Social withdrawal in patients with depression can aggravate depressive symptoms. However, few studies focus on the behavioral changes of social isolation after CUMS. NRF2 had been reported to be down-regulated after CUMS. But whether NRF2 participates in behavioral changes induced by social isolation after CUMS remains unclear. This study aims to develop a new model combined social isolation with CUMS, and investigate whether such behavioral changes are related to NRF2 signaling. This study included two stages. In Stage 1, rats were subjected to 4-week CUMS and CUMS-susceptible rats were selected. In Stage 2, the CUMS-susceptible rats received 4-week social isolation or social support. Behavioral tests were carried out to observe behavioral changes, including sucrose preference test, forced swimming test, open field test, novel object recognition and social interaction test. QRT-PCR, western blot and immunofluorescence staining detected the ERK/KEAP1/NRF2 signaling. CUMS-susceptible rats exhibited depressive-like behaviors accompanied by the down-regulated ERK/KEAP1/NRF2 signaling in hippocampus. In Stage 2, compared with 4-week social support (group CUMSG), 4-week social isolation (group CUMSI) perpetuated the depressive-like behaviors, memory deficits and social withdrawal in CUMS-susceptible rats, as well as lower levels of p-ERK, NRF2, p-NRF2, HO-1 and NQO1, and the higher levels of KEAP1 in hippocampus. These findings suggested that social isolation after CUMS perpetuated depressive-like behaviors, memory deficits and social withdrawal via inhibiting ERK/KEAP1/NRF2 signaling. This study provided molecular evidence for the effects of post-stress social isolation on mental health, and the antioxidant stress signaling might be a target to rescue these.

Background: Major depressive disorder (MDD) is a common mental illness that affects millions of people worldwide and imposes a heavy burden on individuals, families and society. Previous studies on MDD predominantly focused on neurons and employed bulk homogenates of brain tissues. This paper aims to decipher the relationship between oligodendrocyte lineage (OL) development and MDD at the single-cell resolution level. Methods: Here, we present the use of a guided regularized random forest (GRRF) algorithm to explore single-nucleus RNA sequencing profiles (GSE144136) of the OL at four developmental stages, which contains dorsolateral prefrontal cortex of 17 healthy controls (HC) and 17 MDD cases, generated by Nagy C et al. We prioritized and ordered differentially expressed genes (DEGs) based on Nagy et al., which could predominantly discriminate cells in the four developmental stages and two adjacent developmental stages of the OL. We further screened top-ranked genes that distinguished between HC and MDD in four developmental stages. Moreover, we estimated the performance of the GRRF model via the area under the curve value. Additionally, we validated the pivotal candidate gene Malat1 in animal models. Results: We found that, among the four developmental stages, the onset development of OL (OPC2) possesses the best predictive power for distinguishing HC and MDD, and long noncoding RNA MALAT1 has top-ranked importance value in candidate genes of four developmental stages. In addition, results of fluorescence in situ hybridization assay showed that Malat1 plays a critical role in the occurrence of depression. Conclusions: Our work elucidates the mechanism of MDD from the perspective of OL development at the single-cell resolution level and provides novel insight into the occurrence of depression.

Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5′-C-phosphate-G-3′ (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a “dominant” region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.

This paper addresses the stability problem of genetic regulatory networks by involving the spatial diffusion of concentration, discrete and infinite distributed delays. By using the theories of partial differential equation and Lyapunov stability, the global exponential stability criteria in algebraic form are derived for reaction diffusion genetic regulatory networks (RDGRNs) with discrete and distributed delays. The derived stability conditions are simple and can be directly calculated by using the parameters of the networks. Moreover, the theoretical results are universal and can be applied to deal with the stability problem of RDGRNs with or without distributed delays. Eventually, the validity and feasibility of the results are illustrated by numerical simulations.