Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2.

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model.

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

CircRNA identification and feature interpretability analysis.

BACKGROUND: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation. RESULTS: We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification. CONCLUSIONS: CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .

Deep learning models for disease-associated circRNA prediction: a review.

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

Recently urodynamic and quality of life assessment in the non-menopausal women with cervical cancer after radical hysterectomy.

PURPOSE: The aim of this study was to evaluate the pre- and postoperative changes in the recently urodynamic and quality of life (QoL) in nonmenopausal women diagnosed with cervical cancer and treated with radical hysterectomy (RH). PATIENTS AND METHODS: Twenty-eight nonmenopausal women (28-49 years) with cervical carcinoma (FIGO stage Ia2-IIa) underwent a radical hysterectomy. Urodynamic studies were performed 1 week before (U0) and 3-6 months (U1) after surgery. A self-administered condition-specific QoL questionnaire (PFDI-20, PFIQ-7) was applied at U0 and U1. RESULTS: Data from the urodynamics analysis performed at U1 showed that the average first sensation volume (119.39 ± 12.28 ml vs 150.43 ± 31.45 ml, P < 0.001), the residual urine volume (6.39 ± 10.44 ml vs. 42.32 ± 33.72 ml, P < 0.001), and the time of urination (46.10 ± 16.65 s vs. 74.31 ± 23.94 s, P < 0.001) were increased, while the bladder volume at a strong desire to void (448.89 ± 86.62 ml vs. 322.82 ± 50.89 ml, P < 0.001), the bladder compliance (82.63 ± 58.06 ml/cmH2O vs. 37.45 ± 28.66 ml/cmH2O, P < 0.001), the average flow rate (Qave) (23.86 ± 4.25 ml/s vs. 12.57 ± 2.37 ml/s, P < 0.001), the maximum natural flow rate (Qmax) (25.42 ± 6.46 ml/s vs. 14.43 ± 5.32 ml/s, P < 0.001), and the pressure at a peak flow rate (PdetQmax) (36.53 ± 11.20 cmH2O vs. 31.43 ± 10.56 cmH2O, P < 0.05) were decreased. At the same time, functional pelvic problems derived from prolapse (PFDI-20 scores) and their impact on the patients' Qol (PFIQ-7 score) were significantly improved at 3-6 months postoperation. CONCLUSION: Radical hysterectomy results in urodynamic changes, and 3-6 months postoperation may be an important period for changes in bladder dysfunction after RH. Urodynamic and QoL analyses may provide methods for assessing symptoms.

Comparative Genomics and Functional Genomics Analysis in Plants.

Comparative genomics and functional genomics are two basic branches of plant genomics [...].

Prevotella as the hub of the cervicovaginal microbiota affects the occurrence of persistent human papillomavirus infection and cervical lesions in women of childbearing age via host NF-κB/C-myc.

There is evidence that coinfection of cervicovaginal high-risk human papillomavirus (HR-HPV) and bacteria is common in women of childbearing age. However, the relationship between bacterial vaginosis (BV) and persistent HR-HPV infection in women of childbearing age and the underlying mechanisms remain unclear. In this study, we determined whether BV affects persistent HR-HPV infection in women aged 20-45 years and explored the possible mechanisms of their interactions. From January 1 to April 30, 2020, we recruited women aged 20-45 years with and without BV at a ratio of 1:2 from Fujian Maternity and Child Health Hospital. All women were followed up at 0, 12, and 24 months. A BV assay, HR-HPV genotyping and cervical cytology were performed at each follow-up. At 0 months, additional vaginal secretions and cervical exfoliated cells were collected for 16S ribosomal RNA sequencing, bacterial metabolite determination, and POU5F1B, C-myc, TLR4, NF-κB, and hTERT quantification. A total of 920 women were included. The abundance of Prevotella (p = 0.016) and Gardnerella (p = 0.027) were higher, whereas the abundance of Lactobacillus was lower (p = 0.001) in women with persistent HR-HPV infection and high-grade squamous intraepithelial lesions (HSIL). The abundance of Prevotella (p = 0.025) and Gardnerella (p = 0.018) increased in the vaginas of women with persistent HPV16 infection, whereas only the abundance of Prevotella (p = 0.026) was increased in women with persistent HPV18 infection. The abundance of Prevotella in the vagina was significantly positively correlated with the expression levels of TLR4, NF-κB, C-myc, and hTERT in host cervical cells (p < 0.05). Our findings suggest that overgrowth of Prevotella in the vagina may influence the occurrence of persistent HR-HPV infection-related cervical lesions through host NF-κB and C-myc signaling.

The prevalence of human papillomavirus and bacterial vaginosis among young women in China: a cross-sectional study.

BACKGROUND: The natural history of human papillomavirus (HPV) is influenced by vaginal microenvironment disorders, such as bacterial vaginosis (BV). The objective of this study was to assess the epidemiology of HPV combined with BV prevalence among Chinese women aged 20-35 years. METHODS: A total of 2000 sexually active women aged 20-35 years voluntarily enrolled in this study and underwent a ThinPrep cytologic test and PCR-reverse dot blot human papillomavirus genotyping (PCR-RDB HPV test). BV was diagnosed if clue cells were observed (20% more than epithelial cells). RESULTS: The overall HPV infection rate in this population was 16.2% (324/2000). Compared with HPV-negative individuals, BV prevalence was higher in the High-risk human papillomavirus (HR-HPV) (5.9% vs. 3.1%, P < 0.001). BV and HPV-51, -52 infection were more commonly associated with each other. In patients with cervical lesions (≥ CIN 1), the BV prevalence rate was higher than in patients with negative for intraepithelial lesion or malignancy (NILM) (11.9% vs. 3.8%, P = 0.002). CONCLUSION: BV was found to be related to HPV-51, -52 infections and cervical lesions. To better manage HPV infected population, more attention should be paid to the prevention and proper treatment of BV.

Expression pattern Nanog is associated with the development of gestational trophoblastic neoplasia in Chinese women from Fujian province: a case-control study.

BACKGROUND: Gestational trophoblastic disease (GTD) is a group of interrelated but distinct diseases and has a serious impact on the reproductive health of women. To analyze the expression of Nanog in GTD and to evaluate its potential to predict the development of gestational trophoblastic neoplasia (GTN). METHODS: The study included 41 normal first-trimester placentas matched by gestational age to 53 regressed-hydatidiform-moles (rHMs), 56 malignant-HMs (mHMs) and 17 choriocarcinomas (CCAs) and evaluated the Nanog expression by immunohistochemistry. The chi-square test, ANOVA, Fisher's exact test and logistic regression were performed to assess the Nanog expression and clinical prognostic factors in GTD. RESULTS: Compared to normal placenta levels, the Nanog expression was increased in GTD samples (p < .05). In HMs, Nanog expression was positively correlated with serum ß-hCG levels,uterine size and theca-lutein cysts (p < .05). Compared with the low-risk metastatic group (Federation of Gynecology and Obstetrics (FIGO) score ≤ 6), the high-risk metastatic group (FIGO score >7) had higher Nanog expression (p = .030). Moreover, logistic regression analysis showed that the positive expression of Nanog had the highest risk of developing into GTN (OR = 4.764, p < .001). CONCLUSIONS: Nanog is an independent predictor of clinical outcomes. It can also be a reliable predictor for GTN development from GTD.

A novel non-invasive molecular biomarker in ovarian endometriosis: estrogen-related receptor α.

PURPOSE: This study aimed to explore whether estrogen-related receptors α (ERRα) can prognosticate the occurrence and development of ovarian endometriosis (EMs) as a non-invasive biomarker. METHODS: The ectopic and its' correspond eutopic endometria from 47 patients with ovarian EMs and the control normal endometria from 32 cases were collected and detected the mRNA expression of ERRα by RT-qPCR. The serum protein of ERRα were tested by ELISA. The menstrual cycle, ovarian cyst sizes, relative clinical tumour markers, such as CA125, CA19-9 and HE-4, and other demographic data were also involved into analysis in these patients by SPSS 22.0 (IBM, Chicago, IL, USA). RESULTS: The ERRα mRNA expression in ectopic endometria were significantly lower than it in eutopic endometria (P < 0.05) and the normal endometria (P < 0.05). Similarly, the serum ERRα levels in patients with EMs were significantly lower than it in control group (P < 0.01). Moreover, the serum ERRα levels were decreasing with the increasing of the pathological stages and ovarian cyst sizes. While, the serum CA125, CA19-9, CA125/ERRα ratio and CA19-9/ERRα ratio in the study group were significantly higher than those in the control group (all P < 0.01). CONCLUSION: The expression of ERRα is correlated with the development of ovarian EMs pathological stages and ovarian cyst sizes and can be used as a novel and non-invasive biomarker for evaluating the progression of ovarian EMs.

Mg2+-Channel-Inspired Nanopores for Mg2+/Li+ Separation: The Effect of Coordination on the Ionic Hydration Microstructures.

The separation behaviors of Mg2+ and Li+ were investigated using molecular dynamics. Two functionalized graphene nanopore models (i.e., co_5 and coo_5) inspired by the characteristic structural features of Mg2+ channels were used. Both nanopores exhibited a higher preference to Mg2+ than to Li+, and the selectivity ratios were higher for coo_5 than for co_5 under all the studied transmembrane voltages. An evaluation of the effect of coordination on the ionic hydration microstructures for both nanopores showed that the positioning of the modified groups could better fit a hydrated Mg2+ than a hydrated Li+, as if Mg2+ was not dehydrated according to hydrogen bond analysis of the ionic hydration shells. This condition led to a lower resistance for Mg2+ than for Li+ when traveling through the nanopores. Moreover, a distinct increase in hydrogen bonds occurred with coo_5 compared with co_5 for hydrated Li+, which made it more difficult for Li+ to pass through coo_5. Thus, a higher Mg2+/Li+ selectivity was found in for coo_5 than for co_5. These findings provide some design principles for developing artificial Mg2+ channels, which have potential applications as Mg2+ sensors and novel devices for Mg2+/Li+ separation.

GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data.

Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate development and heterogeneity. While numerous machine-learning methods have been proposed to infer GRNs from scRNA-seq gene expression data, many of them operate solely in a statistical or black box manner, limiting their capacity for making causal inferences between genes. In this study, we introduce GRN inference with Accuracy and Causal Explanation (GRACE), a novel graph-based causal autoencoder framework that combines a structural causal model (SCM) with graph neural networks (GNNs) to enable GRN inference and gene causal reasoning from scRNA-seq data. By explicitly modeling causal relationships between genes, GRACE facilitates the learning of regulatory context and gene embeddings. With the learned gene signals, our model successfully decoding the causal structures and alleviates the accurate determination of multiple attributes of gene regulation that is important to determine the regulatory levels. Through extensive evaluations on seven benchmarks, we demonstrate that GRACE outperforms 14 state-of-the-art GRN inference methods, with the incorporation of causal mechanisms significantly enhancing the accuracy of GRN and gene causality inference. Furthermore, the application to human peripheral blood mononuclear cell (PBMC) samples reveals cell type-specific regulators in monocyte phagocytosis and immune regulation, validated through network analysis and functional enrichment analysis.

RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs.

Recently, a large number of studies have indicated that circRNAs with covalently closed loops play important roles in biological processes and have potential as diagnostic biomarkers. Therefore, research on the circRNA-disease relationship is helpful in disease diagnosis and treatment. However, traditional biological verification methods require considerable labor and time costs. In this paper, we propose a new computational method (RGCNCDA) to predict circRNA-disease associations based on relational graph convolutional networks (R-GCNs). The method first integrates the circRNA similarity network, miRNA similarity network, disease similarity network and association networks among them to construct a global heterogeneous network. Then, it employs the random walk with restart (RWR) and principal component analysis (PCA) models to learn low-dimensional and high-order information from the global heterogeneous network as the topological features. Finally, a prediction model based on an R-GCN encoder and a DistMult decoder is built to predict the potential disease-associated circRNA. The predicted results demonstrate that RGCNCDA performs significantly better than the other six state-of-the-art methods in a 5-fold cross validation. Furthermore, the case study illustrates that RGCNCDA can effectively discover potential circRNA-disease associations.

Bioinformatics analysis methods for cell-free DNA.

As a kind of novel non-invasive marker for molecular detection, cell-free DNA (cfDNA) has potential value for the early diagnosis of diseases, prognosis assessment, and efficacy monitoring. The constant developments in molecular biology detection technologies have led to an increase in clinical studies on the use of cfDNA detection methods for patients, and many gratifying outcomes have been achieved. In this review, the contributions of bioinformatics tools to the study of cfDNA are well discussed. The focus of the review is on cfDNA identification signals, cfDNA identification methods, and the relationship of cfDNA with human diseases such as hepatic cancer, lung cancer, end-stage kidney disease, and ischemic stroke. The research significance and existing problems of using cfDNA as a biomarker for diseases are also discussed.

Changes of the vaginal microbiota in HPV infection and cervical intraepithelial neoplasia: a cross-sectional analysis.

This study aimed to explore the changes of the vaginal microbiota and enzymes in the women with high-risk human papillomavirus (HR-HPV) infection and cervical lesions. A total of 448 participants were carried out HPV genotyping, cytology tests, and microecology tests, and 28 participants were treated as sub-samples, in which vaginal samples were characterized by sequencing the bacterial 16S V4 ribosomal RNA (rRNA) gene region. The study found the prevalence of HR-HPV was higher in patients with BV (P = 0.036). The HR-HPV infection rate was 72.73% in G. vaginalis women, which was significantly higher than that of women with lactobacillus as the dominant microbiota (44.72%) (P = 0.04). The positive rate of sialidase (SNA) was higher in women with HR-HPV infection (P = 0.004) and women diagnosed with cervical intraepithelial neoplasia (CIN) (P = 0.041). In HPV (+) women, the α-diversity was significantly higher than that in HPV (-) women. The 16S rRNA gene-based amplicon sequencing results showed that Lactobacillus was the dominant bacteria in the normal vaginal microbiota. However, the proportion of Gardnerella and Prevotella were markedly increased in HPV (+) patients. Gardnerella and Prevotella are the most high-risk combination for the development of HPV (+) women. The SNA secreted by Gardnerella and Prevotella may play a significant role in HPV infection progress to cervical lesions.

High quantum efficiency GaP avalanche photodiodes.

Gallium Phosphide (GaP) reach-through avalanche photodiodes (APDs) are reported. The APDs exhibited dark current less than a pico-ampere at unity gain. A quantum efficiency of 70% was achieved with a recessed window structure; this is almost two times higher than previous work.

Nanosphere natural lithography surface texturing as anti-reflective layer on SiC photodiodes.

Natural lithography with 100-nm-diameter SiO(2) spheres followed by inductively coupled plasma etching was used to texture the surface of 4H-SiC for a wide-spectrum large-acceptance-angle anti-reflective layer. The surface showed low normal-incidence reflectance of < 5% over a wide spectrum from 250 nm to 550 nm. Photodiodes fabricated from the surface-textured SiC showed broader spectral and angular responsivity than SiC photodiodes with SiO(2) antireflective coating. The textured SiC photodiodes showed peak responsivity of 116 mA/W, large angle of acceptance angle (< 2% decrease in responsivity at 50° incident angle) and low dark current at 10V.

Bioinformatics Research on Drug Sensitivity Prediction.

Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.

Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer.

PURPOSE: This study assessed the predictive value of the preoperative systemic immune-inflammatory index (SII) for lymph node metastasis (LNM) in endometrial cancer (EC) patients. METHODS: We retrospectively included 392 EC patients between January 2013 and January 2019. Data on clinical indicators including age, body mass index (BMI), menopause, serum inflammatory immune index, serum tumor markers, history of diabetes and hypertension, stage, histological type, and myometrial invasion (MI) were collected. The association between clinical indicators and LNM was evaluated. RESULTS: The results indicated that neutrophil (NE), monocyte (MO) counts, SII, cancer antigen 125 (CA125), cancer antigen 153 (CA153), cancer antigen 199 (CA199), and the expression of estrogen receptor (ER) and Ki67 were higher in EC patients with LNM than in those without LNM (P<0.05). Lymph vascular space invasion (LVSI) was also associated with LNM (P<0.05). Consequently, the SII, CA125, CA153 and LVSI were found to be independent risk factors for LNM, and a nomogram including these indicators was performed. The ROC curve analysis showed that compared with a single index, the combination of the SII, CA125, CA153 and LVSI significantly improved the efficiency of diagnosing LNM in EC patients (AUC=0.865, P < 0.001). Moreover, the SII was significantly associated with age, menopause, and FIGO stage (P < 0.05). Further logistic regression analysis suggested that elevated serum SII was an independent risk factor for MI and progression to a higher pathological grade in young premenopausal EC patients. In addition, elevated SII was an independent risk factor for advanced EC progression in age ≥55 or postmenopausal EC patients. CONCLUSION: An elevated SII is an independent risk factor for LNM in patients with EC. In addition, the SII can be used as a predictor of MI and higher pathological grade in young premenopausal EC patients.