Analysis Of Metabolomics Data Research Articles

COVID-19 and non-alcoholic fatty liver disease: Biological insights from multi-omics data.

We explored the shared pathophysiological mechanisms between COVID-19 and non-alcoholic fatty liver disease (NAFLD) by integrating multi-omics data. We studied common genetic risk factors and underlying biological processes using functional enrichment analysis. To understand the sex-specific pathways involved in the clinical course of SARS-CoV-2 infection, we processed sex-stratified data from COVID-19 genome-wide association datasets. We further explored the transcriptional signature of the liver cells in healthy and COVID-19 tissue specimens. We also integrated genetic and metabolomic information. We found that COVID-19 and NAFLD share biological disease mechanisms, including pathways that regulate the inflammatory and lipopolysaccharide response. Single-cell transcriptomics revealed enrichment of complement-related pathways in Kupffer cells, syndecan-mediated signalling in plasma cells, and epithelial-to-mesenchymal transition in hepatic stellate cells. The strategy of pathway-level analysis of genomic and metabolomic data uncovered l-lactic acid, Krebs cycle intermediate compounds, arachidonic acid and cortisol among the most prominent shared metabolites.

Integrated analysis of transcriptome and metabolome data reveals insights for molecular mechanisms in overwintering Tibetan frogs, Nanorana parkeri.

Nanorana parkeri (Anura, Dicroglossidae) is a unique frog living at high altitude on the Tibetan plateau where they must endure a long winter dormancy at low temperatures without feeding. Here, we presented a comprehensive transcriptomic and metabolomic analysis of liver tissue from summer-active versus overwintering N. parkeri, providing the first broad analysis of altered energy metabolism and gene expression in this frog species. We discovered that significantly up-regulated genes (2,397) in overwintering frogs mainly participated in signal transduction and immune responses, phagosome, endocytosis, lysosome, and autophagy, whereas 2,169 down-regulated genes were mainly involved in metabolic processes, such as oxidation-reduction process, amino acid metabolic process, fatty acid metabolic process, and TCA cycle. Moreover, 35 metabolites were shown to be differentially expressed, including 22 down-regulated and 13 up-regulated in winter. These included particularly notable reductions in the concentrations of most amino acids. These differentially expressed metabolites were mainly involved in amino acid biosynthesis and metabolism. To sum up, these findings suggest that gene expression and metabolic processes show adaptive regulation in overwintering N. parkeri, that contributes to maintaining homeostasis and enhancing protection in the hypometabolic state. This study has greatly expanded our understanding of the winter survival mechanisms in amphibians.

MetChem: a new pipeline to explore structural similarity across metabolite modules.

Computational analysis and interpretation of metabolomic profiling data remains a major challenge in translational research. Exploring metabolic biomarkers and dysregulated metabolic pathways associated with a patient phenotype could offer new opportunities for targeted therapeutic intervention. Metabolite clustering based on structural similarity has the potential to uncover common underpinnings of biological processes. To address this need, we have developed the MetChem package. MetChem is a quick and simple tool that allows to classify metabolites in structurally related modules, thus revealing their functional information. MetChem is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later).

Downloading and Analysis of Metabolomic and Lipidomic Data from Metabolomics Workbench Using MetaboAnalyst 5.0.

Advances in computational and data processing technology have enabled the development of many novel tools for analyzing metabolomic and lipidomic data. These advances involved the catalyst for the creation of publicly accessible complex web-based databases such as the Metabolomics Workbench. Open Source internet-based software packages such as MetaboAnalyst 5.0 enable researchers to perform a wide range of analyte identification and statistical analyses of their own and other researchers' data in order to identify biomarkers and classify compounds. In this paper, we set forth a protocol for obtaining experimental data of interest from a public data repository (Metabolomics Workbench), converting the data into a format suitable for submission to MetaboAnalyst 5.0, and then uploading the data to the MetaboAnalyst server for identification and statistical analysis.

A liquid chromatography-mass spectrometry-based metabolomics strategy to explore plant metabolic diversity.

Plants are expert chemists producing millions of metabolites, only a fraction of which are known to date. Plant metabolomics explores the rationale for highly diverse metabolites evolved and synthesized by plants. Over two-thirds of modern medicines are somehow inspired and/or derived from plants, making the identification of phytochemicals a means of discovering new medicines to challenge existing and emerging diseases. This chapter introduces our established liquid chromatography-tandem mass spectrometry-based untargeted metabolomics approach centered around discovering specialized metabolites (so-called secondary metabolites) across broad lineages of nonmodel plant species. Detecting hundreds to thousands of metabolite peaks, including assigning chemical identity, makes metabolomics data generation and analysis a very complex process. Various mass spectrometry techniques are currently being developed to approach the comprehensive metabolome. Among them, untargeted metabolomics can provide new biological insights by simultaneously and unbiasedly measuring and analyzing all detected metabolites. We have provided a hands-on modular account for untargeted plant metabolomics, from preparing plant biological samples to data analysis and processing using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The methods described here offer a foundation and expert opinion on plant metabolome analysis.

Predictive Modeling of Metabolomics data for the Identification of Biomarkers in Chronic Kidney Disease

Chronic kidney disease is a specific type of Kidney Disease in which, a gradual loss of Kidney Function over a period of 3-30 months is noticeable. Early detection is imperative to prevent this catastrophic event and initiate treatment that may mitigate renal injury. Metabolomics data in Chronic Kidney Disease carries a lot of information about biomarkers. However, it is not clear which of these biomarkers are significant, biostatistical analysis of metabolomics data might provide the clues. In this work, an attempt has been made to find novel biomarkers that may be responsible for causing Chronic Kidney Disease by employing bioinformatics and advanced computational tools. The Chronic Kidney Disease data of the patients (in stages 3 and 4) was selected and data was segregated based on renal and cardiovascular parameters. The study consisted 441 patients and 293 metabolites. Subsequently the identification of top metabolites (as biomarkers) was carried out using statistical methods like t-test, Principal component analysis and partial least square analysis. Nine biomarkers were identified from these statistical analyses i.e Galacturonic acid, p-cresol, L-serine, L-glutamine, Lactose, 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester, Pseudo uridine penta-tms and Myo-inositol. The Reactivity of identified metabolites was confirmed by using quantum chemistry calculations in Gaussian software. Heat Map was constructed to find out the variations in concentrations of biomarkers in healthy and CKD patients and the showed the higher concentrations of L-serine, Galacturonic acid , L-glutamine and lower concentrations of Pseudo uridine penta-tms, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester , 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS , Myo-inositol , p-cresol , Lactose in Death Patients. The biological significance of identified top metabolites has been evaluated by identifying the metabolic pathways in which the metabolites are involved. The metabolites which were found to be toxic are pseudouridine, L-glutamine, and galactouronic acid as per the previous reported literature. The Variations in concentration of these metabolites are responsible for the Death of patient with Chronic Kidney Disease.

Current State and Future Perspectives on Personalized Metabolomics.

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Metabolite Changes during the Transition from Hyperthyroidism to Euthyroidism in Patients with Graves' Disease.

An excess of thyroid hormones in Graves' disease (GD) has profound effects on systemic energy metabolism that are currently partially understood. In this study, we aimed to provide a comprehensive understanding of the metabolite changes that occur when patients with GD transition from hyperthyroidism to euthyroidism with methimazole treatment. Eighteen patients (mean age, 38.6±14.7 years; 66.7% female) with newly diagnosed or relapsed GD attending the endocrinology outpatient clinics in a single institution were recruited between January 2019 and July 2020. All subjects were treated with methimazole to achieve euthyroidism. We explored metabolomics by performing liquid chromatography-mass spectrometry analysis of plasma samples of these patients and then performed multivariate statistical analysis of the metabolomics data. Two hundred metabolites were measured before and after 12 weeks of methimazole treatment in patients with GD. The levels of 61 metabolites, including palmitic acid (C16:0) and oleic acid (C18:1), were elevated in methimazole-naïve patients with GD, and these levels were decreased by methimazole treatment. The levels of another 15 metabolites, including glycine and creatinine, were increased after recovery of euthyroidism upon methimazole treatment in patients with GD. Pathway analysis of metabolomics data showed that hyperthyroidism was closely related to aminoacyl-transfer ribonucleic acid biosynthesis and branched-chain amino acid biosynthesis pathways. In this study, significant variations of plasma metabolomic patterns that occur during the transition from hyperthyroidism to euthyroidism were detected in patients with GD via untargeted metabolomics analysis.

Metabolite profiling of leaves of three <i>Epilobium</i> species

BACKGROUND: The ability of plants to adapt to oxygen deficiency is associated with the presence of various adaptations, many of which are mediated by significant changes of metabolism. These changes allow resistant wetland plants to grow even in oxygen-deficient environment.
 AIM: The aim of the study was to carry out metabolic profiling of the leaves of the wetland species Epilobium palustre and Epilobium hirsutum, and the mesophyte species Epilobium angustifolium in order to identify the most characteristic metabolome traits of hypoxia-resistant plants.
 MATERIALS AND METHODS: Metabolite profiling was performed by GC-MS. Statistical analysis of metabolomics data was processed using R 4.2.1 Funny-Looking Kid.
 RESULTS: The resulting profile included about 360 compounds. 70 of these were identified and 50 compounds were determined to a class. Sugars (64) were the most widely represented in the obtained profiles. 16 amino and 20 carboxylic acids, lipids and secondary compounds have been identified. Significant differences were revealed between the profiles of leaf metabolomes of mesophyte E. angustifolium and hydrophytes E. hirsutum and E. palustre. The mesophyte was characterized by high levels of sugars. The metabolomes of wetland Epilobium species practically did not differ from each other and were characterized by the accumulation of amino acids, including GABA shunt intermediates, dicarboxylic acids of the Krebs cycle, and metabolites of glycolysis and lactic acid fermentation, which reflects the stimulation of anaerobic respiration, nitrogen metabolism, and alternative pathways of NAD(P)H reoxidation in wetland plants.
 CONCLUSIONS: Traits of metabolic profiles detected in hydrophyte Epilobium species can be used to assess the degree of plant resistance to oxygen deficiency.

The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma.

Asthma is a complex chronic airway inflammatory disease that seriously impacts patients' quality of life. As a novel approach to exploring the pathogenesis of diseases, metabolomics provides the potential to identify biomarkers of asthma host susceptibility and elucidate biological pathways. The aim of this study was to screen potential biomarkers and biological pathways so as to provide possible pharmacological therapeutic targets for asthma. In the present study, we merged the differentially expressed genes (DEGs) of asthma in the GEO database with the metabolic genes obtained by Genecard for bioinformatics analysis and successfully screened out the metabolism-related hub genes (HIF1A, OCRL, NNMT, and PER1). Then, untargeted metabolic techniques were utilized to reveal HDM-induced metabolite alterations in 16HBE cells. A total of 45 significant differential metabolites and 5 differential metabolic pathways between the control group and HDM group were identified based on the OPLS-DA model. Finally, three key metabolic pathways, including glycerophospholipid metabolism, galactose metabolism, and alanine, aspartate, and glutamate metabolism, were screened through the integrated analysis of bioinformatics data and untargeted metabolomics data. Taken together, these findings provide valuable insights into the pathophysiology and targeted therapy of asthma and lay a foundation for further research.

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.