PROTEOMICS

Conventional univariate reference intervals (UniRIs) are widely used to identify abnormal biomarker values, but they evaluate each biomarker independently and do not account for coordinated deviations between biomarkers. We developed and evaluated a joint reference region (JRR) framework for plasma proteomics data using the Olink proteomics dataset generated by the UK Biobank Pharma Proteomics Project, covering approximately 3,000 plasma proteins. JRRs were estimated for selected protein pairs in a healthy reference subset, while UniRIs were estimated separately for individual proteins using the nonparametric method. Both approaches were then evaluated in ICD-defined disease subsets. Biomarker discovery revealed sparse and heterogeneous disease--protein associations, with some proteins recurring across multiple phenotypes and others showing more disease-specific patterns. The added value of JRRs varied across diseases and protein pairs. Across evaluated protein pairs, 56.5\% showed higher sensitivity under the JRR framework than the UniRI of the first protein, and 47.3\% showed higher sensitivity than the UniRI of the second protein. At the disease level, the median proportion of protein pairs with improved JRR sensitivity was 0.57. JRRs were most informative when univariate detection was limited but a subset of diseased observations was flagged only by the joint region. These findings suggest that JRRs provide a complementary approach to UniRIs by capturing abnormal joint biomarker configurations in high-dimensional proteomics data.

Parallelization of data acquisition substantially increases the throughput of mass spectrometry-based proteomics. However, parallelization also increases the density of mass spectra and consequently the overlap between ions, frustrating their analysis. To improve sequence identification and quantification from such spectra, we developed an open-source software for Joint Modeling of mass spectra (JMod). JMod models overlapping peaks as linear superpositions of their components in both MS1 and MS2 space, which permits multiplexed DIA with smaller mass offsets to increase the multiplexing capacity and thus proteomics throughput for a given plexDIA tag. This enables 9-plexDIA using 2 Da offset PSMtags, increasing throughput 9-fold while preserving quantitative accuracy and coverage depth. Furthermore, we use JMod to deconvolve simultaneous labeling by mass tags and heavy amino acids, thus increasing the throughput of metabolic pulse experiments measuring protein synthesis and degradation rates in single cells from mouse liver. By supporting enhanced decoding of highly multiplexed DIA spectra, JMod provides an open and flexible software that increases the throughput of sensitive proteomics.

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

Metabolic dysfunction-associated steatotic liver disease (MASLD), the leading cause of chronic liver pathologies worldwide, represents a growing clinical burden. Its diagnosis remains reliant on liver biopsy that limits early detection and the ability to capture molecular changes across disease progression. A systematic understanding of stage-dependent gene expression changes is essential to identify biomarkers and effectively characterize disease mechanisms. Therefore recent studies provided databases for searching genes as well as prediction of multi-gene signatures for disease progression. However, there is still a need for interactive and comprehensive meta-analysis of datasets of MASLD patients with available histological metadata. Herein, we performed a meta-analysis of RNA-seq datasets using NAFLD Activity Score (NAS; n = 897) and fibrosis stage (n = 856) upon conducting pairwise comparisons across histological stages and identified differentially expressed genes associated with disease progression. Most importantly, we provide our findings via a dedicated web server, the MASLD-META NETWORK (https://masld.scilicium.com), enabling users to interactively explore meta-analysis results across diverse network modalities. In addition, we characterized gene expression dynamics across increasing disease stages to identify consistent progression-associated pathways using Louvain clustering. Network-based parameters such as centrality in combination with meta-analysis scores further highlighted central genes and pathways implicated in disease mechanisms. Accordingly, MASLD-META NETWORK enabled an integrative reassessment of recently published gene signatures, identifying COL1A1, COL3A1, THBS2, FBLN5, and PDGFA as the most central genes, and SULF2, MMP14, IL32, GPNMB, and COL3A1 as candidate markers of earlier transcriptional alterations. Network analysis of MASLD associated biological modules further identified LAMA2 and LAMA3 as previously unrecognized central candidate targets.

Recurrent urinary tract infection represents a substantial unmet public health in women. Local administration of estradiol has been shown to reduce recurrence, however in vitro models of the female urinary tract remain limited and the mechanisms underlying the effects of estradiol are incompletely understood. Here, we describe a novel iPSC organoid differentiation protocol and its application to establish a multilayered transwell barrier culture model. Estradiol treatment resulted in reduced expression of innate antimicrobial peptides and cytokines, together with increased expression of demannosylation pathways. Treatment of transwell cultures with a combination of female sex hormones reduced endogenous CXCL8 signaling, independently of a 24-hour uropathogenic Escherichia coli (UPEC) challenge. To our knowledge, this is the first iPSC organoid-derived model of the urinary tract, which provides a platform for investigating interactions between the urothelium, urobiome and hormonal environment.

Hepatocellular carcinoma (HCC) is particularly aggressive and difficult to treat. Due to the lack of early clinical diagnosis and the unsatisfactory clinical treatment effect, it is particularly important to identify novel markers that can predict tumor behavior in HCC. biogenesis of ribosomes BRX1 (BRIX1) is abundant in various tissues of the human body. However, the regulatory mechanisms and its role in various tissues are not fully understood. Here, we analyzed the expression pattern of BRIX1 in HCC from public gene expression databases and tissue samples from clinical HCC. We confirmed that BRIX1 was upregulated in both HCC cell lines and HCC paraffin section samples. BRIX1 depletion significantly dicreased the capacity of cells to grow and migrate in vitro, and knockdown BRIX1 suppressed tumor growth in xenograft tumor model. Mechanistically, BRIX1 depletion suppressed the MAPK/ERK pathway, as reflected by reduced phosphorylated ERK (p-ERK) levels. In summary, we provide a rational clue for the further investigation of BRIX1 as an invaluable biological marker for diagnosing and predicting prognosis of patients with HCC.

Background: Fibrosis, characterised by excessive accumulation of collagen type I (COL1), is a common feature of chronic diseases, including liver diseases (LDs), chronic kidney disease (CKD) and heart failure (HF). COL1 degradation products can be detected in urine by proteomics/ peptidomics analyses and may serve as non-invasive biomarkers of fibrosis. We aimed to identify a common molecular signature of fibrosis across these diseases that may ultimately guide interventions to slow disease progression and prevent organ damage. Methods: Using capillary electrophoresis coupled to mass spectrometry (CE-MS), naturally occurring COL1 degradation products (peptides) in the urine of patients with fibrotic disease, LDs (n=127), CKD (n=263) or HF (n=187), were investigated and compared with the same number of matched controls. Disease-associated COL1 peptides were identified separately for each condition, and peptides showing consistent associations across the three diseases were selected to define a common fibrosis signature. A support vector machine model based on the selected peptides was developed and validated in independent cohorts of patients with LDs (n=110), CKD (n=93), HF (n=32) and controls (n=643). Results: We identified a common fibrotic signature consisting of 50 COL1 degradation products, mainly downregulated in fibrosis. A model based on these peptides achieved a strong performance, with an area under the receiver operating characteristic curve (AUC) of 0.935 (95% confidence interval (CI) 0.917-0.953, p<0.0001) in an external validation cohort comprising pooled disease groups (LDs, CKD, and HF) and controls. Performance was maintained in LDs, CKD and HF, with AUCs of 0.917 (95% CI 0.890-0.944, p<0.0001), 0.951 (95% CI 0.931-0.971, p<0.0001) and 0.950 (95% CI 0.903-0.997, p<0.0001), respectively. The model scores were significantly associated with fibrosis stage in LDs (p=0.0097) and with interstitial fibrosis and tubular atrophy in CKD (p=0.045). Conclusion: A model of urinary COL1 peptides captures a shared collagen degradation signature across organs and diseases, enabling the non-invasive assessment of fibrosis irrespective of its origin. As these peptides exclusively reflect collagen degradation, the findings suggest impaired collagen degradation as a driver in fibrosis. Future clinical studies are warranted to evaluate the utility of this model for early fibrosis detection and earlier implementation of anti-fibrotic interventions.

The folding thermodynamics of proteins are dominated by two opposing forces, the loss in backbone entropy and the packing of hydrophobic groups. The same forces are major contributors to the extension thermodynamics of elastic proteins with the distinction that both processes act in concert, favoring the higher chain and solvent entropy of a relaxed conformation. The relative entropic contributions specify the recoil mechanism; human elastin recoil is primarily driven by hydrophobic forces, whereas fly resilin has a rubber-like mechanism driven by backbone entropy. Despite the importance of elastic proteins to tissue biomechanics, few have been identified, let alone characterized to the same extent as elastin and resilin. We develop a thermodynamic framework that maps proteins by sequence-derived estimates of extension-induced backbone and solvent entropy changes. Putative elastic proteins are proposed and classified by recoil mechanism based on estimated thermodynamic features. Proteins that map to elastic regions are overrepresented by the skin proteome. The set of predicted elastic domains is further extended by incorporating sequence context embedded in protein language models. Protein domains with distinct thermodynamic recoil mechanisms cluster on the latent space manifold. Some of these domains are anticipated to have roles within molecular machines, expanding the scope of elastic protein function beyond mechanical materials like elastin and resilin.

Protein synthesis supports most biological processes. In the brain in particular, protein synthesis plays a critical role in physiological and pathological states. Here, we describe Tellurophene-Alkyne Cycloaddition-mediated Amino acid Tagging (TeACAT), a versatile strategy for fast, facile, and flexible tagging of newly synthesized proteins in mice. TeACAT is based on metabolic incorporation of the non-canonical amino acid TePhe into proteins by the endogenous protein synthesis machinery. Due to their high similarity, TePhe can efficiently replace canonical Phe without dietary or genetic manipulation. The subsequent bio-orthogonal reaction of TePhe with either fluorescent dyes or affinity handles enables both visualization and affinity enrichment of proteins synthesized during TePhe exposure. TeACAT is compatible with immunofluorescence for cell-type specific visualization of protein synthesis with subcellular resolution and can be used in conjunction with routine proteomics to identify and quantify newly synthesized proteins. Robust incorporation into the mouse proteome was observed on the scale of hours to days, allowing the interrogation of various biological processes. In summary, TeACAT enables the visualization and quantification of protein synthesis with minimal perturbation for biological discoveries.

Background: Acute phase response is an early immunometabolic response to brain injuries, primarily coordinated by the liver via the activation of acute phase proteins. These immune responses can be both beneficial, promoting tissue repair, and detrimental, exacerbating neurological deficits, if not properly controlled. Despite the central role of the liver in immunometabolism, how hepatic metabolism dynamically adapts to traumatic brain injury remains under explored, primarily due to limited liver-specific modalities that can assess metabolic pathways in vivo. 13C MRI utilizing hyperpolarized 13C-pyruvate can assess key regulatory enzyme activities in hepatic metabolism. Methods: Rats with controlled cortical impact were studied in vivo using hyperpolarized [1-13C]pyruvate and [2-13C]pyruvate under fed and fasted conditions 3-4 days after injury. Hyperpolarized 13C products, including [13C]bicarbonate from [1-13C]pyruvate and [5-13C]glutamate, [1-13C]acetyl-L-carnitine, and [2-13C]phosphoenolpyruvate from [2-13C]pyruvate, were evaluated to assess mitochondrial and gluconeogenic metabolism. In parallel, liver tissues were collected following [U-13C3]pyruvate injection for NMR isotopomer analysis of phosphoenolpyruvate, glucose, and glutamate. Results: While no metabolic differences were detected under fed condition, [13C]bicarbonate and [2-13C]phosphoenolpyruvate increased after brain injury under fasted condition, indicating an upregulation of the hepatic gluconeogenic pathway after injury. 13C NMR of liver tissue extracts from injured rats showed an elevated [2,3-13C2]glutamate-to-[4,5-13C2]glutamate ratio and increased 13C-labeling in phosphoenolpyruvate than controls, confirming enhanced hepatic gluconeogenic pathway. Conclusion: This study demonstrates that hepatic acute phase response to brain injuries can be monitored in vivo by hyperpolarized pyruvate, which may be further utilized for longitudinal immunometabolic evaluation of the liver during pathogenesis and therapeutic interventions.

Pancreatic ductal adenocarcinoma (PDAC) harbors a distinct intratumoral microbiome. Yet rigorous characterization of its composition is hampered by pervasive environmental and procedural contamination. Sources of contamination have not been thoroughly explored, and the methods of decontamination have not been sufficiently evaluated in a benchmarking manner. We systematically collected >300 negative control (NCT) samples comprising paraffin from formalin-fixed paraffin-embedded (FFPE) samples, lysis buffer and sterile water over a period of four years processed by different laboratory persons (LP). All samples were sequenced using full-length 16S rRNA gene sequencing with Oxford-Nanopore Technologies. We benchmarked four decontamination methods (restrictive filtering, decontam, SCRuB, and the Nejman et al.-derived (Nj) pipeline) against fresh-frozen tumor samples (FF) from LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) mice, using the abovementioned contamination assessment to calculate a composite score for the assessment. Further, we validated those methods via technical replicates. Microbial profiles of NCT samples were significantly determined by control type, LP, year and season reflecting complex batch effects. The 15 most abundant contaminants spanned well-characterized environmental taxa and human commensals from the oral cavity. The LP processing samples left a significant microbial trace highly contributing to the batch effect. Decontamination benchmarking demonstrated that the Nj method consistently outperformed alternatives in both composite score and inter-replicate concordance. Application of Nj to fresh frozen PDAC samples substantially reduced contaminant burden while preserving putative tumor-associated signals in FF but not FFPE samples. Our results support the adoption of the Nj decontamination approach for future intratumoral microbiome studies in fresh frozen tumor samples.

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (GxE) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams--including remote sensing, environmental, and management data--toward data-driven decision making in agriculture.

Plasmodium falciparum heat shock protein 90 (PfHsp90) is a promising antimalarial target, but the molecular pathways influenced by its inhibition remain poorly understood. Herein, we leveraged chemoproteomic profiling employing geldanamycin and XL888 Hsp90 inhibitors to investigate proteins and pathways dependent on the chaperone during the Plasmodium blood stage. This study revealed 131 proteins reduced in abundance after inhibition, of which 40% co-immunoprecipitated with PfHsp90. Bioinformatic analyses identified DNA replication as the most enriched pathway. This link was investigated in phenotypic studies demonstrating reduced parasite DNA content after PfHsp90 inhibition. To assess nascent DNA synthesis, we utilized a 7-deaza-7-ethynyl-2'-deoxyadenosine (EdA) assay, yielding dual-stage attenuation of nucleoside incorporation following Hsp90 inhibition. We further show that parasite co-treatment with Hsp90 and DNA replication inhibitors produces synergistic interactions, highlighting the therapeutic potential of the discovered link. Overall, these findings expand our understanding of PfHsp90 function and uncover novel PfHsp90-dependent pathways.

Low-density lipoprotein (LDL) is a major atherogenic lipoprotein, yet its potential to directly modify the fibrin scaffold of blood clots is incompletely understood. Here, we investigated how LDL alters plasma fibrin network architecture and internal fibrinolysis across defined fibrinogen/thrombin conditions. Pooled normal human plasma was supplemented with LDL and clotted with controlled concentrations of fibrinogen and thrombin. Fibrin architecture was visualized by confocal microscopy and quantified by pore-size analysis; clot formation and lysis were monitored turbidimetrically in the presence of tissue plasminogen activator (tPA). Increasing LDL produced a pronounced reduction in fibrin-network pore size across the tested fibrinogen/thrombin conditions. The LDL dependence of pore diameter was well described by a power-law relationship, D_pore=(6.54 +/- 0.11)[LDL]^(-0.12 +/- 0.02) , (R^2 = 0.90), with a significant negative LDL exponent (p = 4 x 10^5). Increasing LDL also prolonged clot lysis time and altered turbidity kinetics. These findings extend epidemiologic and clinical associations between ApoB-containing lipoproteins and hypofibrinolytic clot phenotypes by demonstrating, in a controlled plasma system, that LDL itself can modify fibrin network architecture and fibrinolytic susceptibility. The results support a structure-function role for LDL within the fibrin biomaterial and motivate direct tests of LDL incorporation, protofibril packing, fibrinolytic-protein binding, and single-fiber mechanics.

The balance between how much human tumors recapitulate fetal tissue programs versus lose adult tissue identity remains unresolved. I used audited vibe coding, a human-mediated, cross-model critique-and-refinement workflow, to re-analyze a public pan-cancer proteomic atlas. A primary large language model wrote and executed the analysis under scientific direction, while a separate model family audited the code, outputs and claims; findings were returned for correction across seven versioned releases. Among 229 tumor-adjacent pairs in seven organs, tumor-minus-adjacent proteomic change partially aligned with reverse fetal-to-adult maturation (organ-balanced cosine, 0.240; 95% interval, 0.138 to 0.335), with positive alignment in 189 of 229 patients (82.5%). The organ-balanced projection coefficient was 0.195 (95% interval, 0.069 to 0.244), indicating movement along only part of the developmental distance. Although reverse maturation overlapped adult-identity loss, a positive developmental component remained after identity loss entered first (0.203; 95% interval, 0.129 to 0.239). Suppression of adult-high proteins contributed to more positive alignment than reactivation of fetal-high proteins. The vibe coding audits identified substantive defects. A common-mask correction reduced the matched-organ advantage from 0.074 to 0.059; a missing-value correction barely changed aggregate geometry but replaced 5 of the top 40 liver contributors; and coupled resampling repaired uncertainty accounting without changing patient scores. As with any single report, the "vibe reanalysis" biological results are candidate discoveries pending independent replication. The workflow is a single feasibility case, not a reliability benchmark, and shows how conversationally generated analysis can be made more inspectable when model-written code is treated as untrusted, versioned and subject to separate-model critique and executable checks.

Background: Multiple sclerosis (MS) is a chronic neurodegenerative disorder in which inflammatory demyelinating lesions affect the brain, optic nerve and spinal cord. MS lesion formation is accompanied by profound changes to blood vessels, including the density of PDGFR{beta}+ mural cells, historically identified as pericytes. Intriguingly, in recent years, single-cell and lineage tracing studies have shown that the PDGFR{beta}+ cell population is heterogeneous, comprising both pericytes and perivascular fibroblasts. Yet, due to their overlapping expression profiles, the spatial distribution of these cell populations in MS lesions remains poorly understood. Methods: We employed multiplex immunohistochemistry for endothelial cells (CD31), basement membrane (laminin), fibroblasts (PDGFR{beta}, COL1A1, SMA), pericytes (PDGFR{beta}, SLC6A12) and immune cells (CD45, CD68) to characterize the spatial localization of fibroblasts and pericytes in MS lesions, and how this relates to perivascular space enlargement and immune cell presence. Results: We analysed 17633 individual vessels across 5 control white matter, 5 normal-appearing white matter, 4 active and 4 chronic active MS lesions. By carefully delineating endothelium and perivascular compartments, we find that perivascular space area but not number of vessels is increased in MS lesions. Through mining of publicly available sequencing datasets, we confirm COL1A1 and SLC6A12 as fibroblast and pericyte markers, respectively, in the human brain. COL1A1+ and SLCA12+ vessels were largely distinct of one another. Unsupervised clustering of the expression profile of PDGFR{beta}, COL1A1 and SLC6A12 in individual vessels distinguished three partially overlapping vessel clusters. Of these, the fibroblast-associated vessel type (COL1A1 high, SLC6A12 low) was increased in chronic active lesion rim and center. Importantly, fibroblast-associated vessels were related to increased perivascular space enlargement and more accumulation of immune cells. Conclusion: We identify distinct fibroblast- and pericyte-associated vascular phenotypes in human white matter. Notably, fibroblast-associated vessels are increased in chronic active lesions, where they are related to immune cell cuffs. These findings provide a spatial link between perivascular fibroblasts and chronic inflammation in MS.

Water availability is critical for plants and their microbial communities, including pathogens. The plant pathogen Phytophthora cinnamomi persists in soils with fluctuating moisture, yet cellular responses to water limitation remain poorly understood in Phytophthora and oomycetes more broadly. Although we recently characterized the proteomic response of P. cinnamomi to NaCl-induced osmotic and ionic stress, its response to PEG-mediated water limitation remains poorly understood, leaving a critical gap in our understanding of drought-relevant stress adaptation. Here, we quantified mycelial growth and profiled time-resolved proteome dynamics of P. cinnamomi during polyethylene glycol (PEG-3350)-treatment, simulating moderate water limiting conditions. Treatment with 5% PEG-3350 enhanced radial mycelial growth relative to controls, with no early growth inhibition observed. Label-free proteomics identified 1,097 protein groups, with 880 proteins shared between conditions and an asymmetric abundance profile dominated by decreasing protein abundance over time. Only a small subset of proteins increased, mainly enzymes involved in redox buffering (e.g., thioredoxin and glutaredoxin-like proteins) and mitochondrial/metabolic regulation (e.g., alternative oxidase) and mitochondrial/metabolic regulation. Hierarchical clustering revealed a potential three-phase temporal program: early translational and regulatory remodeling (1-6 HPT), sustained metabolic adjustment (6-12 HPT), and delayed engagement of redox and proteostasis functions (12-24 HPT). Network analysis demonstrated that redox-associated function was integrated throughout this adaptation, with individual clusters further specialized by cofactor preference (NADP- versus NAD-dependent enzymes) and distinct metabolic roles (malate dehydrogenase, CoA-ligase activity). This coordinated, multi-phase reorganization sustained mycelial growth despite moderate osmotic stress, indicating that P. cinnamomi employs active proteomic adaptation rather than passive stress tolerance. These findings reveal the cellular mechanisms underlying drought persistence in this invasive pathogen and suggest molecular targets for disease management under water-limited conditions.

Genome-wide coexpression analysis of intact tissue samples is a powerful approach for identifying reproducible signatures of cell types and states, since it can survey vast numbers of individuals, cells, and transcripts. However, it can be difficult to optimize gene coexpression network construction and compare results from independent analyses. To address these challenges, we developed OMICON (theomicon.ucsf.edu) for research on human brain gene coexpression networks. OMICON contains gene expression data from >17K normal and neoplastic human brain samples with standardized metadata. Systematic analysis of independent datasets identified >250K gene coexpression modules, which were characterized and compared via enrichment analysis with >40K gene sets. All modules are discoverable via an advanced search engine that can filter by genes, metadata, and enrichment results. Analyses can also be browsed with an interactive workflow visualization tool, and users can communicate within OMICON using @mention functionality to support communal research on human brain gene coexpression networks.

Proteins that distinguish quiescent cells from other non-proliferative states and actively regulate their return to proliferation remain poorly understood. Here, we combined quantitative proteomics with functional image-based screening to identify regulators of the quiescence-to-proliferation transition. Amongst the functional quiescence signature proteins we identified, we focussed on integrin 11 (ITGA11) which is induced across multiple models of reversible quiescence in distinct cell types and that has low expression in proliferating and senescent cells. Although ITGA11 is dispensable for proliferation of asynchronously cycling cells, it is required for efficient cell-cycle re-entry from quiescence. Mechanistically, ITGA11 promotes YAP accumulation and nuclear localization, thereby sustaining SKP2 expression and p27 degradation during cell cycle re-entry. Depletion of p27, or pharmacological activation of YAP signalling rescues the cell-cycle re-entry defect caused by ITGA11 depletion. Together, these findings identify ITGA11 as a functional quiescence signature protein that couples extracellular matrix sensing to YAP-dependent regulation of the Skp2-p27 axis, revealing a mechanism that controls the transition from quiescence to proliferation.