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Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing ‘intelligent’ encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles’ hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs. 

Deep neural networks implementing generative models for dimensionality reduction have been extensively used for the visualization and analysis of genomic data. One of their key limitations is lack of interpretability: it is challenging to quantitatively identify which input features are used to construct the embedding dimensions, thus preventing insight into why cells are organized in a particular data visualization, for example. Here we present a scalable, interpretable variational autoencoder (siVAE) that is interpretable by design: it learns feature embeddings that guide the interpretation of the cell embeddings in a manner analogous to factor loadings of factor analysis. siVAE is as powerful and nearly as fast to train as the standard VAE but achieves full interpretability of the embedding dimensions. Using siVAE, we exploit a number of connections between dimensionality reduction and gene network inference to identify gene neighborhoods and gene hubs, without the explicit need for gene network inference. We observe a systematic difference in the gene neighborhoods identified by dimensionality reduction methods and gene network inference algorithms in general, suggesting they provide complementary information about the underlying structure of the gene co-expression network. Finally, we apply siVAE to implicitly learn gene networks for individual iPSC lines and uncover a correlation between neuronal differentiation efficiency and loss of co-expression of several mitochondrial complexes, including NADH dehydrogenase, cytochrome C oxidase, and cytochrome b. 

In order to explore the consequences of spin–orbit coupling on spin–phonon interactions in a set of chemically similar mixed metal oxides, we measured the infrared vibrational properties of Co4B2O9 (B = Nb, Ta) as a function of temperature and compared our findings with lattice dynamics calculations and several different models of spin–phonon coupling. Frequency vs temperature trends for the Co2+ shearing mode near 150 cm−1 reveal significant shifts across the magnetic ordering temperature that are especially large in relative terms. Bringing these results together and accounting for noncollinearity, we obtain spin–phonon coupling constants of −3.4 and −4.3 cm−1 for Co4Nb2O9 and the Ta analog, respectively. Analysis reveals that these coupling constants are derived from interlayer (rather than intralayer) exchange interactions and that the interlayer interactions contain competing antiferromagnetic and ferromagnetic contributions. At the same time, beyond-Heisenberg terms are minimized due to fortuitous symmetry considerations, different from most other 4d- and 5d-containing oxides. Comparison with other contemporary oxides shows that spin–phonon coupling in this family of materials is among the strongest ever reported, suggesting an origin for magnetoelectric coupling.