ObjectiveTo investigate the status of overlapping hepatitis B virus （HBV） infection in patients with chronic hepatitis C virus （HCV） infection and the serological characteristics of such patients. MethodsA total of 8 637 patients with HCV infection who were hospitalized from January 1， 2010 to December 31， 2020 and had complete data of HBV serological markers were enrolled， and the composition ratio of patients with overlapping HBV serological markers was analyzed among the patients with HCV infection. The patients were divided into groups based on age and year of birth， and serological characteristics were analyzed， and the distribution of HBV-related serological characteristics were analyzed across different HCV genotypes. ResultsThe patients with HCV/HBV overlapping infection accounted for 5.85%， and the patients with previous HBV infection accounted for 48.10%； the patients with protective immunity against HBV accounted for 14.67%， while the patients with a lack of protective immunity against HBV accounted for 31.39%. The patients were divided into groups based on age： in the 0 — 17 years group， the patients with protective immunity against HBV accounted for 61.41% （304 patients）； the 18 — 44 years group was mainly composed of patients with previous HBV infection （698 patients， 37.31%）， the 45 — 59 years group was predominantly composed of patients with previous HBV infection （1 945 patients， 50.38%）， and the ≥60 years group was also predominantly composed of patients with previous HBV infection （1 486 patients， 61.66%）. The patients were divided into groups based on the year of birth： in the pre-1992 group， the patients with previous HBV infection accounted for 51.63% （4 112 patients）； in the 1992 — 2005 group， the patients with protective immunity against HBV accounted for 54.72% （168 patients）； in the post-2005 group， the patients with protective immunity against HBV accounted for 64.38% （235 patients）. In this study， 6 301 patients underwent HCV genotype testing： the patients with genotype 1b accounted for the highest proportion of 51.71% （3 258 patients）， followed by those with genotype 2a （1 769 patients， 28.07%）， genotype 3b （63 patients， 1.00%）， genotype 3a （10 patients， 0.16%）， genotype 4 （21 patients， 0.33%）， and genotype 6a （5 patients， 0.08%）. ConclusionWith the implementation of hepatitis B planned vaccination program in China， there has been a significant reduction in the proportion of patients with previous HBV infection among the patients with HCV/HBV overlapping infection， but there is still a relatively high proportion of patients with a lack of protective immunity against HBV.

The innate immune system can be boosted in response to subsequent triggers by pre-exposure to microbes or microbial products, known as “trained immunity”. Compared to classical immune memory, innate trained immunity has several different features. Firstly, the molecules involved in trained immunity differ from those involved in classical immune memory. Innate trained immunity mainly involves innate immune cells (e.g., myeloid immune cells, natural killer cells, innate lymphoid cells) and their effector molecules (e.g., pattern recognition receptor (PRR), various cytokines), as well as some kinds of non-immune cells (e.g., microglial cells). Secondly, the increased responsiveness to secondary stimuli during innate trained immunity is not specific to a particular pathogen, but influences epigenetic reprogramming in the cell through signaling pathways, leading to the sustained changes in genes transcriptional process, which ultimately affects cellular physiology without permanent genetic changes (e.g., mutations or recombination). Finally, innate trained immunity relies on an altered functional state of innate immune cells that could persist for weeks to months after initial stimulus removal. An appropriate inducer could induce trained immunity in innate lymphocytes, such as exogenous stimulants (including vaccines) and endogenous stimulants, which was firstly discovered in bone marrow derived immune cells. However, mature bone marrow derived immune cells are short-lived cells, that may not be able to transmit memory phenotypes to their offspring and provide long-term protection. Therefore, trained immunity is more likely to be relied on long-lived cells, such as epithelial stem cells, mesenchymal stromal cells and non-immune cells such as fibroblasts. Epigenetic reprogramming is one of the key molecular mechanisms that induces trained immunity, including DNA modifications, non-coding RNAs, histone modifications and chromatin remodeling. In addition to epigenetic reprogramming, different cellular metabolic pathways are involved in the regulation of innate trained immunity, including aerobic glycolysis, glutamine catabolism, cholesterol metabolism and fatty acid synthesis, through a series of intracellular cascade responses triggered by the recognition of PRR specific ligands. In the view of evolutionary, trained immunity is beneficial in enhancing protection against secondary infections with an induction in the evolutionary protective process against infections. Therefore, innate trained immunity plays an important role in therapy against diseases such as tumors and infections, which has signature therapeutic effects in these diseases. In organ transplantation, trained immunity has been associated with acute rejection, which prolongs the survival of allografts. However, trained immunity is not always protective but pathological in some cases, and dysregulated trained immunity contributes to the development of inflammatory and autoimmune diseases. Trained immunity provides a novel form of immune memory, but when inappropriately activated, may lead to an attack on tissues, causing autoinflammation. In autoimmune diseases such as rheumatoid arthritis and atherosclerosis, trained immunity may lead to enhance inflammation and tissue lesion in diseased regions. In Alzheimer’s disease and Parkinson’s disease, trained immunity may lead to over-activation of microglial cells, triggering neuroinflammation even nerve injury. This paper summarizes the basis and mechanisms of innate trained immunity, including the different cell types involved, the impacts on diseases and the effects as a therapeutic strategy to provide novel ideas for different diseases.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Objective To analyze the structural and phylogenetic characteristics of the mitochondrial genome from Bulinus globosus, so as to provide a theoretical basis for classification and identification of species within the Bulinus genus, and to provide insights into understanding of Bulinus-schistosomes interactions and the mechanisms of parasite transmission. Methods B. globosus samples were collected from the Ruya River basin in Zimbabwe. Mitochondrial DNA was extracted from B. globosus samples and the corresponding libraries were constructed for high-throughput sequencing on the Illumina NovaSeq 6000 platform. After raw sequencing data were subjected to quality control using the fastp software, genome assembly was performed using the A5-miseq and SPAdes tools, and genome annotation was conducted using the MITOS online server. Circular maps and sequence plots of the mitochondrial genome were generated using the CGView and OGDRAW software, and the protein conservation motifs and structures were analyzed using the TBtools software. Base composition and codon usage bias were analyzed and visualized using the software MEGA X and the ggplot2 package in the R software. In addition, a phylogenetic tree was created in the software MEGA X after sequence alignment with the software MAFFT 7, and visualized using the software iTOL. Results The mitochondrial genome of B. globosus was a 13 730 bp double-stranded circular molecule, containing 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, and 13 protein-coding genes, with a marked AT preference. The mitochondrial genome composition of B. globosus was similar to that of other species within the Bulinus genus. Phylogenetic analysis revealed that the complete mitochondrial genome sequence of B. globosus was clustered with B. truncatus, B. nasutus, and B. ugandae into the same evolutionary clade, and gene superfamily analysis showed that the metabolism-related proteins of B. globosus were highly conserved, notably the cytochrome c oxidase family, which showed a significant consistency. Conclusions This is the first whole mitochondrial genome sequencing to decode the compositional features of the mitochondrial genome of B. globosus from Zimbabwe and its evolutionary relationship within the Bulinus genus, which provides important insights for further understanding of the phylogeny and mitochondrial genome characteristics of the Bulinus genus.

Malaria, a parasitic disease caused by infection with the species of Plasmodium and transmitted by Anopheles mosquito bites, is one of the major public health challenges that seriously threaten human health. Malaria parasites present diverse immune escape strategies to escape from the recognition and clearance of the host immune system, which poses a great challenge to the malaria control programme. This review presents the advances in the mechanisms underlying the immune escape of Plasmodium, including antigenic variation, epigenetic regulation, antagonism against IgM antibody, activation of the cyclic guanosine monophosphate-adenosine monophosphate (GMP-AMP) synthase-stimulator of interferon genes (cGAS-STING) signaling, suppression of splenic immune functions, and molecular camouflage, so as to provide insights into development of malaria vaccines and antimalarial agents and formulation of the malaria control strategy.

ObjectiveJunctophilin-2 (JPH2) is an essential structural protein that maintains junctional membrane complexes (JMCs) in cardiomyocytes by tethering the plasma membrane to the sarcoplasmic reticulum, thereby facilitating excitation-contraction (E-C) coupling. Mutations in JPH2 have been associated with hypertrophic cardiomyopathy (HCM), but the molecular mechanisms governing its membrane-binding properties and the functional relevance of its membrane occupation and recognition nexus (MORN) repeat motifs remain incompletely understood. This study aimed to elucidate the structural basis of JPH2 membrane association and its implications for HCM pathogenesis. MethodsA recombinant N-terminal fragment of mouse JPH2 (residues1-440), encompassing the MORN repeats and an adjacent helical region, was purified under near-physiological buffer conditions.X-ray crystallography was employed to determine the structure of the JPH2 MORN-Helix domain. Sequence conservation analysis across species and junctophilin isoforms was performed to assess the evolutionary conservation of key structural features. Functional membrane-binding assays were conducted using liposome co-sedimentation and cell-based localization studies in COS7 and HeLa cells. In addition, site-directed mutagenesis targeting positively charged residues and known HCM-associated mutations, including R347C, was used to evaluate their effects on membrane interaction and subcellular localization. ResultsThe crystal structure of the mouse JPH2 MORN-Helix domain was resolved at 2.6 Å, revealing a compact, elongated architecture consisting of multiple tandem MORN motifs arranged in a curved configuration, forming a continuous hydrophobic core stabilized by alternating aromatic residues. A C-terminal α-helix further reinforced structural integrity. Conservation analysis identified the inner groove of the MORN array as a highly conserved surface, suggesting its role as a protein-binding interface. A flexible linker segment enriched in positively charged residues, located adjacent to the MORN motifs, was found to mediate direct electrostatic interactions with negatively charged phospholipid membranes. Functional assays demonstrated that mutation of these basic residues impaired membrane association, while the HCM-linked R347C mutation completely abolished membrane localization in cellular assays, despite preserving the overall MORN-Helix fold in structural modeling. ConclusionThis study provides structural insight into the membrane-binding mechanism of the cardiomyocyte-specific protein JPH2, highlighting the dual roles of its MORN-Helix domain in membrane anchoring and protein interactions. The findings clarify the structural basis for membrane targeting via a positively charged linker and demonstrate that disruption of this interaction—such as that caused by the R347C mutation—likely contributes to HCM pathogenesis. These results not only enhance current understanding of JPH2 function in cardiac E-C coupling but also offer a structural framework for future investigations into the assembly and regulation of JMCs in both physiological and disease contexts.