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Abstract We investigated the biogeography of benthic foraminifera in a highly urbanized tropical seascape, i.e. Hong Kong, in order to assess their utility as bioindicators relative to other marine fauna. Hong Kong is one of the largest coastal cities on the planet and studies of other benthic fauna in the region are available for comparison. We found that: (1) turbid, muddy habitats host a unique foraminiferal fauna; (2) areas with intermediate levels of eutrophication have the highest foraminiferal species diversity; (3) semi-enclosed and heavily polluted environments host a distinct foraminiferal fauna, characterized by low taxonomic diversity and/or high dominance, and that is acclimated to stressful marine conditions. Biodiversity patterns of foraminifera in Hong Kong are generally consistent with those of other soft-sediment macro- and meio-fauna (e.g. polychaetes, molluscs and ostracods); however, foraminifera may be more sensitive than these other groups to eutrophication and associated changes in coastal food webs. The tolerance of some, but not other, species to eutrophic and hypoxic conditions means that foraminiferal faunas can serve as bioindicators across a wide array of environmental conditions, in contrast with corals whose sensitivity to eutrophication results in their absence from eutrophied settings. The well-known autoecology of foraminifera taxa can help to characterize environmental conditions of different habitats and regional environmental gradients. Although the use of fauna as bioindicators may be most robust when data are compared for multiple taxonomic groups, when such broad sampling is not available, benthic foraminifera are particularly well suited for environmental assessments due to their ubiquity, interspecific environmental breadth, and the well-understood environmental preference of individual taxa. 

Abstract Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models. 

The therapeutic potential of recombinant cytokines has been limited by the severe side effects of systemic administration. We describe a strategy to reduce the dose-limiting toxicities of monomeric cytokines by designing two components that require colocalization for activity and that can be independently targeted to restrict activity to cells expressing two surface markers. We demonstrate the approach with a previously designed mimetic of cytokines interleukin-2 and interleukin-15—Neoleukin-2/15 (Neo-2/15)—both fortrans-activating immune cells surrounding targeted tumor cells and forcis-activating directly targeted immune cells. Intrans-activation mode, tumor antigen targeting of the two components enhanced antitumor activity and attenuated toxicity compared with systemic treatment in syngeneic mouse melanoma models. Incis-activation mode, immune cell targeting of the two components selectively expanded CD8⁺ T cells in a syngeneic mouse melanoma model and promoted chimeric antigen receptor T cell activation in a lymphoma xenograft model, enhancing antitumor efficacy in both cases.

 

Many organisms can survive extreme conditions and successfully recover to normal life. This extremotolerant behavior has been attributed in part to repetitive, amphipathic, and intrinsically disordered proteins that are upregulated in the protected state. Here, we assemble a library of approximately 300 naturally-occurring and designed extremotolerance-associated proteins to assess their ability to protect human cells from chemically-induced apoptosis. We show that several proteins from tardigrades, nematodes, and the Chinese giant salamander are apoptosis protective. Notably, we identify a region of the human ApoE protein with similarity to extremotolerance-associated proteins that also protects against apoptosis. This region mirrors the phase separation behavior seen with such proteins, like the tardigrade protein CAHS2. Moreover, we identify a synthetic protein, DHR81, that shares this combination of elevated phase separation propensity and apoptosis protection. Finally, we demonstrate that driving protective proteins into the condensate state increases apoptosis protection, and highlight the ability for DHR81 condensates to sequester caspase-7. Taken together, this work draws a link between extremotolerance-associated proteins, condensate formation, and designing human cellular protection. 

Abstract Nucleation is generally viewed as a structural fluctuation that passes a critical size to eventually become a stable emerging new phase. However, this concept leaves out many details, such as changes in cluster composition and competing pathways to the new phase. In this work, both experimental and computer modeling studies are used to understand the cluster composition and pathways. Monte Carlo and molecular dynamics approaches are used to analyze the thermodynamic and kinetic contributions to the nucleation landscape in barium silicate glasses. Experimental techniques examine the resulting polycrystals that form. Both the modeling and experimental data indicate that a silica rich core plays a dominant role in the nucleation process. 

Deep-learning methods enable the scaffolding of desired functional residues within a well-folded designed protein.