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Abstract SARS-CoV-2 infection causes spike-dependent fusion of infected cells with ACE2 positive neighboring cells, generating multi-nuclear syncytia that are often associated with severe COVID. To better elucidate the mechanism of spike-induced syncytium formation, we combine chemical genetics with 4D confocal imaging to establish the cell surface heparan sulfate (HS) as a critical stimulator for spike-induced cell-cell fusion. We show that HS binds spike and promotes spike-induced ACE2 clustering, forming synapse-like cell-cell contacts that facilitate fusion pore formation between ACE2-expresing and spike-transfected human cells. Chemical or genetic inhibition of HS mitigates ACE2 clustering, and thus, syncytium formation, whereas in a cell-free system comprising purified HS and lipid-anchored ACE2, HS stimulates ACE2 clustering directly in the presence of spike. Furthermore, HS-stimulated syncytium formation and receptor clustering require a conserved ACE2 linker distal from the spike-binding site. Importantly, the cell fusion-boosting function of HS can be targeted by an investigational HS-binding drug, which reduces syncytium formation in vitro and viral infection in mice. Thus, HS, as a host factor exploited by SARS-CoV-2 to facilitate receptor clustering and a stimulator of infection-associated syncytium formation, may be a promising therapeutic target for severe COVID. 

Abstract Injectable hydrogels are valuable tools in tissue engineering and regenerative medicine due to their unique advantages of injectability with minimal invasiveness and usability for irregularly shaped sites. However, it remains challenging to achieve scalable manufacturing together with matching physicochemical properties and on‐demand drug release for a high level of control over biophysical and biomedical cues to direct endogenous cells. Here, the use of an injectable fibro‐gel is demonstrated, a water‐filled network of entangled hydrogel microfibers, whose physicochemical properties and drug release profiles can be tailored to overcome these shortcomings. This fibro‐gel exhibits favorable in vitro biocompatibility and the capability to aid vascularization. The potential use of the fibro‐gel for advancing tissue regeneration is explored with a mice excision skin model. Preliminary in vivo tests indicate that the fibro‐gel promotes wound healing and new healthy tissue regeneration at a faster rate than a commercial gel. Moreover, it is demonstrated that the release of distinct drugs at different rates can further accelerate wound healing with higher efficiency, by using a two‐layer fibro‐gel model. The combination of injectability and tailorable properties of this fibro‐gel offers a promising approach in biomedical fields such as therapeutic delivery, medical dressings, and 3D tissue scaffolds for tissue engineering. 

Learning fair representations is an essential task to reduce bias in data-oriented decision making. It protects minority subgroups by requiring the learned representations to be independent of sensitive attributes. To achieve independence, the vast majority of the existing work primarily relaxes it to the minimization of the mutual information between sensitive attributes and learned representations. However, direct computation of mutual information is computationally intractable, and various upper bounds currently used either are still intractable or contradict the utility of the learned representations. In this paper, we introduce distance covariance as a new dependence measure into fair representation learning. By observing that sensitive attributes (e.g., gender, race, and age group) are typically categorical, the distance covariance can be converted to a tractable penalty term without contradicting the utility desideratum. Based on the tractable penalty, we propose FairDisCo, a variational method to learn fair representations. Experiments demonstrate that FairDisCo outperforms existing competitors for fair representation learning.