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This study integrated non-targeted metabolomics and transcriptomics to investigate dynamic changes in pupae across five developmental stages. Metabolomic analysis identified 1246 metabolites, primarily organic acids, lipids, heterocyclic compounds, and oxygen-containing organics. Principal component analysis revealed stage-specific metabolic profiles: amino acid derivatives (pyruvate, proline, lysine) declined, while pyrimidines (cytidine, uridine, β-alanine) and monosaccharides (glucose, mannose) increased. 18β-glycyrrhetinic and ursolic acids accumulated significantly in the middle and late stages. Transcriptomic analysis identified 7230 differentially expressed genes (DEGs), with 366, 1705, and 5159 significantly differentially expressed genes in the T1, T3, and T5 comparison groups, respectively. KEGG enrichment highlighted ABC transporters, amino acid/pyrimidine metabolism, and tyrosine pathways as developmentally critical, with aminoacyl-tRNA biosynthesis upregulated in later phases. Integrated multi-omics analysis revealed coordinated shifts in metabolites and genes across developmental phases, reflecting dynamic nutrient remodeling during pupal maturation. This study systematically delineates the molecular transitions driving pupal development in pupae, uncovering conserved pathway interactions and mechanistic insights into nutrient metabolism. These findings provide a scientific foundation for leveraging pupal resources in functional food innovation and bioactive compound discovery for pharmaceutical applications.
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http://dx.doi.org/10.3390/insects16070745 | DOI Listing |
Nat Aging
September 2025
Aging Biomarker Consortium (ABC), Beijing, China.
The global surge in the population of people 60 years and older, including that in China, challenges healthcare systems with rising age-related diseases. To address this demographic change, the Aging Biomarker Consortium (ABC) has launched the X-Age Project to develop a comprehensive aging evaluation system tailored to the Chinese population. Our goal is to identify robust biomarkers and construct composite aging clocks that capture biological age, defined as an individual's physiological and molecular state, across diverse Chinese cohorts.
View Article and Find Full Text PDFEcotoxicol Environ Saf
September 2025
Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhua West Road, Jinan, Shandong 250012, China; Shandong Key Laboratory of Reproductive Health and Birth Defects Prevention and Control, China. Electronic address:
Di-isobutyl phthalate (DiBP), a member of the phthalate esters, is frequently used in manufacturing consumer and industrial products as plasticizer to improve durability and flexibility. Despite much research, little is known about the direct mechanisms by which DiBP harms the male reproductive system. In the present study, a male ICR mice model was developed to investigate the reproductive effect and mechanism of DiBP exposure, followed by transcriptomics, non-targeted metabolome, and 16S rDNA sequencing accordingly.
View Article and Find Full Text PDFRedox Biol
September 2025
Multi-Omics Platform, Center for Cancer Immunotherapy and Immunobiology, Kyoto University Graduate School of Medicine, Kyoto, Japan; Human Biology Microbiome Quantum Research Center, Keio University School of Medicine, Tokyo, Japan. Electronic address:
Ferroptosis, an iron-dependent cell death mechanism characterized by excessive lipid peroxidation, has been implicated in numerous human diseases and organ pathologies. However, current detection methods necessitate invasive tissue sampling to assess lipid peroxidation, making noninvasive detection of ferroptosis in human subjects extremely challenging. In this study, we employed oxidative volatolomics to comprehensively characterize the volatile oxidized lipids (VOLs) produced during ferroptosis.
View Article and Find Full Text PDFEmerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
View Article and Find Full Text PDFBrief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
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