Abstract:Transient receptor potential vanilloid 1 (TRPV1) channel, belonging to transient receptor potential (TRP) channel superfamily, is a ligand gated non-selective cation channel which can be activated by multiple physical and chemical stimuli. The abnormal irritation and expression of TRPV1 is involved in pathogenesis of various diseases, so that TRPV1 channel is one of the important targets for drug research and development. For a long time, TRPV1 channel has attracted much attention because of the excellent analgesic effect of TRPV1 modulators. Due to the recognition of the research work of receptors for temperature and touch by the 2021 Nobel Prize in physiology or medicine, TRPV1 channel has become the focus of attention once again. It has been more than 20 years of the research for TRPV1, but the gating mechanism and drug development are still the difficulties. TRPV1 agonists can only be used for topical administration, and the antagonists could be used for oral administration. However, the problem with antagonists is that they cause hyperthermia and damage to noxious heat detection, which is the result of TRPV1 antagonists simultaneously affecting capsaicin, H⁺ and heat gating. Studies have shown that there are common processes of the three gating mechanisms, but no way to affect a single gating mechanism. From the angles of physiological function, gating mechanism and drug discovery, this paper reviews the distribution and expression, functions and features as well as structural characteristics of TRPV1 channel. This paper focuses on three gating mechanisms and the progress of TRPV1 modulators in drug discovery. TRPV1 modulators are a exceptional analgesia drug, and have been studied in cardiovascular diseases, itch, cough, psychiatric disorders and diabetes. With the emerging of artificial intelligence (AI)-assisted drug design and the continuous exploration of gating mechanism, we should have confidence in the future of TRPV1 modulators.

Abstract:Transient receptor potential vanilloid subfamily member 1 (TRPV1), also known as capsaicin receptor (VR1), is a kind of ligand gated non-selective cation channel which can be activated by capsaicin, heat (>43℃) and H⁺ (pH<6.0). TRPV1 is highly permeable to Ca²⁺. Previous studies found that TRPV1 mainly distributes in nervous system and mediates pruritus and pain response. Recent studies have shown that TRPV1 also widely distributes in non-nervous cells such as mast cells, bladder epithelial cells, monocytes, skin keratinized epithelial cells, islet cells and so on. TRPV1 has a wide range of functions and can mediate beneficial or harmful biological effects on the body. In the nervous system, TRPV1 related signal pathway mainly mediates itching and pain response. Relevant studies in pancreatic cells have shown that the upregulation of TRPV1 can alleviate the process of diabetes, but studies in pulmonary epithelial cells, pulmonary vascular endothelial cells, bronchial smooth muscle cells, etc. have shown that the upregulation of TRPV1 can accelerate the development of respiratory diseases. In addition, TRPV1 has dual effects of promotion or inhibition on the disease progression in cardiovascular system, digestive system and skin system. In cancer research, it was also found that the upregulation of TRPV1 played an important antineoplastic effect, which could inhibit the proliferation, invasion and migration of tumor cells in human tongue squamous cell carcinoma, prostate cancer, breast cancer and so on, arrest the cell cycle and induce cell apoptosis. At present, many studies have been carried out on the mechanism of TRPV1, among which the mechanism of TRPV1 mediating itching and pain is relatively in depth. TRPV1 has become a promising therapeutic target due to its extensive functions. New drugs targeted to TRPV1 have been developed to ameliorate diabetes, cardiovascular diseases, and some kinds of cancers. This paper introduces the latest progress in the distribution, structural characteristics and functions of TRPV1, and focuses on the research progress of pruritus and pain related signaling pathways mediated by TRPV1. We also introduced the Chinese herbal medicine with TRPV1 as the target, looking forward to providing theoretical guidance for taking TRPV1 as a potential therapeutic target by combination of traditional Chinese medicine and modern medicine.

Abstract:Atherosclerosis (AS), the pathological basis of most cardiovascular diseases, is a chronic inflammatory vascular disease with disorders of lipid metabolism. It is characterized by excessive lipid accumulation and foaming of macrophages and smooth muscle cells in the intima of blood vessels, which triggers atherosclerotic plaque development and subsequent thrombus formation. High-density lipoprotein (HDL) is a class of cholesterol-rich lipoprotein particles that transport cholesterol from peripheral cells to the liver for biliary excretion via reverse cholesterol transport (RCT), which is thought to be the basis of HDL’s anti-atherogenic properties. The inverse association between high-density lipoprotein-cholesterol (HDL-C) level and the risk of clinical events resulting from atherosclerosis is widely accepted. Therefore, targeting HDL therapeutically presents an attractive strategy for treating atherosclerotic cardiovascular disease. However, multiple epidemiological evidences have demonstrated that raising HDL-C does not certainly confer a clinical benefit. The cholesterol content of HDL may provide limited information on their antiatherogenic properties and the composition and particles’ structure provide more information on their functionality. HDL particles are, however, highly heterogeneous in structure and intravascular metabolism. Many critical proteins and enzymes have been discovered to regulate the levels, composition and structure of HDL. This paper mainly reviews the effects of various molecules on HDL metabolism and remodeling processes, as well as the research progress of related drugs targeting the above processes. Based on the relationship between HDL and RCT, we reviewed four aspects of AS therapeutic strategies for HDL modulation: (1) promoting cholesterol efflux from peripheral cells; (2) enhancing HDL esterification; (3) influencing HDL remodeling; (4) affecting hepatic uptake and intestinal excretion of HDL, which may provide a theoretical reference for a more comprehensive evaluation of the anti-atherogenic effects of HDL.

Abstract:Wilms tumor (WT) is the most common renal malignant tumor in children. The traditional clinical hierarchical diagnosis and treatment scheme cannot achieve the clinical goal of guiding precise risk stratification, especially in patients without high-risk factors. For example, patients with the same clinical phenotype and receiving the same treatment may have significant differences in prognosis, indicating that the disease is much more complex than previously recognized. This individual heterogeneity requires more in-depth molecular phenotypic studies to determine new markers and targets. Circular RNA (circRNA) is a newly discovered non-coding RNA. A large number of studies have reported that dysregulated circRNA mainly plays the role of miRNA sponges to regulate multiple phenotypes of cancer cells, including proliferation, invasion, migration, cycle arrest, and chemotherapy resistance. This molecule may also become a promising marker and target for cancer diagnosis and treatment. Compared with adult cancers, the study of circRNA in WT is still in its infancy. Based on the existing literature, this paper systematically reviewed the latest progress in the expression pattern, biological function, and clinical value of circRNA in WT, and discussed the limitations of current research and future directions.

Abstract:Ketamine is an NMDAR antagonist that has been widely used for clinical anesthesia. In addition, low-dose ketamine has analgesic, anti-inflammatory and antidepressant effects, which have attracted extensive attention in recent years. However, how low-dose ketamine can affect the higher cognitive functions remain unclear. Here we review the effects of low-dose ketamine on working memory (WM). WM is the ability to temporally hold and manipulate information in the brain. It mainly relies on a network involving several key brain areas such as the prefrontal cortex (PFC), hippocampus, etc. WM plays a key role in many complex cognitive processes, thus to understand how it may be affected by low-dose ketamine is informative and important for its clinic use. Here we review the studies showing that acute or chronic use of low-dose ketamine will impair working memory in a sophisticated way. The possible mechanisms underlying such impairments include breaking the excitation/inhibition (E/I) balance in the PFC, changing the short-term and long-term synaptic plasticity, activating the DA pathway, etc. We also pointed out that to fully reveal the effects of low-dose ketamine on WM and related neural mechanism, future multi-facet studies combining behavioral tests, electrophysiology, neuroimaging, histology, etc, and with more comparable dosage and time of medication would be needed. We hope that this review will be instrumental for facilitating appropriate use of low-dose ketamine in clinical settings.

Abstract:Natriuretic peptides (NPs) have been discovered for 30 years, and the clinical use of B-type natriuretic peptides (BNP) and N-terminal pro-B type natriuretic peptide (NT-proBNP) precursors have been a landmark in the management of cardiovascular disease, particularly in heart failure. The BNP has a powerful cardioprotective effect, but the BNP that rises dramatically after heart failure does not show corresponding activity, which is known as the “natriuretic peptide paradox”. In recent years, with the use of mass spectrometry and nuclear magnetic resonance techniques, “natriuretic peptide paradox” is being revealed through novel metabolic findings and testing technology. There are many different biologically active BNP isoforms in the peripheral circulation, and BNP metabolism after heart failure is different from that in the physiological state. Although the significant increase of BNP is detected after heart failure, it is essentially false positive due to bottlenecks in conventional the assay reagents of cross-react to various BNP isoforms, and therefore the bioactive levels of BNPs have been overestimated. So, we believe that it is necessary to strengthen the understanding of BNP in different pathophysiological conditions , and establish sensitive and specific detection methods by biochemical means to identify BNP_1-32, BNP_1-30, BNP_3-32 and pro-B-type natriuretic peptide (proBNP). Accurate detection of BNPs will help us understand the deeper pathophysiological mechanisms of heart failure, and make precise clinical decision on the diagnosis and treatment.

Abstract:Drug addiction is a complicated central nervous system (CNS) disorder. Both basic and clinical studies have confirmed that the neural mechanisms of drug addiction are progressively altered at different stages of the development of addictive behaviors. Genomic studies using technologies such as whole-genome association studies, whole-genome sequencing, whole-exome sequencing or high-throughput transcriptome sequencing have provided insight into the genetic vulnerability of mental disorders, including drug addiction. The single nucleotide polymorphism detection techniques or sequencing technologies mainly predict genetic risk loci for diseases. However, the occurrence of many CNS disorders is closely related to environmental factors and there is brain region-specific cellular heterogeneity information on the expression of relevant genes at different stages of disease development. Therefore, traditional molecular genetic studies have limitations in explaining the pathogenesis of CNS disorder. Single-cell RNA sequencing (ScRNA-seq) technologies, which target individual cells for transcript level determination, avoid the disadvantages of traditional sequencing for detecting the average transcript level of cell populations and can quantitatively describe cellular heterogeneity. In recent years, single-cell transcriptional sequencing technology has been applied to the study of drug addiction. The cortico-mesolimbic system, cortico-striatal-thalamo-cortical pathway and the hippocampus are important brain regions associated with drug addiction. It was found that more neuronal subtypes exist in both the striatum and hippocampus than previously known. In the cortex, pyramidal neurons exhibit a projection-specific gene expression profile. In rodent models experiencing from cocaine, morphine, and tetrahydrocannabinol, recent studies have identified single-cell transcriptome alterations in the nucleus accumbens and prefrontal cortex, the key brain regions for rewarding. These studies have advanced the mechanisms of drug addiction and drug development to the level of cellular resolution in specific brain regions. This review summarizes the important applications of single-cell RNA-Seq in neuroscience research and uses drug addiction as an example to illustrate its value in the study of CNS disorders.

Abstract:Expansion microscopy (ExM) is a new super-resolution imaging technique. With the aid of expandable hydrogel, biological samples are uniformly physically amplified and can be imaged in super resolution by using conventional optical imaging microscopes. In ExM, after immunofluorescence staining, gel embedding, protease digestion and water swelling, the relative distance of fluorescent labeled molecules inside the biological samples was increased, so the sample can bypass the optical diffraction limit in conventional fluorescence microscope to achieve the super-resolution imaging. ExM is widely suitable for many types of biological samples such as cell and tissue sections. Proteins, nucleic acids, lipids and other biological macromolecules can also be imaged by ExM. ExM can be combined with confocal microscopy, light-sheet microscopy and super-resolution microscopy to further improve imaging resolution. In recent years, a variety of derivative technologies have been developed from base ExM, which further promotes the practical application of this technology. Protein retention expansion microscopy (proExM) can avoid complicated sample preparation process and directly image endogenous fluorescent proteins. Magnified analysis of the proteome (MAP) was suitable for super-resolution imaging in large biological samples. Iterative expansion microscopy (iExM) can increase the final expansion factor of biological samples to 16-22 times by changing the gel embedding steps. Cryo-expansion microscopy (Cryo-ExM) can provide better image fidelity. Expansion fluorescent in situ hybridization (ExFISH) and Click-ExM can achieve super-resolution imaging in nonprotein biomolecules, such as RNA, lipids, and polysaccharides. Expansion pathology (ExPath) can be used for clinicopathologic specimens imaging. The combination of ExM and light-sheet microscope can improve the image resolution to super-resolution level in the deep imaging depth. The application of ExM in super-resolution microscopy can further increase the resolution of images to 10-30 nm. In this paper, we reviewed the basic principles of ExM and its derivative technology, the research progress of combining ExM with different imaging technologies, the application progress of ExM in observing different types of biological samples, and the prospective of spreading ExM technology in the future.

Abstract:As the basic structural and functional unit of life, cells have very important research significance in biology, medicine and other fields. With the development of modern science and technology, scientists have a very clear understanding of the spatial structure of cells and organelles with the help of electron microscopes. However, very little is known about their functions and the interactions between cells, and this is precisely the information that disease treatment and drug development urgently need to know. Therefore, the study of subcellular organelles in vitro living cells and in vivo living cells have become very important. However, the structure of many organelles in living cells are at the nanoscale. Traditional optical imaging techniques cannot observe nanoscale biological structures due to the limitation of the optical diffraction limit. Therefore, optical super-resolution imaging technology is an effective tool to study the structure and function of subcellular organelles. Among all optical super-resolution microscopy techniques, stimulated emission depletion (STED) super-resolution imaging technology has the capabilities of real-time, three-dimensional super-resolution and tomographic imaging. Therefore, the STED is very suitable for nanoscale live cell and vivo imaging studies. Moreover, STED super-resolution imaging technology has been widely used for super-resolution dynamic observation of living cells and even living mouse cells after decades of development. This paper summarized the research progress of STED super-resolution imaging in the fields of in vitro living cells and in vivo mouse neurons in recent years, and introduces the development status of fluorescent dyes and fluorescent proteins for STED super-resolution imaging of in vitro and in vivo living cells. In vivo cells super-resolution imaging is very meaningful for understanding the nature of cells, but it has been more than 20 years since the STED super-resolution imaging technology was proposed to the present, and there are still very few literatures on in vivo cells super-resolution imaging. The main problem is that very few probes are available for in vivo cells super-resolution imaging. At the same time, the depth of in vivo cells super-resolution imaging is also very limited, mainly due to the lack of fluorescent probes in the infrared band. Finally, the future application prospects of in vivo cells super-resolution imaging are prospected.

Abstract:Lactic acid (C₃H₆O₃), also known as 2-hydroxypropionic acid, propanoic acid, is a type of hydroxy acid. It is an essential metabolite of human and microbial cells. In diagnosis and medical management, determination of lactate level in serum is greatly required, and it is also important to measure lactate in fermentative foods to access their quality. Therefore, how to detect lactic acid in different samples with high throughput has become the focus of different researches. The traditional lactic acid detection methods are complicated, time-consuming and laborious, or requires expensive detection equipments. However, the electrochemical enzymatic L-lactate biosensors combining the robustness of electrochemical techniques with the specificity of biological recognition processes showed great advantages over the conventional analytical techniques in size, cost, sensitivity, selectivity, response speed and sample pre-treatment, which show a broad application prospects. There are two main types of lactate biosensors based on L-lactate oxidase (L-LOD) and L-lactate dehydrogenase (L-LDH). Designing a successful enzyme-based L-lactate biosensor requires assembling the enzyme onto a solid carrier and selecting an appropriate transduction strategy between the enzyme and the electrode. Due to the restriction of enzyme molecular structures, reaction mechanism and electrode materials, the traditional lactate biosensors have some limitations in sensitivity, selectivity and stability. Therefore, an increased research was performed to improve the performance of lactate sensors according to the characteristic of the enzymes and the electron transfer type. In this paper, we provide an overview of the structural characteristics, origin and catalytic mechanism of L-LOD and L-LDH, and discuss three strategies, including electrode material modification, enzyme immobilization and enzyme engineering modification, to improve the performance of enzyme electrode based lactate biosensors. In addition, the lactate biosensors were compared and analyzed on the basis of different carriers including membrane, transparent gel matrix, hydrogel carrier, nano-particles, etc. Finally, we comprehensively described the merits and demerits of current commercial lactate sensors and preconceive how emerging new technologies may benefit to future lactate biosensor design.

Abstract:Due to their high biological activity, low toxicity, and superior biocompatibility, peptides are often utilized in cancer therapy, cellular activity simulation, and antibody detection. As a result, the detection and analysis of peptides has grown into a significant area of research. The direct detection of peptides by mass spectrometry frequently encounters significant interference, and the outcomes are frequently disappointing. Organic frameworks with plentiful pore size, large specific surface area, and simple surface functionalization are currently prevalent materials for peptide enrichment. Metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) have been used for enriching glycopeptides, phosphopeptides, and endogenous peptides.Protein glycosylation and phosphorylation are the most common post-translational modifications of proteins. Since common organic frameworks have a limited affinity for post-translationally modification peptides and their enrichment impact is not what is expected, several researchers have tried to design functional organic framework materials to obtain effective peptide enrichment. Hydrophilic nanoparticles have a particular affinity for glycosyl groups and have the distinct benefit of not creating enrichment bias when capturing glycopeptides. Hydrophilic organic framework materials may be changed with hydrophilic groups, hydrophilic chemicals, and magnetic functionalization to enhance glycopeptides through hydrogen bonding, electrostatic interactions, and van der Waals forces, building on the synthesis of hydrophilic nanomaterials. The great majority of organic frameworks are made up of organic ligands that particularly bind to phosphate group.The chelation between metals, metal oxides, and phosphate groups provides the basis for capturing the peptides by organic frameworks. Therefore, in order to achieve effective phosphopeptide enrichment, numerous organic frameworks have tried to increase the affinity between organic frameworks and phosphate groups by ligand replacement, ionic ligand modification, and modification. This paper focuses on the principles and applications of MOFs and COFs for peptide enrichment in the last five years.

Abstract:Objective Deep brain stimulation (DBS) utilizes sustained high-frequency stimulation (HFS) of electrical pulses to modulate neuronal activity. The therapy is expected to be used to treat more brain disorders. To deeply understand the mechanisms of the HFS to advance the DBS development, the present study investigates the effect of axonal HFS on neuronal somata during HFS-induced axonal block.Methods Antidromic high-frequency stimulation (A-HFS) with a 100-Hz pulse frequency and a 1-min duration was applied at the axons of pyramidal neurons in the hippocampal CA1 region of anesthetized rats. To investigate the responses of somata, a multi-channel microelectrode array with a vertical linear configuration was implanted to record the evoked potentials in the lamellas around the somata of CA1 pyramidal neurons at the upstream area of stimulation site, including the antidromic population spikes (APS) evoked by the pulses of A-HFS as well as the orthodromic population spikes (OPS) evoked by orthodromic test pulses applied during the A-HFS. Current-source densities (CSD) of the evoked potentials were calculated to evaluate the generation and propagation of action potentials around the somata of pyramidal neurons during A-HFS.Results A-HFS on the axons of pyramidal neurons slowed down the propagation speed of both antidromic and orthodromic excitations around somata. In addition, the occurrence and recovery of the changes of somata were slower than the A-HFS-induced axonal block.Conclusion Axonal HFS can induce soma alterations that might be caused by changes in membrane potentials nearby somata. The finding is helpful for deeply revealing the mechanisms of electrical stimulations of brain nervous system.

Abstract:Objective To investigate the role of histone deacetylase 3 (HDAC3) in the differentiation and function of peripheral CD4⁺ T cells.Methods CD4cre enzyme mediated HDAC3 heterozygous gene deletion mice (Hdac3^fl/flCD4^cre+/-) and wild-type normal control (Hdac3^fl/fl, WT) mice were used. The effects of HDAC3 deletion on the proportion and number of peripheral CD4⁺ and CD8⁺ T cells were detected by flow cytometry. The effects of HDAC3 deletion on the expression of IFN-γ, IL-4 and IL-17A in CD4+ T cells and Tfh cells were detected under the in vitro PMA and Ionomycin stimulation. The effects of HDAC3 deletion on the expression of IFN-γ, IL-4 and IL-17 in serum were detected by ELISA. The naive CD4⁺ T cells of Hdac3^fl/flCD4^cre+/- and WT mice were sorted and cultured in Th1 and Th2 differentiation conditions respectively. The effects of HDAC3 deletion on the expression of Th1, Th2 and Th17 related cytokines and their specific transcription factors were detected by intracellular staining. The effects of HDAC3 deletion on the expression of genes related to CD4⁺ T cell differentiation subsets were detected by gene expression microarray. The mice treated with streptozotocin (STZ) were used to construct type 1 diabetes mellitus (T1DM) disease model, and the effects of HDAC3 deletion on the pathogenesis of T1DM were detected.Results Compared with WT mice, the proportion and number of peripheral CD4⁺ and CD8⁺ T cells in Hdac3^fl/flCD4^cre+/- mice decreased significantly. The expression of IFN-γ in CD4⁺ T cells and serum of Hdac3^fl/flCD4^cre+/- mice decreased significantly, while the expression of IL-4 and IL-17A increased significantly, and the proportion of Tfh cells also increased significantly. HDAC3 deletion inhibited the differentiation of CD4⁺ T cells into Th1 cells, but promoted their differentiation intoTh2 cells. Microarray analysis showed that the deletion of HDAC3 resulted in the decrease of gene expression in Th1 cell lineage, while the increase of gene expression in Th2, Th17 and Tfh cell lineage. Under the condition of STZ induction, HDAC3 deletion inhibited the development of T1DM and the differentiation of CD4⁺ T cells into Th1.Conclusion HDAC3 promotes the differentiation of peripheral CD4⁺ T cells into Th1 cells and aggravates the occurrence of T1DM.

Abstract:Objective The efficacy of repeated transcranial magnetic stimulation (rTMS) as a noninvasive neuromodulation technique in patients with Parkinson’s disease (PD) has not been fully Validated. In this study, we combined clinical scale evaluation, brain electrical source and brain network to explore the effects of high-frequency repetitive transcranial magnetic stimulation on patients with akinetic-rigid Parkinson’s disease (AR-PD).Methods A total of 18 patients with AR-PD were included. The EEG signals were traced by standard low-resolution electromagnetic tomography (sLORETA), then the brain functional network was constructed by complex network theory, and the network topological characteristics were compared and analyzed from the perspective of collaborative work between brain regions.Results The results showed that there were significant differences in prefrontal cortex and primary motor cortex after magnetic stimulation (P<0.05). The network connectivity of brain regions associated with motor sensory production, motor planning, and motor execution was significantly enhanced (P<0.05) and the change in the mean cluster coefficient of topological features of brain functional networks was significantly correlated with the change in Parkinson’s uniform rating scale score (P<0.05).Conclusion It is speculated that rTMS improves the information transmission ability of AR-PD patients from motor sensation generation to motor execution. This study can provide a theoretical basis for the relationship between the improvement of motor symptoms of AR-PD patients by rTMS and the reintegration of sensorimotor network.

Abstract:Objective Locomotor training has been shown to preferentially affect muscle function in many chronic diseases which has been used in the treatment of sarcopenia. In this study, we propose electrical impedance tomography (EIT) to study the electrical characteristics of physiological response in calf muscle compartments induced by locomotor training, which is intended to visualize the effectiveness of locomotor training on the increase in volume of calf muscle fibers.Methods Experimental subjects were asked to perform unilateral heel rises on the right and left legs on 5 consecutive experimental days. EIT was applied to detect the conductivity distribution of calf muscles after locomotor training. In order to quantify the effectiveness of locomotor training, a paired sample t-test was used to analyze the spatial-mean conductivity <σ> from reconstructed EIT images.Results The results show that the spatial-mean conductivity <σ>_M1 of M₁ compartment (which is recognized as the position of the gastrocnemius muscle) is significantly increased in post-training by locomotor training. In addition, the spatial-mean conductivity in pre-training <σ^pre>_M1 shows an increasing tendency in EIT measurement results on 5 consecutive experimental days. The lean mass of all subjects in pre- and post-training parts which was performed in the morning of experimental day 1 is linearly related to <σ>_M1; the increase in <σ>_M1 and spatial-mean conductivity difference ratio Δ<σ>_M1 between pre- and post-training followed the same tendency as extracellular water/intracellular water (ECW/ICW) τ by ECW/ICW difference ratio Δτ.Conclusion Since conductivity is sensitive to changes in extracellular volume which correlates with sarcoplasmic hypertrophy. The <σ^pre>_M1 tended to increase during 5 consecutive experimental days of EIT measurements, which implies an increase in muscle fiber volume with sarcoplasmic hypertrophy occurring. Therefore, EIT is able to effectively detect the effectiveness of locomotor training to increase the volume of human calf muscle fibers.

Abstract:Objective Wool is a high-grade raw textile material, and the physical properties of wool are directly related to the quality of the wool. The present study was to search for the genes affecting wool traits and to explore the complex molecular mechanism affecting wool traits.Methods This study selected 3 Suffolk sheep and 3 small-tailed Han sheep and took samples of their back skin tissue using RNA sequencing (RNA-seq) and proteome sequencing analysis of genes, proteins, and related signalling pathways that cause differences in wool traits.Results RNA-seq showed that after sequencing, 230 406 674 raw data points and 222 049 370 clean data points were obtained, of which the percentage of Q20 bases was over 99.9%, and the percentage of Q30 bases was over 98%. With a fold change (FC)≥1.4 or FC≤0.714 and P<0.05 as the standard, 1 213 differentially expressed genes (DEGs) were screened out, among which there were 644 upregulated genes and 569 downregulated genes in Suffolk sheep in comparison with small-tailed Han sheep. The gene ontology (GO) enrichment found that intermediate filament, calcium ion binding, and keratin filament were significantly enriched, indicating that they might be related to wool traits. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment found that the signalling pathway affecting wool traits might be the ECM-receptor interaction. Proteome sequencing showed that with FC≥1.4 or FC≤0.714 and P<0.05 as the standard, 99 differentially expressed proteins (DEPs) were screened out, among which there were 47 upregulated proteins and 52 downregulated proteins in Suffolk sheep in comparison with small-tailed Han sheep. The GO enrichment found that intermediate filament was significantly enriched, indicating that it might be related to wool traits. The KEGG enrichment found that the signalling pathways affecting wool traits might be the peroxisome proliferator-activated receptor (PPAR) and the ECM-receptor interaction. A combined analysis of RNA-seq and proteome sequencing found that a total of 15 significantly different genes were detected in both RNA-seq and proteome sequencing, of which 13 were positively correlated and 2 were negatively correlated. Intermediate filament was significantly enriched, KRT35, KRT13 and KAP13-1-like genes might be the key candidate genes to affect wool traits. The PPAR signalling pathway was significantly enriched and might be a key candidate pathway to affect wool traits. The FABP4 gene might be a key candidate gene to affect wool traits.Conclusion Among them, KRT35 might affect wool bending and diameter, KRT13 might affect wool differentiation, while KAP13-1-like might affect wool hardness and toughness, FABP4 might affect wool diameter. These results will expand our understanding of the complex molecular mechanisms affecting sheep wool traits and provide a basis for subsequent studies.

Abstract:Objective In multicellular organisms, cell communication allows multiple cells to coordinate with each other and involved in various important biological processes, and the abnormal cell communication pattern have also been demonstrated to be associated with many diseases. In the past decades, the rapid development of single-cell RNA sequencing technology makes it possible to study cell communication pattern through constructing cell communication network based on inferring ligand-receptor interactions, and many methods have been proposed. However, most of them only consider the expression of ligands and receptors to quantify the cell-cell interaction strength, but ignore the effect of specific receptor to its downstream gene regulatory network which reflects whether that receptor participates in particular biological process or not. To fill this gap, we propose a novel method (named IRRG) to construct cell communication networks at cell type level.Methods Our IRRG algorithm consists of the following three main steps. (1) IRRG uses the signaling pathway database to construct the gene regulatory network for each receptor in each cell type, and then calculates receptor impact score (RIS) for each receptor based on a random walk algorithm. RIS represents the degree of receptor impacts on downstream genes during cell communication. (2) IRRG uses permutation test to identify ligand-receptor pairs with significant specificity across cell types in order to discover more biologically significant cellular communication phenomena. (3) IRRG combined ligand-receptor co-expression with RIS to calculate the strength of all ligand-receptor interactions between cell types, and then constructed cellular communication networks.Results To validate the effectiveness of our IRRG, we construct cell communication networks of mouse epidermal tissue and human kidney cancer tissue separately. For mouse interfollicular epidermis (IFE) dataset, IRRG constructs biologically meaningful cellular communication networks, reasonably infers the different roles played by cells at different levels of epidermal tissue in cooperation, and discovers ligand-receptor pairs that are closely related to epidermal physiological processes. We also verified the robustness of the IRRG calculated receptor impact score, indicating that the IRRG calculated receptor impact score can reasonably reflect the signaling of cellular communication processes. By counting the supporting literature possessed of the top ranked ligand-receptor pairs, we also indirectly verified that the cell communication network constructed by IRRG is more reliable. In addition, we identify the cellular communication patterns of tumor tissues in clear cell renal cells (ccRCC) using IRRG and analyze how tumor cells influence normal cells to complete their own growth and development or migration process through cellular communication, further understanding the tumor microenvironment.Conclusion In this paper, we propose a novel method for constructing cellular communication networks named IRRG which integrating receptor-regulated gene expression information through random walk. Case studies on two datasets show that IRRG can construct biologically significant cellular communication networks, which can help us understand the mechanism of biological processes from the perspective of cellular communication. The source code and associated datasets used in this work are publicly available at https://github.com/NWPU-903PR/IRRG.

Abstract:Objective Anticancer drug combination therapies are a promising therapeutic strategy. Drug combinations exhibiting a highly synergistic effect are crucial to improve the treatment for specific cancers. However, identifying such combinations is very complicated and difficult due to the tremendous screening cost. The availability of large-scale high-throughput combination screening data provides opportunities for computational approaches. The purpose of this study is to optimize the high-throughput virtual screening of anticancer drug combinations in a completely data-driven and computational modeling way, and provide theoretical reference for “old drugs repositioning as new combinations”.Methods Inspired by the matrix completion, we present a nuclear norm regularization-based model, termed NNRM, to predict synergy scores and synergy status of anticancer drug combinations. Symmetric observation matrixes of synergy scores were constructed for given cell lines; a folding technique was employed to sparse the observation matrix; alternating direction multiplier method and soft threshold estimation were applied to solve the model.Results NNRM achieved expected predictive result on the dataset released by O’Neil’s team, the root mean square error of the synergy score prediction was 14.78, and the accuracy of the synergy status prediction was 0.94. It is not only significantly superior to the Random Forest and the Support Vector Machine, but also completely comparable to the state-of-the-art deep learning models including DeepSynergy, Deep learning+PCA and AuDNNsynergy. Moreover, NNRM effectively filled the missing synergy scores most of which are consistent with existing research or clinical practice.Conclusion NNRM could predict the synergistic effect of large-scale drug combinations in batches, which greatly lowers the data requirements by existing models, reduces the computational cost, and shortens the screening time. It indicates that NNRM is an alternative tool for high-throughput virtual screening of anticancer drug combinations.

Abstract:Objective In homeostatic conditions the peritoneal cavity is populated by resident macrophages. Inflammatory stimuli trigger a phenomenon called macrophage disappearance reaction (MDR), during which resident macrophages become irretrievable from the lavage of the serous cavity. This phenomenon was already observed after different inflammatory insults , but is still incompletely understood. MDR can be associated with cell death, adhesion to neighbouring tissues or migration to the draining lymph nodes or the omentum. MDR is a strategy to face and annihilate the infection by which macrophages, under the control of GATA6, move from the peritoneum to the closest tissues in order to alert the immune system. However, the specific distribution of peritoneal macrophages in MDR is still unclear. In our study, peritoneal macrophages labelled with cell membrane green fluorescent dye DiO were used to study the tracking of peritoneal macrophages in the macrophages disappearance reaction.Methods Peritoneal macrophages labelled with DiO were transplanted to C57BL/6 mice. The macrophage disappearance reaction was induced by lipopolysaccharide (LPS) in vivo. Fluorescence microscope and flow cytometry were used to detect the number and fluorescence intensity of DiO-labelled peritoneal macrophages. The tissues of mice were separated and collected, and frozen sections were made to detect the distribution of DiO-labelled peritoneal macrophages.Results The observation by fluorescence microscope and flow cytometry showed that intraperitoneal injection of LPS could significantly reduce the number and fluorescence intensity of DiO-labelled peritoneal macrophages. Peritoneal macrophages that disappeared during the macrophage disappearance reaction were found distributed in the liver, thymus and spleen by frozen sections. DiO labelling peritoneal macrophages does not affect cell viability and with long-term stability in vivo, indicating that DiO may be a safe and effective green fluorescent dye for tracking the distribution of peritoneal macrophages.Conclusion This research method will provide a convenient and effective experimental means for exploring the dynamic changes and related biological phenomena of peritoneal macrophages during MDR. Furthermore, it laid a foundation for further research on the causes and mechanisms of MDR.

Abstract:Objective Long non-coding RNA play an important role in genetics, metabolism and gene expression regulation. But it is time-consuming and costly to analyze the RNA structure by experimental approaches. However, prediction software based on co-evolutionary algorithm has not made breakthrough progress in prediction accuracy in recent ten years. Therefore, it is necessary to propose a new prediction algorithm to accurately predict the tertiary structure of RNA. So, this paper develops prediction method of base contact map of RNA that can be used to improve the accuracy of tertiary structure prediction.Methods To utilize the physical and chemical characteristics of RNA, we propose a deep learning algorithm based on multi-layer convolutional neural network and long short-term memory hetworks to predict the contact map between base pair. In addition, we employ attention mechanism to deal with complex global spatial independence features in RNA sequences.Results By combining multilayer neural networks with the attention mechanism, our method can effectively obtain local and global information in RNA features, which improves the robustness and generalization ability of the model. The computations show that the proposed model achieves 0.84, 0.82, 0.82 and 0.75 prediction accuracies for the base contact map of 4 criteria (L/10, L/5, L/2, L) of sequence length L.Conclusion Prediction method based on attention method is better than traditional computational methods and common deep learning algorithms, respectively.

Abstract:Objective Primary liver cancer is a common malignant tumor, seriously threatening people’s life and health. According to the differences in pathogenesis, treatment and prognosis, primary liver cancer can be divided into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and other rare types. Among which HCC accounts for 85%-90%. HCC is usually treated by transcatheter chemoembolization (TACE) or minimally invasive ablation, with good prognosis. While ICC and HCC-ICC mixed type have a high degree of malignancy and are generally treated by surgical resection or liver transplantation, with poor prognosis. In order to improve the diagnostic accuracy of HCC patients, primary liver cancer is usually clinically divided into HCC and non-HCC categories, that is non-HCC includes ICC, HCC-ICC mixed type and other rare tumors. Therefore, accurate screening of HCC from liver cancer lesions is of great clinical significance for the treatment of HCC patients. However, due to the high heterogeneity of tumors, the shape, texture, location and blood flow of liver lesions show complexity and diversity in B-ultrasound and contrast-enhanced ultrasound (CEUS) images. Radiologists need to rely on the naked eyes to obtain multidimensional information at the same time, and evaluate diseases according to different characteristics, which requires high level of expertise and clinical experience. Diagnosis results depend on personal subjective factors, which may lead to some HCC mixed into non-HCC categories, and the detection sensitivity of HCC is not high. In this paper, deep convolutional neural network is used to automatically learn the characteristic information of B-ultrasound and CEUS images, and realize the classification of liver cancer.Methods Multiple 2D (VGG, ResNet, DenseNet) and 3D (3D-CNN, Res3D, Dense3D) classification models based on convolutional neural network (CNN) were established and validated, and the B-ultrasound and CEUS images of 116 patients (including 100 HCC and 16 non-HCC patients) were quantitatively analyzed, and the classification performance of each model was compared and analyzed.Results The experimental results showed that the 3D CNN models was superior to the 2D CNN models in all aspects of performance, which verified that the 3D CNN model could simultaneously extract 2D image features and dynamic changes of blood flow in tumor regions, and was more suitable for classification of HCC and non-HCC. The AUC, accuracy and sensitivity of 3D-CNN model are the highest, reaching 85%, 85% and 80%, respectively. In addition, due to the imbalance between HCC and non-HCC samples, the classification performance of the network can be improved by expanding the number of non-HCC samples.Conclusion The 3D-CNN model proposed in this paper can achieve rapid and accurate classification of liver cancer, and is expected to be applied to assist clinicians in the diagnosis and treatment of liver cancer.

Abstract:Objective Acoustic resolution-based photoacoustic endoscopy is a promising functional imaging technique with its large focusing depth. It is widely used in endoscopic imaging of the rectum and esophagus. Acoustic resolution-based photoacoustic endoscopy imaging usually adopts the lateral scanning method based on a single focused ultrasonic sensor, while using the traditional B-mode method for reconstruction, which will greatly reduce the image quality. In order to obtain high-quality images, several dynamic focusing photoacoustic endoscopic imaging algorithms are proposed in this paper.Methods Numerical simulations were performed using these algorithms. In addition, photoacoustic endoscopic imaging experiments were conducted on the phantom to verify the results of the simulation experiments. In the simulation experiments, photoacoustic endoscopic imaging experiments with a focused transducer were simulated. We chose nine points including the focal point as the targets for reconstruction in the imaging area. The characteristics of each algorithm were compared in terms of imaging effect, resolution and signal-to-noise ratio. We made a phantom to verify the imaging effects of several algorithms using a photoacoustic endoscopic imaging system. Several metal wires were inserted in it to simulate targets at different distances and shapes. We compare the advantages of these algorithms over the B-mode method in reconstructing the above objectives.Results The results show that the transverse resolution and signal-to-noise ratio of defocused region are improved compared with which of the B scan method. In the simulation, the resolution of the defocus area can be improved by about 26 times, and the signal-to-noise ratio can be improved by about 2.3 times after dynamic focusing. In the experiment, the resolution of remote point targets is improved 3-6 times after dynamic focusing reconstruction.Conclusion On the whole, we think that the algorithm based on spatial-temporal response and synthetic aperture focusing technology algorithm are more suitable for experimental conditions. This work has guiding significance for the design of acoustic focusing photoacoustic endoscopy imaging.