Investigating Gut Microbiota with Gamma- Aminobutyric Acid Production Potential in Methamphetamine Addiction Recovery

Investigating Gut Microbiota with Gamma- Aminobutyric Acid Production Potential in Methamphetamine Addiction Recovery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigating Gut Microbiota with Gamma- Aminobutyric Acid Production Potential in Methamphetamine Addiction Recovery Siti Khadijah Kiraman, Hijaz Ridzwan, Norafisah Mohd Arshad, Tengku Mohd Saifuddin Tengku Kamarul Bahri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7033630/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Methamphetamine addiction is rising globally, burdening healthcare systems. With limited treatments beyond abstinence, psychobiotics offer potential aid in recovery. This study aims to identify potential psychobiotics from individuals withdrawing from methamphetamine, focusing on GABA-producing microbes as psychotherapy. We analysed stool samples from 32 individuals withdrawing from methamphetamine (average age 27.34 ± 4.22; range 18–35) and 64 healthy controls (average age 18.84 ± 6.90; range 13–37) using 16S rRNA amplicon sequencing to compare gut microbiota differences linked to addiction. Significant differences in microbial diversity were observed between groups, specifically in α-diversity (Chao1, p -value < 0.05) and β-diversity (Bray-Curtis, p-value = 0.001). Statistical analysis revealed potential biomarkers, including GABA-producing Lactococcus and Weissella based on association with recovery profiles. Functional prediction and genome analyses demonstrated pathways related to glutamate and GABA in the withdrawing individuals. Psychobiotics may offer alternative to support mental health and recovery from methamphetamine addiction by targeting gut microbiota. Health sciences/Diseases Health sciences/Medical research Biological sciences/Microbiology addiction methamphetamine gamma-aminobutyric acid psychobiotic GABAergic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Methamphetamine (METH) abuse has emerged as a significant issue in the context of current public health concerns. Long-term METH abuse leads to cerebral deterioration, metabolic abnormalities, and structural brain changes. It is associated with medical conditions such as cardiovascular diseases 1 and psychiatric issues, the latter including depression and psychosis 2 . Prolonged use or high doses of METH could affect multiple areas in the central nervous system, inducing a phenomenon known as "behavioral sensitization" or “reverse tolerance”, which is believed to underlie METH-induced psychosis 3 . Furthermore, METH abuse also increased the risk of developing schizophrenia 4 . Previous studies have linked the METH abuse with a leaky gut, where the release of neurotransmitters induced by METH may cause damage to enteric neurons. METH use also causes alterations to tight junction proteins, such as Claudin-1 and ZO-1. These tight junctions proteins are thought to be responsible for the regulation of gut epithelial paracellular permeability, malfunction of which can reduce the integrity of blood-brain barrier 5 that resulted in ischemic colitis 6 – 8 and vasculitis of the distal colon 9 . In some cases, METH can trigger vasoconstriction in the gastrointestinal tract, leading to bowel ischemia, which in turn causes abdominal cramps, constipation, diarrhoea, and tissue dehydration 6 – 8 . Therefore, based on these findings, it can be proposed that the gut microbiome composition in METH abusers may be altered because of modification in the intestine. In recent years, a growing body of studies unveiled a role of the gut-brain axis (GBA) in regulating anxiety, depression, cognition and pain. Interestingly, animal studies also linked METH use and/or cessation to alterations in the gut microbiota, including changes in the overall composition 10 , increased microbial diversity 11 , proliferation of pathogenic bacteria 12 , and altered metabolomic profiles 13 . Despite these advancements in animal studies, the role of gut microbiota in human METH abuse and its connections with psychotic syndromes and cognitive impairment remain uncharacterized. Gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter available in foods and produced by certain microbial species, has been explored for its potential therapeutic effects in reducing stress and improving mental health. This study investigated gut microbial differences between individuals recovering from METH addiction and healthy controls. Addicted individuals were further categorized based on early or chronic exposure. Gut microbiota was profiled using V3 region amplicon sequencing and standard bioinformatics analysis to identify potential microbial markers. We assessed bacterial diversity, gut microbiota composition, and distinguishing taxa as potential biomarkers. Associations between METH use duration and microbial profiles were also evaluated to understand the impact of exposure. We hope that this research effort will identify bacteria that could be used for diagnostics or to explore the psychobiotic potential in enhancing therapeutic strategies in our population in the near future. Materials and Methods Ethics approval and consent to participate Prior to patient recruitment and sample collection, the study protocol received ethical approval from the Institutional Research Ethics Committee (IREC) of the Kulliyyah of Medicine at the International Islamic University Malaysia (Research ID: 2023-922), and from the National Medical Research Register (NMRR) (ID: 21-02456-EJA). Written informed consent was obtained from participants at the Cure and Care Rehabilitation Centre (CCRC) prior to enrolment. The sample collection was conducted from 1 December 2022 to 30 November 2023. The study was conducted in accordance with the Declaration of Helsinki and adhered to all relevant institutional and international ethical guidelines. Selection criteria during recruitment process We recruited 32 subjects (average age, 27.34 ± 4.22; age range 18-35) diagnosed with METH use disorder, and 64 healthy subjects (average age 18.84 ± 6.90; age range 13-37) with no prior history of METH or any other illicit substances as controls. These two groups were respectively labeled as “Methamphetamine Withdrawal” (MW) and “Healthy Control” (HC). The MW is diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) by the consensus of two psychiatrists. MW participants were further classified into “Chronic’ and “Early” by employing Addiction Severity Index (ASI) evaluated by the psychiatrists. Additional selection criteria included the absence of antibiotic, probiotic or prebiotic use in the three months leading up to sample collection. Furthermore, participants were not taking any long-term anti-inflammatory or antioxidant medications. Information on METH use was collected. The average age of initial METH use was 19 ± 5.38 years, with a range of 12 to 30 years, and the average duration of METH use was 8 ± 5.01 years, with a range of 1 to 17 years. The average frequency of METH use was 3 ± 2.34 days per week. The subjects in the METH group were hospitalized 0-3 times (Table 1). Fecal sample collection and DNA extraction Fecal samples were collected, homogenised and stored at -80 °C prior to analyses as previously described 14 . DNA were extracted using QIAamp Powerfecal Pro DNA Kit (QIAGEN, Germany), following the manufacturer's instructions with some adjustments 15 . Then, the purity and concentration of DNA were determined by 1% agarose gel electrophoresis. Sample DNA was diluted to 5 ng/µL with sterile distilled water prior library preparation. Library preparation and sequencing Bacterial DNA was amplified by targeting the V3 hypervariable region of the 16S rRNA using primers that incorporated partial Illumina adapters as previously reported 16 . Briefly, the PCR was carried out with the WizBio HotStart PCR mastermix (WizBio, Gyeonggi, Republic of Korea) and followed by adjusted profiles profile: an initial denaturation at 95°C for 3 minutes, then 35 cycles of 95°C for 30 seconds, 55°C for 20 seconds, and 72°C for 20 seconds. Following PCR amplification, the products were purified using SPRI Beads (Beckman Coulter, CA, USA). Subsequently, an index PCR was performed to introduce an Illumina dual index barcode. The barcoded libraries were visualized on a gel, grouped based on band intensity, and further purified using the WizPrep™ Gel/PCR Purification Mini Kit (WizBio, Gyeonggi, Republic of Korea) as per the manufacturer’s guidelines. The quantification of the pooled libraries was conducted using Denovix high sensitivity equipment, and an appropriate volume of the libraries was loaded onto a NovaSeq 6000 System (Illumina, San Diego, CA, USA) for either 2 × 150 paired-end sequencing or 1 × 250 single-end sequencing. Amplicon sequencing data analysis The initial steps in the data processing pipeline included demultiplexing and trimming of primers from the raw paired end reads, which was accomplished using cutadapt v1.18 17 . The resulting trimmed reads were then merged using fastp v0.21 18 . Subsequently, the processed reads were imported into QIIME2 v.2022.8 19 for further analysis. Amplicon Sequence Variants (ASVs) were obtained through the application of the dada2 v1.22 R package 20 with modifications 21 . Taxonomic assignment of these ASVs was performed using q2-feature-classifier 22 . Only ASVs with taxonomic assignments at least to the phylum level were retained for subsequent analysis. The ASV table and taxonomic classification table were exported in tab-separated values (tsv format) using QIIME2 tools and manually formatted to create input data compatible with MicrobiomeAnalyst2 23 with adjusted parameters 24 . To examine the functions of microorganisms, the representative sequences comprising the top 500 taxa were used to infer on Kyoto Encyclopedia of Genes and Genomes (KEGG) database using Phylogenetic Investigation of Communities’ Reconstruction of Unobserved States (PICRUSt2) pipeline (https://github.com/picrust/picrust2/) 25 . Statistical Analysis for Amplicon Data This processed data was used for various analyses, including statistical analysis employing the linear discriminant analysis (LDA) effect size (LEfSe) method 26 . Alpha- (Shannon and Simpson diversity indices) and beta-diversity (Bray–Curtis dissimilarity) were calculated using specialized QIIME2 plug-ins. To explore the relative abundances across taxonomic hierarchies, a filtered relative abundance table was also utilized. Random forest analysis was performed utilising MicrobiomeAnalyst2 to identify influential microbes with Chronic and Early from MW group as input where the model was constructed with 5000 trees evaluated by Mean Decrease Accuracy (MDA). Statistical Analysis of Metabolic Profiles (STAMP) v2.1.3 (https://beikolab.cs.dal.ca/software/STAMP) 27 were utilized to establish metabolic pathways related to GABA and glutamate, while Microbiome Multivariate Association with Linear Models (MaAsLin2) v1.22.0 (https://huttenhower.sph.harvard.edu/maaslin/) 28 were employed to find potential probiotics influencing the group gut microbiome with participants’ group, gender and age covariant. Isolation and Identification of Single Colony Potential Probiotic Chronic participant sample was opted for ten-fold serial dilution and plated on De Man, Rogosa, and Sharpe (MRS) agar and incubated under anaerobic condition with AnaeroPack (Mitsubishi Gas Chemical Co. Inc, Japan) at 37˚C for 24 hours. Colonies were isolated and streaked thrice to ensure pure colony before proceeding with gram staining for morphology. DNA extraction was performed utilizing QIAamp PowerFecal Pro DNA Kit (Qiagen, Germany). Quantification of DNA concentration was made by Qubit 4 Fluorometer (Invitrogen, Thermo Fisher Scientific, US) and gel electrophoresis protocol was executed to assess the DNA quality obtained. PCR targeting V3 region was performed, and the product was outsourced for Sanger sequencing, identification at genus level. Whole Genome Sequencing, Genome Assembly and Assessment Library preparation of MinION (Oxford Nanopore, UK) was conducted with Ligation Sequencing Kit (SQK-LSK110). The prepared DNA library was then loaded to MinION flow cell FLO-MIN106 (R9.4.1). The sequencing was run for 24 hours. The nanopore reads were then quality- and length-filtered to retain the reads longer than 500 bp with a q-score of 7 and above. The filtered nanopore reads were then de novo assembled SPAdes v3.15.0 29 and polished using RACON v1.4.3, generating the final consensus assembly 30 . The contigs less than 500 bp representing mostly sequencing artefact were removed. Genome assembly statistics were generated using QUAST 31 . Genome completeness was assessed using BUSCO5 based on lactobacillales_odb10 linage 32 . Prodigal v2.6 33 was used for protein prediction and the Abricate (https://github.com/tseemann/abricate) was used to screen for possible antimicrobial resistance genes via by comparing nucleotide bases against the curated NCBI AMR 34 and virulence database 35 . Lastly, the genome sequence of bacterial isolate was deposited in the NCBI public database. Phylogenetic tree and Rapid Annotation using Subsystem Technology Phylogenetic tree of the isolate was generated using Automated Multi-Locus Species Tree (autoMLST). The genome sequence in FASTA format were uploaded to autoMLST webserver (https://automlst.ziemertlab.com/) using de novo mode. Automatically generated maximum-likelihood phylogenetic tree was generated where estimated Average Nucleotide Identity (ANI) values with reference genomes are utilised to build the tree 36 . Functional annotation of the sequence was performed by submitting it to Rapid Annotation using Subsystem Technology (RAST) pipeline (https://rast.nmpdr.org/) and the data was presented in subsystem pie chart 37 . Results Characteristics of the Studied Participants A total of 32 subjects identified as having a history of METH addiction were recruited from the Cure and Care Rehabilitation Centre (CCRC). In addition, 64 healthy subjects were recruited at the Sultan Ahmad Shah Medical Centre (SASMEC), Malaysia (Supplementary Table 1). The patients were stratified as having “early exposure” or “chronic exposure” according to substances dependency to evaluate the associations between faecal microbiota and addiction. Clinical and analytical characteristics according to addiction status are shown in Table 1 and Table 2. Table 1. Characteristics of the participants studied. Methamphetamine Withdrawal (MW) Healthy Control (HC) p -value Subjects (n) 32 64 The proportion of males, no. (%) 32 (100%) 32 (50.00%) 8.935e-08 Age (years; means±SD) (range) 27.34 (4.22) (18-35) 18.84 (6.90) (13-37) 6.295e-08 Employed, no. (%) 32 (100%) 13 (20.31%) 2.458e-15 Education 6.609e-04 Primary school, no. (%) 6 (18.75%) 0 (0%) High school, no. (%) 22 (68.75%) 44 (68.75%) University or higher, no. (%) 4 (12.5%) 20 (31.25%) Marriage status Unmarried, no. (%) 28 (87.5%) NA Age of initial METH use (years; mean±SD) (range) 19 (5.38) (12-30) NA Duration of METH use (years; mean±SD) (range) 8 (5.01) (1-17) NA Frequency of METH use (days per week; mean±SD) (range) 3 (2.34) (1-7) NA Number of hospitalization (number; mean±SD) (range) 0 (0.75) (0-3) NA Table 2. Characteristics of the participants studied on methamphetamine exposure from MW group Early Chronic p -value Subjects (n) The proportion of males, no. (%) 17 (100.0%) 15 (100.0%) 1 Age (years; means±SD) (range) 26.24 (4.13) (18-33) 28.6 (4.1) (23-35) 0.1155 Employed, no. (%) 17 (100.0%) 15 (100.0%) 1 Education 0.5014 Primary school, no. (%) 4 (23.53%) 2 (13.33%) High school, no. (%) 10 (58.82%) 12 (80.0%) University or higher, no. (%) 3 (17.65%) 1 (6.67%) Marriage status 0.03796 Unmarried, no. (%) 17 (100.0%) 11 (73.33%) Age of initial METH use (years; mean±SD) (range) 21.7 (5.3) (12-30) 15.7 (3.4) (12-25) 8.913e-06 Duration of METH use (years; mean±SD) (range) 4.2 (2.4) (1-8) 13 (2.4) (10-17) 1.828e-11 Frequency of METH use (days per week; mean±SD) (range) 2.6 (2) (1-7) 3.9 (2.6) (1-7) 0.2277 Number of hospitalization (number; mean±SD) (range) 0.5 (0.9) (0-3) 0.2 (0.6) (0-2) 0.2956 Distinct Gut Microbiome Profiles in Individuals of Methamphetamine Withdrawal Compared to Healthy Control group. The contrast of HC and MW group in relative abundance by phylum is apparent in the abundance of Firmicutes and Proteobacteria in Addicted group in comparison to the abundance of Actinobacteria in HC individuals (Fig 1a). Diving deeper into proportional abundance, there are few bacterial taxa high in MW as compared to HC individuals such as Escherichia (11.07% and 2.03% respectively), Peptostreptococcaceae (7.07% and 4.12% respectively), Ligilactobacillus ruminis (7.15% and 3.44% respectively), Clostridiaceae (8.21% and 0.57% respectively), and Holdemanella biformis (3.03% and 1.03% respectively). Contemporaneously, bacterial taxa that is high in HC in contrast to MW individuals are Bifidobacterium adolescentis (16.2% and 5.17% respectively), Bifidobacterium kashiwanohense (4.35% and 0.68% respectively), Streptococcus (5.05% and 4.06% respectively), and Fusicatenibacter saccharivorans (2.78% and 1.47% respectively) (Fig 1b). Looking to the perspective of intra-diversity within the sample for MW group in measuring richness of the sample including the rare species for Chao, and general richness excluding rare species using Observed display significant difference between the MW and HC group ( p -value < 0.05, t -test = -6.3144 and p -value < 0.05, t -test = -6.3485 respectively) (Fig1c, Fig1d). Conversely, Shannon demonstrated to be insignificant in measuring the evenness and richness of the group sample ( p -value = 0.45597, t -test = -0.75057) (Fig 1e). As for inter-diversity between the group of Addicted and Healthy individuals, permutational multivariate analysis of variance (PERMANOVA) analysis on species-level display distinct differences between these two groups of individuals (F-value of 10.07, R 2 : 0.09866 and p -value = 0.001) (Fig 1f). Linear Discriminant Analysis Effect Size (LefSe) highlights the predominance of certain bacterial taxa in each of the Addicted and Healthy where this data corresponds with relative abundance previously mentioned (Fig 1g). Substance Exposure Shapes the Gut Microbiome Composition This section aims to deepen the understanding of the long-term effects of substance dependence and abuse. Recovering methamphetamine addicted individual groups were further classified into the category of early-staged and chronically addicted, evaluated by psychiatrist in this study utilizing the ASI where this classifies individual’s addiction stages 38 . The difference in Early and Chronic individual gut were evaluated utilizing alpha and beta diversity analysis where the alpha diversity shows no significant difference between each sample group using Chao and Shannon diversity index ( p -value = 0.6053, t -test = -0.52375 and p -value = 0.396, t -test = -0.86206 respectively) (Fig 2c, Fig 2d). On the other hand, the beta diversity PERMANOVA analysis showed no distinct differences between these two groups of individuals (F-value of 0.48382, R 2 : 0.01641 and p -value = 0.936) (Fig 2e). The bacterial community in human guts often reflects the host’s health where dominancy of certain bacteria reflects the physiological state of the host. Harm inflicted by substance abuse are often defined by the period of one consumption. In comparing the bacterial dominancy of Early and Chronic individuals, the relative abundance visualizes Blautia (13.88% and 11.25% respectively), Lachnospiraceae (12.13% and 11.17% respectively), Escherichia (10.79% and 12.10% respectively) and Clostridiaceae (7.8% and 8.67% respectively) (Fig 2a). Three significant microbes were observed to have higher abundance in Chronic as compared to Early group for Peptostreptococcaceae (8.11% and 6.07% respectively), Holdemanella (2.29% and 1.55% respectively) and Streptococcus (6.11% and 2.36% respectively) (Fig 2a). Conversely, prominent probiotics are found to be in lower abundance in Chronic as compared to Early individuals where Bifidobacterium adolescentis with abundance of 3.9% in Chronic and 6.1% in Early individuals and Ligilactobacillus ruminis with abundance of 6.22% in Chronic and 8.43% in Early individuals where the latter is known to be autochthonous microbes in human gastrointestinal tract 39 (Fig 2a). Random forest classification identifies Mean Decrease Accuracy (MDA) analysis using random forest algorithm with 5000 trees generated shown Weizmannia (0.006 MDA), Christensenellales (0.008 MDA) and Weissella (0.010 MDA) in Chronic individuals where the higher the MDA value, the stronger their influence 40 (Fig 2f). Maximum high number of trees, 5000, was chosen to reduce noise and provide more stable MDA values 41 , however due to small sample of 32 MW, the out-of-bag (OOB) value is of 67.7% which is quite high making the error two-thirds of the sample for MW. The presence of Bacteroides (0.0018 MDA) and Bacteroides caccae (0.001 MDA) are also worth highlighting as it is known to be major GABA-producer in the gut microbiota 42 . A study found that Bacteroides make up for the GABA produced in gut microbiota by 31.7% due to its possessing GAD orthologs, followed by Escherichia (22.5%), Fusobacterium (9.9%) and both well-known probiotic genera Bifidobacterium and Lactobacillus contributed only about 2.2% 43 . Functional Insights into the Gut Microbiome: Linking Glutamate and GABA Pathways In exploring the distribution of metabolic pathways correlated with GABA and glutamate in recovering addicted individuals and healthy individuals, Statistical Analysis of Metabolic Profiles (STAMP) was utilized visualizing in box plot. In total 211 pathways were generated in comparing for healthy and recovering addicted individuals. These volumes were then narrowed down to only significant with p -value < 0.05, reduced to 186 significant pathways. Few pathways out of 186 significant ones call attention for being related to glutamate and GABA pathways are then further categorized into indirect and direct related to GABA and glutamate related pathway. Lastly, direct pathways of GABA and glutamate were then evaluated via existing literature on its correlation to GBA. Glutamate and GABA are both vital neurotransmitters in regulating brain function 44 where the former is excitatory, and the latter is inhibitory in which any changes in concentration of these metabolites can have profound effect on cortical excitability 45 . Some of the pathways worth noting are the superpathway of ornithine degradation, pyridoxal 5’-phosphate and 4-aminobutanoate degradation (Fig 3). Superpathway of ornithine degradation is where L-ornithine degraded until the product is 4-aminobutanoate which is GABA via the ornithine aminotransferase catalyzed reaction 46–49 (Fig 3a). This pathway is known to take place in the brain where ornithine is known to be able to pass-through blood brain barrier permeability 50 . On the other hand, pyridoxal 5’-phosphate biosynthesis and salvage are equally important as pyridoxal 5’-phosphate is precursor for glutamate decarboxylase (GAD) to convert glutamate to GABA 51 (Fig 3b, 3c, 4). As for superpathway of L-arginine, putrescine, and 4-aminobutanoate degradation highlights the importance of GABA degradation which in turn convert to glutamate (Fig 3d, 3e). The presence of this GABA and glutamate related pathway strongly suggests the heavy influence of these gut microbiota in GBA of the host, implying possible mental condition at hands. Adding to the fact that METH are known to induce blood-brain barrier disruptions paving way for metabolites to pass through blood brain barrier 52–54 . The previous section highlights the relative abundance of Escherichia and Bacteroides that are known to be major GABA producers in human gut microbiota 55 . Weissella sp. as Potential Psychobiotic of Recovering from Methamphetamine Individual Microbiome Multivariate Association with Linear Models (MaAsLin2) with total sum scaling normalization and log transformation was utilized in finding any potential probiotic that plays role in mental health of the individuals in correlating GBA of WM individuals. Few microbes are to highlight from this analysis which are those are known to be probiotic and some of them with potential probiotic characteristics (Table 3). Next-generation probiotic (NGP) is defined as potential candidate of probiotic that does not come from the genus of Lactobacillus and Bifidobacterium that are traditional probiotics, established in the food industries for their well-known health benefits 57 . Few probiotics and potential NGP were identified and evaluated via existing literature. The negative of coefficient value indicating the microbes are higher in HC than MW whilst positive implying vice versa (Table 3). The presence of well-known probiotics of Bifidobacterium are high in HC, with addition of Lactococcus and Weissella. Diving deeper into the role of these microbes to GBA of the hosts, we further classified these probiotic and potential NGPs into non-GABA producer and GABA producer. (Table 3 & Fig 4). Implying that the presence of the probiotic in MW indicating the recovering gut microbiota from dysbiosis, the presence of Lactococcus and Weissella along with established probiotic such as Bifidobacterium indicate that these two could play same role as the latter. The identified facultative anaerobes are constantly being highlighted in other literatures for their potential in becoming the NGPs 58–62 . Various literature has reported of Weissella potential in producing GABA at different concentrations depending on its strain, namely Weissella cibaria 63,64 and Weissella confusa 65,66 . Moreover, these two are known gut microbes 67,68 yet their roles in human gut flora are still underexplored. The presence of this GABA producer presumably acts as a key factor in contributing to recovering in mental health of MW individuals. Table 3. List of Probiotics, Potential NGPs identified with known GABA producer No. Probiotic/NGP Coefficient value Standard error p -value q -value Literature 1 Bifidobacterium kashiwanohense -1.2548 0.1829 7.8237e-10 4.3128e-08 69 2 Anaerobutyricum -0.3286 0.0670 4.0398e-06 8.0981e-05 70,71 3 Ligilactobacillus ruminis.1 0.4608 0.1707 8.2463e-03 4.0861e-02 72–74 4 Phocaeicola dorei 0.5475 0.2414 2.5668e-02 9.8432e-02 75,76 5 Ligilactobacillus ruminis 0.3311 0.1476 2.7226e-02 1.0350e-01 72–74 6 Clostridium leptum 0.4484 0.2317 5.6001e-02 1.7392e-01 77,78 7 Limosilactobacillus mucosae 0.6887 0.3621 6.0284e-02 1.8172e-01 79,80 GABA Producer 8 Bifidobacterium bifidum -2.3320 0.1937 1.3225e-20 5.8323e-18 81–84 9 Bifidobacterium -1.011 0.1161 1.1544e-13 1.6970e-11 85,86 10 Lactococcus -2.0391 0.2539 3.1136e-12 3.4327e-10 58 11 Bifidobacterium adolescentis -0.4896 0.0956 1.6700e-06 4.0915e-05 87–89 12 Weissella -1.0949 0.2460 2.3889e-05 3.7625e-04 60–62,65 13 Bifidobacterium infantis -0.4914 0.2058 1.9006e-02 7.8335e-02 90,91 14 Phocaeicola vulgatus 0.6297 0.3372 6.5012e-02 1.9242e-01 42 Draft Whole-Genome Sequence of Weissella confusa The genome size obtained was 1.02Mb which is less than 50% coverage of Weissella confusa genome size which was 2.6Mb hence the genome sequence obtained from this protocol was deduced to be draft genome as it is far from reaching the estimated genome size. GC content which tells the percentage of guanine (G) and cytosine (C) is found to be 44.1% in this genome with N50 of 34,965 where this length tells us the quality of genome obtained for which the longer N50 contig length the better it is and good contig of N50 is to be of over 1Mb. 33 number of contigs were obtained from this sequencing and there were 1448 number of Coding Sequence (CDS). The inadequate quality of sequences obtained must be due to the quality of DNA sample loaded into the MinION flow cell (Oxford Nanopore, UK) itself even so the DNA sequences of Weissella confusa was still obtained. Utilising Average Nucleotide Identity (ANI), the draft genome obtained are closely related to Weissella confusa strain DSM20196 by 95.9% as visualised by autoMLST (Fig 4a). The pathways highlight from the draft whole genome sequence is metabolic pathway of Glutamine, Glutamate, Aspartate and Asparagine Biosynthesis where it highlights the synthesis of glutamate which are precursors to GABA. Gene annotation results by RAST show the existence of glutamate metabolic related pathway present in the isolated bacterium, indicating the possible relations to GABA producing pathway (Fig 4b, 4c). As compared to GABA, glutamates are known to pass blood-brain barrier in small amounts hence the presence of it may contribute to being a substrate to produce more GABA in the brain. It was worth noting that the genome obtained here is just draft genome. Other literatures, however, have reported none of Weissella confusa producing glutamate but several have reported producing GABA at various concentrations 65 . Discussion Consistent with findings from other studies, the present data demonstrate that methamphetamine use leads to gut dysbiosis, altering the composition of the gut microbiome. The analysis suggests that predominant microbial populations may play a role in methamphetamine-induced dysbiosis, potentially influencing the gut-brain axis (GBA) and contributing to neuropsychiatric disorders commonly observed in individuals with addiction. Chronic substance abuse, especially involving methamphetamine, which has a long half-life in the human body and can result in significant gut microbial imbalances. This dysbiosis is associated with adverse outcomes such as neurotoxicity and cognitive impairment 92 . These findings imply that methamphetamine exerts a profound impact on the host’s GBA, underscoring the need for further investigation into the specific microbial shifts in substance-dependent individuals. Identifying key bacterial taxa associated with addiction-related gut dysbiosis may pave the way for the development of targeted interventions to restore microbial balance and mitigate neuropsychiatric effects. Here, we observed notable differences in gut microbial composition between HC and MW individuals, marked by the predominance of distinct bacterial taxa in each group. The HC group exhibited a higher relative abundance of beneficial Bifidobacterium species, including Bifidobacterium adolescentis , Bifidobacterium kashiwanohense , and Bifidobacterium bifidum . These species are among the earliest colonizers of the human gut and are well known for their probiotic properties, including roles in immune development, metabolic regulation, and the maintenance of gut homeostasis 93 . In contrast, the MW group showed increased abundance of genera such as Escherichia , and members of the Clostridiceae family. While some strains within these groups function as gut commensals, others are known to be opportunistic pathogens 94,95 . This observation aligns with previous studies reporting a significant reduction in Bifidobacterium and an increased presence of Escherichia-Shigella in methamphetamine users compared to healthy individuals 12 . Notably, among the ten most prevalent bacterial families, only Clostridiales displayed a significant increase in methamphetamine users, further supporting the link between substance abuse and altered gut microbial profiles 96 . Methamphetamine intake and withdrawal dramatically altered the composition of gut bacteria, but did not affect the abundance of bacteria 10 . While high diversity in bacterial abundance usually signifies a healthy gut microbiome 97 , however, our data visualise that withdrawal group has higher diversity than control for Chao 1 and Observed, while Shannon shows no significant in between these two groups. MW is group of individuals that are going through recovery process and are not taking any substance at the time of sample collection. While disease group tend to showcase low diversity of gut microbiome in comparing to their healthy control, there is possibility that this data signifies the recovering from gut dysbiosis for MW. High diversity of gut microbiota deters the colonization of pathogens by blocking and competing nutrients with other microbes 98 . According to a different study, people with methamphetamine use disorder did not differ from healthy controls in terms of faecal microbial diversity, but they did differ in terms of the relative abundance of various microbial taxa. Subjects with methamphetamine use disorder showed reduced levels of Faecalibacterium , Blautia , Dorea , and Streptococcus and increased abundances of Collinsella , Odoribacter , and Megasphaera at the genus level 99 . In the perspective of comparing stages of substance abused, the withdrawal group are categorised into early- and chronic-staged by ASI. The alpha and beta diversity shown insignificant value, as the sample for this group are considered homogenous and stable as all of them are withdrawing from methamphetamine. Certain abundancy of bacterial taxa in Early group was observed such as that of Blautia , Lachnospiraceae, Escherichia , and Clostridiaceae. These four groups are commonly found in human gut microbiome where Lachnospiraceae is abundance, impacting hosts by generating short-chain fatty acids, transforming primary bile acids into secondary bile acids, and promoting colonization resistance against intestinal pathogens 100 . Meanwhile, Blautia is genus under family Lachnospiraceae contributes to biotransformation processes and interact with other gut microbes through crosstalk 101 . Observing the relative abundance, this family is slightly more abundant in Early as compared to Chronic, indirectly showcasing the impact of long-term drug abuse leading to decreasing in abundance of good microflora. On the other hand, Escherichia is a genus that is prevalent in humans, where Escherichia coli is specifically reputed to be the foundation for other species such as Bifidobacterium , Bacteroides and other genera to thrive in gastrointestinal tract in early development of human gut upon birth 102 . Lastly, Clostridiaceae is a symbiotic bacterium in gastrointestinal tract being short-chain fatty acid producers 103 . Understanding the difference between early- and chronic-staged group bacterial taxa abundance could perhaps provide an insight into the effects of substance exposure over certain period. Certain characteristics of gut microbiomes can reveal whether it poses detrimental or beneficial effects on substance abusers 104 . We can assume that over time, as the substance abuser condition get chronic, bacterial taxa such as Peptostreptococcaceae, Holdemanella and Streptococcus can be expected to be slightly higher in composition. Peptostreptococcaceae is a family under order of Clostridia which is usually considered to be normal commensal bacteria. Although conflicting, the study stated that it is related to intestinal inflammation and obesity 105 while another claimed that it assists maintain gut homeostasis 106 . The discrepancies in the outcomes of the study possibly caused by unique research population. Holdemanella in few studies were shown to be a positive bacterium that is commonly found in healthy individual correlate with good host health 107,108 , regulates GLP-1 signaling and improves glucose tolerance 109 . While Streptococcus is often associated with health implications such as coronary atherosclerosis 110 and pancreatic cancer 111 . On the other hand, this analysis revealed the decrease in abundance of certain probiotics such as Bifidobacterium adolescentis and Ligilactobacillus ruminis was observed from chronic- to early-staged groups where this implies the decrease in good bacteria as substance exposure becomes even more chronic. Understanding the relative abundance of certain bacterial taxa in these groups can assist in providing therapeutic recovery treatment by targeting the individual’s gut microbiome. Looking at random forest classification, Weizmannia , Christensenellales and Weissella were found to be highly influential in chronic-staged abuser. Weizmannia , a notable spore-forming lactic acid bacterium, is studied for its probiotic potential in enhancing gut function, reducing inflammation, boosting immunity and regulating the brain-gut axis to alleviate depression particularly with Weizmannia coagulans 112 . Its ability to assist in recovering the gut microbiota balance 113 could have some influence in recovering from methamphetamine addiction subjects. On the other hand, Christensenellales is an autochthonous microbe native to human gastrointestinal tract, was speculated to have anti-obesity potential by preventing adipogenesis particularly for Christensenellales minuta 114 . It is intriguing to note that methamphetamine abusers underwent severe weight loss due to decrease in appetite and the presence of Christensenellales could imply that it plays roles in this scenario. As for Weissella , a study indicated that Weissella could potentially play a role in depression where it was demonstrated that people who are recovering from heroin addiction utilizing methadone maintenance treatment were shown to have less Weissella abundance as compared to those who are still having the addiction where it was also implied could be the role of opportunistic pathogenic in people with addiction 40 . While it is conflicting that some studies highlight the potential probiotic characteristic of Weissella 65,115–117 , this implies more complex relationships and role that Weissella plays in individuals with addiction, possibly opportunistic pathogenic or therapeutic in some strains. Correlating the functional prediction analysis, the presence of major GABA producers was identified which are Bacteroides genus and Bacteroides caccae . This genus possesses GAD orthologs which plays a vital role in converting glutamate to GABA. Results from draft genome analysis of Weissella confusa isolated from the sample of chronic group visualized the possibility of this bacteria producing glutamate. Assuming that it can act as a glutamate feeder to this GABA producing microbes, this can possibly increase the production of GABA in these individuals. In battling mental disorders of anxiety and depression that are so commonly experienced by those who are trying to withdraw from methamphetamine abuse, these findings highlight the possibility of the withdrawal group’s gut microbiota attempts to assist overcome mental health complication endured by these individuals. Few pathways related to that of L-arginine biosynthesis are worth noting as study has shown that with high nitric oxide (NO) concentrations release by L-arginine may assist in increasing permeability of BBB towards peripheral GABA 118 . With increasing permeability of BBB, this would have assisted more of peripheral GABA to pass through and went inside CNS. The gasotransmitter such as NO, has been suggested to be involved in pathophysiology or mood and stress-related disorders 119 . Aside from being shown in Figure 3f, there are few other presences of L-arginine related pathways that are significant to MW group (Supplementary 1). Though, the presence of L-arginine with GABA may help as psychotherapy towards the disorders, the imbalance of it may lead to anxiety as NO was described to be both neurotoxic and neuroprotective and is influential towards anxiety 120,121 . According to STAMP results, it was shown that L-arginine related pathways are very prevalent in MW group compared to HC (Supplementary 1). This further cemented GBA paradigm, that gut microbiome does influence human cognitive behaviors. As MW individuals are going through physical and mental abstinence from METH, anxieties are a common symptom for individuals going through this journey. MaAsLin2 analysis provided a perspective of potential psychobiotic emerging from the study such as Lactococcus and Weissella . While many other potential NGPs are found to be significant in the analysis, the two lactic acid bacteria are known to be GABA producers, proven in few other literatures 58,60–62,115 . The two potential NGPs could be the adjunctive novel therapeutic approach in recovering from addiction. In trying to recover from addiction, substance-dependent users went through physical and mental abstinence to restrain themselves from the use of substance. Presence of GABA-producing microbes could have assisted in the process of recovery for these individuals in battling the mental conditions of anxiety and depression that the individuals went through as their unnatural source of dopamine rush was no longer supplied to them. However, as of date, there was no literature proving that Weissella confusa produce glutamate except for the findings in gene annotations of the bacteria genome itself making it still a prognostic to assume that this Weissella confusa to be a glutamate feeder. On the contrary, numerous studies have reported of Weissella confusa being a GABA producer, with detection using biochemical tests as well as identification by nuclear magnetic resonance (NMR) method 65 . These findings may contribute to a more holistic understanding and treatment of methamphetamine use disorder. Additionally, a plethora of novel probiotic-based goods and associated patents have flooded the health industry due to the exceptional research results and registrations of several probiotic strains 122 . The food, pharmaceutical, and medical sectors have all expressed interest in some strains due to their capacity to generate antimicrobial exopolysaccharides (EPSs) 62 . In preclinical research, probiotics have been demonstrated to lessen stress-related behaviours and enhance stress reactions and cognitive performance in healthy participants 96 . As more animal and human studies have reported the anti-depressive and related gamma-aminobutyric acid-ergic (GABAergic) effects of probiotics developed from Lactobacillus rhamnosus bacterial strains in the gut microbiome, the role of the microbiota-gut-brain (MGB) axis in mood regulation and depression treatment has come to light in recent years 123 . Speculation of the increase of Weissella in chronic individuals could indicate the assistance of gut microbiota in helping to alleviate mental health issues such as anxiety and depression faced by recovering addicted individuals whose dopamine rush came from methamphetamine were taken away. Their abstinence could possibly cause the gut microbiota to react and produce more GABA compounds assisting their health. Although the existence of blood brain barrier could interfere with the passing of GABA into the brain 124,125 , that is not the case with methamphetamine addicted individuals whose tight junctions of Occludin, Claudin-5 and ZO-I are altered 126 , allowing GABA to pass through the BBB. However, some journals highlight that there may be little amount of GABA that can pass through BBB 127,128 . In the case of methamphetamine abusers, the chances of GABA passing through BBB is slightly higher than healthy individuals as methamphetamine intake is known to alter tight junction of endothelial cells 52 , possibly allowing GABA to pass through not just the gut lining but BBB as well. The increase of BBB permeability due to methamphetamine addiction might lead to the individuals are more prone to being sensitive to increase in excitatory neurotransmitters such as glutamate. Presence of GABA throughout human system are intriguing in the role it plays as it is excitatory in enteric nervous system (ENS) but inhibitory for CNS 129,130 . Alterations of circulating and brain GABA levels are linked to shifts in gut microbiota composition where these changes are speculated to contribute to regulation of mental health 125 . Conclusion Methamphetamine remains one of the most potent and challenging drugs to recover from, with no currently approved psychotropic treatments specifically targeting its withdrawal. Mental health is often overlooked during the recovery phase, despite its critical role in determining the success of rehabilitation. This study highlights the potential of gut microbiota in supporting recovery from methamphetamine withdrawal through mechanisms involving GABA production. Psychobiotics with the capacity to produce GABA offer a promising adjunctive therapeutic approach for addiction recovery. While traditional probiotics such as Bifidobacterium and Lactobacillus have shown beneficial effects, emerging next-generation probiotics like Weissella confusa warrant further exploration to fully uncover their potential as psychobiotics in addiction therapy. Declarations Data availability The sequences for the 16S rRNA data were deposited in the National Center for Biotechnology Information (NCBI) database and registered as BioProject PRJNA1270746, Sequence Read Archive (SRA) were deposited as SRR33783416-SRR33783511, and BioSample with accession numbers SAMN48839637-SAMN48839732. The draft genome sequence of Weissella confusa is available at SRR27485677 with BioProject and BioSample accession number of PRJNA1063499 and SAMN39397650 respectively. Author Contributions Statement H.R. and H.F.A. conceived and designed the study. S.K.K., N.M.A., and T.M.S.T.K.B. performed the experiments and analyzed the data together with Y.P. and H.M.T. 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Streptococcus Species Abundance in the Gut Is Linked to Subclinical Coronary Atherosclerosis in 8973 Participants From the SCAPIS Cohort. Circulation 148 , 459–472 (2023). Yang, J. et al. Gut Streptococcus is a microbial marker for the occurrence and liver metastasis of pancreatic cancer. Front. Microbiol. 14 , 1184869 (2023). Togawa, N. et al. Weizmannia coagulans strain SANK70258 combined with galacto-oligosaccharides reduces fecal-p-cresol content and improves scaliness and skin roughness. J. Funct. Foods . 107 , 105665 (2023). Li, C. et al. Weizmannia coagulans BC99 Enhances Intestinal Barrier Function by Modulating Butyrate Formation to Alleviate Acute Alcohol Intoxication in Rats. Nutrients 16 , 4142 (2024). Mazier, W. et al. A new strain of christensenella minuta as a potential biotherapy for obesity and associated metabolic diseases. Cells 10 , 823 (2021). Liu, C. et al. Evaluation of Safety and Probiotic Properties of Weissella spp. in Fermented Vegetables From Xi’an, Shaanxi, China. Food Sci. Nutr. 13 , e4592 (2025). Kang, C. E., Park, Y. J., Kim, J. H., Lee, N. K. & Paik, H. D. Probiotic Weissella cibaria displays antibacterial and anti-biofilm effect against cavity-causing Streptococcus mutans. Microb. Pathog . 180 , 106151 (2023). Thant, E. P. et al. Exploring Weissella confusa W1 and W2 Strains Isolated from Khao-Mahk as Probiotic Candidates: From Phenotypic Traits to Genomic Insights. Antibiotics 13 , 604 (2024). Shyamaladevi, N., Jayakumar, A. R., Sujatha, R., Paul, V. & Subramanian, E. H. Evidence that nitric oxide production increases γ-amino butyric acid permeability of blood-brain barrier. Brain Res. Bull. 57 , 231–236 (2002). Wegener, G. & Volke, V. Nitric Oxide Synthase Inhibitors as Antidepressants. Pharmaceuticals 2010 . 3, Pages 273–299 (3), 273–299 (2010). Hosinian, M., Qujeq, D. & Ahangar, A. A. The Relation Between GABA and L-Arginine Levels With Some Stroke Risk Factors in Acute Ischemic Stroke Patients. Int. J. Mol. Cell. Med. 5 , 100 (2016). Gulati, K., Rai, N. & Ray, A. Nitric Oxide and Anxiety. Vitam. Horm. 103 , 169–192 (2017). Patel, S. et al. Probiotic Formulations: A Patent Landscaping Using the Text Mining Approach. Curr Microbiol 79 , (2022). Tette, F. M., Kwofie, S. K. & Wilson, M. D. Therapeutic Anti-Depressant Potential of Microbial GABA Produced by Lactobacillus rhamnosus Strains for GABAergic Signaling Restoration and Inhibition of Addiction-Induced HPA Axis Hyperactivity. Curr. Issues Mol. Biol. 44 , 1434–1451 (2022). Boonstra, E. et al. Neurotransmitters as food supplements: the effects of GABA on brain and behavior. Front. Psychol. 6 , 1520 (2015). Braga, J. D., Thongngam, M. & Kumrungsee, T. Gamma-aminobutyric acid as a potential postbiotic mediator in the gut–brain axis. npj Science of Food 2024 8:1 8, 1–13 (2024). Martins, T. et al. Methamphetamine transiently increases the blood–brain barrier permeability in the hippocampus: Role of tight junction proteins and matrix metalloproteinase-9. Brain Res. 1411 , 28–40 (2011). Takanaga, H., Ohtsuki, S., Hosoya, K. I. & Terasaki, T. GAT2/BGT-1 as a system responsible for the transport of γ-aminobutyric acid at the mouse blood-brain barrier. J. Cereb. Blood Flow Metab. 21 , 1232–1239 (2001). Kakee, A. et al. Efflux of a suppressive neurotransmitter, GABA, across the blood-brain barrier. J. Neurochem . 79 , 110–118 (2001). Liu, S. et al. Role of Na-K-2Cl symporter in GABA-evoked excitation in rat enteric neurons. The FASEB Journal 27 , (2013). 1160.5-1160.5. Auteri, M., Zizzo, M. G. & Serio, R. GABA and GABA receptors in the gastrointestinal tract: from motility to inflammation. Pharmacol. Res. 93 , 11–21 (2015). Additional Declarations No competing interests reported. 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Bhd, Taman Perdagangan \u0026 Perindustrian Ipoh","correspondingAuthor":false,"prefix":"","firstName":"Seok","middleName":"Fang","lastName":"Oon","suffix":""},{"id":505402811,"identity":"6874bd07-fe83-4f86-aa27-43cc0581e672","order_by":7,"name":"Hajar Fauzan Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBAC9gYInQDEhg9gohL4tPAcQGgxNiBZixlcJX4t7GeMP/zcYZen2354WzVvzh17/gbmg7d5GLYlNuDSwpNjYNh7JrnY7Exa2W3ebc8SZxxgS7bmYbiNU4s9Q45BAm8bc+K2AzlmQC2HExgO8JhJ49PCw//G4ODftvrEbeffmBUDtdjLH+D/hl+LRI5hM2/b4cRtN3LMmIFaGDcc4GEjoOVZMbNs23GglmfFknOBftl4mM3Yco7BbWPcDkve/PFtWzXQYckbP7zddsde7njzwxtvKm7L4tKCDg4wMDCDaAMGRxK0QIE9kTpGwSgYBaNg+AMAWNde6T2pNU8AAAAASUVORK5CYII=","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob","correspondingAuthor":true,"prefix":"","firstName":"Hajar","middleName":"Fauzan","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2025-07-03 03:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7033630/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7033630/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90043385,"identity":"177d81b8-0239-4ad9-b977-cb37e49e28c3","added_by":"auto","created_at":"2025-08-27 17:33:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1171587,"visible":true,"origin":"","legend":"\u003cp\u003e(a)\u003cstrong\u003e \u003c/strong\u003eRelative abundance of bacterial community in gut of HC and MW individuals in the form of bar plots illustrating the taxa summary of merged samples at species level. The box plot and 2D PCoA plot illustrated the (b) (c) (d) alpha-; (e) beta-diversity analysis of bacterial communities of HC and MW individuals. (f) Linear Discriminant Analysis Effect Size (LefSe) analysis of HC and MW in identifying predominant bacterial taxa between the two groups.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/1a9926c2853e1c7308cd6e74.png"},{"id":90043394,"identity":"8105e7d3-c28f-4859-9fe0-d5fd8078280a","added_by":"auto","created_at":"2025-08-27 17:33:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1445078,"visible":true,"origin":"","legend":"\u003cp\u003e(a)\u003cstrong\u003e \u003c/strong\u003eRelative abundance of bacterial community in gut of Early and Chronic individuals in the form of bar plots illustrating the taxa summary of merged samples at species level. The box plot and 2D PCoA plot illustrated the (b) (c) (d) alpha-; (e) beta-diversity analysis of bacterial communities of Early and Chronic individuals; (f) Random Forest variable importance plot (Number of trees = 5000).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/be31ad96995e685f5ae31bf0.png"},{"id":90043807,"identity":"da0b831e-1f6c-4407-a612-a8aa8e003506","added_by":"auto","created_at":"2025-08-27 17:41:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":482813,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical Analysis of Metabolic Profiles (STAMP) analysis (a) Principal Component Analysis (PCA) plot; (b) Glutamate-GABA conversion to GABA\u003csup\u003e56\u003c/sup\u003e; Comparative analysis of metabolic pathways between recovering addicted individuals and healthy individuals (c) Superpathway of ornithine degradation (\u003cem\u003ep\u003c/em\u003e-value = 0.00169); (d) Pyridoxal 5'-phosphate biosynthesis I (\u003cem\u003ep\u003c/em\u003e-value = 0.000808); (e) Superpathway of pyridoxal 5'-phosphate biosynthesis and salvage (\u003cem\u003ep\u003c/em\u003e-value = 0.00122); (f) Superpathway of L-arginine, putrescine, and 4-aminobutanoate degradation (\u003cem\u003ep\u003c/em\u003e-value = 0.00238); (g) 4-aminobutanoate degradation V (\u003cem\u003ep\u003c/em\u003e-value = 0.0000000389).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/1d7c56981fce08919be77ca3.png"},{"id":90043395,"identity":"e7e561b3-e4b5-4ed8-a523-49daaf8c29f4","added_by":"auto","created_at":"2025-08-27 17:33:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":225835,"visible":true,"origin":"","legend":"\u003cp\u003eGABA-producing markers identified by MaAsLin2 adjusting for group, age and gender.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/92a4dbc5284779b0a72b2328.png"},{"id":90043396,"identity":"efc4dc99-936d-4a56-9873-bc8243c4b678","added_by":"auto","created_at":"2025-08-27 17:33:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1252537,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the draft genome obtained (a) Phylogenetic tree generated by Automated Multi-Locus Species Tree (autoMLST); (b) Gene distribution by Rapid Annotation Subsystem Technology (RAST); (c) D-Glutamine and D-Glutamate metabolism pathway; (d) Butanoate metabolism pathway.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/54bfd9bc0610118c03272686.png"},{"id":92285087,"identity":"b271a9b8-0f17-4bfd-8f34-1b10048c1f54","added_by":"auto","created_at":"2025-09-26 18:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5553382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/f5e76dec-64b0-46b9-970a-9fec642485e6.pdf"},{"id":90043409,"identity":"7d5ce402-da9c-4b6a-8953-0a85dfad8a5e","added_by":"auto","created_at":"2025-08-27 17:33:57","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":796302,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7033630/v1/b69f56547935e5e5b93cf1c7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Gut Microbiota with Gamma- Aminobutyric Acid Production Potential in Methamphetamine Addiction Recovery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMethamphetamine (METH) abuse has emerged as a significant issue in the context of current public health concerns. Long-term METH abuse leads to cerebral deterioration, metabolic abnormalities, and structural brain changes. It is associated with medical conditions such as cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and psychiatric issues, the latter including depression and psychosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Prolonged use or high doses of METH could affect multiple areas in the central nervous system, inducing a phenomenon known as \"behavioral sensitization\" or \u0026ldquo;reverse tolerance\u0026rdquo;, which is believed to underlie METH-induced psychosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, METH abuse also increased the risk of developing schizophrenia\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies have linked the METH abuse with a leaky gut, where the release of neurotransmitters induced by METH may cause damage to enteric neurons. METH use also causes alterations to tight junction proteins, such as Claudin-1 and ZO-1. These tight junctions proteins are thought to be responsible for the regulation of gut epithelial paracellular permeability, malfunction of which can reduce the integrity of blood-brain barrier\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e that resulted in ischemic colitis\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and vasculitis of the distal colon\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In some cases, METH can trigger vasoconstriction in the gastrointestinal tract, leading to bowel ischemia, which in turn causes abdominal cramps, constipation, diarrhoea, and tissue dehydration\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, based on these findings, it can be proposed that the gut microbiome composition in METH abusers may be altered because of modification in the intestine.\u003c/p\u003e\u003cp\u003eIn recent years, a growing body of studies unveiled a role of the gut-brain axis (GBA) in regulating anxiety, depression, cognition and pain. Interestingly, animal studies also linked METH use and/or cessation to alterations in the gut microbiota, including changes in the overall composition\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, increased microbial diversity\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, proliferation of pathogenic bacteria\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and altered metabolomic profiles\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Despite these advancements in animal studies, the role of gut microbiota in human METH abuse and its connections with psychotic syndromes and cognitive impairment remain uncharacterized. Gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter available in foods and produced by certain microbial species, has been explored for its potential therapeutic effects in reducing stress and improving mental health.\u003c/p\u003e\u003cp\u003eThis study investigated gut microbial differences between individuals recovering from METH addiction and healthy controls. Addicted individuals were further categorized based on early or chronic exposure. Gut microbiota was profiled using V3 region amplicon sequencing and standard bioinformatics analysis to identify potential microbial markers. We assessed bacterial diversity, gut microbiota composition, and distinguishing taxa as potential biomarkers. Associations between METH use duration and microbial profiles were also evaluated to understand the impact of exposure. We hope that this research effort will identify bacteria that could be used for diagnostics or to explore the psychobiotic potential in enhancing therapeutic strategies in our population in the near future.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePrior to patient recruitment and sample collection, the study protocol received ethical approval from the Institutional Research Ethics Committee (IREC) of the Kulliyyah of Medicine at the International Islamic University Malaysia (Research ID: 2023-922), and from the National Medical Research Register (NMRR) (ID: 21-02456-EJA). Written informed consent was obtained from participants at the Cure and Care Rehabilitation Centre (CCRC) prior to enrolment. The sample collection was conducted from 1 December 2022 to 30 November 2023. The study was conducted in accordance with the Declaration of Helsinki and adhered to all relevant institutional and international ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSelection criteria during recruitment process\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe recruited 32 subjects (average age, 27.34 \u0026plusmn; 4.22; age range 18-35) diagnosed with METH use disorder, and 64 healthy subjects (average age 18.84 \u0026plusmn; 6.90; age range 13-37) with no prior history of METH or any other illicit substances as controls. These two groups were respectively labeled as \u0026ldquo;Methamphetamine Withdrawal\u0026rdquo; (MW) and \u0026ldquo;Healthy Control\u0026rdquo; (HC). The MW is diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) by the consensus of two psychiatrists. MW participants were further classified into \u0026ldquo;Chronic\u0026rsquo; and \u0026ldquo;Early\u0026rdquo; by employing Addiction Severity Index (ASI) evaluated by the psychiatrists. Additional selection criteria included the absence of antibiotic, probiotic or prebiotic use in the three months leading up to sample collection. Furthermore, participants were not taking any long-term anti-inflammatory or antioxidant medications. Information on METH use was collected. The average age of initial METH use was 19 \u0026plusmn; 5.38 years, with a range of 12 to 30 years, and the average duration of METH use was 8 \u0026plusmn; 5.01 years, with a range of 1 to 17 years. The average frequency of METH use was 3 \u0026plusmn; 2.34 days per week. The subjects in the METH group were hospitalized 0-3 times (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFecal sample collection and DNA extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFecal samples were collected, homogenised and stored at -80 \u0026deg;C prior to analyses as previously described\u003csup\u003e14\u003c/sup\u003e. DNA were extracted using QIAamp Powerfecal Pro DNA Kit (QIAGEN, Germany), following the manufacturer\u0026apos;s instructions with some adjustments\u003csup\u003e15\u003c/sup\u003e. Then, the purity and concentration of DNA were determined by 1% agarose gel electrophoresis. Sample DNA was diluted to 5 ng/\u0026micro;L with sterile distilled water prior library preparation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLibrary preparation and sequencing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBacterial DNA was amplified by targeting the V3 hypervariable region of the 16S rRNA using primers that incorporated partial Illumina adapters as previously reported\u003csup\u003e16\u003c/sup\u003e. Briefly, the PCR was carried out with the WizBio HotStart PCR mastermix (WizBio, Gyeonggi, Republic of Korea) and followed by adjusted profiles profile: an initial denaturation at 95\u0026deg;C for 3 minutes, then 35 cycles of 95\u0026deg;C for 30 seconds, 55\u0026deg;C for 20 seconds, and 72\u0026deg;C for 20 seconds. Following PCR amplification, the products were purified using SPRI Beads (Beckman Coulter, CA, USA). Subsequently, an index PCR was performed to introduce an Illumina dual index barcode. The barcoded libraries were visualized on a gel, grouped based on band intensity, and further purified using the WizPrep\u0026trade; Gel/PCR Purification Mini Kit (WizBio, Gyeonggi, Republic of Korea) as per the manufacturer\u0026rsquo;s guidelines. The quantification of the pooled libraries was conducted using Denovix high sensitivity equipment, and an appropriate volume of the libraries was loaded onto a NovaSeq 6000 System (Illumina, San Diego, CA, USA) for either 2 \u0026times; 150 paired-end sequencing or 1 \u0026times; 250 single-end sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAmplicon sequencing data analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe initial steps in the data processing pipeline included demultiplexing and trimming of primers from the raw paired end reads, which was accomplished using cutadapt v1.18\u003csup\u003e17\u003c/sup\u003e. The resulting trimmed reads were then merged using fastp v0.21\u003csup\u003e18\u003c/sup\u003e. Subsequently, the processed reads were imported into QIIME2 v.2022.8\u003csup\u003e19\u003c/sup\u003e for further analysis. Amplicon Sequence Variants (ASVs) were obtained through the application of the dada2 v1.22 R package\u003csup\u003e20\u003c/sup\u003e with modifications\u003csup\u003e21\u003c/sup\u003e. Taxonomic assignment of these ASVs was performed using q2-feature-classifier\u003csup\u003e22\u003c/sup\u003e. Only ASVs with taxonomic assignments at least to the phylum level were retained for subsequent analysis. The ASV table and taxonomic classification table were exported in tab-separated values (tsv format) using QIIME2 tools and manually formatted to create input data compatible with MicrobiomeAnalyst2\u003csup\u003e23\u003c/sup\u003e with adjusted parameters\u003csup\u003e24\u003c/sup\u003e. To examine the functions of microorganisms, the representative sequences comprising the top 500 taxa were used to infer on Kyoto Encyclopedia of Genes and Genomes (KEGG) database using Phylogenetic Investigation of Communities\u0026rsquo; Reconstruction of Unobserved States (PICRUSt2) pipeline (https://github.com/picrust/picrust2/)\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis for Amplicon Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis processed data was used for various analyses, including statistical analysis employing the linear discriminant analysis (LDA) effect size (LEfSe) method\u003csup\u003e26\u003c/sup\u003e. Alpha- (Shannon and Simpson diversity indices) and beta-diversity (Bray\u0026ndash;Curtis dissimilarity) were calculated using specialized QIIME2 plug-ins. To explore the relative abundances across taxonomic hierarchies, a filtered relative abundance table was also utilized. Random forest analysis was performed utilising MicrobiomeAnalyst2 to identify influential microbes with Chronic and Early from MW group as input where the model was constructed with 5000 trees evaluated by Mean Decrease Accuracy (MDA). Statistical Analysis of Metabolic Profiles (STAMP) v2.1.3 (https://beikolab.cs.dal.ca/software/STAMP)\u003csup\u003e27\u003c/sup\u003e were utilized to establish metabolic pathways related to GABA and glutamate, while Microbiome Multivariate Association with Linear Models (MaAsLin2) v1.22.0 (https://huttenhower.sph.harvard.edu/maaslin/)\u003csup\u003e28\u003c/sup\u003e were employed to find potential probiotics influencing the group gut microbiome with participants\u0026rsquo; group, gender and age covariant.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIsolation and Identification of Single Colony Potential Probiotic\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChronic participant sample was opted for ten-fold serial dilution and plated on De Man, Rogosa, and Sharpe (MRS) agar and incubated under anaerobic condition with AnaeroPack (Mitsubishi Gas Chemical Co. Inc, Japan) at 37˚C for 24 hours. Colonies were isolated and streaked thrice to ensure pure colony before proceeding with gram staining for morphology. DNA extraction was performed utilizing QIAamp PowerFecal Pro DNA Kit (Qiagen, Germany). Quantification of DNA concentration was made by Qubit 4 Fluorometer (Invitrogen, Thermo Fisher Scientific, US) and gel electrophoresis protocol was executed to assess the DNA quality obtained. PCR targeting V3 region was performed, and the product was outsourced for Sanger sequencing, identification at genus level.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhole Genome Sequencing, Genome Assembly and Assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLibrary preparation of MinION (Oxford Nanopore, UK) was conducted with Ligation Sequencing Kit (SQK-LSK110). The prepared DNA library was then loaded to MinION flow cell FLO-MIN106 (R9.4.1). The sequencing was run for 24 hours. The nanopore reads were then quality- and length-filtered to retain the reads longer than 500 bp with a q-score of 7 and above. The filtered nanopore reads were then \u003cem\u003ede novo\u003c/em\u003e assembled SPAdes v3.15.0\u003csup\u003e29\u003c/sup\u003e and polished using RACON v1.4.3, generating the final consensus assembly\u003csup\u003e30\u003c/sup\u003e. The contigs less than 500 bp representing mostly sequencing artefact were removed. Genome assembly statistics were generated using QUAST\u003csup\u003e31\u003c/sup\u003e. Genome completeness was assessed using BUSCO5 based on lactobacillales_odb10 linage\u003csup\u003e32\u003c/sup\u003e. Prodigal v2.6\u003csup\u003e33\u003c/sup\u003e was used for protein prediction and the Abricate (https://github.com/tseemann/abricate) was used to screen for possible antimicrobial resistance genes via by comparing nucleotide bases against the curated NCBI AMR\u003csup\u003e34\u003c/sup\u003e and virulence database\u003csup\u003e35\u003c/sup\u003e. Lastly, the genome sequence of bacterial isolate was deposited in the NCBI public database.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhylogenetic tree and Rapid Annotation using Subsystem Technology\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePhylogenetic tree of the isolate was generated using Automated Multi-Locus Species Tree (autoMLST). The genome sequence in FASTA format were uploaded to autoMLST webserver (https://automlst.ziemertlab.com/) using \u003cem\u003ede novo\u003c/em\u003e mode. Automatically generated maximum-likelihood phylogenetic tree was generated where estimated Average Nucleotide Identity (ANI) values with reference genomes are utilised to build the tree\u003csup\u003e36\u003c/sup\u003e. Functional annotation of the sequence was performed by submitting it to Rapid Annotation using Subsystem Technology (RAST) pipeline (https://rast.nmpdr.org/) and the data was presented in subsystem pie chart\u003csup\u003e37\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eCharacteristics of the Studied Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 32 subjects identified as having a history of METH addiction were recruited from the\u0026nbsp;Cure and Care Rehabilitation Centre (CCRC).\u0026nbsp;In addition, 64 healthy subjects were recruited at the Sultan Ahmad Shah Medical Centre (SASMEC), Malaysia (Supplementary Table 1). The patients were stratified as having \u0026ldquo;early exposure\u0026rdquo; or \u0026ldquo;chronic exposure\u0026rdquo; according to substances dependency to evaluate the associations between faecal microbiota and addiction. Clinical and analytical characteristics according to addiction status are shown in Table 1 and Table 2.\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of the participants studied.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethamphetamine Withdrawal (MW)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Control (HC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjects (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eThe proportion of males, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e32 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e32 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e8.935e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eAge (years; means\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e27.34 (4.22) (18-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e18.84 (6.90) (13-37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6.295e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eEmployed, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e32 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e13 (20.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.458e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6.609e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ePrimary school, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e6 (18.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eHigh school, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e22 (68.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e44 (68.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eUniversity or higher, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e20 (31.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarriage status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eUnmarried, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e28 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eAge of initial METH use (years; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e19 (5.38) (12-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eDuration of METH use (years; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e8 (5.01) (1-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eFrequency of METH use (days per week; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3 (2.34) (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eNumber of hospitalization (number; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0 (0.75) (0-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Characteristics of the participants studied on methamphetamine exposure from MW group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjects (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eThe proportion of males, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e17 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eAge (years; means\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e26.24 (4.13) (18-33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e28.6 (4.1) (23-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.1155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eEmployed, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e17 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.5014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003ePrimary school, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4 (23.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2 (13.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eHigh school, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e10 (58.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e12 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eUniversity or higher, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3 (17.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarriage status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.03796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eUnmarried, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e17 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11 (73.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eAge of initial METH use (years; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e21.7 (5.3) (12-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15.7 (3.4) (12-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e8.913e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eDuration of METH use (years; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4.2 (2.4) (1-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e13 (2.4) (10-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.828e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eFrequency of METH use (days per week; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2.6 (2) (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3.9 (2.6) (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.2277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eNumber of hospitalization (number; mean\u0026plusmn;SD) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.5 (0.9) (0-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.2 (0.6) (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.2956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDistinct Gut Microbiome Profiles in Individuals of Methamphetamine Withdrawal Compared to Healthy Control group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe contrast of HC and MW group in relative abundance by phylum is apparent in the abundance of Firmicutes and Proteobacteria in Addicted group in comparison to the abundance of Actinobacteria in HC individuals (Fig 1a). Diving deeper into proportional abundance, there are few bacterial taxa high in MW as compared to HC individuals such as \u003cem\u003eEscherichia\u003c/em\u003e (11.07% and 2.03% respectively), Peptostreptococcaceae (7.07% and 4.12% respectively), \u003cem\u003eLigilactobacillus ruminis\u003c/em\u003e (7.15% and 3.44% respectively), Clostridiaceae (8.21% and 0.57% respectively), and \u003cem\u003eHoldemanella biformis\u003c/em\u003e (3.03% and 1.03% respectively). Contemporaneously, bacterial taxa that is high in HC in contrast to MW individuals are \u003cem\u003eBifidobacterium adolescentis\u003c/em\u003e (16.2% and 5.17% respectively), \u003cem\u003eBifidobacterium kashiwanohense\u003c/em\u003e (4.35% and 0.68% respectively), \u003cem\u003eStreptococcus\u003c/em\u003e (5.05% and 4.06% respectively), and \u003cem\u003eFusicatenibacter saccharivorans\u0026nbsp;\u003c/em\u003e(2.78% and 1.47% respectively) (Fig 1b).\u003c/p\u003e\n\u003cp\u003eLooking to the perspective of intra-diversity within the sample for MW group in measuring richness of the sample including the rare species for Chao, and general richness excluding rare species using Observed display significant difference between the MW and HC group (\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05, \u003cem\u003et\u003c/em\u003e-test = -6.3144 and \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05, \u003cem\u003et\u003c/em\u003e-test = -6.3485 respectively) (Fig1c, Fig1d). Conversely, Shannon demonstrated to be insignificant in measuring the evenness and richness of the group sample (\u003cem\u003ep\u003c/em\u003e-value = 0.45597, \u003cem\u003et\u003c/em\u003e-test = -0.75057) (Fig 1e). As for inter-diversity between the group of Addicted and Healthy individuals, permutational multivariate analysis of variance (PERMANOVA) analysis on species-level display distinct differences between these two groups of individuals (F-value of 10.07, R\u003csup\u003e2\u003c/sup\u003e: 0.09866 and \u003cem\u003ep\u003c/em\u003e-value = 0.001) (Fig 1f). Linear Discriminant Analysis Effect Size (LefSe) highlights the predominance of certain bacterial taxa in each of the Addicted and Healthy where this data corresponds with relative abundance previously mentioned (Fig 1g).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubstance Exposure Shapes the Gut Microbiome Composition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis section aims to deepen the understanding of the long-term effects of substance dependence and abuse. Recovering methamphetamine addicted individual groups were further classified into the category of early-staged and chronically addicted, evaluated by psychiatrist in this study utilizing the ASI where this classifies individual\u0026rsquo;s addiction stages\u003csup\u003e38\u003c/sup\u003e. The difference in Early and Chronic individual gut were evaluated utilizing alpha and beta diversity analysis where the alpha diversity shows no significant difference between each sample group using Chao and Shannon diversity index (\u003cem\u003ep\u003c/em\u003e-value = 0.6053, \u003cem\u003et\u003c/em\u003e-test = -0.52375 and \u003cem\u003ep\u003c/em\u003e-value = 0.396, \u003cem\u003et\u003c/em\u003e-test = -0.86206 respectively) (Fig 2c, Fig 2d). On the other hand, the beta diversity PERMANOVA analysis showed no distinct differences between these two groups of individuals (F-value of 0.48382, R\u003csup\u003e2\u003c/sup\u003e: 0.01641 and \u003cem\u003ep\u003c/em\u003e-value = 0.936) (Fig 2e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe bacterial community in human guts often reflects the host\u0026rsquo;s health where dominancy of certain bacteria reflects the physiological state of the host. Harm inflicted by substance abuse are often defined by the period of one consumption. In comparing the bacterial dominancy of Early and Chronic individuals, the relative abundance visualizes \u003cem\u003eBlautia\u003c/em\u003e (13.88% and 11.25% respectively), Lachnospiraceae (12.13% and 11.17% respectively), \u003cem\u003eEscherichia\u0026nbsp;\u003c/em\u003e(10.79% and 12.10% respectively) and Clostridiaceae (7.8% and 8.67% respectively) (Fig 2a). Three significant microbes were observed to have higher abundance in Chronic as compared to Early group for \u003cem\u003ePeptostreptococcaceae\u003c/em\u003e (8.11% and 6.07% respectively), \u003cem\u003eHoldemanella\u003c/em\u003e (2.29% and 1.55% respectively) and \u003cem\u003eStreptococcus\u003c/em\u003e (6.11% and 2.36% respectively) (Fig 2a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, prominent probiotics are found to be in lower abundance in Chronic as compared to Early individuals where \u003cem\u003eBifidobacterium adolescentis\u003c/em\u003e with abundance of 3.9% in Chronic and 6.1% in Early individuals and \u003cem\u003eLigilactobacillus ruminis\u0026nbsp;\u003c/em\u003ewith abundance of 6.22% in Chronic and 8.43% in Early individuals where the latter is known to be autochthonous microbes in human gastrointestinal tract\u003csup\u003e39\u003c/sup\u003e (Fig 2a).\u003c/p\u003e\n\u003cp\u003eRandom forest classification identifies Mean Decrease Accuracy (MDA) analysis using random forest algorithm with 5000 trees generated shown \u003cem\u003eWeizmannia\u003c/em\u003e (0.006 MDA), Christensenellales (0.008 MDA) and \u003cem\u003eWeissella\u0026nbsp;\u003c/em\u003e(0.010 MDA) in Chronic individuals where the higher the MDA value, the stronger their influence\u003csup\u003e40\u003c/sup\u003e (Fig 2f). Maximum high number of trees, 5000, was chosen to reduce noise and provide more stable MDA values\u003csup\u003e41\u003c/sup\u003e, however due to small sample of 32 MW, the out-of-bag (OOB) value is of 67.7% which is quite high making the error two-thirds of the sample for MW. The presence of \u003cem\u003eBacteroides\u0026nbsp;\u003c/em\u003e(0.0018 MDA) and \u003cem\u003eBacteroides caccae\u003c/em\u003e (0.001 MDA) are also worth highlighting as it is known to be major GABA-producer in the gut microbiota\u003csup\u003e42\u003c/sup\u003e. A study found that Bacteroides make up for the GABA produced in gut microbiota by 31.7% due to its possessing GAD orthologs, followed by \u003cem\u003eEscherichia\u003c/em\u003e (22.5%), \u003cem\u003eFusobacterium\u003c/em\u003e (9.9%) and both well-known probiotic genera \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e contributed only about 2.2%\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional Insights into the Gut Microbiome: Linking Glutamate and GABA Pathways\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn exploring the distribution of metabolic pathways correlated with GABA and glutamate in recovering addicted individuals and healthy individuals, Statistical Analysis of Metabolic Profiles (STAMP) was utilized visualizing in box plot. In total 211 pathways were generated in comparing for healthy and recovering addicted individuals. These volumes were then narrowed down to only significant with \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05, reduced to 186 significant pathways. Few pathways out of 186 significant ones call attention for being related to glutamate and GABA pathways are then further categorized into indirect and direct related to GABA and glutamate related pathway. Lastly, direct pathways of GABA and glutamate were then evaluated via existing literature on its correlation to GBA. Glutamate and GABA are both vital neurotransmitters in regulating brain function\u003csup\u003e44\u003c/sup\u003e where the former is excitatory, and the latter is inhibitory in which any changes in concentration of these metabolites can have profound effect on cortical excitability\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSome of the pathways worth noting are the superpathway of ornithine degradation, pyridoxal 5\u0026rsquo;-phosphate and 4-aminobutanoate degradation (Fig 3). Superpathway of ornithine degradation is where L-ornithine degraded until the product is 4-aminobutanoate which is GABA via the ornithine aminotransferase catalyzed reaction\u003csup\u003e46\u0026ndash;49\u003c/sup\u003e (Fig 3a). This pathway is known to take place in the brain where ornithine is known to be able to pass-through blood brain barrier permeability\u003csup\u003e50\u003c/sup\u003e. On the other hand, pyridoxal 5\u0026rsquo;-phosphate biosynthesis and salvage are equally important as pyridoxal 5\u0026rsquo;-phosphate is precursor for glutamate decarboxylase (GAD) to convert glutamate to GABA\u003csup\u003e51\u003c/sup\u003e (Fig 3b, 3c, 4). As for superpathway of L-arginine, putrescine, and 4-aminobutanoate degradation highlights the importance of GABA degradation which in turn convert to glutamate (Fig 3d, 3e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe presence of this GABA and glutamate related pathway strongly suggests the heavy influence of these gut microbiota in GBA of the host, implying possible mental condition at hands. Adding to the fact that METH are known to induce blood-brain barrier disruptions paving way for metabolites to pass through blood brain barrier\u003csup\u003e52\u0026ndash;54\u003c/sup\u003e. The previous section highlights the relative abundance of \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e that are known to be major GABA producers in human gut microbiota\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWeissella sp. as Potential Psychobiotic of Recovering from Methamphetamine Individual\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMicrobiome Multivariate Association with Linear Models (MaAsLin2) with total sum scaling normalization and log transformation was utilized in finding any potential probiotic that plays role in mental health of the individuals in correlating GBA of WM individuals. Few microbes are to highlight from this analysis which are those are known to be probiotic and some of them with potential probiotic characteristics (Table 3). Next-generation probiotic (NGP) is defined as potential candidate of probiotic that does not come from the genus of \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e that are traditional probiotics, established in the food industries for their well-known health benefits\u003csup\u003e57\u003c/sup\u003e. Few probiotics and potential NGP were identified and evaluated via existing literature. The negative of coefficient value indicating the microbes are higher in HC than MW whilst positive implying vice versa (Table 3). The presence of well-known probiotics of \u003cem\u003eBifidobacterium\u003c/em\u003e are high in HC, with addition of \u003cem\u003eLactococcus\u003c/em\u003e and \u003cem\u003eWeissella.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDiving deeper into the role of these microbes to GBA of the hosts, we further classified these probiotic and potential NGPs into non-GABA producer and GABA producer. (Table 3 \u0026amp; Fig 4). Implying that the presence of the probiotic in MW indicating the recovering gut microbiota from dysbiosis, the presence of \u003cem\u003eLactococcus\u003c/em\u003e and \u003cem\u003eWeissella\u003c/em\u003e along with established probiotic such as \u003cem\u003eBifidobacterium\u003c/em\u003e indicate that these two could play same role as the latter. The identified facultative anaerobes are constantly being highlighted in other literatures for their potential in becoming the NGPs\u003csup\u003e58\u0026ndash;62\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eVarious literature has reported of \u003cem\u003eWeissella\u003c/em\u003e potential in producing GABA at different concentrations depending on its strain, namely \u003cem\u003eWeissella cibaria\u003csup\u003e63,64\u003c/sup\u003e\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Weissella confusa\u003csup\u003e65,66\u003c/sup\u003e\u003c/em\u003e. Moreover, these two are known gut microbes\u003csup\u003e67,68\u003c/sup\u003e yet their roles in human gut flora are still underexplored. The presence of this GABA producer presumably acts as a key factor in contributing to recovering in mental health of MW individuals.\u003c/p\u003e\n\u003cp\u003eTable 3. List of Probiotics, Potential NGPs identified with known GABA producer\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProbiotic/NGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eq\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eBifidobacterium kashiwanohense\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-1.2548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7.8237e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.3128e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e69\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAnaerobutyricum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.3286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.0398e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8.0981e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e70,71\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eLigilactobacillus ruminis.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.4608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8.2463e-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.0861e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e72\u0026ndash;74\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhocaeicola dorei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.5475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.2414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.5668e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9.8432e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e75,76\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eLigilactobacillus ruminis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.3311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.7226e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.0350e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e72\u0026ndash;74\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium leptum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.4484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.2317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5.6001e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.7392e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e77,78\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eLimosilactobacillus mucosae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.6887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.3621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0284e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.8172e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e79,80\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 639px;\"\u003e\n \u003cp\u003eGABA Producer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eBifidobacterium bifidum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-2.3320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.3225e-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5.8323e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e81\u0026ndash;84\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eBifidobacterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.1544e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.6970e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e85,86\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eLactococcus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-2.0391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.2539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.1136e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.4327e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eBifidobacterium adolescentis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.4896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.6700e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.0915e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e87\u0026ndash;89\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eWeissella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-1.0949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.2460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.3889e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.7625e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e60\u0026ndash;62,65\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eBifidobacterium infantis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.4914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.2058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.9006e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e7.8335e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e90,91\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhocaeicola vulgatus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.6297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.3372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.5012e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.9242e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDraft Whole-Genome Sequence of Weissella confusa\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe genome size obtained was 1.02Mb which is less than 50% coverage of \u003cem\u003eWeissella confusa\u003c/em\u003e genome size which was 2.6Mb hence the genome sequence obtained from this protocol was deduced to be draft genome as it is far from reaching the estimated genome size. GC content which tells the percentage of guanine (G) and cytosine (C) is found to be 44.1% in this genome with \u0026nbsp; N50 of 34,965 where this length tells us the quality of genome obtained for which the longer N50 contig length the better it is and good contig of N50 is to be of over 1Mb. 33 number of contigs were obtained from this sequencing and there were 1448 number of Coding Sequence (CDS). The inadequate quality of sequences obtained must be due to the quality of DNA sample loaded into the MinION flow cell (Oxford Nanopore, UK) itself even so the DNA sequences of \u003cem\u003eWeissella confusa\u003c/em\u003e was still obtained.\u003c/p\u003e\n\u003cp\u003eUtilising Average Nucleotide Identity (ANI), the draft genome obtained are closely related to \u003cem\u003eWeissella confusa\u003c/em\u003e strain DSM20196 by 95.9% as visualised by autoMLST (Fig 4a). The pathways highlight from the draft whole genome sequence is metabolic pathway of Glutamine, Glutamate, Aspartate and Asparagine Biosynthesis where it highlights the synthesis of glutamate which are precursors to GABA. Gene annotation results by RAST show the existence of glutamate metabolic related pathway present in the isolated bacterium, indicating the possible relations to GABA producing pathway (Fig 4b, 4c). As compared to GABA, glutamates are known to pass blood-brain barrier in small amounts hence the presence of it may contribute to being a substrate to produce more GABA in the brain. It was worth noting that the genome obtained here is just draft genome. Other literatures, however, have reported none of \u003cem\u003eWeissella confusa\u003c/em\u003e producing glutamate but several have reported producing GABA at various concentrations\u003csup\u003e65\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsistent with findings from other studies, the present data demonstrate that methamphetamine use leads to gut dysbiosis, altering the composition of the gut microbiome. The analysis suggests that predominant microbial populations may play a role in methamphetamine-induced dysbiosis, potentially influencing the gut-brain axis (GBA) and contributing to neuropsychiatric disorders commonly observed in individuals with addiction. Chronic substance abuse, especially involving methamphetamine, which has a long half-life in the human body and can result in significant gut microbial imbalances. This dysbiosis is associated with adverse outcomes such as neurotoxicity and cognitive impairment\u003csup\u003e92\u003c/sup\u003e. These findings imply that methamphetamine exerts a profound impact on the host\u0026rsquo;s GBA, underscoring the need for further investigation into the specific microbial shifts in substance-dependent individuals. Identifying key bacterial taxa associated with addiction-related gut dysbiosis may pave the way for the development of targeted interventions to restore microbial balance and mitigate neuropsychiatric effects.\u003c/p\u003e\n\u003cp\u003eHere, we observed notable differences in gut microbial composition between HC and MW individuals, marked by the predominance of distinct bacterial taxa in each group. The HC group exhibited a higher relative abundance of beneficial \u003cem\u003eBifidobacterium\u003c/em\u003e species, including \u003cem\u003eBifidobacterium adolescentis\u003c/em\u003e, \u003cem\u003eBifidobacterium kashiwanohense\u003c/em\u003e, and \u003cem\u003eBifidobacterium bifidum\u003c/em\u003e. These species are among the earliest colonizers of the human gut and are well known for their probiotic properties, including roles in immune development, metabolic regulation, and the maintenance of gut homeostasis\u003csup\u003e93\u003c/sup\u003e. In contrast, the MW group showed increased abundance of genera such as \u003cem\u003eEscherichia\u003c/em\u003e, and members of the Clostridiceae family. While some strains within these groups function as gut commensals, others are known to be opportunistic pathogens\u003csup\u003e94,95\u003c/sup\u003e. This observation aligns with previous studies reporting a significant reduction in \u003cem\u003eBifidobacterium\u003c/em\u003e and an increased presence of \u003cem\u003eEscherichia-Shigella\u003c/em\u003e in methamphetamine users compared to healthy individuals\u003csup\u003e12\u003c/sup\u003e. Notably, among the ten most prevalent bacterial families, only \u003cem\u003eClostridiales\u003c/em\u003e displayed a significant increase in methamphetamine users, further supporting the link between substance abuse and altered gut microbial profiles\u003csup\u003e96\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethamphetamine intake\u0026nbsp;and withdrawal dramatically altered the composition of gut bacteria, but did not affect the abundance\u0026nbsp;of bacteria\u003csup\u003e10\u003c/sup\u003e. \u0026nbsp; While high diversity in bacterial abundance usually signifies a healthy gut microbiome\u003csup\u003e97\u003c/sup\u003e, however, our data visualise that withdrawal group has higher diversity than control for Chao 1 and Observed, while Shannon shows no significant in between these two groups. MW is group of individuals that are going through recovery process and are not taking any substance at the time of sample collection. While disease group tend to showcase low diversity of gut microbiome in comparing to their healthy control, there is possibility that this data signifies the recovering from gut dysbiosis for MW. High diversity of gut microbiota deters the colonization of pathogens by blocking and competing nutrients with other microbes\u003csup\u003e98\u003c/sup\u003e. According to a different study, people with methamphetamine use disorder did not differ from healthy controls in terms of faecal microbial diversity, but they did differ in terms of the relative abundance of various microbial taxa. Subjects with methamphetamine use disorder showed reduced levels of \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e and increased abundances of \u003cem\u003eCollinsella\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, and \u003cem\u003eMegasphaera\u003c/em\u003e at the genus level\u003csup\u003e99\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the perspective of comparing stages of substance abused, the withdrawal group are categorised into early- and chronic-staged by ASI. The alpha and beta diversity shown insignificant value, as the sample for this group are considered homogenous and stable as all of them are withdrawing from methamphetamine. Certain abundancy of bacterial taxa in Early group was observed such as that of \u003cem\u003eBlautia\u003c/em\u003e, Lachnospiraceae, \u003cem\u003eEscherichia\u003c/em\u003e, and Clostridiaceae. These four groups are commonly found in human gut microbiome where Lachnospiraceae is abundance, impacting hosts by generating short-chain fatty acids, transforming primary bile acids into secondary bile acids, and promoting colonization resistance against intestinal pathogens\u003csup\u003e100\u003c/sup\u003e. Meanwhile, \u003cem\u003eBlautia\u003c/em\u003e is genus under family Lachnospiraceae contributes to biotransformation processes and interact with other gut microbes through crosstalk\u003csup\u003e101\u003c/sup\u003e. Observing the relative abundance, this family is slightly more abundant in Early as compared to Chronic, indirectly showcasing the impact of long-term drug abuse leading to decreasing in abundance of good microflora. On the other hand, \u003cem\u003eEscherichia\u003c/em\u003e is a genus that is prevalent in humans, where \u003cem\u003eEscherichia coli\u003c/em\u003e is specifically reputed to be the foundation for other species such as \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e and other genera to thrive in gastrointestinal tract in early development of human gut upon birth\u003csup\u003e102\u003c/sup\u003e. Lastly, Clostridiaceae is a symbiotic bacterium in gastrointestinal tract being short-chain fatty acid producers\u003csup\u003e103\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUnderstanding the difference between early- and chronic-staged group bacterial taxa abundance could perhaps provide an insight into the effects of substance exposure over certain period. Certain characteristics of gut microbiomes can reveal whether it poses detrimental or beneficial effects on substance abusers\u003csup\u003e104\u003c/sup\u003e. We can assume that over time, as the substance abuser condition get chronic, bacterial taxa such as Peptostreptococcaceae, \u003cem\u003eHoldemanella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e can be expected to be slightly higher in composition. Peptostreptococcaceae is a family under order of Clostridia which is usually considered to be normal commensal bacteria. Although conflicting, the study stated that it is related to intestinal inflammation and obesity\u003csup\u003e105\u003c/sup\u003e while another claimed that it assists maintain gut homeostasis\u003csup\u003e106\u003c/sup\u003e. The discrepancies in the outcomes of the study possibly caused by unique research population. \u003cem\u003eHoldemanella\u003c/em\u003e in few studies were shown to be a positive bacterium that is commonly found in healthy individual correlate with good host health\u003csup\u003e107,108\u003c/sup\u003e, regulates GLP-1 signaling and improves glucose tolerance\u003csup\u003e109\u003c/sup\u003e. While \u003cem\u003eStreptococcus\u003c/em\u003e is often associated with health implications such as coronary atherosclerosis\u003csup\u003e110\u003c/sup\u003e and pancreatic cancer\u003csup\u003e111\u003c/sup\u003e. On the other hand, this analysis revealed the decrease in abundance of certain probiotics such as \u003cem\u003eBifidobacterium adolescentis\u003c/em\u003e and \u003cem\u003eLigilactobacillus ruminis\u003c/em\u003e was observed from chronic- to early-staged groups where this implies the decrease in good bacteria as substance exposure becomes even more chronic. Understanding the relative abundance of certain bacterial taxa in these groups can assist in providing therapeutic recovery treatment by targeting the individual\u0026rsquo;s gut microbiome.\u003c/p\u003e\n\u003cp\u003eLooking at random forest classification, \u003cem\u003eWeizmannia\u003c/em\u003e, Christensenellales and \u003cem\u003eWeissella\u003c/em\u003e were found to be highly influential in chronic-staged abuser. \u003cem\u003eWeizmannia\u003c/em\u003e, a notable spore-forming lactic acid bacterium, is studied for its probiotic potential in enhancing gut function, reducing inflammation, boosting immunity and regulating the brain-gut axis to alleviate depression particularly with \u003cem\u003eWeizmannia coagulans\u003c/em\u003e\u003csup\u003e112\u003c/sup\u003e. Its ability to assist in recovering the gut microbiota balance\u003csup\u003e113\u003c/sup\u003e could have some influence in recovering from methamphetamine addiction subjects. On the other hand, Christensenellales is an autochthonous microbe native to human gastrointestinal tract, was speculated to have anti-obesity potential by preventing adipogenesis particularly for \u003cem\u003eChristensenellales minuta\u003c/em\u003e\u003csup\u003e114\u003c/sup\u003e. It is intriguing to note that methamphetamine abusers underwent severe weight loss due to decrease in appetite and the presence of Christensenellales could imply that it plays roles in this scenario.\u003c/p\u003e\n\u003cp\u003eAs for \u003cem\u003eWeissella\u003c/em\u003e, a study indicated that \u003cem\u003eWeissella\u003c/em\u003e could potentially play a role in depression where it was demonstrated that people who are recovering from heroin addiction utilizing methadone maintenance treatment were shown to have less \u003cem\u003eWeissella\u003c/em\u003e abundance as compared to those who are still having the addiction where it was also implied could be the role of opportunistic pathogenic in people with addiction\u003csup\u003e40\u003c/sup\u003e. While it is conflicting that some studies highlight the potential probiotic characteristic of \u003cem\u003eWeissella\u003csup\u003e65,115\u0026ndash;117\u003c/sup\u003e\u003c/em\u003e, this implies more complex relationships and role that \u003cem\u003eWeissella\u003c/em\u003e plays in individuals with addiction, possibly opportunistic pathogenic or therapeutic in some strains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelating the functional prediction analysis, the presence of major GABA producers was identified which are \u003cem\u003eBacteroides\u003c/em\u003e genus and \u003cem\u003eBacteroides caccae\u003c/em\u003e. This genus possesses GAD orthologs which plays a vital role in converting glutamate to GABA. Results from draft genome analysis of \u003cem\u003eWeissella confusa\u003c/em\u003e isolated from the sample of chronic group visualized the possibility of this bacteria producing glutamate. Assuming that it can act as a glutamate feeder to this GABA producing microbes, this can possibly increase the production of GABA in these individuals. In battling mental disorders of anxiety and depression that are so commonly experienced by those who are trying to withdraw from methamphetamine abuse, these findings highlight the possibility of the withdrawal group\u0026rsquo;s gut microbiota attempts to assist overcome mental health complication endured by these individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFew pathways related to that of L-arginine biosynthesis are worth noting as study has shown that with high nitric oxide (NO) concentrations release by L-arginine may assist in increasing permeability of BBB towards peripheral GABA\u003csup\u003e118\u003c/sup\u003e. With increasing permeability of BBB, this would have assisted more of peripheral GABA to pass through and went inside CNS. The gasotransmitter such as NO, has been suggested to be involved in pathophysiology or mood and stress-related disorders\u003csup\u003e119\u003c/sup\u003e. Aside from being shown in Figure 3f, there are few other presences of L-arginine related pathways that are significant to MW group (Supplementary 1). Though, the presence of L-arginine with GABA may help as psychotherapy towards the disorders, the imbalance of it may lead to anxiety as NO was described to be both neurotoxic and neuroprotective and is influential towards anxiety\u003csup\u003e120,121\u003c/sup\u003e. According to STAMP results, it was shown that L-arginine related pathways are very prevalent in MW group compared to HC (Supplementary 1). This further cemented GBA paradigm, that gut microbiome does influence human cognitive behaviors. As MW individuals are going through physical and mental abstinence from METH, anxieties are a common symptom for individuals going through this journey.\u003c/p\u003e\n\u003cp\u003eMaAsLin2 analysis provided a perspective of potential psychobiotic emerging from the study such as \u003cem\u003eLactococcus\u003c/em\u003e and \u003cem\u003eWeissella\u003c/em\u003e. While many other potential NGPs are found to be significant in the analysis, the two lactic acid bacteria are known to be GABA producers, proven in few other literatures\u003csup\u003e58,60\u0026ndash;62,115\u003c/sup\u003e. The two potential NGPs could be the adjunctive novel therapeutic approach in recovering from addiction. In trying to recover from addiction, substance-dependent users went through physical and mental abstinence to restrain themselves from the use of substance. Presence of GABA-producing microbes could have assisted in the process of recovery for these individuals in battling the mental conditions of anxiety and depression that the individuals went through as their unnatural source of dopamine rush was no longer supplied to them.\u003c/p\u003e\n\u003cp\u003eHowever, as of date, there was no literature proving that \u003cem\u003eWeissella confusa\u003c/em\u003e produce glutamate except for the findings in gene annotations of the bacteria genome itself making it still a prognostic to assume that this \u003cem\u003eWeissella confusa\u003c/em\u003e to be a glutamate feeder. On the contrary, numerous studies have reported of \u003cem\u003eWeissella confusa\u003c/em\u003e being a GABA producer, with detection using biochemical tests as well as identification by nuclear magnetic resonance (NMR) method\u003csup\u003e65\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThese findings may contribute to a more holistic understanding and treatment of methamphetamine use disorder. Additionally, a plethora of novel probiotic-based goods and associated patents have flooded the health industry due to the exceptional research results and registrations of several probiotic strains\u003csup\u003e122\u003c/sup\u003e. The food, pharmaceutical, and medical sectors have all expressed interest in some strains due to their capacity to generate antimicrobial exopolysaccharides (EPSs)\u003csup\u003e62\u003c/sup\u003e. In preclinical research, probiotics have been demonstrated to lessen stress-related behaviours and enhance stress reactions and cognitive performance in healthy participants\u003csup\u003e96\u003c/sup\u003e. As more animal and human studies have reported the anti-depressive and related gamma-aminobutyric acid-ergic (GABAergic) effects of probiotics developed from \u003cem\u003eLactobacillus rhamnosus\u0026nbsp;\u003c/em\u003ebacterial strains in the gut microbiome, the role of the microbiota-gut-brain (MGB) axis in mood regulation and depression treatment has come to light in recent years\u003csup\u003e123\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSpeculation of the increase of \u003cem\u003eWeissella\u0026nbsp;\u003c/em\u003ein chronic individuals could indicate the assistance of gut microbiota in helping to alleviate mental health issues such as anxiety and depression faced by recovering addicted individuals whose dopamine rush came from methamphetamine were taken away. Their abstinence could possibly cause the gut microbiota to react and produce more GABA compounds assisting their health. Although the existence of blood brain barrier could interfere with the passing of GABA into the brain\u003csup\u003e124,125\u003c/sup\u003e, that is not the case with methamphetamine addicted individuals whose tight junctions of Occludin, Claudin-5 and ZO-I are altered\u003csup\u003e126\u003c/sup\u003e, allowing GABA to pass through the BBB. However, some journals highlight that there may be little amount of GABA that can pass through BBB\u003csup\u003e127,128\u003c/sup\u003e. In the case of methamphetamine abusers, the chances of GABA passing through BBB is slightly higher than healthy individuals as methamphetamine intake is known to alter tight junction of endothelial cells\u003csup\u003e52\u003c/sup\u003e, possibly allowing GABA to pass through not just the gut lining but BBB as well. The increase of BBB permeability due to methamphetamine addiction might lead to the individuals are more prone to being sensitive to increase in excitatory neurotransmitters such as glutamate.\u003c/p\u003e\n\u003cp\u003ePresence of GABA throughout human system are intriguing in the role it plays as it is excitatory in enteric nervous system (ENS) but inhibitory for CNS\u003csup\u003e129,130\u003c/sup\u003e. Alterations of circulating and brain GABA levels are linked to shifts in gut microbiota composition where these changes are speculated to contribute to regulation of mental health\u003csup\u003e125\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMethamphetamine remains one of the most potent and challenging drugs to recover from, with no currently approved psychotropic treatments specifically targeting its withdrawal. Mental health is often overlooked during the recovery phase, despite its critical role in determining the success of rehabilitation. This study highlights the potential of gut microbiota in supporting recovery from methamphetamine withdrawal through mechanisms involving GABA production. Psychobiotics with the capacity to produce GABA offer a promising adjunctive therapeutic approach for addiction recovery. While traditional probiotics such as \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e have shown beneficial effects, emerging next-generation probiotics like \u003cem\u003eWeissella confusa\u003c/em\u003e warrant further exploration to fully uncover their potential as psychobiotics in addiction therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequences for the 16S rRNA data were deposited in the National Center for Biotechnology Information (NCBI) \u0026nbsp;database and registered as BioProject PRJNA1270746, Sequence Read Archive (SRA) were deposited as SRR33783416-SRR33783511, and BioSample with accession numbers SAMN48839637-SAMN48839732. The draft genome sequence of \u003cem\u003eWeissella confusa\u003c/em\u003e is available at SRR27485677 with BioProject and BioSample accession number of PRJNA1063499 and SAMN39397650 respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.R. and H.F.A. conceived and designed the study. S.K.K., N.M.A., and T.M.S.T.K.B. performed the experiments and analyzed the data together with Y.P. and H.M.T. S.K.K. and N.M.A. drafted the manuscript with input from H.R., H.F.A., Y.P., H.M.T., and S.F.O. All authors reviewed and approved the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank staff from PUSPEN - a drug rehabilitation centre that provides treatment and rehabilitation programs, and Dr Hidayah Arifin from Mya Clinic who assisted in sample collection during the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis works was supported by UMP-IIUM Sustainable Research Collaboration Grant 2022 - (IUMP-SRCG22-010-0010) and UMPSA Industrial Grant by B-Crobes Laboratory Sdn Bhd (UIC250818).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarr, A. M. et al. The need for speed: an update on methamphetamine addiction. \u003cem\u003eJ. 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Res.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e, 11\u0026ndash;21 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"addiction, methamphetamine, gamma-aminobutyric acid, psychobiotic, GABAergic","lastPublishedDoi":"10.21203/rs.3.rs-7033630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7033630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMethamphetamine addiction is rising globally, burdening healthcare systems. With limited treatments beyond abstinence, psychobiotics offer potential aid in recovery. This study aims to identify potential psychobiotics from individuals withdrawing from methamphetamine, focusing on GABA-producing microbes as psychotherapy. We analysed stool samples from 32 individuals withdrawing from methamphetamine (average age 27.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22; range 18\u0026ndash;35) and 64 healthy controls (average age 18.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.90; range 13\u0026ndash;37) using 16S rRNA amplicon sequencing to compare gut microbiota differences linked to addiction. Significant differences in microbial diversity were observed between groups, specifically in α-diversity (Chao1, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and β-diversity (Bray-Curtis, p-value\u0026thinsp;=\u0026thinsp;0.001). Statistical analysis revealed potential biomarkers, including GABA-producing \u003cem\u003eLactococcus\u003c/em\u003e and \u003cem\u003eWeissella\u003c/em\u003e based on association with recovery profiles. Functional prediction and genome analyses demonstrated pathways related to glutamate and GABA in the withdrawing individuals. Psychobiotics may offer alternative to support mental health and recovery from methamphetamine addiction by targeting gut microbiota.\u003c/p\u003e","manuscriptTitle":"Investigating Gut Microbiota with Gamma- Aminobutyric Acid Production Potential in Methamphetamine Addiction Recovery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 17:33:51","doi":"10.21203/rs.3.rs-7033630/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08c8f63b-e2f5-4313-aa70-9ecc87b3a539","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53681663,"name":"Health sciences/Diseases"},{"id":53681664,"name":"Health sciences/Medical research"},{"id":53681665,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-09-26T18:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 17:33:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7033630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7033630","identity":"rs-7033630","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}