Metabolic phenotyping of opioid and psychostimulant addiction: A novel approach for biomarker discovery and biochemical understanding of the disorder

Serum cytokines and inflammatory proteins in individuals with heroin use disorder: potential mechanistically based biomarkers for diagnosis

Opioid use disorders cause major morbidity and mortality, and there is a pressing need for novel mechanistic targets and biomarkers for diagnosis and prognosis. Exposure to mu-opioid receptor (MOR) agonists causes changes in cytokine and inflammatory protein networks in peripheral blood, and also in brain glia and neurons. Individuals with heroin use disorder (iHUD) show dysregulated levels of several cytokines in the blood. However, there is limited data on a comprehensive panel of such markers in iHUD versus healthy controls (HC), especially considered as a multi-target biomarker. We used a validated proximity extension assay for the relative quantification of 92 cytokines and inflammatory proteins in the serum of iHUD on medication-assisted therapy (MAT; n = 21), compared to HC (n = 24). Twenty-nine targets showed significant group differences (primarily iHUD>HC), surviving multiple comparison corrections (p = 0.05). These targets included 19 members of canonical cytokine families, including specific chemokines, interleukins, growth factors, and tumor necrosis factor (TNF)-related proteins. For dimensionality reduction, data from these 19 cytokines were entered into a principal component (PC) analysis, with PC1 scores showing significant group differences (iHUD > HC; p < 0.0001). A receiver-operating characteristic (ROC) curve analysis yielded an AUROC = 91.7% (p < 0.0001). This PC1 score remained a positive predictor of being in the HUD group in a multivariable logistic regression, that included select demographic/clinical variables. Overall, this study shows a panel of cytokines that differ significantly between iHUD and HC, providing a multi-target "cytokine biomarker score" for potential diagnostic purposes, and future examination of disease severity.

Exploring the progression of drug dependence in a methamphetamine self-administration rat model through targeted and non-targeted metabolomics analyses

Persistent neurochemical and biological disturbances resulting from repeated cycles of drug reward, withdrawal, and relapse contribute to drug dependence. Methamphetamine (MA) is a psychostimulant with substantial abuse potential and neurotoxic effects, primarily affecting monoamine neurotransmitter systems in the brain. In this study, we aimed to explore the progression of drug dependence in rat models of MA self-administration, extinction, and reinstatement through targeted and non-targeted metabolomics analyses. Metabolic profiles were examined in rat plasma during the following phases: after 16 days of MA self-administration (Group M); after 16 days of self-administration followed by 14 days of extinction (Group MS); and after self-administration and extinction followed by a reinstatement injection of MA (Group MSM). Each group of MA self-administration, extinction, and reinstatement induces distinct changes in the metabolic pathways, particularly those related to the TCA cycle, arginine and proline metabolism, and arginine biosynthesis. Additionally, the downregulation of glycerophospholipids and sphingomyelins in Group MSM suggests their potential role in MA reinstatement. These alterations may signify the progressive deterioration of these metabolic pathways, possibly contributing to drug dependence following repeated cycles of drug reward, withdrawal, and relapse. These results provide valuable insights into the metabolic changes associated with MA use at various stages, potentially facilitating the discovery of early diagnostic biomarkers and therapeutic targets for MA use disorders.

Neuroinflammation and Neurometabolomic Profiling in Fentanyl Overdose Mouse Model Treated with Novel β-Lactam, MC-100093, and Ceftriaxone

Opioid-related deaths are attributed to overdoses, and fentanyl overdose has been on the rise in many parts of the world, including the USA. Glutamate transporter 1 (GLT-1) has been identified as a therapeutic target in several preclinical models of substance use disorders, and β-lactams effectively enhance its expression and function. In the current study, we characterized the metabolomic profile of the nucleus accumbens (NAc) in fentanyl-overdose mouse models, and we evaluated the protective effects of the functional enhancement of GLT-1 using β-lactams, ceftriaxone, and MC-100093. BALB/c mice were divided into four groups: control, fentanyl, fentanyl/ceftriaxone, and fentanyl/MC-100093. While the control group was intraperitoneally (i.p.) injected with normal saline simultaneously with other groups, all fentanyl groups were i.p. injected with 1 mg/kg of fentanyl as an overdose after habituation with four repetitive non-consecutive moderate doses (0.05 mg/kg) of fentanyl for a period of seven days. MC-100093 (50 mg/kg) and ceftriaxone (200 mg/kg) were i.p. injected from days 5 to 9. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics, and Western blotting was performed to determine the expression of target proteins. Y-maze spontaneous alternation performance and the open field activity monitoring system were used to measure behavioral manifestations. Fentanyl overdose altered the abundance of about 30 metabolites, reduced the expression of GLT-1, and induced the expression of inflammatory mediators IL-6 and TLR-4 in the NAc. MC-100093 and ceftriaxone attenuated the effects of fentanyl-induced downregulation of GLT-1 and upregulation of IL-6; however, only ceftriaxone attenuated fentanyl-induced upregulation of TRL4 expression. Both of the β-lactams attenuated the effects of fentanyl overdose on locomotor activities but did not induce significant changes in the overall metabolomic profile. Our findings revealed that the exposure to a high dose of fentanyl causes alterations in key metabolic pathways in the NAc. Pretreatment with ceftriaxone and MC-100093 normalized fentanyl-induced downregulation of GLT-1 expression with subsequent attenuation of neuroinflammation as well as the hyperactivity, indicating that β-lactams may be promising drugs for treating fentanyl use disorder.

Establishing a research agenda for the study and assessment of opioid withdrawal

The opioid crisis is an international public health concern. Treatments for opioid use disorder centre largely on the management of opioid withdrawal, an aversive collection of signs and symptoms that contribute to opioid use disorder. Whereas in the past 50 years more than 90 medications have been developed for depression, only five medications have been developed for opioid use disorder during this period. We posit that underinvestment has occurred in part due to an underdeveloped understanding of opioid withdrawal syndrome. This Personal View summarises substantial gaps in our understanding of opioid withdrawal that are likely to continue to limit major advancements in its treatment. There is no firm consensus in the field as to how withdrawal should be precisely defined; 10-550 symptoms of withdrawal can be measured on 18 scales. The imprecise understanding of withdrawal is likely to result in overestimating or underestimating the severity of an individual's withdrawal syndrome or potential therapeutic effects of different candidate medications. The severity of the opioid crisis is not remitting, and an international research agenda for the study and assessment of opioid withdrawal is necessary to support transformational changes in withdrawal management and treatment of opioid use disorder. Nine actionable targets are delineated here: develop a consensus definition of opioid withdrawal; understand withdrawal symptomatology after exposure to different opioids (particularly fentanyl); understand precipitated opioid withdrawal; understand how co-exposure of other drugs (eg, xylazine and stimulants) influences withdrawal expression; examine individual variation in withdrawal phenotypes; precisely characterise the protracted withdrawal syndrome; identify biomarkers of opioid withdrawal severity; identify predictors of opioid withdrawal severity; and understand which symptoms are most closely associated with treatment attrition or relapse. The US Food and Drug Administration recently established a formal indication for opioid withdrawal that has invigorated interest in drug development for opioid withdrawal management. Action is now needed to support these interests and help industry identify new classes of medications so that real change can be achieved for people with opioid use disorder.

Serum cytokine and inflammatory markers in individuals with heroin use disorder: potential biomarkers for diagnosis and disease severity

Opioid use disorders cause major morbidity and mortality, and there is a pressing need for novel mechanistic targets and biomarkers for diagnosis and prognosis. Exposure to mu-opioid receptor (MOR) agonists causes changes in cytokine and inflammatory protein networks in peripheral blood, and also in brain glia and neurons. Individuals with heroin use disorder (iHUD) show dysregulated levels of several cytokines in blood. However, there is limited data on a comprehensive panel of such markers in iHUD versus healthy controls (HC), especially as a multi-target biomarker. We used a validated proximity extension assay for relative quantification of 92 cytokines and inflammatory proteins in serum of iHUD on medication assisted therapy (MAT; n=21), versus HC (n=24). Twenty-nine targets showed significant group differences (primarily iHUD>HC), surviving multiple comparison correction (p=0.05). This included 19 members of canonical cytokine families, including specific chemokines, interleukins, growth factors, and tumor necrosis factor (TNF)-related proteins. For dimensionality reduction, data from these 19 cytokines were entered into a principal component (PC) analysis, and PC1 scores were iHUD>HC (p<0.0001). A receiver-operating characteristic (ROC) curve analysis yielded an AUROC=91.7% (p<0.0001). This PC1 score remained a positive predictor of being in the HUD group in a multivariable logistic regression, which included demographic/clinical variables. Overall, this study shows a panel of cytokines that differ significantly between iHUD and HC, and provides a multi-target "cytokine biomarker score" for potential diagnostic purposes, and examination of disease severity.

Tackling new psychoactive substances through metabolomics: UHPLC-HRMS study on natural and synthetic opioids in male and female murine models

Novel psychoactive substances (NPS) represent a broad class of drugs new to the illicit market that often allow passing drug-screening tests. They are characterized by a variety of structures, rapid transience on the drug scene and mostly unknown metabolic profiles, thus creating an ever-changing scenario with evolving analytical targets. The present study aims at developing an indirect screening strategy for NPS monitoring, and specifically for new synthetic opioids (NSOs), based on assessing changes in endogenous urinary metabolite levels as a consequence of the systemic response following their intake. The experimental design involved in-vivo mice models: 16 animals of both sex received a single administration of morphine or fentanyl. Urine was collected before and after administration at different time points; the samples were then analysed with an untargeted metabolomics LC-HRMS workflow. According to our results, the intake of opioids resulted in an elevated energy demand, that was more pronounced on male animals, as evidenced by the increase in medium and long chain acylcarnitines levels. It was also shown that opioid administration disrupted the pathways related to catecholamines biosynthesis. The observed alterations were common to both morphine and fentanyl: this evidence indicate that they are not related to the chemical structure of the drug, but rather on the drug class. The proposed strategy may reinforce existing NPS screening approaches, by identifying indirect markers of drug assumption.

Biomarker discovery and validation for gastrointestinal tumors: A comprehensive review of colorectal, gastric, and liver cancers

Gastrointestinal (GI) malignancies, encompassing gastric, hepatic, colonic, and rectal cancers, are prevalent forms of cancer globally and contribute substantially to cancer-related mortality. Although there have been improvements in methods for diagnosing and treating GI cancers, the chances of survival for these types of cancers are still extremely low. According to the World Cancer Research International Fund's most recent figures, stomach cancer was responsible for roughly one million deaths worldwide in 2020. This emphasizes the importance of developing more effective tools for detecting, diagnosing, and predicting the outcome of these cancers at an early stage. Biomarkers, quantitative indications of biological processes or disease states, have emerged as promising techniques for enhancing the diagnosis and prognosis of GI malignancies. Recently, there has been a considerable endeavor to discover and authenticate biomarkers for various GI cancers by the utilization of diverse methodologies, including genomics, proteomics, and metabolomics. This review provides a thorough examination of the current state of biomarker research in the field of gastrointestinal malignancies, with a specific emphasis on colorectal, stomach, and liver cancers. A thorough literature search was performed on prominent databases such as PubMed, Scopus, and Web of Science to find pertinent papers published until November, 2023 for the purpose of compiling this review. The diverse categories of biomarkers, encompassing genetic, epigenetic, and protein-based biomarkers, and their potential utility in the fields of diagnosis, prognosis, and treatment selection, are explored. Recent progress in identifying and confirming biomarkers, as well as the obstacles that persist in employing biomarkers in clinical settings are emphasized. The utilization of biomarkers in GI cancers has significant potential in enhancing patient outcomes. Ongoing research is expected to uncover more efficient biomarkers for the diagnosis and prognosis of these cancers.

Long access heroin self-administration significantly alters gut microbiome composition and structure

Introduction

It is well known that chronic opioid use disorder is associated with alterations in gastrointestinal (GI) function that include constipation, reduced motility, and increased bacterial translocation due to compromised gut barrier function. These signs of disrupted GI function can be associated with alterations in the gut microbiome. However, it is not known if long-access opioid self-administration has effects on the gut microbiome.

Methods

We used 16S rRNA gene sequencing to investigate the gut microbiome in three independent cohorts (N=40 for each) of NIH heterogeneous stock rats before onset of long-access heroin self-administration (i.e., naïve status), at the end of a 15-day period of self-administration, and after post-extinction reinstatement. Measures of microbial α- and β-diversity were evaluated for all phases. High-dimensional class comparisons were carried out with MaAsLin2. PICRUSt2 was used for predicting functional pathways impacted by heroin based on marker gene sequences.

Results

Community α-diversity was not altered by heroin at any of the three phases by comparison to saline-yoked controls. Analyses of β-diversity showed that the heroin and saline-yoked groups clustered significantly apart from each other using the Bray-Curtis (community structure) index. Heroin caused significant alterations at the ASV level at the self-administration and extinction phases. At the phylum level, the relative abundance of Firmicutes was increased at the self-administration phase. Deferribacteres was decreased in heroin whereas Patescibacteria was increased in heroin at the extinction phase. Potential biomarkers for heroin emerged from the MaAsLin2 analysis. Bacterial metabolomic pathways relating to degradation of carboxylic acids, nucleotides, nucleosides, carbohydrates, and glycogen were increased by heroin while pathways relating to biosynthesis of vitamins, propionic acid, fatty acids, and lipids were decreased.

Discussion

These findings support the view that long access heroin self-administration significantly alters the structure of the gut microbiome by comparison to saline-yoked controls. Inferred metabolic pathway alterations suggest the development of a microbial imbalance favoring gut inflammation and energy expenditure. Potential microbial biomarkers and related functional pathways likely invoked by heroin self-administration could be targets for therapeutic intervention.

Disulfide bridge-targeted metabolome mining unravels an antiparkinsonian peptide

Peptides are a particular molecule class with inherent attributes of some small-molecule drugs and macromolecular biologics, thereby inspiring continuous searches for peptides with therapeutic and/or agrochemical potentials. However, the success rate is decreasing, presumably because many interesting but less-abundant peptides are so scarce or labile that they are likely 'overlooked' during the characterization effort. Here, we present the biochemical characterization and druggability improvement of an unprecedented minor fungal RiPP (ribosomally synthesized and post-translationally modified peptide), named acalitide, by taking the relevant advantages of metabolomics approach and disulfide-bridged substructure which is more frequently imprinted in the marketed peptide drug molecules. Acalitide is biosynthetically unique in the macrotricyclization via two disulfide bridges and a protease (AcaB)-catalyzed lactamization of AcaA, an unprecedented precursor peptide. Such a biosynthetic logic was successfully re-edited for its sample supply renewal to facilitate the identification of the in vitro and in vivo antiparkinsonian efficacy of acalitide which was further confirmed safe and rendered brain-targetable by the liposome encapsulation strategy. Taken together, the work updates the mining strategy and biosynthetic complexity of RiPPs to unravel an antiparkinsonian drug candidate valuable for combating Parkinson's disease that is globally prevailing in an alarming manner.

Effect of aerobic exercise on brain metabolite profiles in the mouse models of methamphetamine addiction: LC-MS-based metabolomics study

Methamphetamine (MA) abuse is recognized as a brain disorder, and physical activity has clear benefits for MA use disorders. The specific mechanisms by which physical activity alleviates MA use disorders are currently not fully understood. Based on this, the present study used untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) to analyze the metabolic changes induced by MA in the brains of mice by exercise intervention. It was found that after 2 weeks of treadmill training, aerobic exercise modulated MA-induced brain metabolic disorders, in which 129 metabolites existed that were significantly differentiated in response to MA induction, and 32 metabolites were significantly affected by exercise. These differential metabolites were mainly enriched in glycerophospholipid metabolism, steroid hormone biosynthesis and degradation, and renin-angiotensin system pathways. To our knowledge, this study is the first to use LC-MS to investigate the effects of aerobic exercise on MA-induced brain metabolic profiling. The findings of this study provide new insights into exercise therapy using MA.

Whole blood mitochondrial copy number in clinical populations with mood disorders: a meta-analysis

Background

Major depressive disorder (MDD) and bipolar disorder (BD), are globally prevalent, contributing to significant disease burden and adverse health outcomes. These mood disorders are associated with changes in many aspects of brain reward pathways, yet cellular and molecular changes in the brain are not readily available in clinical populations. Therefore, the use of biomarkers as proxies for changes in the brain are necessary. The proliferation of mitochondria in blood has emerged as a potentially useful biomarker, yet a clear consensus on how these mood disorders impact mitochondrial DNA copy number (mtDNAcn) has not been reached.

Methods

Following PRISMA guidelines for a systematic search, 22 papers met inclusion criteria for meta-analysis (10 MDD, 10 BD, 2 both MDD and BD). We extracted demographic, disorder, and methodological information with mtDNAcn. Using the metafor package for R, calculated effect sizes were used in random effects or meta regression models for MDD and BD.

Results

Our results show a trending increase in mtDNAcn in patients with MDD, which reaches significance when one study with outlying demographic characteristics is excluded. Overall, there was no effect of BD on mtDNAcn, however, further subgroup and meta-regression analysis indicated the effects on mtDNAcn are dependent on BD type.

Conclusions

Together our data suggest whole blood/leukocyte mtDNAcn may be a useful biomarker for mood disorders, with MDD and BD Type II associated with higher mtDNAcn, and BD Type I associated with lower mtDNAcn. Further study of blood mtDNAcn could predict downstream health outcomes or treatment responsivity in individuals with mood disorders.

Impact of Opioids on Cellular Metabolism: Implications for Metabolic Pathways Involved in Cancer

Opioid utilization for pain management is prevalent among cancer patients. There is significant evidence describing the many effects of opioids on cancer development. Despite the pivotal role of metabolic reprogramming in facilitating cancer growth and metastasis, the specific impact of opioids on crucial oncogenic metabolic pathways remains inadequately investigated. This review provides an understanding of the current research on opioid-mediated changes to cellular metabolic pathways crucial for oncogenesis, including glycolysis, the tricarboxylic acid cycle, glutaminolysis, and oxidative phosphorylation (OXPHOS). The existing literature suggests that opioids affect energy production pathways via increasing intracellular glucose levels, increasing the production of lactic acid, and reducing ATP levels through impediment of OXPHOS. Opioids modulate pathways involved in redox balance which may allow cancer cells to overcome ROS-mediated apoptotic signaling. The majority of studies have been conducted in healthy tissue with a predominant focus on neuronal cells. To comprehensively understand the impact of opioids on metabolic pathways critical to cancer progression, research must extend beyond healthy tissue and encompass patient-derived cancer tissue, allowing for a better understanding in the context of the metabolic reprogramming already undergone by cancer cells. The current literature is limited by a lack of direct experimentation exploring opioid-induced changes to cancer metabolism as they relate to tumor growth and patient outcome.

Untargeted metabolomic study by liquid chromatography-mass spectrometry in brain tissues on the effects of combined cocaine and ethanol self-administration in male and female young rats

The combined use of ethanol and cocaine is frequent among drug-abuse users and leads to further exacerbation of health consequences compared to individual consumption and this is of special concern during the transition to adulthood. Despite its high prevalence, the effect of combined consumption of cocaine and ethanol has been scarcely studied. In this work, we report the first untargeted metabolomic study in brain tissues to contribute to the advancement in the knowledge of the possible neurobiological effects of this polysubstance dependence. Liquid Chromatography coupled to high resolution Mass Spectrometry was employed to analyze three different brain tissues samples, prefrontal cortex, striatum and hippocampus, from male and female young rats exposed intravenously to a self-administration of these drugs. After optimizing the best sample treatment and selecting the chromatographic and detection conditions to find the maximum number of significant features (possible biomarker metabolites), the high resolution of the Orbitrap analyzer used in this work has made it possible to find up to 761 significant features with assigned molecular formula, of which up to 190 were tentatively identified and 44 unequivocally confirmed. The results demonstrated that the altered metabolic pathways are involved in multiple functions: receptor systems, such as the Glutamine-Glutamic acid-GABA axis or the catecholamine pathway, purinergic and pyrimidine pathways, fatty acids or oxidative stress, among others.

Allostatic load in opioid use disorder: a scoping review protocol

INTRODUCTION

Opioid use disorder affects 2.1 million individuals in the USA, causing more than 100 000 overdose-related deaths annually. While the neurobiological model of addiction is well described and accepted, there is a lack of morbidity and mortality prognosticators for patients struggling with opioid use disorder. Allostatic load index is a promising candidate for the basis of a prognostication tool. Previous studies show that allostatic load predicts both morbidity and mortality in a variety of cohorts. This scoping review protocol provides the rationale and steps for summarising and presenting existing evidence surrounding allostatic load in the context of opioid use disorder. Identification of current knowledge gaps will pave the way for subsequent prospective studies.

METHODS AND ANALYSIS

This scoping review protocol will follow the five-step method designed by Arksey and O'Malley. All studies written in English on allostatic load in the context of opioid use disorder, as defined in our inclusion criteria, will be included. There will be no limit on the year of publication. We will search PubMed, Embase, CINAHL, PsycINFO and Google Scholar. We will hand-review reference lists of included articles, and we will hand search grey literature. We will then group, analyse and present the data in narrative, tabular and diagrammatic format according to themes identified in the scoping review.

ETHICS AND DISSEMINATION

Ethics approval is not necessary, as data are gathered from publicly accessible sources. The results will be disseminated through a peer-reviewed journal and reported at conferences related to addiction medicine.

TRIAL REGISTRATION NUMBER

10.17605/OSF.IO/4J6DQ.

Changes in Plasma Metabolic Signature upon Acute and Chronic Morphine Administration in Morphine-Tolerant Mice

Morphine administration causes system-level metabolic changes. Here, we show that morphine-tolerant mice exhibited distinct plasma metabolic signatures upon acute and chronic administration. We utilized a mouse model of morphine tolerance by exposing mice to increasing doses of the drug over 4 days. We collected plasma samples from mice undergoing acute or chronic morphine or saline injections and analyzed them using targeted GC–MS-based metabolomics to profile approximately 80 metabolites involved in the central carbon, amino acid, nucleotide, and lipid metabolism. Our findings reveal distinct alterations in plasma metabolite concentrations in response to acute or chronic morphine intake, and these changes were linked to the development of tolerance to morphine’s analgesic effects. We identified several metabolites that had been differentially affected by acute versus chronic morphine use, suggesting that metabolic changes may be mitigated by prolonged exposure to the drug. Morphine-tolerant mice showed a restoration of amino acid and glycolytic metabolites. Additionally, we conducted reconstructed metabolic network analysis on the first 30 VIP-ranked metabolites from the PLSDA of the saline, acute, and morphine-tolerant mice groups, which uncovered four interaction networks involving the amino acid metabolism, the TCA cycle, the glutamine-phenylalanine-tyrosine pathway, and glycolysis. These pathways were responsible for the metabolic differences observed following distinct morphine administration regimens. Overall, this study provides a valuable resource for future investigations into the role of metabolites in morphine-induced analgesia and associated effects following acute or chronic use in mice.

Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine Learning Approaches

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg^-1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

Adolescent kratom exposure affects cognitive behaviours and brain metabolite profiles in Sprague-Dawley rats

Adolescence is a critical developmental period during which exposure to psychoactive substances like kratom (Mitragyna speciosa) can cause long-lasting deleterious effects. Here, we evaluated the effects of mitragynine, the main alkaloid of kratom, and lyophilised kratom decoction (LKD) on cognitive behaviours and brain metabolite profiles in adolescent rats. Male Sprague-Dawley rats (Postnatal day, PND31) were given vehicle, morphine (5 mg/kg), mitragynine (3, 10, or 30 mg/kg), or LKD (equivalent dose of 30 mg/kg mitragynine) for 15 consecutive days. Later, a battery of behavioural testing was conducted, brain was extracted and metabolomic analysis was performed using LCMS-QTOF. The results showed that mitragynine did not affect the recognition memory in the novel object recognition task. In the social interaction task, morphine, mitragynine, and LKD caused a marked deficit in social behaviour, while in Morris water maze task, mitragynine and LKD only affected reference memory. Metabolomic analysis revealed distinct metabolite profiles of animals with different treatments. Several pathways that may be involved in the effects of kratom exposure include arachidonic acid, pantothenate and CoA, and tryptophan pathways, with several potential biomarkers identified. These findings suggest that adolescent kratom exposure can cause cognitive behavioural deficits that may be associated with changes in the brain metabolite profiles.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Mitochondria-Related Nuclear Gene Expression in the Nucleus Accumbens and Blood Mitochondrial Copy Number After Developmental Fentanyl Exposure in Adolescent Male and Female C57BL/6 Mice

The potency of the synthetic opioid fentanyl and its increased clinical availability has led to the rapid escalation of use in the general population, increased recreational exposure, and subsequently opioid-related overdoses. The wide-spread use of fentanyl has, consequently, increased the incidence of in utero exposure to the drug, but the long-term effects of this type of developmental exposure are not yet understood. Opioid use has also been linked to reduced mitochondrial copy number in blood in clinical populations, but the link between this peripheral biomarker and genetic or functional changes in reward-related brain circuitry is still unclear. Additionally, mitochondrial-related gene expression in reward-related brain regions has not been examined in the context of fentanyl exposure, despite the growing literature demonstrating drugs of abuse impact mitochondrial function, which subsequently impacts neuronal signaling. The current study uses exposure to fentanyl via dam access to fentanyl drinking water during gestation and lactation as a model for developmental drug exposure. This perinatal drug-exposure is sufficient to impact mitochondrial copy number in circulating blood leukocytes, as well as mitochondrial-related gene expression in the nucleus accumbens (NAc), a reward-related brain structure, in a sex-dependent manner in adolescent offspring. Specific NAc gene expression is correlated with both blood mitochondrial copy number and with anxiety related behaviors dependent on developmental exposure to fentanyl and sex. These data indicate that developmental fentanyl exposure impacts mitochondrial function in both the brain and body in ways that can impact neuronal signaling and may prime the brain for altered reward-related behavior in adolescence and later into adulthood.