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Viral replication through phase separation: Cytosolic and nuclear condensates.

Replication of many RNA and DNA viruses occurs within specialized intracellular hubs organized as membraneless biomolecular condensates (BCs) driven by liquid-liquid phase separation. As obligate intracellular parasites, viruses depend on the host cell machinery to complete their replication cycles and therefore actively remodel the intracellular environment to favor viral genome replication, transcription, and assembly. Cytosolic and nuclear phase-separated replication compartments (RC) provide concentrated and dynamic platforms that promote efficient interactions between viral genomes and viral or host proteins essential for infection. The formation of viral replication BCs is typically facilitated by viral proteins enriched in intrinsically disordered regions and low-complexity domains, which enable multivalent interactions with viral nucleic acids and cellular factors. These interactions are mediated by diverse biophysical forces, including hydrophobic and π interactions, hydrogen bonding, molecular crowding, and osmotic effects. Throughout infection, viral BCs remain highly dynamic, allowing continuous exchange of components and functional maturation of replication hubs. Their properties and activities are further regulated by post-translational modifications of viral and host proteins, such as phosphorylation, acetylation, and methylation. In this review, we summarize current evidence supporting liquid-liquid phase separation as a central organizing principle of viral RCs. We focus on representative RNA and DNA viruses that replicate in the cytosol or nucleus, highlighting virus-specific strategies, conserved mechanisms, and the consequences of BC formation for viral replication efficiency, host antiviral responses, and therapeutic intervention.

Phase Separation

Genome-wide identification of CXE gene family in soybean and functional characterization of GmCXE31 in lipid biosynthesis and salt tolerance.

GmCXE31 negatively regulates salt tolerance and lipid synthesis in soybean, and the cxe31-edited lines improve soybean yield and seed quality. Carboxylesterases (CXEs), as essential lipid hydrolases of the α/β-hydrolase fold superfamily, are critical for plant stress responses, hormone signaling and secondary metabolism. The key candidate gene GmCXE31 was previously identified in our laboratory through a genome‑wide association study (GWAS) of soybean lipid‑related traits. In the present study, we further identified 60 GmCXE family genes in soybean. Phylogenetic analysis clustered them into 11 conserved subfamilies. Cis-acting element analysis showed their promoters are enriched with elements related to abiotic stress, growth and hormone signaling, suggesting potential roles in soybean development and stress adaptation. GmCXE31 is highly expressed in seedling roots and responsive to strigolactones (SLs) and salt stress. Functional assays revealed that GmCXE31 negatively regulates soybean salt tolerance: its overexpression reduced salt tolerance in Arabidopsis and soybean under 150 mM NaCl stress, while its knockout enhanced this trait. Lipid profiling revealed GmCXE31-edited lines had higher seed oil content, elevated oleic/linoleic acid ratio and lower saturated fatty acid proportion, which was achieved by regulating lipid synthesis-related genes like GmNFYA. Agronomic trait analysis showed GmCXE31-edited lines had increased nodule number, plant height and single-plant yield at maturity, with opposite phenotypes in overexpression lines. In conclusion, this study elucidates the multifaceted roles of GmCXE31 in coordinating soybean salt tolerance, lipid metabolism and agronomic traits, providing theoretical and genetic resources for salt-tolerant and high-quality soybean molecular breeding.

Glycine max

Peripheral pain threshold, glycaemic status, and LAMP3 genetic variation: A community-based analysis.

Diabetic polyneuropathy is a common complication of diabetes, yet substantial inter-individual variation in peripheral pain perception suggests underlying genetic influences. This population-based study investigated clinical, metabolic, and genetic determinants of pain threshold using intraepidermal electrical stimulation in 906 participants from the Iwaki Health Promotion Project 2017. Genome-wide association analysis identified 12 loci showing suggestive associations, among which a missense variant in LAMP3 (rs482912) was prioritized as a biologically plausible candidate. Phenotype-stratified analyses showed that individuals carrying the CT or CC genotypes had lower PINT indices than those with the TT genotype, indicating reduced pain thresholds. Notably, the CC genotype retained an association with lower pain threshold using intraepidermal electrical stimulation under conditions of metabolic stress, including impaired glucose tolerance, elevated HbA1c, and obesity, whereas this association was attenuated in the presence of hypertension. Single-cell RNA sequencing analysis of human skin revealed that LAMP3-positive mature dendritic cells, enriched in immunoregulatory molecules, exhibited transcriptional enrichment of inflammatory, antigen-presenting, and nociception-related pathways, including NF-κB, JAK-STAT, cytokine signaling, and neuroimmune sensitization cascades. Autopsy-based skin analysis further demonstrated genotype-associated differences in dermal LAMP3-positive cell infiltration and CD8-positive T-cell abundance, while CD4-positive T-cell abundance and intraepidermal nerve fiber density remained unchanged across genotypes. Taken together, these findings suggest a potential association between LAMP3 variation and individual differences in peripheral pain threshold and provide biological context supporting a role for neuroimmune interactions in early sensory modulation under metabolic stress. Given the suggestive genetic evidence and indirect mechanistic data, these observations should be interpreted as exploratory and hypothesis-generating.

Humans

Sex-dependent protective effects of microglial tumor necrosis factor on post-stroke inflammation and myelin injury.

Tumor necrosis factor (TNF) is rapidly induced after ischemic stroke, but its proposed cell-specific and sex-dependent functions during post-stroke inflammation remain insufficiently understood. Here, we investigated the role of microglia-derived TNF in the acute and subacute response to permanent middle cerebral artery occlusion (pMCAO). Tnf expression was transiently upregulated after stroke, becoming significant at 4 h, peaking at 12-24 h, and returning to baseline by 5 days. In situ hybridization confirmed strong Tnf expression in the infarct and peri-infarct regions. Whole-brain transcriptomic profiling showed that global TNF deficiency reshaped the early post-ischemic response, shifting it from microglia-associated phagocytic and wound-healing pathways toward an interferon-related inflammatory signature. To define the specific contribution of microglial TNF, we used inducible Cx3cr1CreER:Tnffl/fl mice. Microglial TNF deletion had no effect on infarct volume in males at 24 h or 5 days after pMCAO, but significantly increased infarct size in females at both time points. In both sexes, brain TNF levels peaked at 24 h and were significantly reduced in Cx3cr1CreER:Tnffl/fl mice, confirming microglia as a major source of early post-ischemic TNF. However, downstream consequences diverged by sex. At 5 days, male Cx3cr1CreER:Tnffl/fl mice showed reduced microglial reactivity and 18 kDa translocator protein (TSPO) signal, with no change in T-cell infiltration, and exhibited increased density of mature oligodendrocytes. In contrast, female Cx3cr1CreER:Tnffl/fl mice displayed enhanced microglial reactivity, increased TSPO binding, higher peri-infarct T-cell infiltration, and reduced oligodendrocyte density and myelin integrity. Together, these findings identify microglial TNF as a sex-dependent regulator of post-stroke inflammation and myelin injury.

Animals

Phosphorus modulates starch granule development and metabolic partitioning in wheat grain: Insights from SGAP proteomics and nutrition and processing quality.

This study investigates how phosphorus (P) levels are associated with carbon-nitrogen metabolism in wheat grains. Optimal P application (105 kg P₂O₅ ha⁻¹) was associated with enhanced pericarp-endosperm coordination, increased carbon allocation to the endosperm, and early B‑type starch granule formation. Starch granule‑associated protein (SGAP) proteomics showed that optimal P upregulated cytoskeletal and starch‑synthesis proteins bound to starch granules in the endosperm, while reducing storage protein degradation‑related SGAPs in the pericarp. These metabolic adjustments were correlated with increased grain‑filling intensity and duration, and were associated with the highest theoretical grain weight (50.70 mg). Furthermore, optimal P was associated with enrichment of amino acid biosynthesis pathways and with higher levels of essential amino acids (e.g., lysine and threonine by 17.0--26.8%) and an improved essential amino acid profile without altering total protein content. In contrast, excessive P (210 kg P₂O₅ ha⁻¹) was associated with disrupted inter‑tissue coordination but did not simply impair grain filling; instead, HP corresponded to a unique developmental program: it was linked to an early burst of C‑type starch granules (0∼5 µm) at 7 DPA, yet by maturity achieved the highest proportion of large A‑type granules (56.8%) and the highest total starch content (63.5%), together with elevated endosperm phosphorus at 14 DPA and enrichment of spliceosome‑related pathways. HP also showed higher levels of several functional amino acids (glutamate, cysteine, histidine, proline) compared to P0. However, HP was associated with a higher gliadin/globulin ratio and did not improve grain yield. These findings suggest that phosphorus supply is associated with grain quality through tissue‑specific metabolic reprogramming, and that precision management-rather than maximized application-warrants consideration for optimizing both yield and processing quality.

Triticum

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans