FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

SingPro: a knowledge base providing single-cell proteomic data.

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

ADCdb: the database of antibody-drug conjugates.

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Proximal binding of dCas9 at a DNA double strand break stimulates homology-directed repair as a local inhibitor of classical non-homologous end joining.

In CRISPR/Cas9 genome editing, the tight and persistent target binding of Cas9 provides an opportunity for efficient genetic and epigenetic modification on genome. In particular, technologies based on catalytically dead Cas9 (dCas9) have been developed to enable genomic regulation and live imaging in a site-specific manner. While post-cleavage target residence of CRISPR/Cas9 could alter the pathway choice in repair of Cas9-induced DNA double strand breaks (DSBs), it is possible that dCas9 residing adjacent to a break may also determine the repair pathway for this DSB, providing an opportunity to control genome editing. Here, we found that loading dCas9 onto a DSB-adjacent site stimulated homology-directed repair (HDR) of this DSB by locally blocking recruitment of classical non-homologous end-joining (c-NHEJ) factors and suppressing c-NHEJ in mammalian cells. We further repurposed dCas9 proximal binding to increase HDR-mediated CRISPR genome editing by up to 4-fold while avoiding exacerbation of off-target effects. This dCas9-based local inhibitor provided a novel strategy of c-NHEJ inhibition in CRISPR genome editing in place of small molecule c-NHEJ inhibitors, which are often used to increase HDR-mediated genome editing but undesirably exacerbate off-target effects.

DRESIS: the first comprehensive landscape of drug resistance information.

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA.

In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.

The miRNA: a small but powerful RNA for COVID-19.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

ROS-Mediated Selective Killing Effect of Black Phosphorus: Mechanistic Understanding and Its Guidance for Safe Biomedical Applications.

Black phosphorus (BP)-based nanomaterials have distinguished advantages and potential applications in various biomedical fields. However, their biological effects in physiological systems remain largely unexplored. Here, we systematically revealed a reactive oxygen species (ROS)-mediated mechanism for the selective killing of cancer cells by BP-based nanosheets. The treatment with BP-based materials can induce higher levels of ROS in cancer cells than in normal cells, leading to significant changes in the cytoskeleton, cell cycle arrest, DNA damage, and apoptosis in tumor cell lines. We revealed that the decreased superoxide dismutase activity by lipid peroxides could be an essential mechanism of the selectively higher ROS generation induced by BP-based nanosheets in cancer cells. In addition, the selective killing effect only occurred within a certain dosage range (named "SK range" in this study). Once exceeding the SK range, BP-based materials could also induce a high ROS production in normal tissues, leading to detectable DNA damage and pathological characteristics in normal organs and raising safety concerns. These findings not only shed light on a new mechanism for the selective killing of cancer cells by BP-based materials but also provide deep insights into the safe use of BP-based therapies.

The exon 19-deleted EGFR undergoes ubiquitylation-mediated endocytic degradation via dynamin activity-dependent and -independent mechanisms.

BACKGROUND: The epidermal growth factor receptor (EGFR) is closely implicated in cancer, and sequencing analyses have revealed a high mutation rate of EGFR in lung cancer. Recent advances have provided novel insights into the endocytic regulation of wild-type EGFR, but that of mutated EGFR remains elusive. In the present study, we aim to investigate the endocytic degradation of a frequently occurred exon 19-deleted mutant in lung cancer. METHODS: The EGF-induced endocytic degradation of EGFR was examined in a panel of lung cancer cells using immunoblotting. The subcellular distribution of internalized EGFR was investigated using immunofluorescence and confocal microscopy. The effects of dynamin were assessed using its small molecule inhibitors, while the influence of RTN3 was tested using shRNA-mediated knockdown. Finally the ubiquitylation status of EGFR mutant was studied using immunoprecipitation under steady state and tyrosine kinase inhibitor-treated conditions. RESULTS: EGF induced various rates of EGFR endocytic degradation in lung cancer cells. Interestingly, the exon 19 deletion mutant is constantly internalized and sorted to lysosome for degradation, and this process is independent of dynamin activity. EGF stimulation and HSP90 inhibition further enhance the endocytic degradation of the exon 19 deletion mutant, in a dynamin activity-dependent and -independent manner, respectively. Albeit with different modes of internalization, the uptake of the exon 19-deleted EGFR is mediated through receptor ubiquitylation. CONCLUSIONS: The internalized EGFR mutant is constantly routed through endosome to lysosome for degradation. The endocytosis of EGFR mutant occurs through both dynamin activity-dependent and -independent mechanisms. Our findings gain novel insights into the endocytic regulation of mutated EGFR and may have potential clinical implications.

Decoding Drug Response with Structurized Gridding Map-based Cell Representation.

A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repurposing, and resistance reversal. While targeted anticancer therapies have shown promise, not all cancers have well-established biomarkers to stratify drug response. Single-gene associations only explain a small fraction of the observed drug sensitivity, so a more comprehensive method is needed. However, while deep learning models have shown promise in predicting drug response in cell lines, they still face significant challenges when it comes to their application in clinical applications. Therefore, this study proposed a new strategy called DD-Response for cell-line drug response prediction. First, a limitation of narrow modeling horizons was overcome to expand the model training domain by integrating multiple datasets through source-specific label binarization. Second, a modified representation based on a two-dimensional structurized gridding map (SGM) was developed for cell lines & drugs, avoiding feature correlation neglect and potential information loss. Third, a dual-branch, multi-channel convolutional neural network-based model for pairwise response prediction was constructed, enabling accurate outcomes and improved exploration of underlying mechanisms. As a result, the DD-Response demonstrated superior performance, captured cell-line characteristic variations, and provided insights into key factors impacting cell-line drug response. In addition, DD-Response exhibited scalability in predicting clinical patient responses to drug therapy. Overall, because of DD-response's excellent ability to predict drug response and capture key molecules behind them, DD-response is expected to greatly facilitate drug discovery, repurposing, resistance reversal, and therapeutic optimization.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites.

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research.

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

Target residence of Cas9-sgRNA influences DNA double-strand break repair pathway choices in CRISPR/Cas9 genome editing.

BACKGROUND: Due to post-cleavage residence of the Cas9-sgRNA complex at its target, Cas9-induced DNA double-strand breaks (DSBs) have to be exposed to engage DSB repair pathways. Target interaction of Cas9-sgRNA determines its target binding affinity and modulates its post-cleavage target residence duration and exposure of Cas9-induced DSBs. This exposure, via different mechanisms, may initiate variable DNA damage responses, influencing DSB repair pathway choices and contributing to mutational heterogeneity in genome editing. However, this regulation of DSB repair pathway choices is poorly understood. RESULTS: In repair of Cas9-induced DSBs, repair pathway choices vary widely at different target sites and classical nonhomologous end joining (c-NHEJ) is not even engaged at some sites. In mouse embryonic stem cells, weakening the target interaction of Cas9-sgRNA promotes bias towards c-NHEJ and increases target dissociation and reduces target residence of Cas9-sgRNAs in vitro. As an important strategy for enhancing homology-directed repair, inactivation of c-NHEJ aggravates off-target activities of Cas9-sgRNA due to its weak interaction with off-target sites. By dislodging Cas9-sgRNA from its cleaved targets, DNA replication alters DSB end configurations and suppresses c-NHEJ in favor of other repair pathways, whereas transcription has little effect on c-NHEJ engagement. Dissociation of Cas9-sgRNA from its cleaved target by DNA replication may generate three-ended DSBs, resulting in palindromic fusion of sister chromatids, a potential source for CRISPR/Cas9-induced on-target chromosomal rearrangements. CONCLUSIONS: Target residence of Cas9-sgRNA modulates DSB repair pathway choices likely through varying dissociation of Cas9-sgRNA from cleaved DNA, thus widening on-target and off-target mutational spectra in CRISPR/Cas9 genome editing.

Chitinase 3 like 1 contributes to the development of pulmonary vascular remodeling in pulmonary hypertension.

Chitinase 3 like 1 (CHI3L1) is the prototypic chitinase-like protein mediating inflammation, cell proliferation, and tissue remodeling. Limited data suggest CHI3L1 is elevated in human pulmonary arterial hypertension (PAH) and is associated with disease severity. Despite its importance as a regulator of injury/repair responses, the relationship between CHI3L1 and pulmonary vascular remodeling is not well understood. We hypothesize that CHI3L1 and its signaling pathways contribute to the vascular remodeling responses that occur in pulmonary hypertension (PH). We examined the relationship of plasma CHI3L1 levels and severity of PH in patients with various forms of PH, including group 1 PAH and group 3 PH, and found that circulating levels of serum CHI3L1 were associated with worse hemodynamics and correlated directly with mean pulmonary artery pressure and pulmonary vascular resistance. We also used transgenic mice with constitutive knockout and inducible overexpression of CHI3L1 to examine its role in hypoxia-, monocrotaline-, and bleomycin-induced models of pulmonary vascular disease. In all 3 mouse models of pulmonary vascular disease, pulmonary hypertensive responses were mitigated in CHI3L1-null mice and accentuated in transgenic mice that overexpress CHI3L1. Finally, CHI3L1 alone was sufficient to induce pulmonary arterial smooth muscle cell proliferation, inhibit pulmonary vascular endothelial cell apoptosis, induce the loss of endothelial barrier function, and induce endothelial-mesenchymal transition. These findings demonstrate that CHI3L1 and its receptors play an integral role in pulmonary vascular disease pathobiology and may offer a target for the treatment of PAH and PH associated with fibrotic lung disease.

Functional role of RBM10 in lung adenocarcinoma proliferation.

Lung cancer is one of the most common causes of morbidity and mortality among malignant tumors worldwide. The poor prognosis of patients with lung adenocarcinomas is primarily due to its strong ability to invade and metastasize. Recent research has indicated that RNA­binding protein 10 (RBM10) is mutated in lung adenocarcinoma, and is closely associated with tumor proliferation and apoptosis; however, the precise role of RBM10 in lung adenocarcinoma remains unclear. Our preliminary experiments (unpublished data) revealed that RBM10 expression was upregulated in lung adenocarcinoma cell lines and tissues. In this study, we first detected the protein expression level of RBM10 in lung adenocarcinoma cells and tissues, and we then examined the effects of RBM10 overexpression and downregulation (via small interfering RNA) on the proliferation and apoptosis of stable lung adenocarcinoma cells, along with its possible mechanisms of action. We also used clinical samples of lung adenocarcinomas to verify our results. We found that RBM10 protein was overexpressed in lung adenocarcinoma cells and tissues, and it reduced p53 expression (as detected by immunofluorescence assay and western blot analysis) in A549 cells and inhibited apoptosis (as shown by flow cytometric assay). RBM10 also promoted cell growth and proliferation in vitro and increased cell migration in a cell wound scratch assay. Furthermore, we found that RBM10 activated key proliferative signaling pathways [such as the epidermal growth factor receptor (EGFR), mitogen­activated protein kinase (MAPK) and phosphoinositide 3­kinase (PI3K)­AKT pathways] and inhibited apoptotic pathways. In addition, we demonstrated that a high expression of RBM10 protein in patient tissue samples was associated with a shorter overall survival time and a poor prognosis. On the whole, the findings of this study indicate that RBM10 may function as an oncogene in lung cancer, and may thus prove to be a novel therapeutic target for the prophylaxis and treatment of lung adenocarcinomas.

The effect of core fucosylation-mediated regulation of multiple signaling pathways on lung pericyte activation and fibrosis.

The main event in the progression of pulmonary fibrosis is the appearance of myofibroblasts. Recent evidence supports pericytes as a major source of myofibroblasts. TGFß/Smad2/3 and PDGF/Erk signaling pathways are important for regulating pericyte activation. Previous studies have demonstrated that PDGFßR and TGFßR are modified by core fucosylation (CF) catalyzed by α-1,6-fucosyltransferase (FUT8). The aim of this study was to compare the effect of inhibiting CF versus the PDGFßR and TGFßR signaling pathways on pericyte activation and lung fibrosis. FUT8shRNA was used to knock down FUT8-mediated CF both in vivo and in isolated lung pericytes. The small molecule receptor antagonists, ST1571 (imatinib) and LY2109761, were used to block the PDGFß/pErk and TGFß/pSmad2/3 signaling pathways, respectively. Pericyte detachment and myofibroblastic transformation were assessed by immunofluorescence and Western blot. Histochemical and immunohistochemical staining were used to evaluate the effect of the intervention on pulmonary fibrosis. Our findings demonstrate that FUT8shRNA significantly blocked pericyte activation and the progression of pulmonary fibrosis, achieving intervention effects superior to the small molecule inhibitors. The PDGFß and TGFß pathways were simultaneously affected by the CF blockade. FUT8 expression was upregulated with the transformation of pericytes into myofibroblasts, and silencing FUT8 expression inhibited this transformation. In addition, there is a causal relationship between CF modification catalyzed by FUT8 and pulmonary fibrosis. Our findings suggest that FUT8 may be a novel therapeutic target for pulmonary fibrosis.

Polydopamine-Functionalized CA-(PCL-ran-PLA) Nanoparticles for Target Delivery of Docetaxel and Chemo-photothermal Therapy of Breast Cancer.

Current limitations of cancer therapy include the lack of effective strategy for target delivery of chemotherapeutic drugs, and the difficulty of achieving significant efficacy by single treatment. Herein, we reported a synergistic chemo-photothermal strategy based on aptamer (Apt)-polydopamine (pD) functionalized CA-(PCL-ran-PLA) nanoparticles (NPs) for effective delivery of docetaxel (DTX) and enhanced therapeutic effect. The developed DTX-loaded Apt-pD-CA-(PCL-ran-PLA) NPs achieved promising advantages, such as (i) improved drug loading content (LC) and encapsulation efficiency (EE) initiated by star-shaped copolymer CA-(PCL-ran-PLA); (ii) effective target delivery of drugs to tumor sites by incorporating AS1411 aptamers; (iii) significant therapeutic efficacy caused by synergistic chemo-photothermal treatment. In addition, the pD coating strategy with simple procedures could address the contradiction between targeting modification and maintaining formerly excellent bio-properties. Therefore, with excellent bio-properties and simple preparation procedures, the DTX-loaded Apt-pD-CA-(PCL-ran-PLA) NPs effectively increased the local drug concentration in tumor sites, minimized side effects, and significantly eliminated tumors, indicating the promising application of these NPs for cancer therapy.

Harnessing accurate non-homologous end joining for efficient precise deletion in CRISPR/Cas9-mediated genome editing.

BACKGROUND: Many applications of CRISPR/Cas9-mediated genome editing require Cas9-induced non-homologous end joining (NHEJ), which was thought to be error prone. However, with directly ligatable ends, Cas9-induced DNA double strand breaks may be repaired preferentially by accurate NHEJ. RESULTS: In the repair of two adjacent double strand breaks induced by paired Cas9-gRNAs at 71 genome sites, accurate NHEJ accounts for about 50% of NHEJ events. This paired Cas9-gRNA approach underestimates the level of accurate NHEJ due to frequent + 1 templated insertions, which can be avoided by the predefined Watson/Crick orientation of protospacer adjacent motifs (PAMs). The paired Cas9-gRNA strategy also provides a flexible, reporter-less approach for analyzing both accurate and mutagenic NHEJ in cells and in vivo, and it has been validated in cells deficient for XRCC4 and in mouse liver. Due to high frequencies of precise deletions of defined "3n"-, "3n + 1"-, or "3n + 2"-bp length, accurate NHEJ is used to improve the efficiency and homogeneity of gene knockouts and targeted in-frame deletions. Compared to "3n + 1"-bp, "3n + 2"-bp can overcome + 1 templated insertions to increase the frequency of out-of-frame mutations. By applying paired Cas9-gRNAs to edit MDC1 and key 53BP1 domains, we are able to generate predicted, precise deletions for functional analysis. Lastly, a Plk3 inhibitor promotes NHEJ with bias towards accurate NHEJ, providing a chemical approach to improve genome editing requiring precise deletions. CONCLUSIONS: NHEJ is inherently accurate in repair of Cas9-induced DNA double strand breaks and can be harnessed to improve CRISPR/Cas9 genome editing requiring precise deletion of a defined length.