【🎉Latest accepted article】
#NativePlantInvasion mediates coupled homogenization of #SoilMetabolomes and microbial functional groups on the Qinghai-Tibetan Plateau
#Allelopathy | #AlphaDiversity | #BetaDiversity | #BioticHomogenization | #Metabolomics
Why has GC-MS (gas chromatography- electron impact ioniation- mass spectrometry) had such enduring value for the field of metabolomics? How do we identify metabolites based on mass spectrometry data?
I hope you'll enjoy this video explaining the basics of GC-MS and the spectral library methods we use for explaining these spectra!
https://www.youtube.com/watch?v=vTWXkHPHEI0
#bioinformatics
#metabolomics #GCMS
Why would we choose to measure just a few metabolites instead of doing a broad untargeted survey? In two words: "quantitative precision"
This is the first lecture recording I have produced with my new home video setup!
📢 Call for Papers: Metabolome-Microbiome in Health and Diseases for One Health
⏳ Submission Deadline: 31 May 2026
🔗 Submit your work: https://spj.science.org/journal/csbj/si/metabolome-microbiome-health-diseases
#CallForPapers #Metabolomics #Microbiome #SystemsBiology #OneHealth #DiseaseBiology #MedicalInformatics #Phenomics
I really appreciated this detailed post from Nicola Zamboni on #bioinformatics for #metabolomics !
https://metabolomics.blog/2026/03/how-we-analyze-untargeted-metabolomics-data/
As a lab primarily interested in metabolomics applications, our publications usually focus on the biological results and their relevance. Much more rarely do we describe the infrastructure that turns raw LC-MS/MS data into detected features, annotations, and ultimately the files that accompany a publication, whether tables, mzTab exports, or other deliverables. We sometimes allude to […]
Mathematical modelling led by @tsukushi_kamiya reveals the role of #nutrients in the stability of the vaginal #microbiota in a way that is consistent with #genomics and #metabolomics data shared by Jacques Ravel. This is one of the rare #models on this understudied #microbiome.
https://doi.org/10.1371/journal.pbio.3003575
🙏 Thanks to the #FRM for funding Tsukushi's post-doctoral fellowship and to @PeerCommunityIn for the #open peer-review process ( @PLOSBiology editors did not require any additional review!).
The vaginal microbiota is shaped by bacterial access to specific nutritional resources, influencing health outcomes. This study uses a resource-based model supported by clinical data to identify key ecological mechanisms underlying microbiota composition and potential bacterial vaginosis interventions.
Integrated Metagenomic and Metabolomic Analysis Reveals Regional Style Differences in Maotai-Flavour #Baijiu
https://www.sciencedirect.com/science/article/pii/S2666517426000131 #OpenAccess #metabolomics #metagenomics #microbiology
New preprint!
An Integrated Multi-omic Analysis Reveals Novel Gene-Metabolite Relationships in Human Steatohepatitic Hepatocellular Carcinoma
https://www.medrxiv.org/content/10.64898/2026.01.28.26344977v1
Colleagues took paired samples of hepatocellularcarcinoma from several patients (8), and did bulk RNA-Seq, lipidomics, and metabolomics on the samples.
We analyzed the data to see what was different in each modality, as well as what correlations could be found between RNA-Seq and metabolomics.
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the fastest-growing etiology of hepatocellular carcinoma (HCC). A mechanistic understanding of the metabolic heterogeneity of MASLD-driven tumors is crucial to inform strategies for future treatment options. Methods: Paired tumor (n=8) and adjacent non-tumor tissue (n=8) were collected from patients with steatohepatitic HCC at the University of Kentucky Markey Cancer Center. Hematoxylin and eosin (H&E) staining was used for pathological determination of tumor and adjacent nontumor tissue by a board-certified pathologist. Lipidomic, metabolomic, and transcriptomic analyses were performed, and data were integrated across platforms to identify novel relationships across tumor and adjacent nontumor tissue. Results: Histological analysis by H&E showed significant lipid vacuole accumulation and inflammatory foci in HCC tumors relative to nontumor tissue. Across omics platforms, we identified 1,679 genes, 1,696 metabolites, and 292 lipids that were significantly (padj<0.01) increased or decreased in tumors relative to nontumor tissue. We identified significant reductions in total ceramides and increases in fatty acyl chain saturation in tumor tissue. Furthermore, metabolites involved in amino acid and fatty acid metabolism were largely decreased in tumors relative to nontumor tissue. We also identified a total of 303 highly significant and novel transcript-metabolite associations (117 gene-metabolite; 186 gene-lipid) across tumor and nontumor tissue. Conclusions: Taken together, this integrative analysis reveals novel relationships between steady-state gene transcripts and specific metabolites in steatohepatitic tumors, thereby identifying new pharmacological targets that may be exploited for therapeutic benefit. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by, in part, by the National Institute of Health (K01DK128022, R01DK139147), an American Heart Association Career Development Award (23CDA1051959), and an American Cancer Society Award (IRG2215234) to R.N.H. Research reported in this manuscript was also supported by the Biospecimen Procurement & Translational Pathology Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Patients undergoing surgical resection of primary HCC at the University of Kentucky (UK) were consented for tissue donation under institutional review board protocols #44026 and #70872. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at Zenodo: https://dx.doi.org/10.5281/zenodo.18227400 <https://dx.doi.org/10.5281/zenodo.18227400>
Journal of Plant Ecology
Scientific Frontline
Dave Tabb
CSBJ
Ecology & Evolution of Health
David Ojcius
Dr. Robert M Flight