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Abstract Host anti-viral factors are essential for controlling SARS-CoV-2 infection but remain largely unknown due to the biases of previous large-scale studies toward pro-viral host factors. To fill in this knowledge gap, we perform a genome-wide CRISPR dropout screen and integrate analyses of the multi-omics data of the CRISPR screen, genome-wide association studies, single-cell RNA-Seq, and host-virus proteins or protein/RNA interactome. This study uncovers many host factors that are currently underappreciated, including the components of V-ATPases, ESCRT, and N-glycosylation pathways that modulate viral entry and/or replication. The cohesin complex is also identified as an anti-viral pathway, suggesting an important role of three-dimensional chromatin organization in mediating host-viral interaction. Furthermore, we discover another anti-viral regulator KLF5, a transcriptional factor involved in sphingolipid metabolism, which is up-regulated, and harbors genetic variations linked to COVID-19 patients with severe symptoms. Anti-viral effects of three identified candidates (DAZAP2/VTA1/KLF5) are confirmed individually. Molecular characterization of DAZAP2/VTA1/KLF5-knockout cells highlights the involvement of genes related to the coagulation system in determining the severity of COVID-19. Together, our results provide further resources for understanding the host anti-viral network during SARS-CoV-2 infection and may help develop new countermeasure strategies.

Abstract Background Disulfidptosis is a recently discovered programmed cell death pathway. However, the exact molecular mechanism of disulfidptosis in cutaneous melanoma remains unclear. Methods In this study, clustering analysis was performed using data from public databases to construct a prognostic model, which was subsequently externally validated. The biological functions of the model genes were then investigated through various experimental techniques, including qRT-PCR, Western blotting, CCK-8 assay, wound healing assay, and Transwell assay. Results We constructed a signature using cutaneous melanoma (CM) data, which accurately predicts the overall survival (OS) of patients. The predictive value of this signature for prognosis and immune therapy response was validated using multiple external datasets. High-risk CM subgroups may exhibit decreased survival rates, alterations in the tumor microenvironment (TME), and increased tumor mutation burden. We initially verified the expression levels of five optimum disulfidptosis-related genes (ODRGs) in normal tissues and CM. The expression levels of these genes were further confirmed in HaCaT cells and three melanoma cell lines using qPCR and protein blotting analysis. HLA-DQA1 emerged as the gene with the highest regression coefficient in our risk model, highlighting its role in CM. Mechanistically, HLA-DQA1 demonstrated the ability to suppress CM cell growth, proliferation, and migration. Conclusion In this study, a novel signature related to disulfidptosis was constructed, which accurately predicts the survival rate and treatment sensitivity of CM patients. Additionally, HLA-DQA1 is expected to be a feasible therapeutic target for effective clinical treatment of CM.

Abstract The ability to capture pixels' long-distance interdependence is beneficial to semantic segmentation. In addition, semantic segmentation requires the effective use of pixel-to-pixel similarity in the channel direction to enhance pixel regions. Asymmetric Non-local Neural Networks (ANNet) combine multi-scale spatial pyramidal pooling modules and Non-local blocks to reduce model parameters without sacrificing performance. However, ANNet does not consider pixel similarity in the channel direction in the feature map, so its segmentation effect is not ideal. This article proposes a Mutually Reinforcing Non-local Neural Networks (MRNNet) to improve ANNet. MRNNet consists specifically of the channel enhancement regions module (CERM), and the position-enhanced pixels module (PEPM). In contrast to Asymmetric Fusion Non-local Block (AFNB) in ANNet, CERM does not combine the feature maps of the high and low stages, but rather utilizes the auxiliary loss function of ANNet. Calculating the similarity between feature maps in channel direction improves the category representation of feature maps in the channel aspect and reduces matrix multiplication computation. PEPM enhances pixels in the spatial direction of the feature map by calculating the similarity between pixels in the spatial direction of the feature map. Experiments reveal that our segmentation accuracy for cityscapes test data reaches 81.9%. Compared to ANNet, the model's parameters are reduced by 11.35 (M). Given ten different pictures with a size of 2048 × 1024, the average reasoning time of MRNNet is 0.103(s) faster than that of the ANNet model.

BackgroundCuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients.MethodsRNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB).ResultsWe have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients.Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC).Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-β and other pathway signals were correlated with progression.Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration.

Emerging evidence suggests that cryptic translation within long noncoding RNAs (lncRNAs) may produce novel proteins with important developmental/physiological functions. However, the role of this cryptic translation in complex diseases (e.g., cancer) remains elusive. Here, we applied an integrative strategy combining ribosome profiling and CRISPR/Cas9 screening with large-scale analysis of molecular/clinical data for breast cancer (BC) and identified estrogen receptor α–positive (ER+) BC dependency on the cryptic ORFs encoded by lncRNA genes that were upregulated in luminal tumors. We confirmed the in vivo tumor-promoting function of an unannotated protein, GATA3-interacting cryptic protein (GT3-INCP) encoded by LINC00992, the expression of which was associated with poor prognosis in luminal tumors. GTE-INCP was upregulated by estrogen/ER and regulated estrogen-dependent cell growth. Mechanistically, GT3-INCP interacted with GATA3, a master transcription factor key to mammary gland development/BC cell proliferation, and coregulated a gene expression program that involved many BC susceptibility/risk genes and impacted estrogen response/cell proliferation. GT3-INCP/GATA3 bound to common cis regulatory elements and upregulated the expression of the tumor-promoting and estrogen-regulated BC susceptibility/risk genes MYB and PDZK1. Our study indicates that cryptic lncRNA-encoded proteins can be an important integrated component of the master transcriptional regulatory network driving aberrant transcription in cancer, and suggests that the “hidden” lncRNA-encoded proteome might be a new space for therapeutic target discovery.

Battery is the core equipment of the electrical vehicle (EV), battery life, and performance are critical to EV. Only motor variables are considered in most model predictive control (MPC) strategy in which battery indexes are neglected. A model predictive speed control (MPSC) strategy with adjustable C-rate of battery is proposed in this paper on EV with permanent magnet synchronous machine (PMSM). The rest capacity of battery is involved in the cost function, and the operating speed is related to the rest capacity of battery which adjusts the C-rate of the discharging currents further to protect battery. As the EV runs, the C-rate is decreased step by step according to the improved cost function, and the energy is saved to prolong mileage. The benefits of the proposed method including discharging capacities and operating mileage are corroborated by the simulation and repetitive experimental results under same conditions.

Naringin is potential functional and therapeutic ingredient, has low bioavailability because of poor aqueous solubility. In this study, an ovalbumin (OVA)-carboxymethyl konjac glucomannan (CKGM) nano-delivery system was developed to enhance the bioavailability of naringin. The effects of proportion (OVA: CKGM), pH and naringin concentration were studied on the formation, encapsulation efficiency (EE) and bioaccessibility of OVA/CKGM-Naringin nanoparticles (OVA/CKGM-Naringin NPs). Its morphology and size were viewed by Scanning Electron Microscope (SEM) and Transmission Electron Microscopy (TEM). The cross-linkage between OVA and CKGM was verified by Fourier Transform Infrared Spectroscopy (FTIR) and Fluorescence Intensity analysis. The size of OVA/CKGM-Naringin NPs were 463.83 ± 18.50 nm (Polydispersity Index-PDI, 0.42 ± 0.05). It indicated that 2:1 of OVA: CKGM, pH 3 and 7 mg/mL of naringin concentration were optimized processing parameters of OVA/CKGM-Naringin NPs with EE (97.90 ± 2.97 %) and remarkably improved bioaccessibility (85.01 ± 2.52 %). The OVA/CKGM-Naringin NPs was energy efficiently prepared and verified as an ideal carrier of naringin.

Abstract Background The human genome encodes over 14,000 pseudogenes that are evolutionary relics of protein-coding genes and commonly considered as nonfunctional. Emerging evidence suggests that some pseudogenes may exert important functions. However, to what extent human pseudogenes are functionally relevant remains unclear. There has been no large-scale characterization of pseudogene function because of technical challenges, including high sequence similarity between pseudogene and parent genes, and poor annotation of transcription start sites. Results To overcome these technical obstacles, we develop an integrated computational pipeline to design the first genome-wide library of CRISPR interference (CRISPRi) single-guide RNAs (sgRNAs) that target human pseudogene promoter-proximal regions. We perform the first pseudogene-focused CRISPRi screen in luminal A breast cancer cells and reveal approximately 70 pseudogenes that affect breast cancer cell fitness. Among the top hits, we identify a cancer-testis unitary pseudogene, MGAT4EP, that is predominantly localized in the nucleus and interacts with FOXA1, a key regulator in luminal A breast cancer. By enhancing the promoter binding of FOXA1, MGAT4EP upregulates the expression of oncogenic transcription factor FOXM1. Integrative analyses of multi-omic data from the Cancer Genome Atlas (TCGA) reveal many unitary pseudogenes whose expressions are significantly dysregulated and/or associated with overall/relapse-free survival of patients in diverse cancer types. Conclusions Our study represents the first large-scale study characterizing pseudogene function. Our findings suggest the importance of nuclear function of unitary pseudogenes and underscore their underappreciated roles in human diseases. The functional genomic resources developed here will greatly facilitate the study of human pseudogene function.

BackgroundCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied.MethodsCOVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target.ResultBioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment.ConclusionsBased on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD.

Eye lens cells are highly enriched in cholesterol that sustains lens transparency, and disruption of cholesterol biosynthesis leads to cataracts. The authors show that cholesterol biosynthesis regulated by Qki is essential for maintenance of membrane integrity of lens cells and proper protein folding.

Esophageal squamous cell carcinoma (ESCC) is the major type of EC in China. Chemoradiotherapy is a standard definitive treatment for early-stage EC and significantly improves local control and overall survival for late-stage patients. However, chemoradiotherapy resistance, which limits therapeutic efficacy and treatment-induced toxicity, is still a leading problem for treatment break. To optimize the selection of ESCC patients for chemoradiotherapy, we retrospectively analyzed the clinical features and genome landscape of a Chinese ESCC cohort of 58 patients. TP53 was the most frequent mutation gene, followed by NOTCH1. Frequently, copy number variants were found in MCL1 (24/58, 41.4%), FGF19 (23/58, 39.7%), CCND1 (22/58, 37.9%), and MYC (20/58, 34.5%). YAP1 and SOX2 amplifications were mutually exclusive in this cohort. Using univariate and multivariate analyses, the YAP1 variant and BRIP1 mutant were identified as adverse factors for OS. Patients with PI3K-Akt pathway alterations displayed longer PFS and OS than patients with an intact PI3K-Akt pathway. On the contrary, two patients with Keap1-Nrf2 pathway alterations displayed significantly shortened PFS and OS, which may be associated with dCRT resistance. Our data highlighted the prognostic value of aberrant cancer pathways in ESCC patients, which may provide guidance for better chemoradiotherapy management.

Based on the receding horizon principle, cost function of nonlinear model predictive control (NMPC) becomes an accumulating type to extend prediction horizon. A nonlinear model predictive speed control (NMPSC) with prediction horizon self-tuning method is proposed in this paper, and applied into a permanent magnet synchronous motor (PMSM) rotor position control system with inner-loop of speed. The prediction horizon is improved as a positive integral discrete-time variable which needs to be adjusted according to operating states in each sampling period. Control performances such as rotor position and speed integral of time-weighted absolute value of errors (ITAEs), delay time and calculation burden are compared with the conventional control strategy, and advantages including ITAEs, delay time, weighting factor sensitivities and servo stiffness are obtained. The effectiveness and correctness are verified by simulation and experimental results.

The finite control set model predictive torque control (FCS-MPTC) selects the optimal voltage vector (VV) by the composite cost function composed of torque and flux error, which makes it have a faster dynamic response than conventional control methods. However, the prediction state error caused by machine parameter mismatch and the difficulty in setting the weight factor in the composite cost function seriously restrict the popularization and application of FCS-MPTC. In this paper, a model-free parallel predictive torque control (MF-PPTC) based on an ultra-local (UL) model is proposed to solve above problems. The UL model replaces the machine mathematical model without any machine parameters and only uses the input and output of the system, which greatly improves the robustness of the control system. The nonlinear extended state observer proposed for the unknown part of the system has fast convergence and improves the dynamic performance of the system. In addition, the conventional parallel predictive control structure is optimized to reduce the dynamic adjustment process during the selection of optimal voltage vector. Simulation and experimental comparison between the conventional PPTC and the proposed MF-PPTC verified the superiority of the proposed method.

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis‐subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal‐like subtype into two different prognosis‐subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.

Model predictive torque control with duty cycle control (MPTC-DCC) is widely used in motor drive systems because of its low torque ripple and good steady-state performance. However, the selection of the optimal voltage vector and the calculation of the duration are extremely dependent on the accuracy of the motor parameters. In view of this situation, A modified MPTC-DCC is proposed in this paper. According to the variation of error between the measured value and the predicted value, the motor parameters are calculated in real-time. Meanwhile, Model reference adaptive control (MRAC) is adopted in the speed loop to eliminate the disturbance caused by the ripple of real-time update parameters, through which the disturbance caused by parameter mismatch is suppressed effectively. The simulation and experiment are carried out on MATLAB / Simulink software and dSPACE experimental platform, which corroborate the principle analysis and the correctness of the method.

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.