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Objective: There is a scarcity of transcriptome sequencing data available for the Leptopetalum biflorum, and numerous cyclotides remain undiscovered. It is urgent to establish a workflow based on de novo transcriptome assembly and make systematic prediction of cyclotides in Leptopetalum biflorum, to provide a reference for functional analysis of cyclotides. Methods: In this study, we performed RNA-seq on roots, leaves, and flowers of Leptopetalum biflorum to obtain two sets of transcriptome data. The quality assessment of the sequencing was conducted using FastQC and MultiQC. De novo transcriptome assembly of Leptopetalum biflorum was carried out using Trinity, with assembly quality evaluated through the Read Support method and BUSCO tool analysis. The eggnog-mapper and Trinotate were used to annotate functional terms in GO and pathways in KEGG. The Transdecoder was utilized to predict ORFs and coding regions while SignalP software was employed to predict amino acid sequences containing signal peptides and signal peptide splicing sites. The mature protein sequences are subsequently used for cyclotide prediction in Leptopetalum biflorum via FindCRP 2.0 (Find Cyclotide Peptide), a cyclotide prediction tool developed by our team. Results: Trinity assembled a total of 171,310 transcripts and 103,299 isoforms (genes). The average transcript length was 1139.89, while the average gene length was 780.87. Approximately 30% of the genes exhibited homology within other plant species. Among these genes, 23,265 (22.52%) were annotated into 41 GO terms at Level 2. The KEGG pathway annotation revealed that 23,682 genes (22.92%) contained 5171 KO annotations and were involved in 484 pathways. FindCRP predicted 17 potential cyclotides, among which 15 sequences had homologous genes; notably five potential cyclotides showed complete identity (100%) to their respective homologous genes. Additionally, two potential cyclotide sequences without any identified homologous demonstrated circle-forming ability based on the 3D structure prediction results. Conclusion: In this study, we developed a de novo transcriptome assembly workflow for the identification of cyclotides using RNA-seq data from Leptopetalum biflorum. Our custom-built tool, FindCRP, was employed in this workflow to detect potential cyclotides. This meticulously designed workflow ensures the reproducibility and reliability of our study findings. We successfully performed transcript annotation and predicted putative cyclotides. These potential cyclotides show significant homology to known cyclotides.

Abstract Background Memory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD. Methods We identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed. Results Four memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity. Conclusions In summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.

Abstract Background The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug–target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. Methods In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. Results The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. Conclusion The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Uveal melanoma (UM) is the most common intraocular tumor in adults. Recurrent mutations in BRCA1‐associated protein 1 (BAP1) and splicing factor 3B subunit 1 (SF3B1) display a mutually exclusive pattern in UM, but the underlying mechanism is unknown. We show that combined BAP1 deficiency and SF3B1 hotspot mutation lead to senescence and growth arrest in human UM cells. Although p53 protein expression is induced, deletion of TP53 (encoding p53) only modestly rescues the observed senescent phenotype. UM cells with BAP1 loss or SF3B1 mutation are more sensitive to chemotherapeutic drugs compared with their isogenic parental cells. Transcriptome analysis shows that DNA‐repair genes are downregulated upon co‐occurrence of BAP1 deletion and SF3B1 mutation, thus leading to impaired DNA damage response and the induction of senescence. The co‐occurrence of these two mutations reduces invasion of UM cells in zebrafish xenograft models and suppresses growth of melanoma xenografts in nude mice. Our findings provide a mechanistic explanation for the mutual exclusivity of BAP1 and SF3B1 mutations in human UM.

Preeclampsia is believed to be caused by impaired placentation with insufficient trophoblast invasion, leading to impaired uterine spiral artery remodeling and angiogenesis. However, the underlying molecular mechanism remains unknown. We recently carried out transcriptome profiling of placental long noncoding RNAs (lncRNAs) and identified 383 differentially expressed lncRNAs in early-onset severe preeclampsia. Here, we are reporting our identification of lncRNA INHBA-AS1 as a potential causal factor of preeclampsia and its downstream pathways that may be involved in placentation. We found that INHBA-AS1 was upregulated in patients and positively correlated with clinical severity. We systematically searched for potential INHBA-AS1-binding transcription factors and their targets in databases and found that the targets were enriched with differentially expressed genes in the placentae of patients. We further demonstrated that the lncRNA INHBA-AS1 inhibited the invasion and migration of trophoblast cells through restraining the transcription factor CENPB from binding to the promoter of TNF receptor-associated factor 1 (TRAF1). Therefore, we have identified the dysregulated pathway “INHBA-AS1-CENPB-TRAF1” as a contributor to the pathogenesis of preeclampsia through prohibiting the proliferation, invasion, and migration of trophoblasts during placentation.

Summary: Immune activation has been shown to play a critical role in the development of schizophrenia; however its underlying mechanism remains unknown. Our report demonstrates a high-quality protein interaction network for schizophrenia (SCZ Network), constructed using our “neighborhood walk” approach in combination with “random walk with restart”. The spatiotemporal expression pattern of the genes in this disease network revealed two developmental stages sensitive to perturbation by immune activation: mid-to late gestation, and adolescence. Furthermore, we induced immune activation at these stages in mice, carried out transcriptome sequencing on the mouse brains, and illustrated clear potential molecular pathways and key regulators correlating maternal immune activation during gestation and an increased risk for schizophrenia after a second immune activation at puberty. This work provides not only valuable resources for the study on molecular mechanisms underlying schizophrenia, but also a systematic strategy for the discovery of molecular pathways of complex mental disorders.

Abstract Background Previously developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma. Although the role of alternative splicing (AS) in cancer, especially in glioma, has been validated, a comprehensive analysis of AS in glioma has not yet been conducted. In this study, we aimed at classifying glioma based on prognostic AS. Methods Using the TCGA glioblastoma (GBM) and low-grade glioma (LGG) datasets, we analyzed prognostic splicing events. Consensus clustering analysis was conducted to classified glioma samples and correlation analysis was conducted to characterize regulatory network of splicing factors and splicing events. Results We analyzed prognostic splicing events and proposed novel splicing classifications across pan-glioma samples (labeled pST1–7) and across GBM samples (labeled ST1–3). Distinct splicing profiles between GBM and LGG were observed, and the primary discriminator for the pan-glioma splicing classification was tumor grade. Subtype-specific splicing events were identified; one example is AS of zinc finger proteins, which is involved in glioma prognosis. Furthermore, correlation analysis of splicing factors and splicing events identified SNRPB and CELF2 as hub splicing factors that upregulated and downregulated oncogenic AS, respectively. Conclusion A comprehensive analysis of AS in glioma was conducted in this study, shedding new light on glioma heterogeneity and providing new insights into glioma diagnosis and treatment.

Abstract Placenta‐origin pregnancy complications, including preeclampsia (PE), gestational diabetes mellitus (GDM), fetal growth restriction (FGR), and macrosomia (MA) are common occurrences in pregnancy, resulting in significant morbidity and mortality for both mother and fetus. However, despite their frequency, there are no reliable methods for the early diagnosis of these complications. Since cfDNA is mainly derived from placental trophoblasts and maternal hematopoietic cells, it might have information for gene expression which can be used for disease prediction. Here, low coverage whole‐genome sequencing on plasma DNA from 2,199 pregnancies is performed based on retrospective cohorts of 3,200 pregnant women. Read depth in the promoter regions is examined to define read‐depth distribution patterns of promoters for pregnancy complications and controls. Using machine learning methods, classifiers for predicting pregnancy complications are developed. Using these classifiers, complications are successfully predicted with an accuracy of 80.3%, 78.9%, 72.1%, and 83.0% for MA, FGR, GDM, and PE, respectively. The findings suggest that promoter profiling of cfDNA may be used as a biological biomarker for predicting pregnancy complications at early gestational age.