Long-term supplementation of genistein improves immune homeostasis in the aged gut and extends the laying cycle of aged laying hens.

Aging is associated with alterations in gut function, including intestinal inflammation, leaky gut, and impaired epithelial regeneration. Rejuvenating the aged gut is imperative to extend the laying cycle of aged laying hens. Genistein is known to have beneficial effects on age-related diseases, but its precise role in homeostasis of the aged gut of laying hens remains to be elucidated. In this study, 160 45-wk-old Hyline Brown laying hens were continuously fed a basal diet or a diet supplemented with 40 mg/kg genistein until they reached 100 wk of age. The results revealed that long-term genistein supplementation led to an improvement in the egg production rate and feed conversion ratio, as well as an increase in egg quality. Moreover, the expression levels of senescence markers, such as ß-galactosidase, P16, and P21, were decreased in the gut of genistein-treated aged laying hens. Furthermore, genistein ameliorated gut dysfunctions, such as intestinal inflammation, leaky gut, and impaired epithelial regeneration. Treg cell-derived IL-10 plays a crucial role in the genistein-induced regulation of age-related intestinal inflammation. This study demonstrates that long-term consumption of genistein improves homeostasis in the aged gut and extends the laying cycle of aged laying hens. Moreover, the link between genistein and Treg cells provides a rationale for dietary intervention against age-associated gut dysfunction.

Effects of host vimentin on Eimeria tenella sporozoite invasion.

BACKGROUND: Chicken coccidiosis is a parasitic disease caused by Eimeria of Apicomplexa, which has caused great economic loss to the poultry breeding industry. Host vimentin is a key protein in the process of infection of many pathogens. In an earlier phosphorylation proteomics study, we found that the phosphorylation level of host vimentin was significantly regulated after Eimeria tenella sporozoite infection. Therefore, we explored the role of host vimentin in the invasion of host cells by sporozoites. METHODS: Chicken vimentin protein was cloned and expressed. We used qPCR, western blotting, and indirect immunofluorescence to detect levels of mRNA transcription, translation, and phosphorylation, and changes in the distribution of vimentin after E. tenella sporozoite infection. The sporozoite invasion rate in DF-1 cells treated with vimentin polyclonal antibody or with small interfering RNA (siRNA), which downregulated vimentin expression, was assessed by an in vitro invasion test. RESULTS: The results showed that vimentin transcription and translation levels increased continually at 6-72 h after E. tenella sporozoite infection, and the total phosphorylation levels of vimentin also changed. About 24 h after sporozoite infection, vimentin accumulated around sporozoites in DF-1 cells. Treating DF-1 cells with vimentin polyclonal antibody or downregulating vimentin expression by siRNA significantly improved the invasion efficiency of sporozoites. CONCLUSION: In this study, we showed that vimentin played an inhibitory role during the invasion of sporozoites. These data provided a foundation for clarifying the relationship between Eimeria and the host.

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity.

Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.