Advances In Proteomics Research Articles

Omics Analysis of Extracellular Vesicles Recovered from Infant Formula Products and Milk: Towards Personalized Infant Nutrition.

Milk and milk products such as infant formula (IF) play a fundamental role in serving the nutritional needs of the developing infant. Extracellular vesicles (EVs) in human (HM) and cow milk (CM) contain molecular cargo such as proteins and micro(mi)RNAs that serve as functional messengers between cells and may be of importance to infant health. Most IF is derived from a CM protein base, however differences between HM and CM EV molecular cargo have not been extensively studied. This study develops a pipeline using advanced proteomics and transcriptomics to enable cross-species comparison of milk and IF EVs. The number of nanoparticles per mL of IF is significantly reduced compared to unprocessed CM. 130 proteins and 514 miRNAs are differentially abundant between HM and CM EVs. While 90% of CM EV miRNAs are also identified in IF EVs, only 20% of CM EV proteins are identified in IF EVs. This workflow identifies key species-specific differences that can be used to optimize IF recipes and enhance infant nutrition. Improved preservation of EV functional molecular cargo in IF products is of critical importance to retaining molecular drivers of good health and should be the focus of future investigations.

Tandem mass tag (TMT) labeling-based quantitative proteomic analysis reveals the cellular protein characteristics of 16HBE cells infected with coxsackievirus A10 and the potential effect of HMGB1 on viral replication.

Coxsackievirus A10 (CV-A10) is recognized as one of the most important pathogens associated with hand, foot, and mouth disease (HFMD) in young children under 5 years of age worldwide, and it can lead to fatal neurological complications. However, available commercial vaccines fail to protect against CV-A10. Therefore, there is an urgent need to study new protein targets of CV-A10 and develop novel vaccine-based therapeutic strategies. Advances in proteomics in recent years have enabled a comprehensive understanding of host pathogen interactions. Here, to study CV-A10-host interactions, a global quantitative proteomic analysis was conducted to investigate the molecular characteristics of host cell proteins and identify key host proteins involved in CV-A10 infection. Using tandem mass tagging (TMT)-based mass spectrometry, a total of 6615 host proteins were quantified, with 293 proteins being differentially regulated. To ensure the validity and reliability of the proteomics data, three randomly selected proteins were verified by Western blot analysis, and the results were consistent with the TMT results. Further functional analysis showed that the upregulated and downregulated proteins were associated with diverse biological activities and signaling pathways, such as metabolic processes, biosynthetic processes, the AMPK signaling pathway, the neurotrophin signaling pathway, the MAPK signaling pathway, and the GABAergic synaptic signaling. Moreover, subsequent bioinformatics analysis demonstrated that these differentially expressed proteins contained distinct domains, were localized in different subcellular components, and generated a complex network. Finally, high-mobility group box 1 (HMGB1) might be a key host factor involved in CV-A10 replication. In summary, our findings provide comprehensive insights into the proteomic profile during CV-A10 infection, deepen our understanding of the relationship between CV-A10 and host cells, and establish a proteomic signature for this viral infection. Moreover, the observed effect of HMGB1 on CV-A10 replication suggests that it might be a potential therapeutic target treatment of CV-A10 infection.

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review).

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Microbial Pathogenesis in the Era of Spatial Omics.

The biology of a cell, whether it is a unicellular organism or part of a multicellular network, is influenced by cell type, temporal changes in cell state, and the cell's environment. Spatial cues play a critical role in the regulation of microbial pathogenesis strategies. Information about where the pathogen is-in a tissue or in proximity to a host cell-regulates gene expression and the compartmentalization of gene products in the microbe and the host. Our understanding of host and pathogen identity has bloomed with the accessibility of transcriptomics and proteomics techniques. A missing piece of the puzzle has been our ability to evaluate global transcript and protein expression in the context of the subcellular niche, primary cell, or native tissue environment during infection. This barrier is now lower with the advent of new spatial omics techniques to understand how location regulates cellular functions. This review will discuss how recent advances in spatial proteomics and transcriptomics approaches can address outstanding questions in microbial pathogenesis.

Integrative Proteogenomics Using ProteomeGenerator2.

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and protein sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as noncanonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2.

Combination of HBA1, TTR, and SERPINF2 in plasma defines phenotype correlated with severe burn outcome

Recent advancements in proteomics allow for the concurrent identification andquantification of multiple proteins. This study aimed to identify proteins associated with severe burn pathology and establish a clinically useful molecular pathology classification. In a retrospective observational study, blood samples were collected from severe burn patients. Proteins were measured using mass spectrometry, and prognosis-related proteins were extracted by comparing survivors and non-survivors. Enrichment and ROC analyses evaluated the extracted proteins, followed by latent class analysis. Measurements were performed on 83 burn patients. In the non-survivor group, ten proteins significantly changing on the day of injury were associated with metabolic processes and toxin responses. ROC analysis identified HBA1, TTR, and SERPINF2 with AUCs>0.8 as predictors of 28-day mortality. Latent class analysis classified three molecular pathotypes, and plasma mass spectrometry revealed ten proteins associated with severe burn prognosis. Molecular pathotypes based on HBA1, TTR, and SERPINF2 significantly correlated with outcomes.

Nascent Proteomics: Chemical Tools for Monitoring Newly Synthesized Proteins.

Cellular proteins are dynamically regulated in response to environmental stimuli. Conventional proteomics compares the entire proteome in different cellular states to identify differentially expressed proteins, which suffers from limited sensitivity for analyzing acute and subtle changes. To address this challenge, nascent proteomics has been developed, which selectively analyzes the newly synthesized proteins, thus offering a more sensitive and timely insight into the dynamic changes of the proteome. In this Minireview, we discuss recent advancements in nascent proteomics, with an emphasis on methodological developments. Also, we delve into the current challenges and provide an outlook on the future prospects of this exciting field.

Nascent Proteomics: Chemical Tools for Monitoring Newly Synthesized Proteins

AbstractCellular proteins are dynamically regulated in response to environmental stimuli. Conventional proteomics compares the entire proteome in different cellular states to identify differentially expressed proteins, which suffers from limited sensitivity for analyzing acute and subtle changes. To address this challenge, nascent proteomics has been developed, which selectively analyzes the newly synthesized proteins, thus offering a more sensitive and timely insight into the dynamic changes of the proteome. In this Minireview, we discuss recent advancements in nascent proteomics, with an emphasis on methodological developments. Also, we delve into the current challenges and provide an outlook on the future prospects of this exciting field.

Anterior blepharitis is associated with elevated plectin levels consistent with a pronounced intracellular response

PurposeAnterior blepharitis is a frequent ocular condition which may result in severe ocular surface disease. In this study, advanced proteome analysis was performed to elucidate biological mechanisms underlying anterior blepharitis. MethodsAll patients underwent full ophthalmological examination including Ocular Surface Disease Index score (OSDI). Measurement of non-invasive break-up time (NBUT), Oxford score, and meibography were performed. Tear film samples from treatment naïve patients with anterior blepharitis (n = 15) and age-matched controls (n = 11) were collected with Schirmer filtration paper. The samples were analyzed with label-free quantification nano liquid chromatography - tandem mass spectrometry (LFQ nLC-MS/MS). Significantly regulated proteins were identified with a permutation-based calculation with a false discovery rate at 0.05. ResultsAmong the 927 proteins detected, a total of 162 proteins were significantly changed. Regulated proteins were involved in cytoplasmic translation, positive regulation of B cell activation, complement activation and phagocytosis. High levels of plakin proteins, a group of proteins involved in cytoskeleton organization, were observed in anterior blepharitis, including plectin, desmoplakin, envoplakin, epiplakin, periplakin, and vimentin. The upregulation of plectin was confirmed with single reaction monitoring. Patients with anterior blepharitis had lower levels of immunoglobulin chains, VEGF coregulated chemokine 1 (CXCL17), and platelet-derived growth factor C. ConclusionsAnterior blepharitis was associated with a high level of plectin indicating a pronounced intracellular response with cytoskeletal reorganization. Our data suggest a lack of immunoglobulin chains and CXCL17 in anterior blepharitis with potential alterations in the ocular surface immune response.

#2749 QUANTIFICATION OF URINE PROTEINS FOR DISCOVERY OF BIOMARKERS FOR KIDNEY INJURIES BY DIA-PROTEOMICS

Abstract Background and Aims Recent advances in proteomics have enabled to quantify proteins comprehensively. We aimed to establish a platform of urine quantitative proteomics for selection of the biomarkers and assessment of health conditions. Since plasma proteins of varying amounts of were present in urine especially from proteinuric patients, we subtracted the plasma protein conteents from total urine proteomes after the quantitative proteomics for discovery of biomarkers for kidney injuries. On the other hand, urine proteins excreted from plasma may be indicators of pathophysiological conditions in the body, suggesting that the urine proteome profiles figure out health conditions and early discovery of any diseases in the future. Method More than 120,000 urine samples (∼10 ml each) have been collected from ∼15,000 patients with various diseases and healthy volunteers (HV) and stored in -20C freezers. In this study, urine samples were selected from HV, diabetic patients (DM) with or without microalbuminuria and other CKD patients. Proteins were precipitated by a methanol/chloroform precipitation method from 1 ml urine. The protein fractions were digested with trypsin and the tryptic peptides were purified by C18 column chromatography. The tryptic peptides of 200 ng each were analyzed by LC-MS (tims-TOFpro, Bruker) by quantitative Data-independent acquisition (DIA) proteomics and proteins were quantified by the DIA-nn tool. Major top 30 plasma proteins were removed from the quantitative proteomics data and kidney- and other urinary tract organ-derived proteins were normalized to compare their proportions in urine. Results Approximately 2,500 proteins were quantified by the DIA analysis. In urine from HV, proportions of plasma proteins were small, however, in urine from DM patients with microalbuminuria or CKD patients, the plasma protein contents were increased, resulting in relative decreases of urine proportions of other proteins derived from the kidney or other urinary tract organs. By subtraction of the plasma proteins from total urine proteins, protein proportions were comparable to select candidates of biomarkers for kidney injuries. Comparison of total urine protein profiles between HV and DM elucidated their differences, which might indicate changes of pathophysiological conditions. Conclusion By recent quantitative proteomics urine proteins were quantified comprehensively, facilitating discovery of biomarkers for kidney injuries and also evaluation of health conditions in the body.

A Peptidisc-Based Survey of the Plasma Membrane Proteome of a Mammalian Cell

Membrane proteins play critical roles at the cell surface and their misfunction is a hallmark of many human diseases. A precise evaluation of the plasma membrane proteome is therefore essential for cell biology and for discovering novel biomarkers and therapeutic targets. However, the low abundance of this proteome relative to soluble proteins makes it difficult to characterize, even with the most advanced proteomics technologies. Here, we apply the peptidisc membrane mimetic to purify the cell membrane proteome. Using the HeLa cell line as a reference, we capture 500 different integral membrane proteins, with half annotated to the plasma membrane. Notably, the peptidisc library is enriched with several ABC, SLC, GPCR, CD, and cell adhesion molecules that generally exist at low to very low copy numbers in the cell. We extend the method to compare two pancreatic cell lines, Panc-1 and hPSC. Here we observe a striking difference in the relative abundance of the cell surface cancer markers L1CAM, ANPEP, ITGB4, and CD70. We also identify two novel SLC transporters, SLC30A1 and SLC12A7, that are highly present in the Panc-1 cell only. The peptidisc library thus emerges as an effective way to survey and compare the membrane proteome of mammalian cells. Furthermore, since the method stabilizes membrane proteins in a water-soluble state, members of the library, here SLC12A7, can be specifically isolated.

Analysis of the receptor for advanced glycation end-products proteome and protein-protein interactome in PANC-1 cancer cells

Background: Pancreatic adenocarcinoma (PA) remains one of the leading causes of cancer related deaths worldwide. The pathogenesis of PA is unclear. However, studies show that the receptor for advanced glycation end-products (RAGE), a member of the immunoglobulin superfamily and pattern recognition receptor, likely plays a role in oncogenesis and cell proliferation. RAGE has multiple functions including amplification and perpetuation of the inflammatory response, protein transport and degradation, maintaining cell polarity, and promoting cell differentiation and division. RAGE may contribute to oncogenesis of PA through dysregulation of ubiquitin and tumor suppressor genes such as p53.

Thiol redox proteomics: Characterization of thiol-based post-translational modifications.

Redox post-translational modifications on cysteine thiols (redox PTMs) have profound effects on protein structure and function, thus enabling regulation of various biological processes. Redox proteomics approaches aim to characterize the landscape of redox PTMs at the systems level. These approaches facilitate studies of condition-specific, dynamic processes implicating redox PTMs and have furthered our understanding of redox signaling and regulation. Mass spectrometry (MS) is a powerful tool for such analyses which has been demonstrated by significant advances in redox proteomics during the last decade. A group of well-established approaches involves the initial blocking of free thiols followed by selective reduction of oxidized PTMs and subsequent enrichment for downstream detection. Alternatively, novel chemoselective probe-based approaches have been developed for various redox PTMs. Direct detection of redox PTMs without any enrichment has also been demonstrated given the sensitivity of contemporary MS instruments. This review discusses the general principles behind different analytical strategies and covers recent advances in redox proteomics. Several applications of redox proteomics are also highlighted to illustrate how large-scale redox proteomics data can lead to novel biological insights.

Pathogenic cellular and molecular mediators in lupus nephritis.

Kidney involvement in patients with systemic lupus erythematosus - lupus nephritis (LN) - is one of the most important and common clinical manifestations of this disease and occurs in 40-60% of patients. Current treatment regimens achieve a complete kidney response in only a minority of affected individuals, and 10-15% of patients with LN develop kidney failure, with its attendant morbidity and considerable prognostic implications. Moreover, the medications most often used to treat LN - corticosteroids in combination with immunosuppressive or cytotoxic drugs - are associated with substantial side effects. Advances in proteomics, flow cytometry and RNA sequencing have led to important new insights into immune cells, molecules and mechanistic pathways that are instrumental in the pathogenesis of LN. These insights, together with a renewed focus on the study of human LN kidney tissue, suggest new therapeutic targets that are already being tested in lupus animal models and early-phase clinical trials and, as such, are hoped to eventually lead to meaningful improvements in the care of patients with systemic lupus erythematosus-associated kidney disease.

Matrix metalloproteinases in arthritis: towards precision medicine

Proteolysis of structural molecules of the extracellular matrix (ECM) is an irreversible post-translational modification in all arthropathies. Common joint disorders, including osteoarthritis and rheumatoid arthritis, have been associated with increased levels of matrix remodelling enzymes, including matrix metalloproteinases (MMPs). MMPs, in concert with other host proteinases and glycanases, destroy proteoglycans, collagens and other ECM molecules. MMPs may also control joint remodelling indirectly by signalling through cell-surface receptors or by proteolysis of cytokines and receptor molecules. After synthesis as pro-forms, MMPs can be activated by various types of post-translational modifications, including proteolysis. Once activated, MMPs are controlled by general and specific tissue inhibitors of metalloproteinases (TIMPs). In rheumatoid arthritis, proteolysis of the ECM results in so-called remnant epitopes that enhance and perpetuate autoimmune processes in susceptible hosts. In osteoarthritis, the considerable production of MMP-13 by chondrocytes, often concurrent with mechanical overload, is a key event. Hence, information about the regulation, timing, localization and activities of MMPs in specific disease phases and arthritic entities will help to develop better diagnostics. Insights into beneficial and detrimental effects of MMPs on joint tissue inflammation are also necessary to plan and execute (pre)clinical studies for better therapy and precision medicine with MMP inhibitors. With the advances in proteomics and single-cell transcriptomics, two critical points need attention: neglected neutrophil MMP biology, and the analysis of net proteolytic activities as the result of balances between MMPs and their inhibitors.

Autism Spectrum Disorders: Advances in Proteomics.

Autism is a class of developmental disorders with extremely high rates of disability affecting patients throughout their lives. There is no cure to date clinically, and early rehabilitation interventions can improve some of the behavioral problems of autistic patients, but these are limited by age and often have minimal effects in older adults with autism. Early diagnosis is also necessary while developing effective autism therapies. At present, the early diagnosis of autism is dependent on the search for effective markers in an attempt to screen differentially expressed proteins in autistic patients using high-throughput assays, such as synaptic scaffolding proteins, microtubule-associated proteins, apolipoproteins, immunoglobulin G complement factor-related proteins, etc. It would also be a big step forward for mechanistic studies of autism if a valid biomarker for autism could be found.

Quantitative proteomic studies addressing unmet clinical needs in sarcoma.

Sarcoma is a rare and complex disease comprising over 80 malignant subtypes that is frequently characterized by poor prognosis. Challenges in clinical management include uncertainties in diagnosis and disease classification, limited prognostic and predictive biomarkers, incompletely understood disease heterogeneity among and within subtypes, lack of effective treatment options, and limited progress in identifying new drug targets and novel therapeutics. Proteomics refers to the study of the entire complement of proteins expressed in specific cells or tissues. Advances in proteomics have included the development of quantitative mass spectrometry (MS)-based technologies which enable analysis of large numbers of proteins with relatively high throughput, enabling proteomics to be studied on a scale that has not previously been possible. Cellular function is determined by the levels of various proteins and their interactions, so proteomics offers the possibility of new insights into cancer biology. Sarcoma proteomics therefore has the potential to address some of the key current challenges described above, but it is still in its infancy. This review covers key quantitative proteomic sarcoma studies with findings that pertain to clinical utility. Proteomic methodologies that have been applied to human sarcoma research are briefly described, including recent advances in MS-based proteomic technology. We highlight studies that illustrate how proteomics may aid diagnosis and improve disease classification by distinguishing sarcoma histologies and identify distinct profiles within histological subtypes which may aid understanding of disease heterogeneity. We also review studies where proteomics has been applied to identify prognostic, predictive and therapeutic biomarkers. These studies traverse a range of histological subtypes including chordoma, Ewing sarcoma, gastrointestinal stromal tumors, leiomyosarcoma, liposarcoma, malignant peripheral nerve sheath tumors, myxofibrosarcoma, rhabdomyosarcoma, synovial sarcoma, osteosarcoma, and undifferentiated pleomorphic sarcoma. Critical questions and unmet needs in sarcoma which can potentially be addressed with proteomics are outlined.

Multi-omics approaches to therapeutic target identification

Identifying a suitable drug target is crucial in modern drug discovery methods. Advancement in omics technologies, such as high-throughput genotyping, rigorous statistical analysis and large coordinated genome data of patients, has significantly improved the identification and validation of potential drug targets. This is especially true of next-generation sequencing methods and advanced proteomics approaches, which have played vital roles in therapeutic target identification. This special issue collects together 12 articles focused on omics technologies and their integration towards drug target identification, validation and subsequent therapeutic implications. Data sets obtained from genome sequence generally provide a list of differentially expressed genes associated with disease states. After a series of computational and biochemical experiments, these differentially expressed genes are considered markers or therapeutic targets. However, in-depth analysis of such data types is limited because of a single or a limited number of approaches. Thus, integrating different omics data types will be an attractive strategy to investigate the real cause of disease, biomarker discovery and identification of therapeutic targets that could be further validated through clinical and molecular studies.

Proteomics mining of cancer hallmarks on a single-cell resolution.

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.