Analysis Of Metabolomics Data Research Articles

Thymol ameliorates ethanol-induced hepatotoxicity via regulating metabolism and autophagy

Alcoholic liver disease represents a serious threat to human health. In terms of safety and acceptability, thymol is widely used in or on foodstuffs to generate odour and taste. The present study aimed to investigate the therapeutic effect and mechanism of thymol against ethanol-induced injury in liver cells. Here we found that thymol is an effective agent for reducing ethanol-induced reactive oxygen species production in mouse liver cells. Thymol improves ethanol-induced lipid accumulation, and this corresponded to altered DGAT2 mRNA expression levels. Metabolomics data analysis showed that thymol alleviated ethanol-induced changes in the levels of thirty-four metabolites including nicotinic acid and l-arginine. By utilizing pathway enrichment analysis, altered metabolites in cells treated with ethanol and ethanol plus thymol were enriched in fourteen pathways including metabolic pathways and arginine and proline metabolism. We further confirmed the alleviation of overdose nitric oxide production in cells treated with ethanol plus thymol compared with that in ethanol-treated cells. It was interesting that up-regulated LC3-II/LC3-I ratio together with higher SQSTM1 protein abundance in ethanol-treated cells were attenuated by treatment with ethanol plus thymol. Thymol ameliorated ethanol-induced reduction of HSPA8 protein abundance. In addition, chloroquine-treated cells exhibited lower HSPA8 protein abundance compared with cells simulated with ethanol plus thymol. These data reveal that improving effect of thymol on ethanol-induced metabolic alteration is related to autophagic flux restoration. Our findings indicate that thymol is an attractive option for treating ethanol-induced liver damage.

How Low Can You Go? Selecting Intensity Thresholds for Untargeted Metabolomics Data Preprocessing

Untargeted mass spectrometry (MS) metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data preprocessing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features, but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresholds (1) improved the limit of detection, (2) increased the number of features detected with an associated isotope pattern and/or an MS-MS fragmentation spectrum, and (3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low- and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in the cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.

A model for microbial interactions and metabolomic alterations in Candida glabrata-Staphylococcus epidermidis dual-species biofilms.

The fungus Candida glabrata and the bacterium Staphylococcus epidermidis are important biofilm-forming microorganisms responsible of nosocomial infections in patients. In addition to causing single-species disease, these microorganisms are also involved in polymicrobial infections leading to an increased antimicrobial resistance. To expand knowledge about polymicrobial biofilms, in this study we investigate the formation of single- and dual-species biofilms of these two opportunistic pathogens employing several complementary approaches. First, biofilm biomass, biofilm metabolic activity and the microbial composition in single- and dual-species biofilms were assessed and compared. Then, the expression of three genes of C. glabrata and three genes of S. epidermidis positively related to the process of biofilm formation was evaluated. Although S. epidermidis is a stronger biofilm producer than C. glabrata, both biological and genetic data indicate that S. epidermidis growth is inhibited by C. glabrata which dominates the dual-species biofilms. To better understand the mechanisms of the interactions between the two microorganisms, a broad GC-MS metabolomic dataset of extracellular metabolites for planktonic, single- and dual-species biofilm cultures of C. glabrata and S. epidermidis was collected. As demonstrated by Partial Least Squares Discriminant Analysis (PLS-DA) of GC-MS metabolomic data, planktonic cultures, single- and dual-species biofilms can be sharply differentiated from each other by the nature and levels of an assortment of primary and secondary metabolites secreted in the culture medium. However, according to our data, 2-phenylethanol (secreted by C. glabrata) and the synergistically combined antifungal activity of 3-phenyllactic acid and of the cyclic dipeptide cyclo-(l-Pro-l-Trp) (secreted by S. epidermidis) play a major role in the race of the two microorganisms for predominance and survival.

Genomic, metabonomic and transcriptomic analyses of sweet osmanthus varieties provide insights into floral aroma formation

Sweet osmanthus is an important ornamental evergreen tree or shrub, which has been widely used in the world's subtropical landscapes and the fragrance industry due to its deliciously fragrant flowers. In this study, whole-genome resequencing, RNA sequencing, and GC-MS analysis were used to compare 30 accessions of Osmanthus fragrans to gain insights into taxonomic classification and the diversity of volatile floral scents. A total of 13,466,389 high confidence SNPs were identified from the accessions with an average sequencing depth of 16.96 × . Phylogenetic analysis of the sequences of the 30 ancient or improved O. fragrans genotypes with diverse origins could be roughly divided into two differentiated sweet osmanthus populations, corresponding to the autumn-flowering and the four-seasons flowering characteristics. The odor activity values and orthogonal projections to latent structures-discriminant analysis of GC-MS metabolomics data indicated that linalool was one of the most important volatile organic compounds (VOCs) which can determine the scent diversity among different sweet osmanthus cultivars. Weighted gene co-expression network analysis (WGCNA) identified 23 distinct modules, four of which were highly associated with important floral VOCs. Based on WGCNA analysis, a critical enzyme gene OfHDR2.1 in the MEP pathway and its 12 co-expression transcription factors from the turquoise module were considered to play important roles in linalool synthesis among differently scented O. fragrans populations. Thus, our study provided new clues for the classification of O. fragrans cultivars and valuable genetic resources for future molecular breeding of O. fragrans accessions improved with respect to floral aroma.

Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component.

Integrative Analysis of Transcriptomic and Metabolomic Data for Identification of Pathways Related to Matrine-Induced Hepatotoxicity.

Matrine (MT) is a major bioactive compound extracted from Sophorae tonkinensis. However, the clinical application of MT is relatively restricted due to its potentially toxic effects, especially hepatotoxicity. Although MT-induced liver injury has been reported, little is known about the underlying molecular mechanisms. In this study, transcriptomics and metabolomics were applied to investigate the hepatotoxicity of MT in mice. The results indicated that liver injury occurred when the administration of MT (30 or 60 mg/kg, i.g) lasted for 2 weeks, including dramatically increased alanine aminotransferase (ALT), aspartate aminotransferase (AST), etc. The metabolomic results revealed that steroid biosynthesis, purine metabolism, glutathione metabolism, and pyruvate metabolism were involved in the occurrence and development of MT-induced hepatotoxicity. Further, the transcriptomic data indicated that the downregulation of NSDHL with CYP51, FDFT1, and DHCR7, involved in steroid biosynthesis, resulted in a lower level of cholic acid. Besides, Gstps and Nat8f1 were related to the disorder of glutathione metabolism, and HMGCS1 could be treated as the marker gene of the development of MT-induced hepatotoxicity. In addition, other metabolites, such as taurine, flavin mononucleotide (FMN), and inosine monophosphate (IMP), also made a contribution to the boosting of MT-induced hepatotoxicity. In this work, our results provide clues for the mechanism investigation of MT-induced hepatotoxicity, and several biomarkers (metabolites and genes) closely related to the liver injury caused by MT are also provided. Meanwhile, new insights into the understanding of the development of MT-induced hepatotoxicity or other monomer-induced hepatotoxicity were also provided.

Analysis of the Antioxidant Mechanism of Tamarix ramosissima Roots under NaCl Stress Based on Physiology, Transcriptomic and Metabolomic.

There is a serious problem with soil salinization that affects the growth and development of plants. Tamarix ramosissima Ledeb (T. ramosissima), as a halophyte, is widely used for afforestation in salinized soils. At present, there are few reports on the antioxidant mechanism of T. ramosissima under NaCl stress. In this study, we learned about the superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities, and hydrogen peroxide (H2O2) and malondialdehyde (MDA) content changes in T. ramosissima. We also mined the relevant metabolic pathways in the antioxidant mechanism, candidate key genes, and their related differential metabolites and verified them using quantitative real-time PCR (qRT-PCR). The results show that the SOD, POD, and CAT activities, and the H2O2 and MDA content reached the highest values in the roots of T. ramosissima. Simultaneously, 92 differentially expressed genes (DEGs) related to antioxidant enzyme activities changed during 48 and 168 h of NaCl stress, and these DEGs were mainly upregulated in 168 h. Based on the association analysis of transcriptomic and metabolomic data, we found Unigene0089358 and Unigene0007782 as genes related to key enzymes in the flavonoid biosynthesis pathway. They were located in the upstream positive regulation at 48 and 168 h under NaCl stress, and their respective related metabolites (phloretin and pinocembrin) were involved in resistance to NaCl stress, and they were significantly correlated with their respective metabolites. In conclusion, at 48 and 168 h under NaCl stress, the roots of T. ramosissima resist NaCl stress by enhancing enzymatic and nonenzymatic antioxidant mechanisms, scavenging ROS generated by high-salt stress, alleviating NaCl toxicity, and maintaining the growth of T. ramosissima. This study provides genetic resources and a scientific theoretical basis for further breeding of salt-tolerant Tamarix plants and the molecular mechanism of antioxidants to alleviate NaCl toxicity.

Additive-induced pH determines bacterial community composition and metabolome in traditional mustard seed fermented products

IntroductionKahudi and Kharoli are unique naturally fermented mustard seed products prepared and consumed in the northeastern region of India. The pre-fermentation processing of mustard seeds (soaking, pan-frying, mixing with alkaline or acidic additives, airtight packaging) renders a stringent fermentation environment. The metabolic activities of fermenting bacterial populations yield a myriad of glucosinolate-derived bioactive components which have not been described earlier.MethodsThis present study employed integrated 16S rRNA amplicon sequencing and LC-MS-based metabolomics to elucidate the bacterial diversity and metabolome of the two fermented mustard seed food products.Results and DiscussionUnivariate and multivariate analyses of metabolomics data revealed differential abundances of a few therapeutically-important metabolites viz., sinapine, indole-3-carbinol, γ-linolenic acid in Kahudi, and metabolites viz., β-sitosterol acetate, 3-butylene glucosinolate, erucic acid in Kharoli. A metagenomic investigation involving the 16S rRNA (V3–V4) amplicon sequencing showed the dominance of Firmicutes (99.1 ± 0.18%) in Kahudi, and Firmicutes (79.6 ± 1.92%) and Proteobacteria (20.37 ± 1.94%) in Kharoli. The most abundant genera were Bacillus (88.7 ± 1.67% in Kahudi; 12.5 ± 1.75% in Kharoli) followed by Lysinibacillus (67.1 ± 2.37% in Kharoli; 10.4 ± 1.74% in Kahudi). Members of both these genera are well known for proteolytic and endospore-forming abilities which could have helped in colonizing and thriving in the stringent fermentation environments.

A Time-Series Metabolomic Analysis of SARS-CoV-2 Infection in a Ferret Model

The global threat of COVID-19 has led to an increased use of metabolomics to study SARS-CoV-2 infections in animals and humans. In spite of these efforts, however, understanding the metabolome of SARS-CoV-2 during an infection remains difficult and incomplete. In this study, metabolic responses to a SAS-CoV-2 challenge experiment were studied in nasal washes collected from an asymptomatic ferret model (n = 20) at different time points before and after infection using an LC-MS-based metabolomics approach. A multivariate analysis of the nasal wash metabolome data revealed several statistically significant features. Despite no effects of sex or interaction between sex and time on the time course of SARS-CoV-2 infection, 16 metabolites were significantly different at all time points post-infection. Among these altered metabolites, the relative abundance of taurine was elevated post-infection, which could be an indication of hepatotoxicity, while the accumulation of sialic acids could indicate SARS-CoV-2 invasion. Enrichment analysis identified several pathways influenced by SARS-CoV-2 infection. Of these, sugar, glycan, and amino acid metabolisms were the key altered pathways in the upper respiratory channel during infection. These findings provide some new insights into the progression of SARS-CoV-2 infection in ferrets at the metabolic level, which could be useful for the development of early clinical diagnosis tools and new or repurposed drug therapies.

Integrated analysis of transcriptomics, proteomics and metabolomics data reveals the role of SLC39A1 in renal cell carcinoma.

Purpose: Accumulating evidence suggests that solute carrier family 39 member 1 (SLC39A1) conceivably function as a tumor suppressor, but the underlying mechanism in renal cell carcinoma (RCC) is poorly understood. Methods: OSRC-2 renal cancer cells were first transfected with SLC39A1 overexpressed vectors and empty vectors and then used in transcriptomics, proteomics, and metabolomics integrated analyses. Results: SLC39A1 significantly altered several metabolisms at transcriptional, protein and metabolic levels, including purine and pyrimidine metabolism, amino acids and derivatives metabolism, lactose metabolism, and free fatty acid metabolism. Additionally, SLC39A1 could promote ferroptosis, and triggered significant crosstalk in PI3K-AKT signal pathway, cAMP signal pathway, and peroxisome proliferators-activated receptor (PPAR) signal pathway. Conclusion: We found SLC39A1 transfection impaired tumor metabolism and perturbed tumor metabolism-related pathways, which was a likely cause of the alteration in cell proliferation, migration, and cell cycle progression in RCC cells. These multi-omics analyses results provided both a macroscopic picture of molecular perturbation by SLC39A1 and novel insights into RCC tumorigenesis and development.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

The gut microbiome-metabolome dataset collection: a curated resource for integrative meta-analysis

Integrative analysis of microbiome and metabolome data obtained from human fecal samples is a promising avenue for better understanding the interplay between bacteria and metabolites in the human gut, in both health and disease. However, acquiring, processing, and unifying such datasets from multiple sources is a daunting and challenging task. Here we present a publicly available, simple-to-use, curated dataset collection of paired fecal microbiome-metabolome data from multiple cohorts. This data resource allows researchers to easily obtain multiple fully processed and integrated microbiome-metabolome datasets, facilitating the discovery of universal microbe-metabolite links, benchmark various microbiome-metabolome integration tools, and compare newly identified microbe-metabolite findings to other published datasets.

Discovery of Homogentisic Acid as a Precursor in Trimethoprim Metabolism and Natural Product Biosynthesis.

Opportunistic infections by Burkholderia cenocepacia are life threatening for patients suffering from cystic fibrosis and chronic granulomatous disease. These infections are often associated with variable clinical outcomes, prompting an interest in molecular investigations of phenotypes associated with disease severity. The production of the pyomelanin pigment is one such phenotype, which was recently linked to the ability of clinical strains to carry out biotransformation of the antibiotic trimethoprim. However, this biotransformation product was not identified, and differences in metabolite production associated with pyomelanin pigmentation are poorly understood. Here, we identify several key metabolites produced exclusively by the pyomelanin-producing strains. To provide insight into the structures and biosynthetic origin of these metabolites, we developed a mass spectrometry-based strategy coupling unsupervised in silico substructure prediction with stable isotope labeling referred to as MAS-SILAC (Metabolite Annotation assisted by Substructure discovery and Stable Isotope Labeling by Amino acids in Cell culture). This approach led to discovery of homogentisic acid as a precursor for biosynthesis of several natural products and for biotransformation of trimethoprim, representing a previously unknown mechanism of antibiotic tolerance. This work presents application of computational methods for analysis of untargeted metabolomic data to link the chemotype of pathogenic microorganisms with a specific phenotype. The observations made in this study provide insights into the clinical significance of the melanated phenotype.

Sevoflurane but not propofol enhances ovarian cancer cell biology through regulating cellular metabolic and signaling mechanisms.

Perioperative risk factors, including the choice of anesthetics, may influence ovarian cancer recurrence after surgery. Inhalational anesthetic sevoflurane and intravenous agent propofol might affect cancer cell metabolism and signaling, which, in turn, may influence the malignancy of ovarian cancer cells. The different effects between sevoflurane and propofol on ovarian cancer cell biology and underlying mechanisms were studied. Cultured ovarian cancer cells were exposed to 2.5% sevoflurane, 4μg/mL propofol, or sham condition as the control for 2h followed by 24-h recovery. Glucose transporter 1 (GLUT1), mitochondrial pyruvate carrier 1 (MPC1), glutamate dehydrogenase 1 (GLUD1), pigment epithelium-derived factor (PEDF), p-Erk1/2, and hypoxia-inducible factor 1-alpha (HIF-1α) expressions were determined with immunostaining and/or Western blot. Cultured media were collected for 1H-NMR spectroscopy-based metabolomics analysis. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were used to analyze metabolomics data. Sevoflurane increased the GLUT1, MPC1, GLUD1, p-Erk1/2, and HIF-1α expressions but decreased the PEDF expression relative to the controls. In contrast to sevoflurane, propofol decreased GLUT1, MPC1, GLUD1, p-Erk1/2, and HIF-1α but increased PEDF expression. Sevoflurane increased metabolite isopropanol and decreased glucose and glutamine energy substrates in the media, but the opposite changes were found after propofol treatment. Our data indicated that, unlike the pro-tumor property of sevoflurane, propofol negatively modulated PEDF/Erk/HIF-1α cellular signaling pathway and inhibited ovarian cancer metabolic efficiency and survival, and hence decreased malignancy. The translational value of this work warrants further study. • Sevoflurane promoted but propofol inhibited ovarian cancer cell biology. • Sevoflurane upregulated but propofol downregulated the GLUT1, MPC1, and GLUD1 expressions of ovarian cancer cells. • Sevoflurane enhanced but propofol inhibited ovarian cancer cellular glucose. metabolism and glutaminolysis. • Sevoflurane downregulated PEDF but upregulated the Erk pathway and HIF-1α, while propofol had the adverse effects on ovarian cancer cells.

The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

Plant metabolomics studies haves revealed new bioactive compounds. However, like other omics disciplines, the generated data are not fully exploited, mainly because the commonly performed analyses focus on elucidating the presence/absence of distinctive metabolites (and/or their precursors) and not on providing a holistic view of metabolomic changes and their participation in organismal adaptation to biotic and abiotic stress conditions. Therefore, spectral libraries generated from Cecropia obtusifolia cell suspension cultures in a previous study were considered as a case study and were reanalyzed herein. These libraries were obtained from a time-course experiment under nitrate starvation conditions using both electrospray ionization modes. The applied methodology included the use of ecological analytical tools in a systematic four-step process, including a population analysis of metabolite α diversity, richness, and evenness (i); a chemometrics analysis to identify discriminant groups (ii); differential metabolic marker identification (iii); and enrichment analyses and annotation of active metabolic pathways enriched by differential metabolites (iv). Our species α diversity results referring to the diversity of metabolites represented by mass-to-charge ratio (m/z) values detected at a specific retention time (rt) (an uncommon way to analyze untargeted metabolomic data) suggest that the metabolome is dynamic and is modulated by abiotic stress. A total of 147 and 371 m/z_rt pairs was identified as differential markers responsive to nitrate starvation in ESI− and ESI+ modes, respectively. Subsequent enrichment analysis showed a high degree of completeness of biosynthetic pathways such as those of brassinosteroids, flavonoids, and phenylpropanoids.

Metabology: Analysis of metabolomics data using community ecology tools

Several areas such as microbiology, botany, and medicine use genetic information and computational tools to organize, classify and analyze data. However, only recently has it been possible to obtain the chemical ontology of metabolites computationally. The systematic classification of metabolites into classes opens the way for adapting methods that previously used genetic taxonomy to now accept chemical ontology. Community ecology tools are ideal for this adaptation as they have mature methods and enable exploratory data analysis with established statistical tools. This study introduces the Metabology approach, which transforms metabolites into an ecosystem where the metabolites (species) are related by chemical ontology. In the present work, we demonstrate the applicability of this new approach using publicly available data from a metabolomics study of human plasma that searched for prognostic markers of COVID-19, and in an untargeted metabolomics study carried out by our laboratory using Lasiodiplodia theobromae fungal pathogen supernatants.

Acute exposure to microplastics induces metabolic disturbances and gut dysbiosis in adult zebrafish (Danio rerio)

There is limited knowledge of the ecotoxicological impacts of MPs at the environmentally relevant concentration on freshwater animals, even though numerous studies have demonstrated the toxic effects of MPs on living organisms. In this study, zebrafish (Danio rerio) was used as a model organism to investigate the ecotoxicological effects of acute exposure of virgin MPs on changes in metabolome and gut microbiota. High-throughput untargeted metabolomics using liquid chromatography with tandem mass spectrometry (LC-MS/MS) provided comprehensive insights into the metabolic responses of zebrafish exposed to PE (polyethylene) and PES (polyester) MPs. Statistical analysis of metabolomics data indicated that 39 and 27 metabolites, such as lysophosphatidylcholine, phosphocholine, phosphatidylserine, triglyceride, glycosphingolipid, psychosine, 8-amino-7-oxononanoate, cholesterol fatty acid ester, phosphatidylinositol, n-Triacontanol, were significantly altered in PE- and PES-exposed zebrafish, respectively. Furthermore, the enrichment pathway analysis unveiled the synthesis of the structural and functional lipids, signaling molecules, fatty alcohol metabolism, and amino acid metabolism, which was considerably perturbated in MPs-exposed zebrafish. In addition, high-throughput DNA sequencing was conducted to examine changes in gut microbiota in the MPs-treated zebrafish. The MPs exposure increased in the relative abundance of Fusobacteria and Proteobacteria, while the relative abundance of Firmicutes declined in MPs-treated zebrafish. Also, microbial diversity and linear discriminant analyses indicated microbiota dysbiosis, metabolomic dysregulation, and oxidative stress. Taken together, the acute exposure of MPs at environmentally relevant concentrations could disrupt the metabolic interaction via the microbiota-gut-liver-brain relationship, implying gastrointestinal and neurological/immune disorders in zebrafish.

Bifidobacterium longum 070103 Fermented Milk Improve Glucose and Lipid Metabolism Disorders by Regulating Gut Microbiota in Mice.

Background: Fermented milk is beneficial for metabolic disorders, while the underlying mechanisms of action remain unclear. This study explored the benefits and underlying mechanisms of Bifidobacterium longum 070103 fermented milk (BLFM) in thirteen-week high-fat and high-sugar (HFHS) fed mice using omics techniques. Methods and results: BLFM with activated glucokinase (GK) was screened by a double-enzyme coupling method. After supplementing BLFM with 10 mL/kg BW per day, fasting blood glucose, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and leptin were significantly reduced compared with the HFHS group. Among them, the final body weight (BW), epididymal fat, perirenal fat, and brown fat in BLFM group had better change trends than Lacticaseibacillus rhamnosus GG fermented milk (LGGFM) group. The amplicon and metabolomic data analysis identified Bifibacterium as a key gut microbiota at regulating glycolipid metabolism. BLFM reverses HFHS-induced reduction in bifidobacteria abundance. Further studies showed that BLFM significantly reduces the content of 3-indoxyl sulofphate associated with intestinal barrier damage. In addition, mice treated with BLFM improved BW, glucose tolerance, insulin resistance, and hepatic steatosis. Conclusion: BLFM consumption attenuates obesity and related symptoms in HFHS-fed mice probably via the modulation of gut microbes and metabolites.

Homologue series detection and management in LC-MS data with homologueDiscoverer.

Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer. Supplementary data are available at Bioinformatics online.

Metabolomic Characterization of the Antiproliferative Activity of Bioactive Compounds from Fruit By-Products Against Colon Cancer Cells.

This methodological work demonstrates the potential of metabolomic approaches based on liquid chromatography coupled to high-resolution mass spectrometry (LC-ESI(+/-)-HRMS) to investigate the antiproliferative capacity of underexplored biomasses (e.g., Passiflora mollissima seeds and Physalys peruviana calyx), by evaluating the molecular changes induced at the metabolite expression levels on HT-29 human colon cancer cells. This protocol describes in detail the optimal conditions to obtain bioactive extracts by pressurized liquid extraction (PLE), the experimental procedure to grow and treat HT-29 human colon cancer cells and CCD-18Co normal human colon fibroblasts with the target extracts, the metabolites extraction from the cytosolic fraction, and subsequent metabolomic fingerprinting. After treatment for 48 and 72h, the viability of HT-29 colon cancer cells is markedly affected, and metabolites can be extracted for investigation. Following the proposed metabolomic data analysis and interpretation workflow, altered cellular redox homeostasis, as well as inactivation or dysfunction on other metabolic pathways, constitutes valuable biological information to understand the mechanisms underlying the antiproliferative effect.