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MetabolomicsDeep functional analysis of synII, a 770 kb synthetic yeast chromosome
Here we report the design, construction and characterization of a 770 Kb synthetic yeast chromosome II (synII). With the UPLC-MS method, we demonstrated synII maintains a nearly wild-type phenotype and fitness despite hundreds of designer editing to it. Our phenomics, transcriptomics, proteomics dat...
2016-10-13 | MTBLS296 | MetaboLights
MetabolomicsProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the...

2021-09-01 | MTBLS1903 | MetaboLights
MetabolomicsOceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges
Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compo...
2023-05-12 | MTBLS1388 | MetaboLights
MetabolomicsDeciphering metabolomic alterations in seminal plasma of crossbred (Bos taurus X Bos indicus) bulls through comparative deep metabolomic analysis
The incidence of sub-fertility is higher in crossbred bulls compared to zebu bulls. In the present study, we analysed the metabolomic profile of seminal plasma from crossbred and zebu bulls and uncovered differentially expressed metabolites between these two breeds. Using a high-throughput LC-MS/MS-...
2021-12-22 | MTBLS1642 | MetaboLights
MetabolomicsDissolved organic carbon compounds in deep-sea hydrothermal vent fluids from the East Pacific Rise at 9°50′N

Deep-sea hydrothermal vents are unique ecosystems that may release chemically distinct dissolved organic matter to the deep ocean. Here, we describe the composition and concentrations of polar dissolved organic compounds observed in low and high temperature hydrothermal vent fluids at 9°50′N on t...

2017-05-23 | MTBLS428 | MetaboLights
MetabolomicsRespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing and agriculture problems. However, the DBTL cycle’s Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing dat...

2023-06-29 | MTBLS3830 | MetaboLights
MetabolomicsExperimental and computational approaches for deep metabolome annotation with application to the ecotoxicological model organism Daphnia magna

Identifying metabolomes with greater coverage and confidence is a critical step towards interpreting the metabolic basis of human and environmental health. Yet for over two decades the field of metabolomics has been inhibited by limited metabolite identification. To address this, we developed the...

MTBLS2273 | MetaboLights
MetabolomicsDeep longitudinal multi-omics analysis of Bordetella pertussis cultivated in bioreactors highlights medium starvations and transitory metabolisms, associated to vaccine antigen biosynthesis variations and global virulence regulation

Bordetella pertussis is the bacterial causative agent of whooping cough, a serious respiratory illness. An extensive knowledge on its virulence regulation and metabolism is a key factor to ensure pertussis vaccine manufacturing process robustness. The aim of this study was to refine our ...

2023-02-21 | MTBLS5762 | MetaboLights
MetabolomicsLongitudinal Deep Multi-Omics Profiling in aCLN3Δex7/8Minipig Model Reveals Novel Biomarker Signatures for Batten Disease
Development of therapies for CLN3 Batten disease, a rare pediatric lysosomal storage disorder, has been hindered by the lack of etiological insights and translatable biomarkers to clinics. Here, we used a deep multi-omics approach to discover new biomarkers using longitudinal serum samples from a po...
2025-08-13 | MTBLS1107 | MetaboLights
MetabolomicsWT 2.0: Unveiling the “dark matter” in the metabolome using all-ion stepwise fragmentation acquisition mode (ASFAM) and deep learning-based feature extraction

Current metabolomics methods often miss low-abundance compounds and yield incomplete or ambiguous MS2 spectra, resulting in the presence of “dark matter” within the metabolome. Here, we introduce WT 2.0, which employs an all-ion stepwise fragmentation acquisition mode (ASFAM) to acquire comprehe...

2025-09-23 | MTBLS13023 | MetaboLights
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