Drug abuse is a major global public health and social challenge that adversely affects physical and mental health and contributes to substantial socioeconomic burdens (Strang et al., 2020). The 2023 World Drug Report by the United Nations Office on Drugs and Crime (United Nations Office on Drugs and Crime (UNODC), 2023) estimated that in 2021, over 296 million people worldwide had used a drug in the past 12 months, of whom 60 million people engaged in non-medical opioid use, accounting for 1.2% of the global population. The opioid crisis in North America continues unabated, mainly attributed to the unprecedented high number of deaths caused by the use of fentanyl. Long-term drug abuse can seriously damage the structure and function of the brain, leading to compulsive nonmedical drug seeking and use despite negative health and social effects, which is so-called drug addiction (Wise and Robble, 2020). Fentanyl is a synthetic opioid that was initially manufactured for anesthesia and severe pain management; in addition to its clinical use, it is a powerfully addictive drug and its illegal use is related to the addiction crisis in the United States (Volkow, 2021). Research suggests that fentanyl abuse causes persistent changes in the physical structure and physiology of the brain, thus producing addictive behaviors that are difficult to reverse (Han et al., 2022).

Drug addiction has been proposed to involve profound metabolic dysregulation that contributes to persistent neurochemical and behavioral alterations (Caspani et al., 2022; Ghanbari and Sumner, 2018; Okeoma et al., 2025; Xu et al., 2021). By providing comprehensive biochemical data from biological samples, metabolomics aims to capture the metabolic state that reflects the overall health status of an individual (Quinones and Kaddurah-Daouk, 2009). To achieve different research objectives, metabolomics can be conducted through targeted and untargeted methods; the former aims to identify a limited number of specific metabolites and perform absolute quantification, while the latter focuses on acquiring as many known and unknown metabolites as possible and performing relative quantification (Schrimpe-Rutledge et al., 2016). Currently, the four major analytical platforms used in metabolomics include liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, and capillary electrophoresis-mass spectrometry (CE-MS). Among these platforms, ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is widely used in untargeted metabolomics research due to its high sensitivity, high peak resolution, and broad metabolome coverage across physicochemical properties and concentration ranges (Perez de Souza et al., 2021).

Given that chronic opioid exposure induces persistent neurochemical and metabolic adaptations in the brain, metabolomics offers a powerful systems-level approach to characterize these alterations (Zaitsu et al., 2016). In recent years, metabolomics has been applied to investigate the biochemical basis of the addiction cycle and predict prognosis by understanding the biochemical disorders associated with substance abuse (Caspani et al., 2022). Previous investigations have reported unique metabolic alterations in opioid addiction and have primarily focused on morphine and heroin abuse (Dinis-Oliveira, 2019). Regarding fentanyl, studies have shown metabolic alterations in fentanyl-overdose mouse models using urine, liver, and nucleus accumbens (NAc) samples (Alasmari et al., 2023; Alasmari et al., 2024; Di Francesco et al., 2024). The NAc (located in the ventral striatum) is a major neural node of the mesolimbic dopaminergic system (MLDS) which guides animals to addictive drugs. Dopamine (DA) neurons in mesolimbic regions such as the ventral tegmental area (VTA) and substantia nigra (SN) send projections to the NAc, caudate putamen (CPu, located in the dorsal striatum), amygdala (Amy), prefrontal cortex (PFC), and hippocampus (Hip), regions traditionally associated with action selection, reinforcement learning, and encoding and retrieval of conditioning to drug cues (Volkow et al., 2017). However, these studies primarily focused on overdose or toxicity models and did not address reward-related learning or metabolic alterations across interconnected mesolimbic brain regions. Additionally, profiling peripheral blood metabolites may provide clinically accessible biomarkers that reflect central nervous system alterations associated with fentanyl dependence. Therefore, metabolomic studies of the blood and associated brain regions underlying fentanyl dependence are promising for uncovering the pharmacological mechanisms of addiction therapy.

Researchers have developed a number of animal models, including conditioned place preference (CPP), intracranial self-stimulation (ICSS), drug self-administration (SA), and so on, to investigate the specific pathological brain processes underlying addiction. Compared to other animal models, CPP reflects associative learning between drug reward and environmental context, a process critically involving the Hip and PFC. The CPP paradigm provides a measure of motivated behaviors and is widely used to explore the mechanisms underlying the rewarding properties of addictive drugs (Garcia Pardo et al., 2017; Spanagel, 2017). Therefore, the CPP paradigm in mice was used as an animal model in our present study to explore the mechanism of fentanyl-induced rewarding behavior. The metabolites in the plasma, CPu, PFC, and Hip of fentanyl-dependent mice were profiled using UHPLC-HRMS, following a robust untargeted metabolomics workflow. This study aimed to characterize fentanyl-induced metabolic reprogramming in mesolimbic brain regions and plasma in order to define the global metabolic signature of the integrated fentanyl-CPP phenotype, thereby elucidate underlying metabolic pathways and reveal potential peripheral biomarkers of fentanyl dependence.

In the present study, fentanyl disrupted glycerophospholipid metabolism, increasing PC, PS, PE, and PI levels indicating membrane damage. Dysregulated M-Ins and D-myo-inositol 4-phosphate further support membrane disruption and suggest altered astrocytic activity, which affects neurotransmission and brain energy metabolism in addiction (Miguel-Hidalgo, 2009). Indeed, specific glycerophospholipids act as messengers, linking their dysregulation to cognitive disorders (Bou Khalil et al., 2010; Yu et al., 2020). Furthermore, altered lipid metabolism may compensate for heightened energy demands in addiction (Caspani et al., 2022).

Neuronal activity is extremely energy demanding, and the energy supply of the brain is almost entirely dependent on the oxidative metabolism of glucose in mitochondria. In fentanyl-CPP mice, we observed elevated glucose levels, suggestive of disrupted energy homeostasis and oxidative stress (Mei et al., 2013). This coincides with increased intermediates of glycolysis and the TCA cycle in key brain regions, collectively indicating an upregulation of carbohydrate metabolism to meet heightened energy requirements. Concurrently, the rise in Gln—which fuels the TCA cycle beyond its role as a glutamate (Glu) precursor—and its conversion to Glu further supports enhanced de novo synthesis and energy production (Brown and Yamamoto, 2003). In parallel, the elevation of creatinine across plasma and brain tissues (CPu, PFC) points to a protective response against energy depletion and oxidative damage. Conversely, levels of ketone bodies (AcAc, 3-HB, acetone), which serve as alternative brain fuels during glucose scarcity (Cotter et al., 2013; Morris, 2005), were decreased. This suppression of fatty acid β-oxidation and ketogenesis (Zaitsu et al., 2014) suggests a metabolic shift away from lipid-derived energy sources. Taken together, the coordinated elevation of glucose-related metabolites and reduction in ketone bodies reveal a metabolic reprogramming favoriting carbohydrates over lipids for energy supply in fentanyl dependence.

To date, purine and pyrimidine metabolites have been proven to be involved in various neuropsychiatric disorders, such as depression (Yao et al., 2013), neurodegenerative diseases (Burnstock, 2008), and drug addiction (Liu et al., 2017; Mannelli et al., 2009; Patkar et al., 2009). In the present study, purine and pyrimidine metabolism was significantly changed in the fentanyl group. Consistent with prior studies (Li et al., 2011; Liu et al., 2007; Song et al., 2013; Yang et al., 2006), the observed changes in nucleotide metabolites indicate that fentanyl administration inhibits the anabolism and enhances the catabolism of purine nucleotides in the brain, increasing uridine release from the CPu and Hip. The increased guanine, adenine and xanthine may indicate the conversion into their corresponding nucleotides through salvage pathways which could alter energy production. In fact, adenosine and uridine serve as potential endogenous neuromodulators in the CNS that might be responsible for modification of addictive behaviors through interact with the endocannabinoid (EC), γ-aminobutyric acid (GABA) receptors, serotoninergic, and dopaminergic system to regulate DA-related behaviors (Guarneri et al., 1985; Guarneri et al., 1983; Kimura et al., 2001; Pasquini et al., 2022). Attributed to the inhibitory effect on enhanced DA release and the ability to acquire associations between reward and cues, uridine, uracil, and β-alanine could also significantly attenuate opioid dependence and addictive behaviors (Liu et al., 2017; Liu et al., 2014; Song et al., 2013). In the same way, adenosine receptor agonists could significantly attenuate opioid dependence, while adenosine antagonists exacerbate these effects in animals (Kaplan and Sears, 1996; Listos et al., 2011; Wu et al., 2013). Notably, nucleoside derivatives M6A and ac4C—the most common RNA modifications involved in chromatin remodeling and transcriptional regulation—are implicated in neurogenesis, synaptic function, learning, memory, and reward (Broly et al., 2022; Shafik et al., 2022). Studies confirm that persistent OUD stems partly from long-term neuronal plasticity changes driven by transcriptomic (Huggett et al., 2022) and epigenetic (Browne et al., 2020) shifts. Our results further support that M6A and ac4C modifications regulate neural responses to opioids and may drive opioid-induced transcriptomic dysregulation (Dabrowski and Daws, 2024).

Elevated free AAs like valine, arginine, and proline likely reflect protein hydrolysis-induced negative nitrogen balance (Murin et al., 2009; Wu et al., 2009). Concomitantly, increased urea cycle intermediates (arginine, ornithine, citrulline, argininosuccinate) further confirm nitrogen imbalance. Notably, these AAs—alongside leucine, isoleucine, serine, and citrulline—modulate TCA cycle flux. Glycine and serine additionally support purine nucleotide synthesis, glycerophospholipid metabolism, creatine production, and pyruvate cycling. Collectively, these changes indicate dysregulated energy metabolism. Elevated arginine and methionine may counteract oxidative stress as reactiveoxygenspecies (ROS) scavengers (Liang et al., 2018; Luo and Levine, 2009). Furthermore, altered methionine and its precursor homocysteine imply disrupted transmethylation—critical for epigenetic regulation of DNA/RNA expression. Elevated histidine and histamine metabolites suggest enhanced catabolism of these compounds in the brain. Previous studies report significantly reduced histamine levels in the VTA and NAc of morphine-CPP rats—likely due to morphine-triggered histamine depletion from nerve terminals. Simultaneously, both histidine and histamine inhibit morphine-induced CPP by suppressing dopaminergic activity (Gong et al., 2007). Although we observed no direct decrease of histidine/histamine in reward-related brain regions, increased metabolites in these regions—along with peripheral histamine elevation—may indicate weakened inhibition of fentanyl’s rewarding effects.

Aromatic amino acids (AAs)—phenylalanine, tyrosine, and tryptophan—regulate key neurotransmitter pathways. Phenylalanine converts to tyrosine, a precursor of catecholamines (CAs, including tyramine, DA, epinephrine, norepinephrine/NE). Dysregulated phenylalanine/tyrosine metabolism associates not only with depression and neurodegeneration but also impairs DA transmission in the MLDS (Hufner et al., 2019; Volkow et al., 2017). Elevated plasma 3-Methoxy-4-hydroxyphenylethyleneglycol sulfate (an NE metabolite) further indicates CAs disturbances. Tryptophan generates serotonin (5-HT), whose deficiency contributes to opioid withdrawal symptoms (Welsch et al., 2020). Reduced plasma tryptophan metabolites suggest compensatory brain uptake to maintain 5-HT levels (Zaitsu et al., 2014). Collectively, these AAs abnormalities could disrupt catecholaminergic/monoaminergic neurotransmission—particularly for DA and 5-HT, which cannot cross the blood-brain barrier directly.

Compared to saline controls, fentanyl increased Gln in plasma and CPu. Paradoxically, GABA decreased in plasma but increased in CPu. These shifts likely reflect fentanyl-induced disruption of the Gln-Glu-GABA axis—a conversion pathway where Gln metabolizes to GABA via Glu. Elevated valine, arginine, and proline (which modulate Gln/Glu metabolism) further support this dysregulation (Meng et al., 2012). GABA is an endogenous GABA receptor agonist. Opioid reinforcement involves co-activation of GABA-A and µ receptors on GABAergic interneurons. This suppresses GABA release from VTA interneurons onto DA nerve endings, disinhibiting DA neurons and enhancing DA efflux (Saigusa et al., 2008). Based on our results, fentanyl likely mediates reinforcement through this GABAergic disinhibition pathway (Sustkova-Fiserova et al., 2017). In fact, Increased GABA in MLDS may represent an adaptive response to opioids (Hu et al., 2012). GABA also contributes to DA-independent opioid reinforcement via EC systems (Befort, 2015; Fields and Margolis, 2015; Maldonado et al., 2006; Vigano et al., 2005) 61–64. Cannabinoid CB1 receptor (CB1) and µ-receptors co-localize on GABA presynaptic terminals (Fields and Margolis, 2015; Maldonado et al., 2006). CB1 antagonists could reduce opioid rewarding effects, while agonists enhance them (Singh et al., 2004; Solinas et al., 2003; Solinas et al., 2005). ECs (notably anandamide/AEA and 2-arachidonoylglycerol/2-AG) act as retrograde messengers, modulating GABA release via presynaptic CB1 activation (Piomelli, 2003). Heroin, morphine, and fentanyl increase AEA in reward circuits (Caille et al., 2007; Sustkova-Fiserova et al., 2017; Vigano et al., 2004)—a mechanism confirmed by primate self-administration studies (Justinova et al., 2005). Our data show that fentanyl elevates AEA in CPu and Hip, further implicating ECs in opioid reward.

Our study shows that perturbed vitamins regulate energy production and neurotransmission: TPP facilitates mitochondrial oxidative decarboxylation (pyruvate, α-ketoglutarate, branched chain AAs) and supports acetylcholine synthesis; NAD+ provides protons for oxidative phosphorylation; the active form of vitamin B5 including coenzyme-A (CoA) and acyl carrier protein (ACP) are essential cofactors in pyruvate/α-ketoglutarate dehydrogenase complexes; biotin (B7) enables carboxylation reactions in gluconeogenesis and fatty acid oxidation; vitamin C, recognized as an antioxidant and anti-inflammatory agent involved in glutathione recycling, further functions as an electron donor for CAs and 5-HT biosynthesis (Depeint et al., 2006a; b; Zelfand, 2020). Disrupted vitamin metabolism—combined with glucose dysregulation—impairs acetyl-CoA production and downstream pathways (fatty acid/carbohydrate metabolism). While substance abuse alters vitamin absorption (el-Nakah et al., 1979; Nabipour et al., 2014), we observed elevated plasma/brain levels of neurotransmission-associated vitamins in fentanyl-CPP mice. This selective upregulation suggests compensatory modulation of central neurotransmission.

In summary, the exploration of metabolite profiles in the plasma, CPu, PFC, and Hip of fentanyl-CPP mice provides valuable insights into the complex interplay between neurochemistry and the rewarding effects of fentanyl administration. Our results revealed disturbances in key metabolites and metabolic pathways associated with cell membrane metabolism, energy metabolism, AA metabolism, and neurotransmitter regulation within the MLDS of fentanyl-CPP mice. This finding not only contributes to elucidating the mechanism of the conditioned state associated with fentanyl-induced rewarding effect but also holds promise for developing novel interventions for fentanyl dependence in future research and prevention strategies.

While our sample size (n = 8 per group) was sufficient to detect robust and significant metabolic alterations associated with fentanyl-induced CPP, it remains a limitation common to exploratory metabolomics. Future studies with larger sample sizes are needed to confirm these findings, investigate less abundant metabolites, and reduce the false-negative rate. Nevertheless, the strong statistical signals and validated multivariate models presented here provide a solid foundation for such subsequent work.

Additionally, considering that the preference for addictive drugs fluctuates with estradiol levels during the estrous cycle in females (Gaulden et al., 2021; Quigley et al., 2021), our study focused primarily on male mice. In future research, given the growing emphasis on sex differences in neuroscience, investigating sex-specific metabolomic variations in fentanyl-dependent mice will yield more comprehensive neurochemical insights into fentanyl addiction mechanisms.