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At least 19 recordsLinked to original sources

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↗

PubMed2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data are essential. Unfortunately, accurately resolving cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires - inherently imperfect - cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3272 proteins, using cross-validation to determine regularisation parameters, and identified 539 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

Journal Article↗

PubMed2026

Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.

Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.

Humans↗

PubMed2024

Advances in solid-state NMR methods for studying RNA structures and dynamics.

Ribonucleic acid (RNA) structures and dynamics play a crucial role in elucidating RNA functions and facilitating the design of drugs targeting RNA and RNA-protein complexes. However, obtaining RNA structures using conventional biophysical techniques, such as X-ray crystallography and solution nuclear magnetic resonance (NMR), presents challenges due to the inherent flexibility and susceptibility to degradation of RNA. In recent years, solid-state NMR (SSNMR) has rapidly emerged as a promising alternative technique for characterizing RNA structure and dynamics. SSNMR has several distinct advantages, including flexibility in sample states, the ability to capture dynamic features of RNA in solid form, and suitability to character RNAs in various sizes. Recent decade witnessed the growth of 1H-detected SSNMR methods on RNA, which targeted elucidating RNA topology and base pair dynamics in solid state. They have been applied to determine the topology of RNA segment in human immunodeficiency virus (HIV) genome and the base pair dynamics of riboswitch RNA. These advancements have expanded the utility of SSNMR techniques within the RNA research field. This review provides a comprehensive discussion of recent progress in 1H-detected SSNMR investigations into RNA structure and dynamics. We focus on the established 1H-detected SSNMR methods, sample preparation protocols, and the implementation of rapid data acquisition approaches.

Dynamics↗

PubMed2026

Dynamic metabolic modelling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic flux balance analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Humans↗

PubMed2026

Estradiol dynamics during superovulation are associated with oocyte quantity and maturation stage in cynomolgus macaques.

OBJECTIVE: To examine whether dynamic changes in circulating estradiol during ovarian stimulation are associated with the number and maturation status of oocytes recovered in cynomolgus macaques. DESIGN: Observational study based on retrospective analysis of ovarian stimulation cycles. SUBJECTS: Sixty-six ovarian stimulation cycles from adult female cynomolgus macaques (Macaca fascicularis) housed at the Primate Resources Center. EXPOSURE: Animals underwent a standardized gonadotropin-based ovarian stimulation protocol. Circulating estradiol concentrations were measured at multiple time points during the late follicular phase before oocyte recovery, and estradiol dynamics were defined as the difference between the maximum and minimum values observed across these measurements. MAIN OUTCOME MEASURES: Total number of oocytes recovered per stimulation cycle, number of mature oocytes at the metaphase two stage, and proportional distribution of oocyte maturation stages. RESULTS: Dynamic changes in estradiol concentrations were positively associated with the total number of oocytes recovered per cycle (correlation coefficient 0.45). A similar but weaker association was observed with the number of mature oocytes recovered (correlation coefficient 0.36). In contrast, estradiol dynamics were not associated with the proportional distribution of oocyte maturation stages. Donor age and body weight were not significantly associated with oocyte recovery outcomes. CONCLUSION: Dynamic changes in circulating estradiol during ovarian stimulation primarily reflect the quantitative dimension of ovarian response in cynomolgus macaques, with limited relevance for the maturation stage composition of recovered oocytes.

Animals↗

PubMed2025

Microfluidics to Follow Spatiotemporal Dynamics at the Nucleo-Cytoplasmic Interface During Plant Root Growth.

Nuclear dynamics refers to global/local changes in the molecular and spatial organization of genomic DNA that can occur during development or in response to environmental stress signals and eventually impact genomic functions. In plants, nuclear dynamics relies notably on the connection of the nucleus with the cytoskeleton during development. It orchestrates genomic functions in response to developmental and environmental cues. This is particularly true in the plant root system, which is constantly exposed to a wide range of internal and external stimuli. Currently, studying nuclear dynamics in a growing root is challenging due to limitations regarding real-time imaging for quantitative analyses under controlled conditions. Microfluidic systems for plant cell studies are valuable analytical tools that provide precise control of culture conditions together with live-imaging capabilities at high temporal and spatial resolutions. Herein, we describe a microfluidic platform to unravel dynamically and noninvasively nuclear organization in the seedling root system exposed to various treatments. As exemplified here, our microfluidic platform can be conveniently used for real-time microscopy imaging and quantitative analysis of fine nuclear morphological changes upon modifying cytoskeleton dynamics. Importantly, our system can be applied to a wide variety of microscopic means including high-resolution microscopy to investigate diverse subcellular compartments or nuclear domains in Arabidopsis thaliana roots.

Plant Roots↗

PubMed2025

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.

Post-traumatic stress (PTS) encompasses a range of psychological responses following trauma, which may lead to more severe outcomes such as post-traumatic stress disorder (PTSD). Identifying early neuroimaging biomarkers that link brain function to PTS outcomes is critical for understanding PTSD risk. This longitudinal study examines the association between brain dynamic functional network connectivity (dFNC) and current/future PTS symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.197, p corrected = 0.0079) and future (r=-0.176, p corrected = 0.0176) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.205, p corrected = 0.0079). We additionally observed that the association between the network dynamics of the inter-network and intra-network brain state with symptom severity is more pronounced in female group. Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger effect in female group, underscoring the importance of sex differences.

Journal Article↗

PubMed2025

The effects of cryptic diversity on diversification dynamics analyses in Crocodylia.

Incomplete taxon sampling due to underestimation of present-day biodiversity biases diversification analysis by favouring slowdowns in speciation rates towards the recent time. For instance, in diversification dynamics studies in Crocodylia, long-term low net-diversification rates and slowdowns in speciation rates have been suggested to characterize crocodylian evolution. However, crocodylian cryptic diversity has never been considered. Here, we explore the effects of incorporating cryptic diversity into a diversification dynamics analysis of extant crocodylians. We inferred a time-calibrated cryptic-species-level phylogeny using cytochrome b sequences of 45 lineages compared with the formally recognized 26 crocodylian species. Diversification rate estimates using the cryptic-species-level phylogeny show increasing speciation and net-diversification rates towards the present time, which contrasts with previous findings. Cryptic diversity should be considered in future macroevolutionary analyses; however, the representation of cryptic extinct taxa represents a major challenge. Additionally, further investigation of crocodylian diversification dynamics under different underlying genomic data is encouraged upon advances in population genetics. Our case study adds to the diversification dynamics knowledge of extant taxa and demonstrates that cryptic species and robust taxonomic assessment are essential to study recent biodiversity dynamics with broad implications for evolutionary biology and ecology.

Animals↗

PubMed2025

Bistable Mutation-Selection Equilibria and Violations of Fisher's Theorem in Tetraploids: Insights from Nonlinear Dynamics.

Polyploidy and whole genome duplication (WGD) are widespread biological phenomena with substantial cellular, meiotic, and genetic effects. Despite their prevalence and significance across the tree of life, population genetics theory for polyploids is not well developed. The lack of theoretical models limits our understanding of polyploid evolution and restricts our ability to harness polyploidy for crop improvement amidst increasing environmental stress. To address this gap, we developed and analyzed deterministic models of mutation-selection balance for tetraploids under polysomic (autotetraploid) and disomic (allotetraploid) inheritance patterns and arbitrary dominance relationships. We also introduced a new mathematical framework based on ordinary differential equations and nonlinear dynamics for analyzing the models. We find that autotetraploids approach Hardy-Weinberg Equilibrium 33% faster than allotetraploids, but the different tetraploid inheritance models show little differences in mutation load and allele frequency at mutation-selection balance. Our model also reveals two bistable points of mutation-selection balance for dominant alleles with biased mutation rates over a wide range of selection coefficients in the tetraploid models compared to bistability in only a narrow range for diploids. Finally, using discrete time simulations, we explore the temporal dynamics of allele frequency and fitness change and compare these dynamics to the predictions of Fisher's Fundamental Theorem of Natural Selection. While Fisher's predictions generally hold, we show that the bistable dynamics for dominant mutations fundamentally alter the associated temporal dynamics. Overall, this work develops foundational theoretical models that will facilitate the development of population genetic models and methodologies to study evolution in empirical tetraploid populations.

Fisher’s Fundamental Theorem↗

PubMed2025

DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.

Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.

Leukemia↗

PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗

PubMed2026

Integrative analysis of transcriptome and DNA methylome dynamics during caudal fin regeneration in silver pomfret (Pampus argenteus).

Caudal fin regeneration in teleost fish is a complex, multi-stage process involving coordinated molecular and cellular changes. While the role of epigenetic regulation particularly DNA methylation has been studied in model freshwater species such as zebrafish, its contribution to regeneration in marine teleosts remains largely unexplored. In this study, we integrated transcriptomic and DNA methylomic data to characterize the temporal dynamics of gene expression and methylation during caudal fin regeneration in the silver pomfret (Pampus argenteus). Using RNA-sequencing and reduced representation bisulfite sequencing (RRBS) at three biologically critical time points 1, 3, and 7 days post-amputation (dpa), we characterized the spatiotemporal molecular landscape of caudal fin regeneration. These time points capture the key transitional phases of wound healing and inflammation (1 dpa), blastema formation and progenitor proliferation (3 dpa), and regenerative outgrowth with tissue remodeling (7 dpa), enabling robust detection of the major molecular programs underlying epimorphic regeneration. Concurrently, CG-methylome analysis identified thousands of dynamically changing differentially methylated regions (DMRs). A strong global inverse correlation was observed between promoter methylation and gene expression. Integrative analysis pinpointed key regeneration genes (fgf20a, msxb, sox9b) whose expression was associated with dynamic methylation changes in their promoters or gene bodies. We conclude that DNA methylation is a dynamic and key regulatory layer that acts in concert with transcriptional reprogramming to coordinate tissue regeneration, providing new insights into the epigenetic mechanisms underlying complex regenerative processes in teleosts.

Animals↗

PubMed2026

The dynamic pool of Rec8-cohesin is crucial for meiotic recombination and transcription regulation in the yeast Saccharomyces cerevisiae.

Cohesin is a ring-shaped protein complex that mediates sister-chromatid cohesion (SCC) to ensure accurate chromosome segregation during mitosis and meiosis. In Saccharomyces cerevisiae, cohesin consists of four core subunits-Smc1, Smc3, Scc1/Mcd1, and Scc3. During meiosis, the mitotic α-kleisin Scc1/Mcd1 is replaced by the meiosis-specific α-kleisin Rec8. Rec8-containing cohesin is essential for multiple meiotic processes, including chromosome morphogenesis, homologous recombination, axis and synaptonemal complex formation, SCC, and transcriptional regulation. While stable association of Rec8-cohesin with chromatin is required to maintain SCC from premeiotic S phase through anaphase II, dynamic chromatin association is thought to underlie its roles in recombination, chromosome architecture, and transcription via loop extrusion. Whether distinct stable and dynamic pools of Rec8-cohesin coexist during meiosis and how their functions are partitioned remained unclear. Here, we employed an anchor-away strategy to conditionally deplete only the dynamic pool of Rec8-cohesin from the nucleus while preserving the stable pool. Selective depletion reduced sporulation efficiency and spore viability without compromising SCC. Calibrated ChIP-seq revealed a genome-wide reduction in Rec8-cohesin levels rather than locus-specific loss. Functional analyses demonstrated that the dynamic pool of Rec8-cohesin is required for efficient meiotic recombination, establishment of meiosis-specific chromosome architecture and synaptonemal complex formation, and proper transcriptional regulation of key meiotic regulators. In contrast, the stable pool alone was sufficient to maintain spindle pole body cohesion. Together, our findings demonstrate the existence of two functionally distinct pools of Rec8-cohesin during yeast meiosis.

Saccharomyces cerevisiae↗

PubMed2024

Proteome Dynamics in iPSC-Derived Human Dopaminergic Neurons.

Dopaminergic neurons participate in fundamental physiological processes and are the cell type primarily affected in Parkinson's disease. Their analysis is challenging due to the intricate nature of their function, involvement in diverse neurological processes, and heterogeneity and localization in deep brain regions. Consequently, most of the research on the protein dynamics of dopaminergic neurons has been performed in animal cells ex vivo. Here we use iPSC-derived human mid-brain-specific dopaminergic neurons to study general features of their proteome biology and provide datasets for protein turnover and dynamics, including a human axonal translatome. We cover the proteome to a depth of 9409 proteins and use dynamic SILAC to measure the half-life of more than 4300 proteins. We report uniform turnover rates of conserved cytosolic protein complexes such as the proteasome and map the variable rates of turnover of the respiratory chain complexes in these cells. We use differential dynamic SILAC labeling in combination with microfluidic devices to analyze local protein synthesis and transport between axons and soma. We report 105 potentially novel axonal markers and detect translocation of 269 proteins between axons and the soma in the time frame of our analysis (120 h). Importantly, we provide evidence for local synthesis of 154 proteins in the axon and their retrograde transport to the soma, among them several proteins involved in RNA editing such as ADAR1 and the RNA helicase DHX30, involved in the assembly of mitochondrial ribosomes. Our study provides a workflow and resource for the future applications of quantitative proteomics in iPSC-derived human neurons.

Humans↗

PubMed2026

Host-virus dynamics in anaerobic digesters facing abiotic inhibition.

Viruses play a major role in controlling the structure and dynamics of microbial communities in anaerobic digesters, ecosystems sensitive to disturbances that inhibit methane production. Here, we studied the interplay between abiotic disturbances, microbiome and virome composition, and process performance, to assess whether provirus induction can be triggered by abiotic stresses known to inhibit anaerobic digestion (ammonium, phenol and sodium chloride). We monitored viral dynamics in batch mesophilic anaerobic digesters fed with biowaste through shotgun metavirome sequencing. The diversity of both prokaryotes and viruses was high, with Clostridiales dominating the prokaryotic community and Caudoviricetes dominating the viromes. We identified 132 viral contigs and 19 host genera that were differentially abundant under disturbed conditions. No significant impact of the tested abiotic stresses on provirus induction was observed under the current experimental and analytical framework. The results were consistent with viruses exerting steady, background-level predation through a putative combination of kill-the-winner dynamics at the sub-genus level and piggyback-the-winner dynamics, rather than stress-triggered, synchronous lytic bursts. A few auxiliary metabolic genes were detected, potentially targeting carbon, sulfur and cofactor metabolism in anaerobic digestion. Temperate viruses were dominant, representing up to 71% of the viral genomes confirmed as complete across all conditions. Electron microscopy analysis revealed diverse virus-like particles, including head-tailed particles typical of Caudoviricetes, but also spherical, rod-shaped and spindle-shaped particles typical of archaeal viruses. Notably, we present a new virus family, Eurekaviridae, of spindle-shaped viruses associated with methanogenic archaea.

Anaerobiosis↗

PubMed2026

Unraveling the epidemiological and dispersal dynamics of the 2024-2025 chikungunya virus epidemic on Réunion Island.

Réunion Island experienced a massive chikungunya virus epidemic in 2024-2025, with >54,000 confirmed cases. This is the second major chikungunya epidemic on the island, following the first one that peaked 20 years ago. It has been asserted that this new outbreak finds its origin in a single introduction event into the island, offering an opportunity to exploit viral genomic data to understand the epidemiological and dispersal dynamics of the introduced transmission chain. We sequenced >3,000 viral genomes collected during the epidemic. Harnessing this genomic dataset, we used several phylogeographic and phylodynamic approaches to unravel the paths taken by the transmission chain and the external factors that might have impacted its dispersal and epidemiological dynamics on the island. Our analyses highlight a dispersal pattern in line with a gravity-model dynamic with viral transition events being more frequent from and toward more populated areas. Our analyses reveal that the transmission chain was overall spatially intermixed, with frequent exchanges among residential areas. In addition, we show that the temporal dynamic and intensity of the epidemic were associated with climatic variables, namely temperature and precipitation. Our results also show that in theory, the population immunity-resulting from this epidemic and the previous one (2005-2006)-could be sufficient to explain on its own the decrease in the transmission rate that led to the end of the epidemic. While a short-term resurgence cannot be excluded, the risk of a large-scale circulation of the virus in the human population appears therefore relatively limited in the upcoming seasons.

Reunion↗

PubMed2026

Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.

OBJECTIVES: Platelet storage lesion (PSL) severely impairs platelet function during storage, presenting a major hurdle in transfusion medicine; however, the dynamic interplay between global proteomic changes and post-translational modifications (PTMs) underlying these functional deteriorations remains insufficiently characterized. Here, we report the first comprehensive multi-omics analysis integrating global proteomics, acetylomics, and succinylomics to dissect the molecular dynamics during platelet storage. METHODS: We performed quantification of global proteomics, acetylome and succinylome based on TMT-labeled LC-MS/MS analysis, combined with antibody-affinity enrichment and purification. Dynamic molecular changes and functional transformation of platelet were also characterized under proper conditions stored for 1, 3, 5, 7 days, respectively. RESULTS: We systematically characterized 3,609 proteins, 1,308 acetylation sites, and 1,947 succinylation sites across multiple storage time points (D1, D3, D5, D7). We distinct temporal patterns of post-translational modifications, with succinylation showing more extensive coverage than acetylation in platelets. Pathway enrichment analysis revealed extensive metabolic reprogramming involving complement activation, energy metabolism, and cellular detoxification processes. The identification of specific motif patterns provided mechanistic insights into the functional specificity of these modifications. Random forest machine learning identified 20 core regulatory proteins representing critical nodes in PSL development. Furthermore, we employed real - time quantitative polymerase chain reaction (RT - QPCR) to measure the expression levels of key genes related to platelet function and PTM - associated pathways. CONCLUSION: By mapping the interplay between proteomic abundance shifts and PTM dynamics, this study provides a multidimensional understanding of PSL, establishing a foundational framework for optimizing storage protocols and enhancing transfusion safety.

Blood Platelets↗