Viruses manipulate host cellular functions to promote replication and evade antiviral responses, and many processes in host–virus interactions are regulated by protein post-translational modifications (PTMs). While previous studies have examined individual PTMs in host–virus interactions, a comprehensive, multi-PTM perspective of host remodeling during coronavirus infection remains lacking. To address this gap, we applied our multi-PTM omics platform to simultaneously quantify protein abundance, cysteine oxidation, phosphorylation, and lysine acetylation in human lung fibroblasts (MRC5) and epithelial cells (A549) infected with human coronavirus strain 229E (HCoV-229E) at 8, 16, and 24 hours post-infection. We observed modest changes in the global host proteome, with only a small fraction of proteins affected even 24 hours post-infection. In contrast, host PTM landscapes were rapidly and extensively remodeled, exhibiting pronounced and cell type–specific alterations in redox and phosphorylation states as early as 8 hours post-infection. Phosphorylation profiling revealed widespread remodeling of host signaling networks with distinct temporal and directional patterns between MRC5 and A549 cells, while redox profiling uncovered divergent oxidative regulation of proteins involved in infection-related pathways. Notably, a subset of host proteins showed coordinated regulation across multiple PTM types without corresponding changes in abundance, highlighting potential PTM crosstalk during HCoV-229E infection. Among these, heat shock protein 90 beta (HSP90B) displayed dynamic regulation across cysteine oxidation, phosphorylation, and acetylation, and pharmacological inhibition of HSP90B significantly suppressed HCoV-229E replication. Together, these results provide a comprehensive, multi-dimensional view of host PTM remodeling during HCoV-229E infection and demonstrate that integrated multi-PTM omics can reveal functionally relevant host factors and therapeutic vulnerabilities not apparent from protein abundance measurements.

Abstract Prochlorococcus MED4 is a minimal photoautotroph whose extreme genome streamlining extends to its transcriptional regulatory architecture, yet it dominates high-light oligotrophic surface waters and drives marine carbon cycling. Despite ecological significance, MED4 remains genetically intractable, lacking molecular tools to characterize regulatory mechanisms and construct a transcriptome-wide regulatory map. To address this, we assembled an RNA-seq compendium of 253 samples, including 207 new samples capturing transcriptional responses across three classes of experiments: diverse environmental perturbations, a 24-hour circadian cycle, and phage infection. Using independent component analysis (ICA) applied to 247 quality-filtered samples, we identify 32 independently regulated gene set modules in MED4 (iModulons). By comparison, we previously identified 78 iModulons in the model cyanobacterium Synechococcus elongatus PCC 7942, revealing how dramatically genome reduction has simplified MED4’s regulatory architecture. Of the 32 iModulons in MED4, 13 are conserved modules that correlate with experimentally validated transcriptional regulons in PCC 7942, identifying regulatory programs that resisted elimination under extreme selective pressure. These conserved modules reveal regulatory programs governing photosynthesis and light responses (RpaB), circadian rhythms (RpaA), and nutrient assimilation (NtcA, PhoB). Known regulator-specific DNA-binding motifs upstream of genes in conserved modules independently support their identification as regulatory targets. Notably, RpaA governs circadian rhythms through three temporally distinct modules in MED4 versus one in PCC 7942, and the RpaB photoprotection module similarly splits into two. This work uncovers the minimal regulatory core governing photosynthesis, circadian rhythms, and C/N/P metabolism in a globally critical but genetically intractable photoautotroph. This approach offers a generalizable framework for regulatory inference beyond model organisms. Importance The minimal photoautotroph Prochlorococcus MED4 possesses only 28 transcriptional regulators, versus 150+ found in most cyanobacteria, reflecting a genome streamlined by billions of years of natural selection. This streamlining minimized metabolic costs, enabling MED4 to dominate nutrient-depleted oceans today. This raises a fundamental question: which regulatory programs did nature choose to keep? The answer matters, but MED4 cannot be studied by conventional genetics, placing its molecular machinery beyond direct experimental reach. Instead, we use large-scale computational methods to define groups of co-regulated genes in MED4. By comparing MED4 with a genetically tractable model cyanobacterium, we can distinguish which regulatory programs nature preserved from those it discarded. This work reveals the minimal regulatory architecture sufficient to sustain the smallest oxygenic photoautotroph on Earth. The principles uncovered here, distinguishing essential from dispensable regulatory programs in a naturally streamlined organism, inform the design of minimal photosynthetic platforms for biotechnology.

Single amino acid variants (SAAVs) in protein sequences are often a direct result of single-nucleotide polymorphisms (SNPs). Certain germline SAAVs have shown biological relevance in different disease conditions but lack precise quantification in circulation, which could hinder functional investigations and progress in biomarker development. Here, we have developed a multiplexed liquid chromatography-selected reaction monitoring (LC-SRM) assay that monitors 5 wild-type and variant peptide pairs (Complement Factor B: CFB-R32Q/R32W, Clusterin: CLU-N317H, Fetuin B: FETUB-K360R, and Kininogen: KNG1-L212P) in nondepleted human plasma. The assay was optimized for imprecision, linearity, stability, and calibration assessments with CVs of under 20%. The wild-type and variant peptide pairs were characterized in a set of healthy individual plasma samples. These target identifications were also validated by SNP genotyping with more than 99% accuracy. For all protein targets, we observed significantly lower concentrations of WT species in the presence variant peptides. In CFB, the concentration of R32Q was significantly lower than its counterpart R32W variant and WT species. Furthermore, our results distinguished phenotypes of homozygosity and heterozygosity of the SAAV presence through direct concentration level characterization. These findings provide some insights into how SAAVs affect quantitative assessments of target peptides. The assay demonstrates a platform for proteogenomic analyses with potential applications in both research and clinical settings.

Type 1 diabetes (T1D) results from dysfunction and loss of the insulin-producing pancreatic β cells. The body’s lipid metabolism is strongly regulated during this process but there is a need to understand how this regulation contributes to the β-cell death. Here, we investigated the role of free fatty acids (FFAs) in T1D development. Lipidomics data from the case-control study of The Determinants of Diabetes in the Young (TEDDY) consortium were re-analyzed to determine temporal changes in lipid profiles during T1D development. Fatty acid distribution across the pancreas was measured by mass spectrometry imaging. To model islet inflammation, FFAs were measured in human islets treated with the pro-inflammatory cytokines IL-1β + IFNγ by gas chromatography-mass spectrometry. Further, the effects of FFAs on MIN6 insulin-producing cells were measured by proteomics analysis along with biochemical and cell biology assays. We investigated if similar effects occur in vivo during T1D on the islets’ single-cell RNA sequencing data from Human Pancreas Analysis Program (HPAP). During islet autoimmunity, prior to T1D onset, plasma phosphatidylcholine and triacylglycerols are reduced with a simultaneous increase of FFAs. Similarly in human islets, IL-1β + IFNγ induce the increase of palmitate. Moreover, FFAs are abundantly detected across islets and surrounding exocrine tissue. FFAs synergistically enhanced cytokine-mediated apoptosis in MIN6 cells by downregulating the production of nicotinamide adenosine dinucleotide (NAD) via downregulating nicotinamide phosphoribosyltransferase (NAMPT), a rate-limiting enzyme of the NAD salvage pathway. The enhancement of cytokine-mediated apoptosis was reverted by supplementing cells with nicotinic acid and nicotinamide mononucleotide, metabolites that bypass NAMPT in NAD biosynthesis. NAMPT downregulation was further observed during T1D development, supporting that NAD production might be compromised in vivo. Our findings show that fatty acids are released during islet autoimmunity. These fatty acids enhance pro-inflammatory cytokine-mediated apoptosis through impaired NAD metabolism.

Reversible oxidation of cysteine residues (redox modifications) plays a crucial role in regulating protein function, signaling, and cellular homeostasis. These dynamic modifications act as molecular switches that transduce redox signals and modulate stress responses, metabolism, and pathogenesis. Redox proteomics enables systematic profiling of these modifications, quantifying the oxidation levels of tens of thousands of cysteine sites across the proteome and providing rich data to understand redox-regulated networks. However, conventional redox proteomic workflows are often limited by low throughput and high sample requirements. Here, we present a high-throughput sample processing workflow for redox proteomics analysis from as little as 2 μg of protein, enabling, for the first time, rapid redox-state profiling of cells cultured in 96-well plates. The workflow integrates 96-well plate-based cell culture, lysis, digestion, and cysteine-peptide enrichment, substantially increasing throughput and reducing hands-on processing time. Incorporating field asymmetric ion mobility spectrometry (FAIMS) further enhances redox proteome coverage by removing singly charged species in low-input samples, thereby increasing the signal of cysteine-containing peptides. Applying the workflow to RAW264.7 cells cultured in 96-well plates (40,000 cells per well), DIA identified >10,000 cysteine sites and revealed a global increase in cysteine oxidation upon diamide treatment. To assess robustness, we repeated the 96-well experiment across three independent batches processed on different days and observed consistent coverage, reproducible quantification, and comparable diamide-induced oxidation of heat shock proteins, transcription factors, and protein kinases. Together, this workflow and new data acquisition scheme enable comprehensive redox proteomics from minimal inputs, paving the way for high-throughput sophisticated studies of redox modifications in cell signaling, disease, or large-scale screening of redox-modulating compounds.

Exercise training confers broad health benefits, yet molecular regulators of skeletal muscle adaptation, particularly sex-specific mechanisms, remain incompletely understood. Integrating new and previously published multi-omics data from the molecular transducers of physical activity consortium (MoTrPAC), we characterized metabolomic, epigenomic, transcriptomic, proteomic, and post-translational modification (PTM) responses to 1-8 weeks of endurance exercise training in male and female rat gastrocnemius. While transcriptomic and proteomic responses were largely sex-concordant, there were distinct sex-specific training-induced PTM signatures, particularly in the redox proteome. Females exhibited decreased mitochondrial protein cysteine oxidation alongside increased oxidation of glycolytic proteins relative to males, suggesting sex-biased subcellular reactive oxygen species (ROS) dynamics. Multi-omic factor analysis (MOFA) identified coordinated sex-concordant molecular programs and further supported female-specific mechanisms of redox buffering with training. Together, these findings indicate that sex-specific skeletal muscle exercise adaptations are particularly evident at the PTM level in rats, and identify future avenues for precision exercise health and medicine.

Phosphoregulatory events underlie vascular responses to environmental and pathological stimuli, regulate hemostasis and thrombosis, and drive vascular remodeling in health and disease. Consequently, defining phosphorylation-dependent signaling networks in vascular physiology and pathology has enabled biomarker discovery and informed therapeutic interventions. Advances in mass spectrometry-based phosphoproteomics have enabled accurate and high-resolution mapping of dynamic phosphorylation events at the systems level, providing comprehensive mechanistic insights into vascular signaling and pathology. In this review, we contextualize phosphorylation in the vascular niche and summarize the current state of phosphoproteomics in vascular research, highlighting experimental design considerations, technological advances, quantification strategies, and data analysis approaches to uncover biological insights from large-scale phosphoproteomic data sets. Finally, we discuss recent discoveries in vascular signaling and disease, along with current challenges and emerging directions for applying phosphoproteomics to critical questions in vascular biology.

The human pancreas is a structurally and functionally complex organ in which endocrine islets are embedded within an exocrine matrix. Despite advances in spatial omics, three-dimensional (3D) proteomic map of the human islet microenvironment remains lacking. Here, we present a 3D spatial proteomics workflow that integrates immunofluorescence imaging, laser capture microdissection, nanoPOTS processing, and LC-MS/MS to map the islet microenvironment at 50 μm resolution, achieving ~3,000 protein identifications with spatial fidelity. Unsupervised clustering analyses revealed four molecularly distinct microenvironments spanning the acinar-to-islet axis, including a previously underappreciated peri-islet ductal-stromal niche enriched with canonical collagens and other extracellular matrix (ECM) proteins. Spatial correlation analysis (linking abundances to relative distance from the islet center) revealed proteins with interesting, reversed correlation patterns between islet and acinar compartments, which were enriched with ECM and cytoskeletal components. Finally, we provide a publicly accessible interactive web-based platform, enabling integrated 3D visualization of the spatial proteome alongside immunofluorescence imaging. Collectively, this work establishes a proof-of-principle framework for studying spatial tissue proteomic profiles, advances our understanding of the islet microenvironment, and lays a potential foundation for future applications in disease research.

Abstract Background Apurinic/apyrimidinic endonuclease 1/redox factor-1 (Ref-1/APE1) is a central regulator of redox-dependent transcriptional signaling that promotes pancreatic ductal adenocarcinoma (PDAC) progression, therapeutic resistance, and metabolic adaptation. While pharmacologic inhibition of Ref-1 suppresses tumor growth and alters cellular metabolism, immediate molecular events linking Ref-1 inhibition to downstream cellular adaptation remain poorly understood. We therefore sought to characterize proteome-wide signaling responses induced by second-generation Ref-1 redox inhibitor, APX2014. Methods We applied an integrated multiplexed proteomics workflow to simultaneously quantify global protein abundance together with cysteine oxidation, phosphorylation, and lysine acetylation in Pa03C PDAC cells following acute treatment (30–120 min) with selective Ref-1 redox inhibitor APX2014. Differential post-translational modification (PTM) analysis, pathway enrichment, structural mapping of regulated sites, and functional mitochondrial substrate utilization assays were performed to define early signaling responses. Results APX2014 induced rapid and extensive remodeling of PTM landscape while producing minimal changes in global protein abundance. Cysteine oxidation represented the earliest and most sustained response, accompanied by widespread phosphorylation and delayed lysine acetylation. Integrated pathway analyses identified mitochondrial translation, respiratory electron transport, TCA cycle metabolism, and mitochondrial redox homeostasis as the earliest and most consistently regulated processes. Functional mitochondrial assays confirmed impaired utilization of TCA cycle substrates following APX2014 treatment. Coordinated PTM remodeling was observed on Ref-1-associated signaling proteins, including NF-κB1 and p53, revealing simultaneous regulation of oxidation, phosphorylation, and acetylation within functionally important domains. Early redox-sensitive protein networks were also associated with subsequent disruption of mitotic organization. Conclusions Integrated multi-PTM proteomics reveals that pharmacologic Ref-1 redox inhibition rapidly rewires regulatory signaling networks before detectable changes in protein abundance. Our findings identify mitochondrial redox remodeling as an early consequence of Ref-1 inhibition, providing systems-level insight into how Ref-1-targeted therapies disrupt metabolic and stress-adaptive programs in pancreatic cancer. This work establishes a framework for understanding the molecular basis of Ref-1-directed therapeutics and highlights integrated PTM profiling as a powerful strategy for defining early drug response mechanisms. These findings provide a strong translational rationale for advancing next-generation Ref-1 redox inhibitors such as APX2014, developed from the first-in-class inhibitor APX3330 currently in clinical trials, and underscore the broader therapeutic potential of targeting Ref-1 redox signaling in pancreatic cancer.

Abstract In type 1 diabetes (T1D), insulin-producing β cells are destroyed by an autoimmune response driven by pro-inflammatory cytokines, including interferons. β-cell cytokine signaling is mediated in part by post-translational modifications, such as phosphorylation and acetylation. However, the role of other post-translational modifications in β-cell cytokine signaling represents an important knowledge gap. In the context of autoimmune diseases, lysine carbamylation has gained attention for its role in pathogenesis. Here, we investigate the role of carbamylation in T1D. We found that pancreatic islet cells from the T1D model, non-obese diabetic (NOD) mice, exhibit 11% carbamylation-positive cells, whereas non-diabetic CD1 mice have only 5%. Proteomics analysis of the MIN6 insulin-producing cell line treated with a cocktail of three pro-inflammatory cytokines IFNγ + IL-1β + TNFα identified 284 carbamylated peptides from 222 proteins impacted by the cytokine treatment. Integration of carbamylation and acetylation provided a deep view of the cytokine-regulated PTMs and potential points of interplay. A functional-enrichment analysis revealed that carbamylation was enriched in pathways related to autoimmune diseases, metabolism, DNA replication, and protein translation. Moreover, functional testing demonstrated that carbamylation inhibits the glycolytic enzyme aldolase A and the insulin-processing enzyme carboxypeptidase E, identifying a possible role for cytokine-induced β-cell dysfunction. In summary, protein carbamylation is elevated in islets from NOD mice, and pro-inflammatory cytokine treatment regulates protein carbamylation in MIN6 cells. These data identify carbamylation as a potential regulatory mechanism for β-cell metabolism and insulin production in the context of islet inflammation.

Photosynthetic microorganisms rely on multiple central-carbon-metabolism pathways to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes that operate across disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. Here, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial, subcellular organization of key carbon fixation enzymes and genetic information processes coupled to a non-spatial genome-scale metabolic model (GSMM). We combine perturbative, time-resolved multi-omics measurements and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulate carbon fixation by controlling the structural assembly of a protein megacomplex, the "dark complex," at a conserved regulatory node of the Calvin-Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates the importance of spatial heterogeneity in understanding phenotypic robustness. Spatiotemporal sequestration creates a timing hierarchy spanning seconds to hours and noise-buffering behavior that cannot be recovered from well-mixed phenomenological models or purely time-resolved descriptions. Constrained by spatial heterogeneity, local enzyme stoichiometry and diffusion-limited assembly/disassembly determine effective stochastically varying control kinetics. Diffusion-driven fluctuations amplify transcription of highly expressed genes, whereas PTM-dependent regulation on enzyme stoichiometry maintains perturbation-driven phenotypic outcomes. 4D whole cell modeling with perturbation-based multi-modal experiments unlocks the ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and light-dependent central carbon metabolism. The outcome of this work addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions, but previous studies mostly inferred their roles from annotations, ignoring system-wide impacts and phage production. Here we integrate AMGs and phage assembly into a genome-scale metabolic model of Prochloroccocus marinus MED4 infected by P-HM2. We show that 17 directly hijacked reactions substantially affect more than 30% of the reactions in MED4 metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis, distinguishing these AMGs as either phage aligned-shifting feasible reaction velocities in accordance with maximal phage production-or phage antialigned. Pareto optimization reveals that phage-aligned reactions alter phage-host growth trade-offs, while phage-antialigned reactions do not. We experimentally validate our predictions of system-level AMG impacts by measuring the N-dependent effect of P-HM2 cp12 expression on growth in a model relative of the genetically intractable MED4, Synechococcus elongatus. We also show that AMGs' indirect impacts are synergistically and antagonistically coupled, providing systems-level insight into AMG perturbations and highlighting how nontrivial cascading effects shape host metabolism.

Systemic perturbations trigger extensive changes across the proteome-altering protein abundance, post-translational modifications (PTMs), conformational states, and complex assembly. Interpreting these effects demands computational pipelines capable of integrating diverse proteomics modalities, such as multi-PTM profiling, limited proteolysis mass spectrometry (LiP-MS), and cross-linking mass spectrometry (XL-MS), within a unified and interoperable framework. Because instrument data are quantified at the peptide level, mapping these measurements to individual residues or modification sites is essential for biologically meaningful interpretation. We introduce ProteoMeter, an open-source Python library designed to integrate multi-modal proteomics datasets and map them to single-residue resolution using a standardized coordinate framework. We showcase its capabilities in a combined multi-PTM and LiP-MS analysis profiling the proteomic response to human coronavirus 229e (HCoV-229E) infection. ProteoMeter is actively maintained and is freely available-including all source code and figure-generation scripts-at the following repository: https://github.com/PNNL-Predictive-Phenomics/ProteoMeter.

The mechanisms driving progressive β cell dysfunction in type 2 diabetes remain incompletely understood. This study aimed to identify pancreatic islet proteome changes that could predict diabetes onset. We isolated islets from individuals without diabetes undergoing partial pancreatectomy, previously characterized for glucose tolerance, insulin sensitivity, and insulin secretion, using laser capture microdissection, and analyzed them via high-performance liquid chromatography-mass spectrometry. Proteomic analysis revealed that individuals with impaired glucose tolerance (IGT) had reductions in proteins regulating glycolysis (PGK1, G3P), lipid metabolism (ACBP, ARF1), glucose transport (14-3-3B), and insulin secretion (STARD10, CAPDS) compared with normal glucose-tolerant (NGT) individuals. Additionally, IGT islets showed impaired expression of proteins involved in glucose- and incretin-stimulated insulin response (CREB1, IQGA1). Stratification by β cell glucose sensitivity (βGS) indicated that individuals with lower βGS exhibited reduced levels of insulin maturation (ERO1B) and antiapoptotic proteins (CASP8, PAK2, SKP1), along with increased SEL1L, a factor promoting endocrine precursor differentiation. These findings suggest that early defects in glucose metabolism and insulin secretion characterize IGT, while reduced βGS may trigger compensatory mechanisms, through enhanced β cell survival or neogenesis, to delay type 2 diabetes progression. Overall, proteomic alterations in prediabetic islets provide potential early predictive markers and targets for interventions aimed at preserving β cell function.

Bottom-up proteomic workflows rely on sequential preprocessing steps, commonly including peptide-to-protein aggregation ("roll-up"), to enhance data reliability and interpretability. While roll-up is effective for protein-centered analyses, it may be suboptimal for applications focused on post-translational modifications (PTMs) or protein structural changes, such as limited proteolysis-mass spectrometry (LiP-MS). Here, we investigate how different roll-up strategies influence site-level quantification in PTM differential analysis. Moreover, we introduce a novel site-centric roll-up approach tailored for LiP-MS, which quantifies proteolytic fragments rather than solely tryptic peptides. We benchmark these methods through simulation studies, comparing their sensitivity and specificity in detecting structural and PTM-driven changes. We found that the median and mean roll-up methods outperform the sum method in both PTM and LiP proteomics, and site-level quantification in LiP outperforms peptide-level quantification. Our findings offer the first systematic, data-driven guidance for selecting roll-up techniques in site-level proteomic analyses, with implications for both PTM-focused and structural proteomics studies.

Background: Obesity is a major global public health challenge that contributes to numerous comorbidities and increased mortality. A better understanding of the biological mechanisms and disease risks associated with obesity requires robust monitoring of key circulating biomarkers, including adipokines, apolipoproteins, and inflammatory proteins. Targeted mass spectrometry (MS) offers a promising platform for developing specific, standardized, and multiplexed assays for biomarker quantification. In this study, we developed a multiplex targeted MS assay for the quantification of 42 obesity-associated biomarker candidates in human plasma. Methods: The assay was optimized for surrogate peptide selection, digestion incubation time, and LC gradient, and evaluated for linearity, lower limit of quantification (LLOQ), imprecision, and stability. A semi-automated sample preparation workflow using 96-well plates was also established to support high-throughput implementation. The assay was applied to a clinical cohort undergoing weight loss interventions to evaluate differences in protein abundance across obesity-related groups and to monitor biomarker changes over time. Statistical analyses were performed to identify proteins with significantly different abundances between study groups and before versus after intervention. Results: The assay demonstrated acceptable linearity, LLOQ, imprecision, and stability. Inter-laboratory validation using samples from 70 healthy individuals showed strong correlation with the finalized standard operating procedure (SOP). Application of the assay to an obesity cohort revealed significant differences in the levels of 6 proteins across obese, overweight, and healthy control groups, as well as 16 proteins that were differentially abundant in obese individuals compared with non-obese individuals (overweight and healthy controls combined). In subjects undergoing weight loss interventions, four proteins—CRP, PRG4, SERPINF1, and SHBG—showed significant concentration changes in individuals who achieved > 5% weight loss. Conclusions: These results demonstrate the robustness and high-throughput capability of this multiplex targeted MS assay for measuring obesity-associated plasma biomarkers. The assay shows potential clinical utility for improving the diagnosis, stratification, and risk assessment of obesity-related conditions, as well as for monitoring responses to weight loss interventions.

This protocol describes a solvent-solvent extraction method to obtain Metabolites, Lipids and Proteins from the same sample. This method, termed MPLEx, can be used on a variety of samples including biofluids (e.g., bronchoalvolar lavage fluid, plasma, blood, urine), mammalian tissues, bacteria, viruses, soil, etc.