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Genome editing holds promise for correcting pathogenic mutations. However, it is difficult to determine off-target effects of editing due to single-nucleotide polymorphism in individuals. Here we developed a method named GOTI (genome-wide off-target analysis by two-cell embryo injection) to detect off-target mutations by editing one blastomere of two-cell mouse embryos using either CRISPR-Cas9 or base editors. Comparison of the whole-genome sequences of progeny cells of edited and nonedited blastomeres at embryonic day 14.5 showed that off-target single-nucleotide variants (SNVs) were rare in embryos edited by CRISPR-Cas9 or adenine base editor, with a frequency close to the spontaneous mutation rate. By contrast, cytosine base editing induced SNVs at more than 20-fold higher frequencies, requiring a solution to address its fidelity.

Key Points Drug combinations are highly useful for enhanced therapeutics. Understanding of their mechanisms facilitates the discovery of new multicomponent and multi-target therapeutics. This article describes the extensive investigation of the published literature on a large number of drug combinations from interaction and network perspectives, which reveals general and specific modes of action. Understanding the molecular mechanisms underlying the effects of efficacious drug combinations could aid the discovery of novel combinations and multi-targeted drugs. This article presents an extensive investigation of drug combinations for which rigorous analytical information is available in published literature, which illustrates several general types of combination mechanisms and highlights the potential value of molecular interaction profiles for studying such mechanisms. Understanding the molecular mechanisms underlying synergistic, potentiative and antagonistic effects of drug combinations could facilitate the discovery of novel efficacious combinations and multi-targeted agents. In this article, we describe an extensive investigation of the published literature on drug combinations for which the combination effect has been evaluated by rigorous analysis methods and for which relevant molecular interaction profiles of the drugs involved are available. Analysis of the 117 drug combinations identified reveals general and specific modes of action, and highlights the potential value of molecular interaction profiles in the discovery of novel multicomponent therapies.

Kawasaki disease (KD) is the most common cause of acquired heart disease in children in developed countries. Although functional and phenotypic changes of immune cells have been reported, a global understanding of immune responses underlying acute KD is unclear. Here, using single-cell RNA sequencing, we profile peripheral blood mononuclear cells from seven patients with acute KD before and after intravenous immunoglobulin therapy and from three age-matched healthy controls. The most differentially expressed genes are identified in monocytes, with high expression of pro-inflammatory mediators, immunoglobulin receptors and low expression of MHC class II genes in acute KD. Single-cell RNA sequencing and flow cytometry analyses, of cells from an additional 16 KD patients, show that although the percentage of total B cells is substantially decreased after therapy, the percentage of plasma cells among the B cells is significantly increased. The percentage of CD8 + T cells is decreased in acute KD, notably effector memory CD8 + T cells compared with healthy controls. Oligoclonal expansions of both B cell receptors and T cell receptors are observed after therapy. We identify biological processes potentially underlying the changes of each cell type. The single-cell landscape of both innate and adaptive immune responses provides insights into pathogenesis and therapy of KD. Immune cell changes are associated with Kawasaki disease (KD) pathogenesis. Here, using single cell RNA sequencing of PBMC, the authors show monocyte inflammatory genes are over-expressed in KD and TCR and BCR clonotype sequences show oligoclonal expansions after intravenous immunoglobulin therapy.

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.

Cytosine base editors (CBEs) offer a powerful tool for correcting point mutations, yet their DNA and RNA off-target activities have caused concerns in biomedical applications. We describe screens of 23 rationally engineered CBE variants, which reveal mutation residues in the predicted DNA-binding site can dramatically decrease the Cas9-independent off-target effects. Furthermore, we obtained a CBE variant—YE1-BE3-FNLS—that retains high on-target editing efficiency while causing extremely low off-target edits and bystander edits. Structural and biochemical insights help engineer a cytosine base editor variant that possesses improved on-target activity with minimal DNA and RNA off-target editing.

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

In the current context of global urbanization and climate change, balancing ecological protection and economic development is a particular challenge in the optimal allocation of regional land use. Here, we propose a research framework for the optimal allocation of land use that considers the regional ecological security pattern (ESP) and allocates space for land-use activities to areas with low ecological risk. Taking Baicheng, China as our study area, ecological sources were first identified by integrating their ecological importance and landscape connectivity, and ecological corridors and functional zones were extracted using the minimum cumulative resistance difference and circuit theory. The ecological source areas were then taken as limiting factors, and four future scenarios were established for 2030 using the parcel-level land-use simulator (PLUS) model. The ecological corridors and functional zones served as areas having restricted ecological conditions, and the four future scenarios were coupled into the corresponding functional zones to optimize the land-use structure in 2030. The results indicate that under the coupled ESP–PLUS scenario, the spatial distribution and structure of land use in Baicheng balance the needs of ecological source area protection and economic development, resulting in greater sustainability. By 2030, the cultivated land area will steadily increase, but attention will also be given to the protection of ecological land (e.g., woodland and marshland), aligning with current policy planning demands. An analysis of the landscape indices for each future scenario found all scenarios to be effective in reducing negative changes in landscape patterns. These findings provide a novel perspective for the rational allocation of future land resources and the optimization of land-use structures.

Background Advancements in single-cell RNA sequencing have enabled the analysis of millions of cells, but integrating such data across samples and methods while mitigating batch effects remains challenging. Deep learning approaches address this by learning biologically conserved gene expression representations, yet systematic benchmarking of loss functions and integration performance is lacking. Results We evaluate 16 integration methods using a unified variational autoencoder framework, incorporating batch and cell-type information. Results reveal limitations in the single-cell integration benchmarking index (scIB) for preserving intra-cell-type information. To address this, we introduce a correlation-based loss function and enhance benchmarking metrics to better capture biological conservation. Using cell annotations from lung and breast atlases, our approach improves biological signal preservation. We propose a refined integration framework, scIB-E, and metrics that provide deeper insights into the integration process and offer guidance for advanced developments in integrating increasingly complex single-cell data. Conclusions This benchmark highlights the potential of deep learning-based approaches for single-cell data integration, emphasizing the importance of biologically informed metrics and improved benchmarking strategies.

Background Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers due to its high metastasis rate in the liver. However, little is known about the molecular features of hepatic metastases due to difficulty in obtaining fresh tissues and low tumor cellularity. Results We conduct exome sequencing and RNA sequencing for synchronous surgically resected primary tumors and the paired hepatic metastases from 17 hepatic oligometastatic pancreatic ductal adenocarcinoma and validate our findings in specimens from 35 of such cases. The comprehensive analysis of somatic mutations, copy number alterations, and gene expressions show high similarity between primary tumors and hepatic metastases. However, hepatic metastases also show unique characteristics, such as a higher degree of 3p21.1 loss, stronger abilities of proliferation, downregulation of epithelial to mesenchymal transition activity, and metabolic rewiring. More interesting, altered tumor microenvironments are observed in hepatic metastases, especially a higher proportion of tumor infiltrating M2 macrophage and upregulation of complement cascade. Further experiments demonstrate that expression of C1q increases in primary tumors and hepatic metastases, C1q is mainly produced by M2 macrophage, and C1q promotes migration and invasion of PDAC cells. Conclusion Taken together, we find potential factors that contribute to different stages of PDAC metastasis. Our study broadens the understanding of molecular mechanisms driving PDAC metastasis.

Background The CRISPR/Cas9 system has become an efficient gene editing method for generating cells carrying precise gene mutations, including the rearrangement and deletion of chromosomal segments. However, whether an entire chromosome could be eliminated by this technology is still unknown. Results Here we demonstrate the use of the CRISPR/Cas9 system to eliminate targeted chromosomes. Using either multiple cleavages induced by a single-guide RNA (sgRNA) that targets multiple chromosome-specific sites or a cocktail of multiple sgRNAs, each targeting one specific site, we found that a sex chromosome could be selectively eliminated in cultured cells, embryos, and tissues in vivo. Furthermore, this approach was able to produce a targeted autosome loss in aneuploid mouse embryonic stem cells with an extra human chromosome and human induced pluripotent stem cells with trisomy 21, as well as cancer cells. Conclusions CRISPR/Cas9-mediated targeted chromosome elimination offers a new approach to develop animal models with chromosome deletions, and a potential therapeutic strategy for human aneuploidy diseases involving additional chromosomes.