| Literature DB >> 34158061 |
Enrico Patrono1, Jan Svoboda2, Aleš Stuchlík3.
Abstract
Schizophrenia research arose in the twentieth century and is currently rapidly developing, focusing on many parallel research pathways and evaluating various concepts of disease etiology. Today, we have relatively good knowledge about the generation of positive and negative symptoms in patients with schizophrenia. However, the neural basis and pathophysiology of schizophrenia, especially cognitive symptoms, are still poorly understood. Finding new methods to uncover the physiological basis of the mental inabilities related to schizophrenia is an urgent task for modern neuroscience because of the lack of specific therapies for cognitive deficits in the disease. Researchers have begun investigating functional crosstalk between NMDARs and GABAergic neurons associated with schizophrenia at different resolutions. In another direction, the gut microbiota is getting increasing interest from neuroscientists. Recent findings have highlighted the role of a gut-brain axis, with the gut microbiota playing a crucial role in several psychopathologies, including schizophrenia and autism.There have also been investigations into potential therapies aimed at normalizing altered microbiota signaling to the enteric nervous system (ENS) and the central nervous system (CNS). Probiotics diets and fecal microbiota transplantation (FMT) are currently the most common therapies. Interestingly, in rodent models of binge feeding, optogenetic applications have been shown to affect gut colony sensitivity, thus increasing colonic transit. Here, we review recent findings on the gut microbiota-schizophrenia relationship using in vivo optogenetics. Moreover, we evaluate if manipulating actors in either the brain or the gut might improve potential treatment research. Such research and techniques will increase our knowledge of how the gut microbiota can manipulate GABA production, and therefore accompany changes in CNS GABAergic activity.Entities:
Keywords: Fecal microbiota transplantation; Gut microbiota; Gut optogenetics; NMDA hypoactivity; NMDARs/GABA interaction; Probiotic dietaries; Schizophrenia
Mesh:
Year: 2021 PMID: 34158061 PMCID: PMC8218443 DOI: 10.1186/s12993-021-00180-2
Source DB: PubMed Journal: Behav Brain Funct ISSN: 1744-9081 Impact factor: 3.759
Evidence for a role of gut microbiota in SCZ from human and animal studies
| References | Subject type | Investigated actors | Methods | Results |
|---|---|---|---|---|
| Pyndt Jørgensen et al. [ | Rats | Cognitive abilities (memory performances) | Sub-chronic PCP model to induce SCZ-like behaviors | SubPCP model impaired NORT |
| Increased locomotor sensitivity up to 6 weeks after washout | ||||
| NORT | ||||
| 16S rRNA gene MiSeq-based high throughput sequencing | ||||
| Locomotor activity | Gut microbiota profiles correlated to SCZ-like memory performance | |||
| Administration of ampicillin (restoring gut microbiota) abolished the subPCP-induced memory deficit | ||||
| Gut microbiota | ||||
| Nguyen et al. [ | Human | α-diversity | Stool sampling | Phylum level |
| 16S rRNA amplicon extraction protocol | ↓ Proteobacteria in SCZ vs HC | |||
| Illumina primers to target the V4 region of the 16S ribosomal RNA gene | ||||
| Genus level | ||||
| ↑ | ||||
| ↓ | ||||
| β-diversity | ||||
| Within SCZ | ||||
| | ||||
| Zheng et al. [ | Human, mice | Gut microbiota of SCZ and HC | 16S rRNA gene sequencing | ↓ Microbiome α-diversity index in SCZ |
| ↓ Disturbances of gut microbiota composition | ||||
| Human-to-mice Gut microbiota transplant | ||||
| SCZ-relevant behavioral phenotypes in GF mice | ||||
| Whole-genome shotgun sequencing of cecum | GF mice receiving SCZ microbiome | |||
| HPC ↓glutamate and ↑glutamine and GABA | ||||
| Non-targeted metabolomics analysis | ||||
| SCZ vs HC mice microbiota analysis | ||||
| SCZ-relevant behaviors similar to those with GLU hypofunction | ||||
| Severance et al. [ | Human | Plasma biomarkers for general inflammation and gut microbiota derived inflammation: hs-CRP, LBP, sCD14 | 409 SCZ individuals | Multivariate regression models |
| GI and endocrine conditions was additive for LBP, with associations only when both conditions were present compared to when were absent | ||||
| Multivariate and univariate regression models | hs-CRP strongly associated with primarily endocrine conditions | |||
| Univariate comparisons | ||||
| | ||||
| IgG antibodies to | ||||
| Babulas et al. [ | Human | Maternal G/R infections prenatal exposure and SCZ relationship in offspring | 7794 offspring reported maternal G/R infections from obstetric records | Exposure to G/R infections during the periconceptional period is associated with increased SCZ risk, with adjustment for maternal race, education, age, and mental illness |
| Diagnosed 71 cases of SCZ and SCZ spectrum disorders | ||||
| Dunphy-Doherty et al. [ | Rats | Social isolation/altered gut microbiota correlation | Behavioral testing: OF/NORT, EPM, CFR, restraint | SI rats showed a few signs of anxiety phenotype (↑ locomotion, ↓ defecation, ↓ CFR). No differences in NORT and EPM |
| ELISA | No changes in corticosterone after stress | |||
| HPC neurogenesis and brain cytokine levels | 16S rRNA gene MiSeq-based high throughput sequencing | ↓ BrdU/NeuN in dentate gyrus | ||
| Post-mortem caecal microbiota composition | Cytokine and mTOR analysis | ↓ Il-6 and IL-10 in HPC | ||
| Kannan et al. [ | Human, mice | Mice received 2 | In mice | |
| Serum collection from blood tail 20 weeks post infection | | |||
| Enzyme-linked immunosorbent assays | In humans (USA and Germany cohorts) | |||
| IgG class antibodies to the NMDAR | ↑ NMDAR IgG levels in | |||
| Serum IgG antibodies ( | ||||
| Humans: 2 cohorts (USA, Germany) with | ||||
| = NMDAR IgG levels in medicated | ||||
| Blood sampling | ||||
| Subject selection with RBANS | ||||
| Maes et al. [ | Human | Plasma IgA/IgM against 5 g-negative bacteria | Recruitment (80 SCZ, 38 HC) | IgA/IgM values |
| IgA values associated with the SCZ Deficit Phenotype | ||||
| SDS screening (40 out of 80 SCZ) | No associations between IgM and the 5 Gram-negative bacteria | |||
| IgM MDA and azelaic acid | ||||
| Assessments (MINI, PANSS, BPRS) | Low IgM to MDA and azelaic acid in SCZ deficit phenotype | |||
| Immunoassays for | ||||
| IgA and IgM against 5 g-negative bacteria | ||||
| SCZ deficit phenotype | IgM-mediated autoimmune responses directed against MDA and azelaic acid | |||
| Xu et al. [ | Human | GMEs | Recruitment (84 SCZ + HC) | MWAS found 19 different taxonomies in SCZ vs HC; and 12 were increased in SCZ |
| Stool sampling and analysis | ||||
| ↑ MD index in SCZ vs HC | ||||
| MEs analysis | ||||
| ↑ MEs diversity in SCZ, + correlation with the MD index | ||||
| Gut microbiota taxonomies | 16S rRNA gene MiSeq-based high throughput sequencing | |||
| ↑ GOGAT in the SCZ guts | ||||
| MWAS | ||||
| ROC analysis showed that MD index, IgA and GOGAT reached AUC 0.86 → potential gut markers of SCZ | ||||
| MD index | ||||
| Correlation and regression analysis | ||||
| MD analysis | ||||
| ROC analysis | ||||
| GOGAT | ||||
| ELISA | ||||
| Shen et al. [ | Human | SCZ and HC gut microbiota | 16S rRNA gene MiSeq-based high throughput sequencing | Similar α-diversity SCZ vs HC |
| ROC analysis | β-diversity altered → increased phylum | |||
| Proteobacteria, Fusobacteria, Firmicutes, Bacteroidetes | ||||
| α-diversity | ||||
| PICRUSt analysis | β-diversity altered → decreased phylum | |||
| Mainly Firmicutes (Clostridia, Streptococcus, etc.) | ||||
| β-diversity | ||||
| Olde Loohuis et al. [ | Human | Microbial communities composition in the blood | Recruitment (192 with SCZ, ALS, BPD) + HC | ↑ Microbial diversity in SCZ vs ALS, BPD, HC |
| High-quality unmapped RNA sequencing reads | ||||
| ↑ Microbial diversity is inversely correlated with estimated abundance of antigenic CD8 + T cells in HC | ||||
| Kanayama et al. [ | Human | Gut microbiota of a SCZ patient after ECT | Stool sampling before and after ECT | After ECT |
| ↓ Scores in BPRS and BFCRS | ||||
| Gut microbiota differences before and after ECT | ||||
| Assessments (BPRS, BFCRS) | ↓ | |||
| ↑ | ||||
| ECT (14 sessions) | ||||
| ↑ | ||||
| He et al. [ | Human | Gut microbiota differences | Stool sampling | ↑ Clostridiales, Lactobacillales, and Bacteroidales in UHR vs HR and HC |
| MRS scans | ||||
| ↑ SCFAs | ||||
| Recruitment (81 HR SCZ, 19 UHR SCZ, 69 HC) | ↑ ACC choline in UHR vs HR and HC | |||
| Choline concentrations in the ACC | ||||
| Yuan et al. [ | Human | Metabolic parameters | Recruitment (41 SCZ, 41 HC) | ↓ |
| Assessment (PANSS) | ↑ Clostridium | |||
| Post 24-weeks risperidone | ||||
| ↑ Body weight, BMI, hs-CRP, SOD, HOMA-IR | ||||
| Blood and stool sampling | ||||
| ↑ | ||||
| ↓ | ||||
| Only changes in | ||||
| Standard enzymatic methods + electro-chemiluminescence immunoassay | ||||
| Automatic biochemical analyzer | ||||
| Particle-enhanced assay + chemical colorimetric assay | ||||
| QIAamp fast DNA stool mini kit | ||||
| SOD | qRT-PCR | |||
| hs-CRP | ||||
| Gut microbiota | ||||
| Metabolic/microbiota changes relationship | ||||
| 24-weeks risperidone treatment | ||||
| Schwarz et al. [ | Human | Fecal microbiota | Assessment (extended BPRS, SANS, GAF, food habits, physical activity) | Similar α-diversity SCZ vs HC |
| Difference in β-diversity | ||||
| ↑ and ↓ different strains of Phyla (Proteobacteria, Fusobacteria, Firmicutes, Bacteroidetes) | ||||
| Stool sampling | In active SCZ patients: | |||
| ↑ Lactobacillaceae | ||||
| ↓ Veillonellaceae | ||||
| SCZ-first episode, HC | qRT-PCR | |||
| Okubo et al. [ | Human | 4-weeks | ↑ HADS and PANSS scores after | |
| Assessment (HADS, PANSS) | ||||
| ↑ Relative abundance of gut | ||||
| Blood test findings | ||||
| Fecal microbiota composition | ||||
| Flowers et al. [ | Human | AAP treatment | Starch tolerability assessment | = overall microbiota composition at baseline between AAP vs non-AAP |
| Stool sampling | ||||
| ↑ Alistipes in non-AAP | ||||
| AAP-microbiome varied with resistant starch admin | ||||
| ↑ Actinobacteria phylum in AAP-treated | ||||
| PowerMag soil DNA isolation kit | ||||
| 16S rRNA gene MiSeq-based high throughput sequencing | ||||
| 6 months prebiotics (resistant starch) | Illumina expression array | |||
| Recruitment (37 SCZ, BPD) | ||||
| Ghaderi et al. [ | Human | Effects of vitamin D/probiotic combination on metabolic and clinical SCZ symptoms | Assessment (PANSS, BPRS) | Vitamin D |
| Vitamin D3/probiotic every 2 weeks or placebo (12 weeks) | ↑ PANSS scores | |||
| Vitamin D/probiotic co-supplementation | ||||
| ↑ TAC | ||||
| ↓ MDA, hs-CRP | ||||
| ↓ Fasting plasma glucose, QUICKI, HOMA-IR | ||||
| Fasting blood sampling | ||||
| ELISA | ||||
| Antioxidant markers (TAC, GSH, MDA) | ||||
| Insulin markers (HOMA-IR, QUICKI) | ||||
| Enzymatic kits | ||||
| Zhu et al. [ | Human, mice | FMT from SCZ humans to HC mice | Behavioral testing (OF, RSIT, TCST, NORT, FST, EPM, BM, TST) | SCZ-transplanted mice showed behavioral (cognitive and locomotor) impairments up to 10 days post FMT |
| Cognitive and motor abilities in SCZ-induced mice | Mice stool sampling | |||
| ↑ Kyn/KynA pathway of Tr degradation in both periphery and brain | ||||
| 16S rRNA gene MiSeq-based high throughput sequencing | Increased DA in PFC, and 5-HT in HPC | |||
| ELISA | ||||
| qRT-PCR | ||||
| Kyn/KynA pathway of Tr degradation | ||||
| Extracellular DA in PFC, 5-HT in HPC |
The table provides a summary of the most relevant studies cited in the manuscript related to the role of the gut microbiota in the onset and development of SCZ
PCP phencyclidine; SCZ schizophrenia; NORT novel object recognition test; HC healthy controls; GF germ free; GLU glutamate; OF open field; EPM elevated plus maze; DA dopamine; 5-HT serotonin; qRT-PCR quantitative real time-PCR; PFC prefrontal cortex; HPC hippocampus; Tr tryptophan; Kyn kynurenine; KynA kynurenic acid; hs-CRP high-sensitivity C-reactive protein; LBP lipopolysaccharide-binding protein; SCD14 soluble CD14; GI gastrointestinal; G/R genital/reproductive; CFR conditioning freezing response; SI social isolation; RBANS repeated battery for the assessment of neuropsychological status; MDA malondialdehyde; SDS schedule for deficit syndrome; MINI mini international neuropsychiatric interview; PANSS positive and negative syndrome scale; BPRS brief psychiatric rating scale; GMEs gut-microbiota associated epitopes; MD microbial dysbiosis; GOGAT glutamate synthase; MWAS metagenomic-wide association study; ROC receiver operating characteristic; BPD bipolar disorder; ECT electroconvulsive therapy; BFCRS Bush–Francis catatonia rating survey; ACC anterior cingulate cortex; MRS magnetic resonance spectroscopy; HR high risk; UHR ultra high risk; SCFA short-chain fatty acids; SOD antioxidant superoxide dismutase; HOMA-IR homeostasis model of assessment-insulin resistance; SANS assessment of negative symptoms; GAF global assessment of functions; HADS hospital anxiety and depression scale; AAP atypical antipsychotics; TAC total antioxidant capacity; GSH total glutathione; QUICKI quantitative insulin sensitivity check index; RSIT reciprocal social interaction test; TCST three-chamber social test; BM Barnes maze; FST forced swimming test; TST tail suspension test