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Metal detoxification is essential for bacteria’s survival in adverse environments and their pathogenesis in hosts. Understanding the underlying mechanisms is crucial for devising antibacterial treatments. In the Gram-negative bacteriumEscherichia coli, membrane-bound sensor CusS and its response regulator CusR together regulate the transcription of thecusoperon that plays important roles in cells’ resistance to copper/silver, and they belong to the two-component systems (TCSs) that are ubiquitous across various organisms and regulate diverse cellular functions. In vitro protein reconstitution and associated biochemical/physical studies have provided significant insights into the functions and mechanisms of CusS–CusR and related TCSs. Such studies are challenging regarding multidomain membrane proteins like CusS and also lack the physiological environment, particularly the native spatial context of proteins inside a cell. Here, we use stroboscopic single-molecule imaging and tracking to probe the dynamic behaviors of both CusS and CusR in live cells, in combination with protein- or residue-specific genetic manipulations. We find that copper stress leads to a cellular protein concentration increase and a concurrent mobilization of CusS out of clustered states in the membrane. We show that the mobilized CusS has significant interactions with CusR for signal transduction and that CusS’s affinity toward CusR switches on upon sensing copper at the interfacial metal-binding sites in CusS’s periplasmic sensor domains, prior to ATP binding and autophosphorylation at CusS’s cytoplasmic kinase domain(s). The observed CusS mobilization upon stimulation and its surprisingly early interaction with CusR likely ensure an efficient signal transduction by providing proper conformation and avoiding futile cross talks.

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An adaptive scheme for handling spelling errors by an e-Iearner while responding to the e-Iearning system through typed-in single-word responses is presented in this paper. To simulate the behaviour of a human instructor, the system preprocesses the input word with respect to spelling errors due to wrong letter or missing letter. The appropriately encoded input is then to fed into a neural net that intelligently recognizes the correct response, in spite of minor spelling mistakes committed by the learner. Results show that the scheme intelligent recognises the misspelled words as expected to be done by a human instructor.

No abstract