Emerging evidence suggests that the gut microbiota may interact with the host brain and play pivotal roles in the pathogenesis of neuropsychiatric disorders. However, the mechanism underlying reciprocal interactions along the microbiota-gut-brain axis in depression remains unclear. In this study, a murine model of chronic restraint stress (CRS) was established to investigate the metabolic signaling of tryptophan (Trp) neurotransmission at the intestinal and central levels in depression. The results showed that CRS mice displayed depression- and anxiety-like behaviors. Additionally, kynurenine (Kyn) and its metabolites, an important Trp metabolic pathway, were strongly activated in the brain. Intriguingly, the Kyn toxic signaling was exacerbated in the gut, especially in the colon. Indoleamine 2,3-dioxygenase (IDO), a rate-limiting enzyme responsible for Kyn metabolic pathway initiation, was significantly upregulated in the brain and gut in CRS mice compared with control mice, promoting transfer of Trp metabolic pathway to Kyn signaling. Additionally, administration of IDO inhibitor, 1-methyl-tryptophan (1-MT), partially rescued CRS-induced depression- and anxiety-like changes. Moreover, the enhanced intestinal permeability mediated by CRS allowed toxic metabolites to “leak” into the bloodstream. The microbiome profiles of CRS mice displayed obviously altered taxonomic composition and negative correlations were observed between Enterorhabdus, Parabacteroides and Kyn levels in the brain. Reciprocal crosstalk between the brain and gut was further validated by citalopram treatment, IDO inhibitor and microbiota intervention, which counteracted depression-like behavior, Kyn metabolic signaling and microbiota composition in CRS mice. Meanwhile, Parabacteroides treatment affected Trp metabolism in mouse hippocampus, manifesting as elevated concentration of 5-HT as well as ratio of 5-HT to Trp. These results suggest that long-term stress disrupts Kyn metabolism and endocrine function along the gut-brain axis, accompanied by the disrupted homeostasis of certain microbiota, which collectively contribute to the development of depression-like behavior.

Background: Adolescent depression is becoming one of the major public health concerns, because of its increased prevalence and risk of significant functional impairment and suicidality. Clinical depression commonly emerges in adolescence; therefore, the prevention and intervention of depression at this stage is crucial. Recent evidence supports the importance of the gut microbiota (GM) in the modulation of multiple functions associated with depression through the gut-brain axis (GBA). However, the underlying mechanisms remain poorly understood. Therefore, in the current study, we aimed to screen the microbiota out from healthy and depressive adolescents, delineate the association of the targeted microbiota and the adolescent depression, address the salutary effects of the targeted microbiota on anti-depressive behaviors in mice involving the metabolism of the tryptophan (Trp)-derived neurotransmitters along the GBA. Results: Here, we found the gut microbiota from healthy adolescent volunteers, first diagnosis patients of adolescent depression, and sertraline interveners after first diagnosis displayed significant difference, the relative abundance of Faecalibacterium, Roseburia, Collinsella, Blautia, Phascolarctobacterium, Lachnospiraceae-unclassified decreased in adolescent depressive patients, while restored after sertraline treatment. Of note, the Roseburia abundance exhibited a high efficiency in predicting adolescent depression. Intriguingly, transplantation of the fecal microbiota from healthy adolescent volunteers to the chronic restraint stress (CRS)-induced adolescent depressed mice significantly ameliorated mouse depressive behaviors, in which the Roseburia exerted critical roles, since its effective colonization in the mouse colon resulted in remarkably increased 5-HT level and reciprocally decreased kynurenine (Kyn) toxic metabolites quinolinic acid (Quin) and 3-hydroxykynurenine (3-HK) levels in both the mouse brain and colon. The specific roles of the Roseburia were further validated by the target bacteria transplantation mouse model, Roseburia intestinalis (Ri.) was gavaged to mice and importantly, it dramatically ameliorated CRS-induced mouse depressive behaviors, increased 5-HT levels in the brain and colon via promoting tryptophan hydroxylase-2 (TPH2) or -1 (TPH1) expression. Reciprocally, Ri. markedly restrained the limit-step enzyme responsible for kynurenine (indoleamine2,3-dioxygenase 1, IDO1) and quinolinic acid (3-hydroxyanthranilic acid 3,4-dioxygenase, 3HAO) generation, thereby decreased Kyn and Quin levels. Additionally, Ri. administration exerted a pivotal role in the protection of CRS-induced synaptic loss, microglial activation, and astrocyte maintenance. Conclusions: This study is the first to delineate the beneficial effects of Ri. on adolescent depression by balancing Trp-derived neurotransmitter metabolism and improving synaptogenesis and glial maintenance, which may yield novel insights into the microbial markers and therapeutic strategies of GBA in adolescent depression.

Change detection is an important method of analyzing information about changes in geographical features. However, existing deep learning feature difference methods often lead to the loss of detailed information. Differences in features can arise from factors like illumination or geometric variations rather than actual change regions, resulting in inaccurate change detection. This leads to poor detection of fine-grained boundaries and internal hole problems. To alleviate this, we propose a novel change detection network guided by change boundary awareness and incorporating the concept of boundary-to-center. Our network introduces a change boundary-aware module (CBM) to capture boundary information of change regions. This module enhances boundaries, reducing the influence of noise in feature differences and providing rich contextual information to improve the accuracy of change boundaries. Additionally, we propose a bi-temporal feature aggregation module (BFAM) based on spatial-temporal features. The BFAM aggregates multiple receptive fields features and complements texture information. Both modules utilize the SimAM attention mechanism to enhance the fine-grained nature of the features. In addition, we introduce a deep feature extraction module (DFEM) to extract deep features and minimize information loss during the decoupling process. The proposed change detection network in this paper is guided by change boundary perception, progressively integrating semantic and spatial texture information to refine edges and enhance internal integrity. The performance and efficiency of B2CNet have been validated on four publicly available remote sensing image change detection datasets. Through extensive experiments, the effectiveness of the proposed method has been demonstrated. For example, in terms of IOU for LEVIR, WHU, SYSU and HRCUS datasets, the improvements compared to the baseline are 1.89%, 2.86%, 4.70% and 3.79%, respectively. The code of the proposed approach can be found at https://github.com/bao11seven/B2CNet.