Yunfei Hu

Pattern recognition receptors confer plant resistance to pathogen infection by recognizing the conserved pathogen-associated molecular patterns. The cell surface receptor chitin elicitor receptor kinase 1 of Arabidopsis (AtCERK1) directly binds chitin through its lysine motif (LysM)-containing ectodomain (AtCERK1-ECD) to activate immune responses. The crystal structure that we solved of an AtCERK1-ECD complexed with a chitin pentamer reveals that their interaction is primarily mediated by a LysM and three chitin residues. By acting as a bivalent ligand, a chitin octamer induces AtCERK1-ECD dimerization that is inhibited by shorter chitin oligomers. A mutation attenuating chitin-induced AtCERK1-ECD dimerization or formation of nonproductive AtCERK1 dimer by overexpression of AtCERK1-ECD compromises AtCERK1-mediated signaling in plant cells. Together, our data support the notion that chitin-induced AtCERK1 dimerization is critical for its activation.

Nuclear magnetic resonance (NMR) spectroscopy is a well-established method for analyzing protein structure, interaction, and dynamics at atomic resolution and in various sample states including solution state, solid state, and membranous environment. Thanks to rapid NMR methodology development, the past decade has witnessed a growing number of protein NMR studies in complex systems ranging from membrane mimetics to living cells, which pushes the research frontier further toward physiological environments and offers unique insights in elucidating protein functional mechanisms. In particular, in-cell NMR has become a method of choice for bridging the huge gap between structural biology and cell biology. Herein, we review the recent developments and applications of NMR methods for protein analysis in close-to-physiological environments, with special emphasis on in-cell protein structural determination and the analysis of protein dynamics, both difficult to be accessed by traditional methods.

The conversion of prion protein (PrP) to the pathogenic PrPSc conformation is central to prion disease. Previous studies revealed that PrP interacts with lipids and the interaction induces PrP conformational changes, yet it remains unclear whether in the absence of any denaturing treatment, PrP-lipid interaction is sufficient to convert PrP to the classic proteinase K-resistant conformation. Using recombinant mouse PrP, we analyzed PrP-lipid interaction under physiological conditions and followed lipid-induced PrP conformational change with proteinase K (PK) digestion. We found that the PrP-lipid interaction was initiated by electrostatic contact and followed by hydrophobic interaction. The PrP-lipid interaction converted full-length alpha-helix-rich recombinant PrP to different forms. A significant portion of PrP gained a conformation reminiscent of PrPSc, with a PrPSc-like PK-resistant core and increased beta-sheet content. The efficiency for lipid-induced PrP conversion depended on lipid headgroup structure and/or the arrangement of lipids on the surface of vesicles. When lipid vesicles were disrupted by Triton X-100, PrP aggregation was necessary to maintain the lipid-induced PrPSc-like conformation. However, the PK resistance of lipid-induced PrPSc-like conformation does not depend on amyloid fiber formation. Our results clearly revealed that the lipid interaction can overcome the energy barrier and convert full-length alpha-helix-rich PrP to a PrPSc-like conformation under physiological conditions, supporting the relevance of lipid-induced PrP conformational change to in vivo PrP conversion.

BACKGROUND: Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development. RESULTS: In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets. CONCLUSIONS: Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.