Molecular Omics

Metabolic syndrome (MetS), characterized by abdominal obesity, insulin resistance, dyslipidemia, and hypertension, affects a substantial proportion of the global population and increases the risk for cardiovascular disease, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). Despite its prevalence, there are currently no effective pharmacological therapies targeting MetS, highlighting the need to identify novel etiological mechanisms, particularly within the gastrointestinal (GI) tract. Using a mouse model of MetS and healthy lean controls, we assessed the colonic microenvironment through metabolomic, transcriptomic, and microbiome analyses. Colonic organoids were cultured to further explore epithelial alterations. Additionally, human MetS fecal metabolomics data were cross-compared with the mouse model to validate translational relevance. MetS mice exhibited upregulation of colonic anabolic pathways, including glycolysis, the pentose phosphate pathway, and the tryptophan/kynurenine pathway, without evidence of intestinal inflammation. Microbiome analysis revealed an increased abundance of the genus Lactobacillus in MS NASH mice. Colonic organoids from MetS mice showed altered goblet cell differentiation. Comparative analysis with human MetS fecal metabolomics demonstrated similar dysregulated pathways, underscoring the translational relevance of these findings. Our study reveals significant metabolic and microbial alterations in the colon of MS NASH mice, implicating a dysfunctional GI tract as a potential etiological factor in MetS. These findings highlight specific metabolic pathways and microbial signatures that could serve as future therapeutic targets for MetS. NEW & NOTEWORTHYThis study identifies the colon as a metabolically active tissue affected in metabolic syndrome. Despite the absence of intestinal inflammation, MS NASH mice displayed altered colonic metabolism and microbiota composition, with conserved metabolite changes matching those seen in humans with metabolic syndrome. These findings highlight colonic metabolic dysfunction as a potential driver of gut dysbiosis and disease progression in metabolic syndrome and MASLD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/716131v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@1b7c685org.highwire.dtl.DTLVardef@4a832aorg.highwire.dtl.DTLVardef@1e95c66org.highwire.dtl.DTLVardef@1b14209_HPS_FORMAT_FIGEXP M_FIG C_FIG

The use of decellularized diseased livers in regenerative medicine is a promising approach for eliminating organ shortages. Bioengineering studies have shown that ECM can impact cell physiology, inducing cell activation, function, and ECM deposition, which suggests that the ECM has a "memory" that is involved in the outcome after recellularization. However, the effect of diseased ECM memory on new cells in vitro and in vivo has not been thoroughly investigated. Since it has been increasingly recognized that liver ECM changes due to different factors, it is comprehensively that diseased ECM obtained from discarded organs will ensure a distinct environment and impact cell survival and physiology. Thus, we aimed at investigating the impact of the memory of diseased ECM obtained from metabolic dysfunction-associated steatohepatitis (MASH)-derived organs on steatohepatitis establishment. To address this aim, we explored decellularized ECM obtained from rats and humans with MASH in different contexts. First, MASH ECM was characterized and then submitted to transplantation to investigate whether a MASH-derived ECM could be used as a scaffold for transplantation and to promote steatohepatitis features in control animals. Histological analysis revealed that the MASH-ECM was completely recellularized after transplantation in both control and MASH recipient rats. However, steatosis and fibrosis were observed in MASH ECM after transplantation in both groups. Molecular analysis showed that MASH ECM stimulates de novo lipogenesis and fibrosis 30 days after transplantation. Untargeted metabolomic analysis revealed that cells grown on MASH ECM had a similar metabolic profile, even when transplanted into healthy or MASH recipient rats. In addition, we observed that MASH ECM promoted impaired lipid oxidation and mitochondrial dysfunction when transplanted into healthy recipients. Altered lipid turnover and inflammatory signaling were observed in MASH ECM transplanted in MASH recipients. In vitro analysis revealed that MASH ECM induced lipid accumulation in HepG2 cells after 10 days of culture. Calcium signalling experiments obtained from HepG2 cells cultured in MASH ECM showed a lower response to ATP, a reduced calcium signalling amplitude, and a distinct response profile than that observed in healthy ECM. On the other hand, a diseased human-derived ECM could still provide an environment that allows cell development. Taken together, our data showed that MASH ECM impacts cell metabolism, promoting steatohepatitis maintenance. In conclusion, our data confirm that diseased ECM memory can impact cell physiology contributing to disease progression.

Primordial germ cells (PGCs) are the population of cells that, in the human embryo, specify day 12 post-fertilization, and form the precursor cells for the future egg or sperm cells. Although in vitro differentiation of PGCs from human stem cells has been achieved, these primordial germ cell-like cells (hPGCLCs) fail to further mature. The reason for this is unclear. Previous studies in mice revealed that several specific metabolic changes occur during the maturation of these cells, which are essential for their developmental progress. However, very little is known about the metabolic profile of human primordial germ cells. In the severe scarcity of human PGCs, hPGCLCs serve as a research model to study PGC formation. To investigate this, we differentiated hPGCLCs using induced-pluripotent stem cells and performed a mass spectrometry analysis to establish their metabolome and proteome. These cells revealed distinct metabolic profile, with changes particularly at the proteome level. This included a shift between canonical and non-canonical citric acid cycle in hPGCLC, downregulation of late-stage glycolysis and reduction of nucleotide de novo synthesis. By providing an integrative map of these metabolic networks, we aim to provide insight on the influence of metabolism on human PGC development that could help improve methods for in vitro differentiation and maturation hPGCLCs.

DNA methylation plays a central role in the regulation of gene expression. In plants, methylation occurs in the CG, CHG and CHH contexts, via distinct DNA methyltransferases including MET1, CMT3 and the RNA-directed DNA Methylation (RdDM) pathway via DRM2. In interspecific hybrids, these epigenetic mechanisms are confronted to a mixed small RNA population and two subgenomes harbouring specific methylation patterns, therefore generating unique expression profiles. The aim of this work was to understand these regulations by analysing gene expression, DNA methylation and small RNAs in a Citrus hybrid resulting from the cross between C. reticulata (mandarin) and C. australasica (finger lime). Haplotype-resolved subgenomes assembly identified hundreds of allele-specifically expressed genes. Asymmetric reprogramming of methylation was observed, in particular an increase in CHH in C. australasica haplotype. Surprisingly, CHH methylation, usually associated with gene silencing, was correlated here with increased expression, but also 24nt small RNA populations at their promoter regions. Similar analyses of the parental lines and other citrus species suggest the correlation between CHH methylation-enriched promoter and high expression level is not due to the hybridization, but seem to be generally true for all citrus. These observations suggest that, in citrus fruit, RdDM could activate transcription. This work also provides a full pipeline to analyse the expression profiles and DNA methylation in complex hybrids, which could be crucial for anticipating varieties resistant to diseases and the current threats affecting citriculture such as the Huanglongbing disease.

Harbour porpoises (Phocoena phocoena) in the North and Baltic Seas are increasingly impacted by anthropogenic pressures, including underwater noise, fisheries and pollution. These pressures correlate with declining population health, particularly affecting the respiratory system. Growing pathological lesions, partly resulting from high prevalence of parasitic infestations and subsequent diseases, can impair tissue function and oxygen supply to distant end-organs. In this study, we applied an integrative MultiOmics approach (proteomics, metabolomics, lipidomics) to analyse the lungs and muscles of 12 wild harbour porpoises with compromised respiratory health. Our aim was to identify dysregulated biological pathways across omics layers to advance insights into adaptive physiological responses and to define disease-associated molecular signatures that could assist health assessments. Our analysis revealed pronounced immune system and antioxidative responses in the lungs and muscles, indicated by enhanced immunoglobulins, plasmalogens and glutathione-related proteins. In the lungs, high cardiolipin levels and reduced collagen suggest impaired tissue structure and function, while tissue maintenance processes were elevated in the muscle. Both tissues exhibited metabolic alterations suggestive of energetic imbalance, including increased purine metabolism in the lung and decreased lipid metabolism in the muscle. Several dysregulated molecules were shared across tissues, pointing to pathophysiological effects. The proposed disease-associated molecular signatures included the protein SLC25A4, the metabolite O-phosphoethanolamine and the lipid TG O-16:0_16:0_20:4 for the lung, and the protein SPEG, the metabolite pipecolic acid, and the lipid BMP 18:1_22:6 in the muscle. Our findings elucidate the complexity of molecular mechanisms linking anthropogenic and environmental stressors with vulnerability and resilience in a marine sentinel species. Furthermore, this study highlights the potential of integrative omics to define disease-related marker panels, thereby supporting ongoing and future health monitoring and conservation efforts.

Accurate, simultaneous, and efficient quantification of chemically diverse phytohormone species is a critical task towards understanding the complex system of phytohormone signaling pathways. Quantification of phytohormones with the commonly used technique liquid chromatography coupled to tandem mass spectrometry is susceptible to the influence of non-phytohormone components present in the sample, a phenomenon referred to as matrix effect. To reduce matrix effect, some phytohormone quantification methods include additional steps of cleanup of crude extracts. However, to what extent additional purification steps provide increased accuracy compared to simpler, less laborious methods is seldomly evaluated. We evaluated three previously described phytohormone extraction methods, two of which include solid-phase extraction and one that does not, in their ability to minimize matrix effect and generate accurate estimates of phytohormone species spanning six classifications, from fruit and leaf tissue of Solanum lycopersicum cv. Micro-Tom (tomato). Our results show that, while the methods that included solid phase extraction occasionally outperformed each other regarding matrix effect and/or recovery efficiency for broad range of phytohormones, they rarely outperformed the simpler single-phase extraction method. Short AbstractAccurate, simultaneous quantification of chemically diverse phytohormones by LC-MS/MS is frequently confounded by matrix effects, leading to the incorporation of additional purification steps. We systematically compared three published extraction protocols with or without solid-phase extraction in tomato tissues across six hormone classes. Solid-phase methods occasionally improved matrix suppression or recovery, but did not consistently outperform the single-phase approach, questioning the added value of extra cleanup steps, particularly when high-throughput is desired, as in the case of systems biology interrogations.

Spatiotemporal organisation of biological molecules is a key driver of cellular processes, including many post-transcriptional epigenetic processes. The germline-specific germ granules are biomolecular condensates that act as hubs for mRNA and small RNA processing and are core regulators of germline gene expression programming. Germ granules have been studied extensively in C. elegans, and recent developments have led to many subdivisions of the germ granule into specialised compartments. Rapid advancements in microscopy and protein-protein interaction (PPI) screening techniques have produced a large amount of data towards characterising the localisation of proteins to specific granules. However, common methods used to probe PPIs are limited in their ability to robustly detect valid interactions, especially the multivalent and sometimes transient ones observed in granule environments. Here we perform a meta-analysis of granule protein interaction screens. While these experiments generally enrich for proteins matching the profile of granule-associated proteins, we find that when considering screens individually, reproducibility is surprisingly low, highlighting not only the variability inherent in these methods but also the dynamic nature of the PPI networks present in granules. We developed an algorithm to provide a measure of each proteins association with specific granules across various experiments. By further clustering and investigation of the resulting score matrix, we demonstrate the power of this holistic approach to provide deeper insights into germ granule organisation and highlight novel can provide a resource to better inform future investigations into granules and their constituent proteins.

Cryopreservation is essential for long-term storage of biological tissues. Yet, surprisingly, the precise molecular impact of cryopreservation on tissue transcriptomes remains poorly defined. This study provides the first resource of whole-genome transcriptomic changes following cryopreservation. This study used bulk RNA sequencing to examine how preservation method (snap freezing or vitrification) affects transcriptomes in mouse cerebral cortex and hippocampus. This allowed us to separate cryoprotectant-specific changes from cold induced-changes via snap freezing. In a subset of genes, tissues processed under vitrification conditions showed selective under-representation of a small but structurally coherent group of transcripts, with the hippocampus exhibiting greater vulnerability than the cortex. UniProt annotation revealed that affected transcripts were strongly enriched for proteins with membrane-associated, secretory-pathway, and multi-pass topologies, indicating that structurally complex membrane-integrated architectures are disproportionately sensitive to vitrification. Pathway-level analysis using iPANDA further showed that negative preservation scores in vitrified tissue clustered primarily within signal transduction and metabolic pathways, suggesting coordinated pathway-level disruption rather than global transcript loss. Together, these results demonstrate that vitrification conditions induce selective and structured molecular perturbations in neural tissue, defined by the under-recovery of transcripts associated with membrane and secretory pathway organization. This work highlights molecular vulnerability during vitrification and emphasizes the need for transcript-level evaluation when optimizing cryopreservation approaches for neural systems.

During drought, numerous compounds accumulate in plant tissues, but their physiological roles remain unclear - they may function as osmolytes, osmoprotectants, or merely arise as by-products of stress-induced metabolic shifts. We developed an experimental approach to link accumulation patterns with specific functions, using Scots pine (Pinus sylvestris L.) saplings subjected to water deprivation and subsequent rewatering as a model system. We monitored changes in relative water content (RWC) and osmotic adjustment dynamics, employed untargeted primary metabolite profiling for preliminary screening of compounds correlated with water status, and performed quantitative GC-MS and LC-MS analyses of selected metabolites. Major inorganic cations (K, Ca{superscript 2}, Mg{superscript 2}) were also quantified to assess their potential roles. Our results revealed that tryptophan, valine, and lysine - though generally present in low abundance - exhibited selective accumulation under severely reduced RWC ([≤] 70%), suggesting their involvement as osmoprotectants. Major organic acids, particularly shikimic acid, showed trends consistent with osmotic adjustment. Notably, neither sucrose nor inorganic cations appeared to function as primary osmolytes in this context. The proposed approach offers a viable strategy for identifying compounds involved in plant adaptation to water deficit, with potential applications in breeding programs aimed at improving drought tolerance. HighlightsAn approach to identify osmolytes and osmoprotectants was implemented Accumulation of Trp, Val and Lys was consistent with their role in osmoprotection Osmotic adjustment relied predominantly on organic acids than on inorganic ions Monosaccharides but not sucrose correlates with changes in needle water status

Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides additional resolution that further enhances scientific certainty in both product development and safety characterization. However, the industry adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods to achieve comparable performance. Specifically, an optimal digestion time of 3 hours at 37{degrees}C and the use of polymer-based desalting columns significantly enhanced protein identification ([~]4500 - 5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision ([~]6500 vs 5000 IDs). The validated Standard Operating Procedures presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat, facilitating the generation of reliable and comparable data required for robust multi-omics characterization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/713501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b61b8org.highwire.dtl.DTLVardef@16c7e65org.highwire.dtl.DTLVardef@1de21d2org.highwire.dtl.DTLVardef@7e984a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComplexity and non-standardization limit MS-proteomics use in cultivated meat (CM). C_LIO_LICM protein profile varies with sample prep, LC-MS, and data processing pipeline. C_LIO_LIDevice-based and optimized cost-effective protocols offer a high proteome coverage. C_LIO_LIProteomics can complement transcriptomics for a comprehensive CM characterization. C_LIO_LIProposed standardized methods ensure reliable data for future regulatory submissions. C_LI

The unfolded protein response (UPR) helps reinstate cellular proteostasis upon an accumulation of misfolded proteins in the endoplasmic reticulum (ER), in part through ER-associated degradation (ERAD). Ube2j2 is an ER-localized E2 ubiquitin-conjugating enzyme that participates in ERAD. We used mass spectrometry analysis of cultured U2OS cells to investigate how the loss of Ube2j2 affects the cellular proteome in response to tunicamycin-induced ER stress. We constructed a network of twelve statistically distinct modules of protein abundance profiles across conditions. We describe the Gene Ontology annotations for each module along with the "hub gene" proteins whose abundance levels most closely adhere to each modules protein abundance profile. Our analysis identifies known Ube2j2-associated pathways (e.g., the UPR and ERAD) and cellular functions that were previously unassociated with Ube2j2 (e.g., RNA metabolism, ER-Golgi transport, and cell-cycle progression). These data are available via ProteomeXchange with identifier PXD076153 and provide avenues for further investigation into the cellular functions of Ube2j2 under basal and ER-stressed conditions.

Introduction: The accumulation of senescent cells is a recognized hallmark of biological aging and is associated with the onset of multiple chronic medical conditions. Senescent cells exhibit a distinct secretory profile, known as the senescence-associated secretory phenotype (SASP), which can propagate cellular senescence to neighboring and distant tissues. Measuring SASP factors in blood serves as a practical proxy for cellular senescence burden and may help track disease states and intervention outcomes. Methods: We developed and validated a composite SASP Score by integrating large-scale population proteomics data with a semi-supervised deep learning framework. The analytical workflow included: (1) selection of biologically curated SASP proteins; (2) development of a Guided autoencoder with Transformer (GAET) model using data from the UK Biobank Pharma Proteomics Project (UKB-PPP); (3) internal evaluation and association analyses within the UK Biobank; and (4) external validation and longitudinal assessment in an independent randomized clinical trial cohort. Results: The deep learning-based SASP Score was a strong, independent predictor of mortality risk and incident serious, chronic medical conditions (e.g., dementia, COPD, myocardial infarction, stroke). In an independent cohort, multimodal exercise significantly changed the SASP Score trajectory over 18 months. Discussion: Our findings support the potential of a deep learning-derived SASP Score as a biomarker for systemic cellular senescence burden. Our statistical approach can offer enhanced interpretability and cross-platform utility, providing a valuable tool for aging research and the evaluation of geroscience-guided interventions.

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

Introduction The complete blood count with differential (CBD) is one of the most commonly performed blood tests worldwide, used in nearly all areas of medicine. Although modern CBD analyzers generate flow cytometry based single cell measurements,the resultant CBD markers are limited to coarse summary features, such as total cell counts and average cell sizes. This means, the markers cannotdetect subtle cell population shifts that may signal early stage pathogenesis. To test this, we evaluate whether AI based analysis of the raw single cell data underlying the CBD can be used to develop novel, clinically prognostic biomarkers, across patient settings. Method We developed two complementary methods for biomarker discovery using CBD tests and evaluated them with longitudinal data from an academic medical center. To create interpretable biomarkers, we clustered cells into physiologically meaningful subpopulations and performed robust statistical summarization. In tandem, self supervised autoencoders were developed to extract novel nonlinear markers. We evaluated the utility of these clustering (CLS) and autoencoder (AE) markers for patient prognostication across a range of outcomes (mortality, inpatient admission, and future disease development). Results Our study included 242,623 CBD samples from 127,545 patients. Both clustering and embedding approaches successfully generated hundreds of new clinical biomarkers. Many biomarkers showed strong prognostic associations for all cause mortality, inpatient admission, and development of anemia, cancer, or cardiovascular disease, with associations remaining significant after adjustment for demographics and clinical CBD markers. A large subset of these prognostic markers also showed high novelty, having low correlations to existing CBD markers, while also exhibiting significant correlations with broader physiologic signals, such as inflammatory, hormonal, infectious, and coagulopathic markers. Conclusion Collectively, these results demonstrate how modern AI techniques can allow for deeper phenotyping of routine clinical blood counts, generating novel biomarkers that capture more subtle physiologic signals than what are currently clinically utilized.

Altered iron homeostasis has long been implicated in Parkinson's Disease (PD), although the mechanisms have not been clear. Given the critical role of PD-related activating mutations in LRRK2 (leucine-rich repeat protein kinase 2) within membrane trafficking pathways we examined the impact of a homozygous mutant LRRK2G2019S on iron homeostasis within the RAW macrophage cell line with high iron capacity. Proteomics analysis revealed a dysregulation of iron-related proteins in steady state with highly elevated levels of ferritin light chain and a reduction of ferritin heavy chain. LRRK2G2019S mutant cells showed efficient ferritinophagy upon iron chelation, but upon iron overload there was a near complete block in the degradation of the ferritinophagy adaptor NCOA4. These conditions lead to an accumulation of phosphorylated Rab8 at the plasma membrane, which is selectively inhibited by LRRK type II kinase inhibitors. Iron overload then leads to increased oxidative stress and ferroptotic cell death. These data implicate LRRK2 as a key regulator of iron homeostasis and point to the need for an increased focus on the mechanisms of iron dysregulation in PD.

Aging is typically accompanied by a progressive decline in cognitive function, yet some individuals maintain exceptional cognitive performance, even across the transition from middle to older age, defining exceptional cognitive resilience. While existing measures of resilience primarily rely on clinical assessments, its molecular determinants and early predictive markers remain poorly understood. Here, we performed untargeted LC-MS/MS profiling of longitudinal serum samples to identify metabolic signatures associated with cognitive resilience, which was established based on cognitive tests conducted over 28 years in a cohort of 237 participants. We observed associations across multiple chemical classes, including carnitines, glutamine conjugates, phosphocholines, as well as diet-and drug-derived metabolites. Chemical class-specific analyses revealed distinct metabolic profiles, including predominantly negative associations of medium-chain acylcarnitines with cognitive resilience, increased accumulation of glucuronide conjugates in individuals with low cognitive resilience, altered metabolism of the antihypertensive drug, metoprolol, and elevated levels of dietary compounds such as piperine and lutein in individuals with high cognitive resilience. By leveraging public metabolomics data, we further contextualized the metabolic signatures with respect to their organ specificity, microbial origin, and disease associations. Collectively, these metabolic features, including several previously underexplored compounds, represent promising candidates for functional characterization in mechanisms of aging biology and provide mechanistic insights into the molecular basis of cognitive resilience. HighlightsO_LISerum metabolite MS/MS features, including acyl carnitines, phosphocholines, and hippuric acid conjugates, are associated with cognitive resilience in 237 individuals transitioning from middle to older age. C_LIO_LIDiet-derived piperine is positively associated with cognitive resilience. C_LIO_LIDifferences in {beta}-blocker drug metabolism, rather than parent drug levels, are associated with cognitive resilience. C_LIO_LIRepository-scale searches for the resilience-associated metabolites reveal organ specificity, microbial contributions, and their presence across multiple disease contexts. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/715122v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1038203org.highwire.dtl.DTLVardef@14ca5f1org.highwire.dtl.DTLVardef@12275fcorg.highwire.dtl.DTLVardef@16ff70c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Dengue virus (DENV) is a major burden to global public health, affecting hundreds of millions annually. Children represent the major proportion of global dengue cases, ranging from asymptomatic or subclinical presentation to dengue fever (DF) and severe dengue hemorrhagic fever or shock syndrome (DHF/DSS). The factors that distinguish this range of disease severity are still poorly understood. To identify biomarkers of severity, we analyzed the plasma proteome of acute DENV infected children including both subclinical and hospitalized cases. Proteins associated with the acute-phase response, innate immune and lysosomal activation, and components of the coagulation cascade showed marked differences between hospitalized and subclinical cases during early infection. Longitudinal profiling demonstrated that endothelial dysfunction emerges early, with PTX3 showing the strongest and most rapid upregulation in hospitalized patients, supporting its potential role as a marker of imminent vascular involvement. When comparing severe (DHF/DSS) and classical DF hospitalized cases, CLEC11A displayed the highest fold change at hospital admittance. We used machine-learning analysis to predict disease severity at the acute phase of infection, distinguishing subclinical from hospitalized cases and patients that develop classical dengue fever or severe disease based on the identified complement regulators and inflammatory markers. The panel of identified plasma proteins shed light on the mechanisms of dengue related disease progression and may provide a handle to predict disease severity based on blood markers present during the acute phase of infection.

Tauopathies, including Alzheimers disease, are age-related neurodegenerative disorders characterized by abnormal phosphorylation and buildup of microtubule-associated protein tau. Gene expression dysregulation is a key molecular feature of tauopathies, but how aging and disease interact to disrupt crucial transcriptional regulators and pathways remains largely unknown. Here, we examined how pathological tau affects gene expression programs in age-related neurodegenerative disease using a well-established Drosophila melanogaster tauopathy model with neuronal expression of the toxic human tauR406W. Transcriptomic analysis of tau-expressing fly heads showed a preferential downregulation of long neuronal genes with long introns. Notably, we found that these downregulated genes in the tauopathy model are marked by increased accumulation of initiating RNA polymerase II (RNAP II) near the transcription start site and reduced elongating RNAP II within gene bodies, indicating a problem with the transition from initiation to elongation. By calculating an RNAP II Pause Index (PI) for each gene, we identified a strong link between promoter-proximal RNAP II stalling, gene expression deficits, and gene length in the tauopathy model. Overall, we have uncovered the genomic and transcriptomic features of tau-dependent downregulated genes and identified increased RNAP II promoter-proximal stalling as a significant mechanism of transcription stress in tauopathy.

Psychosocial stress is a significant risk factor for mental and physical illness, and emerging evidence suggests that altered oral microRNAs (miRNAs) and microbiome may act as biomarkers or mediators of stress responses. This study investigated stress-associated molecular changes in saliva from 113 male police officers. Based on repeated administrations of the Karasek Demand/Control and Effort/Reward Imbalance questionnaires, subjects were stratified by perceived stress response (SR) to homogeneous occupational stressors into low, intermediate, or high responders. Salivary miRNA profiles were analyzed using small RNA sequencing, and microbiome composition was assessed through shotgun metagenomics. Eighteen miRNAs were significantly differentially expressed between high- and low-SR groups, with four miRNAs with increasing (miR-10400-5p, miR-1290, miR-6074-5p, and miR-9902) and fourteen with decreasing (including miR-21-5p and mirR-142-3p) levels in the high SR group (adj.p<0.05). The identified salivary miRNAs showed a progressive alteration from low- to high-SR groups. Functional enrichment analysis indicated that dysregulated miRNA targets are involved in apoptosis, cellular stress responses, and metabolic regulation. Distinct salivary microbial communities were also observed across SR groups. Several taxa displayed progressive abundance shifts, with Prevotella baroniae and Schaalia odontolytica increasing and Actinomyces naeslundii and Capnocytophaga ochracea decreasing in the high SR group. Functional predictions revealed, in this group, a significant enrichment of inositol degradation pathways, paralleled by a reduction in bacteria involved in L-tryptophan and thiamine biosynthesis. These findings suggest that salivary miRNAs and microbiota profiles may serve as non-invasive biomarkers of psychosocial stress and provide insight into molecular mechanisms linking chronic stress to physiological and behavioral outcomes.

Integrating transcriptome, translatome, and proteome data remains challenging because changes in mRNA, ribosome occupancy, and protein abundance do not always occur simultaneously. To address this, we applied tensor-decomposition-based unsupervised feature extraction to a tri-omics dataset generated under branched-chain amino acid starvation. The three layers were organized into a tensor and analyzed to extract singular value vectors representing coordinated variation across omics, conditions, and replicates. This approach distinguished patterns consistent with ribosome stacking, in which the transcriptome and translatome increase while the proteome decreases, from those of translational buffering, in which the proteome remains stable despite variations in upstream layers. Using components associated with these signatures, we selected 1,781 genes related mainly to reduced translational efficiency and 221 related to buffer-ing. Functional interpretation via enrichment analysis, supported by generative artificial intelligence and a manual literature review, revealed six major biological units: genome replication and maintenance, extracellular matrix remodeling, mitochondrial biogenesis and oxidative phosphorylation, proteostasis and secretion, vesicle transport and signal integration, and epigenetic/RNA regulation. These results indicate that tensor decomposition can effectively integrate triomics data and uncover the biologically meaningful gene clusters and mechanisms underlying cell fate transitions. This framework is valuable for interpreting complex multilayer regulation beyond conventional pairwise analyses.