3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

The Cross-Regulation Between Set1, Clr4, and Lsd1/2 in Schizosaccharomyces pombe.

Eukaryotic chromatin is organized into either silenced heterochromatin or relaxed euchromatin regions, which controls the accessibility of transcriptional machinery and thus regulates gene expression. In fission yeast, Schizosaccharomyces pombe, Set1 is the sole H3K4 methyltransferase and is mainly enriched at the promoters of actively transcribed genes. In contrast, Clr4 methyltransferase initiates H3K9 methylation, which has long been regarded as a hallmark of heterochromatic silencing. Lsd1 and Lsd2 are two highly conserved H3K4 and H3K9 demethylases. As these histone-modifying enzymes perform critical roles in maintaining histone methylation patterns and, consequently, gene expression profiles, cross-regulations among these enzymes are part of the complex regulatory networks. Thus, elucidating the mechanisms that govern their signaling and mutual regulations remains crucial. Here, we demonstrated that C-terminal truncation mutants, lsd1-ΔHMG and lsd2-ΔC, do not compromise the integrity of the Lsd1/2 complex but impair their chromatin-binding capacity at the promoter region of target genomic loci. We identified protein-protein interactions between Lsd1/2 and Raf2 or Swd2, which are the subunits of the Clr4 complex (CLRC) and Set1-associated complex (COMPASS), respectively. We showed that Clr4 and Set1 modulate the protein levels of Lsd1 and Lsd2 in opposite ways through the ubiquitin-proteasome-dependent pathway. During heat stress, the protein levels of Lsd1 and Lsd2 are upregulated in a Set1-dependent manner. The increase in protein levels is crucial for differential gene expression under stress conditions. Together, our results support a cross-regulatory model by which Set1 and Clr4 methyltransferases control the protein levels of Lsd1/2 demethylases to shape the dynamic chromatin landscape.

xSiGra: explainable model for single-cell spatial data elucidation.

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.

AntiFormer: graph enhanced large language model for binding affinity prediction.

Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.

Gene expression prediction from histology images via hypergraph neural networks.

Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.

Structural basis of a small monomeric Clivia fluorogenic RNA with a large Stokes shift.

RNA-based fluorogenic modules have revolutionized the spatiotemporal localization of RNA molecules. Recently, a fluorophore named 5-((Z)-4-((2-hydroxyethyl)(methyl)amino)benzylidene)-3-methyl-2-((E)-styryl)-3,5-dihydro-4H-imidazol-4-one (NBSI), emitting in red spectrum, and its cognate aptamer named Clivia were identified, exhibiting a large Stokes shift. To explore the underlying molecular basis of this unique RNA-fluorophore complex, we determined the tertiary structure of Clivia-NBSI. The overall structure uses a monomeric, non-G-quadruplex compact coaxial architecture, with NBSI sandwiched at the core junction. Structure-based fluorophore recognition pattern analysis, combined with fluorescence assays, enables the orthogonal use of Clivia-NBSI and other fluorogenic aptamers, paving the way for both dual-emission fluorescence and bioluminescence imaging of RNA molecules within living cells. Furthermore, on the basis of the structure-based substitution assay, we developed a multivalent Clivia fluorogenic aptamer containing multiple minimal NBSI-binding modules. This innovative design notably enhances the recognition sensitivity of fluorophores both in vitro and in vivo, shedding light on future efficient applications in various biomedical and research contexts.

DRMref: comprehensive reference map of drug resistance mechanisms in human cancer.

Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

ARR1 and ARR12 modulate arsenite toxicity responses in Arabidopsis roots by transcriptionally controlling the actions of NIP1;1 and NIP6;1.

Cytokinin is central to coordinating plant adaptation to environmental stresses. Here, we first demonstrated the involvement of cytokinin in Arabidopsis responses to arsenite [As(III)] stress. As(III) treatment reduced cytokinin contents, while cytokinin treatment repressed further primary root growth in Arabidopsis plants under As(III) stress. Subsequently, we revealed that the cytokinin signaling members ARR1 and ARR12, the type-B ARABIDOPSIS RESPONSE REGULATORs, participate in cytokinin signaling-mediated As(III) responses in plants as negative regulators. A comprehensive transcriptome analysis of the arr1 and arr12 single and arr1,12 double mutants was then performed to decipher the cytokinin signaling-mediated mechanisms underlying plant As(III) stress adaptation. Results revealed important roles for ARR1 and ARR12 in ion transport, nutrient responses, and secondary metabolite accumulation. Furthermore, using hierarchical clustering and regulatory network analyses, we identified two NODULIN 26-LIKE INTRINSIC PROTEIN (NIP)-encoding genes, NIP1;1 and NIP6;1, potentially involved in ARR1/12-mediated As(III) uptake and transport in Arabidopsis. By analyzing various combinations of arr and nip mutants, including high-order triple and quadruple mutants, we demonstrated that ARR1 and ARR12 redundantly function as negative regulators of As(III) tolerance by acting upstream of NIP1;1 and NIP6;1 to modulate their function in arsenic accumulation. ChIP-qPCR, EMSA, and transient dual-LUC reporter assays revealed that ARR1 and ARR12 transcriptionally activate the expression of NIP1;1 and NIP6;1 by directly binding to their promoters and upregulating their expression, leading to increased arsenic accumulation under As(III) stress. These findings collectively provide insights into cytokinin signaling-mediated plant adaptation to excessive As(III), contributing to the development of crops with low arsenic accumulation.

spaCI: deciphering spatial cellular communications through adaptive graph model.

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.

SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment.

Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functional regions at an allosteric site. In addition to the known effects of mutations around functional sites, mutations at allosteric sites have been associated with protein structure, dynamics, and energy communication. As a result, identifying driver mutations at allosteric sites will be beneficial for deciphering the mechanisms of cancer and developing allosteric drugs. In this study, we provided a platform called DeepAlloDriver to predict driver mutations using a deep learning method that exhibited >93% accuracy and precision. Using this server, we found that a missense mutation in RRAS2 (Gln72 to Leu) might serve as an allosteric driver of tumorigenesis, revealing the mechanism of the mutation in knock-in mice and cancer patients. Overall, DeepAlloDriver would facilitate the elucidation of the mechanisms underlying cancer progression and help prioritize cancer therapeutic targets. The web server is freely available at: https://mdl.shsmu.edu.cn/DeepAlloDriver.

Integration of multiomics features for blood-based early detection of colorectal cancer.

BACKGROUND: Early detection of colorectal cancer (CRC) significantly enhances patient outcomes. Conventional CRC screening tools, like endoscopy and stool-based tests, have constraints due to their invasiveness or suboptimal patient adherence. Recently, liquid biopsy employing plasma cell-free DNA (cfDNA) has emerged as a potential noninvasive screening technique for various malignancies. METHODS: In this research, we harnessed the Mutation Capsule Plus (MCP) technology to profile an array of genomic characteristics from cfDNA procured from a single blood draw. This profiling encompassed DNA methylation, the 5' end motif, copy number variation (CNV), and genetic mutations. An integrated model built upon selected multiomics biomarkers was trained using a cohort of 93 CRC patients and 96 healthy controls. RESULTS: This model was subsequently validated in another cohort comprising 89 CRC patients and 95 healthy controls. Remarkably, the model achieved an area under the curve (AUC) of 0.981 (95% confidence interval (CI), 0.965-0.998) in the validation set, boasting a sensitivity of 92.1% (95% CI, 84.5%-96.8%) and a specificity of 94.7% (95% CI, 88.1%-98.3%). These numbers surpassed the performance of any single genomic feature. Importantly, the sensitivities reached 80% for stage I, 89.2% for stage II, and were 100% for stages III and IV. CONCLUSION: Our findings underscore the clinical potential of our multiomics liquid biopsy test, indicating its prospective role as a noninvasive method for early-stage CRC detection. This multiomics approach holds promise for further refinement and broader clinical application.

A noninvasive multianalytical approach establishment for risk assessment and gastric cancer screening.

Effective screening and early detection are critical to improve the prognosis of gastric cancer (GC). Our study aims to explore noninvasive multianalytical biomarkers and construct integrative models for preliminary risk assessment and GC detection. Whole genomewide methylation marker discovery was conducted with CpG tandems target amplification (CTTA) in cfDNA from large asymptomatic screening participants in a high-risk area of GC. The methylation and mutation candidates were validated simultaneously using one plasma from patients at various gastric lesion stages by multiplex profiling with Mutation Capsule Plus (MCP). Helicobacter pylori specific antibodies were detected with a recomLine assay. Integrated models were constructed and validated by the combination of multianalytical biomarkers. A total of 146 and 120 novel methylation markers were found in CpG islands and promoter regions across the genome with CTTA. The methylation markers together with the candidate mutations were validated with MCP and used to establish a 133-methylation-marker panel for risk assessment of suspicious precancerous lesions and GC cases and a 49-methylation-marker panel as well as a 144-amplicon-mutation panel for GC detection. An integrated model comprising both methylation and specific antibody panels performed better for risk assessment than a traditional model (AUC, 0.83 and 0.63, P < .001). A second model for GC detection integrating methylation and mutation panels also outperformed the traditional model (AUC, 0.82 and 0.68, P = .005). Our study established methylation, mutation and H. pylori-specific antibody panels and constructed two integrated models for risk assessment and GC screening. Our findings provide new insights for a more precise GC screening strategy in the future.

Early genetic divergence of high-grade carcinomas originating from low-grade ovarian serous neoplasms.

The current paradigm implicates a fallopian tube precursor as the origin of most ovarian high-grade serous carcinomas (HGSC). However, a rare subset of HGSCs develop via a distinct pathway from low-grade serous ovarian neoplasms (namely, serous borderline tumor and low-grade serous carcinoma). This alternate pathway for the development of HGSC and other poorly differentiated carcinomas of the ovary is not well understood. To elucidate the molecular pathogenesis and evolutionary trajectory of histologic transformation of low-grade serous neoplasms, we performed whole exome sequencing on microdissected low-grade and higher-grade components from 7 cases of serous borderline tumor or low-grade serous carcinoma associated with a synchronous or metachronous indeterminate/high-grade carcinoma. In most cases, there were relatively few somatic mutations shared between matched low-grade and higher-grade tumors compared to private mutations specific to each component (i.e., phylogenetic trees with short trunks and long branches). Truncal mutations, present across all tumor samples from a given patient, included known drivers of low-grade serous neoplasms: KRAS (G12D, n=4), BRAF (G469A, n=1), NF2 (n=1), and USP9X (n=1). Transformation to HGSC was associated with a TP53 mutation with bi-allelic inactivation in 3 cases, all with severe nuclear atypia, and associated with genome-wide copy number alterations and allelic imbalances. TP53-wildtype tumors comprised a morphologic spectrum, which included indeterminate-grade serous carcinomas with moderate nuclear atypia and high mitotic activity, while lacking extensive chromosomal instability (n=2), and poorly-differentiated carcinomas (n=2, including a high-grade Mullerian carcinoma and an undifferentiated carcinoma with sarcomatoid features). In summary, synchronous and metachronous low-grade serous neoplasms and higher-grade carcinomas are clonally related. Early genetic divergence, most evident in cases with TP53 mutations, suggests that high-grade transformation may be a relatively early molecular event.

Modifying Polyvinylidene Chloride Resin with Fluorine Monomer and Cross-Linking Monomers.

Modified polyvinylidene chloride (PVDC) resin was prepared using octafluoropentyl methacrylate and trimethylolpropane trimethacrylate as modifying monomers through seeded emulsion polymerization. The successful incorporation of octafluoropentyl methacrylate into the PVDC resin was confirmed by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) analyses. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and XPS were utilized to investigate the element distribution in the modified monomer emulsion and the mechanism of monomer modification. The results demonstrated that the fluorine monomer was reacted in the resin, and mainly concentrated on the surface of the resin. The addition of octafluoropentyl methacrylate and trimethylolpropane trimethacrylate improved the water resistance of the resin. Compared to unmodified PVDC resin, the contact angle of the modified PVDC resin increased from 89.46° to 109.51°, and the water resistance at room temperature increased from 120 to 500 h. Furthermore, the modified resin exhibited excellent mechanical properties, thermal stability, and storage stability.

Proapoptotic effect of WS-299 induced by NOXA accumulation and NRF2-counterbalanced oxidative stress damage through targeting RBX1-UBE2M interaction in gastric cancers.

The abnormal activation of Cullin RING E3 Ligases (CRLs) is closely associated with the occurrence and development of various cancers. Targeting the neddylation pathway represents an effective approach for cancer treatment. In this work, we reported that WS-299, structurally featuring a coumarin moiety attached to the triazolopyrimidine, exhibited excellent anti-proliferative activity in MGC-803 and HGC-27 cells. WS-299 exerted potent anticancer effects by inhibiting clone formation, EdU incorporation and inducing cell cycle arrest. WS-299 inhibited CUL3/5 neddylation and caused an obvious accumulation of Nrf2 and NOXA, substrates of CRL3 and CRL5, respectively. Biochemical studies showed that WS-299 inhibited CUL3 neddylation by inhibiting RBX1-UBE2M interaction. The anti-proliferative effect of WS-299 was mainly induced by NOXA-mediated apoptosis. Of note, Nrf2 attenuated WS-299-induced reactive oxygen species (ROS) levels. Furthermore, Nrf2 accumulation also had an antagonistic effect on NOXA-induced apoptosis. Therefore, WS-299 and siNrf2 synergistically increased ROS levels, apoptotic cells and suppressed tumor growth in vivo. Taken together, our research clarified the anti-cancer mechanisms of WS-299 through targeting the RBX1-UBE2M protein-protein interaction and inhibiting the neddylation modification of CUL3 and CUL5. More importantly, our studies also demonstrated that combination of WS-299 with shNrf2 could be an effective strategy for treating gastric cancers.

Microbial strategies for effective microplastics biodegradation: Insights and innovations in environmental remediation.

Microplastics (MPs), diminutive yet ubiquitous fragments arising from the degradation of plastic waste, pervade environmental matrices, posing substantial risks to ecological systems and trophic dynamics. This review meticulously examines the origins, distribution, and biological impacts of MPs, with an incisive focus on elucidating the molecular and cellular mechanisms underpinning their toxicity. We highlight the indispensable role of microbial consortia and enzymatic pathways in the oxidative degradation of MPs, offering insights into enhanced biodegradation processes facilitated by innovative pretreatment methodologies. Central to our discourse is the interplay between MPs and biota, emphasizing the detoxification capabilities of microbial metabolisms and enzymatic functions in ameliorating MPs' deleterious effects. Additionally, we address the practical implementations of MP biodegradation in environmental remediation, advocating for intensified research to unravel the complex biodegradation pathways and to forge effective strategies for the expeditious elimination of MPs from diverse ecosystems. This review not only articulates the pervasive challenges posed by MPs but also positions microbial strategies at the forefront of remedial interventions, thereby paving the way for groundbreaking advancements in environmental conservation.

Targeting DNA polymerase to DNA double-strand breaks reduces DNA deletion size and increases templated insertions generated by CRISPR/Cas9.

Most insertions or deletions generated by CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) endonucleases are short (<25 bp), but unpredictable on-target long DNA deletions (>500 bp) can be observed. The possibility of generating long on-target DNA deletions poses safety risks to somatic genome editing and makes the outcomes of genome editing less predictable. Methods for generating refined mutations are desirable but currently unavailable. Here, we show that fusing Escherichia coli DNA polymerase I or the Klenow fragment to Cas9 greatly increases the frequencies of 1-bp deletions and decreases >1-bp deletions or insertions. Importantly, doing so also greatly decreases the generation of long deletions, including those >2 kb. In addition, templated insertions (the insertion of the nucleotide 4 nt upstream of the protospacer adjacent motif) were increased relative to other insertions. Counteracting DNA resection was one of the mechanisms perturbing deletion sizes. Targeting DNA polymerase to double-strand breaks did not increase off-targets or base substitution rates around the cleavage sites, yet increased editing efficiency in primary cells. Our strategy makes it possible to generate refined DNA mutations for improved safety without sacrificing efficiency of genome editing.

Structure-based insights into fluorogenic RNA aptamers.

Fluorogenic RNA aptamers are in vitro-selected RNA molecules capable of binding to specific fluorophores, significantly increasing their intrinsic fluorescence. Over the past decade, the color palette of fluorescent RNA aptamers has greatly expanded. The emergence and development of these fluorogenic RNA aptamers has introduced a powerful approach for visualizing RNA localization and transport with high spatiotemporal resolution in live cells. To date, a variety of tertiary structures of fluorogenic RNA aptamers have been determined using X-ray crystallography or NMR spectroscopy. Many of these fluorogenic RNA aptamers feature base quadruples or base triples in their fluorophore-binding sites. This review summarizes the structure-based investigations of fluorogenic RNA aptamers, with a focus on their overall folds, ligand-binding pockets and fluorescence activation mechanisms. Additionally, the exploration of how structures guide rational optimization to enhance RNA visualization techniques is discussed.