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PubMed2026

The R2R3-MYB transcription factor ScMYB20 negatively regulates drought and salt tolerance through a dual-repression of ScCHALCONE SYNTHASE-1 (ScCHS1)-mediated flavonoid biosynthesis in the desert moss Syntrichia caninervis.

The desert moss Syntrichia caninervis is one of the most desiccation-tolerant land plants known and provides a powerful system for dissecting the molecular foundations of extreme stress adaptation in early-diverging land lineages. The MYB transcription factor superfamily orchestrates secondary metabolism and stress signaling across plants, yet its lineage-specific evolution and mechanistic deployment in bryophytes remain poorly understood. Here, we identified 65 ScMYB genes in the S. caninervis genome and showed that the family expanded predominantly through dispersed duplication, with no detectable synteny to vascular-plant MYBs, indicating bryophyte-specific neo-functionalization. Integrating phylogenetic clustering, cis-element architecture and stress-responsive expression profiling, we pinpointed ScMYB20, a nuclear-localized, S13-subgroup R2R3-MYB that is rapidly and strongly induced by dehydration and salinity. Heterologous overexpression in Arabidopsis, together with overexpression and RNAi in S. caninervis, demonstrated that ScMYB20 negatively regulates drought and salt tolerance by suppressing antioxidant capacity, osmotic adjustment and photosynthetic performance, while concomitantly elevating ROS and MDA accumulation. Mechanistically, ScMYB20 directly binds a TAACCA motif in the ScCHS1 promoter to repress its transcription, and simultaneously sequesters the WD40 protein ScTTG1, a positive transcriptional activator of ScCHS1, thereby antagonising ScTTG1-mediated activation. Transient ScCHS1 overexpression restored flavonoid accumulation, antioxidant capacity and stress tolerance. Together, our findings define a dual-repression module (ScMYB20-ScTTG1-ScCHS1) that fine-tunes flavonoid flux under abiotic stress, and provide evolutionary and mechanistic insights into how R2R3-MYB repressors evolved to balance metabolic investment and stress survival in land plants.

Syntrichia caninervis↗
PubMed2026

Gene expression patterns in the intestines of sea urchins (Strongylocentrotus intermedius) under prolonged high-salinity stress.

The effective development of high-salinity aquaculture for the sea urchin Strongylocentrotus intermedius depends on understanding its molecular mechanisms. Therefore, we conducted a 60-day experiment to investigate the effects of prolonged high-salinity stress on the survival, growth, amino acid levels, antioxidant enzyme activity, and gene expression of S. intermedius. The experiment involved the preparation of two groups: one with a salinity of 32 (group S32) and another with 36 (group S36). The results showed that the survival rate of S. intermedius in group S36 was 80%&#xa0;&#xb1;&#xa0;6.7%, while the weight gain rate was only 61.58%&#xa0;&#xb1;&#xa0;1.92%. Both parameters were significantly lower than those in group S32 (P&#xa0;<&#xa0;0.05). In addition, the GSH, Cys, and Glu expression in S. intermedius was significantly higher than that observed in group S32 (P&#xa0;<&#xa0;0.05). The transcriptomic results showed that, when comparing groups S32 and S36, 179 differentially expressed genes were identified. These genes were predominantly enriched in pathways related to metabolism and amino acid biosynthesis. We highlight the genes CGL, EAAT3, AMY, and NADH, which are associated with the energy metabolism, cysteine transport, and amino acid biosynthesis of S. intermedius. We speculated that S. intermedius exposed to high salinity enhances energy metabolism, as well as Cys synthesis and transport, to mitigate oxidative stress. This study provides a theoretical reference for the healthy aquaculture of S. intermedius in high-salinity environments.

Animals↗
PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5&#x200b; concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗
PubMed2026

Whole-transcriptome RNA sequencing and ceRNA network analyses provide novel insights into the antibacterial immune response of Hippocampus abdominalis against Vibrio harveyi.

Long non-coding RNAs (lncRNAs) stand as newly-arisen molecular types that exert regulatory effects, able to operate as competitive endogenous RNAs (ceRNAs) to engage microRNAs (miRNAs) in interaction, resulting in the recovery of target mRNA expression and activity. Increasing evidences indicate that the ceRNA network affects various biological processes in mammals, including development, cellular differentiation, metabolism, immune response, and disease pathogenesis. In teleost fish, the lncRNA-miRNA-mRNA regulatory networks have been reported occasionally. However, up to now, the roles of lncRNAs in the big-belly seahorse (Hippocampus abdominalis) remains unclear. In this study, we reported for the first time, via whole-transcriptome RNA sequencing, the lncRNA mediated ceRNA regulatory network in Vibrio harveyi-infected H. abdominalis. A total of 4197 differentially expressed mRNAs (DE-mRNAs), 1317 DE-lncRNAs, and 183 DE-miRNAs were identified. Furthermore, the crosstalk between miRNAs and lncRNAs as well as between miRNAs and mRNAs was inferred based on the negative correlations between miRNAs and their target lncRNAs/mRNAs. A core immune associated lncRNA-miRNA-mRNA putative regulatory network was thus constructed, comprising 211 lncRNA-miRNA and 224 mRNA-miRNA pairs. In conclusion, our findings provide an integrative overview of the ceRNA regulatory networks on the underlying immune responses to V. harveyi infection in the big-belly seahorse, and offer a solid theoretical foundation for the comparative immunological research of teleost fish.

Animals↗
PubMed2026

Integrated physiological and transcriptomic analyses reveal coordinated gill responses to heat stress in pikeperch (Sander lucioperca).

Climate change-driven warming of aquatic environments has made thermal stress an increasingly important factor influencing fish physiological homeostasis. Given their central roles in respiration and osmoregulation, gills are particularly responsive to variations in ambient temperature. Histological examination, physiological measurements, and transcriptome profiling were integrated to investigate the mechanisms associated with heat stress-induced gill injury in pikeperch (Sander lucioperca). Histological analysis revealed that exposure to 29&#xa0;&#xb0;C directly caused structural damage to the gills of pikeperch. Oxidative status was evaluated by measuring malondialdehyde (MDA) levels and the activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). MDA accumulation was significantly enhanced under heat stress, while antioxidant enzyme activities (SOD, POD, and CAT) displayed a transient increase followed by a subsequent decline. Transcriptome profiling showed marked enrichment of the protein processing in endoplasmic reticulum pathway after heat stress, suggesting activation of endoplasmic reticulum (ER) stress in pikeperch gills. With increasing stress duration, the unfolded protein response (UPR) appeared unable to re-establish ER homeostasis, shifting ire1 and atf6 toward a pro-apoptotic state. Protein-protein interaction (PPI) analysis further highlighted hub genes potentially involved in heat stress-induced ER stress and apoptosis. TUNEL staining and western blotting collectively confirmed that heat stress triggered apoptosis in pikeperch gill tissue. Overall, this study provides new insights into the physiological and molecular responses of pikeperch gills to heat stress and enhances our understanding of thermal stress adaptation in cold-water aquaculture species under climate change.

Animals↗
PubMed2026

Contrasting redox-related physiological responses associated with HaGATA23 and HaGATA36 during Orobanche cumana parasitism in sunflower (Helianthus annuus L.).

Helianthus annuus is an economically important Asteraceae species used for seed oil production and ornamental purposes, but its production is seriously affected by the root-parasitic plant Orobanche cumana. GATA transcription factors are zinc-finger DNA-binding regulators involved in plant development and stress adaptation. However, the molecular characteristics of GATA transcription factors in Helianthus annuus and their contribution to Helianthus annuus -Orobanche cumana interaction remain poorly understood. Here, 36 HaGATA members were retrieved from the Helianthus annuus genome and classified into four phylogenetic clades. Chromosomal placement, collinearity, gene structure, motif composition, and promoter elements varied among the 36 HaGATA members, indicating evolutionary conservation coupled with functional diversification. Expression analysis and RT-qPCR analyses revealed differential expression patterns among HaGATA genes under O. cumana stress, with HaGATA23 markedly downregulated and HaGATA36 strongly upregulated. Overexpression of HaGATA23 was associated with increased malondialdehyde (MDA) accumulation and unfavorable changes in antioxidant enzyme activities, whereas its silencing showed the opposite physiological tendency. In contrast, overexpression of HaGATA36 reduced malondialdehyde accumulation, increased peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD) activities, while its silencing showed the reverse tendency. These results indicate that HaGATA23 and HaGATA36 are candidate genes associated with contrasting redox-related physiological responses during O. cumana stress. This work provides evidence that GATA transcription factors are associated with redox-related physiological responses in sunflower under O. cumana treatment and identifies HaGATA23 and HaGATA36 as functionally divergent candidate genes for further validation.

Helianthus↗
PubMed2026

Culture of infectious human norovirus isolated from live contaminated oysters.

Human noroviruses are a major cause of foodborne outbreaks worldwide. Filter-feeding shellfish, such as oysters, can bioaccumulate these viruses in their digestive tissue when grown in sewage-impacted coastal areas and are often implicated in norovirus foodborne outbreaks. Despite the high sensitivity of current molecular assays, these methods for norovirus detection in shellfish fail to distinguish between infectious and non-infectious particles. Assessing norovirus infectivity in shellfish remains a challenge due to the lack of suitable isolation methods that maintain capsid integrity. In this study, a protocol for isolating infectious norovirus from oyster tissues, based on chloroform-butanol elution and polyethylene glycol concentration (CB-PEG), was optimized for the recovery of human norovirus GI and GII. While CB-PEG method recovered various norovirus GI and GII genotypes, it was less efficient at the genomic level than a protocol based on proteinase K elution (adapted from ISO 15216) and showed genotype-dependent viral recovery rates. By optimizing the flocculation step, we improved the method's compatibility with human intestinal enteroid (HIE) cultures. Using this approach, we successfully quantified infectious norovirus GII.3 titers recovered from artificially-contaminated live oysters. Interestingly, infectious virus was better isolated following a freezing step of the digestive tissues, with titers ranging from 13 to 40 TCID50/mL for positive samples. In conclusion, this study established an optimized methodological approach for the relative quantification of infectious norovirus GII.3 in shellfish, paving the way for future research on viral persistence and inactivation strategies in this foodstuff.

Norovirus↗
PubMed2026

Transcriptomic responses to developmental temperature in two field-collected Spodoptera exigua populations from Korea.

The beet armyworm, Spodoptera exigua, is a polyphagous insect whose development and seasonal occurrence are strongly influenced by temperature. However, transcriptomic responses to developmental thermal regimes remain insufficiently characterized in field-collected populations. In this study, we compared two Korean field-collected populations of S. exigua: a Haenam population collected in May and initially maintained at 15&#xa0;&#xb1;&#xa0;1&#xa0;&#xb0;C (HN), and a Jeju population collected in July and initially maintained at 27&#xa0;&#xb1;&#xa0;1&#xa0;&#xb0;C (JJ). F1 larvae from each population were reared under three fluctuating developmental temperature regimes: low (15-21&#xa0;&#xb0;C), middle (21-27&#xa0;&#xb0;C), and high (27-33&#xa0;&#xb0;C), followed by RNA-seq analysis. Differential expression analysis revealed population-associated variation in transcriptomic responses across developmental temperatures. HN exhibited a larger number of differentially expressed genes under the high-temperature regime, suggesting stronger transcriptomic sensitivity to elevated developmental temperature. Functional enrichment analyses identified population-associated differences in pathways related to heat response, oxidative metabolism, cytoskeletal organization, cuticle-associated processes, lipid metabolism, and immune-related functions. In JJ, heat-response and cuticle-related expression patterns were more prominent under warmer developmental conditions, whereas HN showed broader changes in stress- and metabolism-associated pathways under high temperature. Overall, this study provides a comparative transcriptomic analysis of two field-collected S. exigua populations under different developmental temperature regimes and identifies RNA-seq-based molecular response patterns associated with population-specific thermal response profiles.

Animals↗
PubMed2026

Early infantile developmental and epileptic encephalopathy: clinical spectrum, diagnosis, outcomes, and evolving treatment strategies.

Early infantile developmental and epileptic encephalopathy (EIDEE) is among the most severe epilepsy syndromes, with onset before three months of age and an estimated incidence of approximately 10 per 100,000 live births. The 2022 International League Against Epilepsy classification unified the historically distinct Ohtahara syndrome and early myoclonic encephalopathy under a single diagnostic framework defined by frequent drug-resistant tonic and/or myoclonic seizures, an abnormal neurological examination, and an abnormal interictal electroencephalogram-most characteristically a burst-suppression pattern. This narrative review synthesizes the clinical, electrophysiological, neuroimaging, genetic, and therapeutic literature within the EIDEE framework. The clinical phenotype is characterized by central hypotonia, postnatal microcephaly, cortical visual impairment, and age-dependent syndromic evolution toward infantile epileptic spasms syndrome or Lennox-Gastaut syndrome in the majority of patients. Electroencephalography remains essential for syndromic classification, while systematic metabolic screening and early trio whole-exome or whole-genome sequencing are central to the etiologic workup, achieving diagnostic yields of 60-65%. The most commonly identified genetic causes include STXBP1, KCNQ2, and SCN2A variants. Outcomes are poor overall and strongly etiology-dependent: vitamin-responsive disorders carry a substantially more favorable prognosis, whereas mortality reaches 25% in genetic cohorts. Genotype-guided pharmacotherapy is now applicable to a clinically meaningful subset of patients, with sodium channel blockers, potassium channel openers, and emerging antisense oligonucleotide therapies representing important therapeutic advances. Gene therapy trials are underway but have encountered early safety signals, underscoring the vulnerability of this population. Critical unmet needs include earlier molecular diagnosis, precision therapies targeting developmental outcomes beyond seizure control, and prospective international registries to characterize the long-term natural history of EIDEE.

Humans↗
PubMed2026

Pelvic lymph node dissection in prostate cancer: current evidence, controversies, and future directions.

BACKGROUND: Pelvic lymph node dissection (PLND) remains controversial in the management of prostate cancer. Although it provides the most accurate pathological staging, its therapeutic value beyond staging has long been debated due to conflicting evidence and concerns regarding procedure-related morbidity. OBJECTIVE: To critically evaluate the contemporary role of PLND, particularly extended pelvic lymph node dissection (ePLND), in prostate cancer management in the context of modern imaging, risk stratification tools, and evolving oncologic endpoints. EVIDENCE ACQUISITION: A narrative review of recent literature was conducted, focusing on high-level evidence including randomized trials, observational studies, and contemporary guideline recommendations addressing the indications, extent, oncologic outcomes, and complications of PLND. EVIDENCE SYNTHESIS: Recent randomized and observational studies suggest that ePLND improves nodal staging accuracy and may be associated with modest improvements in metastasis-free survival (MFS) in selected patients with intermediate- and high-risk prostate cancer, although the absolute benefit remains limited and causality is not definitively established. Advances in molecular imaging, particularly prostate-specific membrane antigen (PSMA) PET/CT, together with multiparametric MRI, validated nomograms, and emerging genomic classifiers, now allow more precise identification of patients most likely to benefit from ePLND. The integration of these tools supports a more individualized surgical strategy, including image-guided and sentinel lymph node approaches designed to maximize staging accuracy while minimizing unnecessary dissection. CONCLUSIONS: In the contemporary PSMA imaging era, ePLND continues to play an important role in nodal staging and may contribute to improved oncologic outcomes in carefully selected patients.

Humans↗
PubMed2026

Characteristics and functions of a cell adhesion molecule PvCadN in Penaeus vannamei during WSSV infection.

Cell adhesion not only maintains the integrity of the organism, but also plays an important role in the immune system, which is involved in modulation in the interaction between host and virus. In this study, a novel cell adhesion molecule from Penaeus vannamei, designated as PvCadN, was investigated. It had the typical molecular characteristics of cadherin family, with multiple extracellular cadherin repeat domains, a transmembrane region, and a conserved &#x3b2;-catenin-binding motif. Pvcadn is expressed ubiquitously across all detected tissues, with the highest transcriptional level in gills. RNA interference-mediated silencing of pvcadn significantly impaired the adhesion ability of shrimp hemocytes. Upon WSSV infection, pvcadn showed a tissue-specific expression pattern, with upregulation in gills and downregulation in hemocytes. Knockdown of pvcadn markedly suppressed the transcription of WSSV immediate-early gene ie1 and replication of the viral genome in vivo, suggesting that PvCadN acted as a potential virus-associated molecule. Furthermore, it was found that PvCadN was regulated by Lv&#x3b2;-catenin, a core molecule in the Wnt signaling pathway that functions in innate immunity, at the transcriptional and protein levels. Silencing of lv&#x3b2;-catenin significantly downregulated pvcadn transcription, and Lv&#x3b2;-catenin bound directly to the Cadherin C domain of PvCadN. In summary, the study revealed that PvCadN was a key cell adhesion molecule involved in WSSV infection, which was regulated by Lv&#x3b2;-catenin. Our findings will provide fundamental data for further investigation into cadherin-mediated immune regulation in shrimp, and offer new insights for the prevention and control of WSSV.

Animals↗
PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24&#x2009;months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗
PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n&#xa0;=&#xa0;907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed2026

Normoalbuminuric and albuminuric diabetic kidney disease exhibit divergent renal proteomic characteristics: implications for management.

BACKGROUND: The pathogenesis of diabetic kidney disease (DKD) is complex. Normoalbuminuric diabetic kidney disease (NADKD) is a special subtype of DKD that often progresses insidiously without detectable albuminuria, posing diagnostic and therapeutic challenges. Its pathogenesis remains unclear. Proteomic analysis of renal tissues may offer insights into its pathogenesis and identify biomarkers. METHODS: Clinicopathological data from 295 biopsy-proven DKD patients were collected and classified into normoalbuminuric (UACR&#xa0;<&#xa0;30&#xa0;mg/g, n&#xa0;=&#xa0;25), microalbuminuric (UACR 30-300&#xa0;mg/g, n&#xa0;=&#xa0;26), and macroalbuminuric (UACR&#xa0;>&#xa0;300&#xa0;mg/g, n&#xa0;=&#xa0;244) groups. Laser microdissection combined with mass spectrometry (LMD/MS) was used to analyze glomerular and proximal tubule proteomics in 5 patients per DKD subgroup and 5 control subjects. Associations with clinical features were examined. RESULTS: Glomerular proteomic analysis revealed that oxidative stress and metabolic pathways (UQCRC1) were upregulated in NADKD group, whereas the complement and coagulation cascades (C3, C5, C6, C9, CFH, CFHR1) were significantly upregulated in the microalbuminuric and macroalbuminuric DKD groups. The proximal tubule proteomics analysis showed that oxidative phosphorylation-related proteins (SDHA, CYCS, UQCRQ) were upregulated in NADKD, and collagen I related proteins (COL1A1, COL1A2) were significantly upregulated. CONCLUSION: Oxidative stress and mitochondrial dysfunction are involved in the progression of NADKD, lesions predominantly located in the tubulointerstitium. The complement pathway participates in the pathogenesis and progression of albuminuric DKD (ADKD). These divergent molecular profiles suggest that NADKD and ADKD may reflect different pathophysiological mechanisms and have important implications for therapeutic strategies in diabetes management.

Humans↗
PubMed2026

Integrative modeling of the genome structure and dynamics in fission yeast.

Genome organization in the nucleus is highly structured and dynamic. Recent advances in genomic technology have enabled the measurement of genome-wide architecture and locus-specific motion, yielding contact maps and live-cell trajectories. However, these outcomes are derived from different modalities and are not directly comparable, with their quantitative integration being a key challenge. Here we establish a genome-wide live-cell imaging platform in fission yeast Schizosaccharomyces pombe, tracking 131 chromosomal loci, along with the spindle pole body (SPB) and nucleolus, to construct a quantitative map of locus dynamics. By integrating these dynamics with contact data through polymer modeling of Hi-C data, we build a physics-based "digital twin" of the S. pombe genome consistent with the spatiotemporal dynamics of interphase chromatin. We validate it against genome-wide mobility patterns and known architectural features, including centromere and telomere clustering. The model also identifies distinct dynamical regimes: centromere- and telomere-proximal loci relax within [Formula: see text]150 s, whereas the remaining loci relax within [Formula: see text]70 s. We measure semiperiodic dynamics of SPB motion, including a characteristic peak near 225 s and [Formula: see text] fluctuations. We use the model with SPB-directed forcing to show how these low-frequency components propagate through the genome to drive genome-wide chromatin displacements. Together, this predictive physics-based modeling framework integrates genome structure and dynamics to reveal how nuclear mechanical driving forces shape chromosome motion, linking mechanically driven chromatin responses to genome maintenance and regulation.

Schizosaccharomyces↗