BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities.

RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.

Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains.

To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k-mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains' evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.

The nnU-Net based method for automatic segmenting fetal brain tissues.

The magnetic resonance (MR) images of fetuses make it possible for doctors to detect out pathological fetal brains in early stages. Brain tissue segmentation is prerequisite for making brain morphology and volume analyses. nnU-Net is an automatic segmentation method based on deep learning. It can adaptively configure itself, so as to adapt to a specific task via preprocessing, network architecture, training, and post-processing. Therefore, we adapt nnU-Net to segment seven types of fetal brain tissues, including external cerebrospinal fluid, gray matter, white matter, ventricle, cerebellum, deep gray matter, and brainstem. With regard to the characteristics of the FeTA 2021 data, some adjustments are made to the original nnU-Net, so that it can segment seven types of fetal brain tissues precisely as far as possible. The average segmentation results on FeTA 2021 training data demonstrate that our advanced nnU-Net is superior to the peers including SegNet, CoTr, AC U-Net and ResUnet. Its average segmentation results are 0.842, 11.759 and 0.957 in terms of Dice, HD95 and VS criteria. Moreover, the experimental results on FeTA 2021 test data further demonstrate that our advanced nnU-Net has obtained good segmentation performance of 0.774, 14.699 and 0.875 in terms of Dice, HD95 and VS, ranked the third in FeTA 2021 challenge. Our advanced nnU-Net realized the task for segmenting the fetal brain tissues using MR images of different gestational ages, which can help doctors to make correct and seasonable diagnoses.

Corosolic acid isolated from Eriobotrya japonica leaves reduces glucose level in human hepatocellular carcinoma cells, zebrafish and rats.

Type 2 diabetes (T2D) with high morbidity and mortality is characterized by abnormal glucose and lipid metabolism due in part to insulin resistance in liver, which lead to elevated hyperglycemia and hyperlipidemia. This study sough to explore the effects of corosolic acid (CA) in different T2D models and explored the underlying mechanism. Separated from Eriobotrya japonica leaves, CA purity was above 95% measured by a HPLC method. Compared with cAMP and DEX induced T2D HepG2 model, CA significantly stimulated glucose consumption and improved glycogen accumulation by inhibiting PEPCK mRNA expression. And in cAMP and DEX induced T2D zebrafish model, CA reduced glycogen degradation and increased glucose consumption by regulating some key enzymes in carbon metabolism including GLUT1, GLUT2, GLUT3, LDHA, LDHB, GP, G6Pase, GYS1, and PFKFB3. In addition, insulin receptor signals were also involved in CA-regulated hypoglycemic action. Furthermore, in STZ-induced T2D rat model, compared with diabetic control groups, CA remarkably downregulated the levels of serum lipid, blood glucose, ICAM-1, malonaldehyde and insulin resistance index, while upregulated SOD activity and impaired glucose tolerance. In a conclusion, CA can regulate glucose and lipid metabolic adaptation in T2D like HepG2, zebrafish and rat models partly through reducing inflammation and oxidative stress and suppressing PEPCK.

MicroRNA­675­3p promotes esophageal squamous cell cancer cell migration and invasion.

Esophageal cancer ranks fourth in cancer­associated mortality in China and the incidence of esophageal adenocarcinoma has risen dramatically over the past two decades. MicroRNA (miRNA/miR) serves a pivotal role in human cancer cell growth, invasion and migration. MiR­675­3p is highly expressed in esophageal squamous cell cancer (ESCC) tissues, and may have an influence on ESCC cell migration and invasion. ESCC tumor tissue samples from 35 patients were profiled. MiR­675­3p expression was confirmed by reverse transcription­quantitative polymerase chain reaction. Manipulation of miR­675­3p via knockdown was carried out with subsequent evaluation of effects on cell proliferation, invasion, migration, and use of western blotting and ELISA assays. MiR­675­3p was overexpressed in ESCC tissues compared with normal tissues, and had higher expression levels in ESCC cells compared with the healthy esophageal epithelial cell line. The results revealed a predominant upregulation of cell migration and invasion ability. MiR­675­3p inhibitor inhibited ESCC cell proliferation, migration and invasion ability. It was also demonstrated that downregulation of miR­675­3p decreased the levels of matrix metalloproteinase (MMP) 2 and 9 and increased the level of E­cadherin. In addition, the effects of miR­675­3p inhibitor on ESCC cell lines were eliminated by con­transfection with miR­675­3p inhibitor and miR­675­3p mimic. In conclusion, the results indicated that miR­675­3p may be involved in the progression of ESCC through regulating ESCC cell migration and invasion capacity via modulating epithelial mesenchymal transition markers (MMP2, MMP 9 and E­cadherin).