scholarly journals STATISTICAL MODELS OF THE METABOLOME OF LITHOBIONTIC COMMUNITIES IN NATURAL AND URBANIZED CONDITIONS

2021 ◽  
Vol 1 (6) ◽  
pp. 155-160
Author(s):  
K.V. Sazanova ◽  
Keyword(s):  
Urban Environment ◽  
Statistical Models ◽  
Metabolomic Profiling ◽  
Metabolomic Data ◽  
Community Types ◽  
Marble Surface

The composition of metabolites in various types of biolayering on the marble surface in natural outcrops and in an urban environment has been studied. Metabolomic profiling was performed by gas chromatographymass spectrometry. It was found that biolayering in the urban environment is much less diverse biochemically than in anthropogenically undisturbed conditions. The differences in metabolomic data were significantly greater between sampling sites than between community types. Lithobiontic communities of organisms are an interesting and promising for bioindication and biomonitoring of the environment.

scholarly journals Metabolomic Profiling of Biolayers on the Surface of Marble in Nature and Urban Environment. Case Study of Karelia and St. Petersburg

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1033
Author(s):  
Katerina V. Sazanova ◽  
Marina S. Zelenskaya ◽  
Oksana A. Rodina ◽  
Alexey L. Shavarda ◽  
Dmitry Yu. Vlasov
Keyword(s):  
Urban Environment ◽  
Environmental Conditions ◽  
Biochemical Composition ◽  
Soil Formation ◽  
Seasonal Differences ◽  
Metabolomic Profiling ◽  
Metabolome Profiling ◽  
Metabolite Network ◽  
Marble Surface

The formation of biolayers of various taxonomic and biochemical composition occurs on the rock surfaces under various environmental conditions. The composition of metabolites in various types of biolayers on the marble surface in natural outcrops and urban environment was studied. Metabolome profiling was fulfilled by GC-MS. It was found that communities in urban environment are much less biochemically diverse than in a quarry. The seasonal differences in metabolite network between samples dominate over taxonomic ones in biolayers with predomination of algae and cyanobacteria and in biolayers with predomination of fungi. The biolayers of different stage of soil formation are less susceptible to seasonal variability.

scholarly journals Metabolomics datasets in the Born in Bradford cohort

2020 ◽  
Vol 5 ◽  
pp. 264
Author(s):  
Kurt Taylor ◽  
Nancy McBride ◽  
Neil J Goulding ◽  
Kimberley Burrows ◽  
Dan Mason ◽  
...  
Keyword(s):  
Cord Blood ◽  
Early Life ◽  
Large Scale ◽  
Epidemiological Studies ◽  
Metabolomic Profiling ◽  
Born In Bradford ◽  
Large Numbers ◽  
Metabolomic Data ◽  
Phenotype Data ◽  
Offspring Health

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

Metabo-Endotypes of Asthma Reveal Clinically Important Differences in Lung Function: Discovery and validation in two TOPMed Cohorts

2021 ◽  
Author(s):  
Rachel Kelly ◽  
Kevin Mendez ◽  
Mengna Huang ◽  
Brian Hobbs ◽  
Clary Clish ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Pulmonary Surfactant ◽  
Vital Capacity ◽  
Molecular Classification ◽  
Forced Expiratory Volume ◽  
Metabolomic Profiling ◽  
Metabolomic Data ◽  
Heterogeneous Nature ◽  
Function Discovery ◽  
Current Guidelines

Abstract Current guidelines do not sufficiently capture the heterogeneous nature of asthma; a detailed molecular classification is needed. Metabolomics represents a novel and compelling approach to derive asthma endotypes, i.e., subtypes defined by functional/pathobiological mechanisms. In two cohorts of asthmatics, untargeted metabolomic profiling and Similarity Network Fusion was used to derive and validate five “metabo-endotypes” of asthma, which displayed significant differences in asthma-relevant phenotypes including pre-bronchodilator and post-bronchodilator forced expiratory volume/forced vital capacity (FEV1/FVC). The “most-severe” asthma metabo-endotype was defined by the lowest FEV1/FVC and characterized by altered levels of phospholipids and polyunsaturated fatty acids, suggesting dysregulation of pulmonary surfactant homeostasis. This was supported by genetic analyses as members of this endotype were more likely to carry variants in key pulmonary surfactant regulation genes including BMPR1B (meta-analyzed p=2.8x10-4) and BMP3 (meta-analyzed p=5.23x10-4). These findings suggest clinically meaningful endotypes can be derived and validated using metabolomic data. Interrogating the drivers of these metabo-endotypes can help understand their pathophysiology.

scholarly journals LC-MS based metabolomic profiling for renal cell carcinoma histologic subtypes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Lun Jing ◽  
Jean-Marie Guigonis ◽  
Delphine Borchiellini ◽  
Matthieu Durand ◽  
Thierry Pourcher ◽  
...  
Keyword(s):  
Renal Cell ◽  
Acid Metabolism ◽  
Clear Cell ◽  
Liquid Chromatography Mass Spectrometry ◽  
Metabolic Dysregulation ◽  
Metabolomic Profiling ◽  
Metabolomic Data ◽  
Metabolic Variation ◽  
Independent Cohort

Abstract Renal cell carcinomas (RCC) are classified according to their histological features. Accurate classification of RCC and comprehensive understanding of their metabolic dysregulation are of critical importance. Here we investigate the use of metabolomic analyses to classify the main RCC subtypes and to describe the metabolic variation for each subtype. To this end, we performed metabolomic profiling of 65 RCC frozen samples (40 clear cell, 14 papillary and 11 chromophobe) using liquid chromatography-mass spectrometry. OPLS-DA multivariate analysis based on metabolomic data showed clear discrimination of all three main subtypes of RCC (R2 = 75.0%, Q2 = 59.7%). The prognostic performance was evaluated using an independent cohort and showed an AUROC of 0.924, 0.991 and 1 for clear cell, papillary and chromophobe RCC, respectively. Further pathway analysis using the 21 top metabolites showed significant differences in amino acid and fatty acid metabolism between three RCC subtypes. In conclusion, this study shows that metabolomic profiling could serve as a tool that is complementary to histology for RCC subtype classification. An overview of metabolic dysregulation in RCC subtypes was established giving new insights into the understanding of their clinical behaviour and for the development of targeted therapeutic strategies.

Conversion of Metabolomic Data to Genomic Marker for Genetic Characterization of Piper betle L. Chemotypes: A Review

2021 ◽  
Author(s):  
Bidisha Mondal
Keyword(s):  
Essential Oil ◽  
Genomic Analysis ◽  
Piper Betle ◽  
Germplasm Management ◽  
Metabolomic Profiling ◽  
Metabolomic Data ◽  
Genomic Marker ◽  
Unique Trace ◽  
Metabolome Profiling

The Indian perfumery industry is shifting towards natural product. In India including West Bengal betel leaves produces high quality essential oil as well contribute to Indian fresh vegetable export. The crop is cultivated from stem cutting and suffers from authenticity problem of cultivars with redundant names. The genetic screening and characterization of cultivars were not initiated due to unavailability of reliable markers. The essential oil metabolomic study identified some polar and non-polar volatile signature compounds. Metabolomic profiling of cultivars is not consistent due to seasonal variation in the production of secondary metabolites and ignorance in marking of unique trace discriminatory compounds. In this paper gene ontogeny study was made on major signature compounds to obtain the complete coding sequence (CDS) of the aroma-genes. The CDS information of aroma-genes could be utilized to construct robust DNA markers to eradicate authentication problem and germplasm management of Piper. The direct genomic analysis could supersede the metabolome profiling. Information available in NCBI, DDBJ and EMBL database were searched for gene ontogeny study utilizing available metabolomic data. The information and method depicted could be efficiently utilized for Piper genomics. Aroma-scientists could apply this technique to validate promising cultivars and competent germplasm management.

scholarly journals Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs

mSystems ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Julia M. Gauglitz ◽  
James T. Morton ◽  
Anupriya Tripathi ◽  
Shalisa Hansen ◽  
Michele Gaffney ◽  
...  
Keyword(s):  
Clinical Information ◽  
Exposed Group ◽  
Fecal Microbiome ◽  
Metabolomic Profiling ◽  
Metabolomic Data ◽  
Sample Data ◽  
In Captivity ◽  
Using Data ◽  
Molecular Patterns ◽  
The Impact

ABSTRACT Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats. IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses.

scholarly journals Metabolomics datasets in the Born in Bradford cohort

2021 ◽  
Vol 5 ◽  
pp. 264
Author(s):  
Kurt Taylor ◽  
Nancy McBride ◽  
Neil J Goulding ◽  
Kimberley Burrows ◽  
Dan Mason ◽  
...  
Keyword(s):  
Cord Blood ◽  
Early Life ◽  
Large Scale ◽  
Epidemiological Studies ◽  
Metabolomic Profiling ◽  
Born In Bradford ◽  
Large Numbers ◽  
Metabolomic Data ◽  
Phenotype Data ◽  
Offspring Health

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

scholarly journals SPME-LC/MS-based serum metabolomic phenotyping for distinguishing ovarian cancer histologic subtypes: a pilot study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mariola Olkowicz ◽  
Hernando Rosales-Solano ◽  
Vathany Kulasingam ◽  
Janusz Pawliszyn
Keyword(s):  
Ovarian Cancer ◽  
Operating Characteristic ◽  
Solid Phase ◽  
Gynecological Cancer ◽  
Serum Samples ◽  
Metabolomic Profiling ◽  
Metabolomic Data ◽  
Wide Range ◽  
Auc Value ◽  
Comprehensive Mapping

AbstractEpithelial ovarian cancer (EOC) is the most common cause of death from gynecological cancer. The outcomes of EOC are complicated, as it is often diagnosed late and comprises several heterogenous subtypes. As such, upfront treatment can be highly challenging. Although many significant advances in EOC management have been made over the past several decades, further work must be done to develop early detection tools capable of distinguishing between the various EOC subtypes. In this paper, we present a sophisticated analytical pipeline based on solid-phase microextraction (SPME) and three orthogonal LC/MS acquisition modes that facilitates the comprehensive mapping of a wide range of analytes in serum samples from patients with EOC. PLS-DA multivariate analysis of the metabolomic data was able to provide clear discrimination between all four main EOC subtypes: serous, endometrioid, clear cell, and mucinous carcinomas. The prognostic performance of discriminative metabolites and lipids was confirmed via multivariate receiver operating characteristic (ROC) analysis (AUC value > 88% with 20 features). Further pathway analysis using the top 57 dysregulated metabolic features showed distinct differences in amino acid, lipid, and steroids metabolism among the four EOC subtypes. Thus, metabolomic profiling can serve as a powerful tool for complementing histology in classifying EOC subtypes.

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