Selection of texture-associated biomarkers in chilled and iced grass carp (Ctenopharyngodon idella) fillets via DIA-based proteomics.

Consumers care about the texture of fresh fish flesh, but a rapid quantitative analytical method for this has not been properly established. In this study, texture-associated biomarkers were selected by DIA-based proteomics for possible future application. Results indicated a significant decline in texture and moisture characteristics with extended storage under chilled and iced conditions, and flesh quality was categorized into three intervals. A total of 8 texture-associated biomarkers were identified in the chilled storage group, and 3 distinct ones in the iced storage group. Biomarkers were further refined based on their expression levels. Isobutyryl-CoA dehydrogenase, mitochondrial and [Phosphatase 2A protein]-leucine-carboxy methyltransferase were identified as effective texture-associated biomarkers for chilled fish, and Staphylococcal nuclease domain-containing protein 1 for iced fish. This study provided suitable proteins as indicators of fresh fish flesh texture, which could help establish a rapid and convenient texture testing method in future studies.

Protease S of entomopathogenic bacterium Photorhabdus laumondii: expression, purification and effect on greater wax moth Galleria mellonella.

BACKGROUND: Protease S (PrtS) from Photorhabdus laumondii belongs to the group of protealysin-like proteases (PLPs), which are understudied factors thought to play a role in the interaction of bacteria with other organisms. Since P. laumondii is an insect pathogen and a nematode symbiont, the analysis of the biological functions of PLPs using the PrtS model provides novel data on diverse types of interactions between bacteria and hosts. METHODS AND RESULTS: Recombinant PrtS was produced in Escherichia coli. Efficient inhibition of PrtS activity by photorin, a recently discovered emfourin-like protein inhibitor from P. laumondii, was demonstrated. The Galleria mellonella was utilized to examine the insect toxicity of PrtS and the impact of PrtS on hemolymph proteins in vitro. The insect toxicity of PrtS is reduced compared to protease homologues from non-pathogenic bacteria and is likely not essential for the infection process. However, using proteomic analysis, potential PrtS targets have been identified in the hemolymph. CONCLUSIONS: The spectrum of identified proteins indicates that the function of PrtS is to modulate the insect immune response. Further studies of PLPs' biological role in the PrtS and P. laumondii model must clarify the details of PrtS interaction with the insect immune system during bacterial infection.

Molecular distinctions of bronchoalveolar and alveolar organoids under differentiation conditions.

The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex-vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre-differentiated and post-differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre- and post-differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting-edge AI-driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.

Extracellular vesicle proteins as breast cancer biomarkers: Mass spectrometry-based analysis.

Extracellular vesicles (EVs) are membrane-surrounded vesicles released by various cell types into the extracellular microenvironment. Although EVs vary in size, biological function, and components, their importance in cancer progression and the potential use of EV molecular species to serve as novel cancer biomarkers have become increasingly evident. Cancer cells actively release EVs into surrounding tissues, which play vital roles in cancer progression and metastasis, including invasion and immune modulation. EVs released by cancer cells are usually chosen as a gateway in the search for biomarkers for cancer. In this review, we mainly focused on molecular profiling of EV protein constituents from breast cancer, emphasizing mass spectrometry (MS)-based proteomic approaches. To further investigate the potential use of EVs as a source of breast cancer biomarkers, we have discussed the use of these proteins as predictive marker candidates. Besides, we have also summarized the key characteristics of EVs as potential therapeutic targets in breast cancer and provided significant information on their implications in breast cancer development and progression. Information provided in this review may help understand the recent progress in understanding EV biology and their potential role as new noninvasive biomarkers as well as emerging therapeutic opportunities and associated challenges.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Identification of cytokines in benign and malignant thymus tumors: based on Mendelian randomization and proteomics.

Objective: The aim of this study was to identify potential causal cytokines in thymic malignancies and benign tumors from the FinnGen database using Mendelian randomization (MR). Methods: In this study, data from genome-wide association studies (GWAS) of 91 cytokines were used as exposure factors, and those of thymic malignant tumors and thymic benign tumors were the outcome variables. Two methods were used to determine the causal relationship between exposure factors and outcome variables: inverse variance weighting (IVW) and MR-Egger regression. Sensitivity analysis was performed using three methods, namely, the heterogeneity test, the pleiotropy test, and the leave-one-out test. Results: There was a causal relationship between the expression of fibroblast growth factor 5, which is a risk factor for thymic malignant tumors, and thymic malignant tumors. C-C motif chemokine 19 expression, T-cell surface glycoprotein CD5 levels, and interleukin-12 subunit beta levels were causally related to thymic malignant tumors and were protective. Adenosine deaminase levels, interleukin-10 receptor subunit beta expression, tumor necrosis factor (TNF)-related apoptosis-inducing ligand levels, and TNF-related activation-induced cytokine levels showed a causal relationship with thymic benign tumors, which are its risk factors. Caspase 8 levels, C-C motif chemokine 28 levels, interleukin-12 subunit beta levels, latency-associated peptide transforming growth factor beta 1 levels, and programmed cell death 1 ligand 1 expression showed a causal relationship with thymic benign tumors, which are protective factors. Sensitivity analysis showed no heterogeneity. Conclusion: Cytokines showed a causal relationship with benign and malignant thymic tumors. Interleukin-12 subunit beta is a common cytokine that affects malignant and benign thymic tumors.

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.

Association between plasma proteome and pulmonary heart disease: A two-stage Mendelian randomization analysis.

Pulmonary heart disease (PHD) involves altered structure and function of the right ventricle caused by an abnormal respiratory system that causes pulmonary hypertension. However, the association between changes in plasma proteomics and PHD remains unclear. Hence, we aimed to identify causal associations between genetically predicted plasma protein levels and PHD. Mendelian randomization was performed to test the target proteins associated with PHD. Summary statistics for the human plasma proteome and pulmonary heart disease were acquired from the UK Biobank (6038 cases and 426 977 controls) and the FinnGen study (6753 cases and 302 401 controls). Publicly available pQTLs datasets for human plasma proteins were obtained from a largescale genome-wide association study in the INTERVAL study. The results were validated using a case-control cohort. We first enrolled 3622 plasma proteins with conditionally independent genetic variants; three proteins (histo-blood group ABO system transferase, activating signal cointegration 1 complex subunit 1, and calcium/calmodulin-dependent protein kinase I [CAMK1]) were significantly associated with the risk of pulmonary heart disease in the UK Biobank cohort. Only CAMK1 was successfully replicated (odds ratio: 1.1056, 95% confidence interval: 1.019-1.095, p = 0.0029) in the FinnGen population. In addition, the level of CAMK1 in 40 patients with PHD was significantly higher (p = 0.023) than that in the control group. This work proposes that CAMK1 is associated with PHD, underscoring the importance of the calcium signaling pathway in the pathophysiology to improve therapies for PHD.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

TSWIFT, a novel method for iterative staining of embedded and mounted human brain sections.

Comprehensive characterization of protein networks in mounted brain tissue represents a major challenge in brain and neurodegenerative disease research. In this study, we develop a simple staining method, called TSWIFT, to iteratively stain pre-mounted formalin fixed, paraffin embedded (FFPE) brain sections, thus enabling high-dimensional sample phenotyping. We show that TSWIFT conserves tissue architecture and allows for relabeling a single mounted FFPE sample more than 10 times, even after prolonged storage at 4 °C. Our results establish TSWIFT as an efficient method to obtain integrated high-dimensional knowledge of cellular proteomes by analyzing mounted FFPE human brain tissue.

Patient-derived tumoroids and proteomic signatures: tools for early drug discovery.

Onco-virotherapy is an emergent treatment for cancer based on viral vectors. The therapeutic activity is based on two different mechanisms including tumor-specific oncolysis and immunostimulatory properties. In this study, we evaluated onco-virotherapy in vitro responses on immunocompetent non-small cell lung cancer (NSCLC) patient-derived tumoroids (PDTs) and healthy organoids. PDTs are accurate tools to predict patient's clinical responses at the in vitro stage. We showed that onco-virotherapy could exert specific antitumoral effects by producing a higher number of viral particles in PDTs than in healthy organoids. In the present work, we used multiplex protein screening, based on proximity extension assay to highlight different response profiles. Our results pointed to the increase of proteins implied in T cell activation, such as IFN-γ following onco-virotherapy treatment. Based on our observation, oncolytic viruses-based therapy responders are dependent on several factors: a high PD-L1 expression, which is a biomarker of greater immune response under immunotherapies, and the number of viral particles present in tumor tissue, which is dependent to the metabolic state of tumoral cells. Herein, we highlight the use of PDTs as an alternative in vitro model to assess patient-specific responses to onco-virotherapy at the early stage of the preclinical phases.

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses.

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

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/.

Advancements in clinical approaches, analytical methods, and smart sampling for LC-MS-based protein determination from dried matrix spots.

Determination of proteins from dried matrix spots using MS is an expanding research area. Mainly, the collected dried matrix sample is whole blood from a finger or heal prick, resulting in dried blood spots. However as other matrices such as plasma, serum, urine, and tear fluid also can be collected in this way, the term dried matrix spot is used as an overarching term. In this review, the focus is on advancements in the field made from 2017 up to 2023. In the first part reviews concerning the subject are discussed. After this, advancements made for clinical purposes are highlighted. Both targeted protein analyses, with and without the use of affinity extractions, as well as untargeted, global proteomic approaches are discussed. In the last part, both methodological advancements are being reviewed as well as the possibility to integrate sample preparation steps during the sample handling. The focus, of this so-called smart sampling, is on the incorporation of cell separation, proteolysis, and antibody-based affinity capture.

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.

Thermotolerance in S. cerevisiae as a model to study extracellular vesicle biology.

The budding yeast Saccharomyces cerevisiae is a proven model organism for elucidating conserved eukaryotic biology, but to date its extracellular vesicle (EV) biology is understudied. Here, we show yeast transmit information through the extracellular medium that increases survival when confronted with heat stress and demonstrate the EV-enriched samples mediate this thermotolerance transfer. These samples contain vesicle-like particles that are exosome-sized and disrupting exosome biogenesis by targeting endosomal sorting complexes required for transport (ESCRT) machinery inhibits thermotolerance transfer. We find that Bro1, the yeast ortholog of the human exosome biomarker ALIX, is present in EV samples, and use Bro1 tagged with green fluorescent protein (GFP) to track EV release and uptake by endocytosis. Proteomics analysis reveals that heat shock protein 70 (HSP70) family proteins are enriched in EV samples that provide thermotolerance. We confirm the presence of the HSP70 ortholog stress-seventy subunit A2 (Ssa2) in EV samples and find that mutant yeast cells lacking SSA2 produce EVs but they fail to transfer thermotolerance. We conclude that Ssa2 within exosomes shared between yeast cells contributes to thermotolerance. Through this work, we advance Saccharomyces cerevisiae as an emerging model organism for elucidating molecular details of eukaryotic EV biology and establish a role for exosomes in heat stress and proteostasis that seems to be evolutionarily conserved.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Temperature effects on plasmalogen profile and quality characteristics in Pacific oyster (Crassostrea gigas) during depuration.

The quality of Pacific oyster (Crassostrea gigas) can be affected by many factors during depuration, in which temperature is the major element. In this study, we aim to determine the quality and plasmalogen changes in C. gigas depurated at different temperatures. The quality was significantly affected by temperature, represented by varying survival rate, glycogen content, total antioxidant capacity, alkaline phosphatase activity between control and stressed groups. Targeted MS analysis demonstrated that plasmalogen profile was significantly changed during depuration with PUFA-containing plasmalogen species being most affected by temperature. Proteomics analysis and gene expression assay further verified that plasmalogen metabolism is regulated by temperature, specifically, the plasmalogen synthesis enzyme EPT1 was significantly downregulated by high temperature and four plasmalogen-related genes (GPDH, PEDS, Pex11, and PLD1) were transcriptionally regulated. The positive correlations between the plasmalogen level and quality characteristics suggested plasmalogen could be regarded as a quality indicator of oysters during depuration.