The metabolic slowdown caused by the deletion of pspA accelerates protein aggregation during stationary phase facilitating antibiotic persistence.

Entering a dormant state is a prevailing mechanism used by bacterial cells to transiently evade antibiotic attacks and become persisters. The dynamic progression of bacterial dormancy depths driven by protein aggregation has been found to be critical for antibiotic persistence in recent years. However, our current understanding of the endogenous genes that affects dormancy depth remains limited. Here, we discovered a novel role of phage shock protein A (pspA) gene in modulating bacterial dormancy depth. Deletion of pspA of Escherichia coli resulted in increased bacterial dormancy depths and prolonged lag times for resuscitation during the stationary phase. ∆pspA exhibited a higher persister ratio compared to the wild type when challenged with various antibiotics. Microscopic images revealed that ∆pspA showed accelerated formation of protein aggresomes, which were collections of endogenous protein aggregates. Time-lapse imaging established the positive correlation between protein aggregation and antibiotic persistence of ∆pspA at the single-cell level. To investigate the molecular mechanism underlying accelerated protein aggregation, we performed transcriptome profiling and found the increased abundance of chaperons and a general metabolic slowdown in the absence of pspA. Consistent with the transcriptomic results, the ∆pspA strain showed a decreased cellular ATP level, which could be rescued by glucose supplementation. Then, we verified that replenishment of cellular ATP levels by adding glucose could inhibit protein aggregation and reduce persister formation in ∆pspA. This study highlights the novel role of pspA in maintaining proteostasis, regulating dormancy depth, and affecting antibiotic persistence during stationary phase.

Progressive Frame-Proposal Mining for Weakly Supervised Video Object Detection.

In this paper, we focus on the weakly supervised video object detection problem, where each training video is only tagged with object labels, without any bounding box annotations of objects. To effectively train object detectors from such weakly-annotated videos, we propose a Progressive Frame-Proposal Mining (PFPM) framework by exploiting discriminative proposals in a coarse-to-fine manner. First, we design a flexible Multi-Level Selection (MLS) scheme, with explicit guidance of video tags. By selecting object-relevant frames and mining important proposals from these frames, the proposed MLS can effectively reduce frame redundancy as well as improve proposal effectiveness to boost weakly-supervised detectors. Moreover, we develop a novel Holistic-View Refinement (HVR) scheme, which can globally evaluate importance of proposals among frames, and thus correctly refine pseudo ground truth boxes for training video detectors in a self-supervised manner. Finally, we evaluate the proposed PFPM on a large-scale benchmark for video object detection, on ImageNet VID, under the setting of weak annotations. The experimental results demonstrate that our PFPM significantly outperforms the state-of-the-art weakly-supervised detectors.