Prediction of efficiencies for diverse prime editing systems in multiple cell types.

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.

Synonymous but Not Silent: The Codon Usage Code for Gene Expression and Protein Folding.

Codon usage bias, the preference for certain synonymous codons, is found in all genomes. Although synonymous mutations were previously thought to be silent, a large body of evidence has demonstrated that codon usage can play major roles in determining gene expression levels and protein structures. Codon usage influences translation elongation speed and regulates translation efficiency and accuracy. Adaptation of codon usage to tRNA expression determines the proteome landscape. In addition, codon usage biases result in nonuniform ribosome decoding rates on mRNAs, which in turn influence the cotranslational protein folding process that is critical for protein function in diverse biological processes. Conserved genome-wide correlations have also been found between codon usage and protein structures. Furthermore, codon usage is a major determinant of mRNA levels through translation-dependent effects on mRNA decay and translation-independent effects on transcriptional and posttranscriptional processes. Here, we discuss the multifaceted roles and mechanisms of codon usage in different gene regulatory processes.

A Tradeoff in the Neural Code across Regions and Species.

Many evolutionary years separate humans and macaques, and although the amygdala and cingulate cortex evolved to enable emotion and cognition in both, an evident functional gap exists. Although they were traditionally attributed to differential neuroanatomy, functional differences might also arise from coding mechanisms. Here we find that human neurons better utilize information capacity (efficient coding) than macaque neurons in both regions, and that cingulate neurons are more efficient than amygdala neurons in both species. In contrast, we find more overlap in the neural vocabulary and more synchronized activity (robustness coding) in monkeys in both regions and in the amygdala of both species. Our findings demonstrate a tradeoff between robustness and efficiency across species and regions. We suggest that this tradeoff can contribute to differential cognitive functions between species and underlie the complementary roles of the amygdala and the cingulate cortex. In turn, it can contribute to fragility underlying human psychopathologies.

Pervasive Regulatory Functions of mRNA Structure Revealed by High-Resolution SHAPE Probing.

mRNAs can fold into complex structures that regulate gene expression. Resolving such structures de novo has remained challenging and has limited our understanding of the prevalence and functions of mRNA structure. We use SHAPE-MaP experiments in living E. coli cells to derive quantitative, nucleotide-resolution structure models for 194 endogenous transcripts encompassing approximately 400 genes. Individual mRNAs have exceptionally diverse architectures, and most contain well-defined structures. Active translation destabilizes mRNA structure in cells. Nevertheless, mRNA structure remains similar between in-cell and cell-free environments, indicating broad potential for structure-mediated gene regulation. We find that the translation efficiency of endogenous genes is regulated by unfolding kinetics of structures overlapping the ribosome binding site. We discover conserved structured elements in 35% of UTRs, several of which we validate as novel protein binding motifs. RNA structure regulates every gene studied here in a meaningful way, implying that most functional structures remain to be discovered.

Transcription Impacts the Efficiency of mRNA Translation via Co-transcriptional N6-adenosine Methylation.

Transcription and translation are two main pillars of gene expression. Due to the different timings, spots of action, and mechanisms of regulation, these processes are mainly regarded as distinct and generally uncoupled, despite serving a common purpose. Here, we sought for a possible connection between transcription and translation. Employing an unbiased screen of multiple human promoters, we identified a positive effect of TATA box on translation and a general coupling between mRNA expression and translational efficiency. Using a CRISPR-Cas9-mediated approach, genome-wide analyses, and in vitro experiments, we show that the rate of transcription regulates the efficiency of translation. Furthermore, we demonstrate that m6A modification of mRNAs is co-transcriptional and depends upon the dynamics of the transcribing RNAPII. Suboptimal transcription rates lead to elevated m6A content, which may result in reduced translation. This study uncovers a general and widespread link between transcription and translation that is governed by epigenetic modification of mRNAs.

CRISPR/Cas9 boosts wheat yield by reducing brassinosteroid signaling.

A modern green revolution is needed to ensure global food security. Recently, Song et al. reported a new strategy to create high-yielding, semi-dwarf wheat varieties with improved nitrogen-use efficiency by inhibiting brassinosteroid (BR) signaling through clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein9 (Cas9)-mediated knockout of the ZnF-B gene encoding a zinc-finger RING-type E3 ligase.

Null mutants of a tomato Rho of plants exhibit enhanced water use efficiency without a penalty to yield.

Improving water use efficiency in crops is a significant challenge as it involves balancing water transpiration and CO2 uptake through stomatal pores. This study investigates the role of SlROP9, a tomato Rho of Plants protein, in guard cells and its impact on plant transpiration. The results reveal that SlROP9 null mutants exhibit reduced stomatal conductance while photosynthetic CO2 assimilation remains largely unaffected. Notably, there is a notable decrease in whole-plant transpiration in the rop9 mutants compared to the wild type, especially during noon hours when the water pressure deficit is high. The elevated stomatal closure observed in rop9 mutants is linked to an increase in reactive oxygen species formation. This is very likely dependent on the respiratory burst oxidase homolog (RBOH) NADPH oxidase and is not influenced by abscisic acid (ABA). Consistently, activated ROP9 can interact with RBOHB in both yeast and plants. In diverse tomato accessions, drought stress represses ROP9 expression, and in Arabidopsis stomatal guard cells, ABA suppresses ROP signaling. Therefore, the phenotype of the rop9 mutants may arise from a disruption in ROP9-regulated RBOH activity. Remarkably, large-scale field experiments demonstrate that the rop9 mutants display improved water use efficiency without compromising fruit yield. These findings provide insights into the role of ROPs in guard cells and their potential as targets for enhancing water use efficiency in crops.

m6A modification plays an integral role in mRNA stability and translation during pattern-triggered immunity.

Plants employ distinct mechanisms to respond to environmental changes. Modification of mRNA by N 6-methyladenosine (m6A), known to affect the fate of mRNA, may be one such mechanism to reprogram mRNA processing and translatability upon stress. However, it is difficult to distinguish a direct role from a pleiotropic effect for this modification due to its prevalence in RNA. Through characterization of the transient knockdown-mutants of m6A writer components and mutants of specific m6A readers, we demonstrate the essential role that m6A plays in basal resistance and pattern-triggered immunity (PTI). A global m6A profiling of mock and PTI-induced Arabidopsis plants as well as formaldehyde fixation and cross-linking immunoprecipitation-sequencing of the m6A reader, EVOLUTIONARILY CONSERVED C-TERMINAL REGION2 (ECT2) showed that while dynamic changes in m6A modification and binding by ECT2 were detected upon PTI induction, most of the m6A sites and their association with ECT2 remained static. Interestingly, RNA degradation assay identified a dual role of m6A in stabilizing the overall transcriptome while facilitating rapid turnover of immune-induced mRNAs during PTI. Moreover, polysome profiling showed that m6A enhances immune-associated translation by binding to the ECT2/3/4 readers. We propose that m6A plays a positive role in plant immunity by destabilizing defense mRNAs while enhancing their translation efficiency to create a transient surge in the production of defense proteins.

Design principles of heterointerfacial redox chemistry for highly reversible lithium metal anode.

High electrochemical reversibility is required for the application of high-energy-density lithium (Li) metal batteries; however, inactive Li formation and SEI (solid electrolyte interface)-instability-induced electrolyte consumption cause low Coulombic efficiency (CE). The prior interfacial chemical designs in terms of alloying kinetics have been used to enhance the CE of Li metal anode; however, the role of its redox chemistry at heterointerfaces remains a mystery. Herein, the relationship between heterointerfacial redox chemistry and electrochemical transformation reversibility is investigated. It is demonstrated that the lower redox potential at heterointerface contributes to higher CE, and this enhancement in CE is primarily due to the regulation of redox chemistry to Li deposition behavior rather than the formation of SEI films. Low oxidation potential facilitates the formation of the surface with the highly electrochemical binding feature after Li stripping, and low reduction potential can maintain binding ability well during subsequent Li plating, both of which homogenize Li deposition and thus optimize CE. In particular, Mg hetero-metal with ultra-low redox potential enables Li metal anode with significantly improved CE (99.6%) and stable cycle life for 700 cycles at 3.0 mA cm-2. This work provides insight into the heterointerfacial design principle of next-generation negative electrodes for highly reversible metal batteries.

Bio-catalyzed oxidation self-charging zinc-polymer batteries.

Oxidation self-charging batteries have emerged with the demand for powering electronic devices around the clock. The low efficiency of self-charging has been the key challenge at present. Here, a more efficient autoxidation self-charging mechanism is realized by introducing hemoglobin (Hb) as a positive electrode additive in the polyaniline (PANI)-zinc battery system. The heme acts as a catalyst that reduces the energy barrier of the autoxidation reaction by regulating the charge and spin state of O2. To realize self-charging, the adsorbed O2 molecules capture electrons of the reduced (discharged state) PANI, leading to the desorption of zinc ions and the oxidation of PANI to complete self-charging. The battery can discharge for 12 min (0.5 C) after 50 self-charging/discharge cycles, while there is nearly no discharge capacity in the absence of Hb. This biology-inspired electronic regulation strategy may inspire new ideas to boost the performance of self-charging batteries.

Enhanced charge-carrier dynamics and efficient solar-to-urea conversion on Si-based photocathodes.

Photoelectrochemical (PEC) coupling of CO2 and nitrate can provide a useful and green source of urea, but the process is affected by the photocathodes with poor charge-carrier dynamics and low conversion efficiency. Here, a NiFe diatomic catalysts/TiO2 layer/nanostructured n+p-Si photocathode is rationally designed, achieving a good charge-separation efficiency of 78.8% and charge-injection efficiency of 56.9% in the process of PEC urea synthesis. Compared with the electrocatalytic urea synthesis by using the same catalysts, the Si-based photocathode shows a similar urea yield rate (81.1 mg·h-1·cm-2) with a higher faradic efficiency (24.2%, almost twice than the electrocatalysis) at a lower applied potential under 1 sun illumination, meaning that a lower energy-consumption method acquires more aimed productions. Integrating the PEC measurements and characterization results, the synergistic effect of hierarchical structure is the dominating factor for enhancing the charge-carrier separation, transfer, and injection by the matched band structure and favorable electron-migration channels. This work provides a direct and efficient route of solar-to-urea conversion.

Local O2 concentrating boosts the electro-Fenton process for energy-efficient water remediation.

The electro-Fenton process is a state-of-the-art water treatment technology used to remove organic contaminants. However, the low O2 utilization efficiency (OUE, <1%) and high energy consumption remain the biggest obstacles to practical application. Here, we propose a local O2 concentrating (LOC) approach to increase the OUE by over 11-fold compared to the conventional simple O2 diffusion route. Due to the well-designed molecular structure, the LOC approach enables direct extraction of O2 from the bulk solution to the reaction interface; this eliminates the need to pump O2/air to overcome the sluggish O2 mass transfer and results in high Faradaic efficiencies (~50%) even under natural air diffusion conditions. Long-term operation of a flow-through pilot device indicated that the LOC approach saved more than 65% of the electric energy normally consumed in treating actual industrial wastewater, demonstrating the great potential of this system-level design to boost the electro-Fenton process for energy-efficient water remediation.

Mechanisms controlling metabolite concentrations of the Calvin Benson Cycle.

Maintaining proper metabolite levels in a complex metabolic network is crucial for maintaining a high flux through the network. In this paper, we discuss major regulatory mechanisms over the Calvin Benson Cycle (CBC) with regard to their roles in conferring homeostasis of metabolite levels in CBC. These include: 1) Redox regulation of enzymes in the CBC on one hand ensures that metabolite levels stay above certain lower bounds under low light while on the other hand increases the flux through the CBC under high light. 2) Metabolite regulations, especially allosteric regulations of major regulatory enzymes, ensure the rapid up-regulation of fluxes to ensure sufficient amount of triose phosphate is available for end product synthesis and concurrently avoid phosphate limitation. 3) A balanced activities of enzymes in the CBC help maintain balanced flux through CBC; some innate product feedback mechanisms, in particular the ADP feedback regulation of GAPDH and F6P feedback regulation of FBPase, exist in CBC to achieve such a balanced enzyme activities and hence flux distribution in the CBC for greater photosynthetic efficiency. Transcriptional regulation and natural variations of enzymes controlling CBC metabolite homeostasis should be further explored to maximize the potential of engineering CBC for greater efficiency.

High-throughput quantitation of protein-RNA UV-crosslinking efficiencies as a predictive tool for high-confidence identification of RNA-binding proteins.

UV-crosslinking has proven to be an invaluable tool for the identification of RNA-protein interactomes. The paucity of methods for distinguishing background from bona fide RNA-protein interactions, however, makes attribution of RNA-binding function on UV-crosslinking alone challenging. To address this need, we previously reported an RNA-binding protein (RBP) confidence scoring metric (RCS), incorporating both signal-to-noise (S:N) and protein abundance determinations to distinguish high- and low-confidence candidate RBPs. Although RCS has utility, we sought a direct metric for quantification and comparative evaluation of protein-RNA interactions. Here we propose the use of protein-specific UV-crosslinking efficiency (%CL), representing the molar fraction of a protein that is crosslinked to RNA, for functional evaluation of candidate RBPs. Application to the HeLa RNA interactome yielded %CL values for 1097 proteins. Remarkably, %CL values span over five orders of magnitude. For the HeLa RNA interactome, %CL values comprise a range from high efficiency, high specificity interactions, e.g., the Elav protein HuR and the Pumilio homolog Pum2, with %CL values of 45.9 and 24.2, respectively, to very low efficiency and specificity interactions, for example, the metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and alpha-enolase, with %CL values of 0.0016, 0.006, and 0.008, respectively. We further extend the utility of %CL through prediction of protein domains and classes with known RNA-binding functions, thus establishing it as a useful metric for RNA interactome analysis. We anticipate that this approach will benefit efforts to establish functional RNA interactomes and support the development of more predictive computational approaches for RBP identification.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

Factors contributing to the potency of CD8+ T cells.

CD8+ cytotoxic T lymphocytes (CTLs) play a crucial role in targeting virus-infected and cancer cells. Although other cytotoxic lymphocytes such as CD4+ T and natural killer (NK) cells, as well as chimeric antigen receptor (CAR)-T cells, can also identify and destroy aberrant cells, they seem to be significantly less potent based on available experimental data. Here, I contemplate the molecular mechanisms controlling the sensitivity and kinetics of granule-mediated CD8+ T cell cytolytic responses. I posit that the clustering of MHC-I molecules and T cell receptors (TCRs) on the cell surface, as well as the contribution of the CD8 co-receptor, are major factors driving exceptionally potent cytolytic responses. I also contend that CD8+ T cells with known specificity and engineered TCR-T cells might be among the most efficient cytolytic effectors for treating patients suffering from viral infections or cancer.

The productive performance of intercropping.

Crop diversification has been put forward as a way to reduce the environmental impact of agriculture without penalizing its productivity. In this context, intercropping, the planned combination of two or more crop species in one field, is a promising practice. On an average, intercropping saves land compared with the component sole crops, but it remains unclear whether intercropping produces a higher yield than the most productive single crop per unit area, i.e., whether intercropping achieves transgressive overyielding. Here, we quantified the performance of intercropping for the production of grain, calories, and protein in a global meta-analysis of several production indices. The results show that intercrops outperform sole crops when the objective is to achieve a diversity of crop products on a given land area. However, when intercropping is evaluated for its ability to produce raw products without concern for diversity, intercrops on average generate a small loss in grain or calorie yield compared with the most productive sole crop (-4%) but achieve similar or higher protein yield, especially with maize/legume combinations grown at moderate N supply. Overall, although intercropping does not achieve transgressive overyielding on average, our results show that intercropping performs well in producing a diverse set of crop products and performs almost similar to the most productive component sole crop to produce raw products, while improving crop resilience, enhancing ecosystem services, and improving nutrient use efficiency. Our study, therefore, confirms the great interest of intercropping for the development of a more sustainable agricultural production, supporting diversified diets.

Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency.

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

Dissipation and energy propagation across scales in an active cytoskeletal material.

Living systems are intrinsically nonequilibrium: They use metabolically derived chemical energy to power their emergent dynamics and self-organization. A crucial driver of these dynamics is the cellular cytoskeleton, a defining example of an active material where the energy injected by molecular motors cascades across length scales, allowing the material to break the constraints of thermodynamic equilibrium and display emergent nonequilibrium dynamics only possible due to the constant influx of energy. Notwithstanding recent experimental advances in the use of local probes to quantify entropy production and the breaking of detailed balance, little is known about the energetics of active materials or how energy propagates from the molecular to emergent length scales. Here, we use a recently developed picowatt calorimeter to experimentally measure the energetics of an active microtubule gel that displays emergent large-scale flows. We find that only approximately one-billionth of the system's total energy consumption contributes to these emergent flows. We develop a chemical kinetics model that quantitatively captures how the system's total thermal dissipation varies with ATP and microtubule concentrations but that breaks down at high motor concentration, signaling an interference between motors. Finally, we estimate how energy losses accumulate across scales. Taken together, these results highlight energetic efficiency as a key consideration for the engineering of active materials and are a powerful step toward developing a nonequilibrium thermodynamics of living systems.

Multilayered optofluidics for sustainable buildings.

Indoor climate control is among the most energy-intensive activities conducted by humans. A building facade that can achieve versatile climate control directly, through independent and multifunctional optical reconfigurations, could significantly reduce this energy footprint, and its development represents a pertinent unmet challenge toward global sustainability. Drawing from optically adaptive multilayer skins within biological organisms, we report a multilayered millifluidic interface for achieving a comprehensive suite of independent optical responses in buildings. We digitally control the flow of aqueous solutions within confined milliscale channels, demonstrating independent command over total transmitted light intensity (95% modulation between 250 and 2,500 nm), near-infrared-selective absorption (70% modulation between 740 and 2,500 nm), and dispersion (scattering). This combinatorial optical tunability enables configurable optimization of the amount, wavelength, and position of transmitted solar radiation within buildings over time, resulting in annual modeled energy reductions of more than 43% over existing technologies. Our scalable "optofluidic" platform, leveraging a versatile range of aqueous chemistries, may represent a general solution for the climate control of buildings.