Abstract:The 2024 Nobel Prize in Physiology or Medicine was awarded to American scientists Victor Ambros and Gary Ruvkun in recognition of their discovery of microRNA (miRNA) and its role in regulating gene expression at the post-transcriptional level. miRNA is a type of small non-coding RNA (ncRNA) that regulates gene expression by binding to messenger RNA (mRNA). It exists not only in model organisms such as Caenorhabditis elegans (C. elegans) but also plays an important role in multicellular organisms, including humans, participating in regulating key life activities such as the cell cycle, cell death, and tissue differentiation. miRNAs have also been found in viruses, where they are involved in the process of viral infection. The discovery of miRNA has not only opened up a new research field in ncRNA but also challenged the classic “central dogma” of molecular biology. This dogma traditionally transcribes the linear transmission of genetic information: from DNA to mRNA, then translated into proteins, which ultimately carry out biological functions. However, due to the competitive binding of miRNAs with mRNA and other ncRNAs in cells, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), a vast and complex gene expression regulatory network, known as the competing endogenous RNA (ceRNA) network, has emerged. The complexity and sophistication of the ceRNA regulatory network offer new perspectives for transcriptome research, aid in the exploration of gene functions and regulatory mechanisms at a deeper level, and then enable a more comprehensive understanding of various biological phenomena. Moreover, a complex interaction and regulatory network exists between miRNA and other ncRNAs. miRNA and other ncRNAs may also be generated through the splicing of the same genes, which have complex transcripts capable of simultaneously producing multiple types of ncRNAs, including miRNA, lncRNA, circRNA, etc., all of which are involved in a variety of biological processes. Meanwhile, miRNA itself is encoded by genes in the genome, and its expression is also regulated by other coding or ncRNAs. Together with mRNA and other ncRNAs, miRNA finely regulates the life activities of cells and affects the physiological and pathological functions of the body. The dysregulation of miRNA expression is closely linked to the onset and progression of many diseases, particularly cancers, cardiovascular diseases, and neurodegenerative disordors. Furthermore, miRNA provides new molecular markers and targets for the diagnosis and treatment of these diseases. In terms of disease diagnosis, miRNA can stably exist in body fluids and serve as a biomarker for many diseases. The research and development of miRNA drugs is currently advancing rapidly. At present, the research and development of miRNA drugs mainly includes endogenous miRNA analogs and inhibitors targeting endogenous miRNA. Although challenges such as stability, immunogenicity, and permeability remain, advances in chemical modification and delivery technologies are gradually overcoming these obstacles, promoting the clinical translation of miRNA-based drugs. This article summarizes the discovery, mechanism of action, and biological functions of miRNAs, as well as their interaction networks with other ncRNAs, and explores the future prospects of miRNA applications.

Abstract:In recent years, deep learning-based methods have achieved significant breakthroughs in protein structure prediction. The open-source release of AlphaFold 2 (AF2) in 2021 enabled high-precision prediction of three-dimensional structures for both individual proteins and protein complexes, allowing researchers to rapidly obtain reliable structural information and greatly accelerating advancements in protein structure and function studies. The release of AlphaFold 3 (AF3) in 2024 took this further by achieving accurate predictions of three-dimensional structures for protein-nucleic acid and protein-small molecule complexes. With improved algorithms and a more efficient model, AF3 significantly enhanced prediction accuracy, especially demonstrating outstanding performance in antigen-antibody and protein-small molecule complexes. The success of AlphaFold has not only brought revolutionary progress to structural biology but also showcased immense application potential in fields such as drug development, protein design, and molecular function research, driving innovation in biomedical studies. This article will review the development history of AlphaFold and related protein structure prediction methods, summarize their key technologies and current applications, and, by considering their limitations, provide an outlook on future research directions and applications.

Abstract:Proteins are essential fundamental substance for life processes, performing a variety of key roles in organisms, such as constructing cell structures, participating in metabolism and energy transformation, regulating physiological functions, providing immune protection, and transmitting signals. The diverse functions of proteins are achieved through their unique amino acid sequences and corresponding three-dimensional structures. Protein engineering involves altering or designing protein sequences and structures to achieve specific functions, and these efforts enhance our knowledge of proteins and offer powerful tools and technical support for research in biomedicine, biomaterials, bioengineering, and related fields. In recent years, with the advancements in algorithms, the accumulation of big data, and improvements in hardware computational power, artificial intelligence technology has rapidly developed and gradually been applied in the field of protein engineering, leading to the emergence of intelligent protein engineering. By utilizing biological big data such as genomics, proteomics, protein structure databases, and establishing various advanced deep learning models based on the data, intelligent protein engineering can achieve efficient, precise, and predictable protein design and modification. This article primarily focuses on four aspects of intelligent protein engineering, including structure design, backbone-free sequence design, backbone-based sequence design, and other auxiliary design approaches, summarizing the latest progress in the artificial intelligence technologies employed in these fields, and compiling the practical results achieved in recent years using intelligent protein engineering technology. As an emerging technology and method, intelligent protein engineering demonstrates significant potential and prospects, bringing substantial impacts on future scientific research and technological innovation, and providing new solutions and tools for addressing global challenges.

Abstract:Chimeric antigen receptor T (CAR-T) cell therapy is an innovative and cutting-edge treatment in the field of adoptive cell therapy. It represents an important milestone in personalized and precision medicine. T cell immunotherapy has gone through more than 30 years of development, making CAR-T cell therapy increasingly mature. Currently, CAR-T cell therapy has achieved significant success in the treatment of hematological system tumors, and the FDA has approved 6 CAR-T cell therapies for the treatment of hematopoietic cancers. However, on one hand, the preparation of CAR-T cells is a highly technical process involving multiple steps, each requiring precise operation and strict condition control to ensure the quality and activity of the cells. The high-quality materials, specialized equipment, and highly specialized personnel required in the production process have led to very high preparation costs for CAR-T cell therapy. The high cost has led to increased treatment fees, which may limit the popularization and accessibility of CAR-T therapy. On the other hand, CAR-T cell therapy faces a series of difficulties and challenges in the treatment of solid tumors. The first is the insufficient targeting and infiltration ability of CAR-T cells to tumors. The tumor microenvironment (TME) of solid tumors is usually composed of dense extracellular matrix, forming a physical barrier that severely limits the targeting and penetration ability of CAR-T cells to tumors. The second is the immunosuppressive factors in the TME. In the TME, there are a large number of immunosuppressive factors, such as interleukin-10, transforming growth factor-β, and suppressive cells including regulatory T cells, tumor-associated macrophages, and myeloid-derived suppressor cells. These factors not only weaken the persistence of CAR-T cells but also severely hinder their effective anti-tumor effect. Finally, CAR-T cell therapy can cause serious cytotoxicity. The activation of CAR-T cells may cause cytokine release syndrome and attack normal cells expressing the CAR-T target antigen, causing “off-target” toxicity, and thus causing systemic inflammatory reactions and potential serious side effects. These factors lead to unsatisfactory therapeutic effects of CAR-T cell therapy. Fortunately, the advancement of nanotechnology has brought new hope to this field. In particular, nano drug delivery systems have become an extremely active research direction in the development of anti-tumor drugs. Nanoparticle delivery systems can address the challenges encountered by CAR-T cell therapy in treating solid tumors through various mechanisms. These mechanisms include enhancing tumor targeting and CAR-T cell penetration ability, regulating the tumor’s suppressive microenvironment, and overcoming the side effects of CAR-T cell therapy. The implementation of these strategies is expected to significantly improve the efficacy of CAR-T cell therapy in the treatment of solid tumors, thereby bringing more significant therapeutic effects to patients. This article focuses on the background of CAR-T therapy and solid tumor treatment, systematically reviews the application of nanotechnology in CAR-T cell preparation and solid tumor treatment in vitro and in vivo in recent years, and provides a forward-looking perspective on future development directions.

Abstract:Enzyme therapy, known for its high efficiency and high selectivity, is an emerging treatment method that utilizes the catalytic activity of exogenous enzyme molecules to initiate specific chemical reactions in the diseased area for disease treatment. With the development of nanoscience and nanotechnology, nanomaterials have brought a new revolution in enzyme therapy. Firstly, nanomaterials with enzyme-like activity (known as nanozymes) have the ability to replace enzymes for catalytic therapy due to their advantages such as tunable nanostructures, high stability, and low cost. Secondly, the construction of nanohybrid enzymes using enzyme engineering techniques can improve the poor stability and limited application performance of enzymes. Finally, many nanomaterials exhibit unique responsiveness to external stimuli such as light, electricity, magnetism, and sound, allowing the catalytic activity of nanozymes and nanohybrid enzymes to be precisely controlled by remote physical fields. Compared to other stimuli, magnetic fields have advantages such as deep tissue penetration, no radiation hazard, remote manipulability, and high spatiotemporal resolution. Under the action of different magnetic fields, magnetic nanomaterials can produce magnetothermal,magnetomechanical,and magnetoelectric effects, respectively. In recent years, significant research progress has been made in utilizing these effects to regulate the catalytic behaviors of nanobiocatalysts. The magnetothermal effect is the process in which magnetic nanomaterials convert electromagnetic energy into heat energy when subjected to a high frequency alternating magnetic field. This effect has been harnessed to remotely regulate the nanobiocatalysts by inducing changes in the surrounding temperature. The magnetomechanical effect refers to the magnetic force generated by the interaction between the magnetic field and magnetic particle when exposed to a low frequency static magnetic field, rotating magnetic field, or gradient magnetic field. This effect regulates enzyme catalytic reactions by altering enzyme conformation or the interaction between an enzyme and its substrate. The magnetoelectric effect involves the charge polarization of a material under the influence of an external alternating magnetic field. This effect enables the energy conversion between magnetic and electric fields. The electrons generated in this process can trigger the redox reaction of nanozymes. These three effects are shown to control the catalytic activity of nanozymes or nanohybrid enzymes under different settings, leading to improved performance of nanobiocatalysts in various biomedical applications. Currently, the concept of magneto-controlled nanobiocatalysis has been applied in the treatment of cancer, bacterial infection and Alzheimer’s disease, demonstrating tremendous potential in precision catalytic therapy. In this paper, the magnetothermal, magnetomechanical, and magnetoelectric effects mediated by magnetic materials were first introduced. Then, current research status on the regulation of nanobiocatalysts under control of magnetic field was comprehensively discussed. Finally, future research suggestions in the field of magneto-controlled nanobiocatalysis was proposed.

Abstract:Extracellular vesicles (EVs) are nanoscale vesicles secreted by cells and play a pivotal role in intercellular communication. As crucial mediators in cell-to-cell signaling, EVs are instrumental in physiological and pathological processes. They serve not only as significant biomarkers in disease diagnosis but also hold promise as new drug and drug delivery system candidates due to their unique biological properties. The process begins with the cell membrane invagination to form a cup-like structure, selectively encapsulating surface proteins and soluble proteins to create early endosomes. Under the influence of the endosomal sorting complex required for transport (ESCRT), Rab-GTPase, and tetraspanins, these early endosomes evolve into late sorting endosomes, which form multivesicular bodies. Upon fusion with the plasma membrane, these bodies release EVs into the extracellular space. EVs are internalized by target cells through ligand-receptor interactions, endocytosis, and membrane fusion, thereby executing biological functions. Endocytosis is a common uptake mechanism for EVs, with various pathways including clathrin-dependent pathways, caveolae-mediated uptake, macropinocytosis, phagocytosis, and lipid raft-mediated internalization. Once inside the recipient cell, EVs interact with the endosomal system, fuse, and release their contents into the cytoplasm. The absorption and distribution of EVs in the body are influenced by factors such as their origin, targeting, administration method, size, and surface characteristics. Through engineering, EVs can be loaded with specific proteins or RNA to achieve targeted drug delivery to specific organs or cells. In terms of disease diagnosis, the components of EVs can serve as biomarkers, offering new avenues for early detection, progression monitoring, and therapeutic efficacy assessment. They carry RNA and protein molecules that can reveal pathological changes in their originating cells. In terms of disease treatment, EVs have the potential for targeted delivery, serving as platforms for vaccine development and as drug delivery systems to transport drugs directly to specific cells or tissues. Moreover, EVs themselves can be used as therapeutic agents for autoimmune diseases and cancer. In the realm of EV separation and purification technology, common methods include ultracentrifugation, immunoaffinity chromatography, polymer co-precipitation, ultrafiltration, size exclusion chromatography, and microfluidics. However, due to the limitations of a single separation technique in meeting the demand for high-quality and high-purity EVs, multiple methods are often combined to separate and purify EVs effectively. This article concludes by summarizing the broad application prospects of EVs in the prevention and treatment of human diseases and highlights several key scientific questions that require further in-depth research. The potential of EVs in diagnostics and therapeutics, as well as the challenges in their isolation and characterization, underscores the need for continued exploration and innovation in this field.

Abstract:Alternative splicing is an important regulatory mechanism in organisms, influencing the expression of genes involved in processes such as drug metabolism, pathway activation, and apoptosis. It refers to the process of removing introns from precursor mRNA and joining the remaining exons to produce mature mRNA. During this process, different combinations of exons can result in multiple mature mRNAs. This process is known as alternative splicing. Alternative splicing allows the same gene to produce different transcript variants and protein isoforms, increasing protein diversity and functional complexity. Transcriptomics and proteomics are two main approaches for identifying alternative splicing events. Transcriptomics identifies alternative splicing by analyzing differences between RNA sequencing data and reference sequences in databases. This method relies on the development of modern sequencing technologies. It also depends on increasingly improved splicing identification algorithms. Examples of these algorithms include alignment mapping and sequencing data quality control. The other approach is proteomic data analysis, which identifies corresponding protein products. We consider alternative splicing events more meaningful when they can be detected at the protein level. Alternative splicing proteoforms can be identified using bottom-up proteomics based on mass spectrometry. Due to the high sequence similarity between these alternative splicing proteoforms, general proteomic data analysis pipelines do not achieve good discrimination between them. To improve the identification of proteoforms and obtain differentiation information for different isoforms in proteomic data, two strategies have been developed for improving data processing: the construction of special databases and targeted identification algorithms. We believe that this potential protein isoform information may play a crucial role in life science research. In terms of databases, it is not enough to only use ordinary public databases for searching. To ensure the discovery of as many isoforms as possible, the method of constructing sample-specific databases assisted by RNA sequencing data has been widely used, which can increase the probability of detecting proteoforms. Another key strategy is the improvement of protein identification algorithms. Traditional identification algorithms often struggle to distinguish between highly similar or mutually inclusive proteoforms. To address the complex identification of alternative splicing proteoforms, several inference algorithms have been developed, which are combined with existing search engines to better characterize and detect alternative splicing proteoforms. These include peptide grouping (PeptideClassifier, SEPepQuant, GpGrouper), peptide quantitative correlation (PQPQ, PeCorA, COPF, SpliceVista), machine learning (IsoSVM, Re-Fraction, LibSVM), and major splice isoform theory (ASV-ID). Such methods have shown promising results in focusing on alternative splicing proteoforms. When using these algorithms, we should try different ones based on actual situations. Additionally, the performance of these algorithms is limited by the quality of input data. To ensure reliable identification, it is also essential to perform proper peptide identification and quality control at the front end. In general, the detection and differentiation of spliced protein isoforms are still inadequate, requiring continued attention. This article reviews recent research progress on alternative splicing and its biological functions, as well as the detection of alternative splicing at different levels, and introduces the main methods for identifying alternative splicing proteoforms using bottom-up proteomic data. Identifying different alternative splicing proteoforms helps us understand the comprehensive functions of proteins and is of great significance for discovering related biomarkers and key drug targets.

Abstract:Neuroinflammation is a complex process triggered by various factors such as injury, infection, oxidative stress, and other activators. In central immune system, microglia and astrocytes release a wide range of inflammatory mediators like cytokines and chemokines in response. Initially, acute neuroinflammation can have protective effects by promoting neuronal repair and maintaining homeostasis. However, chronic activation of neuroinflammation leads to excessive production of inflammatory mediators, resulting in neuronal dysfunction and degeneration. This can contribute to various neurological disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), and Huntington’s disease (HD).In vitro cellular models are crucial for elucidating the underlying mechanisms of neuroinflammation. Investigating neuroinflammatory signaling pathways is essential for understanding the intricate network of molecules and cells involved. Key signaling pathways such as NF-κB, MAPK, PI3K/AKT, Nrf2/HO-1, and NLRP3 play critical roles in regulating neuroinflammation. During inflammation, activation of glial cells involves multiple signaling pathways simultaneously, primarily orchestrated by two key factors: MAPK and NF-κB. These pathways guide the inflammatory cascade, leading to the release of numerous inflammatory factors and reactive oxygen species (ROS). These inflammatory factors and ROS have dual effects. Firstly, they can directly harm neighboring neurons, promoting the accumulation of abnormal proteins and triggering neuronal apoptosis. Secondly, inflammatory factor receptors on cell membranes can initiate positive feedback loops that exacerbate the inflammatory response. Neuroinflammation encompasses various cell types within the central nervous system, forming a complex and interconnected malignant cycle. This ultimately culminates in irreversible brain damage. Moreover, innovative therapeutic approaches targeting specific signaling pathways and molecular targets show promise in treating diseases related to neuroinflammation.Various cellular models are commonly employed to investigate neuroinflammation, each focusing on different aspects: pathogen-related models involve substances like lipopolysaccharide(LPS), amyloid β-protein(Aβ), CpG-DNA, and viruses; cytokine models utilize interferon-γ(IFN-γ); metabolic stress models include oxygen-glucose deprivation(OGD), 1-methyl-4-phenylpyridinium (MPP⁺), rotenone, and oxyhemoglobin; environmental toxin models encompass substances such as bisphenol A (BPA), particulate matter (PM2.5), various metals, and nanoparticles; additive substance models involve alcohol, morphine, and methamphetamine (METH). Each model offers distinct advantages and drawbacks for studying neuroinflammation. In conclusion, research on these cellular models and their associated signaling pathways provides crucial insights into the mechanisms underlying neuroinflammation-related diseases. These insights are essential for developing effective therapeutic strategies and advancing clinical practice to address the complexities of neuroinflammatory diseases.

Abstract:Integrated stress response (ISR) is an evolutionarily conserved intracellular signaling network. When the body encounters adverse stimuli, ISR is activated to assist cells, tissues, and the body in adapting to the changing environment and maintaining health by reprogramming genes. ISR is implicated in the onset and progression of various diseases, including cardiovascular disease, diabetes, obesity, cancer, and neurological disorders. A key factor in ISR is the eukaryotic initiation factor 2α (eIF2α) kinase. Four eIF2α kinases have been identified, namely general control non-derepressible-2 (GCN2), protein kinase double-stranded RNA-dependent (PKR), PKR-like ER kinase (PERK), and heme-regulated inhibitor (HRI). GCN2, PKR, PERK, and HRI kinases share a common kinase catalytic domain but have distinct regulatory domains that are activated by endoplasmic reticulum stress (ERS), viral infection, heme deficiency, and amino acid deficiency, respectively. Various stress conditions promote the phosphorylation of eIF2α at serine 51 by its 4 kinases. This inhibits the eIF2B-mediated GTP acquisition of eIF2α and reduces the translation rate. At the same time, ISR upregulates ATF4 expression. ATF4 and CCAAT-enhancer binding protein (CHOP) can promote downstream growth arrest and DNA damage-inducible protein 34 (GADD34) to mediate eIF2α dephosphorylation. At the same time, it can promote the downstream expression of Sestrin 2 (SESN2) protein, increase autophagy induced by mTORC1 and AMPK, and thereby reduce the risk of cardiovascular disease. Numerous animal and cellular studies have demonstrated that exercise, drugs, and molecular compounds can prevent and improve pathological myocardial hypertrophy, diabetic cardiomyopathy, ischemic cardiomyopathy, cardiotoxicity, and atherosclerosis by modulating ISR. The relevant mechanism involves gene knockout or inhibitors that directly inhibit the expression of eIF2α kinase. Aerobic exercise, editing of specific molecules, or drugs can indirectly inhibit the expression of eIF2α kinase, ultimately leading to the inhibition of the downstream expression of eIF2α/ATF4. In light of the significant pathological role of ISR in cardiovascular disease, current research on ISR primarily aims to develop medications that can regulate the upstream and downstream signaling activities of ISR. This involves targeting ISR to regulate intracellular protein homeostasis, ultimately aiming to delay or reverse the progression of cardiovascular disease. At present, drugs targeting ISR in cardiovascular disease research mainly include ISRIB, 4-PBA, and Salubrinal. ISRIB reverses eIF2α phosphorylation by suppressing the inhibitory effect of eIF2α on protein synthesis and blocking eIF2α/ATF4 signaling. 4-PBA can inhibit endoplasmic reticulum stress. Salubrinal inhibits eIF2α dephosphorylation by inhibiting the binding of GADD34-PP1 and CReP-PP1 complexes to eIF2α. In conclusion, the integrated stress response mediated by the four eIF2α kinases is essential for the body to adapt to various stress stimuli affecting the heart and blood vessels under normal or pathological conditions. Integrated stress response inhibitors should be promptly administered to clinical cardiovascular patients to assess their effectiveness in the onset and development of various cardiovascular diseases, as well as to evaluate potential side effects. Future studies are needed to explore the role and mechanism of eIF2α kinase-mediated integrative stress response in various diseases. It is also essential to investigate whether the integrative stress response yields different effects in various organs and can potentially exert cross-organ efficacy through inter-organ interaction.

Abstract:With changes in human lifestyle, chronic diseases caused by metabolic disorders, such as obesity, type 2 diabetes, and non-alcoholic fatty liver disease, have become serious public health issues threatening human health. These diseases not only significantly increase the disease burden on humans but also put immense pressure on global healthcare systems. Therefore, understanding and exploring the molecular mechanisms leading to these diseases, especially the role of metabolic regulators, is crucial for developing effective prevention and treatment strategies. KLF15, one of the highly conserved members of the KLF family, has gained widespread attention due to its expression and regulatory roles in various metabolically active organs. Recent studies have shown that KLF15 regulates glucose, lipid, and amino acid metabolism in adipose tissue, skeletal muscle, and liver, and is closely related to the acquisition, transport, and utilization of nutrients. The role of KLF15 in glucose metabolism is primarily reflected in its regulation of gluconeogenesis and glucose uptake. KLF15 influences blood glucose levels by regulating the expression of key gluconeogenic enzyme phosphoenolpyruvate carboxykinase (PEPCK). Research has shown that KLF15 knockout (KO) mice exhibit severe hypoglycemia and reduced liver glycogen content after 18 h of fasting. Additionally, KLF15 interacts with muscle enhancer factor 2 (MEF2A) to activate the GLUT4 promoter, significantly enhancing glucose uptake in skeletal muscle and adipose tissue. In insulin-resistant individuals, KLF15 expression is reduced, affecting insulin sensitivity by regulating genes related to lipid metabolism and mitochondrial function. In terms of lipid metabolism, KLF15 expression significantly increases during adipocyte differentiation, regulating the expression of genes such as C/EBPβ, C/EBPδ, and PPARγ. KLF15 KO mice show reduced lipogenesis and increased lipolysis, highlighting its importance in fat storage and energy balance. In brown adipose tissue (BAT), KLF15 regulates genes involved in lipid uptake and thermogenesis, such as CD36, Slc25a20, and Cpt1a. KLF15 KO mice fail to maintain body temperature during fasting-induced cold exposure, demonstrating the critical role of KLF15 in BAT metabolism and energy balance. Specifically, KLF15 forms positive feedback loops with adipogenic transcription factors such as glucocorticoid receptor (GR), PPARγ, and C/EBP, promoting adipocyte differentiation and maturation. In BAT, KLF15 is crucial not only for regulating lipid uptake but also for promoting non-shivering thermogenesis by regulating thermogenic genes, thereby helping to maintain body temperature in cold environments. In protein metabolism, KLF15 regulates key enzymes involved in branched chain amino acid (BCAA) metabolism, such as BCAT2 and ALT, which are essential for gluconeogenesis and maintaining blood glucose levels. KLF15 KO mice show reduced expression of these enzymes, leading to impaired amino acid catabolism. KLF15 regulates muscle protein synthesis and degradation through the mTOR pathway and E3 ubiquitin ligases (e.g., Atrogin-1 and MuRF1), indicating its significance in muscle protein metabolism and stress response, especially in glucocorticoid-induced muscle atrophy. Studies have shown that KLF15 expression in muscle tissue is regulated by GR. Glucocorticoids regulate KLF15 expression through GR, which in turn affects the mTOR signaling pathway, inhibiting protein synthesis and promoting protein degradation. This mechanism is particularly significant in glucocorticoid-induced muscle atrophy. KLF15 also responds significantly to exercise, particularly acute endurance exercise and long-term aerobic training. Acute endurance exercise increases KLF15 expression in muscle and adipose tissue, enhancing lipid synthesis and protein catabolism. In contrast, chronic exercise reduces KLF15 expression, improving insulin sensitivity and mitigating diabetes-induced myopathy. However, further research is needed to explore the effects of different forms of exercise on KLF15 and its specific roles in various tissues. In conclusion, KLF15 plays a crucial role in maintaining overall metabolic balance. It regulates glucose, lipid, amino acid, and protein metabolism, responding to nutritional status and exercise to maintain energy homeostasis. The role of KLF15 in glucose metabolism involves regulating gluconeogenesis and glucose uptake, in lipid metabolism through regulating fat synthesis and breakdown, and in protein metabolism through influencing branched-chain amino acid metabolism and muscle protein synthesis and degradation. Future research should continue to delve into the specific mechanisms of KLF15 in different metabolic pathways, especially its regulatory roles under various exercise forms and nutritional states, to provide new perspectives and theoretical foundations for treating metabolic diseases.

Abstract:Benzo[a]pyrene (B[α]P) is a common environmental carcinogen, mainly from the smoke generated by the incomplete combustion of coal, oil and natural gas in the industrial production and living process, which undergoes a series of metabolic reactions in vivo, and ultimately generates the active metabolite, benzopyrene dihydroxy epoxide (B[α]PDE) to exert a strong carcinogenic effect. In this paper, we provide an overview of the mechanisms involved in the malignant transformation of bronchial epithelial cells induced by B[α]PDE in terms of DNA base mutations, DNA repair function, related signaling pathways and epigenetic variations. B[α]PDE covalently binds to DNA bases to form B[α]PDE-DNA adducts, which cause DNA base mutations, inducing malignant transformation of bronchial epithelial cells and ultimate tumor formation. Interestingly, it was found that B[α]PDE-DNA adducts showed a high GC-dependent distribution and the single-nucleotide resolution profile of DNA damage profile was highly similar to that of mutations previously identified in the lung cancer genomes of smokers. B[α]PDE can also regulate the expression or silencing of proto-oncogenes and oncogenes by activating the classical AhR signaling pathway, as well as the PI3K/AKT/mTOR and NF-κB signaling pathways, inducing epithelial-mesenchymal transition (EMT) in bronchial epithelial cells, and interfering with cellular metabolism and the cell cycle, thereby inducing the development of lung cancer. The genes mutated in B[α]PDE-induced malignant transformation of bronchial epithelial cells include the proto-oncogenes RAS, KIF11, and PPP1R13L as well as the oncogenes PHLPP2 and p53. B[α]PDE exposure leads to single nucleotide polymorphisms in the 3"-UTR of DNA repair enzyme gene, which inhibits the transcription of genes encoding proteins related to DNA damage repair, and subsequently affects the cell cycle, proliferation, and apoptosis of tumor cells. B[α]PDE exposure can induce lung carcinogenesis and progression by inducing hypomethylation of specific gene promoter regions to activate proto-oncogenes and hypermethylation to silence oncogenes. The aberrantly expressed miRNAs or lncRNAs may regulate the expression and signaling of lung cancer-related genes, thereby affecting lung cancer-related biological functions, including cell proliferation, apoptosis, migration and invasion. Poly(ADP-ribose) glycohydrolase (PARG) regulates DNA damage repair and maintains genomic stability, whereas silencing PARG inhibits B[α]PDE-induced deterioration of bronchial epithelial cells. B[α]PDE exposure induces metabolic reprogramming in cancer cells, which provides energy to cancer cells rapidly proliferation by increasing glucose uptake and glycolysis, and also regulates cancer cell growth and survival by affecting lipid and nucleic acid metabolism. In conclusion, in B[α]PDE-induced lung cancer, epigenetic changes such as DNA methylation, miRNAs, lncRNAs, metabolic reprogramming, and PARG work together to form a complex regulatory network that affects gene expression, cellular metabolism, and genomic stability. An in-depth study of the mechanism of B[α]PDE-induced malignant transformation of bronchial epithelial cells can provide a theoretical basis for the study of potential targets for the development of anti-tumor drugs, which will help to guide the prevention and treatment of lung cancer in polluted environments and exposure to smoky environments, and also provide theoretical support for the Healthy China measures of tobacco control and smoking ban.

Abstract:Sleep deprivation (SD) not only directly affects an individual’s work efficiency but also negatively impacts various cognitive functions such as memory, attention, and learning as fatigue increases. Over the past few decades, numerous researchers have conducted lots of studies on the effects of SD on cognition, particularly memory. In this paper, we first review the effects of SD on memory function based on behavioral studies. Then, we further elaborate on recent advances in the physiological mechanisms of SD, including synaptic plasticity in structure and function, levels of excitatory and inhibitory neurotransmitters, and the expression of related synaptic protein signals. It has been observed that SD modulates the expression of synaptic protein signals and downstream signaling pathways by influencing changes in synaptic activities (such as dendritic spine density, synaptic connectivity strength, and the balance of excitatory and inhibitory synapses), ultimately affecting behavior. This review aims to provide insights into the research progress on the effects of SD on memory and its underlying mechanisms, providing a reference for future studies on sleep function and related mechanisms, as well as the development of strategies to mitigate memory deficits caused by SD.

Abstract:Objective To investigate the expression of cyclin-dependent kinase 8 (CDK8) in esophageal squamous cell carcinoma (ESCC) and its effect on ESCC cells, and to explore its potential molecular mechanism.Methods The expression level of CDK8 mRNA was analyzed using UALCAN database, and then the expression level of CDK8 protein in tumor tissues of ESCC patients was detected by immunohistochemistry (IHC). Esophageal cancer cell lines Kyse-30 and Kyse-150 were stably transfected with lentivirus to achieve knockdown and overexpression of CDK8. EdU proliferation assay, cell colony formation assay, cell cycle assay, cell scratch assay and invasion assay were used to explore the effect of CDK8 protein expression level on the phenotype of ESCC cells. Subsequently, the effect of CDK8 on the growth of esophageal cancer xenografts in vitro was observed by subcutaneous tumor formation assay in mice. Finally, the expression of proliferation and metastasis related proteins was detected by Western blot.Results CDK8 showed high transcription and protein expression levels in ESCC tissues compared with normal esophageal tissues. Knockdown of CDK8 expression significantly inhibited the proliferation, migration and invasion of ESCC cells. In addition, inhibition of CDK8 expression significantly affected the JAK2/STAT3 pathway and the expression of E-cadherin/N-cadherin, while overexpression of CDK8 reversed these effects. Inhibition of STAT3 pathway reversed the promoting effect of CDK8 overexpression on ESCC cell phenotype.Conclusion CDK8 is a cancer-promoting factor of ESCC, which mediates the phosphorylation of JAK2/ STAT3 and epithelial-mesenchymal transition (EMT).

Abstract:Objective Formononetin (FOR), a traditional Chinese medicine, has been widely used for nerve protection and nerve function rehabilitation after cerebral stroke. However, the role of FOR in autophagic lysosome function in cerebral ischemia-reperfusion damage has not been investigated. This study aimed to explore whether the therapeutic benefits of FOR were influenced by the regulation of autophagy flux.Methods Male Sprague-Dawley rats were separated into sham, model, and MCAO+FOR (30 mg/kg) groups after undergoing middle cerebral artery occlusion (MCAO) and ischemia-reperfusion (I/R). Then, the brain tissues in the ischemic penumbra were obtained to detect the proteins in autophagic/lysosomal pathway with antibodies of Beclin-1, LC3, SQSTM1/P62, Ubiquitin, LAMP-2, Cathepsin B (CTSB) and Cathepsin D (CTSD) by Western blot and immunofluorescence, respectively. Meanwhile, the therapeutic effectiveness was evaluated by measuring infarct volume, neurological impairments, and neuronal necrosis.Results The findings of this study demonstrate that FOR treatment exhibits a dual effect by enhancing the autophagic activities of Beclin-1 and LC3 in neurons, while simultaneously improving the autophagic clearance function, as evidenced by reinforced lysosomal activities of LAMP-2, CTSB, and CTSD, as well as reduced autophagic accumulation of Ubiquitin and P62 in the MCAO+FOR group compared to the MCAO group. Additionally, 7 d of FOR treatment dramatically reduced neurological deficits, infarct volume, and neuronal death caused by cerebral ischemia.Conclusion These findings suggest that the neuroprotective mechanism of FOR therapy in accelerating recovery from ischemic stroke may involve the increase of autophagy flux in the penumbra.

Abstract:Objective Chronic stress can induce cognitive dysfunction, but the underlying mechanisms remain unknown. Studies have confirmed that the high mobility group box 1/Toll-like receptor 4 (HMGB1/TLR4) pathway is closely associated with cognitive impairment. Therefore, this research aimed to explore whether the HMGB1/TLR4 pathway involves in chronic stress-induced cognitive dysfunction.Methods The chronic unpredictable mild stress (CUMS) mouse model was established by randomly giving different types of stress every day for four consecutive weeks. Cognitive function was detected by novel object recognition test, Y-maze test, and Morris water maze test. The protein expressions of HMGB1, TLR4, B-cell lymphoma 2 (BCL2), and BCL2 associated X (BAX) were determined by Western blot. The damage of neurons in the hippocampal CA1 region was observed by hematoxylin-eosin (HE) staining.Results The protein expressions of HMGB1 and TLR4 were significantly increased in the hippocampus of chronic stress mice. Furthermore, inhibition of the HMGB1/TLR4 pathway induced by ethyl pyruvate (EP, a specific inhibitor of HMGB1) and TAK242 (a selective inhibitor of TLR4) treatment attenuated cognitive impairment in chronic stress mice, according to the novel object recognition test, Y-maze test, and Morris water maze test. In addition, administration of EP and TAK242 also mitigated the increase of apoptosis in the hippocampus of chronic stress mice.Conclusion These results indicate that the hippocampal HMGB1/TLR4 pathway contributes to chronic stress-induced apoptosis and cognitive dysfunction.

Abstract:Objective This work examines the impact of external electric fields at terahertz (THz) frequencies on double-stranded deoxyribonucleic acid (dsDNA) systems adsorbed on Au(111) surfaces in aqueous environments.Methods The investigation utilizes a molecular dynamics (MD) approach at the atomic level and vibrational dynamics calculations using the GolDNA-Amber force field.Results The results reveal that the sugar-phosphate backbone of the DNA exhibits reduced adherence to the gold surface, while the side chains display a stronger affinity. When subjecting the hydrated DNA strands to an electric field with frequencies up to 10 THz, peak intensities of vibrational dynamic density (VDoS) are observed at five different frequencies. Moreover, the strong electric field causes hydrogen bonds in the DNA within the slit to break. The sensitivity to the electric field is particularly pronounced at 8.8 THz and 9.6 THz, with different vibrational modes observed at varying electric field strengths.Conclusion These findings contribute to an enhanced understanding of the molecular organization of gold-plated charged biological interfaces.

Abstract:Objective The inference of biogeographical ancestry (BGA) using DNA is a significant focus within anthropology and forensic science. Current methods often utilize dozens of ancestry-informative SNPs, employing principal component analysis (PCA) and likelihood ratios (LR) to ascertain individual ancestries. Nonetheless, the selection of these SNPs tends to be population-specific and shows limitations in population differentiation. With the development of high-throughput sequencing technologies, acquiring high-density SNP datasets has become easier, challenging traditional statistical models which are often reliant on prior assumptions and struggle with high-density genetic data. The integration of machine learning, which prioritizes data learning and algorithmic iteration over prior knowledge, has propelled forward new developments in BGA research. This study aims to construct a BGA inference model suitable for high-density SNP data, characterized by broad population applicability, higher accuracy, and strong generalization capabilities.Methods Initially, intersection sites of autosomes from the phase III data of the 1000 Genomes Project and commonly used commercial chips were selected to build a reference dataset after thorough site quality control and filtering. This dataset was analyzed using PCA and ADMIXTURE to study population clustering, ancestral component mixing, and genetic substructures. Utilizing spaces of different principal component (PC), combinations, this study visually assessed the PCs’ capabilities to differentiate between continental and intercontinental populations. Following this, the study employed the supervised learning classification model XGBoost, establishing a multidimensional PC-based PCA-XGBoost model with hyperparameters set through ten-fold cross-validation and a greedy strategy. Subsequently, the model was optimized and evaluated based on the LR, considering accuracy and runtime to determine the optimal number of PCs and training rounds, culminating in the study’s optimal BGA inference model. Finally, the performance of the model was subsequently validated at national and regional levels using test sets from other public data to assess its post-optimization generalization capabilities.Results The reference dataset created contains 307 866 SNP sites. Top PCs reflect varying levels of population differentiation capabilities, with some PCs showing population specificity. Under smaller K values in ADMIXTURE results, genetic ancestral components between continents are elucidated, while larger K values reveal some specific ancestral components of certain populations within continents. The number of PCs and training rounds significantly affect the classification accuracy and efficiency of the XGBoost supervised model. With LR-based evaluation methods, the optimized PCA-XGBoost model achieved a continental prediction accuracy of over 98% in the reference set. For subcontinental population levels within the continents, the model achieved an accuracy of over 95% in the reference set and over 90% in the test set.Conclusion The reference dataset effectively represents the genetic substructures of populations at selected sites. Information derived from PC dimensions significantly aids in population differentiation and inference issues, and incorporating more PC dimensions as features in supervised learning models can increase the accuracy of BGA inference. The model of this study is suitable for high-density SNP data and is not confined to specific regional populations, offering enhanced population-wide applicability. Compared to previous ancestry inference models, the optimized PCA-XGBoost model demonstrates high intercontinental population predictive accuracy. LR-based evaluation methods further enhance the reliability of predictions. Additionally, the model’s strong generalization capabilities suggest that updating the reference population data could enable more detailed population analysis and inference.

Abstract:Objective Dust has steadily emerged as a frontier research in the field of forensic science because it is a material evidence with significant features and application potential that carries rich environmental DNA information. However, as a crucial foundational step in forensic applications, the collection and DNA extraction research of dust on object surfaces from the perspective of practical applications in forensic science are still in urgent need of development.Methods Dust was collected from object surfaces using a Copan Liquid Amies Elution Swab. DNA was extracted separately from the swab head, sediment, and supernatant within the sample collection tube to evaluate DNA content, thereby determining which components within the tube should be processed and lysed. Dust samples were collected according to five different sampling areas (25-400 cm2) and the DNA concentration was measured to determine the optimal sampling area. The extraction efficiency of three commercial DNA extraction kits for dust samples was compared. The size of the DNA fragments extracted from the dust was analyzed, as well as the presence of human DNA. Additionally, 16S rDNA amplicon sequencing was used to analyze the bacterial information in dust DNA from object surfaces. This process aimed to establish a quality control method for dust DNA extraction. Regarding the critical step of cell lysis in DNA extraction, the quantity of DNA extracted was compared and evaluated under different cell lysis methods and varying vortexing times. This was done to establish an appropriate cell lysis method for dust DNA extraction.Results The sediment and swab head in the dust sampling tube are the primary sources of DNA, and both should be included in subsequent extraction processes. The sampling area of dust is positively correlated with dust DNA concentration, and it is recommended that the sampling area be larger than 5×5 cm2. Using the DNeasy PowerSoil Pro kit can yield a higher amount of DNA. Additionally, there were no significant differences in the sizes of DNA fragments extracted by the three different DNA extraction kits. No human DNA was detected in the DNA extracted from the dust samples, while bacterial DNA was present in the dust from object surfaces. Furthermore, there were differences in microbial species composition between different sampling points. Additionally, using a biological sample homogenizer to grind and lyse for 4 min (2 min× 2 times) resulted in the highest concentration of dust DNA.Conclusion The extraction of dust DNA is influenced by the sampling area, extraction kits, and lysis methods. It is crucial to establish a comprehensive and suitable dust DNA extraction scheme. This not only lays the foundation for researching and extracting environmental DNA data from dust, but also provides a methodological reference for forensic case work involving environmental samples.

Abstract:In a recent publication, Hu et al. (2023) have reported that individuals with high trait anxiety exhibit attentional deficits characterized by reduced inhibition of distractors and delayed attentional selection of targets, indicating impaired top-down attentional control. This commentary underscores their significant contributions to the cognitive theory of anxiety. Based on their findings, we propose a novel training approach called attentional inhibition training (AIT), aimed at improving top-down attentional control to alleviate symptoms of anxiety. Furthermore, we explore the potential application of non-invasive transcranial magnetic stimulation (TMS) for rapidly enhancing attentional control function.