PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

C6orf15 promotes liver metastasis via WNT/ß-catenin signalling in colorectal cancer.

BACKGROUND: Colon cancer ranks third among global tumours and second in cancer-related mortality, prompting an urgent need to explore new therapeutic targets. C6orf15 is a novel gene that has been reported only in Sjogren's syndrome and systemic lupus erythematosus patients. We found a close correlation between increased C6orf15 expression and the occurrence of colon cancer. The aim of this study was to explore the potential of C6orf15 as a therapeutic target for colorectal cancer. METHOD: RNA-seq differential expression analysis of the TCGA database was performed using the R package 'limma.' The correlation between target genes and survival as well as tumour analysis was analysed using GEPIA. Western blot and PCR were used to assess C6orf15 expression in colorectal cancer tissue samples. Immunofluorescence and immunohistochemistry were used to assess C6orf15 subcellular localization and tissue expression. The role of C6orf15 in liver metastasis progression was investigated via a mouse spleen infection liver metastasis model. The association of C6orf15 with signalling pathways was assessed using the GSEA-Hallmark database. Immunohistochemistry (IHC), qPCR and western blotting were performed to assess the expression of related mRNAs or proteins. Biological characteristics were evaluated through cell migration assays, MTT assays, and Seahorse XF96 analysis to monitor fatty acid metabolism. RESULTS: C6orf15 was significantly associated with liver metastasis and survival in CRC patients as determined by the bioinformatic analysis and further verified by immunohistochemistry (IHC), qPCR and western blot results. The upregulation of C6orf15 expression in CRC cells can promote the nuclear translocation of ß-catenin and cause an increase in downstream transcription. This leads to changes in the epithelial-mesenchymal transition (EMT) and alterations in fatty acid metabolism, which together promote liver metastasis of CRC. CONCLUSION: Our study identified C6orf15 as a marker of liver metastasis in CRC. C6orf15 can activate the WNT/ß-catenin signalling pathway to promote EMT and fatty acid metabolism in CRC.

Relationships between blood chromium exposure and liver injury: Exploring the mediating role of systemic inflammation in a chromate-exposed population.

Hexavalent chromium and its compounds are prevalent pollutants, especially in the work environment, pose a significant risk for multisystem toxicity and cancers. While it is known that chromium accumulation in the liver can cause damage, the dose-response relationship between blood chromium (Cr) and liver injury, as well as the possible potential toxic mechanisms involved, remains poorly understood. To address this, we conducted a follow-up study of 590 visits from 305 participants to investigate the associations of blood Cr with biomarkers for liver injury, including serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and direct bilirubin (DBIL), and to evaluate the mediating effects of systemic inflammation. Platelet (PLT) and the platelet-to-lymphocyte ratio (PLR) were utilized as biomarkers of systemic inflammation. In the linear mixed-effects analyses, each 1-unit increase in blood Cr level was associated with estimated effect percentage increases of 0.82% (0.11%, 1.53%) in TBIL, 1.67% (0.06%, 3.28%) in DBIL, 0.73% (0.04%, 1.43%) in ALT and 2.08% (0.29%, 3.87%) in AST, respectively. Furthermore, PLT mediated 10.04%, 11.35%, and 10.77% increases in TBIL, DBIL, and ALT levels induced by chromate, respectively. In addition, PLR mediated 8.26% and 15.58% of the association between blood Cr and TBIL or ALT. These findings shed light on the mechanisms underlying blood Cr-induced liver injury, which is partly due to worsening systemic inflammation.

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) account for a significant proportion of gynecological malignancies and represent a major global health concern. Globally, CESC is ranked as the fourth most common cancer among women. Conventional treatment of this disease has a less favorable prognosis for most patients. However, the discovery of early molecular biomarkers is therefore important for the diagnosis of CESC, as well as for slowing down their progression process. Methods: To identify differentially expressed genes strongly associated with prognosis, univariate Cox proportional hazard analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used. Using multiple Cox proportional hazard regression, a multifactorial model for prognostic risk assessment was then created. Results: The expression of biological clock-related genes, which varied considerably among distinct subtypes and were associated with significantly diverse prognoses, was used to categorize CESC patients. These findings demonstrate how the nomogram developed based on the 7-CRGs signature may assist physicians in creating more precise, accurate, and successful treatment plans that can aid CESC patients at 1, 3, and 5 years. Conclusions: By using machine learning techniques, we thoroughly investigated the impact of CRGs on the prognosis of CESC patients in this study. By creating a unique nomogram, we were able to accurately predict patient prognosis. At the same time, we showed new perspectives on the development of CESC and its treatment by analyzing the associations of the prognostic model with immunity, enrichment pathways, chemotherapy sensitivity, and so on. This research provides a new direction for clinical treatment.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

Deciphering a Prognostic Signature Based on Soluble Mediators Defines the Immune Landscape and Predicts Prognosis in HNSCC.

BACKGROUND: The study on Head and Neck Squamous Cell Carcinoma (HNSCC), a prevalent and aggressive form of head and neck cancer, focuses on the often-overlooked role of soluble mediators. The objective is to leverage a transcriptome-based risk analysis utilizing soluble mediator-related genes (SMRGs) to provide novel insights into prognosis and immunotherapy efficacy in HNSCC patients. METHODS: We analyzed the expression and prognostic significance of 10,859 SMRGs using 502 HNSCC and 44 normal samples from the TCGA-HNSC cohort in The Cancer Genome Atlas (TCGA). The samples were divided into training and test sets in a 7:3 ratio, with an additional external validation using 40 tumor samples from the International Cancer Genome Consortium (ICGC). Key differentially expressed genes (DEGs) with prognostic significance were identified through univariate and Lasso-Cox regression analyses. A prognostic model based on 20 SMRGs was developed using Lasso and multivariate Cox regression. We assessed the clinical outcomes and immune status in high-risk (HR) and low-risk (LR) HNSCC patients utilizing the BEST databases and single-sample Gene Set Enrichment Analysis (ssGSEA). RESULTS: The 20 SMRGs were crucial in predicting the prognosis of HNSCC, with the SMRG signature emerging as an independent prognostic indicator. Patients classified in the HR group exhibited poorer outcomes compared to those in the LR group. A nomogram, integrating clinical characteristics and risk scores, demonstrated substantial prognostic value. Immunotherapy appeared to be more effective in the LR group, possibly attributed to enhanced immune infiltration and expression of immune checkpoints. CONCLUSIONS: The model based on soluble mediator-associated genes offers a fresh perspective for assessing the pre-immune efficacy and showcases robust predictive capabilities. This innovative approach holds significant promise in advancing the field of precision immuno-oncology research, providing valuable insights for personalized treatment strategies in HNSCC.

Corrigendum: Prediction of severe CRS and determination of biomarkers in B cell-acute lymphoblastic leukemia treated with CAR-T cells.

[This corrects the article DOI: 10.3389/fimmu.2023.1273507.].

Mendelian randomization study confirms causal relationship between myopia and vitreous disorders.

PURPOSE: This study aims to investigate the potential bidirectional causal relationship between myopia and vitreous disorders from a genetic perspective, as vitreous disorders have been found to be closely associated with myopia development. METHODS: To achieve this, a two-sample Mendelian randomization (MR) design was employed. The study utilized pooled statistics from independent genome-wide association studies. Myopia was chosen as the exposure factor, while five different vitreous disorders were considered as outcomes. The primary analytical method was the inverse variance weighting (IVW) method, supplemented by sensitivity analysis. RESULTS: The study yielded significant findings indicating a positive association between myopia and vitreous disorders. The genetic prediction of myopia consistently demonstrated a positive correlation with vitreous disorders, as evidenced by IVW (odds ratio [OR] = 18.387; P < 0.01), MR Egger (OR = 2784.954; P < 0.01), weighted median (OR = 30.284; P < 0.01), and weighted mode (OR = 57.381; P < 0.01). All sensitivity analyses further validated these associations. Furthermore, a significant association was observed between myopia and other unspecified vitreous body disorders (IVW: OR = 57.729; P < 0.01). CONCLUSION: Studies mainly conducted in European populations have confirmed that myopia, extending beyond early high myopia, plays a crucial role in influencing vitreous disorders and that there is a unidirectional causal relationship between myopia and vitreous disorders. Additionally, a causal relationship was identified between myopia and other unspecified vitreous disordes. These findings introduce fresh perspectives for the clinical management of unspecified vitreous disorders and contribute to the understanding of the effect of myopia on vitreous disorders. Myopia prevention and treatment will aid in slowing down the process of vitreous liquefaction and subsequently decrease the incidence of malignant eye conditions.

Prediction of severe CRS and determination of biomarkers in B cell-acute lymphoblastic leukemia treated with CAR-T cells.

Introduction: CAR-T cell therapy is a novel approach in the treatment of hematological tumors. However, it is associated with life-threatening side effects, such as the severe cytokine release syndrome (sCRS). Therefore, predicting the occurrence and development of sCRS is of great significance for clinical CAR-T therapy. The study of existing clinical data by artificial intelligence may bring useful information. Methods: By analyzing the heat map of clinical factors and comparing them between severe and non-severe CRS, we can identify significant differences among these factors and understand their interrelationships. Ultimately, a decision tree approach was employed to predict the timing of severe CRS in both children and adults, considering variables such as the same day, the day before, and initial values. Results: We measured cytokines and clinical biomarkers in 202 patients who received CAR-T therapy. Peak levels of 25 clinical factors, including IFN-γ, IL6, IL10, ferritin, and D-dimer, were highly associated with severe CRS after CAR T cell infusion. Using the decision tree model, we were able to accurately predict which patients would develop severe CRS consisting of three clinical factors, classified as same-day, day-ahead, and initial value prediction. Changes in serum biomarkers, including C-reactive protein and ferritin, were associated with CRS, but did not alone predict the development of severe CRS. Conclusion: Our research will provide significant information for the timely prevention and treatment of sCRS, during CAR-T immunotherapy for tumors, which is essential to reduce the mortality rate of patients.

Hepatic-Specific FGF21 Knockout Abrogates Ovariectomy-Induced Obesity by Reversing Corticosterone Production.

Increased glucocorticoid (GC) levels act as a master contributor to central obesity in estrogen-depleted females; however, what factors cause their increased GC production is unclear. Given (1) liver fibroblast growth factor 21 (FGF21) and GCs regulate each other's production in a feed-forward loop, and (2) circulating FGF21 and GCs are parallelly increased in menopausal women and ovariectomized mice, we thus hypothesized that elevation of hepatic FGF21 secretion causes increased GGs production in estrogen-depleted females. Using the ovariectomized mice as a model for menopausal women, we found that ovariectomy (OVX) increased circulating corticosterone levels, which in turn increased visceral adipose Hsd11b1 expression, thus causing visceral obesity in females. In contrast, liver-specific FGF21 knockout (FGF21 LKO) completely reversed OVX-induced high GCs and high visceral adipose Hsd11b1 expression, thus abrogating OVX-induced obesity in females. Even though FGF21 LKO failed to rescue OVX-induced dyslipidemia, hepatic steatosis, and insulin resistance. What's worse, FGF21 LKO even further exacerbated whole-body glucose metabolic dysfunction as evidenced by more impaired glucose and pyruvate tolerance and worsened insulin resistance. Mechanically, we found that FGF21 LKO reduced circulating insulin levels, thus causing the dissociation between decreased central obesity and the improvement of obesity-related metabolic syndromes in OVX mice. Collectively, our results suggest that liver FGF21 plays an essential role in mediating OVX-induced central obesity by promoting GC production. However, lack of liver FGF21 signaling reduces insulin production and in turn causes the dissociation between decreased central obesity and the improvement of obesity-related metabolic syndromes, highlighting a detrimental role for hepatic FGF21 signals in mediating the development of central obesity but a beneficial role in preventing metabolic abnormality from further exacerbation in estrogen-depleted females.

An Aggrephagy-Related LncRNA Signature for the Prognosis of Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a common, highly malignant, and aggressive gastrointestinal tumor. The conventional treatment of PAAD shows poor results, and patients have poor prognosis. The synthesis and degradation of proteins are essential for the occurrence and development of tumors. Aggrephagy is a type of autophagy that selectively degrades aggregated proteins. It decreases the formation of aggregates by degrading proteins, thus reducing the harm to cells. By breaking down proteins, it decreases the formation of aggregates; thus, minimizing damage to cells. For evaluating the response to immunotherapy and prognosis in PAAD patients, in this study, we developed a reliable signature based on aggrephagy-related genes (ARGs). We obtained 298 AGGLncRNAs. Based on the results of one-way Cox and LASSO analyses, the lncRNA signature was constructed. In the risk model, the prognosis of patients in the low-risk group was noticeably better than that of the patients in the high-risk group. Additionally, the ROC curves and nomograms validated the capacity of the risk model to predict the prognosis of PAAD. The patients in the low-risk and high-risk groups showed considerable variations in functional enrichment and immunological analysis. Regarding drug sensitivity, the low-risk and high-risk groups had different half-maximal inhibitory concentrations (IC50).

Tongqiao Mingmu formula alleviates retinal ganglion cell autophagy through PI3K/AKT/mTOR pathway.

Glaucoma is a severe blindness-causing optic nerve disease characterized by a loss of retinal ganglion cells (RGCs). Previous studies have shown that the Tongqiao Mingmu (TQMM) formula can reduce retinal and optic nerve damage, but its mechanism of action requires further elucidation. In this study, an RGC injury model was prepared using glutamate and then treated with serum-containing drug from the TQMM formula (hereafter called "TQMM formula serum"). In the glutamate-induced RGC injury model, cell viability decreased with an increase in glutamate concentration, whereas the expression of autophagy-related biomarkers LC3 and Belicin-1 increased. An adenovirus transfection experiment revealed that glutamate markedly promoted autophagic flux in RGCs. Notably, TQMM formula serum inhibited the expression of autophagy-related biomarkers, reduced autophagy flux, and reversed the damage caused by glutamate to RGCs. Furthermore, the PI3K inhibitor LY294002 was used to intervene in the RGC autophagy model and was found to suppress the PI3K/AKT/mTOR pathway and enhance RGC autophagy. However, TQMM formula serum could generate an opposite effect and upregulate the expressions of the PI3K/AKT/mTOR pathway genes and proteins. In conclusion, the TQMM formula can prevent glutamate-induced autophagy in RGCs, possibly by activating the PI3K/AKT/mTOR pathway and reducing the expression of autophagy-related biomarkers LC3 and Belicin-1 to attenuate autophagy and maintain RGC viability.

Changes of Volatile Organic Compounds of Different Flesh Texture Pears during Shelf Life Based on Headspace Solid-Phase Microextraction with Gas Chromatography-Mass Spectrometry.

Aroma is an important sensory factor in evaluating the quality of pear fruits. This study used headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) to analyze the volatile organic compounds (VOCs) of three crispy pears and five soft pears during shelf life, and the changes in soluble solids content (SSC) were analyzed. The results showed that the SSC of the soft pears such as Nanguoli, Jingbaili and Louis was always higher than that of the crispy pears throughout shelf life. A total of 160 VOCs were detected in the eight pear varieties. Orthogonal partial least squares discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA) combined with predictor variable importance projection (VIP) showed that the eight pear varieties could be obviously classified into six groups according to the differences in their VOCs, and 31 differential VOCs were screened out, which could be used to differentiate between pears with different flesh textures. The results of clustering heat map analysis showed that, with the extension of shelf life, the content of each different VOC did not change much in crispy pears, whereas the difference in soft pears was larger. This study confirmed the potential of determining the optimal shelf life of different pear varieties about aroma evaluation and studying the mechanism of differences in VOCs in the future.

A Retrospective Study of Trifluridine/Tipiracil with Fruquintinib in Patients with Chemorefractory Metastatic Colorectal Cancer.

INTRODUCTION: Trifluridine/tipiracil (TAS-102) and fruquintinib are novel antitumor agents for patients with refractory metastatic colorectal cancer (mCRC). We conducted a retrospective study to explore the clinical efficacy and drug toxicities of combination therapy with TAS-102 and fruquintinib in real-life clinical practice. METHODS: Between March 2021 and February 2023, patients at two different centers with mCRC who failed two or more lines of prior therapy and received TAS-102 in combination with fruquintinib were recruited. RESULTS: In total, 32 mCRC patients were included in the analysis. The objective response rate (ORR) and the disease control rate (DCR) were 9.4% and 75%. The median progression-free survival (PFS) and overall survival (OS) were 6.3 (95% CI: 5.3-7.3) and 13.5 (95% CI: 9.5-17.5) months, respectively. Patients without liver metastasis or peritoneal metastasis obtained better median PFS (7.1 m vs. 5.6 m, p = 0.03 and 6.3 m vs. 3.4 m, p = 0.04), and OS (15.2 m vs. 10.4 m, p = 0.01 and 13.6 m vs. 7.1 m, p = 0.03), respectively. Other clinicopathological features, including age, tumor site, KRAS status, dosage of fruquintinib, and treatment line, did not affect the clinical efficacy of TAS-102 combined with fruquintinib. The most common grade three-four toxicities were neutropenia (46.9%), anemia (21.9%), diarrhea (15.6%), nausea (12.5%), and hand-foot syndrome rash (12.5%). CONCLUSIONS: Our results suggest that TAS-102 combined with fruquintinib has promising clinical efficacy and manageable safety for refractory mCRC patients in a real-world clinical setting. Further prospective trials are warranted to confirm our results.

Genetic damage and potential mechanism exploration under different air pollution patterns by multi-omics.

Ambient air pollution was classified as carcinogenic to humans (Group 1) for lung cancer. DNA damage was an important first step in the process of carcinogenesis, and could also be induced by air pollution. In this study, intratracheal instillation and real-time air exposure system were combined to establish SHP (short-term high-level PM2.5) and LLPO (long-term low-level PM2.5 and O3) exposure patterns, respectively. Hierarchical levels of genetic biomarkers were analyzed to explore DNA damage effects in rats. Representative DNA repair genes from different repair pathways were selected to explore the relative expression levels. The methylation level of differentially expressed repair genes were also determined. Besides, miRNA sequencing and non-targeted metabolomic analysis were performed in rat lungs. KEGG and multi-omics analysis were used to explore the potential mechanism of genetic damage under different air pollution patterns. We found that LLPO exposure induced DSBs and chromosome damage. SHP exposure could induce DSBs and DNA oxidative damage, and the effects of genetic damage under this pollution pattern could be repaired by natural repair. Repair genes involved in two pattern were different. SHP exposure could induce higher methylation levels of RAD51, which might be a potential epigenetic mechanism for high-level PM2.5 induced down-regulated expression of RAD51 and DSBs. Besides, 29 overlapped alterations in metabolic pathways were identified by metabolomic and miRNA sequencing, including purine metabolism and pyrimidine metabolism after LLPO exposure. Differential miRNAs expression in lung tissue were associated with apoptosis, DNA damage and damage repair. We concluded that under different air pollution patterns, DNA damage biomarkers and activated targets of DNA damage repair network were both different. The genetic damage effects caused by high-level short-term PM2.5 can be alleviated by natural repair. We provided possible mechanisms by multi-omics which could explain the increased carcinogenic risk caused by air pollution.

5-methylcytosine (m5C) RNA modification controls the innate immune response to virus infection by regulating type I interferons.

5-methylcytosine (m5C) is one of the most prevalent modifications of RNA, playing important roles in RNA metabolism, nuclear export, and translation. However, the potential role of RNA m5C methylation in innate immunity remains elusive. Here, we show that depletion of NSUN2, an m5C methyltransferase, significantly inhibits the replication and gene expression of a wide range of RNA and DNA viruses. Notably, we found that this antiviral effect is largely driven by an enhanced type I interferon (IFN) response. The antiviral signaling pathway is dependent on the cytosolic RNA sensor RIG-I but not MDA5. Transcriptome-wide mapping of m5C following NSUN2 depletion in human A549 cells revealed a marked reduction in the m5C methylation of several abundant noncoding RNAs (ncRNAs). However, m5C methylation of viral RNA was not noticeably altered by NSUN2 depletion. In NSUN2-depleted cells, the host RNA polymerase (Pol) III transcribed ncRNAs, in particular RPPH1 and 7SL RNAs, were substantially up-regulated, leading to an increase of unshielded 7SL RNA in cytoplasm, which served as a direct ligand for the RIG-I-mediated IFN response. In NSUN2-depleted cells, inhibition of Pol III transcription or silencing of RPPH1 and 7SL RNA dampened IFN signaling, partially rescuing viral replication and gene expression. Finally, depletion of NSUN2 in an ex vivo human lung model and a mouse model inhibits viral replication and reduces pathogenesis, which is accompanied by enhanced type I IFN responses. Collectively, our data demonstrate that RNA m5C methylation controls antiviral innate immunity through modulating the m5C methylome of ncRNAs and their expression.

Investigation of CRS-associated cytokines in CAR-T therapy with meta-GNN and pathway crosstalk.

BACKGROUND: Chimeric antigen receptor T-cell (CAR-T) therapy is a new and efficient cellular immunotherapy. The therapy shows significant efficacy, but also has serious side effects, collectively known as cytokine release syndrome (CRS). At present, some CRS-related cytokines and their roles in CAR-T therapy have been confirmed by experimental studies. However, the mechanism of CRS remains to be fully understood. METHODS: Based on big data for human protein interactions and meta-learning graph neural network, we employed known CRS-related cytokines to comprehensively investigate the CRS associated cytokines in CAR-T therapy through protein interactions. Subsequently, the clinical data for 119 patients who received CAR-T therapy were examined to validate our prediction results. Finally, we systematically explored the roles of the predicted cytokines in CRS occurrence by protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis. RESULTS: We identified some novel cytokines that would play important roles in biological process of CRS, and investigated the biological mechanism of CRS from the perspective of functional analysis. CONCLUSIONS: 128 cytokines and related molecules had been found to be closely related to CRS in CAR-T therapy, where several important ones such as IL6, IFN-γ, TNF-α, ICAM-1, VCAM-1 and VEGFA were highlighted, which can be the key factors to predict CRS.

Context-aware learning for cancer cell nucleus recognition in pathology images.

MOTIVATION: Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for nucleus subtyping. Current CNN-based methods do not explicitly encode contextual information within the input images and point annotations. RESULTS: In this article, we propose a novel framework with context to locate and classify nuclei in microscopy image data. Specifically, first we use state-of-the-art network architectures to extract multi-scale feature representations from multi-field-of-view, multi-resolution input images and then conduct feature aggregation on-the-fly with stacked convolutional operations. Then, two auxiliary tasks are added to the model to effectively utilize the contextual information. One for predicting the frequencies of nuclei, and the other for extracting the regional distribution information of the same kind of nuclei. The entire framework is trained in an end-to-end, pixel-to-pixel fashion. We evaluate our method on two histopathological image datasets with different tissue and stain preparations, and experimental results demonstrate that our method outperforms other recent state-of-the-art models in nucleus identification. AVAILABILITY AND IMPLEMENTATION: The source code of our method is freely available at https://github.com/qjxjy123/DonRabbit. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

(-)-Epigallocatechin-3-Gallate (EGCG) Modulates the Composition of the Gut Microbiota to Protect Against Radiation-Induced Intestinal Injury in Mice.

The high radiosensitivity of the intestinal epithelium limits the outcomes of radiotherapy against abdominal malignancies, which results in poor prognosis. Currently, no effective prophylactic or therapeutic strategy is available to mitigate radiation toxicity in the intestine. Our previous study revealed that the green tea polyphenol (-)-epigallocatechin-3-gallate (EGCG) attenuates radiation-induced intestinal injury (RIII). The aim of the present study was to determine the effect of EGCG on the intestinal flora of irradiated mice. EGCG administration reduced radiation-induced intestinal mucosal injury, and significantly increased the number of Lgr5+ intestinal stem cells (ISCs) and Ki67+ crypt cells. In addition, EGCG reversed radiation-induced gut dysbiosis, restored the Firmicutes/Bacteroidetes ratio, and increased the abundance of beneficial bacteria. Our findings provide novel insight into EGCG-mediated remission of RIII, revealing that EGCG could be a potential modulator of gut microbiota to prevent and treat RIII.