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 .

A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https://github.com/Cuteu/SMSC/.

scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder.

The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/.

Special Protein or RNA Molecules Computational Identification.

The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases [...].

Bacterial and Fungal Biocontrol Agents for Plant Disease Protection: Journey from Lab to Field, Current Status, Challenges, and Global Perspectives.

Plants are constantly exposed to various phytopathogens such as fungi, Oomycetes, nematodes, bacteria, and viruses. These pathogens can significantly reduce the productivity of important crops worldwide, with annual crop yield losses ranging from 20% to 40% caused by various pathogenic diseases. While the use of chemical pesticides has been effective at controlling multiple diseases in major crops, excessive use of synthetic chemicals has detrimental effects on the environment and human health, which discourages pesticide application in the agriculture sector. As a result, researchers worldwide have shifted their focus towards alternative eco-friendly strategies to prevent plant diseases. Biocontrol of phytopathogens is a less toxic and safer method that reduces the severity of various crop diseases. A variety of biological control agents (BCAs) are available for use, but further research is needed to identify potential microbes and their natural products with a broad-spectrum antagonistic activity to control crop diseases. This review aims to highlight the importance of biocontrol strategies for managing crop diseases. Furthermore, the role of beneficial microbes in controlling plant diseases and the current status of their biocontrol mechanisms will be summarized. The review will also cover the challenges and the need for the future development of biocontrol methods to ensure efficient crop disease management for sustainable agriculture.

Clustering and classification methods for single-cell RNA-sequencing data.

Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.

Evaluation of Recombinase Polymerase Amplification Assay for Detecting Meloidogyne javanica.

Meloidogyne javanica is one of the most widespread and economically important nematodes in many countries, including China. In this study, a recombinase polymerase amplification (RPA) assay was evaluated for the detection of M. javanica based on the sequences of a sequence-characterized amplified regions marker gene segment. The RPA assay specifically detected M. javanica from individual juvenile or adult female, M. javanica-induced galls, and nematodes in the soil samples. The detection limit of M. javanica RPA assay was 1 pg of purified genomic DNA, 0.01 adult female, or 0.1 second-stage juvenile, which was 10 times more sensitive than conventional PCR assay. Furthermore, combined with lateral flow dipstick (LFD), a visual detection method of LFD-RPA assay was developed, which is suitable for onsite surveys and routine diagnostics. Results indicate that the RPA assay is rapid, sensitive, and reliable for detection and molecular identification of M. javanica.

Rutaecarpine Reverses the Altered Connexin Expression Pattern Induced by Oxidized Low-density Lipoprotein in Monocytes.

Adhesion of monocytes to the vascular endothelium is crucial in atherosclerosis development. Connexins (Cxs) which form hemichannels or gap junctions, modulate monocyte-endothelium interaction. We previously found that rutaecarpine, an active ingredient of the Chinese herbal medicine Evodia, reversed the altered Cx expression induced by oxidized low-density lipoprotein (ox-LDL) in human umbilical vein endothelial cells, and consequently decreases the adhesive properties of endothelial cells to monocytes. This study further investigated the effect of rutaecarpine on Cx expression in monocytes exposed to ox-LDL. In cultured human monocytic cell line THP-1, ox-LDL rapidly reduced the level of atheroprotective Cx37 but enhanced that of atherogenic Cx43, thereby inhibiting adenosine triphosphate release through hemichannels. Pretreatment with rutaecarpine recovered the expression of Cx37 but inhibited the upregulation of Cx43 induced by ox-LDL, thereby improving adenosine triphosphate-dependent hemichannel activity and preventing monocyte adhesion. These effects of rutaecarpine were attenuated by capsazepine, an antagonist of transient receptor potential vanilloid subtype 1. The antiadhesive effects of rutaecarpine were also attenuated by hemichannel blocker 18α-GA. This study provides additional evidence that rutaecarpine can modulate Cx expression through transient receptor potential vanilloid subtype 1 activation in monocytes, which contributes to the antiadhesive properties of rutaecarpine.

Osteopontin-induced brown adipogenesis from white preadipocytes through a PI3K-AKT dependent signaling.

Recent studies have shown that OPN (osteopontin) plays critical roles in cell survival, differentiation, bio-mineralization, cancer and cardiovascular remodeling. However, its roles in the differentiation of brown adipocytes and the underlying mechanisms remain unclear. Therefore, the aim of this study was to investigate the roles of OPN in the brown adipogenesis and the underlying mechanisms. It was shown that the OPN successfully induced the differentiation of 3T3-L1 white preadipocytes into the PRDM16(+) (PRD1-BF1-RIZ1 homologous domain containing 16) and UCP-1(+) (uncoupling protein-1) brown adipocytes in a concentration and time-dependent manner. Also, activation of PI3K (phosphatidylinositol 3-kinase)-AKT pathway was required for the OPN-induced brown adipogenesis. The findings suggest OPN plays an important role in promoting the differentiation of the brown adipocytes and might provide a potential novel therapeutic approach for the treatment of obesity and related disorders.

DJ-1 Mediates the Delayed Cardioprotection of Hypoxic Preconditioning Through Activation of Nrf2 and Subsequent Upregulation of Antioxidative Enzymes.

We have recently shown that DJ-1 is implicated in the delayed cardioprotective effect of hypoxic preconditioning (HPC) against hypoxia/reoxygenation (H/R) injury as an endogenous protective protein. This study aims to further investigate the underlying mechanism by which DJ-1 mediates the delayed cardioprotection of HPC against H/R-induced oxidative stress. Using a well-characterized cellular model of HPC from rat heart-derived H9c2 cells, we found that HPC promoted nuclear factor erythroid 2-related factor 2 (Nrf2) and its cytoplasmic inhibitor Kelch-like ECH-associated protein-1 (Keap1) dissociation and resulted in increased nuclear translocation, antioxidant response element-binding, and transcriptional activity of Nrf2 24 hours after HPC, with subsequent upregulation of manganese superoxide dismutase (MnSOD) and heme oxygenase-1 (HO-1), which provided delayed protection against H/R-induced oxidative stress in normal H9c2 cells. However, the aforementioned effects of HPC were abolished in DJ-1-knockdown H9c2 cells, which were restored by restoration of DJ-1 expression. Importantly, we showed that inhibition of the Nrf2 pathway in H9c2 cells mimicked the effects of DJ-1 knockdown and abolished HPC-derived induction of antioxidative enzymes (MnSOD and HO-1) and the delayed cardioprotection. In addition, inhibition of Nrf2 also reversed the effects of restored DJ-1 expression on induction of antioxidative enzymes and delayed cardioprotection by HPC in DJ-1-knockdown H9c2 cells. Taken together, this work revealed that activation of Nrf2 pathway and subsequent upregulation of antioxidative enzymes could be a critical mechanism by which DJ-1 mediates the delayed cardioprotection of HPC against H/R-induced oxidative stress in H9c2 cells.

Ginsenoside Rg1 relieves tert-Butyl hydroperoxide-induced cell impairment in mouse microglial BV2 cells.

Microglial activation plays an important role in neurodegenerative diseases associated with oxidative stress. tert-Butyl hydroperoxide (t-BHP), an analog of hydroperoxide, mimics the oxidative damage to microglial cells. It has been reported that ginsenoside Rg1 (G-Rg1), an active ingredient of Panax ginseng, has anti-stress and anti-inflammatory properties. The present study aims to investigate the ability of G-Rg1 to decrease the t-BHP-mediated cell damage of BV2 microglial cells. We performed flow cytometry assays to facilitate the detection of reactive oxygen species as well as Western blotting analyses and immunofluorescence assays using specific antibodies, such as antibodies against phospho-mitogen-activated protein kinases (p-MAPKs), phospho-nuclear factor-κB (p-NF-κB), B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X (Bax), Caspase-3, autophagy marker light chain 3 (LC3), and Becline-1. We found that treatment with 50 µM G-Rg1 protected microglial cells against oxidative damage induced by 10 µM t-BHP.

Nrf2-dependent upregulation of antioxidative enzymes: a novel pathway for hypoxic preconditioning-mediated delayed cardioprotection.

It has been well demonstrated that hypoxic preconditioning (HPC) can attenuate hypoxia/reoxygenation (H/R)-induced oxidant stress and elicit delayed cardioprotection by upregulating the expression of multiple antioxidative enzymes such as heme oxygenase-1 (HO-1), manganese superoxide dismutase (MnSOD) and so on. However, the underlying mechanisms of HPC-induced upregulation of antioxidative enzymes are not fully understood. Nuclear factor erythroid 2-related factor 2 (Nrf2) is an essential transcription factor that regulates expression of several antioxidant genes via binding to the antioxidant response element (ARE) and plays a crucial role in cellular defence against oxidative stress. Here, we wondered whether activation of the Nrf2-ARE pathway is responsible for the induction of antioxidative enzymes by HPC and contributes to the delayed cardioprotection of HPC. Cellular model of HPC from rat heart-derived H9c2 cells was induced 24 h prior to H/R. The results showed that HPC efficiently attenuated H/R-induced viability loss and lactate dehydrogenase leakage. In addition, HPC increased nuclear translocation and ARE binding of Nrf2 during the late phase, upregulated the expression of antioxidative enzymes (HO-1 and MnSOD), inhibited H/R-induced oxidant stress. However, when Nrf2 was specifically knocked down by siRNA, the induction of antioxidative enzymes by HPC was completely abolished and, as a result, the inhibitory effect of HPC on H/R-induced oxidant stress was reversed, and the delayed cardioprotection induced by HPC was also abolished. These results suggest that HPC upregulates antioxidative enzymes through activating the Nrf2-ARE pathway and confers delayed cardioprotection against H/R-induced oxidative stress.

FK506 inhibits the mice glomerular mesangial cells proliferation by affecting the transforming growth factor-ß and Smads signal pathways.

Abstract TGF-ß1 plays an important role in the pathogenesis of chronic renal diseases. Although the specific mechanism is unknown, a major factor is the potent fibrogenic activity of TGF-ß1 in the chronic progression of renal diseases. TGF-ß1 closely correlates with renal fibrosis in cooperation with several fibrosis-promoting molecules. Recently it has been studied that, Smad proteins as intracellular mediators of TGF-ß signaling pathways provide important insights into the mechanisms determining the specificity of TGF-ß action in various renal cells. Some studies have proved that immunosuppressants can affect TGF-ß expression, but the mechanisms are unclear. In this study, we investigated the effect of FK506 on mesangial cells via TGF-ß and Smads signal pathways. Our results shows that FK506 effectively blocked the TGF-ß/Smad signaling pathway by downregulation of TGF-ß receptor, and played an important role in TGF-ß1-induced Smad2 expression in mice mesangial cells. FK506 can inhibit the TGF-ß1-stimulated cell proliferation via Smad-related pathways. And reduced the Smad2 protein and mRNA expression. Altogether, this study provided a theoretical proof for the protective and treating effect of FK506 on kidneys.

PseU-KeMRF: A novel method for identifying RNA pseudouridine sites.

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models. On the three species' training datasets, the testing accuracy of PseU-KeMRF was 0.66%, 3.66%, and 2.76% higher, respectively, than the best available methods. Moreover, PseU-KeMRF's accuracy on independent testing datasets was 15.15% and 11.0% higher, respectively, than the best available methods. The above results can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.

A new method for handling heterogeneous data in bioinformatics.

Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique.

Joint masking and self-supervised strategies for inferring small molecule-miRNA associations.

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.

Biocontrol of plant parasitic nematodes by bacteria and fungi: a multi-omics approach for the exploration of novel nematicides in sustainable agriculture.

Plant parasitic nematodes (PPNs) pose a significant threat to global crop productivity, causing an estimated annual loss of US $157 billion in the agriculture industry. While synthetic chemical nematicides can effectively control PPNs, their overuse has detrimental effects on human health and the environment. Biocontrol agents (BCAs), such as bacteria and fungi in the rhizosphere, are safe and promising alternatives for PPNs control. These BCAs interact with plant roots and produce extracellular enzymes, secondary metabolites, toxins, and volatile organic compounds (VOCs) to suppress nematodes. Plant root exudates also play a crucial role in attracting beneficial microbes toward infested roots. The complex interaction between plants and microbes in the rhizosphere against PPNs is mostly untapped which opens new avenues for discovering novel nematicides through multi-omics techniques. Advanced omics approaches, including metagenomics, transcriptomics, proteomics, and metabolomics, have led to the discovery of nematicidal compounds. This review summarizes the status of bacterial and fungal biocontrol strategies and their mechanisms for PPNs control. The importance of omics-based approaches for the exploration of novel nematicides and future directions in the biocontrol of PPNs are also addressed. The review highlighted the potential significance of multi-omics techniques in biocontrol of PPNs to ensure sustainable agriculture.

Stable overexpression of DJ-1 protects H9c2 cells against oxidative stress under a hypoxia condition.

It has been well accepted that increased reactive oxygen species (ROS) and the subsequent oxidative stress is one of the major causes of ischemia/reperfusion (I/R) injury. DJ-1 protein, as a multifunctional intracellular protein, plays an important role in regulating cell survival and antioxidant stress. Here, we wondered whether DJ-1 overexpression attenuates simulated ischemia/reperfusion (sI/R)-induced oxidative stress. A rat cDNA encoding DJ-1 was inserted into a mammalian expression vector. After introduction of this construct into H9c2 myocytes, stable clones were obtained. Western blot analysis of the derived clones showed a 2.6-fold increase in DJ-1 protein expressing. Subsequently, the DJ-1 gene-transfected and control H9c2 cells were subjected to sI/R, and then cell viability, lactate dehydrogenase, malondialdehyde, intracellular ROS and antioxidant enzymes (superoxide dismutase, catalase and glutathione peroxidase) were measured appropriately. The results showed that stable overexpression of DJ-1 efficiently attenuated sI/R-induced viability loss and lactate dehydrogenase leakage. Additionally, stable overexpression of DJ-1 inhibited sI/R-induced the elevation of ROS and MDA contents followed by the increase of antioxidant enzymes (superoxide dismutase, catalase and glutathione peroxidase) activities and expression. Our data indicate that overexpression of DJ-1 attenuates ROS generation, enhances the cellular antioxidant capacity and prevents sI/R-induced oxidative stress, revealing a novel mechanism of cardioprotection. Importantly, DJ-1 overexpression may be an important part of a protective strategy against ischemia/reperfusion injury.

[Effects of antagonist and agonist of nicotinic acetylcholine receptors on injury of rat neurons induced by amyloid ß-protein].

OBJECTIVE: To explore the chronic effects of nicotinic antagonist and agonist on rat neurons injury induced by ß-amyloid protein. METHODS: The rat model of neuron injury was established by the exposure to Aß25-35 and the intervention agent was either methyllycaconitine (MLA) or nicotine (Nic). And the experimental groups were control (distilled water), Aß25-35, MLA (MLA and Aß25-35) and Nic (Nic and Aß25-35). Cellular viability was detected by methyl thiazolyl tetrazolium (MTT) chromatometry while apoptosis and necrosis were detected by flow cytometer. RESULTS: Compared with control, cellular viability decreased while the apoptotic and necrotic rates increased in Aß25-35 group(P = 0.00). The values of cellular viability at (0.75 ± 0.02) and (0.75 ± 0.09) in Aß25-35 and MLA groups respectively were significantly lower than that of Nic group (0.81 ± 0.02, P = 0.01) at Day 3 and 7. No significant differences existed in cellular viability between Aß25-35 and MLA groups. At Day 14, the differences of cellular viability were not obvious in all groups. At Day 21, cell viability of MLA group (0.64 ± 0.10) was significantly higher than those of Aß25-35 (0.57 ± 0.04, P = 0.019) and Nic groups (0.56 ± 0.04, P = 0.008). The apoptotic rate was lower than that of Aß25-35 group (3.70% ± 0.20% vs 4.70% ± 0.46%, P = 0.008) while the necrotic rate lower than that of Aß25-35 group (7.73% ± 0.86% vs 16.30% ± 1.05%, P = 0.00) and Nic group (16.03% ± 1.53%, P = 0.00). However, no significant differences existed in cellular viability or apoptotic and necrotic rate between Aß25-35 and Nic groups. CONCLUSION: With chronic treatment, the protective effect of α7 nicotinic antagonist methyllycaconitine increases whereas that of nicotinic agonist nicotine decreases.

Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.

Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.