Applying mass spectrometry-based proteomics to genetics, genomics and network biology

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Expression of filaments of the Geobacter extracellular cytochrome OmcS in Shewanella oneidensis

The physiological role of Geobacter sulfurreducens extracellular cytochrome filaments is a matter of debate and the development of proposed electronic device applications of cytochrome filaments awaits methods for large-scale cytochrome nanowire production. Functional studies in G. sulfurreducens are stymied by the broad diversity of redox-active proteins on the outer cell surface and the redundancy and plasticity of extracellular electron transport routes. G. sulfurreducens is a poor chassis for producing cytochrome nanowires for electronics because of its slow, low-yield, anaerobic growth. Here we report that filaments of the G. sulfurreducens cytochrome OmcS can be heterologously expressed in Shewanella oneidensis. Multiple lines of evidence demonstrated that a strain of S. oneidensis, expressing the G. sulfurreducens OmcS gene on a plasmid, localized OmcS on the outer cell surface. Atomic force microscopy revealed filaments with the unique morphology of OmcS filaments emanating from cells. Electron transfer to OmcS appeared to require a functional outer-membrane porin-cytochrome conduit. The results suggest that S. oneidensis, which grows rapidly to high culture densities under aerobic conditions, may be suitable for the development of a chassis for producing cytochrome nanowires for electronics applications and may also be a good model microbe for elucidating cytochrome filament function in anaerobic extracellular electron transfer.

Using machine learning to identify proteomic and metabolomic signatures of stroke in atrial fibrillation

Atrial fibrillation (AF) is a common cardiac arrhythmia, with stroke being its most detrimental comorbidity. The exact mechanism of AF related stroke (AFS) still needs to be explored. In this study, we integrated proteomics and metabolomics platform to explore disordered plasma proteins and metabolites between AF patients and AFS patients. There were 22 up-regulated and 31 down-regulated differentially expressed proteins (DEPs) in AFS plasma samples. Moreover, 63 up-regulated and 51 down-regulated differentially expressed metabolites (DEMs) were discovered in AFS plasma samples. We integrated proteomics and metabolomics based on the topological interactions of DEPs and DEMs, which yielded revealed several related pathways such as arachidonic acid metabolism, serotonergic synapse, purine metabolism, tyrosine metabolism and steroid hormone biosynthesis. We then performed a machine learning model to identify potential biomarkers of stroke in AF. Finally, we selected 6 proteins and 6 metabolites as candidate biomarkers for predicting stroke in AF by random forest, the area under the curve being 0.976. In conclusion, this study provides new perspectives for understanding the progressive mechanisms of AF related stroke and discovering innovative biomarkers for determining the prognosis of stroke in AF.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks

IMPORTANCE

The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

SEPepQuant enhances the detection of possible isoform regulations in shotgun proteomics

Shotgun proteomics is essential for protein identification and quantification in biomedical research, but protein isoform characterization is challenging due to the extensive number of peptides shared across proteins, hindering our understanding of protein isoform regulation and their roles in normal and disease biology. We systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico and experimental data, and then present SEPepQuant, a graph theory-based approach to maximize isoform characterization. Using published data from one induced pluripotent stem cell study and two human hepatocellular carcinoma studies, we demonstrate the ability of SEPepQuant in addressing the key limitations of existing methods, providing more comprehensive isoform-level characterization, identifying hundreds of isoform-level regulation events, and facilitating streamlined cross-study comparisons. Our analysis provides solid evidence to support a widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant has broad applications to biological and translational research.

Development of an in situ cell-type specific proteome analysis method using antibody-mediated biotinylation

Since proteins are essential molecules exerting cellular functions, decoding proteome changes is the key to understanding the normal physiology and pathogenesis mechanism of various diseases. However, conventional proteomic studies are often conducted on tissue lumps, in which multiple cell types are entangled, presenting challenges in interpreting the biological dynamics among diverse cell types. While recent cell-specific proteome analysis techniques, like BONCAT, TurboID, and APEX, have emerged, their necessity for genetic modifications limits their usage. The alternative, laser capture microdissection (LCM), although it does not require genetic alterations, is labor-intensive, time-consuming, and requires specialized expertise, making it less suitable for large-scale studies. In this study, we develop the method for in situ cell-type specific proteome analysis using antibody-mediated biotinylation (iCAB), in which we combined immunohistochemistry (IHC) with the biotin-tyramide signal amplification approach. Poly-horseradish peroxidase (HRP) conjugated to the secondary antibody will be localized at a target cell type via a primary antibody specific to the target cell type and biotin-tyramide activated by HRP will biotinylate the nearby proteins. Therefore, the iCAB method can be applied to any tissues that can be used for IHC. As a proof-of-concept, we employed iCAB for mouse brain tissue enriching proteins for neuronal cell bodies, astrocytes, and microglia, followed by identifying the enriched proteins using 16-plex TMT-based proteomics. In total, we identified ~8,400 and ~6,200 proteins from enriched and non-enriched samples. Most proteins from the enriched samples showed differential expressions when we compared different cell type data, while there were no differentially expressed proteins from non-enriched samples. The cell type enrichment analysis with the increased proteins in respective cell types using Azimuth showed that neuronal cell bodies, astrocytes, and microglia data exhibited Glutamatergic Neuron, Astrocyte and Microglia/Perivascular Macrophage as the representative cell types, respectively. The proteome data of the enriched proteins showed similar subcellular distribution as non-enriched proteins, indicating that the iCAB-proteome is not biased toward any subcellular compartment. To our best knowledge, this study represents the first implementation of a cell-type-specific proteome analysis method using an antibody-mediated biotinylation approach. This development paves the way for the routine and widespread use of cell-type-specific proteome analysis. Ultimately, this could accelerate our understanding of biological and pathological phenomena.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Decoding the mechanism of hypertension through multiomics profiling

Hypertension, characterised by a constant high blood pressure, is the primary risk factor for multiple cardiovascular events and a major cause of death in adults. Excitingly, innovations in high-throughput technologies have enabled the global exploration of the whole genome (genomics), revealing dysregulated genes that are linked to hypertension. Moreover, post-genomic biomarkers, from the emerging fields of transcriptomics, proteomics, glycomics and lipidomics, have provided new insights into the molecular underpinnings of hypertension. In this paper, we review the pathophysiology of hypertension, and highlight the multi-omics approaches for hypertension prediction and diagnosis.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Integration of proteomic and metabolomic characterization in atrial fibrillation-induced heart failure

BACKGROUND

The exact mechanism of atrial fibrillation (AF)-induced heart failure (HF) remains unclear. Proteomics and metabolomics were integrated to in this study, as to describe AF patients' dysregulated proteins and metabolites, comparing patients without HF to patients with HF.

METHODS

Plasma samples of 20 AF patients without HF and another 20 with HF were analyzed by multi-omics platforms. Proteomics was performed with data independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS), as metabolomics was performed with LC-MS/MS platform. Proteomic and metabolomic results were analyzed separately and integrated using univariate statistical methods, multivariate statistical methods or machine learning model.

RESULTS

We found 35 up-regulated and 15 down-regulated differentially expressed proteins (DEPs) in AF patients with HF compared to AF patients without HF. Moreover, 121 up-regulated and 14 down-regulated differentially expressed metabolites (DEMs) were discovered in HF patients compared to AF patients without HF. An integrated analysis of proteomics and metabolomics revealed several significantly enriched pathways, including Glycolysis or Gluconeogenesis, Tyrosine metabolism and Pentose phosphate pathway. A total of 10 DEPs and DEMs selected as potential biomarkers provided excellent predictive performance, with an AUC of 0.94. In addition, subgroup analysis of HF classification was performed based on metabolomics, which yielded 9 DEMs that can distinguish between AF and HF for HF classification.

CONCLUSIONS

This study provides novel insights to understanding the mechanisms of AF-induced HF progression and identifying novel biomarkers for prognosis of AF with HF by using metabolomics and proteomics analyses.

SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update

The SIGnaling Network Open Resource (SIGNOR 3.0, https://signor.uniroma2.it) is a public repository that captures causal information and represents it according to an 'activity-flow' model. SIGNOR provides freely-accessible static maps of causal interactions that can be tailored, pruned and refined to build dynamic and predictive models. Each signaling relationship is annotated with an effect (up/down-regulation) and with the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the regulation of the target entity. Since its latest release, SIGNOR has undergone a significant upgrade including: (i) a new website that offers an improved user experience and novel advanced search and graph tools; (ii) a significant content growth adding up to a total of approx. 33,000 manually-annotated causal relationships between more than 8900 biological entities; (iii) an increase in the number of manually annotated pathways, currently including pathways deregulated by SARS-CoV-2 infection or involved in neurodevelopment synaptic transmission and metabolism, among others; (iv) additional features such as new model to represent metabolic reactions and a new confidence score assigned to each interaction.

Application of quantitative proteomics to investigate fruit ripening and eating quality

The consumption of fruit and vegetables play an important role in human nutrition, dietary diversity and health. Fruit and vegetable industries impart significant impact on our society, economy, and environment, contributing towards sustainable development in both developing and developed countries. The eating quality of fruit is determined by its appearance, color, firmness, flavor, nutritional components, and the absence of defects from physiological disorders. However, all of these components are affected by many pre- and postharvest factors that influence fruit ripening and senescence. Significant efforts have been made to maintain and improve fruit eating quality by expanding our knowledge of fruit ripening and senescence, as well as by controlling and reducing losses. Innovative approaches are required to gain better understanding of the management of eating quality. With completion of the genome sequence for many horticultural products in recent years and development of the proteomic research technique, quantitative proteomic research on fruit is changing rapidly and represents a complementary research platform to address how genetics and environment influence the quality attributes of various produce. Quantiative proteomic research on fruit is advancing from protein abundance and protein quantitation to gene-protein interactions and post-translational modifications of proteins that occur during fruit development, ripening and in response to environmental influences. All of these techniques help to provide a comprehensive understanding of eating quality. This review focuses on current developments in the field as well as limitations and challenges, both in broad term and with specific examples. These examples include our own research experience in applying quantitative proteomic techniques to identify and quantify the protein changes in association with fruit ripening, quality and development of disorders, as well as possible control mechanisms.

Osmoregulatory strategies of estuarine fish Scatophagus argus in response to environmental salinity changes

BACKGROUND

Scatophagus argus, an estuarine inhabitant, can rapidly adapt to different salinity environments. However, the knowledge of the molecular mechanisms underlying its strong salinity tolerance remains unclear. The gill, as the main osmoregulatory organ, plays a vital role in the salinity adaptation of the fish, and thus relative studies are constructive to reveal unique osmoregulatory mechanisms in S. argus.

RESULTS

In the present study, iTRAQ coupled with nanoLC-MS/MS techniques were employed to explore branchial osmoregulatory mechanisms in S. argus acclimated to different salinities. Among 1,604 identified proteins, 796 differentially expressed proteins (DEPs) were detected. To further assess osmoregulatory strategies in the gills under different salinities, DEPs related to osmoregulatory (22), non-directional (18), hypo- (52), and hypersaline (40) stress responses were selected. Functional annotation analysis of these selected DEPs indicated that the cellular ion regulation (e.g. Na⁺-K⁺-ATPase [NKA] and Na⁺-K⁺-2Cl^- cotransporter 1 [NKCC1]) and ATP synthesis were deeply involved in the osmoregulatory process. As an osmoregulatory protein, NKCC1 expression was inhibited under hyposaline stress but showed the opposite trend in hypersaline conditions. The expression levels of NKA α1 and β1 were only increased under hypersaline challenge. However, hyposaline treatments could enhance branchial NKA activity, which was inhibited under hypersaline environments, and correspondingly, reduced ATP content was observed in gill tissues exposed to hyposaline conditions, while its contents were increased in hypersaline groups. In vitro experiments indicated that Na⁺, K⁺, and Cl^- ions were pumped out of branchial cells under hypoosmotic stress, whereas they were absorbed into cells under hyperosmotic conditions. Based on our results, we speculated that NKCC1-mediated Na⁺ influx was inhibited, and proper Na⁺ efflux was maintained by improving NKA activity under hyposaline stress, promoting the rapid adaptation of branchial cells to the hyposaline condition. Meanwhile, branchial cells prevented excessive loss of ions by increasing NKA internalization and reducing ATP synthesis. In contrast, excess ions in cells exposed to the hyperosmotic medium were excreted with sufficient energy supply, and reduced NKA activity and enhanced NKCC1-mediated Na⁺ influx were considered a compensatory regulation.

CONCLUSIONS

S. argus exhibited divergent osmoregulatory strategies in the gills when encountering hypoosmotic and hyperosmotic stresses, facilitating effective adaptabilities to a wide range of environmental salinity fluctuation.

The New Era of Salivaomics in Dentistry: Frontiers and Facts in the Early Diagnosis and Prevention of Oral Diseases and Cancer

Nowadays, with the development of new and highly sensitive, blood is not the only medium of choice for the diagnosis of several diseases and pathological conditions. Saliva is now considered a safe and non-invasive sample to study oral and systemic diseases, showing great diagnostic potential. According to several recent studies, saliva has emerged as an emerging biofluid for the early diagnosis of several diseases, indicated as a mirror of oral and systemic health and a valuable source of clinically relevant information. Indeed, several studies have observed that saliva is useful for detecting and diagnosing malignant tumours, human immunodeficiency virus, heart disease, and autoimmune diseases. The growing realisation that saliva is an inexhaustible source of information has led to the coining of the term ‘Salivaomics’, which includes five “omics” in connection with the main constituents of saliva: genome and epigenome, transcriptomics, metabolomics, lipidomics, proteomics and microbiota. All those may be changed by disease state, so they offer significant advantages in the early diagnosis and prognosis of oral diseases. The aim of the present review isto update and highlight the new frontiers of salivaomics in diagnosing and managing oral disorders, such as periodontitis, premalignant disorders, and oral squamous cell carcinoma (OSCC).

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Psoriasis to Psoriatic Arthritis: The Application of Proteomics Technologies

Psoriatic disease (PsD) is a spectrum of diseases that affect both skin [cutaneous psoriasis (PsC)] and musculoskeletal features [psoriatic arthritis (PsA)]. A considerable number of patients with PsC have asymptomatic synovio-entheseal inflammations, and approximately one-third of those eventually progress to PsA with an enigmatic mechanism. Published studies have shown that early interventions to the very early-stage PsA would effectively prevent substantial bone destructions or deformities, suggesting an unmet goal for exploring early PsA biomarkers. The emergence of proteomics technologies brings a complete view of all involved proteins in PsA transitions, offers a unique chance to map all potential peptides, and allows a direct head-to-head comparison of interaction pathways in PsC and PsA. This review summarized the latest development of proteomics technologies, highlighted its application in PsA biomarker discovery, and discussed the possible clinical detectable PsA risk factors in patients with PsC.

Fast Sampling of the Cellular Metabolome

Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally, a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells and may interfere with the quantification of the endo metabolome. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, this release can be minimized by adaptation of the quenching method. For prokaryotic cells, this has not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction, for measurement of metabolites in total broth samples, washed cell samples, and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

The Emerging Landscapes of Long Noncoding RNA in Thyroid Carcinoma: Biological Functions and Clinical Significance

Thyroid carcinoma (TC) is one of the most prevalent primary endocrine tumors, and its incidence is steadily and gradually increasing worldwide. Accumulating evidence has revealed the critical functions of long noncoding RNAs (lncRNAs) in the tumorigenesis and development of TC. Many TC-associated lncRNAs have been documented to be implicated in TC malignant behaviors, including abnormal cell proliferation, enhanced stem cell properties and aggressiveness, and resistance to therapeutics, through interaction with proteins, DNA, or RNA or encoding small peptides. Therefore, further elucidating the lncRNA dysregulation sheds additional insights into TC tumorigenesis and progression and opens new avenues for the early diagnosis and clinical therapy of TC. In this review, we summarize the abnormal expression of lncRNA in TC and the fundamental characteristics in TC tumorigenesis and development. Additionally, we introduce the potential prognostic and therapeutic significance of lncRNAs in TC.

α-Active Pyrylium Salt 2,4,5-Triphenylpyrylium for Improved Mass Spectrometry-Based Detection of Peptides

Pyrylium salts are considered efficient chemical tags for amino groups. However, the apparent steric selectivity of pyrylium salts limits their application in the field of chemical labeling, especially during the labeling of sterically hindered compounds like amino acids, peptides, and proteins. Herein, we have investigated the effects of the α-substitution of pyrylium salts on their reactivity. We have also investigated the mechanism of nucleophilic reactions with pyrylium salts and further proposed that the reactivity of pyrylium salts mainly depends on the position and type of their substituents. A series of pyrylium salts were synthesized, and a highly active α-monosubstituted pyrylium salt, 2,4,5-triphenylpyrylium, was developed for efficient chemical labeling. All of the 15 amino acids studied were efficiently labeled under optimized reaction conditions. The 2,4,5-triphenylpyrylium salt was highly efficient in comparison to the previously reported 2,4,6-triphenylpyrylium salt developed for lysine-specific modifications. Furthermore, we successfully used 2,4,5-triphenylpyrylium salt for the hydrophobic labeling of peptides and protein hydrolysates. The most striking observation was that the ionization efficiency of short-chain multilabeled peptides in mixed samples, after derivatization, increased by up to 60 times. The increase in ionization efficiency gradually decreased with increasing peptide chain length. During the "soft" collision-induced dissociation (CID) process, the peptide was tagged at the N-terminus with 2,4,5-triphenylpyrylium, producing abundant a-type ions and b-type ions (Δ = 28), which eases the peptide resequencing process and assists in cracking the peptide codes. Moreover, 2,4,5-triphenylpyrylium has been utilized for the proteomic analysis of HeLa cell digests. In addition, 215 additional proteins were identified in the labeled products and the coverage of most proteins was improved.

Forensic proteomics

Protein is a major component of all biological evidence, often the matrix that embeds other biomolecules such as polynucleotides, lipids, carbohydrates, and small molecules. The proteins in a sample reflect the transcriptional and translational program of the originating cell types. Because of this, proteins can be used to identify body fluids and tissues, as well as convey genetic information in the form of single amino acid polymorphisms, the result of non-synonymous SNPs. This review explores the application and potential of forensic proteomics. The historical role that protein analysis played in the development of forensic science is examined. This review details how innovations in proteomic mass spectrometry have addressed many of the historical limitations of forensic protein science, and how the application of forensic proteomics differs from proteomics in the life sciences. Two more developed applications of forensic proteomics are examined in detail: body fluid and tissue identification, and proteomic genotyping. The review then highlights developing areas of proteomics that have the potential to impact forensic science in the near future: fingermark analysis, species identification, peptide toxicology, proteomic sex estimation, and estimation of post-mortem intervals. Finally, the review highlights some of the newer innovations in proteomics that may drive further development of the field. In addition to potential impact, this review also attempts to evaluate the stage of each application in the development, validation and implementation process. This review is targeted at investigators who are interested in learning about proteomics in a forensic context and expanding the amount of information they can extract from biological evidence.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

A pilot study indicating the dysregulation of the complement and coagulation cascades in treated schizophrenia and bipolar disorder patients

A better understanding of the proteome profile after bipolar disorder (BD) and schizophrenia (SCZ) treatment, besides monitoring disease progression, may assist on the development of novel therapeutic strategies with the ability to reduce or control possible side effects. In this pilot study, proteomics analysis employing nano liquid chromatography coupled to mass spectrometry (nLC-MS) and bioinformatic tools were applied to identify differentially abundant proteins in serum of treated BD and SCZ patients. In total, 10 BD patients, 10 SCZ patients, and 14 healthy controls (HC) were included in this study. 24 serum proteins were significantly altered (p < 0.05) in BD and SCZ treated patients and, considering log₂FC > 0.58, 8 proteins presented lower abundance in the BD group, while 7 proteins presented higher abundance and 2 lower abundance in SCZ group when compared against HC. Bioinformatics analysis based on these 24 proteins indicated two main altered pathways previously described in the literature; furthermore, it revealed that opposite abundances of the complement and coagulation cascades were the most significant biological processes involved in these pathologies. Moreover, we describe disease-related proteins and pathways associations suggesting the necessity of clinical follow-up improvement besides treatment, as a precaution or safety measure, along with the disease progression. Further biological validation and investigations are required to define whether there is a correlation between complement and coagulation cascade expression for BD and SCZ and cardiovascular diseases.

The impact of mass spectrometry application to screen new proteomics biomarkers in Ophthalmology

INTRODUCTION

In the search for molecular markers that aid in the early diagnosis and treatment of various human diseases, many studies have focused on changes in genes, their transcripts and protein products. Recent advances in proteomic methodologies, such as mass spectrometry (MS), generate new opportunities to obtain relevant information on normal and abnormal processes that occur in many important cell pathways. The human eye is a highly specialized and compartmentalized organ, and the interpretation of molecular biomarkers helps to evaluate its cellular structure, providing a broader molecular understanding that corroborates in the pathophysiology of ophthalmological diseases, with marked improvements in their diagnosis, prognosis and treatment. This review summarizes the most important protein biomarkers in Ophthalmology screened by MS tools.

CONCLUSION

The use of translational medicine techniques (as MS), integrating basic and clinical research, still transforms scientific findings, from laboratory researches to clinical applications, from the bedside into the community.

Sca1+ Progenitor Cells (Ex vivo) Exhibits Differential Proteomic Signatures From the Culture Adapted Sca1+ Cells (In vitro), Both Isolated From Murine Skeletal Muscle Tissue

Stem cell antigen-1 (Sca-1) is a glycosyl-phosphatidylinositol-anchored membrane protein that is expressed in a sub-population of muscle stem and progenitor cell types. Reportedly, Sca-1 regulates the myogenic property of myoblasts and Sca-1^-/- mice exhibited defective muscle regeneration. Although the role of Sca-1 in muscle development and maintenance is well-acknowledged, molecular composition of muscle derived Sca-1⁺ cells is not characterized. Here, we applied a high-resolution mass spectrometry-based workflow to characterize the proteomic landscape of mouse hindlimb skeletal muscle derived Sca-1⁺ cells. Furthermore, we characterized the impact of the cellular microenvironments on the proteomes of Sca-1⁺ cells. The proteome component of freshly isolated Sca-1⁺ cells (ex vivo) was compared with that of Sca-1⁺ cells expanded in cell culture (in vitro). The analysis revealed significant differences in the protein abundances in the two conditions reflective of their functional variations. The identified proteins were enriched in various biological pathways. Notably, we identified proteins related to myotube differentiation, myotube cell development and myoblast fusion. We also identified a panel of cell surface marker proteins that can be leveraged in future to enrich Sca-1⁺ cells using combinatorial strategies. Comparative analysis implicated the activation of various pathways leading to increased protein synthesis under in vitro condition. We report here the most comprehensive proteome map of Sca-1⁺ cells that provides insights into the molecular networks operative in Sca-1⁺ cells. Importantly, through our work we generated the proteomic blueprint of protein abundances significantly altered in Sca-1⁺ cells under ex vivo and in vitro conditions. The curated data can also be visualized at https://yenepoya.res.in/database/Sca-1-Proteomics .

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Mass-Spectrometry-Based Near-Complete Draft of the Saccharomyces cerevisiae Proteome

Proteomics approaches designed to catalogue all open reading frames (ORFs) under a defined set of growth conditions of an organism have flourished in recent years. However, no proteome has been sequenced completely so far. Here, we generate the largest yeast proteome data set, including 5610 identified proteins, using a strategy based on optimized sample preparation and high-resolution mass spectrometry. Among the 5610 identified proteins, 94.1% are core proteins, which achieves near-complete coverage of the yeast ORFs. Comprehensive analysis of missing proteins showed that proteins are missed mainly due to physical properties. A review of protein abundance shows that our proteome encompasses a uniquely broad dynamic range. Additionally, these values highly correlate with mRNA abundance, implying a high level of accuracy, sensitivity, and precision. We present examples of how the data could be used, including reannotating gene localization, providing expression evidence of pseudogenes. Our near-complete yeast proteome data set will be a useful and important resource for further systematic studies.

Integrated bioinformatic analysis of RNA binding proteins in hepatocellular carcinoma

RNA binding proteins (RBPs) are aberrantly expressed in a tissue-specific manner across many tumors. These proteins, which play a vital role in post-transcriptional gene regulation, are involved in RNA splicing, maturation, transport, stability, degradation, and translation. We set out to establish an accurate risk score model based on RBPs to estimate prognosis in hepatocellular carcinoma (HCC). RNA-sequencing data, proteomic data and corresponding clinical information were acquired from the Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database respectively. We identified 406 differentially expressed RBPs between HCC tumor and normal tissues at the transcriptional and protein level. Overall, 11 RBPs (BRIX1, DYNC1H1, GTPBP4, PRKDC, RAN, RBM19, SF3B4, SMG5, SPATS2, TAF9, and THOC5) were selected to establish a risk score model. We divided HCC patients into low-risk and high-risk groups based on the median of risk score values. The survival analysis indicated that patients in the high-risk group had poorer overall survival compared to patients in the low-risk group. Our study demonstrated that 11 RBPs were associated with the overall survival of HCC patients. These RBPs may represent potential drug targets and can help optimize future clinical treatment.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

A combined proteomic and metabolomic analyses of the priming phase during rat liver regeneration

By comparing differentially abundant proteins and metabolites, the protein expression, metabolic changes and metabolic regulation mechanisms during the priming phase of liver regeneration (LR) were investigated. We combined proteomic analysis via isobaric tags for relative and absolute quantification (iTRAQ) with metabolomic analysis via nontargeted liquid chromatography-mass spectrometry (LC-MS). LC-MS was used to examine 29 energy metabolites expression alterations in targeted metabolomics. A total number of 441 differentially expressed proteins and 65 metabolites were identified. PSMB10, PSMB5, RCG_63409, PSME4 and PSMB7 were key node proteins, these proteins are involved in the proteasome pathway. The most strongly enriched transcription factor motif was TP63. These results point out a critical role of the proteasome pathway (defense mechanisms) and of TP63 (metabolic regulator) as the key transcription factor during the priming phase of LR. Metabolomic and metabolite analysis showed that profiling indicates upregulation of arginine biosynthesis and glycolysis as the main ATP-delivering pathway. Integrative proteomic and metabolomic analysis showed that biomolecular changes were primarily related to the neurological disease, cell death and survival and cell morphology. What's more, neurotransmitters may play an important role in the regulation of LR.

Intron retention and its impact on gene expression and protein diversity: A review and a practical guide

Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis. Here we provide a review of IR and its biological implications, as well as a practical guide for how to detect and analyze it. Several methods, including long read third generation direct RNA sequencing, are described. We have developed an R package, FakIR, to facilitate the execution of the bioinformatic tasks recommended in this review and a tutorial on how to fit them to users aims. Additionally, we provide guidelines and experimental protocols to validate IR discovery and to evaluate the potential impact of IR on gene expression and protein output. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Splicing Regulation/Alternative Splicing RNA Methods > RNA Analyses in vitro and In Silico.

Molecular mechanisms by which iNOS uncoupling can induce cardiovascular dysfunction during sepsis: Role of posttranslational modifications (PTMs)

Human sepsis is the result of a multifaceted pathological process causing marked dysregulation of cardiovascular responses. A more sophisticated understanding of the pathogenesis of sepsis is certainly prerequisite. Evidence from studies provide further insight into the role of inducible nitric oxide synthase (iNOS) isoform. Results on inhibition of iNOS in sepsis models remain inconclusive. Concern has been devoted to improving our knowledge and understanding of the role of iNOS. The aim of this review is to define the role of iNOS in redox homeostasis disturbance, the detailed mechanisms linking iNOS and posttranslational modifications (PTMs) to cardiovascular dysfunctions, and their future implications in sepsis settings. Many questions related to the iNOS and PTMs still remain open, and much more work is needed on this.

A Signaling Pathway to Mediate the Combined Immunomodulation of Acetylcholine and Enkephalin in Oyster Crassostrea gigas

Molluscs have evolved a primitive but complete neuroendocrine-immune (NEI) system with a vast array of neurotransmitters to conduct both humoral and cellular immunomodulation. Previous studies have illustrated the immune functions of several key neurotransmitters. However, the combined effects of multiple neurotransmitters and the signaling pathway to mediate such immunomodulation have not been well-understood. In the present study, iTRAQ and LC-ESI-MS/MS approaches were employed to investigate the combined immunomodulation functions of two crucial neurotransmitters, acetylcholine (ACh), and [Met⁵]-enkephalin (ENK), in oyster Crassostrea gigas. A total number of 5,379 proteins were identified from hemocytes of oysters after the treatments with Ach and ENK separately or simultaneously, and 1,475 of them were found to be significantly up-regulated, while 1,115 of them were significantly down-regulated. The protein expression patterns in the groups treated by ACh and ENK separately were quite similar, which were dramatically different from that in the group treated by ACh+ENK. One hundred seventy-two proteins were found to be differentially expressed in all the three neurotransmitter treatment groups. Functional validation suggested that ACh and ENK possibly modulate the immune response in oyster hemocytes by enhancing pathogen recognition, cell apoptosis, and the enzyme activities of superoxide dismutase (SOD). Moreover, GO enrichment and co-expression network analyses implied that the combined immunomodulation of ACh and ENK might be mediated by p53, EGF-R–ErbB, and Fc gamma R (FcγR) signaling pathways. These results collectively indicated that multiple neurotransmitters executed a combined and ordered immune regulation through common signaling cascades in molluscs, which was under delicate control to maintain the homeostasis.

Optical control of MAP kinase kinase 6 (MKK6) reveals that it has divergent roles in pro-apoptotic and anti-proliferative signaling

The selective pressure imposed by extrinsic death signals and stressors adds to the challenge of isolating and interpreting the roles of proteins in stress-activated signaling networks. By expressing a kinase with activating mutations and a caged lysine blocking the active site, we can rapidly switch on catalytic activity with light and monitor the ensuing dynamics. Applying this approach to MAP kinase 6 (MKK6), which activates the p38 subfamily of MAPKs, we found that decaging active MKK6 in fibroblasts is sufficient to trigger apoptosis in a p38-dependent manner. Both in fibroblasts and in a murine melanoma cell line expressing mutant B-Raf, MKK6 activation rapidly and potently inhibited the pro-proliferative extracellular signal-regulated kinase (ERK) pathway; to our surprise, this negative cross-regulation was equally robust when all p38 isoforms were inhibited. These results position MKK6 as a new pleiotropic signal transducer that promotes both pro-apoptotic and anti-proliferative signaling, and they highlight the utility of caged, light-activated kinases for dissecting stress-activated signaling networks.

Integration of proteomics and metabolomics reveals promotion of proliferation by exposure of bisphenol S in human breast epithelial MCF-10A cells

Bisphenol S (BPS) has been reported to have similar estrogenic effects as bisphenol A (BPA). Considering the endocrine disrupting effects of BPS, in this study, we investigated the effects of BPS exposure on normal human breast epithelial cell line MCF-10A by using mass spectrometry (MS)-based metabolomics and quantitative proteomics. We found that exposure to BPS for 24 h altered the proliferation of MCF-10A cells in a hormetic manner with the highest proliferation rate at the dosage of 1 μM. A total of 200 proteins were identified to be significantly changed by 1 μM of BPS exposure. The upregulation of epidermal growth factor receptor (EGFR) and Ras/mTOR-related proteins implied that EGFR-mediated pathways were involved in BPS-induced proliferation of MCF-10A cells. In addition, several proliferation-related protein markers were found to be elevated, such as MKI67 and CDH1, further indicating the promotion of proliferation by low dose of BPS exposure. Besides, 35 endogenous metabolites were found to be significantly changed. The joint pathway analysis of the altered metabolites and proteins suggested changes in pathways of tricarboxylic acid (TCA) cycle, purine metabolism, pyruvate metabolism and lipid metabolism, which were involved in sustaining cell proliferation and cellular signal transduction. Taken together, this study provides insights into the effects and the potential mechanisms of BPS on estrogen receptor α-negative normal breast cell line MCF-10A, broadening our knowledge about the risk of using BPS as the alternative of BPA.

Analysis of proximal ALOX5 promoter binding proteins by quantitative proteomics

5-Lipoxygenase (5-LO) is the initial enzyme in the biosynthesis of leukotrienes, which are mediators involved in pathophysiological conditions such as asthma and certain cancer types. Knowledge of proteins involved in 5-LO pathway regulation, including gene regulatory proteins, is needed to evaluate all options for therapeutic intervention in these diseases. Here, we present a mass spectrometric screening of ALOX5 promoter-interacting proteins, obtained by DNA pulldown and label-free quantitative mass spectrometry. Protein preparations from myeloid and B-lymphocytic cell lines were screened for promoter DNA interactors. Through statistical analysis, 66 proteins were identified as specific ALOX5 promotor binding proteins. Among those, the 15 most likely candidates for a prominent role in ALOX5 gene regulation are the known ALOX5 interactors Sp1 and Sp3, the related factor Sp2, two Krüppel-like factors (KLF13 and KLF16) and six other zinc finger proteins (MAZ, PRDM10, VEZF1, ZBTB7A, ZNF281 and ZNF579). Intriguingly, we also identified two helicases (BLM and DHX36) and the proteins hnRNPD and hnRNPK, which are, together with the protein MAZ, known to interact with DNA G-quadruplex structures. As G-quadruplexes are implicated in gene regulation, spectroscopic and antibody-based methods were used to confirm their presence within the GC-rich sequence of the ALOX5 promoter. In summary, we have systematically characterized the interactome of the ALOX5 promoter, identifying several zinc finger proteins as novel potential ALOX5 gene regulators. Further, we have shown that the ALOX5 promoter can form DNA G-quadruplex structures, which may play a functional role in ALOX5 gene regulation.

Microarray and Mass Spectrometry-Based Methodology for Lipid Profiling of Tissues and Cell Cultures

The recent developments in mass spectrometry have revealed the importance of lipids as biomarkers in the context of different diseases and as indicators of the cell's homeostasis. However, further advances are required to unveil the complex relationships between lipid classes and lipid species with proteins. Here, we present a new methodology that combines microarrays with mass spectrometry to obtain the lipid fingerprint of samples of a different nature in a standardized and fast way, with minimal sample consumption. As a proof of concept, we use the methodology to obtain the lipid fingerprint of 20 rat tissues and to create a lipid library for tissue classification. Then, we combine those results with immunohistochemistry and enzymatic assays to unveil the relationship between some lipid species and two enzymes. Finally, we demonstrate the performance of the methodology to explore changes in lipid composition of the nucleus accumbens from mice subjected to two lipid diets.

Proteomic Analysis of the Xanthan-Degrading Pathway of Microbacterium sp. XT11

Xanthan, a highly stable polysaccharide which is not easily degraded by most microorganisms, contains a cellulosic backbone with trisaccharide side chains composed of mannosyl-glucuronyl-mannose attached α-1,3 to alternating glucosyl residues. Different digestion strategies were first applied to demonstrate the complexity about the proteomes of Microbacterium sp. XT11 in xanthan medium and glucose medium. Significantly up-regulated proteins induced by xanthan were screened out by the label-free quantitation of the proteomes of Microbacterium sp. XT11 in xanthan medium and glucose medium. Consequently, 2746 and 2878 proteins were identified in proteomes of Microbacterium sp. XT11 in xanthan medium and glucose medium individually, which represent 80.6 and 84.4% of total protein dataset predicted to be expressed by the gene. In the list of 430 induced proteins containing the proteins specifically expressed or up-regulated in xanthan medium, 19 proteins involved in carbohydrate-active enzymes database and 38 proteins annotated with transporter activity were critical in the degrading pathway of xanthan. Four CAZymes (GH3, GH38, GH9, and PL8) and one ABC transporter (LX1-1GL001097) were verified with quantitative real-time polymerase chain reaction. Four CAZymes (GH3, GH38, GH9, and PL8) were further verified with the enzyme assay. This study suggests a xanthan-degrading pathway in Microbacterium sp. XT11, and other potential xanthan degradation-related proteins still need further investigation.

Redox Proteomes in Human Physiology and Disease Mechanisms

Redox proteomics is a field of proteomics that is concerned with the characterization of the oxidation state of proteins to gain information about their modulated structure, function, activity, and involvement in different physiological pathways. Oxidative modifications of proteins have been shown to be implicated in normal physiological processes of cells as well as in pathomechanisms leading to the development of cancer, diabetes, neurodegenerative diseases, and some rare hereditary metabolic diseases, like classic galactosemia. Reactive oxygen species generate a variety of reversible and irreversible modifications in amino acid residue side chains and within the protein backbone. These oxidative post-translational modifications (Ox-PTMs) can participate in the activation of signal transduction pathways and mediate the toxicity of harmful oxidants. Thus the application of advanced redox proteomics technologies is important for gaining insights into molecular mechanisms of diseases. Mass-spectrometry-based proteomics is one of the most powerful methods that can be used to give detailed qualitative and quantitative information on protein modifications and allows us to characterize redox proteomes associated with diseases. This Review illustrates the role and biological consequences of Ox-PTMs under basal and oxidative stress conditions by focusing on protein carbonylation and S-glutathionylation, two abundant modifications with an impact on cellular pathways that have been intensively studied during the past decade.

A systematic approach to orient the human protein-protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Comprehensive metabolomic and proteomic analyses reveal candidate biomarkers and related metabolic networks in atrial fibrillation

INTRODUCTION

Atrial fibrillation (AF) is an abnormal heart rhythm characterized by an irregular beating of the atria and is associated with an increased risk of heart failure, dementia, and stroke. Currently, the perturbation of plasma content due to AF disease onset is not well known.

OBJECTIVES

To investigate dysregulated molecules in blood plasma of untreated AF patients, with the goal of identifying biomarkers for disease screening and pathological studies.

METHODS

LC-MS based untargeted metabolomics, lipidomics and proteomics analyses were performed to find candidate biomarkers. A targeted quantification assay and an ELISA were performed to validate the results of the omics analyses.

RESULTS

We found that 24 metabolites, 16 lipids and 16 proteins were significantly dysregulated in AF patients. Pathway enrichment analysis showed that the purine metabolic pathway and fatty acid metabolism were perturbed by AF onset. FA 20:2 and FA 22:4 show great linear correlational relationship with the left atrial area and could be considered for AF disease stage monitoring or prognosis evaluation.

CONCLUSION

we used a comprehensive multiple-omics strategy to systematically investigate the dysregulated molecules in the plasma of AF patients, thereby revealing potential biomarkers for diagnosis and providing information for pathological studies.

Proteomic profiling sheds light on alkali tolerance of common wheat (Triticum aestivum L.)

Alkali (high-pH) stress is an important factor limiting agricultural production and has complex effects on plant metabolism. Transcriptomics is widely used in the discovery of stress-response genes, but it provides only a rough estimation for gene expression. Proteomics may be more helpful than transcriptomics for the discovery and identification of stress-response genes. In this study, wheat plants were treated with sodic alkaline stress (50 mM, NaHCO₃: Na₂CO₃ = 1:1; pH 9.7), and then proteomic analysis was carried out on control and stressed plants. We detected 3,104 proteins, including 69 alkaline stress-response proteins. Five superoxide dismutases, three malate dehydrogenases, three dehydrin proteins, and one V-ATPase protein were upregulated in sodic alkaline-stressed wheat roots. We propose that these salinity response proteins may be important for ion homeostasis and osmotic regulation of sodic alkaline-stressed wheat. Additionally, two malic enzymes and many enzymes involved in the tricarboxylic acid cycle (TCA) were downregulated in the roots. The upregulation of malate dehydrogenase and the downregulation of TCA enzymes and malic enzymes may enhance the accumulation of malate in sodic alkaline-stressed wheat roots. Previous studies have demonstrated that the accumulation of malate in roots is a crucial adaptive mechanism of wheat to sodic alkaline stress. Herein, our proteomics results provided molecular insights into this adaptive mechanism.

Mass Spectrometry for Proteomics and Recent Developments in ESI, MALDI and other Ionization Methodologies

Background:

Mass spectrometry is a tool used in analytical chemistry to identify components in a chemical compound and it is of tremendous importance in the field of biology for high throughput analysis of biomolecules, among which protein is of great interest.

Objective:

Advancement in proteomics based on mass spectrometry has led the way to quantify multiple protein complexes, and proteins interactions with DNA/RNA or other chemical compounds which is a breakthrough in the field of bioinformatics.

Methods:

Many new technologies have been introduced in electrospray ionization (ESI) and Matrixassisted Laser Desorption/Ionization (MALDI) techniques which have enhanced sensitivity, resolution and many other key features for the characterization of proteins.

Results:

The advent of ambient mass spectrometry and its different versions like Desorption Electrospray Ionization (DESI), DART and ELDI has brought a huge revolution in proteomics research. Different imaging techniques are also introduced in MS to map proteins and other significant biomolecules. These drastic developments have paved the way to analyze large proteins of >200kDa easily.

Conclusion:

Here, we discuss the recent advancement in mass spectrometry, which is of great importance and it could lead us to further deep analysis of the molecules from different perspectives and further advancement in these techniques will enable us to find better ways for prediction of molecules and their behavioral properties.

A protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mRNA into a binomial probability scoring model

Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior probability of protein identification and integrated to re-score in PSM using the binomial probability distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.

α-Amidated Peptides: Approaches for Analysis

α-Amidation is a terminal modification in peptide biosynthesis that can itself be rate limiting in the overall production of bioactive α-amidated peptides. More than half of the known neural and endocrine peptides are α-amidated and in most cases this structural feature is essential for receptor recognition, signal transduction, and thus biologic function. This chapter describes methods for developing and using analytical tools to study the biology of α-amidated peptides. The principal analytical method used to quantify α-amidated peptides is the radioimmunoassay (RIA). Detailed protocols are provided for (1) primary antibody production and characterization; (2) radiolabeling of RIA peptides; (3) sample preparation; and (4) performance of the RIA itself. Techniques are also described for the identification and verification of α-amidated peptides. Lastly, in vivo models used for studying the biology of α-amidation are discussed.