Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes

Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.

Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex

Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.

Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning methods. By changing the species origin and relative position of uracil-DNA glycosylase and deaminase, together with codon optimization, we obtain optimized C-to-G BEs (OPTI-CGBEs) for efficient C-to-G transversion. The motif preference of OPTI-CGBEs for editing 100 endogenous sites is determined in HEK293T cells. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr-edited offspring. Thus, these engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites.

C->G transversions can be highly desirable editing outcomes. Here the authors optimise CGBEs and provide a deep learning model for predicting editing outcomes based on sequence context.

MULTIPHYSICS MODELING OF PRECURSORS IN MOLTEN SALT FAST REACTORS USING PROTEUS AND Nek5000

The goal of this work was to calculate the impact of the delayed neutron precursor drift in fast spectrum Molten Salt Reactors (MSRs) using coupled solutions from the neutronics code PROTEUS and the computational fluid dynamics code Nek5000. Specifically, using a multiphysics approach to solve the effective delayed neutron fraction (βeff) or delayed neutron precursor distribution for reactors with flowing fuel salts would provide valuable information for transient simulations and safety assessments. Given the multiple options for the flux solution and geometric resolution/fidelity in PROTEUS, two approaches were developed and applied to various test cases: PROTEUS-NODAL/Nek5000 and PROTEUS-SN/Nek5000. For the former, the precursors are tracked in the built-in precursor drift model in PROTEUS-NODAL, whereas in the latter, Nek5000 directly tracks the precursors. Both approaches were used to solve a single test channel problem and showed excellent agreement in the calculated βeff. Separately, a 3D hourglass-shaped core was modeled using the PROTEUS-SN/Nek5000 approach. This problem was designed to demonstrate the capability of the discrete ordinates (SN) solver and Nek5000 to model complex core designs with axially varying geometries and the ability for Nek5000 to track the precursors and calculate the resulting βeff. In addition, the Nek5000 calculations revealed the presence of recirculation zones in the hourglass design, which could lead to significant temperatures in the fuel salt and surrounding materials. These first coupled solutions show why these approaches may be necessary for not only predicting the precursor drift effect in fast MSRs but also for reactor design and performance assessments.

MSRE TRANSIENT BENCHMARKS USING SAM

In recent years, there has been renewed interest in Molten Salter Reactors (MSRs) for their potential advantages compared to reactors that rely on solid fuel. In response to such interest, the System Analysis Module (SAM) was enhanced to include MSR-specific modeling features including a delayed neutron precursor drift model and a modified point kinetics model. This paper discusses the validation of these features using the experiments conducted in the Molten Salt Reactor Experiment (MSRE). These experiments include the pump start-up and coast-down tests at zero power and a thermal convection test. For the zero power tests, the change in pump speeds induces flow rate changes in the core that impact the precursor concentrations. This introduces a neutron imbalance and requires the adjustment of the control rods to counter-balance this effect. SAM was used to evaluate the precursor concentration in the core as a function of time, and the resulting changes in reactivity were evaluated through the modified point kinetics equation. The results show good agreement with the experimental data. It should be noted that the pump performance curve used in this analysis was re-constructed based on the initial water test data of the fuel pump. The steady-state pump curve is assumed to be applicable to transient flow operations. The thermal convection test was conducted by shutting off the pumps, reducing the inlet core temperature for 360 minutes, and allowing the power to be adjusted by the inherent feedbacks of the system. The power level during this transient was evaluated by SAM as a function of time.

GSK3 beta mediates suppression of cyclin D2 expression by tumor suppressor PTEN

PTEN, encoding a lipid phosphatase, is a tumor suppressor gene and is mutated in various types of cancers. It is reported to regulate G1 to S phase transition of the cell cycle by influencing the expression, protein stability and subcellular location of cyclin D1. Here, we provide evidence that PTEN modulates the transcription and protein stability of cyclin D2. Targeted deletion of Pten in mouse embryonic fibroblasts (MEFs) endowed cells with greater potential to overcome G1 arrest than wild-type MEFs and led to the elevated expression of cyclin D2, which was suppressed by the introduction of PTEN. We further defined a pathway involving GSK3beta and beta-catenin/TCF in PTEN-mediated suppression of cyclin D2 transcription. LiCl, an inhibitor of GSK3beta, abolished inhibitory effect of PTEN on cyclin D2 expression, and TCF members could directly bind to the promoter of cyclin D2 and regulate its transcription in a CREB-dependent manner. Our results indicate that the downregulation of cyclin D2 expression by PTEN is mediated by the GSK3beta/beta-catenin/TCF pathway in cooperation with CREB, and suggest a convergence from the PI-3 kinase/PTEN pathway and the Wnt pathway in modulation of cyclin D2 expression.