Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes^1,2. Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance^3–9. In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host’s overall energy extraction¹⁰, thereby playing a role in the pathogenesis of obesity and prediabetes^3,4,6. Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host–microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance.

Mouse mothers transfer metabolic mode: Obesity and metabolic diseases tend to go together, and humans who become obese are also prone to type 2 diabetes and cardiovascular problems. Starting with the observation that offspring of germ-free mice tended to become obese on high-fat diets, Kimura et al. investigated how the presence of the microbiota might be protective in mice (see the Perspective by Ferguson). Short-chain fatty acids (SCFAs) from the microbiota are known to suppress insulin signaling and reduce fat deposition in adipocytes. Further experiments showed that SCFAs in the bloodstream were able to pass from a non–germ-free mother's gut microbiota across the placenta and into the developing embryos. The authors found that in the embryos, the SCFA propionate mediates not only insulin levels through GPR43 signaling but also sympathetic nervous system development through GPR41 signaling. A high-fiber diet promoted propionate production from the maternal microbiota, and maternal antibiotic treatment resulted in obese-prone offspring. Science , this issue p. eaaw8429 ; see also p. 978

Selective destruction of senescent cells: Senescent cells are associated with a variety of age-related medical conditions and thus have been proposed as potential targets for therapy, but we do not yet have a full understanding of the underlying mechanisms. Johmura et al. used RNA interference to screen for enzymes essential to the survival of senescent cells (see the Perspective by Pan and Locasale). The authors identified a key role for glutamine metabolism, particularly the enzyme glutaminase 1, and demonstrated that inhibition of this pathway induced the death of senescent cells. Glutaminase targeting also ameliorated aging-related organ dysfunction and obesity-related disorders in mouse models, suggesting the potential therapeutic value of this approach. Science , this issue p. 265 ; see also p. 234

Lipidomics and metabolomics communities comprise various informatics tools; however, software programs handling multimodal mass spectrometry (MS) data with structural annotations guided by the Lipidomics Standards Initiative are limited. Here, we provide MS-DIAL 5 for in-depth lipidome structural elucidation through electron-activated dissociation (EAD)-based tandem MS and determining their molecular localization through MS imaging (MSI) data using a species/tissue-specific lipidome database containing the predicted collision-cross section values. With the optimized EAD settings using 14 eV kinetic energy, the program correctly delineated lipid structures for 96.4% of authentic standards, among which 78.0% had the sn-, OH-, and/or C = C positions correctly assigned at concentrations exceeding 1 μM. We showcased our workflow by annotating the sn- and double-bond positions of eye-specific phosphatidylcholines containing very-long-chain polyunsaturated fatty acids (VLC-PUFAs), characterized as PC n-3-VLC-PUFA/FA. Using MSI data from the eye and n-3-VLC-PUFA-supplemented HeLa cells, we identified glycerol 3-phosphate acyltransferase as an enzyme candidate responsible for incorporating n-3 VLC-PUFAs into the sn1 position of phospholipids in mammalian cells, which was confirmed using EAD-MS/MS and recombinant proteins in a cell-free system. Therefore, the MS-DIAL 5 environment, combined with optimized MS data acquisition methods, facilitates a better understanding of lipid structures and their localization, offering insights into lipid biology.