AI-Based multimodal Multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: A retrospective study.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. METHODS: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I-III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep learning multimodal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). RESULTS: This study included a total of 1,011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, we categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T cell subtypes and reduced B cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an AUC of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates (P<0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS (P<0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. We utilized the GradCAM algorithm to generate heatmaps from pathology-based deep learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians' understanding of the model's predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. CONCLUSION: The AI-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations' impact. The integrated deep learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.

Exploring the impact of N4-acetylcytidine modification in RNA on non-neoplastic disease: unveiling its role in pathogenesis and therapeutic opportunities.

RNA modifications include not only methylation modifications, such as m6A, but also acetylation modifications, which constitute a complex interaction involving "writers," "readers," and "erasers" that play crucial roles in growth, genetics, and disease. N4-acetylcytidine (ac4C) is an ancient and highly conserved RNA modification that plays a profound role in the pathogenesis of a wide range of diseases. This review provides insights into the functional impact of ac4C modifications in disease and introduces new perspectives for disease treatment. These studies provide important insights into the biological functions of post-transcriptional RNA modifications and their potential roles in disease mechanisms, offering new perspectives and strategies for disease treatment.

A library-on-library screen reveals the breadth expansion landscape of a broadly neutralizing betacoronavirus antibody.

Broadly neutralizing antibodies (bnAbs) typically evolve cross-reactivity breadth through acquiring somatic hypermutations. While evolution of breadth requires improvement of binding to multiple antigenic variants, most experimental evolution platforms select against only one antigenic variant at a time. In this study, a yeast display library-on-library approach was applied to delineate the affinity maturation of a betacoronavirus bnAb, S2P6, against 27 spike stem helix peptides in a single experiment. Our results revealed that the binding affinity landscape of S2P6 varies among different stem helix peptides. However, somatic hypermutations that confer general improvement in binding affinity across different stem helix peptides could also be identified. We further showed that a key somatic hypermutation for breadth expansion involves long-range interaction. Overall, our work not only provides a proof-of-concept for using a library-on-library approach to analyze the evolution of antibody breadth, but also has important implications for the development of broadly protective vaccines.

Epistasis mediates the evolution of the receptor binding mode in recent human H3N2 hemagglutinin.

The receptor-binding site of influenza A virus hemagglutinin partially overlaps with major antigenic sites and constantly evolves. In this study, we observe that mutations G186D and D190N in the hemagglutinin receptor-binding site have coevolved in two recent human H3N2 clades. X-ray crystallography results show that these mutations coordinately drive the evolution of the hemagglutinin receptor binding mode. Epistasis between G186D and D190N is further demonstrated by glycan binding and thermostability analyses. Immunization and neutralization experiments using mouse and human samples indicate that the evolution of receptor binding mode is accompanied by a change in antigenicity. Besides, combinatorial mutagenesis reveals that G186D and D190N, along with other natural mutations in recent H3N2 strains, alter the compatibility with a common egg-adaptive mutation in seasonal influenza vaccines. Overall, our findings elucidate the role of epistasis in shaping the recent evolution of human H3N2 hemagglutinin and substantiate the high evolvability of its receptor-binding mode.

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively. Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

BACKGROUND: Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. METHODS: This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. RESULTS: A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. CONCLUSIONS: The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning.

The fusion peptide of SARS-CoV-2 spike protein is functionally important for membrane fusion during virus entry and is part of a broadly neutralizing epitope. However, sequence determinants at the fusion peptide and its adjacent regions for pathogenicity and antigenicity remain elusive. In this study, we perform a series of deep mutational scanning (DMS) experiments on an S2 region spanning the fusion peptide of authentic SARS-CoV-2 in different cell lines and in the presence of broadly neutralizing antibodies. We identify mutations at residue 813 of the spike protein that reduced TMPRSS2-mediated entry with decreased virulence. In addition, we show that an F823Y mutation, present in bat betacoronavirus HKU9 spike protein, confers resistance to broadly neutralizing antibodies. Our findings provide mechanistic insights into SARS-CoV-2 pathogenicity and also highlight a potential challenge in developing broadly protective S2-based coronavirus vaccines.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy.

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Nanotechnology-enabled sonodynamic therapy against malignant tumors.

Sonodynamic therapy (SDT) is an emerging approach for malignant tumor treatment, offering high precision, deep tissue penetration, and minimal side effects. The rapid advancements in nanotechnology, particularly in cancer treatment, have enhanced the efficacy and targeting specificity of SDT. Combining sonodynamic therapy with nanotechnology offers a promising direction for future cancer treatments. In this review, we first systematically discussed the anti-tumor mechanism of SDT and then summarized the common nanotechnology-related sonosensitizers and their recent applications. Subsequently, nanotechnology-related therapies derived using the SDT mechanism were elaborated. Finally, the role of nanomaterials in SDT combined therapy was also introduced.

Synergistic role of activated CD4+ memory T cells and CXCL13 in augmenting cancer immunotherapy efficacy.

The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4+ memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4+ memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.

Unveiling the best predictive models for early­onset metastatic cancer: Insights and innovations (Review).

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early­onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing.

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C-X-C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single-cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune-inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune-related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune-related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G-related genes was developed using multi-level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune-active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Tumor-associated macrophages in non-small-cell lung cancer: From treatment resistance mechanisms to therapeutic targets.

Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related deaths worldwide. Different treatment approaches are typically employed based on the stage of NSCLC. Common clinical treatment methods include surgical resection, drug therapy, and radiation therapy. However, with the introduction and utilization of immune checkpoint inhibitors, cancer treatment has entered a new era, completely revolutionizing the treatment landscape for various cancers and significantly improving overall patient survival. Concurrently, treatment resistance often poses a critical challenge, with many patients experiencing disease progression following an initial response due to treatment resistance. Increasing evidence suggests that the tumor microenvironment (TME) plays a pivotal role in treatment resistance. Tumor-associated macrophages (TAMs) within the TME can promote treatment resistance in NSCLC by secreting various cytokines activating signaling pathways, and interacting with other immune cells. Therefore, this article will focus on elucidating the key mechanisms of TAMs in treatment resistance and analyze how targeting TAMs can reduce the levels of treatment resistance in NSCLC, providing a comprehensive understanding of the principles and approaches to overcome treatment resistance in NSCLC.

Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning.

The fusion peptide of SARS-CoV-2 spike protein is functionally important for membrane fusion during virus entry and is part of a broadly neutralizing epitope. However, sequence determinants at the fusion peptide and its adjacent regions for pathogenicity and antigenicity remain elusive. In this study, we performed a series of deep mutational scanning (DMS) experiments on an S2 region spanning the fusion peptide of authentic SARS-CoV-2 in different cell lines and in the presence of broadly neutralizing antibodies. We identified mutations at residue 813 of the spike protein that reduced TMPRSS2-mediated entry with decreased virulence. In addition, we showed that an F823Y mutation, present in bat betacoronavirus HKU9 spike protein, confers resistance to broadly neutralizing antibodies. Our findings provide mechanistic insights into SARS-CoV-2 pathogenicity and also highlight a potential challenge in developing broadly protective S2-based coronavirus vaccines.

Leveraging vaccination-induced protective antibodies to define conserved epitopes on influenza N2 neuraminidase.

There is growing appreciation for neuraminidase (NA) as an influenza vaccine target; however, its antigenicity remains poorly characterized. In this study, we isolated three broadly reactive N2 antibodies from the plasmablasts of a single vaccinee, including one that cross-reacts with NAs from seasonal H3N2 strains spanning five decades. Although these three antibodies have diverse germline usages, they recognize similar epitopes that are distant from the NA active site and instead involve the highly conserved underside of NA head domain. We also showed that all three antibodies confer prophylactic and therapeutic protection in vivo, due to both Fc effector functions and NA inhibition through steric hindrance. Additionally, the contribution of Fc effector functions to protection in vivo inversely correlates with viral growth inhibition activity in vitro. Overall, our findings advance the understanding of NA antibody response and provide important insights into the development of a broadly protective influenza vaccine.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Widespread impact of immunoglobulin V-gene allelic polymorphisms on antibody reactivity.

The ability of the human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V-gene allelic polymorphisms. However, previous studies have provided only limited examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many V-gene allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiments further demonstrate that paratope allelic polymorphisms on both heavy and light chains often abolish antibody binding. We also illustrate the importance of minor V-gene allelic polymorphisms with low frequency in several broadly neutralizing antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus. Overall, this study not only highlights the pervasive impact of V-gene allelic polymorphisms on antibody binding but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery.

Widespread impact of immunoglobulin V gene allelic polymorphisms on antibody reactivity.

The ability of human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V gene (IGV) allelic polymorphisms. However, previous studies have provided only a limited number of examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many IGV allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiment further demonstrates that paratope allelic mutations on both heavy and light chain often abolish antibody binding. We also illustrate the importance of minor IGV allelic variants with low frequency in several broadly neutralizing antibodies to SARS-CoV-2 and influenza virus. Overall, this study not only highlights the pervasive impact of IGV allelic polymorphisms on antibody binding, but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery.

Ang-(1-7) attenuates podocyte injury induced by high glucose in vitro.

Objective: The incidence of diabetic nephropathy (DN) is gradually increasing worldwide. Podocyte injury, such as podocyte apoptosis and loss of the slit diaphragm (SD)-specific markers are early pathogenic features of DN. Materials and methods: The cultured mouse podocytes were separated into a high glucose-treated (HG, 30mM) group to mimic DN in vitro, a low glucose-treated (LG, 5mM) group as a control and HG+ angiotensin-(1-7)(Ang-(1-7)) and HG+Ang-(1-7) + D-Ala7-Ang-(1-7) (A779, Ang-(1-7)/Mas receptor antagonist) experimental groups. The Cell Counting Kit-8 (CCK-8) method and flow cytometry was used to detect podocyte activity and podocyte apoptosis respectively. The expression of angiotensin type 1 receptor (AT1R), Mas receptor (MasR) and podocyte-specific markers were examined by q-PCR and Western blot, respectively. Results: The results showed that the decrease in podocyte activity; the increase in podocyte apoptosis; the decreased mRNA and protein expression of nephrin, podocin, WT-1 and MasR; and the upregulated expression of AT1R induced by HG could be reversed by Ang-(1-7). However, these effects were blocked by A779. The possible mechanisms of the Ang-(1-7)-mediated effect depended on MasR. In addition, the protective effect of Ang-(1-7) on podocyte activity was dose-dependent and most obvious at 10 µM. A779 had the greatest antagonistic action against Ang-(1-7) at a concentration of 10 µM. Conclusion: This study reveals that binding of Ang-(1-7) to its specific receptor MasR may counteract the effects of Ang II mediated by AT1R to significantly attenuate podocyte injury induced by high glucose. Ang-(1-7)/MasR targeting in podocytes may be a therapeutic approach to attenuate renal injury in DN.

High-throughput identification of prefusion-stabilizing mutations in SARS-CoV-2 spike.

Designing prefusion-stabilized SARS-CoV-2 spike is critical for the effectiveness of COVID-19 vaccines. All COVID-19 vaccines in the US encode spike with K986P/V987P mutations to stabilize its prefusion conformation. However, contemporary methods on engineering prefusion-stabilized spike immunogens involve tedious experimental work and heavily rely on structural information. Here, we establish a systematic and unbiased method of identifying mutations that concomitantly improve expression and stabilize the prefusion conformation of the SARS-CoV-2 spike. Our method integrates a fluorescence-based fusion assay, mammalian cell display technology, and deep mutational scanning. As a proof-of-concept, we apply this method to a region in the S2 domain that includes the first heptad repeat and central helix. Our results reveal that besides K986P and V987P, several mutations simultaneously improve expression and significantly lower the fusogenicity of the spike. As prefusion stabilization is a common challenge for viral immunogen design, this work will help accelerate vaccine development against different viruses.