bims-obesme 2025-09-14 papers

Cell Metab. 2025 Aug 29. pii: S1550-4131(25)00361-4. [Epub ahead of print]

Portal vein-enriched metabolites as intermediate regulators of the gut microbiome in insulin resistance.

Vitor Rosetto Muñoz, Francois Moreau, Marion Soto, Yoshiyuki Watanabe, Loc-Duyen Pham, Jimmy Zhong, Sam Zimmerman, Bruna B Brandao, Khyati Girdhar, Julian Avila, Hui Pan, Jonathan M Dreyfuss, Michael Y Mi, Robert E Gerszten, Emrah Altindis, Aleksandar Kostic, Clary B Clish, C Ronald Kahn.

Diet and obesity contribute to insulin resistance and type 2 diabetes, in part via the gut microbiome. To explore the role of gut-derived metabolites in this process, we assessed portal/peripheral blood metabolites in mice with different risks of obesity/diabetes, challenged with a high-fat diet (HFD) + antibiotics. In diabetes/obesity-prone C57BL/6J mice, 111 metabolites were portally enriched and 74 were peripherally enriched, many of which differed in metabolic-syndrome-resistant 129S1/129S6 mice. Vancomycin treatment of HFD-fed C57BL/6J mice modified the microbiome and the portal/peripheral ratio of many metabolites, including upregulating tricarboxylic acid (TCA) cycle-related metabolites, like mesaconate, in portal blood. Treatment of isolated hepatocytes with mesaconate, itaconate, or citraconate improved insulin signaling and transcriptionally regulated genes involved in gluconeogenesis, fatty acid oxidation, and lipogenesis in vitro and in vivo. In humans, citraconate levels are inversely correlated with plasma glucose. Thus, portal versus peripheral metabolites play important roles in mediating effects of the microbiome on hepatic metabolism and the pathogenesis of HFD-related insulin resistance.

Keywords: TCA cycle; diabetes; gene regulation; gut microbiota; insulin signaling; mesaconate; metabolomics; microbial metagenomics; portal circulation

DOI: https://doi.org/10.1016/j.cmet.2025.08.005

Nat Metab. 2025 Sep 09.

Cellular pan-chain acyl-CoA profiling reveals SLC25A42/SLC25A16 in mitochondrial CoA import and metabolism.

Ran Liu, Zihan Zhang, Aye K Kyaw, Kariona A Grabińska, Hardik Shah, Hongying Shen.

The essential cofactor coenzyme A (CoASH) and its thioester derivatives (acyl-CoAs) have pivotal roles in cellular metabolism. However, the mechanism by which different acyl-CoAs are accurately partitioned into different subcellular compartments to support site-specific reactions, and the physiological impact of such compartmentalization, remain poorly understood. Here, we report an optimized liquid chromatography-mass spectrometry-based pan-chain acyl-CoA extraction and profiling method that enables a robust detection of 33 cellular and 23 mitochondrial acyl-CoAs from cultured human cells. We reveal that SLC25A16 and SLC25A42 are critical for mitochondrial import of free CoASH. This CoASH import process supports an enriched mitochondrial CoA pool and CoA-dependent pathways in the matrix, including the high-flux TCA cycle and fatty acid oxidation. Despite a small fraction of the mitochondria-localized CoA synthase COASY, de novo CoA biosynthesis is primarily cytosolic and supports cytosolic lipid anabolism. This mitochondrial acyl-CoA compartmentalization enables a spatial regulation of anabolic and energy-related catabolic processes, which promises to shed light on pathophysiology in the inborn errors of CoA metabolism.

DOI: https://doi.org/10.1038/s42255-025-01358-y

Nat Metab. 2025 Sep 10.

In vivo itaconate tracing reveals degradation pathway and turnover kinetics.

Hanna F Willenbockel, Alexander T Williams, Alfredo Lucas, Mack B Reynolds, Emeline Joulia, Maureen L Ruchhoeft, Birte Dowerg, Pedro Cabrales, Christian M Metallo, Thekla Cordes.

Itaconate is an immunomodulatory metabolite that alters mitochondrial metabolism and immune cell function. This organic acid is endogenously synthesized by tricarboxylic acid (TCA) metabolism downstream of TLR signalling. Itaconate-based treatment strategies are under investigation to mitigate numerous inflammatory conditions. However, little is known about the turnover rate of itaconate in circulation, the kinetics of its degradation and the broader consequences on metabolism. By combining mass spectrometry and in vivo 13C itaconate tracing in male mice, we demonstrate that itaconate is rapidly eliminated from plasma, excreted via urine and fuels TCA cycle metabolism specifically in the liver and kidneys. Our results further reveal that itaconate is converted into acetyl-CoA, mesaconate and citramalate. Itaconate administration also influences branched-chain amino acid metabolism and succinate levels, indicating a functional impact on succinate dehydrogenase and methylmalonyl-CoA mutase activity in male rats and mice. Our findings uncover a previously unknown aspect of itaconate metabolism, highlighting its rapid catabolism in vivo that contrasts findings in cultured cells.

DOI: https://doi.org/10.1038/s42255-025-01363-1

Cell Rep. 2025 Sep 05. pii: S2211-1247(25)01001-0. [Epub ahead of print]44(9): 116230

ADSL deficiency is a secondary mitochondrial disease affecting organelle homeostasis and ERK2/AKT signaling in a linear genotype-phenotype relation.

Adenylosuccinate lyase deficiency (ADSLd) is a rare autosomal recessive purine metabolism disorder with several clinical manifestations. While toxic substrate accumulation is a known hallmark, no additional molecular mechanisms have been established. Here, we show that ADSLd is associated with mitochondrial dysfunction, including increased fragmentation, impaired respiration, and reduced ATP production. The severity of mitochondrial impairment correlates with ADSLd pathology, especially in mitochondria-dependent tissues. We also identify defects in mitochondrial dynamics and transport linked to ERK2 and AKT suppression. Notably, overexpressing constitutively active ERK2 or supplementing purine intermediates partially rescues the mitochondrial phenotype. These findings suggest an alternative disease mechanism and highlight mitochondrial metabolism as a potential therapeutic target in ADSLd.

Keywords: ADSL; CP: Metabolism; ERK; mitochondria; purine metabolism; rare genetic disease

DOI: https://doi.org/10.1016/j.celrep.2025.116230

Nat Aging. 2025 Sep 09.

The X-Age Project to construct a Chinese aging clock.

Jiaming Li, Mengmeng Jiang, Qiaoran Wang, Zikai Zheng, Jianghua Shen, Jingyi Li, Muzhao Xiong, Yandong Zheng, Xiaoyong Lu, Yusheng Cai, Yanling Fan, Lingling Geng, Qianzhao Ji, Qianqian Peng, Shuhui Sun, Yuanyuan Wang, Zijuan Xin, Kaowen Yan, Yuanhan Yang, Jun Yu, Haoteng Yan, Ding Ai, Yongping Bai, Yan Bi, Xiu-Wu Bian, Pengcheng Bu, Jian-Ping Cai, Chun-Mei Cao, Feng Cao, Zhongwei Cao, Renjie Chai, Piu Chan, Chang Chen, Cheng-Shui Chen, Chunying Chen, Di Chen, Hou-Zao Chen, Lin Chen, Quan Chen, Xiao Chen, Xiaochun Chen, Yu Chen, Zi-Jiang Chen, Weimin Ci, Zhe Dai, Qiurong Ding, Birong Dong, Jiahong Dong, Jian-Gao Fan, Shiqing Feng, Xin Feng, Yun Feng, Xiaobing Fu, Xiaolong Fu, Feng Gao, Jiangang Gao, Qiang Gao, Shaorong Gao, Yonghao Gu, Youfei Guan, Feifan Guo, Jing-Dong J Han, Haiping Hao, Jihui Hao, Fuchu He, Jinhan He, Ming He, Mingguang He, Qiyang He, Zhiying He, Zuhong He, Huashan Hong, Jiaxu Hong, Shengping Hou, Cheng Hu, Ping Hu, Zhibin Hu, Canhua Huang, Jun Huang, Kai Huang, Pengyu Huang, Xunming Ji, Yong Ji, Shunji Jia, Hong Jiang, Wenjian Jiang, Lingjing Jin, Zi-Bing Jin, Shenghong Ju, Zhenyu Ju, Qing-Peng Kong, Wei Kong, Wei-Jia Kong, Xiangqing Kong, Guanghua Lei, Geng-Lin Li, Ji Li, Jian Li, Mengfeng Li, Rong Li, Wei Li, Wei Li, Xiao-Jun Li, Xin Li, Qingfeng Liang, Zhen Liang, Haotian Lin, Baohua Liu, Cai-Yue Liu, Changsheng Liu, Feng Liu, Jianfeng Liu, Jun-Ping Liu, Ke Liu, Lin Liu, Pingsheng Liu, Qiang Liu, Qiang Liu, Tiemin Liu, Wenwen Liu, Xingguo Liu, Yajun Liu, Yong Liu, Youhua Liu, Youshuo Liu, Zhili Liu, Xiao Long, Yao Lu, Jian Luo, Xianghang Luo, Chunhong Ma, Shuai Ma, Xinran Ma, Jianhua Mao, Zhiyong Mao, Shyh-Chang Ng, Guangjun Nie, Yuyu Niu, Yaojin Peng, Jun Pu, Jieyu Qi, Li Qiang, Jie Qiao, Yingying Qin, Aijuan Qu, Jing Qu, Jie Ren, Ruibao Ren, Xiong Z Ruan, Anbing Shi, Haibo Shi, Jie Shi, Kwok-Fai So, Moshi Song, Weihong Song, Zhou Songyang, Jiacan Su, Aijun Sun, Liang Sun, Qiang Sun, Yi Eve Sun, Yu Sun, Peifu Tang, Qi-Qun Tang, Yi Tang, Jun Tao, Ling Tao, Mei Tian, Xiao-Li Tian, Ye Tian, Xiaolin Tong, Cong-Yi Wang, Haibo Wang, Hongmei Wang, Huating Wang, Jianan Wang, Jianwei Wang, Jianwei Wang, Jiqiu Wang, Liheng Wang, Lin Wang, Miao Wang, Qiang Wang, Si Wang, Sijia Wang, Songlin Wang, Wengong Wang, Xiaoming Wang, Xiaoning Wang, Yan Wang, Yan-Jiang Wang, Yuan Wang, Yunfang Wang, Zhenning Wang, Xiawei Wei, Jianping Weng, Haitao Wu, Jihong Wu, Xiaohuan Xia, Yang Xia, Andy Peng Xiang, Guozhi Xiao, Junjie Xiao, Yichuan Xiao, Zhi-Xiong Jim Xiao, Zhengwei Xie, Wei Xiong, Aimin Xu, Hua Xu, Lingyan Xu, Ming Xu, Liying Yan, Jiayin Yang, Jichun Yang, Liu Yang, Yun-Gui Yang, Ze Yang, Zhenglin Yang, Hongjie Yao, Jing Ye, Chengqi Yi, Fan Yi, Honghua Yu, Yang Yu, Zhengrong Yu, Ti-Fei Yuan, Jirong Yue, Rui Yue, Chen Zhang, Chunxiang Zhang, Cuntai Zhang, Feng Zhang, Hongbo Zhang, Hongjia Zhang, Huijie Zhang, Jie Zhang, Jingjing Zhang, Licheng Zhang, Lingqiang Zhang, Luoying Zhang, Qingjiong Zhang, Wei Zhang, Weiping J Zhang, Xin Zhang, Xuan Zhang, Yong Zhang, Yun-Wu Zhang, Zhanjun Zhang, Zhuohua Zhang, Bing Zhao, Guoguang Zhao, Jiajun Zhao, Meng Zhao, Tongbiao Zhao, Jialin C Zheng, Junke Zheng, Zhuozhao Zheng, Huixia Zhou, Lili Zhou, Xiangtian Zhou, Yongsheng Zhou, Zhongjun Zhou, Lan Zhu, Yizhun Zhu, Zhiming Zhu, Wenjuan Zhuang, Weiguo Zou, Weiqi Zhang, Gang Pei, Guang-Hui Liu.

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. This Perspective outlines the core objectives, methodological framework and key deliverables of the X-Age Project, including cohort recruitment, standardized sample collection, multimodal data acquisition and clock model development. By integrating interdisciplinary expertise, we aim to provide a practical and scalable platform for understanding aging complexity and heterogeneity, early detection of accelerated aging and evaluation of aging interventions.

DOI: https://doi.org/10.1038/s43587-025-00935-w

Nat Metab. 2025 Sep 09.

Common genetic variants modify disease risk and clinical presentation in monogenic diabetes.

Jacques Murray Leech, Robin N Beaumont, Ankit M Arni, V Kartik Chundru, Luke N Sharp, Kevin Colclough, Andrew T Hattersley, Michael N Weedon, Kashyap A Patel.

Young-onset monogenic disorders often show variable penetrance, yet the underlying causes remain poorly understood. Uncovering these influences could reveal new biological mechanisms and enhance risk prediction for monogenic diseases. Here we show that polygenic background substantially shapes the clinical presentation of maturity-onset diabetes of the young (MODY), a common monogenic form of diabetes that typically presents in adolescence or early adulthood. We find strong enrichment of type 2 diabetes (T2D) polygenic risk, but not type 1 diabetes risk, in genetically confirmed MODY cases (n = 1,462). This T2D polygenic burden, primarily through beta-cell dysfunction pathways, is strongly associated with earlier age of diagnosis and increased diabetes severity. Common genetic variants collectively account for 24% (P < 0.0001) of the phenotypic variability. Using a large population cohort (n = 424,553), we demonstrate that T2D polygenic burden substantially modifies diabetes onset in individuals with pathogenic variants, with diabetes risk ranging from 11% to 81%. Finally, we show that individuals with MODY-like phenotypes (n = 300) without a causal variant have elevated polygenic burden for T2D and related traits, representing potential polygenic phenocopies. These findings reveal substantial influence of common genetic variation in shaping the clinical presentation of early-onset monogenic disorders. Incorporating these may improve risk estimates for individuals carrying pathogenic variants.

DOI: https://doi.org/10.1038/s42255-025-01372-0

J Hepatol. 2025 Sep 04. pii: S0168-8278(25)02465-1. [Epub ahead of print]

From Glucose Control to Energy Expenditure and Fat Oxidation: The Emerging Role of Glucagon Agonism in Obesity and MASLD.

Amalia Gastaldelli.

Keywords: Glucagon agonists; MASLD; Metabolic dysfunction-Associated Steatotic Liver Disease; Obesity

DOI: https://doi.org/10.1016/j.jhep.2025.08.029