Revealing floral metabolite network in tuberose that underpins scent volatiles synthesis, storage and emission

2021 ◽  
Author(s):  
Nithya N. Kutty ◽  
Upashana Ghissing ◽  
Adinpunya Mitra
Keyword(s):  
Metabolite Network

scholarly journals Antibiotic-Induced Dysbiosis of Microbiota Promotes Chicken Lipogenesis by Altering Metabolomics in the Cecum

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 487
Author(s):  
Tao Zhang ◽  
Hao Ding ◽  
Lan Chen ◽  
Yueyue Lin ◽  
Yongshuang Gong ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
High Performance ◽  
Fat Deposition ◽  
16S Rrna Sequencing ◽  
Oral Antibiotics ◽  
Ms Analysis ◽  
Rrna Sequencing ◽  
Cecal Microbiota ◽  
Metabolite Network

Elucidation of the mechanism of lipogenesis and fat deposition is essential for controlling excessive fat deposition in chicken. Studies have shown that gut microbiota plays an important role in regulating host lipogenesis and lipid metabolism. However, the function of gut microbiota in the lipogenesis of chicken and their relevant mechanisms are poorly understood. In the present study, the gut microbiota of chicken was depleted by oral antibiotics. Changes in cecal microbiota and metabolomics were detected by 16S rRNA sequencing and ultra-high performance liquid chromatography coupled with MS/MS (UHPLC–MS/MS) analysis. The correlation between antibiotic-induced dysbiosis of gut microbiota and metabolites and lipogenesis were analysed. We found that oral antibiotics significantly promoted the lipogenesis of chicken. 16S rRNA sequencing indicated that oral antibiotics significantly reduced the diversity and richness and caused dysbiosis of gut microbiota. Specifically, the abundance of Proteobacteria was increased considerably while the abundances of Bacteroidetes and Firmicutes were significantly decreased. At the genus level, the abundances of genera Escherichia-Shigella and Klebsiella were significantly increased while the abundances of 12 genera were significantly decreased, including Bacteroides. UHPLC-MS/MS analysis showed that antibiotic-induced dysbiosis of gut microbiota significantly altered cecal metabolomics and caused declines in abundance of 799 metabolites and increases in abundance of 945 metabolites. Microbiota-metabolite network revealed significant correlations between 4 differential phyla and 244 differential metabolites as well as 15 differential genera and 304 differential metabolites. Three metabolites of l-glutamic acid, pantothenate acid and N-acetyl-l-aspartic acid were identified as potential metabolites that link gut microbiota and lipogenesis in chicken. In conclusion, our results showed that antibiotic-induced dysbiosis of gut microbiota promotes lipogenesis of chicken by altering relevant metabolomics. The efforts in this study laid a basis for further study of the mechanisms that gut microbiota regulates lipogenesis and fat deposition of chicken.

scholarly journals Metabolomic Changes in Wood Inhabiting Filamentous Fungi during Ontogenesis

2021 ◽  
Author(s):  
Katerina V. Sazanova ◽  
Nadezhda V. Psurtseva ◽  
Alexey L. Shavarda
Keyword(s):  
Tca Cycle ◽  
Stationary Growth Phase ◽  
Mycelium Growth ◽  
Targeted Analysis ◽  
Metabolomic Profiling ◽  
Sparassis Crispa ◽  
Different Strains ◽  
Changes Over Time ◽  
Metabolite Network ◽  
Over Time

GC–MS-based metabolomic profiling of different strains of basidiomycetes Lignomyces vetlinianus, Daedaleopsis tricolor and Sparassis crispa were studied. On different stages of growth in the methanol extracts of mycelium the different compounds including amino acids, organic acid of TCA cycle, sugars, fatty acids, sugar alcohols, and sugar acids were detected. Changes in the metabolite network occurring with age of the mycelium of L. vetlinianus and D. tricolor are discussed. The exponential phase of mycelium growth is characterized by pronounced differences during of growth, which manifests itself both in the analysis of specific compounds and in the modeling of the statistical model of the metabolic network. The metabolomic network in the stationary growth phase is less susceptible to changes over time, and is also characterized by a lower dispersion of samples from one aging group. For some compounds, including biotechnologically significant ones, targeted analysis by GC–MS was performed. 4, 6-dimethoxy-phthalide (4, 6-dimetoxy-1 (3H) -isobenzofuranone) was isolated from the mycelium of Lignomyces vetlinianus, accumulating in the mycelium in the form of large aggregates. The accumulation of sparassol and other orsellinic acid derivatives in Sparassis crispa culture under various conditions is described.

scholarly journals Systems Biology Analysis Reveals Eight SLC22 Transporter Subgroups, Including OATs, OCTs, and OCTNs

2020 ◽  
Vol 21 (5) ◽  
pp. 1791 ◽  
Author(s):  
Darcy C. Engelhart ◽  
Jeffry C. Granados ◽  
Da Shi ◽  
Milton H. Saier Jr. ◽  
Michael E. Baker ◽  
...  
Keyword(s):  
Cyclic Nucleotides ◽  
Signaling Molecules ◽  
In Vitro Assays ◽  
Specific Substrate ◽  
In Vivo Models ◽  
Using Data ◽  
Carnitine Derivatives ◽  
Metabolite Network

The SLC22 family of OATs, OCTs, and OCTNs is emerging as a central hub of endogenous physiology. Despite often being referred to as “drug” transporters, they facilitate the movement of metabolites and key signaling molecules. An in-depth reanalysis supports a reassignment of these proteins into eight functional subgroups, with four new subgroups arising from the previously defined OAT subclade: OATS1 (SLC22A6, SLC22A8, and SLC22A20), OATS2 (SLC22A7), OATS3 (SLC22A11, SLC22A12, and Slc22a22), and OATS4 (SLC22A9, SLC22A10, SLC22A24, and SLC22A25). We propose merging the OCTN (SLC22A4, SLC22A5, and Slc22a21) and OCT-related (SLC22A15 and SLC22A16) subclades into the OCTN/OCTN-related subgroup. Using data from GWAS, in vivo models, and in vitro assays, we developed an SLC22 transporter-metabolite network and similar subgroup networks, which suggest how multiple SLC22 transporters with mono-, oligo-, and multi-specific substrate specificity interact to regulate metabolites. Subgroup associations include: OATS1 with signaling molecules, uremic toxins, and odorants, OATS2 with cyclic nucleotides, OATS3 with uric acid, OATS4 with conjugated sex hormones, particularly etiocholanolone glucuronide, OCT with neurotransmitters, and OCTN/OCTN-related with ergothioneine and carnitine derivatives. Our data suggest that the SLC22 family can work among itself, as well as with other ADME genes, to optimize levels of numerous metabolites and signaling molecules, involved in organ crosstalk and inter-organismal communication, as proposed by the remote sensing and signaling theory.

scholarly journals Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes

2017 ◽  
Vol 49 (1) ◽  
pp. e283-e283 ◽  
Author(s):  
Xiao-Lin Liu ◽  
Ya-Nan Ming ◽  
Jing-Yi Zhang ◽  
Xiao-Yu Chen ◽  
Min-De Zeng ◽  
...  
Keyword(s):  
Liver Disease ◽  
Network Analysis ◽  
Fatty Liver ◽  
Nonalcoholic Fatty Liver Disease ◽  
Fatty Liver Disease ◽  
Nonalcoholic Fatty Liver ◽  
Disease Phenotypes ◽  
Metabolite Network

scholarly journals From single drug targets to synergistic network pharmacology in ischemic stroke

2019 ◽  
Vol 116 (14) ◽  
pp. 7129-7136 ◽  
Author(s):  
Ana I. Casas ◽  
Ahmed A. Hassan ◽  
Simon J. Larsen ◽  
Vanessa Gomez-Rangel ◽  
Mahmoud Elbatreek ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Drug Discovery ◽  
Network Analysis ◽  
Network Pharmacology ◽  
In Vivo Models ◽  
Single Target ◽  
Disease Definition ◽  
Guilt By Association ◽  
Metabolite Network

Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein–protein interactions but also metabolite-dependent interactions. Based on this protein–metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1to3) gene family as the closest target toNox4. Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein–metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.

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