担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

The evolutionary origins of neurons remain unknown. Although recent genome data of extant early-branching animals have shown that neural genes existed in the common ancestor of animals, the physiological and genetic properties of neurons in the early evolutionary phase are still unclear. Here, we performed a mass spectrometry-based comprehensive survey of short peptides from early-branching lineages Cnidaria, Porifera and Ctenophora. We identified a number of mature ctenophore neuropeptides that are expressed in neurons associated with sensory, muscular and digestive systems. The ctenophore peptides are stored in vesicles in cell bodies and neurites, suggesting volume transmission similar to that of cnidarian and bilaterian peptidergic systems. A comparison of genetic characteristics revealed that the peptide-expressing cells of Cnidaria and Ctenophora express the vast majority of genes that have pivotal roles in maturation, secretion and degradation of neuropeptides in Bilateria. Functional analysis of neuropeptides and prediction of receptors with machine learning demonstrated peptide regulation of a wide range of target effector cells, including cells of muscular systems. The striking parallels between the peptidergic neuronal properties of Cnidaria and Bilateria and those of Ctenophora, the most basal neuron-bearing animals, suggest a common evolutionary origin of metazoan peptidergic nervous systems.

DOI： 10.1038/s41559-022-01835-7

Scopus

CiNii Research

PubMed

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135697790&origin=inward

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Background: Pathway enrichment analysis is a crucial method for the biological interpretation of metabolomic data by identifying associations between altered metabolites and biological pathways. However, such traditional approaches often rely on a limited set of predefined metabolic pathways, resulting in a low likelihood of discovering pathways associated with a given metabolic profile. To overcome this limitation, we extended our previously developed iDMET methodology to incorporate a broader range of metabolite sets, including those derived from differential metabolomic profiles. This enhanced approach, termed iDMET+, significantly expands dataset diversity and size, increasing the likelihood of discovering associated metabolite sets for a given metabolic profile, thereby enables more biological insights to be obtained from the metabolic profile. Results: We validated iDMET+ through case studies on three diseases: clear cell renal cell carcinoma, colorectal cancer, and small cell lung cancer. First, using a clear cell renal cell carcinoma study as input, iDMET+ correctly identified another study of the same disease that involved metabolomic analysis. This pair of studies was identified as relevant in our previous iDMET results, showing the consistency between iDMET+ and iDMET. Second, using the metabolomic profile of colorectal cancer as input, iDMET+ identified not only another metabolomic study of the same cancer but, surprisingly, also metabolomic studies on prostate cancer and a high-fat diet. These studies focused on MYC-driven metabolic reprogramming, which was also a major focus of the input study. In both case studies, related studies were enriched because the differential metabolomic profiles of directly associated studies were part of the metabolite set. In contrast, the small cell lung cancer study highlighted limitations in dataset coverage—the absence of directly relevant differential metabolomic profiles resulted in fewer enriched metabolite sets. Nevertheless, the analysis of commonly altered metabolites still yielded some meaningful results. Metabolite alterations associated with inhibition of the purine salvage pathway were observed, suggesting potential involvement in tumor metabolic reprogramming. Conclusions: These results demonstrate that iDMET+ offers broader biologically relevant information than the conventional pathway-based approaches and has the potential to uncover biologically significant findings by searching across diverse datasets. This work also identifies areas of improvements for iDMET+.

DOI： 10.1186/s12859-026-06456-6

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105041837581&origin=inward

記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.1093/bioadv/vbaf246

Kyutacar

記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.1186/s40851-024-00243-y

Kyutacar

開催期間： 2025年06月22日 - 2025年06月25日   記述言語：英語  

開催期間： 2024年10月05日   記述言語：日本語  

開催期間： 2024年06月26日 - 2024年06月28日   記述言語：日本語  

開催期間： 2024年06月16日 - 2024年06月20日   記述言語：英語