Prioritization of genes for translation: a computational approach.

INTRODUCTION: Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED: In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION: Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Comprehensive characterization of protein modifications using mass spectrometry and dry blood spots.

PURPOSE: The main objective of this study is to characterize and analyze modified peptides in DBS samples. This includes deciphering their specific PTMs and understanding their potential impact on the population or disease cohort under study. EXPERIMENTAL DESIGN: Using mass spectrometry-based proteomic approaches, we performed a comprehensive analysis of DBS samples. Our focus was on the identification and quantification of modified peptides. We also took advantage of recent advances in DBS mass spectrometry to ensure accurate detection and quantification. RESULTS: A comprehensive analysis identified 972 modified peptides in DBS samples. Of these, a subset of 211 peptides was consistently present in all samples, highlighting their potential biological importance and relevance. This indicates a diverse spectrum of PTMs in the proteome of DBS samples. CONCLUSIONS AND CLINICAL RELEVANCE: Integration of mass spectrometry and proteomics has revealed a broad spectrum of modified peptides in DBS samples and highlighted their importance in biological processes and disease progression. Accurate detection of these PTMs may be critical for risk stratification and disease management. This study improves the understanding of molecular mechanisms underlying biological processes and disease development, providing important insights for clinical applications.

Diagnostic and monitoring applications using near infrared (NIR) spectroscopy in cancer and other diseases.

Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.

Urinary exosomes: Diagnostic impact with a bioinformatic approach.

Exosomes are tiny membrane-enveloped vesicles of endosomal origin, typically 40-120nm in diameter, produced by most cells in both normal and pathological situations. These exosomes can be isolated from all biofluids, including urine. In this context, many researchers have focused on the analysis of urinary exosomes because urine can be collected in large quantities, regularly, and with minimal effort. Exosomes contain phospholipids, cholesterol, proteins, glycoconjugates, nucleic acids, and metabolites. Because all organs and tissues produce exosomes, their molecular cargo can provide first-hand information about the physiological and biological state of the site of origin. Many potential disease biomarker candidates have already been identified in urinary exosomes. In this chapter, we performed a bibliometric analysis of the keywords "exosome(s)" and "urine" to identify related terms, diseases and molecular/biological processes, and other related terms. This yielded interesting results suggesting that exosomes in urine may play a role in the pathogenesis of various diseases. Moreover, this chapter discusses exosomes isolation and characterization methodologies and highlight the importance of urinary exosomes and their role in the diagnosis, prognosis and therapy of various diseases. We offer a bibliometric approach and an in-depth analysis on several exosomes' isolation techniques, diagnostic potential for urogenital and specific non-urogenital diseases, as well as an overview of miRNAs significance on urinary exosomes, conferring a more complete status to this review, something that was still lacking in the current literature.

Re-Analysis of Published Datasets in Search of Novel Urogenital Diseases Biomarkers.

BACKGROUND: Exosome research is a current trend in functional proteomics as it provides important data on the pathophysiology and pathogenesis of diseases. The scientific outputs regarding these topics often only approach disease-protein/peptide/exosome or mechanismprotein/ peptide/exosome association. Approaching all three aspects could be the key to a better understanding of the pathophysiology and uncovering novel biomarkers for urogenital diseases. The focus of this work is to study exosome datasets to understand the possible role of underlying proteins in disease manifestation. We also attempt to link 4 different diseases that affect renal functions and are genetically inherited. METHODS: For this purpose, the existing literature is consulted to understand the importance of exosomes in disease prediction, diagnosis and therapy. Available biotechnological methods of exosome analysis and the tools of proteomic analysis, data mining and visualization are discussed. The database PRIDE is selected to query the information of several datasets related to urinary exosome analysis. RESULTS: We have obtained a list of 19 proteins/genes involved in the mentioned diseases. On this list, we found a proteomic fingerprint consisting of Rab-7a, PDCD6, and CDC42, among others, and we are exploring their biological significance and underlying processes. CONCLUSION: APOA1, CD59, CD9, IGHG1, RAB7A, RAP1A, SEMG1 and SEMG2 are common in four urogenital diseases, and are involved in interactions with podosomes and endosomes, remodeling of chylomicrons, regulation of interleukin production, regulation of endopeptidase activity, and establishment of apical/basal polarity of epithelial cells.

Multi-Omic Profiling of Macrophages Treated with Phospholipids Containing Omega-3 and Omega-6 Fatty Acids Reveals Complex Immunomodulatory Adaptations at Protein, Lipid and Metabolic Levels.

In recent years, several studies have demonstrated that polyunsaturated fatty acids have strong immunomodulatory properties, altering several functions of macrophages. In the present work, we sought to provide a multi-omic approach combining the analysis of the lipidome, the proteome, and the metabolome of RAW 264.7 macrophages supplemented with phospholipids containing omega-3 (PC 18:0/22:6; ω3-PC) or omega-6 (PC 18:0/20:4; ω6-PC) fatty acids, alone and in the presence of lipopolysaccharide (LPS). Supplementation of macrophages with ω3 and ω6 phospholipids plus LPS produced a significant reprogramming of the proteome of macrophages and amplified the immune response; it also promoted the expression of anti-inflammatory proteins (e.g., pleckstrin). Supplementation with the ω3-PC and ω6-PC induced significant changes in the lipidome, with a marked increase in lipid species linked to the inflammatory response, attributed to several pro-inflammatory signalling pathways (e.g., LPCs) but also to the pro-resolving effect of inflammation (e.g., PIs). Finally, the metabolomic analysis demonstrated that supplementation with ω3-PC and ω6-PC induced the expression of several metabolites with a pronounced inflammatory and anti-inflammatory effect (e.g., succinate). Overall, our data show that supplementation of macrophages with ω3-PC and ω6-PC effectively modulates the lipidome, proteome, and metabolome of these immune cells, affecting several metabolic pathways involved in the immune response that are triggered by inflammation.

Peptidomics and proteogenomics: background, challenges and future needs.

INTRODUCTION: With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development. AREAS COVERED: Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date. EXPERT OPINION: This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.

How can artificial intelligence be used for peptidomics?

INTRODUCTION: Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED: The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1). EXPERT OPINION: Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).

Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases.

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

Microfluidics for Peptidomics, Proteomics, and Cell Analysis.

Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1-100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.

The potential impact of salivary peptides in periodontitis.

Periodontitis is a complex immune-inflammatory condition characterized by the disruption of the periodontal ligament and subsequent formation of periodontal pockets, and by alveolar bone loss, often resulting in tooth loss. A myriad of factors, namely, genetic, metabolic, immunological, and inflammatory, is associated with progression of periodontitis. Periodontitis is also associated with systemic conditions such as neoplastic disorders, obesity, and diabetes. The current diagnosis of this disease relies on clinical measurements such as clinical attachment loss and probing depth, which have poor precision due to patient, operator and probe-related factors. Thus, there is a need to develop reliable, objective, and reproducible biomarkers for early diagnosis of periodontitis. In this regard, saliva, with contributions from the gingival crevicular fluid, holds great potential. However, most of the information on biomarkers of periodontium-related salivary proteins has come from studies on the molecular pathogenesis of periodontitis. In periodontitis, a more holistic approach, such as the use of -omics technologies, for biomarker discovery, is needed. Herein, we review the biomarkers proposed to date for the assessment of periodontitis, with emphasis on the role of salivary peptides in periodontitis and their assessment by high-throughput saliva proteomics. We also discuss the challenges pertaining to the identification of new periodontitis biomarkers in saliva.

Automatic text-mining as an unbiased approach to uncover molecular associations between periodontitis and coronary artery disease.

The increasing prevalence of periodontal and cardiovascular diseases is the result of a sedentary lifestyle associated with poor diet, obesity, hypercholesterolaemia, smoking habits, alcohol consumption and stress. The present study aims to uncover molecular associations between periodontitis and coronary heart disease using an unbiased strategy of automatic text mining traditionally applied to bibliometric studies. A total of 1590 articles on these diseases were retrieved from the Web of knowledge database and searched using the VOS viewer to create a network of keywords associated with both diseases. These data were supplemented with data from DisGeNET, which stores known associations to either periodontitis or coronary heart disease. Overall, the automated text mining approach presented here highlighted inflammatory molecules as common associations between periodontitis and coronary heart disease. Specifically, this study showed that molecules such as C-reactive protein, interleukins 6 and 1-ß, myeloperoxidase, and matrix metalloproteinase 9 are simultaneously associated with periodontitis and coronary artery disease by both text mining and DisGeNET analyses. This association validates the multiplex assessment of salivary inflammatory markers as a tool to assess cardiovascular disease risk and could become an important tool to identify common molecular targets to monitor both diseases simultaneously. In addition, the text mining protocol and subsequent data processing and methods using bioinformatics tools could be useful to uncover links between other diseases.

What can urinary exosomes tell us?

Exosomes are involved in a wide variety of biochemical processes in human body homeostasis. Exosomes also provide important information regarding communications among several organ systems. Additionally, they can serve as molecular vehicles to deliver drugs. Therefore, exosomes have received wide attention in current biomedical research for unraveling pathogenic mechanisms of diseases, searching for novel biomarkers, and discovering new drugs. This paper reviews and discusses the significance of urinary exosomes for a better understanding of human disease pathophysiology and their potential use as therapeutic targets. Isolation methods of exosomes and the latest technological advances are also discussed. Furthermore, novel urinary exosomal biomarkers are highlighted with special emphasis on their clinical applicability (particularly sensitivity, specificity, reliability, and other aspects). Finally, future trends for this field are analyzed and our perspectives are provided.

The role of micropeptides in biology.

Micropeptides are small polypeptides coded by small open-reading frames. Progress in computational biology and the analyses of large-scale transcriptomes and proteomes have revealed that mammalian genomes produce a large number of transcripts encoding micropeptides. Many of these have been previously annotated as long noncoding RNAs. The role of micropeptides in cellular homeostasis maintenance has been demonstrated. This review discusses different types of micropeptides as well as methods to identify them, such as computational approaches, ribosome profiling, and mass spectrometry.

Elucidating Citrullination by Mass Spectrometry and Its Role in Disease Pathogenesis.

This review focuses on discussing key mechanisms in disease pathogenesis mediated by the protein post-translational modification citrullination. These processes are discussed in depth in the context of complex diseases such as rheumatoid arthritis, cancer, central nervous system disorders, and cardiovascular disease. Additionally, a critical evaluation of challenges in laboratory detection of citrullination sites is also outlined. In this context, the role of mass spectrometry is discussed with a focus on contemporary techniques that offer promising options to detect the exact site of protein citrullination. Novel methods described in the paper have the potential to detect and quantify the occurrence of post-translational modification sites for diagnosis and therapeutic purposes with a high degree of specificity and sensitivity. Furthermore, they offer a much faster performance than traditional techniques making them ideal for large-scale experimentation.

De novo sequencing of proteins by mass spectrometry.

INTRODUCTION: Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed. AREAS COVERED: De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data. EXPERT OPINION: As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.

Contact dermatitis: in pursuit of sensitizer's molecular targets through proteomics.

Protein haptenation, i.e., the modification of proteins by small reactive chemicals, is the key step in the sensitization phase of allergic contact dermatitis (ACD). Despite the research effort in past decades, the identification of immunogenic hapten-protein complexes that trigger a relevant pathogenic immune response in ACD, as well as the haptenation reaction molecular site, and the elements of a potentially conditioning environment during each of these stages, remain poorly understood. These questions led us to employ a proteomics-based approach to identify modified proteins in the dendritic-like cell line THP-1 sensitized with fluorescein isothiocyanate (FITC), through a combination of 2D-gel electrophoresis, nano-LC and mass spectrometry. A specific set of 39 targeted proteins was identified and comprised proteins from various cellular locations and biological functions. One of FITC targets was identified as MLK, a member of the mixed-lineage kinase family known to act as a mitogen-activated protein kinase kinase kinase and to control the activity of specific mitogen-activated protein kinase pathways, namely p38 and JNK pathways. Haptenated in the vicinity of its active site, our results point to MLK being a relevant target due to a consistent non-activation at early time points of these pathways upon FITC sensitization in THP-1 cells. Moreover, FITC pre-treatment significantly decrease phospho-p38 and phospho-JNK levels induced upon exposure to a classical activator such as lipopolysaccharide or to the sensitizer 2,4-dinitrofluorobenzene. Overall, our data point to specific amino acid residues haptenation within critical proteins as the key step in the subsequent signaling pathways modulation responsible for DC activation and maturation events.

New Insights on Non-Enzymatic Oxidation of Ganglioside GM1 Using Mass Spectrometry.

Gangliosides are acidic glycosphingolipids that are present in cell membranes and lipid raft domains, being particularly abundant in central nervous systems. They participate in modulating cell membrane properties, cell-cell recognition, cell regulation, and signaling. Disturbance in ganglioside metabolism has been correlated with the development of diseases, such as neurodegenerative diseases, and in inflammation. Both conditions are associated with an increased production of reactive oxidation species (ROS) that can induce changes in the structure of biomolecules, including lipids, leading to the loss or modification of their function. Oxidized phospholipids are usually involved in chronic diseases and inflammation. However, knowledge regarding oxidation of gangliosides is scarce. In order to evaluate the effect of ROS in gangliosides, an in vitro biomimetic model system was used to study the susceptibility of GM1 (Neu5Acα2-3(Galß1-3GalNAcß1-4)Galß1-4Glcß1Cer) to undergo oxidative modifications. Oxidation of GM1 under Fenton reaction conditions was monitored using high resolution electrospray ionization-mass spectrometry (ESI-MS) and tandem mass spectrometry (ESI-MS/MS). Upon oxidation, GM1 underwent oxidative cleavages in the carbohydrate chain, leading to the formation of other gangliosides GM2 (GalNAcß1-4Gal(Neu5Acα2-3)1-4Glcß1Cer), GM3 (Neu5Acα2-3Galß1-4Glcß1Cer), asialo-GM1 (Galß1-3GalNAcß1-4Galß1-4Glcß1Cer), asialo-GM2 (GalNAcß1-4Galß1-4Glcß1Cer), of the small glycolipids lactosylceramide (LacCer), glucosylceramide (GlcCer), and of ceramide (Cer). In addition, oxygenated GM1 and GM2 (as keto and hydroxy derivatives), glycans, oxidized glycans, and oxidized ceramides were also identified. Nonenzymatic oxidation of GM1 under oxidative stress contributes to the generation of other gangliosides that may participate in the imbalance of gangliosides metabolism in vivo, through uncontrolled enzymatic pathways and, consequently, play some role in neurodegenerative processes. Graphical Abstract ᅟ.

Efficient chemo-enzymatic gluten detoxification: reducing toxic epitopes for celiac patients improving functional properties.

Protein engineering of gluten, the exogenous effector in celiac disease, seeking its detoxification by selective chemical modification of toxic epitopes is a very attractive strategy and promising technology when compared to pharmacological treatment or genetic engineering of wheat. Here we present a simple and efficient chemo-enzymatic methodology that decreases celiac disease toxic epitopes of gluten proteins improving its technological value through microbial transglutaminase-mediated transamidation of glutamine with n-butylamine under reducing conditions. First, we found that using low concentrations of amine-nucleophile under non-reducing conditions, the decrease in toxic epitopes is mainly due to transglutaminase-mediated cross-linking. Second, using high amine nucleophile concentrations protein cross-linking is substantially reduced. Third, reducing conditions increase 7-fold the transamidation reaction further decreasing toxic epitopes amount. Fourth, using n-butylamine improves gluten hydrophobicity that strengthens the gluten network. These results open the possibility of tailoring gluten for producing hypoallergenic flours while still taking advantage of the unique viscoelastic properties of gluten.

Toward a standardized saliva proteome analysis methodology.

The present study aimed the evaluation of saliva sample pre-treatment, in particular the sample clearance usually performed by centrifugation, to the contribution of salivary proteome and peptidome. Using in-gel and off-gel approaches, a large content of salivary proteins was detected in the pellet fraction that is usually discarded. In addition, chaotropic/detergent treatment in combination with sonication, before the centrifugation step, resulted in salivary complex disruption and consequently in the extraction of high amounts of proteins. Based on this data, we suggest the use of urea/detergent with sonication as a standard saliva sample pre-treatment procedure. We also described a procedure to extract salivary peptides which can be performed even after saliva sample treatment with chaotropic/detergents. In overall, we reported for the first time the contribution of the pellet fraction to the whole saliva proteome. iTRAQ analysis highlighted a higher number of different peptides as well as distinct quantities of each protein class when after sample treatment with urea and sonication, acetone precipitation followed by solubilization with acetonitrile/HCl was performed.