Mammalian oocytes store proteins for the early embryo on cytoplasmic lattices.

Mammalian oocytes are filled with poorly understood structures called cytoplasmic lattices. First discovered in the 1960s and speculated to correspond to mammalian yolk, ribosomal arrays, or intermediate filaments, their function has remained enigmatic to date. Here, we show that cytoplasmic lattices are sites where oocytes store essential proteins for early embryonic development. Using super-resolution light microscopy and cryoelectron tomography, we show that cytoplasmic lattices are composed of filaments with a high surface area, which contain PADI6 and subcortical maternal complex proteins. The lattices associate with many proteins critical for embryonic development, including proteins that control epigenetic reprogramming of the preimplantation embryo. Loss of cytoplasmic lattices by knocking out PADI6 or the subcortical maternal complex prevents the accumulation of these proteins and results in early embryonic arrest. Our work suggests that cytoplasmic lattices enrich maternally provided proteins to prevent their premature degradation and cellular activity, thereby enabling early mammalian development.

Structure and mechanism of human cystine exporter cystinosin.

Lysosomal amino acid efflux by proton-driven transporters is essential for lysosomal homeostasis, amino acid recycling, mTOR signaling, and maintaining lysosomal pH. To unravel the mechanisms of these transporters, we focus on cystinosin, a prototypical lysosomal amino acid transporter that exports cystine to the cytosol, where its reduction to cysteine supplies this limiting amino acid for diverse fundamental processes and controlling nutrient adaptation. Cystinosin mutations cause cystinosis, a devastating lysosomal storage disease. Here, we present structures of human cystinosin in lumen-open, cytosol-open, and cystine-bound states, which uncover the cystine recognition mechanism and capture the key conformational states of the transport cycle. Our structures, along with functional studies and double electron-electron resonance spectroscopic investigations, reveal the molecular basis for the transporter's conformational transitions and protonation switch, show conformation-dependent Ragulator-Rag complex engagement, and demonstrate an unexpected activation mechanism. These findings provide molecular insights into lysosomal amino acid efflux and a potential therapeutic strategy.

Glycogen accumulation and phase separation drives liver tumor initiation.

Glucose consumption is generally increased in tumor cells to support tumor growth. Interestingly, we report that glycogen accumulation is a key initiating oncogenic event during liver malignant transformation. We found that glucose-6-phosphatase (G6PC) catalyzing the last step of glycogenolysis is frequently downregulated to augment glucose storage in pre-malignant cells. Accumulated glycogen undergoes liquid-liquid phase separation, which results in the assembly of the Laforin-Mst1/2 complex and consequently sequesters Hippo kinases Mst1/2 in glycogen liquid droplets to relieve their inhibition on Yap. Moreover, G6PC or another glycogenolysis enzyme-liver glycogen phosphorylase (PYGL) deficiency in both human and mice results in glycogen storage disease along with liver enlargement and tumorigenesis in a Yap-dependent manner. Consistently, elimination of glycogen accumulation abrogates liver growth and cancer incidence, whereas increasing glycogen storage accelerates tumorigenesis. Thus, we concluded that cancer-initiating cells adapt a glycogen storing mode, which blocks Hippo signaling through glycogen phase separation to augment tumor incidence.

iPSCs from a Hibernator Provide a Platform for Studying Cold Adaptation and Its Potential Medical Applications.

Hibernating mammals survive hypothermia (<10°C) without injury, a remarkable feat of cellular preservation that bears significance for potential medical applications. However, mechanisms imparting cold resistance, such as cytoskeleton stability, remain elusive. Using the first iPSC line from a hibernating mammal (13-lined ground squirrel), we uncovered cellular pathways critical for cold tolerance. Comparison between human and ground squirrel iPSC-derived neurons revealed differential mitochondrial and protein quality control responses to cold. In human iPSC-neurons, cold triggered mitochondrial stress, resulting in reactive oxygen species overproduction and lysosomal membrane permeabilization, contributing to microtubule destruction. Manipulations of these pathways endowed microtubule cold stability upon human iPSC-neurons and rat (a non-hibernator) retina, preserving its light responsiveness after prolonged cold exposure. Furthermore, these treatments significantly improved microtubule integrity in cold-stored kidneys, demonstrating the potential for prolonging shelf-life of organ transplants. Thus, ground squirrel iPSCs offer a unique platform for bringing cold-adaptive strategies from hibernators to humans in clinical applications. VIDEO ABSTRACT.

Glycerophosphodiesters inhibit lysosomal phospholipid catabolism in Batten disease.

Batten disease, the most prevalent form of neurodegeneration in children, is caused by mutations in the CLN3 gene, which encodes a lysosomal transmembrane protein. CLN3 loss leads to significant accumulation of glycerophosphodiesters (GPDs), the end products of glycerophospholipid catabolism in the lysosome. Despite GPD storage being robustly observed upon CLN3 loss, the role of GPDs in neuropathology remains unclear. Here, we demonstrate that GPDs act as potent inhibitors of glycerophospholipid catabolism in the lysosome using human cell lines and mouse models. Mechanistically, GPDs bind and competitively inhibit the lysosomal phospholipases PLA2G15 and PLBD2, which we establish to possess phospholipase B activity. GPDs effectively inhibit the rate-limiting lysophospholipase activity of these phospholipases. Consistently, lysosomes of CLN3-deficient cells and tissues accumulate toxic lysophospholipids. Our work establishes that the storage material in Batten disease directly disrupts lysosomal lipid homeostasis, suggesting GPD clearance as a potential therapeutic approach to this fatal disease.

Atomic observation and structural evolution of covalent organic framework rotamers.

Dynamic 3D covalent organic frameworks (COFs) have shown concerted structural transformation and adaptive gas adsorption due to the conformational diversity of organic linkers. However, the isolation and observation of COF rotamers constitute undergoing challenges due to their comparable free energy and subtle rotational energy barrier. Here, we report the atomic-level observation and structural evolution of COF rotamers by cryo-3D electron diffraction and synchrotron powder X-ray diffraction. Specifically, we optimize the crystallinity and morphology of COF-320 to manifest its coherent dynamic responses upon adaptive inclusion of guest molecules. We observe a significant crystal expansion of 29 vol% upon hydration and a giant swelling with volume change up to 78 vol% upon solvation. We record the structural evolution from a non-porous contracted phase to two narrow-pore intermediate phases and the fully opened expanded phase using n-butane as a stabilizing probe at ambient conditions. We uncover the rotational freedom of biphenylene giving rise to significant conformational changes on the diimine motifs from synclinal to syn-periplanar and anticlinal rotamers. We illustrate the 10-fold increment of pore volumes and 100% enhancement of methane uptake capacity of COF-320 at 100 bar and 298 K. The present findings shed light on the design of smarter organic porous materials to maximize host-guest interaction and boost gas uptake capacity through progressive structural transformation.

Widespread, bipartisan aversion exists to neighbors owning AR-15s or storing guns insecurely.

Over 45,000 gun deaths occur annually in the United States, a country with more than 100 million gun owners and more than 350 million guns. Nevertheless, passing legislation to reduce gun violence is difficult because the issue is intensely polarized. Polls asking about general gun policies (e.g., AR-15 restrictions) demonstrate that, at least in the abstract, Americans disagree vehemently about whether civilians should be able to keep and bear arms. It is possible, however, that a hidden consensus exists in America, which has thus far escaped attention-specifically, that when the focus is on their immediate environments and daily lives, even traditionally pro-gun groups may exhibit aversion to certain types of gun ownership and storage practices. To test this, we conducted two preregistered survey experiments with a large national sample. The first was a conjoint analysis where respondents chose between neighbors (n = 33,596 choices) who randomly varied on seven attributes, including gun ownership (none, pistol, AR-15). No group of respondents, not even traditionally pro-gun groups (e.g., Republicans), exhibited a significant preference for living near gun owners, and every group was averse to AR-15-owning neighbors. The second experiment, per debates about safe-storage laws, was a picture-based factorial vignette that randomized a neighbor's gun storage practices (n = 2,098). Every group of respondents was averse to interacting with a neighbor who stored guns outside of a locked safe. Our findings demonstrate that there is widespread agreement that certain types of gun ownership and storage practices are undesirable for communities.

Proteasome condensate formation is driven by multivalent interactions with shuttle factors and ubiquitin chains.

Stress conditions can cause the relocalization of proteasomes to condensates in yeast and mammalian cells. The interactions that facilitate the formation of proteasome condensates, however, are unclear. Here, we show that the formation of proteasome condensates in yeast depends on ubiquitin chains together with the proteasome shuttle factors Rad23 and Dsk2. These shuttle factors colocalize to these condensates. Strains deleted for the third shuttle factor gene, DDI1, show proteasome condensates in the absence of cellular stress, consistent with the accumulation of substrates with long K48-linked ubiquitin chains that accumulate in this mutant. We propose a model where the long K48-linked ubiquitin chains function as a scaffold for the ubiquitin-binding domains of the shuttle factors and the proteasome, allowing for the multivalent interactions that further drive condensate formation. Indeed, we determined different intrinsic ubiquitin receptors of the proteasome-Rpn1, Rpn10, and Rpn13-and the Ubl domains of Rad23 and Dsk2 are critical under different condensate-inducing conditions. In all, our data support a model where the cellular accumulation of substrates with long ubiquitin chains, potentially due to reduced cellular energy, allows for proteasome condensate formation. This suggests that proteasome condensates are not simply for proteasome storage, but function to sequester soluble ubiquitinated substrates together with inactive proteasomes.

A network model to predict ionic transport in porous materials.

Understanding the dynamics of electric-double-layer (EDL) charging in porous media is essential for advancements in next-generation energy storage devices. Due to the high computational demands of direct numerical simulations and a lack of interfacial boundary conditions for reduced-order models, the current understanding of EDL charging is limited to simple geometries. Here, we present a network model to predict EDL charging in arbitrary networks of long pores in the Debye-Hückel limit without restrictions on EDL thickness and pore radii. We demonstrate that electrolyte transport is described by Kirchhoff's laws in terms of the electrochemical potential of charge (the valence-weighted average of the ion electrochemical potentials) instead of the electric potential. By employing the equivalent circuit representation suggested by these modified Kirchhoff's laws, our methodology accurately captures the spatial and temporal dependencies of charge density and electric potential, matching results obtained from computationally intensive direct numerical simulations. Our network model provides results up to six orders of magnitude faster, enabling the efficient simulation of a triangular lattice of five thousand pores in 6 min. We employ the framework to study the impact of pore connectivity and polydispersity on electrode charging dynamics for pore networks and discuss how these factors affect the time scale, energy density, and power density of capacitive charging. The scalability and versatility of our methodology make it a rational tool for designing 3D-printed electrodes and for interpreting geometric effects on electrode impedance spectroscopy measurements.

Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function.

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning-based methods demand huge training data and are difficult to generalize. Here, we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the target data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2[Formula: see text]3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, and supports customized spatially varying fidelity. BRIEF's multifold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing and promote collaboration and progress in the biomedical field.

Autophagy maintains endosperm quality during seed storage to preserve germination ability in Arabidopsis.

To preserve germination ability, plant seeds must be protected from environmental stresses during the storage period. Here, we demonstrate that autophagy, an intracellular degradation system, maintains seed germination ability in Arabidopsis thaliana. The germination ability of long-term (>5 years) stored dry seeds of autophagy-defective (atg) mutant and wild-type (WT) plants was compared. Long-term stored (old) seeds of atg mutants showed lower germination ability than WT seeds, although short-term stored (new) seeds of atg mutants did not show such a phenotype. After removal of the seed coat and endosperm from old atg mutant seeds, the embryos developed into seedlings. Autophagic flux was maintained in endosperm cells during the storage period, and autophagy defect resulted in the accumulation of oxidized proteins and accelerated endosperm cell death. Consistent with these findings, the transcripts of genes, ENDO-ß-MANNANASE 7 and EXPANSIN 2, which are responsible for degradation/remodeling of the endosperm cell wall during germination, were reduced in old atg mutant seeds. We conclude that autophagy maintains endosperm quality during seed storage by suppressing aging-dependent oxidative damage and cell death, which allows the endosperm to perform optimal functions during germination, i.e., cell wall degradation/remodeling, even after long-term storage.

Direct solar energy conversion on zinc-air battery.

A concept of solar energy convertible zinc-air battery (SZAB) is demonstrated through rational design of an electrode coupled with multifunction. The multifunctional electrode is fabricated using nitrogen-substituted graphdiyne (N-GDY) with large π-conjugated carbonous network, which can work as photoresponsive bifunctional electrocatalyst, enabling a sunlight-promoted process through efficient injection of photoelectrons into the conduction band of N-GDY. SZAB enables direct conversion and storage of solar energy during the charging process. Such a battery exhibits a lowered charge voltage under illumination, corresponding to a high energy efficiency of 90.4% and electric energy saving of 30.3%. The battery can display a power conversion efficiency as high as 1.02%. Density functional theory calculations reveal that the photopromoted oxygen evolution reaction kinetics originates from the transition from the alkyne bonds to double bonds caused by the transfer of excited electrons, which changes the position of highest occupied molecular orbital and lowest unoccupied molecular orbital, thus greatly promoting the formation of intermediates to the conversion process. Our findings provide conceptual and experimental confirmation that batteries are charged directly from solar energy without the external solar cells, providing a way to manufacture future energy devices.

Real-time tracking of electron transfer at catalytically active interfaces in lithium-ion batteries.

Transition metals and related compounds are known to exhibit high catalytic activities in various electrochemical reactions thanks to their intriguing electronic structures. What is lesser known is their unique role in storing and transferring electrons in battery electrodes which undergo additional solid-state conversion reactions and exhibit substantially large extra capacities. Here, a full dynamic picture depicting the generation and evolution of electrochemical interfaces in the presence of metallic nanoparticles is revealed in a model CoCO3/Li battery via an in situ magnetometry technique. Beyond the conventional reduction to a Li2CO3/Co mixture under battery operation, further decomposition of Li2CO3 is realized by releasing interfacially stored electrons from its adjacent Co nanoparticles, whose subtle variation in the electronic structure during this charge transfer process has been monitored in real time. The findings in this work may not only inspire future development of advanced electrode materials for next-generation energy storage devices but also open up opportunities in achieving in situ monitoring of important electrocatalytic processes in many energy conversion and storage systems.

Modeling direct air carbon capture and storage in a 1.5 °C climate future using historical analogs.

Limiting the rise in global temperature to 1.5 °C will rely, in part, on technologies to remove CO2 from the atmosphere. However, many carbon dioxide removal (CDR) technologies are in the early stages of development, and there is limited data to inform predictions of their future adoption. Here, we present an approach to model adoption of early-stage technologies such as CDR and apply it to direct air carbon capture and storage (DACCS). Our approach combines empirical data on historical technology analogs and early adoption indicators to model a range of feasible growth pathways. We use these pathways as inputs to an integrated assessment model (the Global Change Analysis Model, GCAM) and evaluate their effects under an emissions policy to limit end-of-century temperature change to 1.5 °C. Adoption varies widely across analogs, which share different strategic similarities with DACCS. If DACCS growth mirrors high-growth analogs (e.g., solar photovoltaics), it can reach up to 4.9 GtCO2 removal by midcentury, compared to as low as 0.2 GtCO2 for low-growth analogs (e.g., natural gas pipelines). For these slower growing analogs, unabated fossil fuel generation in 2050 is reduced by 44% compared to high-growth analogs, with implications for energy investments and stranded assets. Residual emissions at the end of the century are also substantially lower (by up to 43% and 34% in transportation and industry) under lower DACCS scenarios. The large variation in growth rates observed for different analogs can also point to policy takeaways for enabling DACCS.

Lysosomes and Brain Health.

One of the fundamental properties of the cell is the capability to digest and remodel its own components according to metabolic and developmental needs. This is accomplished via the autophagy-lysosome system, a pathway of critical importance in the brain, where it contributes to neuronal plasticity and must protect nonreplaceable neurons from the potentially harmful accumulation of cellular waste. The study of lysosomal biogenesis and function in the context of common and rare neurodegenerative diseases has revealed that a dysfunctional autophagy-lysosome system is the shared nexus where multiple, interconnected pathogenic events take place. The characterization of pathways and mechanisms regulating the lysosomal system and autophagic clearance offers unprecedented opportunities for the development of polyvalent therapeutic strategies based on the enhancement of the autophagy-lysosome pathway to maintain cellular homeostasis and achieve neuroprotection.

DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage.

DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity. Here, we apply the Bloom Filter, a space-efficient probabilistic data structure, to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin-yang codec is developed.

Data-driven electrolyte design for lithium metal anodes.

Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.

Meeting the challenges of large-scale carbon storage and hydrogen production.

There is a pressing need to rapidly, and massively, scale up negative carbon strategies such as carbon capture and storage (CCS). At the same time, large-scale CCS can enable ramp-up of large-scale hydrogen production, a key component of decarbonized energy systems. We argue here that the safest, and most practical strategy for dramatically increasing CO2 storage in the subsurface is to focus on regions where there are multiple partially depleted oil and gas reservoirs. Many of these reservoirs have adequate storage capacity, are geologically and hydrodynamically well understood and are less prone to injection-induced seismicity than saline aquifers. Once a CO2 storage facility is up and running, it can be used to store CO2 from multiple sources. Integration of CCS with hydrogen production appears to be an economically viable strategy for dramatically reducing greenhouse gas emissions over the next decade, particularly in oil- and gas-producing countries where there are numerous depleted reservoirs that are potentially suitable for large-scale carbon storage.

Shedding light on rechargeable Na/Cl2 battery.

Advancing new ideas of rechargeable batteries represents an important path to meeting the ever-increasing energy storage needs. Recently, we showed rechargeable sodium/chlorine (Na/Cl2) (or lithium/chlorine Li/Cl2) batteries that used a Na (or Li) metal negative electrode, a microporous amorphous carbon nanosphere (aCNS) positive electrode, and an electrolyte containing dissolved aluminum chloride and fluoride additives in thionyl chloride [G. Zhu et al., Nature 596, 525-530 (2021) and G. Zhu et al., J. Am. Chem. Soc. 144, 22505-22513 (2022)]. The main battery redox reaction involved conversion between NaCl and Cl2 trapped in the carbon positive electrode, delivering a cyclable capacity of up to 1,200 mAh g-1 (based on positive electrode mass) at a ~3.5 V discharge voltage [G. Zhu et al., Nature 596, 525-530 (2021) and G. Zhu et al., J. Am. Chem. Soc. 144, 22505-22513 (2022)]. Here, we identified by X-ray photoelectron spectroscopy (XPS) that upon charging a Na/Cl2 battery, chlorination of carbon in the positive electrode occurred to form carbon-chlorine (C-Cl) accompanied by molecular Cl2 infiltrating the porous aCNS, consistent with Cl2 probed by mass spectrometry. Synchrotron X-ray diffraction observed the development of graphitic ordering in the initially amorphous aCNS under battery charging when the carbon matrix was oxidized/chlorinated and infiltrated with Cl2. The C-Cl, Cl2 species and graphitic ordering were reversible upon discharge, accompanied by NaCl formation. The results revealed redox conversion between NaCl and Cl2, reversible graphitic ordering/amorphourization of carbon through battery charge/discharge, and probed trapped Cl2 in porous carbon by XPS.

Parallel molecular computation on digital data stored in DNA.

DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limited computation capabilities. Inspired by the computational power of "DNA strand displacement," we augment DNA storage with "in-memory" molecular computation using strand displacement reactions to algorithmically modify data in a parallel manner. We show programs for binary counting and Turing universal cellular automaton Rule 110, the latter of which is, in principle, capable of implementing any computer algorithm. Information is stored in the nicks of DNA, and a secondary sequence-level encoding allows high-throughput sequencing-based readout. We conducted multiple rounds of computation on 4-bit data registers, as well as random access of data (selective access and erasure). We demonstrate that large strand displacement cascades with 244 distinct strand exchanges (sequential and in parallel) can use naturally occurring DNA sequence from M13 bacteriophage without stringent sequence design, which has the potential to improve the scale of computation and decrease cost. Our work merges DNA storage and DNA computing, setting the foundation of entirely molecular algorithms for parallel manipulation of digital information preserved in DNA.