Publications by authors named "Wesley Wei Qian"

2 Publications

  • Page 1 of 1

Comprehensive interactome profiling of the human Hsp70 network highlights functional differentiation of J domains.

Mol Cell 2021 06 5;81(12):2549-2565.e8. Epub 2021 May 5.

Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada. Electronic address:

Hsp70s comprise a deeply conserved chaperone family that has a central role in maintaining protein homeostasis. In humans, Hsp70 client specificity is provided by 49 different co-factors known as J domain proteins (JDPs). However, the cellular function and client specificity of JDPs have largely remained elusive. We have combined affinity purification-mass spectrometry (AP-MS) and proximity-dependent biotinylation (BioID) to characterize the interactome of all human JDPs and Hsp70s. The resulting network suggests specific functions for many uncharacterized JDPs, and we establish a role of conserved JDPs DNAJC9 and DNAJC27 in histone chaperoning and ciliogenesis, respectively. Unexpectedly, we find that the J domain of DNAJC27 but not of other JDPs can fully replace the function of endogenous DNAJC27, suggesting a previously unappreciated role for J domains themselves in JDP specificity. More broadly, our work expands the role of the Hsp70-regulated proteostasis network and provides a platform for further discovery of JDP-dependent functions.
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http://dx.doi.org/10.1016/j.molcel.2021.04.012DOI Listing
June 2021

Batch equalization with a generative adversarial network.

Bioinformatics 2020 12;36(Suppl_2):i875-i883

Google Research, 1600 Amphitheatre Parkway Mountain View, CA 94043.

Motivation: Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-batch variation (i.e. batch effect) due to uncontrollable experimental noise (e.g. varying stain intensity or cell density). Previous approaches to minimize the batch effect have commonly focused on normalizing the low-dimensional image measurements such as an embedding generated by a neural network. However, normalization of the embedding could suffer from over-correction and alter true biological features (e.g. cell size) due to our limited ability to interpret the effect of the normalization on the embedding space. Although techniques like flat-field correction can be applied to normalize the image values directly, they are limited transformations that handle only simple artifacts due to batch effect.

Results: We present a neural network-based batch equalization method that can transfer images from one batch to another while preserving the biological phenotype. The equalization method is trained as a generative adversarial network (GAN), using the StarGAN architecture that has shown considerable ability in style transfer. After incorporating new objectives that disentangle batch effect from biological features, we show that the equalized images have less batch information and preserve the biological information. We also demonstrate that the same model training parameters can generalize to two dramatically different types of cells, indicating this approach could be broadly applicable.

Availability And Implementation: https://github.com/tensorflow/gan/tree/master/tensorflow_gan/examples/stargan.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa819DOI Listing
December 2020
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