Plasma proteome profiling reveals dynamic of cholesterol marker after dual blocker therapy.

Dual blocker therapy (DBT) has the enhanced antitumor benefits than the monotherapy. Yet, few effective biomarkers are developed to monitor the therapy response. Herein, we investigate the DBT longitudinal plasma proteome profiling including 113 longitudinal samples from 22 patients who received anti-PD1 and anti-CTLA4 DBT therapy. The results show the immune response and cholesterol metabolism are upregulated after the first DBT cycle. Notably, the cholesterol metabolism is activated in the disease non-progressive group (DNP) during the therapy. Correspondingly, the clinical indicator prealbumin (PA), free triiodothyronine (FT3) and triiodothyronine (T3) show significantly positive association with the cholesterol metabolism. Furthermore, by integrating proteome and radiology approach, we observe the high-density lipoprotein partial remodeling are activated in DNP group and identify a candidate biomarker APOC3 that can reflect DBT response. Above, we establish a machine learning model to predict the DBT response and the model performance is validated by an independent cohort with balanced accuracy is 0.96. Thus, the plasma proteome profiling strategy evaluates the alteration of cholesterol metabolism and identifies a panel of biomarkers in DBT.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference.

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Super-resolution proximity labeling with enhanced direct identification of biotinylation sites.

Promiscuous labeling enzymes, such as APEX2 or TurboID, are commonly used in in situ biotinylation studies of subcellular proteomes or protein-protein interactions. Although the conventional approach of enriching biotinylated proteins is widely implemented, in-depth identification of specific biotinylation sites remains challenging, and current approaches are technically demanding with low yields. A novel method to systematically identify specific biotinylation sites for LC-MS analysis followed by proximity labeling showed excellent performance compared with that of related approaches in terms of identification depth with high enrichment power. The systematic identification of biotinylation sites enabled a simpler and more efficient experimental design to identify subcellular localized proteins within membranous organelles. Applying this method to the processing body (PB), a non-membranous organelle, successfully allowed unbiased identification of PB core proteins, including novel candidates. We anticipate that our newly developed method will replace the conventional method for identifying biotinylated proteins labeled by promiscuous labeling enzymes.

Dataset of seminal plasma proteome of Nellore bulls with high and low percentage of abnormalities sperm.

OBJECTIVES: Bovine seminal plasma proteins perform several functions related to sperm function. Changes in the expression pattern or abundance of seminal proteins are related to changes in the fertilizing capacity of bulls. Considering the role of seminal plasma proteins in sperm function and animal reproduction, we investigated changes in the protein abundance profile in response to sperm morphological changes using a proteomic approach. DATADESCRIPTION: In our present investigation, we employed liquid chromatography coupled with mass spectrometry to elucidate the proteomic composition of seminal plasma obtained from Nellore bulls exhibiting varying percentages of sperm abnormalities. Following semen collection, seminal plasma was promptly isolated from sperm, and proteins were subsequently precipitated, enzymatically digested using porcine trypsin, and subjected to analysis utilizing the Acquity nano UHPLC System in conjunction with a mass spectrometer. This dataset encompasses a total of 297 proteins, marking the inaugural instance in which a comparative profile of seminal plasma proteins in young Nellore bulls, categorized by their sperm abnormality percentages, has been delineated using LC-MS/MS. The comprehensive nature of this dataset contributes pivotal proteomic insights, representing a noteworthy advancement in our understanding of the reproductive biology of the Nellore breed.

Exosomes Secreted by Wharton's Jelly-Derived Mesenchymal Stem Cells Promote the Ability of Cell Proliferation and Migration for Keratinocyte.

Mesenchymal stem cells (MSCs) isolated from Wharton's jelly (WJ-MSCs) and adipose tissue (AD-MSCs) are alternative sources for bone marrow-derived MSCs. Owing to their multiple functions in angiogenesis, immune modulation, proliferation, migration, and nerve regeneration, MSC-derived exosomes can be applied in "cell-free cell therapy". Here, we investigated the functional protein components between the exosomes from WJ-MSCs and AD-MSCs to explain their distinct functions. Proteins of WJ-MSC and AD-MSC exosomes were collected and compared based on iTRAQ gel-free proteomics data. Results: In total, 1695 proteins were detected in exosomes. Of these, 315 were more abundant (>1.25-fold) in AD-MSC exosomes and 362 kept higher levels in WJ-MSC exosomes, including fibrinogen proteins. Pathway enrichment analysis suggested that WJ-MSC exosomes had higher potential for wound healing than AD-MSC exosomes. Therefore, we treated keratinocyte cells with exosomes and the recombinant protein of fibrinogen beta chain (FGB). It turned out that WJ-MSC exosomes better promoted keratinocyte growth and migration than AD-MSC exosomes. In addition, FGB treatment had similar results to WJ-MSC exosomes. The fact that WJ-MSC exosomes promoted keratinocyte growth and migration better than AD-MSC exosomes can be explained by their higher FGB abundance. Exploring the various components of AD-MSC and WJ-MSC exosomes can aid in their different clinical applications.

Energy flux couples sulfur isotope fractionation to proteomic and metabolite profiles in Desulfovibrio vulgaris.

Microbial sulfate reduction is central to the global carbon cycle and the redox evolution of Earth's surface. Tracking the activity of sulfate reducing microorganisms over space and time relies on a nuanced understanding of stable sulfur isotope fractionation in the context of the biochemical machinery of the metabolism. Here, we link the magnitude of stable sulfur isotopic fractionation to proteomic and metabolite profiles under different cellular energetic regimes. When energy availability is limited, cell-specific sulfate respiration rates and net sulfur isotope fractionation inversely covary. Beyond net S isotope fractionation values, we also quantified shifts in protein expression, abundances and isotopic composition of intracellular S metabolites, and lipid structures and lipid/water H isotope fractionation values. These coupled approaches reveal which protein abundances shift directly as a function of energy flux, those that vary minimally, and those that may vary independent of energy flux and likely do not contribute to shifts in S-isotope fractionation. By coupling the bulk S-isotope observations with quantitative proteomics, we provide novel constraints for metabolic isotope models. Together, these results lay the foundation for more predictive metabolic fractionation models, alongside interpretations of environmental sulfur and sulfate reducer lipid-H isotope data.

Transcriptome- and proteome-wide effects of a circular RNA encompassing four early exons of the spinal muscular atrophy genes.

Spinal muscular atrophy (SMA) genes, SMN1 and SMN2 (hereinafter referred to as SMN1/2), produce multiple circular RNAs (circRNAs), including C2A-2B-3-4 that encompasses early exons 2A, 2B, 3 and 4. C2A-2B-3-4 is a universally and abundantly expressed circRNA of SMN1/2. Here we report the transcriptome- and proteome-wide effects of overexpression of C2A-2B-3-4 in inducible HEK293 cells. Our RNA-Seq analysis revealed altered expression of ~ 15% genes (4172 genes) by C2A-2B-3-4. About half of the affected genes by C2A-2B-3-4 remained unaffected by L2A-2B-3-4, a linear transcript encompassing exons 2A, 2B, 3 and 4 of SMN1/2. These findings underscore the unique role of the structural context of C2A-2B-3-4 in gene regulation. A surprisingly high number of upregulated genes by C2A-2B-3-4 were located on chromosomes 4 and 7, whereas many of the downregulated genes were located on chromosomes 10 and X. Supporting a cross-regulation of SMN1/2 transcripts, C2A-2B-3-4 and L2A-2B-3-4 upregulated and downregulated SMN1/2 mRNAs, respectively. Proteome analysis revealed 61 upregulated and 57 downregulated proteins by C2A-2B-3-4 with very limited overlap with those affected by L2A-2B-3-4. Independent validations confirmed the effect of C2A-2B-3-4 on expression of genes associated with chromatin remodeling, transcription, spliceosome function, ribosome biogenesis, lipid metabolism, cytoskeletal formation, cell proliferation and neuromuscular junction formation. Our findings reveal a broad role of C2A-2B-3-4, and expands our understanding of functions of SMN1/2 genes.

Comparative proteomic analysis of the ovarian fluid and eggs of Siberian sturgeon.

BACKGROUND: Sturgeon species are living fossils that exhibit unique reproductive characteristics, and elucidation of the molecular processes governing the formation and quality of sturgeon eggs is crucial. However, comprehensive data on the protein composition of sturgeon ovarian fluid (OF) and eggs and their functional significance are lacking. To address this knowledge gap, the aim of the present study was to conduct a comprehensive comparative proteomic analysis of Siberian sturgeon OF and eggs using liquid chromatography-mass spectrometry (LC-MS/MS). RESULTS: A total of 617 proteins were identified in OF, and 565 proteins were identified in eggs. A total of 772 proteins showed differential abundance. Among the differentially abundant proteins, 365 were more abundant in OFs, while 407 were more abundant in eggs. We identified 339 proteins unique to OFs and 287 proteins specific to eggs, and further investigated the top 10 most abundant proteins in each. The functional annotation of the OF proteins highlighted their predominant association with immune system processes, including the complement and coagulation cascade, neutrophil and leukocyte-mediated immunity, cholesterol metabolism, and regulation of the actin cytoskeleton. Analysis of egg proteins revealed enrichment in metabolic pathways, such as oxidative phosphorylation and fatty acid metabolism, and protein ubiquitination and translation. OF-specific proteins included extracellular matrix and secretory vesicles, and eggs were enriched in proteins localized to mitochondria and ribosome components. CONCLUSIONS: This study presents the first comprehensive characterization of the protein composition of sturgeon OF and eggs and elucidates their distinct functional roles. These findings advance our understanding of sturgeon reproduction, OF-egg signaling and the origin of OF proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD044168 to ensure accessibility for further research.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Cord Blood Proteomic Profiles, Birth Weight, and Early Life Growth Trajectories.

Importance: The cord blood proteome, a repository of proteins derived from both mother and fetus, might offer valuable insights into the physiological and pathological state of the fetus. However, its association with birth weight and growth trajectories early in life remains unexplored. Objective: To identify cord blood proteins associated with birth weight and the birth weight ratio (BWR) and to evaluate the associations of these cord blood proteins with early growth trajectories. Design, Setting, and Participants: This cohort study included 288 mother-child pairs from the ongoing prospective Environmental Influence on Early Aging birth cohort study. Newborns were recruited from East-Limburg Hospital in Genk, Belgium, between February 2010 and November 2017 and followed up until ages 4 to 6 years. Data were analyzed from February 2022 to September 2023. Main Outcomes and Measures: The outcome of interest was the associations of 368 inflammatory-related cord blood proteins with birth weight or BWR and with early life growth trajectories (ie, rapid growth at age 12 months and weight, body mass index [BMI] z score, waist circumference, and overweight at age 4-6 years) using multiple linear regression models. The BWR was calculated by dividing the birth weight by the median birth weight of the population-specific reference growth curve, considering parity, sex, and gestational age. Results are presented as estimates or odds ratios (ORs) for each doubling in proteins. Results: The sample included 288 infants (125 [43.4%] male; mean [SD] gestation age, 277.2 [11.6] days). The mean (SD) age of the child at the follow-up examination was 4.6 (0.4) years old. After multiple testing correction, there were significant associations of birth weight and BWR with 7 proteins: 2 positive associations: afamin (birth weight: coefficient, 341.16 [95% CI, 192.76 to 489.50]) and secreted frizzled-related protein 4 (SFRP4; birth weight: coefficient, 242.60 [95% CI, 142.77 to 342.43]; BWR: coefficient, 0.07 [95% CI, 0.04 to 0.10]) and 5 negative associations: cadherin EGF LAG 7-pass G-type receptor 2 (CELSR2; birth weight: coefficient, -237.52 [95% CI, -343.15 to -131.89]), ephrin type-A receptor 4 (EPHA4; birth weight: coefficient, -342.78 [95% CI, -463.10 to -222.47]; BWR: coefficient, -0.11 [95% CI, -0.14 to -0.07]), SLIT and NTRK-like protein 1 (SLITRK1; birth weight: coefficient, -366.32 [95% CI, -476.66 to -255.97]; BWR: coefficient, -0.11 [95% CI, -0.15 to -0.08]), transcobalamin-1 (TCN1; birth weight: coefficient, -208.75 [95% CI, -305.23 to -112.26]), and unc-5 netrin receptor D (UNC5D; birth weight: coefficient, -209.27 [95% CI, -295.14 to -123.40]; BWR: coefficient, -0.07 [95% CI, -0.09 to -0.04]). Further evaluation showed that 2 proteins were still associated with rapid growth at age 12 months (afamin: OR, 0.32 [95% CI, 0.11-0.88]; TCN1: OR, 2.44 [95% CI, 1.26-4.80]). At age 4 to 6 years, CELSR2, EPHA4, SLITRK1, and UNC5D were negatively associated with weight (coefficients, -1.33 to -0.68 kg) and body mass index z score (coefficients, -0.41 to -0.23), and EPHA4, SLITRK1, and UNC5D were negatively associated with waist circumference (coefficients, -1.98 to -0.87 cm). At ages 4 to 6 years, afamin (OR, 0.19 [95% CI, 0.05-0.70]) and SLITRK1 (OR, 0.32 [95% CI, 0.10-0.99]) were associated with lower odds for overweight. Conclusions and Relevance: This cohort study found 7 cord blood proteins associated with birth weight and growth trajectories early in life. Overall, these findings suggest that stressors that could affect the cord blood proteome during pregnancy might have long-lasting associations with weight and body anthropometrics.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Proteome partitioning constraints in long-term laboratory evolution.

Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.

Multi-omics integration highlights the role of ubiquitination in endometriosis fibrosis.

BACKGROUND: Endometriosis, characterized by the presence of active endometrial-like tissues outside the uterus, causes symptoms like dysmenorrhea and infertility due to the fibrosis of endometrial cells, which involves excessive deposition of extracellular matrix (ECM) proteins. Ubiquitination, an important post-transcriptional modification, regulates various biological processes in human diseases. However, its role in the fibrosis process in endometriosis remains unclear. METHODS: We employed multi-omics approaches on two cohorts of endometriosis patients with 39 samples. GO terms and KEGG pathways enrichment analyses were used to investigate the functional changes involved in endometriosis. Pearson's correlation coefficient analysis was conducted to explore the relationship between global proteome and ubiquitylome in endometriosis. The protein expression levels of ubiquitin-, fibrosis-related proteins, and E3 ubiquitin-protein ligase TRIM33 were validated via Western blot. Transfecting human endometrial stroma cells (hESCs) with TRIM33 small interfering RNA (siRNA) in vitro to explore how TRIM33 affects fibrosis-related proteins. RESULTS: Integration of proteomics and transcriptomics showed genes with concurrent change of both mRNA and protein level which involved in ECM production in ectopic endometria. Ubiquitylomics distinguished 1647 and 1698 ubiquitinated lysine sites in the ectopic (EC) group compared to the normal (NC) and eutopic (EU) groups, respectively. Further multi-omics integration highlighted the essential role of ubiquitination in key fibrosis regulators in endometriosis. Correlation analysis between proteome and ubiquitylome showed correlation coefficients of 0.32 and 0.36 for ubiquitinated fibrosis proteins in EC/NC and EC/EU groups, respectively, indicating positive regulation of fibrosis-related protein expression by ubiquitination in ectopic lesions. We identified ubiquitination in 41 pivotal proteins within the fibrosis-related pathway of endometriosis. Finally, the elevated expression of TGFBR1/α-SMA/FAP/FN1/Collagen1 proteins in EC tissues were validated across independent samples. More importantly, we demonstrated that both the mRNA and protein levels of TRIM33 were reduced in endometriotic tissues. Knockdown of TRIM33 promoted TGFBR1/p-SMAD2/α-SMA/FN1 protein expressions in hESCs but did not significantly affect Collagen1/FAP levels, suggesting its inhibitory effect on fibrosis in vitro. CONCLUSIONS: This study, employing multi-omics approaches, provides novel insights into endometriosis ubiquitination profiles and reveals aberrant expression of the E3 ubiquitin ligase TRIM33 in endometriotic tissues, emphasizing their critical involvement in fibrosis pathogenesis and potential therapeutic targets.

Comparative proteomic analysis of renal tissue of normotensive and hypertensive rats.

Comparative proteomic analysis of kidney tissue from normotensive (WKY) and spontaneously hypertensive (SHR) rats revealed quantitative and qualitative changes in renal proteins. The number of renal proteins specific for WKY rats (blood pressure 110-120 mm Hg) was 13-16. There were 20-24 renal proteins specific for SHR (blood pressure 180 mm Hg and more). The total number of identified renal proteins common for both rat strains included 972-975 proteins. A pairwise comparison of all possible (SHR-WKY) variants identified 8 proteins specific only for normotensive (WKY) animals, and 7 proteins specific only for hypertensive ones (SHR). Taking into consideration their biological roles, the lack of some enzyme proteins in hypertensive rats (for example, biliverdin reductase A) reduces the production of molecules exhibiting antihypertensive properties, while the appearance of others (e.g. betaine-homocysteine S-methyltransferase 2, septin 2, etc.) can be interpreted as a compensatory reaction. Renal proteins with altered relative content (with more than 2.5-fold change) accounted for no more than 5% of all identified proteins. Among the proteins with an increased relative content in hypertensive animals, the largest group consisted of proteins involved in the processes of energy generation and carbohydrate metabolism, as well as antioxidant and protective proteins. In the context of the development of hypertension, the identified relative changes can apparently be considered compensatory. Among the proteins with the most pronounced decrease in the relative content in hypertensive rats, the dramatic reduction in acyl-CoA medium-chain synthetase-3 (ACSM3) appears to make an important contribution to the development of renal pathology in these animals.

REMEMProt: a resource of membrane-enriched proteome profiles, their disease associations, and biomarker status.

The differential expression of plasma membrane proteins is integrally analyzed for their diagnosis, prognosis, and therapeutic applications in diverse clinical manifestations. Necessarily, distinct membrane protein enrichment methods and mass spectrometry platforms are employed for their global and relative quantitation. First of its kind to explore, we compiled membrane-associated proteomes in human and mouse systems into a database named, Resource of Experimental Membrane-Enriched Mass spectrometry-derived Proteome (REMEMProt). It currently hosts 14,626 proteins (9,507 proteins in Homo sapiens; 5,119 proteins in Mus musculus) with information on their membrane-protein enrichment methods, experimental/physiological context of detection in cells or tissues, transmembrane domain analysis, and their current attribution as biomarkers. Based on these annotations and the transmembrane domain analysis in proteins or their binary/complex protein-protein interactors, REMEMProt facilitates the assessment of the plasma membrane localization potential of proteins through batch query. A cross-study enrichment analysis platform is enabled in REMEMProt for comparative analysis of proteomes using novel/modified membrane enrichment methods and evaluation of methods for targeted enrichment of membrane proteins. REMEMProt data are made freely accessible to explore and download at https://rememprot.ciods.in/.

Discovery of active mouse, plant and fungal cytochrome P450s in endogenous proteomes and upon expression in planta.

Eukaryotes produce a large number of cytochrome P450s that mediate the synthesis and degradation of diverse endogenous and exogenous metabolites. Yet, most of these P450s are uncharacterized and global tools to study these challenging, membrane-resident enzymes remain to be exploited. Here, we applied activity profiling of plant, mouse and fungal P450s with chemical probes that become reactive when oxidized by P450 enzymes. Identification by mass spectrometry revealed labeling of a wide range of active P450s, including six plant P450s, 40 mouse P450s and 13 P450s of the fungal wheat pathogen Zymoseptoria tritici. We next used transient expression of GFP-tagged P450s by agroinfiltration to show ER-targeting and NADPH-dependent, activity-based labeling of plant, mouse and fungal P450s. Both global profiling and transient expression can be used to detect a broad range of active P450s to study e.g. their regulation and discover selective inhibitors.

The proteomic profile is altered but not repaired after bariatric surgery in type 2 diabetes pigs.

To reveal the sources of obesity and type 2 diabetes (T2D) in humans, animal models, mainly rodents, have been used. Here, we propose a pig model of T2D. Weaned piglets were fed high fat/high sugar diet suppling 150% of metabolizable energy. Measurements of weight gain, blood morphology, glucose plasma levels, cholesterol, and triglycerides, as well as glucose tolerance (oral glucose tolerance test, OGTT) were employed to observe T2D development. The histology and mass spectrometry analyses were made post mortem. Within 6 months, the high fat-high sugar (HFHS) fed pigs showed gradual and significant increase in plasma triglycerides and glucose levels in comparison to the controls. Using OGTT test, we found stable glucose intolerance in 10 out of 14 HFHS pigs. Mass spectrometry analysis indicated significant changes in 330 proteins in the intestine, liver, and pancreas of the HFHS pigs. These pigs showed also an increase in DNA base modifications and elevated level of the ALKBH proteins in the tissues. Six diabetic HFHS pigs underwent Scopinaro bariatric surgery restoring glycaemia one month after surgery. In conclusion, a high energy diet applied to piglets resulted in the development of hyperlipidaemia, hyperglycaemia, and type 2 diabetes being reversed by a bariatric procedure, excluding the proteomic profile utill one month after the surgery.

Temporal dynamics of the multi-omic response to endurance exercise training.

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

Interleukin-13 Treatment of Living Lung Tissue Model Alters the Metabolome and Proteome-A Nano-DESI MS Metabolomics and Shotgun Proteomics Study.

Asthma is a chronic respiratory disease with one of the largest numbers of cases in the world; thus, constant investigation and technical development are needed to unravel the underlying biochemical mechanisms. In this study, we aimed to develop a nano-DESI MS method for the in vivo characterization of the cellular metabolome. Using air-liquid interface (ALI) cell layers, we studied the role of Interleukin-13 (IL-13) on differentiated lung epithelial cells acting as a lung tissue model. We demonstrate the feasibility of nano-DESI MS for the in vivo monitoring of basal-apical molecular transport, and the subsequent endogenous metabolic response, for the first time. Conserving the integrity of the ALI lung-cell layer enabled us to perform temporally resolved metabolomic characterization followed by "bottom-up" proteomics on the same population of cells. Metabolic remodeling was observed upon histamine and corticosteroid treatment of the IL-13-exposed lung cell monolayers, in correlation with alterations in the proteomic profile. This proof of principle study demonstrates the utility of in vivo nano-DESI MS for characterizing ALI tissue layers, and the new markers identified in our study provide a good starting point for future, larger-scale studies.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.