ABSTRACT
The development of sequencing technology has promoted discovery of variants in the human genome. Identifying functions of these variants is important for us to link genotype to phenotype, and to diagnose diseases. However, it usually requires researchers to visit multiple databases. Here, we presented a one-stop webserver for variant function annotation tools (VCAT, https://biomed.nscc-gz.cn/zhaolab/VCAT/ ) that is the first one connecting variant to functions via the epigenome, protein, drug and RNA. VCAT is also the first one to make all annotations visualized in interactive charts or molecular structures. VCAT allows users to upload data in VCF format, and download results via a URL. Moreover, VCAT has annotated a huge number (1,262,041,068) of variants collected from dbSNP, 1000 Genomes projects, gnomAD, ICGC, TCGA, and HPRC Pangenome project. For these variants, users are able to searcher their functions, related diseases and drugs from VCAT. In summary, VCAT provides a one-stop webserver to explore the potential functions of human genomic variants including their relationship with diseases and drugs.
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Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3, HIST1H1E, KRAS, NRAS, and TP53, phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.
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Glioblastoma (GBM) is an aggressive brain cancer with limited therapeutic options. Natural killer (NK) cells are innate immune cells with strong anti-tumor activity and may offer a promising treatment strategy for GBM. We compared the anti-GBM activity of NK cells engineered to express interleukin (IL)-15 or IL-21. Using multiple in vivo models, IL-21 NK cells were superior to IL-15 NK cells both in terms of safety and long-term anti-tumor activity, with locoregionally administered IL-15 NK cells proving toxic and ineffective at tumor control. IL-21 NK cells displayed a unique chromatin accessibility signature, with CCAAT/enhancer-binding proteins (C/EBP), especially CEBPD, serving as key transcription factors regulating their enhanced function. Deletion of CEBPD resulted in loss of IL-21 NK cell potency while its overexpression increased NK cell long-term cytotoxicity and metabolic fitness. These results suggest that IL-21, through C/EBP transcription factors, drives epigenetic reprogramming of NK cells, enhancing their anti-tumor efficacy against GBM.
Subject(s)
Brain Neoplasms , CCAAT-Enhancer-Binding Protein-delta , Glioblastoma , Interleukins , Killer Cells, Natural , Killer Cells, Natural/immunology , Killer Cells, Natural/metabolism , Glioblastoma/immunology , Glioblastoma/genetics , Glioblastoma/pathology , Glioblastoma/therapy , Interleukins/genetics , Interleukins/metabolism , Interleukins/immunology , Humans , Animals , Mice , CCAAT-Enhancer-Binding Protein-delta/metabolism , CCAAT-Enhancer-Binding Protein-delta/genetics , Brain Neoplasms/immunology , Brain Neoplasms/genetics , Brain Neoplasms/pathology , Brain Neoplasms/therapy , Cell Line, Tumor , Interleukin-15/genetics , Interleukin-15/metabolism , Interleukin-15/immunology , Xenograft Model Antitumor AssaysABSTRACT
Acute kidney injury (AKI) is the sudden decrease in renal function that can be attributed to dysregulated reactive oxygen species (ROS) production and impaired mitochondrial function. Irisin, a type I membrane protein secreted by skeletal muscles in response to physical activity, has been reported to alleviate kidney damage through regulation of mitochondrial biogenesis and oxidative metabolism. In this study, a macrophage membrane-coated metal-organic framework (MCM@MOF) is developed as a nanocarrier for encapsulating irisin to overcome the inherent characteristics of irisin, including a short circulation time, limited kidney-targeting ability, and low membrane permeability. The engineered irisin-mediated biomimetic nanotherapeutics have extended circulation time and enhanced targeting capability toward injured kidneys due to the preservation of macrophage membrane proteins. The irisin-encapsulated biomimetic nanotherapeutics effectively mitigate acute ischemia-reperfusion injury by protecting mitochondrial function and modulating SOD2 levels in renal tubular epithelial cells. The present study provides novel insights to advance the development of irisin as a potential therapeutic approach for AKI.
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OBJECTIVE: To evaluate the effectiveness and safety of nusinersen for the treatment of 5q-spinal muscular atrophy (SMA) among Chinese pediatric patients. METHODS: Using a longitudinal, multi-center registry, both prospective and retrospective data were collected from pediatric patients with 5q-SMA receiving nusinersen treatment across 18 centers in China. All patients fulfilling the eligibility criteria were included consecutively. Motor function outcomes were assessed post-treatment by SMA type. Safety profile was evaluated among patients starting nusinersen treatment post-enrollment. Descriptive analyses were used to report baseline characteristics, effectiveness, and safety results. RESULTS: As of March 2nd, 2023, 385 patients were included. Most patients demonstrated improvements or stability in motor function across all SMA types. Type II patients demonstrated mean changes [95% confidence interval (CI)] of 4.4 (3.4-5.4) and 4.1 (2.8-5.4) in Hammersmith Functional Motor Scale-Expanded (HFMSE), and 2.4 (1.7-3.1) and 2.3 (1.2-3.4) in Revised Upper Limb Module (RULM) scores at months 6 and 10. Type III patients exhibited mean changes (95% CI) of 3.9 (2.5-5.3) and 4.3 (2.6-6.0) in HFMSE, and 2.1 (1.2-3.0) and 1.5 (0.0-3.0) in RULM scores at months 6 and 10. Of the 132 patients, 62.9% experienced adverse events (AEs). Two patients experienced mild AEs (aseptic meningitis and myalgia) considered to be related to nusinersen by the investigator, with no sequelae. CONCLUSIONS: These data underscore the significance of nusinersen in Chinese pediatric patients with SMA regarding motor function improvement or stability, and support recommendations on nusinersen treatment by Chinese SMA guidelines and continuous coverage of nusinersen by basic medical insurance.
Subject(s)
Oligonucleotides , Registries , Spinal Muscular Atrophies of Childhood , Humans , Male , Female , Oligonucleotides/administration & dosage , Oligonucleotides/pharmacology , Oligonucleotides/adverse effects , China , Child, Preschool , Infant , Spinal Muscular Atrophies of Childhood/drug therapy , Spinal Muscular Atrophies of Childhood/physiopathology , Child , Retrospective Studies , Treatment Outcome , Longitudinal Studies , Adolescent , Prospective StudiesABSTRACT
Cancer cells with BRCA1/2 deficiencies are sensitive to poly (ADP-ribose) polymerase (PARP) inhibitors. We evaluated the efficacy of talazoparib in DNA-Damage Repair (DDR)-altered patients. In this phase II trial, patients were enrolled onto one of four cohorts based on molecular alterations: (1) somatic BRCA1/2, (2) other homologous recombination repair pathway, (3) PTEN and (4) germline BRCA1/2. The primary endpoint was a clinical benefit rate (CBR): complete response, partial response or stable disease ≥24 weeks. 79 patients with a median of 4 lines of therapy were enrolled. CBR for cohorts 1-4 were: 32.5%, 19.7%, 9.4% and 30.6%, respectively. PTEN mutations correlated with reduced survival and a trend towards shorter time to progression.Talazoparib demonstrated clinical benefit in selected DDR-altered patients. PTEN mutations/loss patients derived limited clinical benefit. Further study is needed to determine whether PTEN is prognostic or predictive of response to PARP inhibitors.
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Surgical intervention for medication-related jaw osteonecrosis (MRONJ) is currently the main treatment method, offering a higher healing rate than conservative approaches. However, the management of bony defects after sequestrectomy remains a challenging issue due to poor vascularization from the drug effect. The use of pedicled buccal fat pad (PBFP) for filling bone defects has become common and effective but is limited to the posterior maxillary region. To add to the advantages of the buccal fat pad, we explored a novel treatment approach using a free buccal fat pad (FBFP) to fill bone defects other than the posterior maxilla. While the FBFP has been employed in oral defect reconstruction, currently published cases have been utilized in recipient sites with good blood supply. There has yet to be any usage in poor vascularization defects like MRONJ. This article describes that the FBFP was used to fill the surgical defects of 8 patients who were diagnosed with MRONJ and who underwent sequestrectomy and saucerization. During follow-up visits, there was excellent wound healing and no significant tissue depression. Based on successful treatment experiences, FBFP is a reliable therapeutic option for the management of poor vascularization defects like MRONJ treated through surgical intervention.
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Background/purpose: Additive manufacturing (AM) technology, such as selective laser melting (SLM), has been used to fabricate medical devices of Ti-6wt.% Al-4wt.%V (Ti6Al4V) alloys in dentistry. Strontium (Sr) has been shown to have the potential to treat osteoporosis. The aim of this study was to investigate the physicochemical and biological properties of strontium-containing coatings on selective laser melted Ti6Al4V (SLM-Ti6Al4V) substrate. Materials and methods: The disk of Ti6Al4V was prepared by SLM method. The strontium-containing coatings were prepared by micro-arc oxidation (MAO) in aqueous electrolytes. The surface topography, chemical composition, and phase of strontium-containing MAO (SrMAO) coatings were performed by scanning electron microscope (SEM), energy dispersive X-ray spectrometer (EDS), and thin film X-ray diffraction (TF-XRD), respectively. The apatite-forming ability of the MAO coatings was conducted in simulating body fluid (SBF), and the cell proliferation was determined by methylthiazoletetrazolium (MTT) assay. Results: The microstructure of SLM-Ti6Al4V displays acicular α-phase organization. The TF-XRD results indicated that the phase of SrMAO coating was anatase, rutile, and titanium. The calcium, phosphorus, and strontium were detected in the coatings by EDS. Using the SEM, the surface morphology of SrMAO coatings exhibited a uniform 3D porous structure. The SrMAO coatings could induce a bone-like apatite layer after immersion in SBF, and presented significantly higher cell proliferation than untreated specimens in in-vitro experiments. Conclusion: All findings in this study indicate that SrMAO coatings formed on SLM-Ti6Al4V surfaces exhibit a benefit on biological responses and thereby are suitable for biomedical applications.
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Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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OBJECTIVES: Back pain subsequent to intervertebral disc (IVD) injury is a common clinical problem. Previous work examining early molecular changes post injury mainly used a candidate marker approach. In this study, gene expression in the injured and intact mouse tail IVDs was determined with a nonbiased whole transcriptome approach. DESIGN: Mouse tail IVD injury was induced by a needle puncture. Whole murine transcriptome was determined by RNASeq. Transcriptomes of injured IVDs were compared with those of intact controls by bioinformatic methods. RESULTS: Among the 18,078 murine genes examined, 592 genes were differentially expressed (P.adj < 0.01). Novel genes upregulated in injured compared with intact IVDs included Chl1, Lum, etc. Ontology study of upregulated genes revealed that leukocyte migration was the most enriched biological process, and network analysis showed that Tnfa had the most protein-protein interactions. Novel downregulated genes in the injured IVDs included 4833412C05Rik, Myoc, etc. The most enriched downregulated pathways were related to cytoskeletal organization. CONCLUSION: Novel genes highly regulated post disc injury were identified with an unbiased approach; they may serve as biomarkers of injury and response to treatments in future experiments. Enriched biological pathways and molecules with high numbers of connections may be targets for treatments post injury.
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With the rapid advancement of electronic technology, traditional textiles are challenged to keep up with the demands of wearable electronics. It is anticipated that multifunctional textile-based electronics incorporating energy storage, electromagnetic interference (EMI) shielding, and photothermal conversion are expected to alleviate this problem. Herein, a multifunctional cotton fabric with hierarchical array structure (PPy/NiCoAl-LDH/Cotton) is fabricated by the introduction of NiCoAl-layered double hydroxide (NiCoAl-LDH) nanosheet arrays on cotton fibers, followed by polymerization and growth of continuous dense polypyrrole (PPy) conductive layers. The multifunctional cotton fabric shows a high specific areal capacitance of 754.72 mF cm-2 at 5 mA cm-2 and maintains a long cycling life (80.95% retention after 1000 cycles). The symmetrical supercapacitor assembled with this fabric achieves an energy density of 20.83 Wh cm-2 and a power density of 0.23 mWcm-2. Moreover, the excellent electromagnetic interference shielding (38.83 dB), photothermal conversion (70.2 °C at 1000 mW cm-2), flexibility and durability are also possess by the multifunctional cotton fabric. Such a multifunctional cotton fabric has great potential for using in new energy, smart electronics, and thermal management applications.
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Multiple factors in the design of a chimeric antigen receptor (CAR) influence CAR T-cell activity, with costimulatory signals being a key component. Yet, the impact of costimulatory domains on the downstream signaling and subsequent functionality of CAR-engineered natural killer (NK) cells remains largely unexplored. Here, we evaluated the impact of various costimulatory domains on CAR-NK cell activity, using a CD70-targeting CAR. We found that CD28, a costimulatory molecule not inherently present in mature NK cells, significantly enhanced the antitumor efficacy and long-term cytotoxicity of CAR-NK cells both in vitro and in multiple xenograft models of hematologic and solid tumors. Mechanistically, we showed that CD28 linked to CD3Z creates a platform that recruits critical kinases, such as LCK and ZAP70, initiating a signaling cascade that enhances CAR-NK cell function. Our study provides insights into how CD28 costimulation enhances CAR-NK cell function and supports its incorporation in NK-based CARs for cancer immunotherapy.
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Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples.
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Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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Introduction: The starter used in solid-state fermentation (SSF) vinegar, known as seed Pei is a microbial inoculant from the previous batch that is utilized during the acetic acid fermentation stage. The seed Pei, which has a notable impact on vinegar fermentation and flavor, is under-researched with comparative studies on microorganisms. Methods: Herein metagenomics was employed to reveal the microbes and their potential metabolic functions of four seed Pei from three regions in China. Results: The predominant microbial taxa in all four starters were bacteria, followed by viruses, eukaryotes, and archaea, with Lactobacillus sp. or Acetobacter sp. as main functional taxa. The seed Pei used in Shanxi aged vinegar (SAV) and Sichuan bran vinegar (SBV) exhibited a higher similarity in microbial composition and distribution of functional genes, while those used in two Zhenjiang aromatic vinegar (ZAV) differed significantly. Redundancy analysis (RDA) of physicochemical factors and microbial communities indicated that moisture content, pH, and reducing sugar content are significant factors influencing microbial distribution. Moreover, seven metagenome-assembled genomes (MAGs) that could potentially represent novel species were identified. Conclusions: There are distinctions in the microbiome and functional genes among different seed Pei. The vinegar starters were rich in genes related to carbohydrate metabolism. This research provides a new perspective on formulating vinegar fermentation starters and developing commercial fermentation agents for vinegar production.
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BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. OBJECTIVE: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. METHODS: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. RESULTS: Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. CONCLUSION: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.
Subject(s)
Acute Kidney Injury , Electronic Health Records , Intensive Care Units , Acute Kidney Injury/therapy , Humans , Longitudinal Studies , Renal Replacement Therapy , Artificial Intelligence , Forecasting , Length of Stay , Male , Databases, Factual , FemaleABSTRACT
Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.
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Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.