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Synthetic lethality through combinatorial targeting DNA damage response (DDR) pathways provides exciting anticancer therapeutic benefit. Currently, the long noncoding RNAs (lncRNAs) have been implicated in tumor drug resistance; however, their potential significance in DDR is still largely unknown. Here, we report that a human lncRNA, CTD-2256P15.2, encodes a micropeptide, named PAR-amplifying and CtIP-maintaining micropeptide (PACMP), with a dual function to maintain CtIP abundance and promote poly(ADP-ribosyl)ation. PACMP not only prevents CtIP from ubiquitination through inhibiting the CtIP-KLHL15 association but also directly binds DNA damage-induced poly(ADP-ribose) chains to enhance PARP1-dependent poly(ADP-ribosyl)ation. Targeting PACMP alone inhibits tumor growth by causing a synthetic lethal interaction between CtIP and PARP inhibitions and confers sensitivity to PARP/ATR/CDK4/6 inhibitors, ionizing radiation, epirubicin, and camptothecin. Our findings reveal that a lncRNA-derived micropeptide regulates cancer progression and drug resistance by modulating DDR, whose inhibition could be employed to augment the existing anticancer therapeutic strategies.
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Endodesoxirribonucleases , Neoplasias , Peptídeos , Poli ADP Ribosilação , RNA Longo não Codificante , Reparo do DNA , Endodesoxirribonucleases/metabolismo , Humanos , Proteínas dos Microfilamentos/genética , Proteínas dos Microfilamentos/metabolismo , Neoplasias/genética , Neoplasias/metabolismo , Peptídeos/farmacologia , Poli Adenosina Difosfato Ribose/metabolismo , Inibidores de Poli(ADP-Ribose) Polimerases/farmacologia , Poli(ADP-Ribose) Polimerases/genética , Poli(ADP-Ribose) Polimerases/metabolismo , RNA Longo não Codificante/genética , RNA Longo não Codificante/metabolismoRESUMO
Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.
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Perfilação da Expressão Gênica , Transcriptoma , Redes Neurais de ComputaçãoRESUMO
Spatially resolved transcriptomics (SRT) has emerged as a powerful tool for investigating gene expression in spatial contexts, providing insights into the molecular mechanisms underlying organ development and disease pathology. However, the expression sparsity poses a computational challenge to integrate other modalities (e.g. histological images and spatial locations) that are simultaneously captured in SRT datasets for spatial clustering and variation analyses. In this study, to meet such a challenge, we propose multi-modal domain adaption for spatial transcriptomics (stMDA), a novel multi-modal unsupervised domain adaptation method, which integrates gene expression and other modalities to reveal the spatial functional landscape. Specifically, stMDA first learns the modality-specific representations from spatial multi-modal data using multiple neural network architectures and then aligns the spatial distributions across modal representations to integrate these multi-modal representations, thus facilitating the integration of global and spatially local information and improving the consistency of clustering assignments. Our results demonstrate that stMDA outperforms existing methods in identifying spatial domains across diverse platforms and species. Furthermore, stMDA excels in identifying spatially variable genes with high prognostic potential in cancer tissues. In conclusion, stMDA as a new tool of multi-modal data integration provides a powerful and flexible framework for analyzing SRT datasets, thereby advancing our understanding of intricate biological systems.
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Perfilação da Expressão Gênica , Transcriptoma , Humanos , Perfilação da Expressão Gênica/métodos , Análise por Conglomerados , Biologia Computacional/métodos , Redes Neurais de Computação , Neoplasias/genética , AlgoritmosRESUMO
Advances in single-cell multi-omics technology provide an unprecedented opportunity to fully understand cellular heterogeneity. However, integrating omics data from multiple modalities is challenging due to the individual characteristics of each measurement. Here, to solve such a problem, we propose a contrastive and generative deep self-expression model, called single-cell multimodal self-expressive integration (scMSI), which integrates the heterogeneous multimodal data into a unified manifold space. Specifically, scMSI first learns each omics-specific latent representation and self-expression relationship to consider the characteristics of different omics data by deep self-expressive generative model. Then, scMSI combines these omics-specific self-expression relations through contrastive learning. In such a way, scMSI provides a paradigm to integrate multiple omics data even with weak relation, which effectively achieves the representation learning and data integration into a unified framework. We demonstrate that scMSI provides a cohesive solution for a variety of analysis tasks, such as integration analysis, data denoising, batch correction and spatial domain detection. We have applied scMSI on various single-cell and spatial multimodal datasets to validate its high effectiveness and robustness in diverse data types and application scenarios.
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Aprendizagem , MultiômicaRESUMO
Spatially resolved transcriptomics (SRT) enable the comprehensive characterization of transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial transcriptional heterogeneity needs to effectively incorporate spatial information accounting for the substantial spatial correlation of expression measurements. Here, we develop a computational method, SpaSRL (spatially aware self-representation learning), which flexibly enhances and decodes spatial transcriptional signals to simultaneously achieve spatial domain detection and spatial functional genes identification. This novel tunable spatially aware strategy of SpaSRL not only balances spatial and transcriptional coherence for the two tasks, but also can transfer spatial correlation constraint between them based on a unified model. In addition, this joint analysis by SpaSRL deciphers accurate and fine-grained tissue structures and ensures the effective extraction of biologically informative genes underlying spatial architecture. We verified the superiority of SpaSRL on spatial domain detection, spatial functional genes identification and data denoising using multiple SRT datasets obtained by different platforms and tissue sections. Our results illustrate SpaSRL's utility in flexible integration of spatial information and novel discovery of biological insights from spatial transcriptomic datasets.
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Perfilação da Expressão Gênica , Aprendizagem , TranscriptomaRESUMO
The integration of multi-omics data makes it possible to understand complex biological organisms at the system level. Numerous integration approaches have been developed by assuming a common underlying data space. Due to the noise and heterogeneity of biological data, the performance of these approaches is greatly affected. In this work, we propose a novel deep neural network architecture, named Deep Latent Space Fusion (DLSF), which integrates the multi-omics data by learning consistent manifold in the sample latent space for disease subtypes identification. DLSF is built upon a cycle autoencoder with a shared self-expressive layer, which can naturally and adaptively merge nonlinear features at each omics level into one unified sample manifold and produce adaptive representation of heterogeneous samples at the multi-omics level. We have assessed DLSF on various biological and biomedical datasets to validate its effectiveness. DLSF can efficiently and accurately capture the intrinsic manifold of the sample structures or sample clusters compared with other state-of-the-art methods, and DLSF yielded more significant outcomes for biological significance, survival prognosis and clinical relevance in application of cancer study in The Cancer Genome Atlas. Notably, as a deep case study, we determined a new molecular subtype of kidney renal clear cell carcinoma that may benefit immunotherapy in the viewpoint of multi-omics, and we further found potential subtype-specific biomarkers from multiple omics data, which were validated by independent datasets. In addition, we applied DLSF to identify potential therapeutic agents of different molecular subtypes of chronic lymphocytic leukemia, demonstrating the scalability of DLSF in diverse omics data types and application scenarios.
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Neoplasias , Humanos , Neoplasias/genéticaRESUMO
Anti-reflection (AR) coating is a critical technology and an ongoing challenge for terahertz systems. The subwavelength structure (SWS) is an effective AR method, whereas the current manufacturing techniques, such as chemical etching and ultrafast laser processing, are low-efficient and low-quality for processing structures at the hundred-micron scale on hard brittle materials. We present a study of broadband SWSs directly ablated on the surface of quartz crystal by precisely controlled CO2 laser pulses, instead of commonly used ultra-fast lasers. The processing time of SWS can be shortened by two orders of magnitude compared with that by ultra-fast laser pulses. The SWS samples exhibit excellent AR properties with maximum transmittance of 97% at 0.71 THz, peak transmittance improvement of 13.5%, and optimal efficiency spectrum of 0.28-1.21 THz with transmittance >90%. The AR properties of SWS samples are in agreement with the simulated expectation and exist over a wide range of incidence angles up to â¼40°. The imaging of an object using SWS as the substrate shows an obvious improvement in imaging quality. We present an efficient and practical way to improve the transmission of optical components of materials, such as quartz crystal, alumina, and sapphire, in the terahertz band.
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In a large-scale, high-power laser facility, fused silica optics plays an irreplaceable role to transmit extremely intense lasers. However, the surface fractures, such as surface pit, crack, and scratch and laser damage site, of fused silica optics will shorten the lifetime of the optics and thus limit the output performance of the laser facility. In this work, besides experimental study, finite difference time domain (FDTD) simulation is performed to study hydrofluoric acid-based (HF-based) etching effect on the surface fractures. The effect of local surface curvature on etching rate is discussed and an explicit local-curvature-dependent etching model is proposed. Based on this model, the result from FDTD simulation qualitatively agrees very well with that of the experiment. It is demonstrated that the FDTD simulation is efficient to predict the morphological evolution of the surface fractures during etching. In addition, it is found that the surface fractures will be passivated and HF-based etching can greatly suppress the laser-damage growth of laser-induced damage to the surface site of fused silica optics.
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MOTIVATION: Integrating different omics profiles is a challenging task, which provides a comprehensive way to understand complex diseases in a multi-view manner. One key for such an integration is to extract intrinsic patterns in concordance with data structures, so as to discover consistent information across various data types even with noise pollution. Thus, we proposed a novel framework called 'pattern fusion analysis' (PFA), which performs automated information alignment and bias correction, to fuse local sample-patterns (e.g. from each data type) into a global sample-pattern corresponding to phenotypes (e.g. across most data types). In particular, PFA can identify significant sample-patterns from different omics profiles by optimally adjusting the effects of each data type to the patterns, thereby alleviating the problems to process different platforms and different reliability levels of heterogeneous data. RESULTS: To validate the effectiveness of our method, we first tested PFA on various synthetic datasets, and found that PFA can not only capture the intrinsic sample clustering structures from the multi-omics data in contrast to the state-of-the-art methods, such as iClusterPlus, SNF and moCluster, but also provide an automatic weight-scheme to measure the corresponding contributions by data types or even samples. In addition, the computational results show that PFA can reveal shared and complementary sample-patterns across data types with distinct signal-to-noise ratios in Cancer Cell Line Encyclopedia (CCLE) datasets, and outperforms over other works at identifying clinically distinct cancer subtypes in The Cancer Genome Atlas (TCGA) datasets. AVAILABILITY AND IMPLEMENTATION: PFA has been implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/PFApackage_0.1.rar . CONTACT: lnchen@sibs.ac.cn , liujuan@whu.edu.cn or zengtao@sibs.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Algoritmos , Biologia Computacional/métodos , Neoplasias/genética , Análise por Conglomerados , Humanos , Reprodutibilidade dos Testes , Razão Sinal-Ruído , SoftwareRESUMO
Transcription factors (TFs) could regulate physiological transitions or determine stable phenotypic diversity. The accurate estimation on TF regulatory signals or functional activities is of great significance to guide biological experiments or elucidate molecular mechanisms, but still remains challenging. Traditional methods identify TF regulatory signals at the population level, which masks heterogeneous regulation mechanisms in individuals or subgroups, thus resulting in inaccurate analyses. Here, we propose a novel computational framework, namely local network component analysis (LNCA), to exploit data heterogeneity and automatically quantify accurate transcription factor activity (TFA) in practical terms, through integrating the partitioned expression sets (i.e., local information) and prior TF-gene regulatory knowledge. Specifically, LNCA adopts an adaptive optimization strategy, which evaluates the local similarities of regulation controls and corrects biases during data integration, to construct the TFA landscape. In particular, we first numerically demonstrate the effectiveness of LNCA for the simulated data sets, compared with traditional methods, such as FastNCA, ROBNCA and NINCA. Then, we apply our model to two real data sets with implicit temporal or spatial regulation variations. The results show that LNCA not only recognizes the periodic mode along the S. cerevisiae cell cycle process, but also substantially outperforms over other methods in terms of accuracy and consistency. In addition, the cross-validation study for glioblastomas multiforme (GBM) indicates that the TFAs, identified by LNCA, can better distinguish clinically distinct tumor groups than the expression values of the corresponding TFs, thus opening a new way to classify tumor subtypes and also providing a novel insight into cancer heterogeneity. AVAILABILITY: LNCA was implemented as a Matlab package, which is available at http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm/LNCApackage_0.1.rar.
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Algoritmos , Neoplasias Encefálicas/genética , Glioblastoma/genética , Proteínas de Neoplasias/genética , Fatores de Transcrição/genética , Transcrição Gênica , Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/metabolismo , Neoplasias Encefálicas/mortalidade , Ciclo Celular/genética , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Glioblastoma/diagnóstico , Glioblastoma/metabolismo , Glioblastoma/mortalidade , Humanos , Prognóstico , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Transdução de Sinais , Análise de Sobrevida , Fatores de Transcrição/metabolismoRESUMO
Herein, pressure-induced phase transitions of RDX up to 50 GPa were systematically studied under different compression conditions. Precise phase transition points were obtained based on high-quality Raman spectra with small pressure intervals. This favors the correctness of the theoretical formula for detonation and the design of a precision weapon. The experimental results indicated that α-RDX immediately transformed to γ-RDX at 3.5 GPa due to hydrostatic conditions and possible interaction between the penetrating helium and RDX, with helium gas as the pressure-transmitting medium (PTM). Mapping of pressure distribution in samples demonstrates that the pressure gradient is generated in the chamber and independent of other PTMs. The gradient induced the first phase transition starts at 2.3 GPa and completed at 4.1 GPa. The larger pressure gradient promoted phase transition in advance under higher pressures. Experimental results supported that there existed two conformers of AAI and AAE for γ-RDX, as proposed by another group. δ-RDX was considered to only occur in a hydrostatic environment around 18 GPa using helium as the PTM. This study confirms that δ-RDX is independent of PTM and exists under non-hydrostatic conditions. Evidence for a new phase (ζ) was found at about 28 GPa. These 4 phases have also been verified via XRD under high pressures. In addition to this, another new phase (η) may exist above 38 GPa, and it needs to be further confirmed in the future. Moreover, all the phase transitions were reversible after the pressure was released, and original α-RDX was always obtained at ambient pressure.
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BACKGROUND: A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. RESULTS: In this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches. CONCLUSION: Therefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar .
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Biologia Computacional , Bases de Dados Genéticas , Biomarcadores/sangue , Diabetes Mellitus Tipo 1/genética , Diabetes Mellitus Tipo 2/genética , Humanos , Insulina/sangue , Insulina/metabolismo , Secreção de Insulina , Modelos Teóricos , Medicina de PrecisãoRESUMO
A simple and convenient means of fabricating concave microlens arrays direct on silica glass by using the local fictive temperature modification of fused silica is presented. This method is based on the fact that an increased fictive temperature results in a much higher HF acid etching rate of fused silica. Combining the abrupt local fictive temperature enhancement by the CO2 laser pulse and the subsequent etching by the HF acid solution, concave microlens arrays with high fill factors, excellent smoothness, and optical performance are generated on fused silica.
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There is no effective cure nowadays for many complex diseases, and thus it is crucial to detect and further treat diseases in earlier stages. Generally, the development and progression of complex diseases include three stages: normal stage, pre-disease stage, and disease stage. For diagnosis and treatment, it is necessary to reveal dynamical organizations of molecular modules during the early development of the disease from the pre-disease stage to the disease stage. Thus, we develop a new framework, i.e. we identify the modules presenting at the pre-disease stage (pre-disease module) based on dynamical network biomarkers (DNBs), detect the modules observed at the advanced stage (disease-responsive module) by cross-tissue gene expression analysis, and finally find the modules related to early development (progressive module) by progressive module network (PMN). As an application example, we used this new method to analyze the gene expression data for NOD mouse model of Type 1 diabetes mellitus (T1DM). After the comprehensive comparison with the previously reported milestone molecules, we found by PMN: (1) the critical transition point was identified and confirmed by the tissue-specific modules or DNBs relevant to the pre-disease stage, which is considered as an earlier event during disease development and progression; (2) several key tissues-common modules related to the disease stage were significantly enriched on known T1DM associated genes with the rewired association networks, which are marks of later events during T1DM development and progression; (3) the tissue-specific modules associated with early development revealed several common essential progressive genes, and a few of pathways representing the effect of environmental factors during the early T1DM development. Totally, we developed a new method to detect the critical stage and the key modules during the disease occurrence and progression, and show that the pre-disease modules can serve as warning signals for the pre-disease state (e.g. T1DM early diagnosis) whereas the progressive modules can be used as the therapy targets for the disease state (e.g. advanced T1DM), which were also validated by experimental data.
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Diabetes Mellitus/diagnóstico , Modelos Teóricos , Animais , Diabetes Mellitus/genética , Diabetes Mellitus/patologia , Progressão da Doença , Diagnóstico Precoce , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Camundongos Endogâmicos NOD , PrognósticoRESUMO
Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.
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BACKGROUND: Targeted immunotherapy with monoclonal antibodies (mAbs) is an effective and safe method for the treatment of malignancies. Development of mAbs with improved cytotoxicity, targeting new and known tumor-associated antigens, therefore continues to be an active research area. We reported that Dickkopf-1 (DKK1) is a good target for immunotherapy of human cancers based on its wide expression in different cancers but not in normal tissues. As DKK1 is a secreted protein, mAbs binding directly to DKK1 have limited effects on cancer cells in vivo. METHODS: The specificity and antibody-binding capacity of DKK1-A2 mAbs were determined using indirect ELISA, confocal imaging, QIFIKIT antibody-binding capacity and cell surface binding assays. The affinity of mAbs was determined using a surface plasmon resonance biosensor. A flow cytometry-based cell death was performed to detect tumor cell apoptosis. Antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) assays were used to evaluate the ability of DKK1-A2 mAbs to mediate ADCC and CDC activities against tumor cells in vitro. Flow cytometry data were collected with an FACSymphony A3 cell analyzer and analyzed with FlowJo V.10.1 software. Human cancer xenograft mouse models were used to determine the in vivo therapeutic efficacy and the potential safety and toxicity of DKK1-A2 mAbs. In situ TUNEL assay was performed to detect apoptosis in tumors and mouse organs. RESULTS: We generated novel DKK1-A2 mAbs that recognize the DKK1 P20 peptide presented by human HLA-A*0201 (HLA-A2) molecules (DKK1-A2 complexes) that are naturally expressed by HLA-A2+DKK1+ cancer cells. These mAbs directly induced apoptosis in HLA-A2+DKK1+ hematologic and solid cancer cells by activating the caspase-9 cascade, effectively lysed the cancer cells in vitro by mediating CDC and ADCC and were therapeutic against established cancers in their xenograft mouse models. As DKK1 is not detected in most human tissues, DKK1-A2 mAbs neither bound to or killed HLA-A2+ blood cells in vitro nor caused tissue damage in tumor-free or tumor-bearing HLA-A2-transgenic mice. CONCLUSION: Our study suggests that DKK1-A2 mAbs may be a promising therapeutic agent to treat human cancers.
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Antígeno HLA-A2 , Neoplasias , Humanos , Animais , Camundongos , Anticorpos Monoclonais/farmacologia , Anticorpos Monoclonais/uso terapêutico , Peptídeos , Imunoterapia , Neoplasias/tratamento farmacológico , Modelos Animais de Doenças , Peptídeos e Proteínas de Sinalização IntercelularRESUMO
Multiple myeloma (MM) is a hematologic malignancy characterized by uncontrolled proliferation of plasma cells in the bone marrow. MM patients with aggressive progression have poor survival, emphasizing the urgent need for identifying new therapeutic targets. Here, we show that the leukocyte immunoglobulin-like receptor B1 (LILRB1), a transmembrane receptor conducting negative immune response, is a top-ranked gene associated with poor prognosis in MM patients. LILRB1 deficiency inhibits MM progression in vivo by enhancing the ferroptosis of MM cells. Mechanistic studies reveal that LILRB1 forms a complex with the low-density lipoprotein receptor (LDLR) and LDLR adapter protein 1 (LDLRAP1) to facilitate LDL/cholesterol uptake. Loss of LILRB1 impairs cholesterol uptake but activates the de novo cholesterol synthesis pathway to maintain cellular cholesterol homeostasis, leading to the decrease of anti-ferroptotic metabolite squalene. Our study uncovers the function of LILRB1 in regulating cholesterol metabolism and protecting MM cells from ferroptosis, implicating LILRB1 as a promising therapeutic target for MM patients.
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Colesterol , Ferroptose , Homeostase , Receptor B1 de Leucócitos Semelhante a Imunoglobulina , Mieloma Múltiplo , Receptores de LDL , Humanos , Mieloma Múltiplo/metabolismo , Mieloma Múltiplo/patologia , Mieloma Múltiplo/genética , Receptor B1 de Leucócitos Semelhante a Imunoglobulina/metabolismo , Ferroptose/genética , Colesterol/metabolismo , Receptores de LDL/metabolismo , Receptores de LDL/genética , Animais , Linhagem Celular Tumoral , Camundongos , Antígenos CDRESUMO
Growing evidence indicates that cellular metabolism is a critical determinant of immune cell viability and function in antitumor immunity and lipid metabolism is important for immune cell activation and adaptation to the tumor microenvironment (TME). Lipid peroxidation is a process in which oxidants attack lipid-containing carbon-carbon double bonds and is an important part of lipid metabolism. In the past decades, studies have shown that lipid peroxidation participates in signal transduction to control cell proliferation, differentiation, and cell death, which is essential for cell function execution and human health. More importantly, recent studies have shown that lipid peroxidation affects immune cell function to modulate tumor immunity and antitumor ability. In this review, we briefly overview the effect of lipid peroxidation on the adaptive and innate immune cell activation and function in TME and discuss the effectiveness and sensitivity of the antitumor ability of immune cells by regulating lipid peroxidation.
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Neoplasias , Humanos , Peroxidação de Lipídeos , Morte Celular , Diferenciação Celular , Carbono , Microambiente TumoralRESUMO
The generation of ectoderm, mesoderm, and endoderm layers is the most critical biological process during the gastrulation of embryo development. Such a differentiation process in human embryonic stem cells (hESCs) is an inherently nonlinear multi-stage dynamical process which contain multiple tipping points playing crucial roles in the cell-fate decision. However, the tipping points of the process are largely unknown, letting alone the understanding of the molecular regulation on these critical events. Here by designing a module-based dynamic network biomarker (M-DNB) model, we quantitatively pinpointed two tipping points of the differentiation of hESCs toward definitive endoderm, which leads to the identification of M-DNB factors (FOS, HSF1, MYCN, TP53, and MYC) of this process. We demonstrate that before the tipping points, M-DNB factors are able to maintain the cell states and orchestrate cell-fate determination during hESC (ES)-to-ME and ME-to-DE differentiation processes, which not only leads to better understanding of endodermal specification of hESCs but also reveals the power of the M-DNB model to identify critical transition points with their key factors in diverse biological processes, including cell differentiation and transdifferentiation dynamics.
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Self-contained microfluidic platforms with on-chip integration of flow control units, microreactors, (bio)sensors, etc. are ideal systems for point-of-care (POC) testing. However, current approaches such as micropumps and microvalves, increase the cost and the control system, and it is rather difficult to integrate into a single chip. Herein, we demonstrated a versatile acoustofluidic platform actuated by a Lamb wave resonator (LWR) array, in which pumping, mixing, fluidic switching, and particle trapping are all achieved on a single chip. The high-speed microscale acoustic streaming triggered by the LWR in the confined microchannel can be utilized to realize a flow resistor and switch. Variable unidirectional pumping was realized by regulating the relative position of the LWR in various custom-designed microfluidic structures and adoption of different geometric parameters for the microchannel. In addition, to realize quantitative biomarker detection, the on-chip flow resistor, micropump, micromixer and particle trapper were also integrated with a CMOS photo sensor and electronic driver circuit, resulting in an automated handheld microfluidic system with no moving parts. Finally, the acoustofluidic platform was tested for prostate-specific antigen (PSA) sensing, which demonstrates the biocompatibility and applied potency of this proposed self-contained system in POC biomedical applications.