Search Results (216)

Marine-derived bioactive peptides (MBPs) are emerging as promising natural agents for regulating inflammatory responses. MBPs, typically obtained through enzymatic hydrolysis of proteins from various marine organisms such as fish, mollusks, and algae, exhibit diverse biological activities, including antioxidant, immunomodulatory, and anti-inflammatory effects. The ability of MBPs to modulate key inflammatory mediators such as TNF-α, IL-6, and COX-2, primarily through pathways like NF-κB and MAPK, highlights the therapeutic potential of MBPs in managing chronic inflammatory diseases. However, most existing studies are confined to in vitro assays or animal models, with limited translation to human clinical applications. This review explores the stability, bioavailability, and metabolic rate of MBPs under physiological conditions, which remain poorly understood. In addition, a lack of standardized protocols for peptide extraction, purification, and efficacy evaluation hinders comparative analysis across studies and also different proteomics approaches for separation, purification, identification, and quantification of marine-derived peptides with therapeutic properties. The structure–function relationship of MBPs is also underexplored, limiting rational design and targeted applications in functional foods or therapeutic products. These limitations are largely due to a lack of consolidated information and integrated research efforts. To address these challenges, this review summarizes recent progress in identifying MBPs with anti-inflammatory potentials, outlines key mechanisms, and highlights current limitations. Additionally, this review also emphasizes the need to enhance mechanistic understanding, optimize delivery strategies, and advance clinical validation to fully realize the therapeutic potential of MBPs. Full article

Circular RNAs are a class of stable non-coding RNAs generated through a back-splicing mechanism. They are now recognized as central players in cell function and are no longer considered byproducts of transcription. CircRNAs regulate gene expression at the transcriptional, post-transcriptional, and translational levels by interacting with various molecules. They act as sponges for miRNAs and proteins, molecular scaffolds, and can also be translated into peptides. Although advances in next-generation sequencing and PCR methods have improved their identification and quantification, technical and bioinformatic challenges remain. Increasing evidence shows their involvement in cardiovascular diseases such as heart failure, hypertrophy, fibrosis, and atherosclerosis, with protective or deleterious effects depending on the context. Given their presence in biological fluids and extracellular vesicles, they can be considered promising biomarkers, but their therapeutic applications are still under investigation. Future studies including a better understanding of their mechanisms of action, the development of standardized validation methods, and potential clinical applications (prevention, early diagnosis, personalized therapies) in diseases are still needed. This review provides an updated overview of the knowledge regarding circRNAs and their translational role in health and disease with a particular focus on cardiovascular diseases. Full article

A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and destructive. Near-infrared (NIR) spectroscopy enables proximate composition analysis in a non-destructive, rapid, inexpensive, and practical manner, providing results similar to well-established conventional methods. This study aimed to evaluate the feasibility of NIRs-based selection to identify more nutritious rice genotypes. A collection of 155 rice genotypes grown in Southern Brazil was used. After harvest, grains were hulled, polished, and milled. NIRs was used to determine moisture, starch, protein, fat, ash, and fiber contents in rice flour. It was possible to differentiate genotypes with higher and lower levels of the investigated components. Similar and distinct values were observed in comparison to other studies, indicating the accuracy of NIRs and the effect of genotype and environment, respectively. Starch is correlated negatively with protein and fat, preventing the identification of genotypes with high levels of these three components. PCA enabled the separation of the genotypes but highlighted the complexity of sample distribution. NIRs is an effective and accurate method to determine the proximate composition of rice, enabling the selection of more nutritious genotypes. Full article

Redox signaling is central to plant adaptation, influencing metabolic regulation, stress responses, and developmental processes through thiol-based oxidative post-translational modifications (oxiPTMs) of redox-sensitive proteins. These modifications, particularly those involving cysteine (Cys) residues, act as molecular switches that alter protein function, structure, and interactions. Advances in mass spectrometry-based redox proteomics have greatly enhanced the identification and quantification of oxiPTMs, enabling a more refined understanding of redox dynamics in plant cells. In parallel, the emergence of computational modeling, artificial intelligence (AI), and machine learning (ML) has revolutionized the ability to predict redox-sensitive residues and characterize redox-dependent signaling networks. This review provides a comprehensive synthesis of methodological advancements in redox proteomics, including enrichment strategies, quantification techniques, and real-time redox sensing technologies. It also explores the integration of computational tools for predicting S-nitrosation, sulfenylation, S-glutathionylation, persulfidation, and disulfide bond formation, highlighting key models such as CysQuant, BiGRUD-SA, DLF-Sul, and Plant PTM Viewer. Furthermore, the functional significance of redox modifications is examined in plant development, seed germination, fruit ripening, and pathogen responses. By bridging experimental proteomics with AI-driven prediction platforms, this review underscores the future potential of integrated redox systems biology and emphasizes the importance of validating computational predictions, through experimental proteomics, for enhancing crop resilience, metabolic efficiency, and precision agriculture under climate variability. Full article

Background: Cochlear implantation is widely used to provide auditory rehabilitation to individuals with severe-to-profound sensorineural hearing loss. However, electrode insertion during cochlear implantation leads to inner ear trauma, damage to sensory structures, and consequently, loss of residual hearing. There is very limited information regarding the target proteins involved in electrode insertion trauma (EIT) following cochlear implantation. Methods: The aim of our study was to identify target proteins and host molecular pathways involved in cochlear damage following EIT utilizing the iTRAQ™ (isobaric tags for relative and absolute quantification) technique using our ex vivo model. The organ of Corti (OC) explants were dissected from postnatal day 3 rats and subjected to EIT or left untreated (control). The proteins were extracted, labelled, and subjected to ultra-high performance liquid chromatography–tandem mass spectrometry. Results: We identified distinct molecular pathways involved in EIT-induced cochlear damage. Confocal microscopy confirmed the expression of these identified proteins in OC explants subjected to EIT. By separating the apical, middle, and basal cochlear turns, we deciphered a topographic array of host molecular pathways that extend from the base to the apex of the cochlea, which are activated post-trauma following cochlear implantation. Conclusions: The identification of target proteins involved in cochlear damage will provide novel therapeutic targets for the development of effective treatment modalities for the preservation of residual hearing in implanted individuals. Full article

Background: Previous proteomic studies provided valuable information about cataracts, but unclarified issues, such as sex and ethnicity-associated differences, remain. This study aimed to provide additional data on cataract-related proteins regarding age, sex, and cataract type. Methods: Twenty-six female and seven male Turkish cataract patients were screened for visual acuity and dysfunctional lens index. A nano-LC-MS/MS system and Progenesis QI software v3.0 were used for protein identification and quantification. The remaining data were evaluated with SPSS Version 29.0 software. Results: Proteins that showed age-associated changes were mainly involved in cytoskeletal organization. A glyoxalase enzyme, caveolin 1, and HS90B were lower, and RAB8B and ATP6V1B1 were higher in lenses in women. Proteins with lower levels in cataractous lenses than in transparent lenses included filensin and phakinin, concurrent with previous publications, and LCTL, GDI, HSPB1, and EIF4A2, not reported before. Corticonuclear cataracts constituted the only group showing depletions in putatively protective proteins, while the cortical type was the least influenced. ANXA1 and DNHD1 positively, and TCPD, SEC14L2, and PRPS1 proteins negatively correlated with visual acuity. Conclusions: This study revealed cataract-related proteins concurrent with earlier studies and new ones hitherto unreported. Despite the low number of patients investigated, the results merit further research, as these new proteins are highly likely to be involved in cataractogenesis. Full article

Background: Snake envenomation is a major public health issue in Nigeria, primarily due to bites from Echis ocellatus, Naja nigricollis, and Bitis arietans. Understanding their venom composition is essential for effective antivenom development. This study characterizes and compares the venom proteomes of these snakes using iTRAQ-based proteomics, focusing on key toxin families and their relative abundances. Methods: Venom samples were ethically collected from adult snakes, pooled by species, lyophilized, and stored for proteomic analysis. Proteins were extracted, digested with trypsin, and labeled with iTRAQ. Peptides were analyzed via mass spectrometry, and data were processed using Mascot and IQuant for protein identification and quantification. Results: E. ocellatus and B. arietans venoms had similar profiles, rich in C-type lectins, serine proteases, and phospholipase A₂s. These comprised 17%, 11%, and 5% in E. ocellatus and 47%, 10%, and 7% in B. arietans, with metalloproteinases dominating both (53% and 47%). In N. nigricollis, three-finger toxins (9%) were most abundant, followed by metalloproteinases (3%). All species shared four core protein families, with N. nigricollis also containing four uncharacterized proteins. Conclusions: This study highlights venom compositional differences, advancing snake venom biology and informing targeted antivenom development. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, heterogeneous at the molecular level and characterized by diverse and complex pathological features. Such features are known to accumulate silently in the brain over years or even decades before the onset of detectable symptoms. Despite long years of intense research activities, the disease remains orphaned of either disease-modifying therapies or a specific blood test capable of predicting the disease in the pre-symptomatic stages. This disappointing outcome of such efforts can be attributed to a number of factors. One of these factors is the failure of earlier research to capture the heterogeneity of the disease. Such failure has the direct consequence of poor patient stratification, which in turn impacts negatively on the development of specific and effective therapy. The second factor is the absence of detailed and accurate information on proteins and associated post-translational modifications, which may influence the initiation and progress of the disease. Recent studies have demonstrated that the quantification of various isoforms of phosphorylated tau protein in plasma and other biofluids can be considered as potential biomarkers for the early detection of Alzheimer’s disease. Mass spectrometry-based proteomics and immunoassay-based multiplex proteomics are the two technologies in current use for probing the human proteome, both in tissues and biofluids. In the present review, we discuss the contribution of MS-based proteomics to efforts aimed at the identification and eventual characterization of the heterogeneity of the disease, and the key role of the same technique in the analysis of protein post-translational modifications associated with the disease is also discussed. Full article

Drug conjugates, in which chemotherapeutic or cytotoxic agents are coupled to targeting or delivering macromolecules like peptides or proteins via a linker, revolutionize cancer treatment. While protein-drug and antibody-drug conjugates have already secured a role in clinical oncology, peptide–drug conjugates (PDCs) are emerging as a promising alternative, offering enhanced efficacy and fewer side effects compared to the free drug molecules. Comprehensive chemical and biological investigation of PDCs is crucial during drug development and optimization, paving the way for the next generation of targeted therapies. Anthracycline-containing peptide conjugates have emerged as promising candidates in targeted cancer therapies due to their ability to deliver cytotoxic agents directly to tumor cells. However, their structural complexity poses significant analytical challenges, particularly in mass spectrometric characterization. Accurate identification and quantification of these conjugates are critical for assessing their stability, efficacy, and mechanism of action. This article explores the major difficulties encountered during mass spectrometry (MS) analysis of anthracycline-peptide conjugates, focusing on ionization issues, fragmentation behavior, and challenges of detection from biological matrix. Full article

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy, caused by either sarcomere protein or other gene mutations. It is a complex and highly heterogeneous disorder, with phenotypes ranging from asymptomatic to severe disease, characterized by asymmetric left ventricular (LV) hypertrophy unexplained by loading conditions, which is also associated with myocardial fiber disarray, and preserved or increased ejection fraction without LV dilation. Comprehensive personal and family history, physical examination, and ECG testing raise suspicion of HCM, and echocardiogram represents the first-line imaging modality for confirming a diagnosis. Moreover, contrast-enhanced cardiac magnetic resonance (CMR) imaging has increasingly emerged as a fundamental diagnostic and prognostic tool in HCM management. This article reviews the role of CMR in HCM identification and differentiation from phenotypic mimics, characterization of HCM phenotypes, monitoring of disease progression, evaluation of pre- and post-septal reduction treatments, and selection of candidates for implantable cardioverter-defibrillator. By providing information on cardiac morphology and function and tissue characterization, CMR is particularly helpful in the quantification of myocardial wall thickness, the detection of hypertrophy in areas blind to echocardiogram, subtle morphologic features in the absence of LV hypertrophy, myocardial fibrosis, and apical aneurysm, the evaluation of LV outflow tract obstruction, and the assessment of LV function in end-stage dilated HCM. Full article

Background/Objectives: Progressive pulmonary fibrosis (PPF) represents one of the most severe and complex challenges in respiratory medicine, characterized by a rapid decline in lung function and often poor prognosis, making it a priority in research on interstitial lung diseases (ILDs). The aim of this study is to correlate classical clinical features and three genetic biomarkers with the diagnosis and prognosis of progressive pulmonary fibrosis in ILDs. Methods: This study involved 19 patients with progressive pulmonary fibrosis (PPF) and 20 patients with non-progressive pulmonary fibrosis (nPPF) from the S.C. of Respiratory System Diseases at the Policlinico of Foggia (Italy) between 2015 and 2022. All participants underwent pulmonary function tests (PFTs), a 6 min walk test (6MWT), and bronchoalveolar lavage (BAL) sampling, following the acquisition of written consent for these procedures. Bayesian analysis with generalized linear models has been applied for both diagnostic and prognostic classification. Results: The proposed Bayesian model enables the estimation of the contribution of each considered feature, and the quantification of the uncertainty that is consequential to the small size of the dataset. The analysis of miRNAs such as miR-21 and miR-92a, alongside the protein biomarker KL-6, was identified as a significant indicator for PPF diagnosis, enhancing both the sensitivity and specificity of predictions. Conclusions: The identification of specific genetic markers such as microRNAs and their integration with traditional clinical characteristics can significantly enhance the management of patients with the disease. This multidimensional approach, which integrates clinical data with omics data, could enable more precise identification and monitoring of the disease and potentially optimize future treatments through larger studies and extended follow-ups. Full article

Natural products play a crucial role in drug discovery because of their structural diversity and biological activity. However, identifying their molecular targets remains a challenge. Traditional target identification approaches such as affinity-based protein profiling and activity-based protein profiling are limited by the need for chemical modification or reactive groups in natural products. The emergence of label-free techniques offers a powerful alternative for studying drug–target engagement in a physiological context. In particular, the cellular thermal shift assay (CETSA) exploits ligand-induced protein stabilization—a phenomenon where ligand binding enhances a protein’s thermal stability by reducing conformational flexibility—to assess drug binding without requiring chemical modifications. CETSA’s integration with advanced mass spectrometry and high-throughput platforms has dramatically expanded proteome coverage and sensitivity, enabling the simultaneous quantification of thousands of proteins and the identification of low-abundance targets in native cellular environments. This review highlights the application of key CETSA-based methods to target identification in natural products including Western blot-based CETSA, isothermal dose–response CETSA, mass spectrometry-based CETSA, and high-throughput CETSA. Case studies are presented that demonstrate their effectiveness in uncovering the mechanisms of action of different drugs. The current limitations of CETSA-based strategies are also explored, and future improvements to optimize their potential for drug discovery are discussed. Integrating CETSA with complementary approaches can enhance the target identification accuracy and efficiency for natural products and ultimately advance development of therapeutic applications. Full article

In recent years, sustainable agricultural practices in wheat cultivation have garnered significant attention, particularly those focused on minimizing pesticide and herbicide usage to safeguard the environment. One effective approach is green manuring, which entails rotating wheat with crops such as soybean and mustard to harness their natural pesticidal and herbicidal properties. While this method presents clear environmental advantages, it also poses a risk of cross-contamination, as these globally recognized allergens may unintentionally pass through wheat-based products. To protect consumers with allergies, there is an urgent need for a reliable analytical method to detect and quantify these allergenic proteins in wheat-derived foodstuffs. In this study, we assessed various protein extraction protocols to optimize the recovery of soybean and mustard allergens from wheat flour. The extracted proteins were analyzed using a bottom-up proteomics approach involving trypsin digestion, coupled with reversed-phase liquid chromatography and mass spectrometry in multiple reaction monitoring (MRM) mode. Two key allergenic proteins, Glycinin G1 and 11S Globulin, were selected as representative for soybean and mustard, respectively. The identified quantifier marker of Glycinin G1 was VLIVPQNFVVAAR (m/z 713.431²⁺), while FYLAGNQEQEFLK (m/z 793.896²⁺) and VFDGELQEGR (m/z 575.280²⁺) were designated as qualifier markers. The selection of specific marker peptides for mustard proved challenging due to the high structural similarity among proteins from Sinapis alba and other members of the Brassicaceae family. For 11S Globulin, FNTLETTLTR (m/z 598.319²⁺) was recognized as the quantifier marker, with VTSVNSYTLPILQYIR (m/z 934.019²⁺) serving as the qualifier marker. The developed method underwent thorough validation for linearity, limit of detection (LOD), limit of quantification (LOQ), recovery, repeatability, and reproducibility, as well as potential matrix and processing effects. This strategy successfully facilitated the identification and quantification of soybean and mustard allergenic proteins in complex, processed food matrices, including naturally contaminated flour and cookies. These findings enhance food safety monitoring and regulatory compliance, thereby helping to mitigate allergen-related risks in wheat-based products. Full article

Aging is a natural process resulting in the progressive impairment of multiple functions in the human body, leading to a decline in cellular functionality and the development of aging-related diseases. External stress factors, such as ultraviolet (UV) radiation, pollution, and toxin exposure, increase oxidative stress, damage cellular repair mechanisms, and speed up aging processes. With the rise in the world’s aging population, there are enlarged demands for the use of sustainable natural products in food, nutrient supplements and cosmetics that can slow down aging and prolong healthy life and longevity. Algae, including both macroalgae and microalgae, have been recognised as a source of valuable proteins, amino acids, fatty acids, vitamins, and minerals useful for human consumption and medical applications. With increasing demands for nutraceutical and pharmaceutical bioproducts from environmentally friendly resources, the biotechnological industry, over recent decades, has had to provide new, advanced solutions using modern high-throughput omics technologies. The application of proteomics in the area of discoveries of natural products with anti-aging properties has become more popular for wide industry applications. New proteomics profiling provides a better understanding of changes occurring in protein and peptide content, their structure, function and interactions, as well as the regulatory processes and molecular pathways. Mass spectrometry-based proteomics has been used for a wide range of applications including protein identification, characterisation, as well as quantification of proteins within the proteome and sub-proteome. The application of chemical proteomics facilitated the identification of natural products approach and included the synthesis of probes and target fishing, allowing the advanced identification of proteins of interest. This review focuses on marine macro- and microalgal anti-aging compounds and novel proteomics approaches, providing recent experimental evidence of their involvement in anti-aging processes that should facilitate their use in innovative approaches and sustainable biotechnological applications. Full article

With the growing interest in health and fitness, whey protein supplements are becoming increasingly popular among fitness enthusiasts and athletes. The surge in demand for whey protein supplements highlights the need for cost-effective methods to characterise product quality throughout the food supply chain. This study presents a rapid and low-cost method for authenticating sports whey protein supplements using smartphone video imaging (SVI) combined with machine learning. A gradient of colours ranging from purple to red is displayed on the front screen of a smartphone to illuminate the sample. The colour change on the sample surface is captured in a short video by the front-facing camera. Then, the video is split into frames, decomposed into RGB colour channels, and converted into spectral data. The relationship between video data and sample labels is established using machine learning models. The proposed method is tested on five tasks, including identifying 15 brands of whey protein concentrate (WPC), quantifying fat content and energy levels, detecting three types of adulterants, and quantifying adulterant levels. Moreover, the performance of SVI was compared to that of hyperspectral imaging (HSI), which has an equipment cost of around 80 times that of SVI. The proposed method achieves accuracies of 0.933 and 0.96 in WPC brand identification and adulterant detection, respectively, which are only around 0.05 lower than those of HSI. It obtains coefficients of determination of 0.897, 0.906 and 0.963 for the quantification of fat content, energy levels and milk powder adulteration, respectively. Such results demonstrate that the combination of smartphones and machine learning offers a low-cost and viable preliminary screening tool for verifying the authenticity of whey protein supplements. Full article