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Background and purposeThe gut-brain axis is bidirectional and the imbalance of the gut microbiota usually coexists with brain diseases, including Alzheimer’s disease (AD). Accumulating evidence indicates that endoplasmic reticulum (ER) stress is a core lesion in AD and persistent ER stress promotes AD pathology and impairs cognition. However, whether the imbalance of the gut microbiota is involved in triggering the ER stress in the brain remains unknown.Materials and methodsIn the present study, fecal microbiota transplantation (FMT) was performed with gut microbiota from AD patients and APP/PS1 mice, respectively, resulting in two mouse models with dysregulated gut microbiota. The ER stress marker protein levels in the cerebral cortex were assessed using western blotting. The composition of the gut microbiota was assessed using 16S rRNA sequencing.ResultsExcessive ER stress was induced in the cerebral cortex of mice after FMT. Elevated ER stress marker proteins (p-perk/perk, p-eIF2α/eIF2α) were observed, which were rescued by 3,3-dimethyl-1-butanol (DMB). Notably, DMB is a compound that significantly attenuates serum trimethylamine-N-oxide (TMAO), a metabolite of the gut microbiota widely reported to affect cognition.ConclusionThe findings indicate that imbalance of the gut microbiota induces ER stress in the cerebral cortex, which may be mediated by TMAO.

Southern corn rust (SCR) caused by Puccinia polysora Underw. poses a major threat to maize production worldwide. The utilization of host SCR-resistance genes and the cultivation of resistant cultivars are the most effective, economical strategies for controlling SCR. Here, we identified and cloned a new SCR resistance gene, RppM, from the elite maize inbred line Jing2416K. RppM was found to encode a typical CC-NBS-LRR protein localized in both the nucleus and cytoplasm. This gene was constitutively expressed at all developmental stages and in all tissues examined, with the strongest expression detected in leaves at the mature stage. A transcriptome analysis provided further evidence that multiple defense systems were initiated in Jing2416K, including pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity, reinforcement of cell walls, accumulation of antimicrobial compounds, and activation of phytohormone signaling pathways. Finally, we developed functional Kompetitive allele-specific PCR markers for RppM using two conserved SNP sites and successfully applied these functional markers for the detection of RppM and the cultivation of resistant maize cultivars, demonstrating their great potential utility in maize breeding.

The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage. Next, we analyzed the phenotypic variation of leaf sheaths of 418 maize inbred lines based on 87 leaf sheath-related phenotypic traits. In order to further analyze the mechanism of leaf sheath phenotype formation, 25 key traits (2 biomass-related, 19 morphology-related and 4 color-related traits) with heritability greater than 0.3 were analyzed by genome-wide association studies (GWAS). And 1816 candidate genes of 17 whole plant leaf sheath traits and 1,297 candidate genes of 8 sixth leaf sheath traits were obtained, respectively. Among them, 46 genes with clear functional descriptions were annotated by single nucleotide polymorphism (SNPs) that both Top1 and multi-method validated. Functional enrichment analysis results showed that candidate genes of leaf sheath traits were enriched into multiple pathways related to cellular component assembly and organization, cell proliferation and epidermal cell differentiation, and response to hunger, nutrition and extracellular stimulation. The results presented here are helpful to further understand phenotypic traits of maize leaf sheath and provide a reference for revealing the genetic mechanism of maize leaf sheath phenotype formation.

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The determination of the total amount of water pollutant emission in different regions is a difficult problem faced by managers and researchers. Previous studies mostly focused on operability and fairness with little attention paid to local water quality. In order to make total emission pollutant control (TEPC) truly serve the improvement of water quality, a water total emission pollutant allocation model was built based on water environmental carrying capacity (WECC) in this paper. This model was used to construct a water pollutant emission control allocation scheme for 28 cities in Henan Province, China. The results showed that the chemical oxygen demand (COD) reduction rates for these cities ranged from 16.8 to 38.6% and ammonia-nitrogen (NH3-N) reduction rates ranged from 5.7 to 43.5% in 2020, which were different from the previous targets for these cities without considering their current status of water quality. The largest COD reduction rates for different types of point sources (industrial, urban, and large-scale livestock sources) were 35.4%, 39.0%, and 38.0%, respectively, and the largest NH3-N reduction rates were 62.2%, 42.5%, and 43.5%, respectively. This study solves the problem of long-term disconnection between TEPC and water quality improvement in China. The results can also be applied to implement the TEPC to improve water quality in other regions with a similar problem.

Additional file 5: Fig. S5. sEV miRNA expression profile. (A–C) sEVs were isolated from glutamate (15 μM), GABA (500 μM), or PBS-treated neurons, and the miRNA composition of sEVs was compared by miRNA-sequencing. (A) miRNA sequencing showed 41 upregulated miRNAs in GABA sEVs and 14 downregulated miRNAs in glutamate sEVs compared to Ctrl sEVs. 4 repeated miRNAs were identified in both groups. (B) There were 64 downregulated miRNAs in GABA sEVs and 47 upregulated miRNAs in glutamate sEVs compared with Ctrl sEVs. A total of 12 miRNAs overlapped in both groups. (C) An aggregate of 16 miRNAs (p < 0.05).

Additional file 7: Fig. S7. sEVs with inhibited or over-expressed miR-132 abrogated Glutamate/GABA sEVs induced alterations of apoptotic molecules and synapse-related proteins in APP/PS1 mice, respectively. (A) Cy3-labeled agomir-132 was loaded into sEVs and injected into mice. Twenty-four hours later, the brain slices showed overlapped Cy3-labeled agomir-132-loaded sEVs (red) and NeuN+-neurons (green). (B-O) The mice were injected with glutamate sEVs/Ctrl sEVs and agomir-132/agomir ctrl (B-H) and GABA sEVs/Ctrl sEVs and antagomir-132/antagomir ctrl (I-O)-loaded sEVs at every other day for seven consecutive injections. The expression of apoptotic molecules (cleaved Caspase-3, Bax, and Bcl-2) and synapse-related proteins (Synapsin I, Synapsin I (phospho S9), postsynaptic density-95 (PSD-95)) in the brain lysate of mice was examined by Western blot. n = 3. Data are presented as the mean ± SEM, *p

Additional file 4: Fig. S4. Internalization of PKH-26 labeled sEVs by neurons in APP/PS1 mice. (A) sEVs were labeled with PKH26 (red) and injected via the tail vein in mice. Brain slices were harvested and subjected to the immunostaining of the neuron marker NeuN (green) and visualized. The control group mice were injected with PKH26 only without sEVs. n = 3.

Additional file 1: Fig. S1. sEVs-depleted medium had no effect on the cell viability of Aβ-treated neurons. (A) Image of cultured primary cortical neurons on DIV 15. (B) Immunostaining of cortical cultures on DIV 15 for the detection of GFAP+ glial cells (red) vs. NeuN+ neurons (green). (C) The morphology of sEVs from glutamate or GABA-treated neurons was observed under a transmission electron microscope. Glutamate sEVs = sEVs derived from 10 μM glutamate-treated neurons, GABA sEVs = sEVs derived from 300 μM GABA-treated neurons. (D-F) Neurons were incubated with PBS (the same amount as the solvent of the sEVs), sEVs and sEV-depleted medium (Negative control, NC), then with Aβ as before. Neuronal viability in Ctrl-sEV-treated group (D), glutamate-sEV-treated group (E), GABA-sEV-treated group (F) and NC group were compared using CCK-8. n = 3. Data are presented as the mean ± SEM, ***p

Additional file 3: Fig. S3. Function of sEVs from neurons treated by various concentrations of glutamate or GABA. (A) Primary neurons on DIV 15 were treated with a gradient concentration of Aβ1–42 for 48 h. Cell viability was measured using a CCK-8 assay. (B–I) sEVs were isolated from low concentrations of glutamate (5 μM)/GABA (100 μM), or high concentrations of glutamate (15 μM)/GABA (500 μM) and added to the culture media. Cell viability was measured using CCK-8 (D, E, H, I) and TUNEL assays (B, C, F, G). Glutamate5 sEVs = sEVs derived from 5 μM glutamate treated neurons, GABA100 sEVs = sEVs derived from 100 μM GABA-treated neurons, Glutamate15 sEVs = sEVs derived from 15 μM glutamate-treated neurons, GABA500 sEVs = sEVs derived from 500 μM GABA treated neurons, Ctrl sEVs = sEVs derived from PBS-treated neurons. n = 3. Data are presented as the mean ± SEM, *p

Additional file 6: Fig. S6. sEVs with inhibited or over-expressed miR-132 abrogated Glutamate/GABA sEVs induced alterations of apoptotic molecules in Aβ treated neurons, respectively. (A, B) TUNEL- positive rate of neurons in Fig. 6C and D were calculated. (C–J) Neurons were cotreated with glutamate sEVs and miR-132 mimic-loaded sEVs (C, E–G) or GABA sEVs and miR-132 inhibitor-loaded sEVs (D, H–J). 24 h later, neurons were subjected to Aβ treatment for another 48 h. Then, the expression of apoptotic molecules (cleaved Caspase-3, Bax, and Bcl-2). (K, L) The proportion of TUNEL-positive cells in brain slices in Fig. 6E and F was detected. n = 3. Data are presented as the mean ± SEM, *p

Additional file 2: Fig. S2. Internalization of PKH26 labeled sEVs in primary cultured neurons. (A) sEVs were labeled with PKH26 (red) and added to primary neurons. A control group (only PKH26 without sEVs) was established to exclude false-positive staining caused by the dye. Then, the neurons were immunostained with βIII-tubulin (green) and visualized under a confocal microscope. PKH26 -labeled sEVs were indicated by white arrows. n = 3.