The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

BackgroundMethamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.MethodFecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.ResultsMUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.ConclusionIn conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings

Population genomics of the Viking world.

The maritime expansion of Scandinavian populations during the Viking Age (about AD 750-1050) was a far-flung transformation in world history1,2. Here we sequenced the genomes of 442 humans from archaeological sites across Europe and Greenland (to a median depth of about 1×) to understand the global influence of this expansion. We find the Viking period involved gene flow into Scandinavia from the south and east. We observe genetic structure within Scandinavia, with diversity hotspots in the south and restricted gene flow within Scandinavia. We find evidence for a major influx of Danish ancestry into England; a Swedish influx into the Baltic; and Norwegian influx into Ireland, Iceland and Greenland. Additionally, we see substantial ancestry from elsewhere in Europe entering Scandinavia during the Viking Age. Our ancient DNA analysis also revealed that a Viking expedition included close family members. By comparing with modern populations, we find that pigmentation-associated loci have undergone strong population differentiation during the past millennium, and trace positively selected loci-including the lactase-persistence allele of LCT and alleles of ANKA that are associated with the immune response-in detail. We conclude that the Viking diaspora was characterized by substantial transregional engagement: distinct populations influenced the genomic makeup of different regions of Europe, and Scandinavia experienced increased contact with the rest of the continent

The complete mitochondrial genome sequence and gene organization of Istigobius campbelli (Perciformes, Gobiidae) with phylogenetic consideration

The complete mitochondrial genome DNA sequence of Istigobius campbelli was 16,527 bp in length. It consists of 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and 1 control region. 28 of the 37 genes were encoded on the heavy strand, and 9 of them were encoded on the light strand. The overall base composition of the genome is 27.84% A, 25.81% T, 29.68% C, and 16.67% G. The phylogenetic tree suggested I. campbelli was genetically closest to Acentrogobius pflaumii and Oxyurichthys formosanus. This study could provide valuable information for further studies on I. campbelli

The complete mitochondrial genome of Dysomma Anguillare (Anguilliformes, Synaphobranchidae) with phylogenetic consideration

In this study, the complete mitochondrial genome of Dysomma anguillare was obtained, which was 16,898 bp in length. It consists of 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNA genes, and a non-coding control region. The overall base composition of the genome is 33.19% for A, 22.9% for T, 28.1% for C, and 15.79% for G. In 13 protein-coding genes, two types of initiation codon (GTG, ATG) and five types of stop codons (AGG,TAA,TAG,TA, and T) were identified. The phylogenetic tree, which is based on complete mtDNA sequences, suggested that D. anguillare was closest to some species of Synaphobranchidae. This study could provide some basic information for further studies on population structure and molecular evolution of D. anguillare

The complete mitochondrial genome sequence and gene organization of Lepidotrigla Kanagashira (Scorpaeniformes, Triglidae) with phylogenetic consideration

The complete mitochondrial genome DNA sequence of Lepidotrigla kanagashira was 16,504 bp in length. It consists of 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes and one control region. Among 22 tRNA genes, 8 tRNAs were encoded on the L-strand. The overall base composition of the genome is 26.65% for A, 25.42% for T, 30.89% for C and17.04% for G. The phylogenetic tree suggested that L. kanagashira was genetically closest to L. microptera and Chelidonichthys kumu among 13 related species. This study could provide some valuable information for further studies on L. kanagashira

Alterations in the fecal microbiota of methamphetamine users with bad sleep quality during abstinence

Abstract Background Methamphetamine (MA) abuse has resulted in a plethora of social issues. Sleep disturbance is a prominent issue about MA addiction, which serve as a risk factor for relapse, and the gut microbiota could play an important role in the pathophysiological mechanisms of sleep disturbances. Therefore, improving sleep quality can be beneficial for treating methamphetamine addiction, and interventions addressing the gut microbiota may represent a promising approach. Method We recruited 70 MA users to investigate the associations between sleep quality and fecal microbiota by the Pittsburgh Sleep Quality Index (PSQI), which was divided into MA-GS (PSQI score < 7, MA users with good sleep quality, n = 49) and MA-BS group (PSQI score ≥ 7, MA users with bad sleep quality, n = 21). In addition, we compared the gut microbiota between the MA-GS and healthy control (HC, n = 38) groups. 16S rRNA sequencing was applied to identify the gut bacteria. Result The study revealed that the relative abundances of the Thermoanaerobacterales at the order level differed between the MA-GS and MA-BS groups. Additionally, a positive correlation was found between the relative abundance of the genus Sutterella and daytime dysfunction. Furthermore, comparisons between MA users and HCs revealed differences in beta diversity and relative abundances of various bacterial taxa. Conclusion In conclusion, the study investigated alterations in the gut microbiota among MA users. Furthermore, we demonstrated that the genus Sutterella changes may be associated with daytime dysfunction, suggesting that the genus Sutterella may be a biomarker for bad sleep quality in MA users

Additional file 1 of Alterations in the fecal microbiota of methamphetamine users with bad sleep quality during abstinence

Supplementary Material

Forward-genetics analysis of sleep in randomly mutagenized mice.

Sleep is conserved from invertebrates to vertebrates, and is tightly regulated in a homeostatic manner. The molecular and cellular mechanisms that determine the amount of rapid eye movement sleep (REMS) and non-REMS (NREMS) remain unknown. Here we identify two dominant mutations that affect sleep and wakefulness by using an electroencephalogram/electromyogram-based screen of randomly mutagenized mice. A splicing mutation in the Sik3 protein kinase gene causes a profound decrease in total wake time, owing to an increase in inherent sleep need. Sleep deprivation affects phosphorylation of regulatory sites on the kinase, suggesting a role for SIK3 in the homeostatic regulation of sleep amount. Sik3 orthologues also regulate sleep in fruitflies and roundworms. A missense, gain-of-function mutation in the sodium leak channel NALCN reduces the total amount and episode duration of REMS, apparently by increasing the excitability of REMS-inhibiting neurons. Our results substantiate the use of a forward-genetics approach for studying sleep behaviours in mice, and demonstrate the role of SIK3 and NALCN in regulating the amount of NREMS and REMS, respectively. Nature 2016 Nov 17; 539(7629):378-383