SummaryIntracellular bacteria have been shown to cause autophagy, which impacts infectious outcomes, whereas extracellular bacteria have not been reported to activate autophagy. Here, we demonstrate that Pseudomonas aeruginosa, a Gram-negative extracellular bacterium, activates autophagy with considerably increased LC3 punctation in both an alveolar macrophage cell line (MH-S) and primary alveolar macrophages. Using the LC3 Gly120 mutant, we successfully demonstrated a hallmark of autophagy, conjugation of LC3 to phosphatidylethanolamine (PE). The accumulation of typical autophagosomes with double membranes was identified morphologically by transmission electron microscopy (TEM). Furthermore, the increase of PE-conjugated LC3 was indeed induced by infection rather than inhibition of lysosome degradation. P. aeruginosa induced autophagy through the classical beclin-1-Atg7-Atg5 pathway as determined by specific siRNA analysis. Rapamycin and IFN-c (autophagy inducers) augmented bacterial clearance, whereas beclin-1 and Atg5 knockdown reduced intracellular bacteria. Thus, P. aeruginosa-induced autophagy represents a host protective mechanism, providing new insight into the pathogenesis of this infection.
Introduction: Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD.Methods: MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A nonlinear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated.
Results:The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions.
Conclusion:The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.
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