Design and synthesis of highly selective Janus kinase 3 covalent inhibitors for the treatment of rheumatoid arthritis

Selective inhibition of Janus kinase 3 (JAK3) is a promising strategy for the treatment of autoimmune diseases. Based on the discovery of a hydrophobic pocket unutilized between the lead compound RB1 and the JAK3 protein, a series of covalent JAK3 inhibitors were prepared by introducing various aromatic fragments to RB1. Among them, J1b (JAK3 IC50  = 7.2 nM, other JAKs IC50  > 1000 nM) stood out because of its low toxicity (MTD > 2 g/kg) and superior anti-inflammatory activity in Institute of Cancer Research mice. Moreover, the acceptable bioavailability (F% = 31.69%) ensured that J1b displayed excellent immune regulation in collagen-induced arthritis mice, whose joints in the high-dose group were almost recovered to a normal state. Given its clear kinase selectivity (Bmx IC50  = 539.9 nM, other Cys909 kinases IC50  > 1000 nM), J1b was nominated as a highly selective JAK3 covalent inhibitor, which could be used to safely treat arthritis and other autoimmune diseases.

Causal relationship between gut microbiota and tuberculosis: a bidirectional two-sample Mendelian randomization analysis

BACKGROUND

Growing evidence from observational studies and clinical trials suggests that the gut microbiota is associated with tuberculosis (TB). However, it is unclear whether any causal relationship exists between them and whether causality is bidirectional.

METHODS

A bidirectional two-sample Mendelian randomization (MR) analysis was performed. The genome-wide association study (GWAS) summary statistics of gut microbiota were obtained from the MiBioGen consortium, while the GWAS summary statistics of TB and its specific phenotypes [respiratory tuberculosis (RTB) and extrapulmonary tuberculosis (EPTB)] were retrieved from the UK Biobank and the FinnGen consortium. And 195 bacterial taxa from phylum to genus were analyzed. Inverse variance weighted (IVW), MR-Egger regression, maximum likelihood (ML), weighted median, and weighted mode methods were applied to the MR analysis. The robustness of causal estimation was tested using the heterogeneity test, horizontal pleiotropy test, and leave-one-out method.

RESULTS

In the UK Biobank database, we found that 11 bacterial taxa had potential causal effects on TB. Three bacterial taxa genus.Akkermansia, family.Verrucomicrobiacea, order.Verrucomicrobiales were validated in the FinnGen database. Based on the results in the FinnGen database, the present study found significant differences in the characteristics of gut microbial distribution between RTB and EPTB. Four bacterial taxa genus.LachnospiraceaeUCG010, genus.Parabacteroides, genus.RuminococcaceaeUCG011, and order.Bacillales were common traits in relation to both RTB and TB, among which order.Bacillales showed a protective effect. Additionally, family.Bacteroidacea and genus.Bacteroides were identified as common traits in relation to both EPTB and TB, positively associating with a higher risk of EPTB. In reverse MR analysis, no causal association was identified. No significant heterogeneity of instrumental variables (IVs) or horizontal pleiotropy was found.

CONCLUSION

Our study supports a one-way causal relationship between gut microbiota and TB, with gut microbiota having a causal effect on TB. The identification of characteristic gut microbiota provides scientific insights for the potential application of the gut microbiota as a preventive, diagnostic, and therapeutic tool for TB.

Metabolomics study of the hepatoprotective effect of total flavonoids of Mallotus apelta leaf in carbon tetrachloride-induced liver fibrosis in rats

Mallotus apelta leaf, recorded in the quality standard of Yao Medicinal Material in Guangxi Zhuang autonomous region, is commonly used in the treatment of liver diseases. Total flavonoids of M. apelta leaf (TFM) had good anti-fibrosis activity, but the anti-fibrosis mechanism of TFM is still unclear. Nuclear magnetic resonance technology was used to study the dynamic changes of urine metabolites in CCl4 -induced liver fibrosis before and after TFM treatment. Ingenuity Path Analysis (IPA) was used to find potential target genes for TFM to improve liver fibrosis and verify the expression of target genes by real-time fluorescent quantitative PCR and Western blotting. TFM can significantly reduce serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) levels, improve liver steatosis and reduce inflammation; in urine metabolomics, a total of seven potential biomarkers were found, mainly involving two metabolic pathways; IPA analysis showed that TNF may be a potential target for TFM to improve liver fibrosis induced by CCl4 in rats. This study found that TNF may be a potential target gene for TFM treatment of liver fibrosis, and shows that the anti-fibrosis mechanism of TFM could improve liver fibrosis by regulating the tricarboxylic acid cycle and subtaurine metabolism.

Prognostic value of EBV DNA and platelet-to-lymphocyte ratio in patients with non-metastatic nasopharyngeal carcinoma: a retrospective study

PURPOSE

Analyzing the prognostic value of Epstein-Barr virus (EBV) DNA load and platelet-to-lymphocyte ratio (PLR) in non-metastatic nasopharyngeal carcinoma (NPC) patients, thereby developing a reliable and effective marker.

METHODS

We compared survival rates among different groups using the Kaplan-Meier method and the Log-rank test. The factors affecting the prognosis of NPC patients were determined using univariate and multivariate cox regression analysis. Receiver operating characteristic (ROC) curves were used to identify the cutoff-value and discriminant performance of the model.

RESULTS

The ROC curve indicated a cut-off value of 775 copies/ml for EBV DNA and 203.3 for PLR. Kaplan-Meier and Log-rank tests showed that 3-year overall survival (OS), local recurrence-free survival (LRFS) and distant metastasis-free survival (DMFS) of NPC patients in high risk group (HRG) were significantly poorer than those in medium risk group (MRG) and low risk group (LRG). The 3-year OS of NPC patients was significantly correlated with age, N stage and EBV DNA-PLR. The 3-year LRFS were significantly correlated with sex, N stage, histology type, and EBV DNA-PLR. The 3-year DMFS were correlated with histology type. The ROC curve showed that area under the curve (AUC) values of EBV DNA-PLR of 3-year OS, LRFS and DMFS in NPC were higher than those of PLR and EBV DNA.

CONCLUSION

EBV DNA-PLR is an independent risk factor for the prognosis of NPC. Compared with PLR or EBV DNA alone, the combination of EBV DNA and PLR may be more accurate in predicting the prognosis of NPC patients.

Analysis of m7G-Related signatures in the tumour immune microenvironment and identification of clinical prognostic regulators in breast cancer

BACKGROUND

Breast cancer is a malignant tumour that seriously threatens women's life and health and exhibits high inter-individual heterogeneity, emphasising the need for more in-depth research on its pathogenesis. While internal 7-methylguanosine (m7G) modifications affect RNA processing and function and are believed to be involved in human diseases, little is currently known about the role of m7G modification in breast cancer.

METHODS AND RESULTS

We elucidated the expression, copy number variation incidence and prognostic value of 24 m7G-related genes (m7GRGs) in breast cancer. Subsequently, based on the expression of these 24 m7GRGs, consensus clustering was used to divide tumour samples from the TCGA-BRCA dataset into four subtypes based on significant differences in their immune cell infiltration and stromal scores. Differentially expressed genes between subtypes were mainly enriched in immune-related pathways such as 'Ribosome', 'TNF signalling pathway' and 'Salmonella infection'. Support vector machines and multivariate Cox regression analysis were applied based on these 24 m7GRGs, and four m7GRGs-AGO2, EIF4E3, DPCS and EIF4E-were identified for constructing the prediction model. An ROC curve indicated that a nomogram model based on the risk model and clinical factors had strong ability to predict the prognosis of breast cancer. The prognoses of patients in the high- and low-TMB groups were significantly different (p = 0.03). Moreover, the four-gene signature was able to predict the response to chemotherapy.

CONCLUSIONS

In conclusion, we identified four different subtypes of breast cancer with significant differences in the immune microenvironment and pathways. We elucidated prognostic biomarkers associated with breast cancer and constructed a prognostic model involving four m7GRGs. In addition, we predicted the candidate drugs related to breast cancer based on the prognosis model.

Application of deep learning algorithm in the recognition of cryptococcosis and talaromycosis skin lesions

BACKGROUND

Cryptococcosis and talaromycosis are known as 'neglected epidemics' due to their high case fatality rates and low concern. Clinically, the skin lesions of the two fungal diseases are similar and easily misdiagnosed. Therefore, this study aims to develop an algorithm to identify cryptococcosis/talaromycosis skin lesions.

METHODS

Skin images of tararomiasis and cryptococcosis were collected from published articles and augmented using the Python Imaging Library (PIL). Then, five deep artificial intelligence models, VGG19, MobileNet, InceptionV3, Incept ResNetV2 and DenseNet201, were developed based on the collected datasets using transfer learning technology. Finally, the performance of the models was evaluated using sensitivity, specificity, F1 score, accuracy, AUC and ROC curve.

RESULTS

In total, 159 articles (79 for cryptococcosis and 80 for talaromycosis), including 101 cryptococcosis skin lesion images and 133 talaromycosis skin lesion images, were collected for further mode construction. Five methods showed good performance for prediction but did not yield satisfactory results for all cases. Among them, DenseNet201 performed best in the validation set, followed by InceptionV3. However, InceptionV3 showed the highest sensitivity, accuracy, F1 score and AUC values in the training set, followed by DenseNet201. The specificity of DenseNet201 in the training set is better than that of InceptionV3.

CONCLUSIONS

DenseNet201 and InceptionV3 are equivalent to the optimal model in these conditions and can be used in clinical settings as decision support tools for the identification and classification of skin lesions of cryptococcus/talaromycosis.

Macrophage-inherited exosome excise tumor immunosuppression to expedite immune-activated ferroptosis

BACKGROUND

Immunosuppressive tumor microenvironment (ITM) remains an obstacle that jeopardizes clinical immunotherapy.

METHODS

To address this concern, we have engineered an exosome inherited from M1-pheototype macrophages, which thereby retain functions and ingredients of the parent M1-phenotype macrophages. The delivered RSL3 that serves as a common ferroptosis inducer can reduce the levels of ferroptosis hallmarkers (eg, glutathione and glutathione peroxidase 4), break the redox homeostasis to magnify oxidative stress accumulation, promote the expression of ferroptosis-related proteins, and induce robust ferroptosis of tumor cells, accompanied with which systematic immune response activation can bbe realized. M1 macrophage-derived exosomes can inherit more functions and genetic substances than nanovesicles since nanovesicles inevitably suffer from substance and function loss caused by extrusion-arised structural damage.

RESULTS

Inspired by it, spontaneous homing to tumor and M2-like macrophage polarization into M1-like ones are attained, which not only significantly magnify oxidative stress but also mitigate ITM including M2-like macrophage polarization and regulatory T cell decrease, and regulate death pathways.

CONCLUSIONS

All these actions accomplish a synergistic antitumor enhancement against tumor progression, thus paving a general route to mitigate ITM, activate immune responses, and magnify ferroptosis.

Erastin induces ferroptosis in cervical cancer cells via Nrf2/HO-1 signaling pathway

OBJECTIVE

Our research aims to assess the influence of erastin, a ferroptosis-inducing agent, on cervical cancer cells.

INTRODUCTION

Cervical cancer is a prevalent malignancy in females. Dysregulation of ferroptosis, a form of cell demise reliant on iron, is implicated in several cancers.

METHODS

The effect of erastin on HeLa and SiHa was detected by transwell assay, scratch test, and colony formation assay, while cell apoptosis was detected using flow cytometry. Cellular reactive oxygen species (ROS) generation was detected using the dichloro-dihydro-fluorescein diacetate assay. Sequencing analysis identified differentially expressed genes (DEGs), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment analyses were employed to identify the target gene. Subsequently, the utilization of small interfering RNA (siRNA) was employed to suppress the targeted gene expression in HeLa cells, thereby effectively mitigating the impact of erastin on various cellular processes including invasion, colony formation, migration, and ROS generation.

RESULTS

The findings indicate that erastin attenuates the viability of both HeLa cells (IC50 = 30.88 µM) and SiHa cells (IC50 = 29.40 µM). Treatment with erastin at 10 µM inhibits the invasion, colony formation, and migration of both HeLa and SiHa cells within 24 h. Ferrostatin-1 (1 µM) notably alleviates the inhibitory effects of erastin of HeLa and SiHa cells. Upregulation of nuclear factor erythroid 2-related factor 2 (Nrf2) and its downstream target, heme oxygenase-1 (HO-1), was found in erastin-treated cells compared to the control group. When knocked down HO-1 in HeLa cells, effectively counteracting the effects of erastin on the invasion, colony formation, migration, and ROS production in HeLa cells.

CONCLUSION

Our research demonstrates that erastin induces ferroptosis and the accumulation of ROS in cervical cancer cells by activating the Nrf2/HO-1 pathway, significantly reducing cell proliferation and motility. These findings propose a potential molecular mechanism of erastin-mediated cervical cancer development.

CASA: a comprehensive database resource for the COVID-19 Alternative Splicing Atlas

BACKGROUND

As a key process in transcriptional regulatory mechanisms, alternative splicing (AS) plays a crucial role in maintaining the diversity of RNA and protein expression, and mediates the immune response in infectious diseases, especially for the COVID-19. Therefore, urgent data gathering and more research of AS profiles in microbe-infected human cells are needed to improve understanding of COVID-19 and related infectious diseases. Herein, we have created CASA, the COVID-19 Alternative Splicing Atlas to provide a convenient computing platform for studies of AS in COVID-19 and COVID-19-related infectious diseases.

METHODS

In CASA, we reanalyzed thousands of RNA-seq datasets generated from 65 different tissues, organoids and cell lines to systematically obtain quantitative data on AS events under different conditions. A total of 262,994 AS events from various infectious diseases with differing severity were detected and visualized in this database. In order to explore the potential function of dynamics AS events, we performed analysis of functional annotations and drug-target interactions affected by AS in each dataset. RNA-binding proteins (RBPs), which may regulate these dynamic AS events are also provided for users in this database.

RESULTS

CASA displays microbe-induced alterations of the host cell splicing landscape across different virus families and helps users identify condition-specific splicing patterns, as well as their potential regulators. CASA may greatly facilitate the exploration of AS profiles and novel mechanisms of host cell splicing by viral manipulation. CASA is freely available at http://www.splicedb.net/casa/ .

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

Objective

Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality.

Methods

We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model.

Results

A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3⁺ T-cell count, CD8⁺ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios.

Conclusion

The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.

Talaromyces marneffei can cause a fatal deeply disseminated fungal infection- talaromycosis. It is widely distributed in Southeast Asia and spreading globally, the disease is insidious and responsible for significant deaths. Clinicians need easy-to-use tools to make decisions on which patients are at a higher risk of dying after infecting T. marneffei. In this study, conducted in Southern China, we have evolved XGBoost machine learning model. 15 clinical indicators and laboratory measures were used to estimate a patient’s risk of dying in the hospital due to the T. marneffei infection. The study showed that the machine learning model has good predictive ability when tested in an internal testing population of patients. We expect that the model could help clinicians assess a patient’s risk of death in just the time of admission to help decide on early treatment timing of high-risk patients who are likely to die.

Optimal stereotactic body radiotherapy dosage for hepatocellular carcinoma: a multicenter study

BACKGROUND

The optimal dose and fractionation scheme of stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC) remains unclear due to different tolerated liver volumes and degrees of cirrhosis. In this study, we aimed to verify the dose-survival relationship to optimize dose selection for treatment of HCC.

METHODS

This multicenter retrospective study included 602 patients with HCC, treated with SBRT between January 2011 and March 2017. The SBRT dosage was classified into high dose, moderate dose, and low dose levels: SaRT (BED10 ≥ 100 Gy), SbRT (EQD2 > 74 Gy to BED10 < 100 Gy), and ScRT (EQD2 < 74 Gy). Overall survival (OS), progression-free survival (PFS), local control (LC), and intrahepatic control (IC) were evaluated in univariable and multivariable analyses.

RESULTS

The median tumor size was 5.6 cm (interquartile range [IQR] 1.1-21.0 cm). The median follow-up time was 50.0 months (IQR 6-100 months). High radiotherapy dose correlated with better outcomes. After classifying into the SaRT, SbRT, and ScRT groups, three notably different curves were obtained for long-term post-SBRT survival and intrahepatic control. On multivariate analysis, higher radiation dose was associated with improved OS, PFS, and intrahepatic control.

CONCLUSIONS

If tolerated by normal tissue, we recommend SaRT (BED10 ≥ 100 Gy) as a first-line ablative dose or SbRT (EQD2 ≥ 74 Gy) as a second-line radical dose. Otherwise, ScRT (EQD2 < 74 Gy) is recommended as palliative irradiation.

Role of alternative splicing signatures in the prognosis of glioblastoma

BACKGROUND

Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking.

METHODS

The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan-Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM.

RESULTS

A total of 2355 survival-associated AS events were identified. The splicing prognostic model revealed that patients in the high-risk group have worse survival rates than those in the low-risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = -.506).

CONCLUSIONS

Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.