The Mammalian Disaggregase Machinery: Hsp110 Synergizes with Hsp70 and Hsp40 to Catalyze Protein Disaggregation and Reactivation in a Cell-Free System

Bacteria, fungi, protozoa, chromista and plants all harbor homologues of Hsp104, a AAA+ ATPase that collaborates with Hsp70 and Hsp40 to promote protein disaggregation and reactivation. Curiously, however, metazoa do not possess an Hsp104 homologue. Thus, whether animal cells renature large protein aggregates has long remained unclear. Here, it is established that mammalian cytosol prepared from different sources possesses a potent, ATP-dependent protein disaggregase and reactivation activity, which can be accelerated and stimulated by Hsp104. This activity did not require the AAA+ ATPase, p97. Rather, mammalian Hsp110 (Apg-2), Hsp70 (Hsc70 or Hsp70) and Hsp40 (Hdj1) were necessary and sufficient to slowly dissolve large disordered aggregates and recover natively folded protein. This slow disaggregase activity was conserved to yeast Hsp110 (Sse1), Hsp70 (Ssa1) and Hsp40 (Sis1 or Ydj1). Hsp110 must engage substrate, engage Hsp70, promote nucleotide exchange on Hsp70, and hydrolyze ATP to promote disaggregation of disordered aggregates. Similarly, Hsp70 must engage substrate and Hsp110, and hydrolyze ATP for protein disaggregation. Hsp40 must harbor a functional J domain to promote protein disaggregation, but the J domain alone is insufficient. Optimal disaggregase activity is achieved when the Hsp40 can stimulate the ATPase activity of Hsp110 and Hsp70. Finally, Hsp110, Hsp70 and Hsp40 fail to rapidly remodel amyloid forms of the yeast prion protein, Sup35, or the Parkinson's disease protein, alpha-synuclein. However, Hsp110, Hsp70 and Hsp40 enhanced the activity of Hsp104 against these amyloid substrates. Taken together, these findings suggest that Hsp110 fulfils a subset of Hsp104 activities in mammals. Moreover, they suggest that Hsp104 can collaborate with the mammalian disaggregase machinery to rapidly remodel amyloid conformers

Algorithm to demodulate an electromyogram signal modulated by essential tremor

Abstract Essential tremor is a disorder that causes involuntary oscillations in patients both while they are engaged in actions and when maintaining a posture. Such patients face serious difficulties in performing daily living activities such as meal movement. We have been developing an electromyogram (EMG)-controlled exoskeleton to suppress tremors to support the movements of these patients. The problem is that the EMG signal of the patients is modulated by the tremor signal as multiplicative noise. In this paper, we proposed a novel signal processing method to demodulate patients’ EMG signals. We modelled the multiplicative tremor signal with a powered sine wave and the tremor signal in the EMG signal was removed by dividing the modelled tremor signal into the EMG signal. To evaluate the effectiveness of the demodulation, we applied the method to a real patient’s EMG signal, extracted from biceps brachii while performing an elbow flexion. We quantified the effect of the demodulation by root mean square error between two kinds of muscle torques, an estimated torque from the EMG signal and calculated torque from inverse dynamics based on the motion data. The proposed method succeeded in reducing the error by approximately 15–45% compared with using a low-pass filter, typical processing for additive noise, and showed its effectiveness in the demodulation of the patients’ EMG signal