Proceedings of the National Academy of Sciences of the United States of America最新文献

In the central region of the human retina, the high-acuity foveola is notable for its dense packing of green (M) and red (L) cones and absence of blue (S) cones. To identify mechanisms that pattern cones in the foveola, we examined human fetal retinas and differentiated retinal organoids. During development, sparse S-opsin-expressing cones are initially observed in the foveola. Later in fetal development, the foveola contains a mix of cones that either coexpress S- and M/L-opsins or exclusively express M/L-opsin. In adults, only M/L cones are present. Two signaling pathway regulators are highly and continuously expressed in the central retina: Cytochrome P450 26 subfamily A member 1 (CYP26A1), which degrades retinoic acid (RA) and Deiodinase 2 (DIO2), which promotes thyroid hormone (TH) signaling. Both CYP26A1 mutant organoids and high RA conditions increased the number of S cones and reduced the number of M/L cones in retinal organoids. In contrast, sustained TH signaling promoted the generation of M/L-opsin-expressing cones and induced M/L-opsin expression in S-opsin-expressing cones, showing that cone fate is plastic. Our data suggest that CYP26A1 degrades RA to specify M/L cones and limit S cones and that continuous DIO2 expression sustains high levels of TH to transition S-opsin-expressing cones into M/L cone fate, resulting in the foveola containing only M/L cones. Given the vulnerability of the foveola in macular degeneration and other retinal disorders, these findings provide a mechanistic framework for engineering organoids for therapeutic applications.

The self-assembly of matter into ordered structures is ubiquitous throughout nature and engineered systems. Programming a material's macroscopic properties via molecular-level structural control is a grand scientific challenge, requiring methods for inverse design that can design a targeted molecule to achieve a given self-assembled structure. One model system that serves as a common proving ground for inverse design algorithms is block copolymers. In these systems, self-consistent field-theory (SCFT) provides a robust thermodynamic model for predicting self-assembly for a given molecular sequence. This work presents a computational algorithm which learns the reverse translation, allowing a target structure to be achieved by varying molecular sequence. The algorithm is based on development of an adjoint solution of the SCFT equations allowing incorporation of automatic differentiation. The power of this algorithm is demonstrated by inverse designing polymer sequences to yield equilibrium structures, resolving the long-standing dilemma of navigating the combinatorial explosion of sequence possibilities offered by complex copolymer designs. The inverse designed sequences show that the algorithm learns to modulate unfavorable block interactions to stabilize these complex morphologies. By learning how to program self-assembly at the molecular-level using only a thermodynamic model, this work opens the door to similar computational inverse design across other soft matter systems.

Ependymomas (EPN) are rare central nervous system tumors that account for approximately 10% of intracranial tumors in children and 4% in adults. Despite their clinical and molecular heterogeneity, spanning supratentorial, posterior fossa, and spinal subtypes, treatment remains limited to surgery and radiotherapy, with chemotherapy offering minimal benefit. Here, we performed transcriptomic analysis of 370 human ependymoma samples and identified two distinct molecular subgroups: EPN-E1 and EPN-E2. The EPN-E1 cluster is enriched for supratentorial tumors harboring ZFTA-RELA fusions (ZFTA-RELA^fus), which occur in over 70% of cases and are associated with poor prognosis. To identify targeted therapies for this aggressive subtype, we validated a ZFTA-RELA^fus mouse model that recapitulates the human EPN-E1 transcriptome and used it for target discovery. Through Kinome Regularization, a machine learning-driven polypharmacology approach, we identified MERTK as a critical regulator of tumor cell viability. Genetic depletion or pharmacologic inhibition of Mertk reduced cell growth ex vivo, and treatment with a clinical-grade MERTK inhibitor significantly suppressed tumor proliferation in vivo. Both human EPN-E1 tumors and ZFTA-RELA^fus mouse tumors exhibited elevated expression of MERTK and its ligand GAS6, and MERTK inhibition led to suppression of pro-survival signaling pathways including MEK/ERK (Mitogen-Activated Protein Kinase Kinase/Extracellular Signal-Regulated Kinase) and PI3K/AKT (Phosphoinositide 3-Kinase/Protein Kinase B). Notably, over 80% of genes upregulated in ZFTA-RELA^fus tumors were downregulated following MERTK inhibition, indicating a strong dependency on this pathway for tumor maintenance. These findings define a signaling vulnerability in ZFTA-RELA-driven ependymomas and support the clinical development of MERTK-targeted therapies for patients with the high-risk EPN-E1 subtype.

Hydrogen hydrates (HH) are a unique class of materials composed of hydrogen molecules confined within crystalline water frameworks. Among their multiple phases, the filled ice structures, particularly the cubic C2 phase, exhibit exceptionally strong host-guest interactions due to ultra-short H₂-H₂O distances and a 1:1 stoichiometry leading to two interpenetrated identical diamond-like sublattices, one comprised of water molecules, the other of hydrogen molecules. At high pressures, nuclear-quantum effects involving both hydrogen molecules and the water lattice become dominant, giving rise to a dual-lattice quantum system. In this work, we explore the sequence of pressure- and temperature-driven phase transitions in HH, focusing on the interplay between molecular rotation, orientational ordering, lattice symmetry breaking, and hydrogen bond symmetrization. Using a combination of computational modeling based on classical and path-integral molecular dynamics, quantum embedding, and high pressure experiments, including Raman spectroscopy and synchrotron X-ray diffraction at low temperatures and high pressures, we identify signatures of quantum-induced ordering and structural transformations in the C2 phase. Our findings reveal that orientational ordering in HH occurs at much lower pressures than in solid hydrogen, by inducing structural changes in the water network and enhancing the coupling of water and hydrogen dynamics. This work provides insights into the quantum behavior of hydrogen under extreme mechanochemical confinement and establishes hydrogen-filled ices as a promising platform for the design of hydrogen-rich quantum materials.

During the COVID-19 pandemic, a key question for researchers and authorities was to understand the psychological motivations that sustained public engagement in protective behavior such as physical distancing and hygienic protection. While feelings of threat were rampant during the pandemic, theories of health psychology have highlighted appraisals related to the ability to cope (e.g., the feeling of being able to adhere cost-effectively to government advice) and argued that coping appraisals are superior predictors of motivations to protect the self against risks. In this study, we conducted a massive population-based comparison of the association between, on the one hand, threat appraisals and coping appraisals and, on the other hand, protection against actual infection during the COVID-19 pandemic. We built a unique Danish data infrastructure that links surveys of ~8% of the adult population (N = 386,633) with the individual results of the 123 million COVID-19 tests performed during 22 mo of the COVID-19 pandemic. Controlling for a comprehensive range of sociodemographic measures and employing panel data to bolster internal validity, we observe that stronger coping appraisals are consistently associated with lower individual probability of COVID-19 infection risk. We find no consistent evidence of a similar association for threat appraisals. Threat appraisals rather seem to index individual feelings of infection exposure. As appeals to fear also have unintended negative consequences (including anxiety, fatigue, and stigmatization), the findings offer strong support for relying on coping-oriented public health policy and communication in future societal crises in the domain of health and beyond.

Pluralistic ignorance-the systematic misperception of others' attitudes-can entrench suboptimal norms, yet its dynamics remain poorly understood. We develop a mathematical model of the coevolution of actions, private attitudes, and beliefs about others, with societal tightness as a central parameter. Our framework integrates theories of spirals of silence, preference falsification, and cultural mismatch into a single dynamic system capturing the effects of material payoffs, cognitive forces, and social influence. The model shows that pluralistic ignorance can arise from lags between attitude change and belief updating, even without silence or deception. Dynamics unfold faster in loose cultures and slower in tight ones: loose societies display sharp but transient peaks of pluralistic ignorance, while tight societies sustain slower, persistent mismatches. Both can experience cultural evolutionary mismatch but through distinct pathways-internalized norm adherence in loose cultures vs. conformity pressure in tight ones. These mechanisms may help explain global patterns where private support exceeds perceived support, such as climate action, women's rights, and abortion attitudes. Interventions must therefore be culturally tailored: accelerating attitude change through highlighting benefits is effective in loose cultures, whereas lowering expression costs (via anonymity or legal protections) empowers norm entrepreneurs in tight cultures. Our framework identifies policy levers and clarifies when apparent opinion stability conceals underlying shifts, offering insights for democratic societies navigating rapid social change.