meaningful sample-based differences. For all datasets, the sample index assignments that were deduced from VRI-USI were the correct class-based difference when using prior knowledge. VRI-USI holds promise as an exploratory data analysis workflow for studies in which analysts do not readily have a priori class information or want to uncover the underlying nature of their dataset.

Effect of Yeast Culture (Saccharomyces cerevisiae) on Broilers: A Preliminary Study on the Effective Components of Yeast Culture

Simple Summary

The value of yeast culture (YC) as alternative feed additives in poultry farming has been proven. YC is a nutrient-rich and complex micro-ecological fermentation product containing various metabolites. However, the major or specific effective components of YC and their importance in poultry farming are unknown. Herein, we screened the “effective ingredients” of YCs obtained from different fermentation times based on metabolomics and animal feeding experiments. Glycine, fructose, inositol, galactose, and sucrose were identified as potential effective metabolites in YCs. These findings provide an important basis for objective, accurate, and quick evaluation of the quality of YC products, as well as a scientific understanding of their functions.

Abstract

This study was aimed at determining the effective ingredients of yeast culture (YC) for animal breeding. First, the contents of YCs obtained from various fermentation times were detected using gas-chromatography. A total of 85 compounds were identified. Next, 336 Arbor Acres (AA) broilers were randomly divided into seven experimental groups and fed a basal diet, diets supplemented with YCs obtained at various fermentation times, or SZ1 (a commercial YC product). A significant increase in body weight gain (BWG) and a significant decrease in feed conversion ratio (FCR) of AA broiler chicks were observed with YC supplementation. Additionally, most of blood and immunological indices were improved with YC supplementation. According to the production performance and the results of multivariate analysis, glycine, fructose, inositol, galactose, and sucrose were found as the potential effective compounds of YC and were involved in metabolic pathways including glycine, serine, and threonine metabolism. Supplementation with diets based on combinations of effective compounds improved weight gain, feed efficiency, serum immunoglobulin A, and immunoglobulin G, but decreased blood urea concentration. These findings suggest YCs as effective and harmless feed additives with improved nutritional properties for broiler chicks.

Targeted metabolomics study of serum bile acid profile in patients with end-stage renal disease undergoing hemodialysis

Background

Bile acids are important metabolites of intestinal microbiota, which have profound effects on host health. However, whether metabolism of bile acids is involved in the metabolic complications of end-stage renal disease (ESRD), and the effects of bile acids on the prognosis of ESRD remain obscure. Therefore, this study investigated the relationship between altered bile acid profile and the prognosis of ESRD patients.

Methods

A targeted metabolomics approach based on ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to determine the changes in serum bile acids between ESRD patients (n = 77) and healthy controls (n = 30). Univariate and multivariate statistical analyses were performed to screen the differential proportions of bile acids between the two groups.

Results

Six differentially expressed bile acids were identified as potential biomarkers for differentiating ESRD patients from healthy subjects. The decreased concentrations of chenodeoxycholic acid, deoxycholic acid and cholic acid were significantly associated with dyslipidemia in ESRD patients. Subgroup analyses revealed that the significantly increased concentrations of taurocholic acid, taurochenodeoxycholic acid, taurohyocholic acid and tauro α-muricholic acid were correlated to the poor prognosis of ESRD patients.

Conclusions

The serum bile acid profile of ESRD patients differed significantly from that of healthy controls. In addition, the altered serum bile acid profile might contribute to the poor prognosis and metabolic complications of ESRD patients.

Oblique rotation of factors: a novel pattern recognition strategy to classify fluorescence excitation-emission matrices of human blood plasma for early diagnosis of colorectal cancer

Colorectal cancer (CRC) ranks high in both men and women, accounting for about 13% of all cancers. In this study, a novel pattern recognition strategy is proposed to improve early diagnosis of CRC through visualizing the relationship between different spectral patterns in a case-control research. Partial least squares-discriminant analysis (PLS-DA) and supervised Kohonen network (SKN) were used to classify the fluorescence excitation-emission matrices (EEMs) from 289 human blood plasma samples containing CRC patients, adenomas tumor, other non-malignant findings and healthy individuals. To obtain optimal factors, oblique rotation (OR) and genetic algorithm (GA) were used to rotate the factors by optimizing transformation matrix elements. Transformed factors were introduced to SKN to build a classification model and the model performance was examined via comparison with a common classifier; PLS-DA. Classification models were built for CRC-healthy and adenomas-healthy samples and the best results were obtained through applying GA-OR on PLS factors and introducing them to the classifiers. Non-error rates for SKN and PLS-DA models assisted with GA (for selecting more informative PLS factors) and OR were equal to 0.97 and 0.95 in cross validation and 0.93 and 0.90 for prediction of the external test set, respectively. Moreover, according to the acceptable results for adenomas-healthy cases using optimal factors, CRC can be diagnosed in early stages. Combining classifiers and optimal factors proved to be efficient for distinguishing healthy and malignant samples, and OR can significantly improve performance of the classification model.

Application of 2D-HPLC coupled with principal component analysis to study an industrial opiate processing stream

Principal component analysis (PCA) loading plots were used to elucidate key differences between two-dimensional high performance liquid chromatography (2D-HPLC) fingerprint data from samples collected from stages along the Papaver somniferum industrial process chemistry workflow. Data reduction was completed using a 2D-HPLC peak picking approach as a precursor to chemometric analysis. Using comparisons of the final stages of product extraction as an example, PCA analysis of characteristic 2D-HPLC fingerprints accounted for 84.9% of variation between the two sample sets measured in triplicate, with 64.7% explained by PC1. Loadings plots of PC1 on each sample set identified where the significant changes were occurring and normalised bubble plots provided an indication of the relative importance of each of these changes. These findings highlight 2D-HPLC with appropriate chemometric analysis as a useful tool for the exploration of bioactive molecules within biomass.

The fungicide triadimefon affects beer flavor and composition by influencing Saccharomyces cerevisiae metabolism

Despite the fact that beer is produced on a large scale, the effects of pesticide residues on beer have been rarely investigated. In this study, we used micro-brewing settings to determine the effect of triadimefon on the growth of Saccharomyces cerevisiae and beer flavor. The yeast growth in medium was significantly inhibited (45%) at concentrations higher than 5 mg L(-1), reaching 80% and 100% inhibition at 10 mg L(-1) and 50 mg L(-1), respectively. There were significant differences in sensory quality between beer samples fermented with and without triadimefon based on data obtained with an electronic tongue and nose. Such an effect was most likely underlain by changes in yeast fermentation activity, including decreased utilization of maltotriose and most amino acids, reduced production of isobutyl and isoamyl alcohols, and increased ethyl acetate content in the fungicide treated samples. Furthermore, yeast metabolic profiling by phenotype microarray and UPLC/TOF-MS showed that triadimefon caused significant changes in the metabolism of glutathione, phenylalanine and sphingolipids, and in sterol biosynthesis. Thus, triadimefon negatively affects beer sensory qualities by influencing the metabolic activity of S. cerevisiae during fermentation, emphasizing the necessity of stricter control over fungicide residues in brewing by the food industry.

Association between serum bile acid profiles and gestational diabetes mellitus: A targeted metabolomics study

BACKGROUND

Given the potential influence of aberrant bile acid metabolism on glucose homeostasis, we hypothesized that serum bile acid metabolism is altered in gestational diabetes mellitus (GDM). We characterized the metabolic profiling changes of serum bile acids in GDM and to find the potential biomarkers for the diagnosis and differential diagnosis of GDM.

METHODS

Based on ultrahigh performance liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry, a targeted metabolomics study that involved targeted and untargeted screening techniques was performed to explore the changes in serum bile acid metabolism of GDM cases, intrahepatic cholestasis of pregnancy (ICP) cases and healthy controls.

RESULTS

There were 3 significantly different profiling of serum bile acids for GDM, ICP and controls. Compared to the controls, GDM individuals demonstrated significant increases in 8 bile acid species, including 2 dihydroxy conjugated, 1 trihydroxy unconjugated and 5 sulfated bile acids. β-muricholic acid (β-MCA) and di-2 were well-suited to use as the metabolic markers for the diagnosis and differential diagnosis of GDM, respectively.

CONCLUSIONS

These preliminary findings revealed the protective effect of body against cytotoxicity via elimination of increased sulfated bile acids and aberrant enzyme activity participated in the cycle β-MCA→hyodeoxycholic acid (HDCA) of the bile acid metabolism pathway for the women with GDM, which gave us further insights into the etiology and pathophysiology of GDM.

High-Performance Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast

Information about how yeast metabolism is rewired in response to internal and external cues can inform the development of metabolic engineering strategies for food, fuel, and chemical production in this organism. We report a new metabolomics workflow for the characterization of such metabolic rewiring. The workflow combines efficient cell lysis without using chemicals that may interfere with downstream sample analysis and differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) for in-depth yeast metabolome profiling. Using (12)C- and (13)C-dansylation (Dns) labeling to analyze the amine/phenol submetabolome, we detected and quantified a total of 5719 peak pairs or metabolites. Among them, 120 metabolites were positively identified using a library of 275 Dns-metabolite standards, and 2980 metabolites were putatively identified based on accurate mass matches to metabolome databases. We also applied (12)C- and (13)C-dimethylaminophenacyl (DmPA) labeling to profile the carboxylic acid submetabolome and detected over 2286 peak pairs, from which 33 metabolites were positively identified using a library of 188 DmPA-metabolite standards, and 1595 metabolites were putatively identified. Using this workflow for metabolomic profiling of cells challenged by ammonium limitation revealed unexpected links between ammonium assimilation and pantothenate accumulation that might be amenable to engineering for better acetyl-CoA production in yeast. We anticipate that efforts to improve other schemes of metabolic engineering will benefit from application of this workflow to multiple cell types.