Lifestyle modification represents the first-line strategy for the prevention and treatment of type 2 diabetes mellitus (T2DM), which is frequently associated with obesity and characterized by defective pancreatic insulin secretion and/or insulin resistance. Exercise training is an essential component of lifestyle modification and has been shown to ameliorate insulin resistance by reducing body fat mass and by enhancing skeletal muscle mitochondrial biogenesis and insulin-independent glucose uptake. Additionally, exercising stimulates the release of exerkines such as metabolites or cytokines, but also long non-coding RNA, microRNAs, cell-free DNA (cf-DNA), and extracellular vesicles (EVs), which contribute to inter-tissue communication. There is emerging evidence that EV number and content are altered in obesity and T2DM and may be involved in several metabolic processes, specifically either worsening or improving insulin resistance. This review summarizes the current knowledge on the metabolic effects of exercise training and on the potential role of humoral factors and EV as new biomarkers for early diagnosis and tailored treatment of T2DM.

Non-invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence-based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif-based and ML-based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.

The epidemy of metabolic syndrome (MetS) is typically preceded by adoption of a "risky" lifestyle (e.g., dietary habit) among populations. Evidence shows that those with low socioeconomic status (SES) are at an increased risk for MetS. To investigate this, we recruited 123 obese subjects (body mass index [BMI] ≥ 30) from Chicago. Multi-omic data were collected to interrogate fecal microbiota, systemic markers of inflammation and immune activation, plasma metabolites, and plasma glycans. Intestinal permeability was measured using the sugar permeability testing. Our results suggest a heterogenous metabolic dysregulation among obese populations who are at risk of MetS. Systemic inflammation, linked to poor diet, intestinal microbiome dysbiosis, and gut barrier dysfunction may explain the development of MetS in these individuals. Our analysis revealed 37 key features associated with increased numbers of MetS features. These features were used to construct a composite metabolic-inflammatory (MI) score that was able to predict progression of MetS among at-risk individuals. The MI score was correlated with several markers of poor diet quality as well as lower levels of gut microbial diversity and abnormalities in several species of bacteria. This study reveals novel targets to reduce the burden of MetS and suggests access to healthy food options as a practical intervention.

Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.

Cell-derived extracellular vesicles (EVs) are evolutionary-conserved secretory organelles that, based on their molecular composition, are important intercellular signaling regulators. At least three classes of circulating EVs are known based on mechanism of biogenesis: exosomes (sEVs/Exos), microparticles (lEVs/MPs), and shed midbody remnants (lEVs/sMB-Rs). sEVs/Exos are of endosomal pathway origin, microparticles (lEVs/MPs) from plasma membrane blebbing and shed midbody remnants (lEVs/sMB-Rs) arise from symmetric cytokinetic abscission. Here, we isolate sEVs/Exos, lEVs/MPs, and lEVs/sMB-Rs secreted from human isogenic primary (SW480) and metastatic (SW620) colorectal cancer (CRC) cell lines in milligram quantities for label-free MS/MS-based proteomic profiling. Purified EVs revealed selective composition packaging of exosomal protein markers in SW480/SW620-sEVs/Exos, metabolic enzymes in SW480/SW620-lEVs/MPs, while centralspindlin complex proteins, nucleoproteins, splicing factors, RNA granule proteins, translation-initiation factors, and mitochondrial proteins selectively traffic to SW480/SW620- lEVs/sMB-Rs. Collectively, we identify 39 human cancer-associated genes in EVs; 17 associated with SW480-EVs, 22 with SW620-EVs. We highlight oncogenic receptors/transporters selectively enriched in sEVs/Exos (EGFR/FAS in SW480-sEVs/Exos and MET, TGFBR2, ABCB1 in SW620-sEVs/Exos). Interestingly, MDK, STAT1, and TGM2 are selectively enriched in SW480-lEVs/sMB-Rs, and ADAM15 to SW620-lEVs/sMB-Rs. Our study reveals sEVs/Exos, lEVs/MPs, and lEVs/sMB-Rs have distinct protein signatures that open potential diagnostic avenues of distinct types of EVs for clinical utility.

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

Glioblastoma (GBM) is the most common and severe form of brain cancer among adults. Its aggressiveness is largely attributed to its complex and heterogeneous biology that despite maximal surgery and multimodal chemoradiation treatment, inevitably recurs. Traditional large-scale profiling approaches have contributed substantially to the understanding of patient-to-patient inter-tumoral differences in GBM. However, it is now clear that biological differences within an individual (intra-tumoral heterogeneity) are also a prominent factor in treatment resistance and recurrence of GBM and will likely require integration of data from multiple recently developed omics platforms to fully unravel. Here we dissect the growing geospatial model of GBM, which layers intra-tumoral heterogeneity on a GBM stem cell (GSC) precursor, single cell, and spatial level. We discuss potential unique and inter-dependant aspects of the model including potential discordances between observed genotypes and phenotypes in GBM.

Pancreatic β-cell dysfunction is an early hallmark of type 1 diabetes mellitus. Among the potentially critical factors that cause β-cell dysfunction are cytokine attack, glucotoxicity, induction of endoplasmic reticulum (ER) or mitochondria stress. However, the exact molecular mechanism underlying β-cell's inability to maintain glucose homeostasis under severe stresses is unknown. This study used proinflammatory cytokines, thapsigargin, and rotenone in the presence of high concentration glucose to mimicking the conditions experienced by dysfunctional β-cells in human pancreatic islets, and profiled the alterations to the islet proteome with TMT-based proteomics. The results were further verified with label-free quantitative proteomics. The differentially expressed proteins under stress conditions reveal that immune related pathways are mostly perturbed by cytokines, while the respiratory electron transport chains and protein processing in ER pathways by rotenone. Thapsigargin together with high glucose induces dramatic increases of proteins in lipid synthesis and peroxisomal protein import pathways, with energy metabolism and vesicle secretion related pathways downregulated. High concentration glucose, on the other hand, alleviated complex I inhibition induced by rotenone. Our results contribute to a more comprehensive understanding of the molecular events involved in β-cell dysfunction.

Monoclonal antibodies (mAbs) have established themselves as the leading biopharmaceutical therapeutic modality. Once the developability of a mAb drug candidate has been assessed, an important step is to check its in vivo stability through pharmacokinetics (PK) studies. The gold standard is ligand-binding assay (LBA) and liquid chromatography-mass spectrometry (LC-MS) performed at the peptide level (bottom-up approach). However, these analytical techniques do not allow to address the different mAb proteoforms that can arise from biotransformation. In recent years, top-down and middle-down mass spectrometry approaches have gained popularity to characterize proteins at the proteoform level but are not yet widely used for PK studies. We propose here a workflow based on an automated immunocapture followed by top-down and middle-down liquid chromatography-tandem mass spectrometry (LC-MS/MS) approaches to characterize mAb proteoforms spiked in mouse plasma. We demonstrate the applicability of our workflow on a large concentration range using pembrolizumab as a model. We also compare the performance of two state-of-the-art Orbitrap platforms (Tribrid Eclipse and Exploris 480) for these studies. The added value of our workflow for an accurate and sensitive characterization of mAb proteoforms in mouse plasma is highlighted.

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that can be released by all type of cells. Whereas, as one of the most common post-translational modifications, glycosylation plays a vital role in various biological functions of EVs, such as EV biogenesis, sorting, and cellular recognition. Nevertheless, compared with studies on RNAs or proteins, those investigating the glycoconjugates of EVs are limited. An in-depth investigation of N-glycosylation of EVs can improve the understanding of the biological functions of EVs and help to exploit EVs from different perspectives. The general focus of studies on glycosylation of EVs primarily includes isolation and characterization of EVs, preparation of glycoproteome/glycome samples and MS analysis. However, the low content of EVs and non-standard separation methods for downstream analysis are the main limitations of these studies. In this review, we highlight the importance of glycopeptide/glycan enrichment and derivatization owing to the low abundance of glycoproteins and the low ionization efficiency of glycans. Diverse fragmentation patterns and professional analytical software are indispensable for analysing glycosylation via MS. Altogether, this review summarises recent studies on glycosylation of EVs, revealing the role of EVs in disease progression and their remarkable potential as biomarkers.

Cellular communication relies on signaling circuits whose statuses are mainly modulated by soluble biomolecules such as carbohydrates, lipids, proteins, and metabolites as well as extracellular vesicles (EVs). Therefore, the active secretion of such biomolecules is critical for both cell homeostasis and proper pathophysiological responses in a timely fashion. In this context, proteins are among the main modulators of such biological responses. Hence, profiling cell line secretomes may be an opportunity for the identification of "signatures" of specific cell types (i.e., stromal or metastatic cells) with important prognostic/therapeutic value. This review will focus on the biological implications of cell secretomes in the context of cancer, as well as their functional roles in shaping the tumoral microenvironment (TME) and communication status of participating cells.

Aberrant serum N-glycan profiles have been observed in multiple cancers including non-small-cell lung cancer (NSCLC), yet the potential of N-glycans in the early diagnosis of NSCLC remains to be determined. In this study, serum N-glycan profiles of 275 NSCLC patients and 309 healthy controls were characterized by MALDI-TOF-MS. The levels of serum N-glycans and N-glycosylation patterns were compared between NSCLC and control groups. In addition, a panel of N-glycan biomarkers for NSCLC diagnosis was established and validated using machine learning algorithms. As a result, a total of 54 N-glycan structures were identified in human serum. Compared with healthy controls, 29 serum N-glycans were increased or decreased in NSCLC patients. N-glycan abundance in different histological types or clinical stages of NSCLC presented differentiated changes. Furthermore, an optimal biomarker panel of eight N-glycans was constructed based on logistic regression, with an AUC of 0.86 in the validation set. Notably, this model also showed a desirable capacity in distinguishing early-stage patients from healthy controls (AUC = 0.88). In conclusion, our work highlights the abnormal N-glycan profiles in NSCLC and provides supports potential application of N-glycan biomarker panel in clinical NSCLC detection.

Salinity tolerance in fish involves a suite of physiological changes, but a cohesive theory leading to a mechanistic understanding at the organismal level is lacking. To examine the potential of adapting energy homeostasis theory in the context of salinity stress in teleost fish, Oreochromis mossambicus were acclimated to hypersalinity at multiple rates and durations to determine salinity ranges of tolerance and resistance. Over 3000 proteins were quantified simultaneously to analyze molecular phenotypes associated with hypersalinity. A species- and tissue-specific data-independent acquisition (DIA) assay library of MSMS spectra was created. Protein networks representing complex molecular phenotypes associated with salinity acclimation were generated. O. mossambicus has a wide "zone of resistance" from 75 g/kg salinity to 120 g/kg. Crossing into the zone of resistance resulted in marked phenotypic changes including blood osmolality over 400 mOsm/kg, reduced body condition, and cessation of feeding. Protein networks impacted by hypersalinity consist of electron transport chain (ETC) proteins and specific osmoregulatory proteins. Cytoskeletal, cell adhesion, and extracellular matrix proteins are enriched in networks that are sensitive to the critical salinity threshold. These network analyses identify specific proteome changes that are associated with distinct zones described by energy homeostasis theory and distinguish them from general hypersalinity-induced proteome changes.