SEARCH · Search PubMed

Results for “proteome allocation”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

13 recordsLinked to original sources

PubMed2026

Implications of proteome allocation constraints for understanding interbacterial antagonism.

Bacteria live in dense communities where competition influences the composition and, therefore, the function of these communities. Beyond competing for resources, bacteria engage in antagonism by deploying a range of molecular weapon systems to inhibit and kill other bacteria. Investing in antagonism is expected to incur a fitness trade-off, but the nature of this trade-off at the level of molecular physiology remains underexplained. Applying recent advances about the physiological constraints faced by bacterial cells may help us better understand existing studies and design new investigations into interbacterial antagonism. Bacterial cells face two important constraints: a finite amount of protein and a maximum translation speed for ribosomes. As a result, the only way for a cell to grow faster is to allocate more of its finite proteome to synthesizing ribosomes. A cell choosing to attack competitors must therefore allocate some of its limited proteome budget to antagonistic proteins instead of other functions. Conversely, being attacked and resisting the effects of such attacks also require an investment of proteomic resources. The extent to which proteome allocation constraints influence bacterial physiology is not fully understood; consequently, how these constraints influence interbacterial antagonism has not been investigated. Here, I will discuss how proteome allocation constraints can re-contextualize our existing understanding of the costs of both deploying and resisting attacks and how investigation of these constraints may further our understanding of interbacterial antagonism.

Proteome↗

PubMed2026

Membrane and proteome allocation constraints in Escherichia coli models during overflow metabolism.

The allocation of finite cellular resources is a fundamental principle that dictates microbial metabolic strategies and gives rise to complex phenomena, such as overflow metabolism, characterized by the production of respiro-fermentative by-products, including acetate, during rapid growth. Although proteome-constrained models have successfully predicted overflow metabolism in Escherichia coli, they often overlook the distinct biophysical and energetic costs associated with protein localization. The cellular membrane, in particular, represents a critical and constrained compartment where competition for space and synthesis machinery can create significant metabolic bottlenecks. To investigate this, we developed the membrane-associated constrained flux balance analysis (MAFBA), a scalable, genome-scale metabolic model that introduces a tunable constraint on the total protein mass allocated to the cellular membrane. Our model demonstrates that the overall and membrane-associated proteome allocation constraints interact to improve the accuracy of predicting the onset of overflow metabolism. It mechanistically reveals that at high growth rates, competition for limited membrane allocation forces a trade-off between growth-essential functions and respiratory capacity, leading to acetate production. Furthermore, MAFBA quantitatively explains the widely observed experimental phenomenon that expressing heterologous membrane proteins imposes a significantly higher metabolic burden than expressing cytosolic proteins. This study establishes membrane resource allocation as a key constraint governing bacterial physiology, acting in concert with overall proteome limitations. The resulting MAFBA framework provides a powerful and accessible tool for synthetic biology and metabolic engineering, enabling the prediction of metabolic costs associated with expressing membrane-bound proteins and guiding strain design strategies, holding promise for applications in bioproduction and metabolic engineering.

Escherichia coli↗

PubMed2026

Coarse-grained resource allocation modeling for decoding and rewiring microbial metabolism.

Microbial metabolism is a complex, emergent system driven by the coordinated interplay of intricate and dynamic molecular processes. To elucidate cellular behavior and enable biotechnological applications, quantitative models that address the inherent complexity of metabolism have been developed from a resource allocation perspective. Here, we synthesize recent advances in coarse-grained resource allocation frameworks and their applications in understanding microbial physiology and guiding gene circuit design. These frameworks reveal global regulatory constraints and predict cellular adaptation to nutrient and environmental changes. In addition, they enable the quantification of metabolic costs, the dissection of circuit-host interactions, and the development of strategies for burden mitigation. Collectively, these modeling frameworks provide a powerful platform for uncovering quantitative principles of microbial growth and engineering robust synthetic biological systems.

coarse-grained modeling↗

PubMed2026

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

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

Triticum↗

PubMed2025

Effects of nitrogen allocation and photosynthetic proteins response in peanut leaves on photosynthesis under conditions of water scarcity and nitrogen deficiency.

Leaf nitrogen allocation and photosynthetic proteins response can affect net photosynthetic rate (Pn), ultimately influencing crop yield under diverse environmental stresses. However, the internal relationship between Pn with leaf nitrogen allocation and photosynthetic proteins response under nitrogen or water scarcity in peanut (Arachis hypogaea L.) remains elusive. Here, comprehensive physiological property and proteomic analyses of peanut were conducted, revealing that both nitrogen and water scarcity remarkably impeded leaf growth and reduced Pn. Nitrogen deficiency significantly reduced the total nitrogen content per unit leaf area (Narea), chlorophyll content, and Pn, whereas drought stress caused a greater decline in photosynthetic nitrogen use efficiency (PNUE). The allocation of leaf nitrogen to photosynthetic components, including the carboxylation system and electron transport system in leaves, was significantly reduced when subjected to individual or combined deficiency. Proteomic analyses exhibited that several key photosynthetic proteins underwent a decrease under both single and combined water and nitrogen deficiency conditions. Thereby, Pn may decline due to the disruption of nitrogen allocation and down-regulated expression of photosynthetic proteins under these stress conditions. Our findings establish a benchmark for future research exploring the roles of leaf nitrogen allocation and photosynthetic proteins in the plant's response to nitrogen or water deficiency.

Nitrogen↗

PubMed2025

Long COVID in Elderly COPD Patients: Clinical Features, Pulmonary Function Decline, and Proteomic Insights.

BACKGROUND: Elderly patients with chronic obstructive pulmonary disease (COPD) face a heightened risk of developing long coronavirus disease (COVID); however the exact clinical characteristics and underlying mechanisms remain unclear. METHODS: We enrolled 85 elderly COPD patients, of whom 43 reported newly onset persistent fatigue (the most dominant complaint of long COVID) within 1 year after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and they were allocated to the Long-COVID group. The remaining 42 patients were assigned to the Control group. Patients completed questionnaires, pulmonary function tests, chest CT, routine laboratory tests, and blood proteomic analysis. RESULTS: Long-COVID patients had a longer course of COPD (> 5 years, 76.8% vs 52.4%) and duration of SARS-CoV-2 infection (10.0 days vs 7.0 days) (All P < 0.05), higher symptom burden, worse pulmonary ventilation function and a more rapid decrease in DLCO (All P < 0.05). Proteomic analysis indicated disruptions in inflammation and energy metabolism, potentially underlying long COVID in these patients. The machine learning model identified wheezing, the duration of SARS-CoV-2 infection, EIF2S3 (eukaryotic translation initiation factor 2 subunit gamma), current FEV1/FVC (%), and the course of COPD as key features distinguishing Long-COVID patients, and exhibited excellent performance. CONCLUSION: Elderly COPD patients with a longer COPD course and duration of COVID-19 are more prone to develop long COVID, with decreased pulmonary ventilation and diffusion ability. Disordered inflammation regulation and energy metabolism may be the potential mechanisms, highlighting the importance of monitoring inflammation and metabolic dysregulation in elderly COPD patients after recovery from COVID-19.

Humans↗

PubMed2025

Multi-omics reveals an ecdysone-activated Eip75B-FABP signaling axis coordinating nutrient metabolism for development in Hermetia illucens.

INTRODUCTION: Efficient nutrient storage is essential for insect development and energy homeostasis; however, the mechanisms coordinating nutrient allocation during ontogeny are not well understood. Elucidating these systems may yield valuable insights to insect metabolic adaptation. OBJECTIVES: This study aimed to identify regulatory modules governing nutrient metabolism in insects, focusing on hormonal and metabolic interplay. METHODS: Multi-omics profiling (proteomics, phosphoproteomics, and transcriptomics) was conducted throughout the life cycle, from egg to adult, to identify metabolic regulators. RNAi was utilized for gene knockdown, followed by qRT-PCR and mitochondrial DNA quantification to evaluate knockdown efficiency and its metabolic implications. Assessments of nutrient metabolism were performed using assays for triglycerides, crude protein, and fatty acid synthase. EMSA and BODIPY staining examined transcriptional regulation and lipid droplet dynamics. RESULTS: Utilizing an integrative multi-omics approach, this study elucidates the temporal metabolic regulators in insects. A conserved regulatory module was identified in which the PPAR homolog, ecdysone-induced protein 75B (Eip75B), functions as a transcriptional activator of fatty acid binding protein (FABP), sustaining lipid metabolic homeostasis during the larval stage. PPARγ modulators (rosiglitazone and GW9662) alter lipid accumulation, along with the expression of Eip75B and FABP, which was measured by qRT-PCR. Furthermore, the deficiency of FABP may reprogram metabolic pathways by inhibiting lipid storage and promoting mitochondrial β-oxidation, as supported by increased mitochondrial DNA copy number, as well as enhancing protein synthesis. This metabolic change could be modulated by ecdysone signaling, as hormonal supplementation effectively rescued the lipid loss phenotype. Our results establish the ecdysone-Eip75B-FABP signaling axis as a central regulatory module that integrates hormonal and nutrient-sensing signals to control insect nutritional metabolism. CONCLUSION: The ecdysone-Eip75B-FABP axis integrates hormonal and nutrient signals to regulate metabolic plasticity, underscoring a universal strategy for developmental energy allocation. The data also offer potential implications for research on metabolic disorders and bioenergy applications.

Animals↗

PubMed2025

IsoBayes: a Bayesian approach for single-isoform proteomics inference.

MOTIVATION: Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. RESULTS: Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: (i) infer the presence/absence of each protein isoform (via a posterior probability), (ii) estimate its abundance (and credible interval), and (iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. AVAILABILITY AND IMPLEMENTATION: IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Proteomics↗

PubMed2025

ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy.

MOTIVATION: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field. RESULTS: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies. AVAILABILITY AND IMPLEMENTATION: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

Humans↗

PubMed2026

Serum Proteomic Signatures of Rheumatoid Arthritis Risk and Response: Analysis of a Rheumatoid Arthritis Interception Trial.

OBJECTIVE: Our study objective was to identify serum protein signatures associated with progression to rheumatoid arthritis (RA) and response to abatacept in at-risk individuals. METHODS: A total of 440 serum samples from 118 APIPPRA (Arthritis Prevention In the Preclinical Phase of RA with Abatacept) study participants were selected from baseline to RA onset for 46 progressors of RA or to study end for 72 participants who did not develop RA. Samples were analyzed using the SomaScan 7k assay platform. Differential expression analysis was assessed by progression to RA (three pre-RA time intervals to RA, progressors of RA vs nonprogressors, baseline to RA), and by treatment allocation (abatacept vs placebo). Risk and response signatures were identified in the full 7k panel and two prespecified subpanels defined as Inflammatory Mediators and Adaptive Immune Cell panel. RESULTS: We observed significant changes in 80 proteins (68 down-regulated and 12 up-regulated) occurring between RA onset and 6 to 24 months before developing disease. Progression to RA was associated with increased levels of acute-phase reactants SAA1 and SAA2 and reductions in CTLA4, when compared to nonprogressors at the end of treatment. Two up-regulated proteins (CTLA4 and CD86) and seven down-regulated proteins (CXCL13, FCRL4, FCER2, CCL21, LTA|LTB, FDCSP, and IL22RA2) were observed in participants receiving abatacept compared to placebo regardless of RA outcome. CONCLUSION: Protein signatures dominated by acute-phase proteins define progression to RA, whereas changes associated with abatacept therapy highlight potential mechanisms of treatment response. Such signatures provide a better understanding of the immune landscape of the at-risk phase, opening up the possibility of new treatment modalities for RA prevention.

Adult↗

PubMed2025

Predictors of response to terlipressin therapy in hepatorenal syndrome: Metabolomic and proteomic analysis from the CONFIRM trial.

BACKGROUND: Terlipressin is the only FDA-approved vasoconstrictor for hepatorenal syndrome (HRS). The CONFIRM study is the largest trial of terlipressin versus placebo. Novel predictors of HRS response are required to enrich patient selection and optimize outcomes. METHODS: Samples at treatment initiation were tested using (a) liquid chromatography-mass spectrometry of 1594 plasma/1420 urine metabolites (Metabolon Inc.), (b) aptamer-based array of 7289 plasma proteins (SomaScan), and (c) 14 plasma/urine pre-specified assays. The CONFIRM trial's original definition of HRS response [2 serum creatinine (SCr) <1.5 mg/dL separated by >2 h] was used as the primary outcome. RESULTS: In all, 115 patients [79 terlipressin-treated (TT) and 36 placebo-treated (PT)] provided samples. Baseline characteristics, outcomes, and 2:1 TT:PT allocation were preserved from the original 300-patient trial. A total of 36 out of 116 (31.0%) patients achieved HRS reversal. HRS reversal was associated with lower SCr (p=0.001), cystatin C (p=0.005), angiopoietin-2 (p=0.04), and beta-2 microglobulin (p=0.006). In metabolite analysis, PT had the most significant differences in HRS reversal [n=26 plasma, n=50 urine, including lower urine levels of those centered on sulfated secondary bile acids (microbiome-derived), N-acetylated amino acids, catechols (both uremic toxins), and phosphocholines (cell membrane integrity)], with fewer in TT (n=1 plasma, n=2 urine), and in all patients (n=3 plasma, n=7 urine). There were no significant aptamers associated with HRS reversal after false-discovery correction. CONCLUSIONS: SCr, cystatin C, angiopoietin-2, and beta-2 microglobulin were associated with HRS reversal. Protein and metabolite signals centered on microbiome function and uremic toxins appeared more robust in PT patients, likely selecting a subgroup that may recover without terlipressin. Use of novel biomarkers may enrich for terlipressin response.

Humans↗

PubMed2025

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations.

Despite growth being fundamental to all aspects of cell biology, we do not yet know its organizing principles in eukaryotic cells. Classic models derived from the bacteria E. coli posit that protein-synthesis rates are set by mass-action collisions between charged tRNAs produced by metabolic enzymes and mRNA-bound ribosomes. These models show that faster growth is achieved by simultaneously raising both ribosome content and peptide elongation speed. Here, we test if these models are valid for eukaryotes by combining single-molecule tracking, spike-in RNA sequencing, and proteomics in 15 carbon- and nitrogen-limited conditions using the budding yeast S. cerevisiae. Ribosome concentration increases linearly with growth rate, as in bacteria, but the peptide elongation speed remains constant (~9 amino acids/s) and charged tRNAs are not limiting. Total mRNA concentration rises in direct proportion to ribosomes, driven by enhanced RNA polymerase II occupancy of the genome. We show that a simple kinetic model of mRNA-ribosome binding predicts both the fraction of active ribosomes, the growth rate, and responses to transcriptional perturbations. Yeast accelerate growth by coordinately and proportionally co-up-regulating total mRNA and ribosome concentrations, not by speeding elongation. Taken together, our work establishes a new framework for eukaryotic growth control and resource allocation.

Journal Article↗

PubMed2025

Inferring metabolic objectives and trade-offs in single cells during embryogenesis.

While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning. We validated SCOOTI by identifying essential genes from CRISPR-Cas9 screens in embryonic stem cells, and by inferring the metabolic objectives of quiescent cells, during different cell-cycle phases. Applying this to embryonic cell states, we observed a decrease in metabolic entropy upon development. We further uncovered a trade-off between glutathione and biosynthetic precursors in one-cell zygote, two-cell embryo, and blastocyst cells, potentially representing a trade-off between pluripotency and proliferation. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗