Summary In response to stress, human cells coordinately downregulate transcription and translation of housekeeping genes. To downregulate transcription, the negative elongation factor (NELF) is recruited to gene promoters impairing RNA polymerase II elongation. Here we report that NELF rapidly forms nuclear condensates upon stress in human cells. Condensate formation requires NELF dephosphorylation and SUMOylation induced by stress. The intrinsically disordered region (IDR) in NELFA is necessary for nuclear NELF condensation and can be functionally replaced by the IDR of FUS or EWSR1 protein. We find that biomolecular condensation facilitates enhanced recruitment of NELF to promoters upon stress to drive transcriptional downregulation. Importantly, NELF condensation is required for cellular viability under stressful conditions. We propose that stress-induced NELF condensates reported here are nuclear counterparts of cytosolic stress granules. These two stress-inducible condensates may drive the coordinated downregulation of transcription and translation, likely forming a critical node of the stress survival strategy.

The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda (λ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

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Highlights d Comprehensive genetic and physical interactome of nuclear HSP90 in human cells d HSP90 stabilizes HCFC1 complex at chromatin to drive cellcycle gene expression d Synergistic killing of cancer cells by simultaneous inhibition of HSP90 and HCFC1

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