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Lele, Pushkar Prakash

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Lele

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Pushkar Prakash

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Lele, Pushkar Prakash
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    Dynamics of Mechanosensing in the Bacterial Flagellar Motor
    (Proceedings of the National Academy of Sciences, 2013) Lele, Pushkar Prakash; Hosu, Basarab; Berg, Howard
    Mechanosensing by flagella is thought to trigger bacterial swarmer-cell differentiation, an important step in pathogenesis. How flagellar motors sense mechanical stimuli is not known. To study this problem, we suddenly increased the viscous drag on motors by a large factor, from very low loads experienced by motors driving hooks or hooks with short filament stubs, to high loads, experienced by motors driving tethered cells or 1-μm latex beads. From the initial speed (after the load change), we inferred that motors running at very low loads are driven by one or at most two force-generating units. Following the load change, motors gradually adapted by increasing their speeds in a stepwise manner (over a period of a few minutes). Motors initially spun exclusively counterclockwise, but then increased the fraction of time that they spun clockwise over a time span similar to that observed for adaptation in speed. Single-motor total internal reflection fluorescence imaging of YFP–MotB (part of a stator force-generating unit) confirmed that the response to sudden increments in load occurred by the addition of new force-generating units. We estimate that 6–11 force-generating units drive motors at high loads. Wild-type motors and motors locked in the clockwise or counterclockwise state behaved in a similar manner, as did motors in cells deleted for the motor protein gene fliL or for genes in the chemotaxis signaling pathway. Thus, it appears that stators themselves act as dynamic mechanosensors. They change their structure in response to changes in external load. How such changes might impact cellular functions other than motility remains an interesting question.
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    A Rotary Motor Drives Flavobacterium Gliding
    (Elsevier BV, 2015) Shrivastava, Abhishek; Lele, Pushkar Prakash; Berg, Howard
    Cells of Flavobacterium johnsoniae, a rod-shaped bacterium devoid of pili or flagella, glide over glass at speeds of 2–4 μm/s [ 1 ]. Gliding is powered by a protonmotive force [ 2 ], but the machinery required for this motion is not known. Usually, cells move along straight paths, but sometimes they exhibit a reciprocal motion, attach near one pole and flip end over end, or rotate. This behavior is similar to that of a Cytophaga species described earlier [ 3 ]. Development of genetic tools for F. johnsoniae led to discovery of proteins involved in gliding [ 4 ]. These include the surface adhesin SprB that forms filaments about 160 nm long by 6 nm in diameter, which, when labeled with a fluorescent antibody [ 2 ] or a latex bead [ 5 ], are seen to move longitudinally down the length of a cell, occasionally shifting positions to the right or the left. Evidently, interaction of these filaments with a surface produces gliding. To learn more about the gliding motor, we sheared cells to reduce the number and size of SprB filaments and tethered cells to glass by adding anti-SprB antibody. Cells spun about fixed points, mostly counterclockwise, rotating at speeds of 1 Hz or more. The torques required to sustain such speeds were large, comparable to those generated by the flagellar rotary motor. However, we found that a gliding motor runs at constant speed rather than at constant torque. Now, there are three rotary motors powered by protonmotive force: the bacterial flagellar motor, the Fo ATP synthase, and the gliding motor.
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    Publication
    FRAP Analysis: Accounting for Bleaching during Image Capture
    (Public Library of Science, 2012) Wu, Jun; Shekhar, Nandini; Lele, Pushkar Prakash; Lele, Tanmay P.
    The analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments involves mathematical modeling of the fluorescence recovery process. An important feature of FRAP experiments that tends to be ignored in the modeling is that there can be a significant loss of fluorescence due to bleaching during image capture. In this paper, we explicitly include the effects of bleaching during image capture in the model for the recovery process, instead of correcting for the effects of bleaching using reference measurements. Using experimental examples, we demonstrate the usefulness of such an approach in FRAP analysis.