Changes in the metabolic profile of human male postmortem frontal cortex and cerebrospinal fluid samples associated with heavy alcohol use

Targeted and Non-Targeted Metabolomic Evaluation of Cerebrospinal Fluid in Early Phase Schizophrenia: A Pilot Study from the Hopkins First Episode Psychosis Project

(1) Background: The lack of reliable biomarkers remains a significant barrier to improving outcomes for patients with schizophrenia. While metabolomic analyses of blood, urine, and feces have been explored, results have been inconsistent. Compared to peripheral compartments, cerebrospinal fluid (CSF) more closely reflects the chemical composition of brain extracellular fluid. Given that brain dysregulation may be more pronounced during the first episode of psychosis (FEP), we hypothesized that metabolomic analysis of CSF from FEP patients could reveal disease-associated biomarkers. (2) Methods: We recruited 15 patients within 24 months of psychosis onset (DSM-4 criteria) and 14 control participants through the Johns Hopkins Schizophrenia Center. CSF samples were analyzed using both non-targeted and targeted liquid chromatography-mass spectrometry. (3) Results: The non-targeted analysis identified lower levels of N-acetylneuraminic acid and N-acetyl-L-aspartic acid in the FEP group, while levels of uric acid were elevated. The targeted analysis focused on indolic and phenolic molecules previously linked to neuropsychiatric disorders. Notably, L-phenylalanine and 4-hydroxycinnamic acid levels were lower in the FEP group, and this difference remained significant after adjusting for age and sex. However, none of the significant differences in analyte levels between the groups survived an adjustment for multiple comparisons. (4) Conclusions: Our intriguing but preliminary associations align with results from other investigational approaches and highlight potential CSF analytes that warrant further study in larger samples.

Blood metabolomic profiling reveals new targets in the management of psychological symptoms associated with severe alcohol use disorder

Background

Alcohol use disorder (AUD) is a global health problem with limited therapeutic options. The biochemical mechanisms that lead to this disorder are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD. The plasma metabolome contains a plethora of bioactive molecules that reflects the functional changes in host metabolism but also the impact of the gut microbiome and nutritional habits.

Methods

In this study, we investigated the impact of severe AUD (sAUD), and of a 3-week period of alcohol abstinence, on the blood metabolome (non-targeted LC-MS metabolomics analysis) in 96 sAUD patients hospitalized for alcohol withdrawal.

Results

We found that the plasma levels of different lipids ((lyso)phosphatidylcholines, long-chain fatty acids), short-chain fatty acids (i.e. 3-hydroxyvaleric acid) and bile acids were altered in sAUD patients. In addition, several microbial metabolites, including indole-3-propionic acid, p-cresol sulfate, hippuric acid, pyrocatechol sulfate, and metabolites belonging to xanthine class (paraxanthine, theobromine and theophylline) were sensitive to alcohol exposure and alcohol withdrawal. 3-Hydroxyvaleric acid, caffeine metabolites (theobromine, paraxanthine, and theophylline) and microbial metabolites (hippuric acid and pyrocatechol sulfate) were correlated with anxiety, depression and alcohol craving. Metabolomics analysis in postmortem samples of frontal cortex and cerebrospinal fluid of those consuming a high level of alcohol revealed that those metabolites can be found also in brain tissue.

Conclusions

Our data allow the identification of neuroactive metabolites, from interactions between food components and microbiota, which may represent new targets arising in the management of neuropsychiatric diseases such as sAUD.

Funding

Gut2Behave project was initiated from ERA-NET NEURON network (Joint Transnational Call 2019) and was financed by Academy of Finland, French National Research Agency (ANR-19-NEUR-0003-03) and the Fonds de la Recherche Scientifique (FRS-FNRS; PINT-MULTI R.8013.19, Belgium). Metabolomics analysis of the TSDS samples was supported by grant from the Finnish Foundation for Alcohol Studies.

Alcohol use-associated alterations in the circulating metabolite profile in the general population and in individuals with major depressive disorder

Our aim was to evaluate whether alcohol use is associated with changes in the circulating metabolite profile similar to those present in persons with depression. If so, these findings could partially explain the link between alcohol use and depression. We applied a targeted liquid chromatography mass spectrometry method to evaluate correlates between concentrations of 86 circulating metabolites and self-reported alcohol use in a cohort of the non-depressed general population (GP) (n = 247) and a cohort of individuals with major depressive disorder (MDD) (n = 99). Alcohol use was associated with alterations in circulating concentrations of metabolites in both cohorts. Our main finding was that self-reported alcohol use was negatively correlated with serum concentrations of hippuric acid in the GP cohort. In the GP cohort, consumption of six or more doses per week was associated with low hippuric acid concentrations, similar to those observed in the MDD cohort, but in these individuals it was regardless of their level of alcohol use. Reduced serum concentrations of hippuric acid suggest that already-moderate alcohol use is associated with depression-like changes in the serum levels of metabolites associated with gut microbiota and liver function; this may be one possible molecular level link between alcohol use and depression.

Multimodal Approaches Based on Microbial Data for Accurate Postmortem Interval Estimation

Accurate postmortem interval (PMI) estimation is critical for forensic investigations, aiding case classification and providing vital trial evidence. Early postmortem signs, such as body temperature and rigor mortis, are reliable for estimating PMI shortly after death. However, these indicators become less useful as decomposition progresses, making late-stage PMI estimation a significant challenge. Decomposition involves predictable microbial activity, which may serve as an objective criterion for PMI estimation. During decomposition, anaerobic microbes metabolize body tissues, producing gases and organic acids, leading to significant changes in skin and soil microbial communities. These shifts, especially the transition from anaerobic to aerobic microbiomes, can objectively segment decomposition into pre- and post-rupture stages according to rupture point. Microbial communities change markedly after death, with anaerobic bacteria dominating early stages and aerobic bacteria prevalent post-rupture. Different organs exhibit distinct microbial successions, providing valuable PMI insights. Alongside microbial changes, metabolic and volatile organic compound (VOC) profiles also shift, reflecting the body's biochemical environment. Due to insufficient information, unimodal models could not comprehensively reflect the PMI, so a muti-modal model should be used to estimate the PMI. Machine learning (ML) offers promising methods for integrating these multimodal data sources, enabling more accurate PMI predictions. Despite challenges such as data quality and ethical considerations, developing human-specific multimodal databases and exploring microbial-insect interactions can significantly enhance PMI estimation accuracy, advancing forensic science.

Biological aging markers in blood and brain tissue indicate age acceleration in alcohol use disorder

BACKGROUND

Alcohol use disorder (AUD) is associated with increased mortality and morbidity risk. A reason for this could be accelerated biological aging, which is strongly influenced by disease processes such as inflammation. As recent studies of AUD show changes in DNA methylation and gene expression in neuroinflammation-related pathways in the brain, biological aging represents a potentially important construct for understanding the adverse effects of substance use disorders. Epigenetic clocks have shown accelerated aging in blood samples from individuals with AUD. However, no systematic evaluation of biological age measures in AUD across different tissues and brain regions has been undertaken.

METHODS

As markers of biological aging (BioAge markers), we assessed Levine's and Horvath's epigenetic clocks, DNA methylation telomere length (DNAmTL), telomere length (TL), and mitochondrial DNA copy number (mtDNAcn) in postmortem brain samples from Brodmann Area 9 (BA9), caudate nucleus, and ventral striatum (N = 63-94), and in whole blood samples (N = 179) of individuals with and without AUD. To evaluate the association between AUD status and BioAge markers, we performed linear regression analyses while adjusting for covariates.

RESULTS

The majority of BioAge markers were significantly associated with chronological age in all samples. Levine's epigenetic clock and DNAmTL were indicative of accelerated biological aging in AUD in BA9 and whole blood samples, while Horvath's showed the opposite effect in BA9. No significant association of AUD with TL and mtDNAcn was detected. Measured TL and DNAmTL showed only small correlations in blood and none in brain.

CONCLUSIONS

The present study is the first to simultaneously investigate epigenetic clocks, telomere length, and mtDNAcn in postmortem brain and whole blood samples in individuals with AUD. We found evidence for accelerated biological aging in AUD in blood and brain, as measured by Levine's epigenetic clock, and DNAmTL. Additional studies of different tissues from the same individuals are needed to draw valid conclusions about the congruence of biological aging in blood and brain.

Addiction as a brain disease? A meta-regression comparison of error-related brain potentials between addiction and neurological diseases

The notion that addiction is a "brain disorder" is widespread. However, there is a lack of evidence on the degree of disorder in terms of error processing in addiction. The present meta-analysis aimed at shedding light on this by comparing error-processes with populations with well-recognized brain disorders. We included 17 addiction and 32 neurological disorder studies that compared error-related negativity (ERN) or error positivity (Pe) amplitudes/latencies between experimental and healthy-control groups. Meta-regression analyses were performed for the intergroup comparison and other moderators. Both diagnoses were accompanied by a diminished ERN amplitude, although the degree of impairment was marginally larger in neurological disorders. Neurological disorders presented shorter ERN latencies than addiction when compared with controls. The two groups did not differ in Pe amplitude/latency. Except for a reduced ERN amplitude found along with aging, no other moderator contributed significantly to divergent findings about these four ERP indexes. The results support the brain disease model of addiction, while stressing the importance of quantifying the degrees of brain dysfunctions as a next step.

Microbiota-derived metabolites as drivers of gut-brain communication

Alterations in the gut microbiota composition have been associated with a range of neurodevelopmental, neurodegenerative, and neuropsychiatric disorders. The gut microbes transform and metabolize dietary- and host-derived molecules generating a diverse group of metabolites with local and systemic effects. The bi-directional communication between brain and the microbes residing in the gut, the so-called gut-brain axis, consists of a network of immunological, neuronal, and endocrine signaling pathways. Although the full variety of mechanisms of the gut-brain crosstalk is yet to be established, the existing data demonstrates that a single metabolite or its derivatives are likely among the key inductors within the gut-brain axis communication. However, more research is needed to understand the molecular mechanisms underlying how gut microbiota associated metabolites alter brain functions, and to examine if different interventional approaches targeting the gut microbiota could be used in prevention and treatment of neurological disorders, as reviewed herein.Abbreviations:4-EPS 4-ethylphenylsulfate; 5-AVA(B) 5-aminovaleric acid (betaine); Aβ Amyloid beta protein; AhR Aryl hydrocarbon receptor; ASD Autism spectrum disorder; BBB Blood-brain barrier; BDNF Brain-derived neurotrophic factor; CNS Central nervous system; GABA ɣ-aminobutyric acid; GF Germ-free; MIA Maternal immune activation; SCFA Short-chain fatty acid; 3M-4-TMAB 3-methyl-4-(trimethylammonio)butanoate; 4-TMAP 4-(trimethylammonio)pentanoate; TMA(O) Trimethylamine(-N-oxide); TUDCA Tauroursodeoxycholic acid; ZO Zonula occludens proteins.

Diet- and microbiota-related metabolite, 5-aminovaleric acid betaine (5-AVAB), in health and disease

5-Aminovaleric acid betaine (5-AVAB) is a trimethylated compound associated with the gut microbiota, potentially produced endogenously, and related to the dietary intake of certain foods such as whole grains. 5-AVAB accumulates within the metabolically active tissues and has been typically found in higher concentrations in the heart, muscle, and brown adipose tissue. Furthermore, 5-AVAB has been associated with positive health effects such as fetal brain development, insulin secretion, and reduced cancer risk. However, it also has been linked with some negative health outcomes such as cardiovascular disease and fatty liver disease. At the cellular level, 5-AVAB can influence cellular energy metabolism by reducing β-oxidation of fatty acids. This review will focus on the metabolic role of 5-AVAB with respect to both physiology and pathology. Moreover, the analytics and origin of 5-AVAB and related compounds will be reviewed.