Proteomics: Concepts and applications in human medicine

Proteomics in decoding cancer: A review

Cancer remains the second leading cause of death worldwide, posing a significant global health challenge. Extensive research has revealed common biological characteristics across cancer cells, forming the foundation for developing innovative diagnostic and therapeutic strategies. To better understand these shared traits, advanced measurement technologies are critical. Proteomics, the large-scale study of proteins and their functions, has emerged as a transformative tool for uncovering the complexities of cancer biology. This approach provides an in-depth view of cellular activities and protein interactions, offering unprecedented insights into cancer progression and treatment. Unlike traditional methods that investigate specific pathways in isolation, proteomics enables simultaneous analysis of thousands of proteins, generating a comprehensive understanding of cancer biology. This review explores the mechanisms underlying proteomics, its application to understanding cancer hallmarks, and its potential to transform clinical approaches. By examining proteomics' role in metastasis, angiogenesis, proliferation, and resistance mechanisms, this study highlights its contributions to cancer diagnosis, treatment, and personalized medicine. Additionally, prospects in integrating proteomics with other -omics fields and advancements in computational analysis will be discussed. This work aims to illuminate the path toward more effective, precise, and individualized cancer care through proteomics innovations.

The Role of Omics Techniques in Diabetic Wound Healing: Recent Insights into the Application of Single-Cell RNA Sequencing, Bulk RNA Sequencing, Spatial Transcriptomics, and Proteomics

Diabetic foot ulcers (DFUs) are a devastating complication of diabetes mellitus (DM) that affect millions of people worldwide every year. They have a long-term impact on patients' quality of life and pose a significant challenge for both patients and clinicians, alongside negative economic implications on affected individuals. The current therapeutic approaches are costly and, in many cases, ineffective, highlighting the urgent need to develop novel, affordable, more efficient, and personalized treatments. Recent advances in high-throughput omics technologies, including proteomics, bulk RNA sequencing (bulk RNA-seq), single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics in both preclinical animal and human clinical studies, have enhanced our understanding of the molecular function and mechanisms of DFUs, thereby offering potential for targeted therapies. Additionally, these technologies provide valuable insights behind the mechanism of action of novel wound dressings and treatments. In this review, we outline the latest application of omics technologies in DFU preclinical animal and human clinical research on diabetic wound healing, and spotlight recent findings.A graphical abstract is available with this article.

Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review

The presence of carcinogenic substances in beef poses a significant risk to public health, with far-reaching implications for consumer safety and the meat production industry. Despite advancements in food safety measures, traditional breeding methods have proven inadequate in addressing these risks, revealing a substantial gap in knowledge. This review aims to fill this gap by evaluating the potential of healthy breeding techniques to significantly reduce the levels of carcinogenic compounds in beef. We focus on elucidating the molecular pathways that contribute to the formation of key carcinogens, such as heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs), while exploring the transformative capabilities of advanced genomic technologies. These technologies include genomic selection, CRISPR/Cas9, base editing, prime editing, and artificial intelligence-driven predictive models. Additionally, we examine multi-omics approaches to gain new insights into the genetic and environmental factors influencing carcinogen formation. Our findings suggest that healthy breeding strategies could markedly enhance meat quality, thereby offering a unique opportunity to improve public health outcomes. The integration of these innovative technologies into breeding programs not only provides a pathway to safer beef production but also fosters sustainable livestock management practices. The improvement of these strategies, along with careful consideration of ethical and regulatory challenges, will be crucial for their effective implementation and broader impact.

The ocular surface tear film as a biomarker for systemic health

The tear film is a complex structure with rich interactions with the human body. A growing body of evidence suggests that measuring changes in protein, lipid, or other metabolite concentration in the tear film can be used to help detect disease. Particularly in the era of precision medicine, the tear film serves as a promising source of non-invasive insights into systemic health for early diagnosis and treatment. This paper analyzes the latest research in tear film biomarkers for systemic diseases. The review was conducted through PubMed and Embase databases using the PRISMA protocol and includes 54 articles. This paper first reviews the anatomy and physiology of tear film, as well as the latest proteomic analysis techniques on the tear film. We then provide a disease-by-disease review on the tear film as a biomarker including 5 articles related to Alzheimer's Disease, 10 articles related to Cancers, 1 article related to Cystic Fibrosis, 1 article related to Migraines, 4 articles related to Multiple Sclerosis, 15 articles related to Parkinson's Disease, 7 articles related to Rheumatoid Arthritis, and 11 articles related to Thyroid Disease. This paper highlights the promising results of these studies yet also reviews the challenges with limited sample sizes, reproducibility, and biological understanding of biomarkers. We conclude this paper with insights for future work to ensure clinical validity and generalizability. Ultimately, the tear film is a clinically accessible, complex structure that provides a wealth of information that may contribute to a more comprehensive understanding of systemic health.

Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies

Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. Recent advancements in the field of TB diagnostics have focused on the identification and utilization of blood-based biomarkers, offering a non-invasive, rapid, and scalable approach to disease detection. This review provides a comprehensive overview of the latest progress in blood-based biomarkers for TB, highlighting their potential to revolutionize diagnostic strategies. Furthermore, we explore emerging technologies such as NGS, PET-CT, Xpert and line probe assays, which have enhanced the sensitivity, specificity, and accessibility of biomarker-based diagnostics. The integration of artificial intelligence (AI) and machine learning (ML) in biomarker analysis is also examined, showcasing its potential to improve diagnostic accuracy and predictive capabilities. This review underscores the need for multidisciplinary collaboration and continued innovation to translate these promising technologies into practical, point-of-care solutions. By addressing these challenges, blood-based biomarkers and emerging technologies hold the potential to significantly improve TB diagnosis, ultimately contributing to global efforts to eradicate this devastating disease.

AI and omics technologies in biobanking: Applications and challenges for public health

OBJECTIVES

Considering the growing intersection of biobanks, artificial intelligence (AI) and omics research, and their critical impact on public health, this study aimed to explore the current and future public health implications and challenges of AI and omics-driven innovations in biobanking.

STUDY DESIGN

Narrative literature review.

METHODS

A structured literature search was conducted in Scopus, PubMed, Web of Science and IEEExplore databases using relevant search terms. Additional references were identified through backward and forward citation chaining. Key themes were aggregated and analysed through thematic analysis.

RESULTS

Thirty-seven studies were selected for analysis, leading to the identification and categorisation of key developments. Several key technical, ethical and implementation challenges were also identified, including AI model selection, data accessibility, variability and quality issues, lack of robust and standardised validation methods, explainability, accountability, lack of transparency, algorithmic bias, privacy, security and fairness issues, and governance model selection. Based on these results, potential future scenarios of AI and omics integration in biobanking and their related public health implications were considered.

CONCLUSIONS

While AI and omics-driven innovations in biobanking offer specific transformative public health benefits, addressing their technical, ethical and implementation challenges is crucial. Robust regulatory frameworks, feasible governance models, access to quality data, interdisciplinary collaboration, and transparent and validated AI systems are essential to maximise benefits and mitigate risks. Further research and policy development are needed to support the responsible integration of these technologies in biobanking and public health.

Salivary biomarkers: a promising approach for predicting immunotherapy response in head and neck cancers

Head and neck cancers, including cancers of the mouth, throat, voice box, salivary glands, and nose, are a significant global health issue. Radiotherapy and surgery are commonly used treatments. However, due to treatment resistance and disease recurrence, new approaches such as immunotherapy are being explored. Immune checkpoint inhibitors (ICIs) have shown promise, but patient responses vary, necessitating predictive markers to guide appropriate treatment selection. This study investigates the potential of non-invasive biomarkers found in saliva, oral rinses, and tumor-derived exosomes to predict ICI response in head and neck cancer patients. The tumor microenvironment significantly impacts immunotherapy efficacy. Oral biomarkers can provide valuable information on composition, such as immune cell presence and checkpoint expression. Elevated tumor mutation load is also associated with heightened immunogenicity and ICI responsiveness. Furthermore, the oral microbiota may influence treatment outcomes. Current research aims to identify predictive salivary biomarkers. Initial studies indicate that tumor-derived exosomes and miRNAs present in saliva could identify immunosuppressive pathways and predict ICI response. While tissue-based markers like PD-L1 have limitations, combining multiple oral fluid biomarkers could create a robust panel to guide treatment decisions and advance personalized immunotherapy for head and neck cancer patients.

Advancements in Biomarkers for Early Detection and Risk Stratification of Cardiovascular Diseases-A Literature Review

Introduction

CVDs is a leading cause of morbidity, mortality, and healthcare expenditure worldwide. Identifying individuals at risk or in the incipient stages of disease is instrumental in enabling timely interventions, preventive measures, and tailored treatment regimens. The landscape of CVDs is complicated by their heterogeneity, encompassing a spectrum of conditions such as coronary artery disease, heart failure, arrhythmias, and valvular disorders. In recent years, the integration of biomarkers into cardiovascular medicine has emerged as a paradigm-shifting approach with the potential to revolutionize early detection and risk stratification. By synthesizing a multitude of studies, we aim to provide a comprehensive resource that illuminates the transformative potential of biomarkers in ushering in a new era of precision cardiovascular medicine.

Aim

To identify the biomarkers for the detection and diagnosis of CVDs.

Materials and Methods

This review examines key studies from 2015 to the present that investigate the impact of cardiac biomarkers on cardiovascular outcomes. Data were gathered from PubMed, Cochrane Library, and Embase to ensure a comprehensive analysis. The review focuses on various cardiac biomarkers, assessing their levels and changes in relation to cardiovascular health, with special emphasis on advanced biomarkers such as proteomic and metabolomic markers in cardiovascular disease (CVD) diagnosis. Peer-reviewed studies published in English that evaluated the diagnostic, prognostic, or therapeutic role of cardiac biomarkers were included, with priority given to clinical trials, cohort studies, systematic reviews, and meta-analyses providing quantitative biomarker data. Studies unrelated to cardiac biomarkers, case reports, editorials, conference abstracts, and those with small sample sizes or insufficient methodological rigor were excluded. The review also accounts for potential confounding factors and research limitations, ensuring a balanced assessment of the literature. By synthesizing data from academic papers, clinical reports, and research articles, this study provides a comprehensive evaluation of the evolving role of cardiac biomarkers in CVD diagnosis and risk stratification.

Results

Biomarkers play a pivotal role in cardiovascular disease risk prediction, diagnosis, and treatment by providing dynamic biological insights. High-sensitivity cardiac troponins (hs-cTn) enhance myocardial injury detection, while circulating microRNAs (miR-208, miR-499) serve as early indicators of myocardial infarction and heart failure. Lipoprotein(a) [Lp(a)] predicts long-term cardiovascular risk, and inflammatory biomarkers such as C-reactive protein (CRP) and interleukin-6 (IL-6) are linked to adverse outcomes. Multi-biomarker panels, such as hs-cTn with B-type natriuretic peptide (BNP), improve heart failure prognosis, while metabolomic profiling enables precision medicine. Additionally, biomarkers like BNP and NT-proBNP facilitate real-time therapeutic monitoring. These findings underscore the critical role of biomarkers in refining risk stratification, improving diagnostic accuracy, and enabling personalized treatment strategies in cardiovascular medicine.

Conclusion

The advancement of cardiovascular biomarkers has significantly enhanced early detection, risk stratification, and personalized treatment. Emerging biomarkers, including genetic variants, metabolomics, microRNAs, and imaging-based markers, provide deeper insights into disease mechanisms. Integrating multi-omic approaches with artificial intelligence may further refine predictive accuracy and therapeutic decision-making. However, clinical translation requires rigorous validation through large-scale, multicenter studies to ensure reliability and applicability across diverse populations. Standardization, cost-effectiveness assessments, and the development of biomarker panels are essential for clinical adoption. Future research should focus on bridging discovery and implementation, advancing precision medicine to improve cardiovascular outcomes.

Salivary biomarkers of tactical athlete readiness: A systematic review

Tactical athletes must maintain high levels of physical and cognitive readiness to handle the rigorous demands of their roles. They frequently encounter acute stressors like sleep deprivation, muscle fatigue, dehydration, and harsh environmental conditions, which can impair their readiness and increase the risk of mission failure. Given the challenging conditions these athletes face, there is a vital need for non-invasive, rapidly deployable point-of-care assessments to effectively measure the impact of these stressors on their operational readiness. Salivary biomarkers are promising in this regard, as they reflect physiological changes due to stress. This systematic review aims to investigate salivary markers as potential indicators for readiness, specifically focusing on their sensitivity to acute stressors like sleep deprivation, dehydration, environmental factors, and muscle fatigue. A search was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines (PROSPERO; registration #: CRD42022370388). The primary inclusion criteria were the use of a quantitative analysis to assess salivary biomarkers changes in response to acute stressors. Risk of bias and methodological quality were evaluated with the modified Downs and Black checklist. Hormonal salivary biomarkers were the most commonly studied biomarkers. Muscle damage and fatigue were the most frequently studied acute stressors, followed by sleep deprivation, multiple stressors, dehydration, and environmental. Biomarkers such as creatine kinase, aspartate aminotransferase, uric acid, cortisol, testosterone, and the testosterone to cortisol ratio were indicative of muscle damage. Dehydration influenced osmolality, total protein, flow rate, and chloride ion concentrations. Sleep deprivation affected proteins, peptides, and alpha-amylase levels. Environmental stressors, such as hypoxia and cold temperatures, altered cortisol, pH, dehydroepiandrosterone-sulfate (DHEA-s), and salivary IgA levels. The current body of research highlights that various salivary biomarkers react to acute stressors, and proteomic panels appear promising for predicting physical and cognitive outcomes relevant to the operational readiness of tactical athletes.

The Use of Biomarkers in Precision Health Symptom Science-Opportunities and Challenges

OBJECTIVES

Precision health symptom science applies person-centered approaches to elucidate interindividual differences in patients' symptom experiences and incorporates omics methods with social, societal, and environmental determinants of health to develop symptom management strategies. By filling scientific gaps related to patients' symptom experiences and their underlying mechanisms, interventions can be developed to improve quality of life and outcomes. The purposes of this article are to describe symptom phenotype development; review analytical approaches to identify a symptom phenotype; and discuss common and emerging methods for biomarker discovery and their implications in precision health symptom science.

METHODS

Peer-reviewed research studies, review articles, and scientific expertise were synthesized to provide a broad overview of several methods of biomarker discovery and their implications for precision health symptom science.

RESULTS

Approaches to symptom phenotype development and analytical methods for phenotype identification were reviewed. Common (ie, genomic, epigenomic, transcriptomic, proteomic, metabolomic, microbiome) and emerging (ie, polygenic risk scores, microRNA, epigenetic clocks, allostatic load, wearables) methods for biomarker discovery were described. Each method provides unique information to improve our understanding of the complex biological processes that underlie symptoms and may be used for risk prediction, screening, surveillance, and treatment response.

CONCLUSIONS

While the exemplar approaches to conducting precision health symptom science were shared through an oncology lens, they are generalizable across acute and chronic conditions.

IMPLICATIONS FOR NURSING PRACTICE

Symptom biomarker identification is inherently complex and the methods for biomarker collection, processing, measurement, and analysis are continually evolving. Therefore, symptom scientists need to form transdisciplinary teams with experts in omics methodologies and bioinformatics. Despite the challenges, symptom scientists are well suited to lead the way in precision health symptom science to reduce symptom burden and improve quality of life among patients with various chronic conditions.

Breaking the code: Using the Precision Health Model to guide research and clinical care

BACKGROUND

Precision health is a person-centered approach to health and well-being that is operationalized through evaluating omics-level profiles and their associations with the exposome. A precision health approach addresses the challenge that "one size does not fit all" in the management of an individual's health.

PURPOSE

The purpose of this white paper is to introduce a Precision Health Model and its application in research and clinical care.

METHODS

An expert panel reviewed and synthesized the extant literature related to precision health, the current state of omics' science, and common exposome factors that influence the health/illness continuum. A case study provides the framework for the application of the Precision Health Model.

DISCUSSION

Precision health and key domains are defined and serve as the platform for the development of the Precision Health Model.

CONCLUSION

Application of the Precision Health Model will provide inclusive, equitable, person-centered research and clinical care.

Comparing Venous vs. Capillary Blood Collection Methods for Proteomic Measurement in Peripheral Blood

BACKGROUND

Capillary blood collection has a number of advantages over venous collection, especially in the context of increasing decentralized clinical trials and field-based testing. No studies are available comparing venous versus capillary blood collection for proteomics measurement. The aim of this study was to compare venous versus capillary blood collection methods for proteomic measurement in peripheral blood.

METHODS

The expression of 368 different proteins from the Olink Explore 384 Inflammation panel was measured in both venous and capillary blood samples collected from 22 individuals at a single time point. Protein levels from venous and capillary blood samples were compared with descriptive statistics and correlation calculations. Correlations were examined for a subset of proteins identified in recent reports as associated with morbidity and mortality.

RESULTS

Strong positive correlation (r ≥ 0.7) between protein concentrations measured in venous and capillary blood samples was observed for two in three proteins tested (215 of 327, 66%). A further 47 (14%) showed a moderate positive correlation (0.4 ≤ r < 0.7), with weak or very weak correlation (r < 0.4) observed for the remaining 65 (20%). Protein expression was consistently higher in capillary blood samples for proteins with lower correlation (r < 0.6) between sampling methods. Further work is required to understand the underlying reasons why proteins were consistently under-expressed in venous samples/over-expressed in capillary samples in a minority of proteins tested.

CONCLUSIONS

Proteomic measurement utilising capillary blood collection provides very similar results to using venous blood collection. This is a promising sign for the validity and reliability of studies using capillary blood collection, including decentralised and remote studies.

Subtractive proteomics and reverse-vaccinology approaches for novel drug targets and designing a chimeric vaccine against Ruminococcus gnavus strain RJX1120

Mediterraneibacter gnavus, also known as Ruminococcus gnavus, is a Gram-positive anaerobic bacterium that resides in the human gut microbiota. Notably, this bacterium plays dual roles in health and disease. On one side it supports nutrient metabolism essential for bodily functions and on the other it contributes to the development of Inflammatory Bowel Disease (IBD) and other gastrointestinal disorders. R. gnavus strain RJX1120 is an encapsulated strain and has been linked to develop IBD. Despite the advances made on its role in gut homeostasis, limited information is available on strain-specific virulence factors, metabolic pathways, and regulatory mechanisms. The study of such aspects is crucial to make microbiota-targeted therapy and understand its implications in host health. A multi-epitope vaccine against R. gnavus strain RJX1120 was designed using reverse vaccinology-based subtractive proteomics approach. Among the 3,219 proteins identified in the R. gnavus strain RJX1120, two critical virulent and antigenic proteins, a Single-stranded DNA-binding protein SSB (A0A2N5PT08) and Cell division ATP-binding protein FtsE (A0A2N5NK05) were screened and identified as potential targets. The predicted B-cell and T-cell epitopes from these proteins were screened for essential immunological properties such as antigenicity, allergenicity, solubility, MHC binding affinity, and toxicity. Epitopes chosen were cross-linked using suitable spacers and an adjuvant to develop a multi-epitope vaccine. Structural refinement of the construct revealed that 95.7% of the amino acid residues were located in favored regions, indicating a high-quality structural model. Molecular docking analysis demonstrated a robust interaction between the vaccine construct and the human Toll-like receptor 4 (TLR4), with a binding energy of -1277.0 kcal/mol. The results of molecular dynamics simulations further confirmed the stability of the vaccine-receptor complex under physiological conditions. In silico cloning of the vaccine construct yielded a GC content of 48% and a Codon Adaptation Index (CAI) value of 1.0, indicating optimal expression in the host system. These results indicate the possibility of the designed vaccine construct as a candidate for the prevention of R. gnavus-associated diseases. However, experimental validation is required to confirm its immunogenicity and protective efficacy.

Unveiling the Anti-Aging Potential of Marine Natural Bioproducts

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.

Diet-Induced Proteomic and Metabolomic Signatures in Chronic Kidney Disease: A Precision Nutrition Approach

Background: Diet is a key modifiable factor that can either support renal health or accelerate the onset and progression of chronic kidney disease (CKD). Recent advances in multiomics, particularly proteomics and metabolomics, significantly enhanced our understanding of the molecular mechanisms linking diet to CKD risk. Proteomics offers a comprehensive analysis of protein expression, structure, and interactions, revealing how dietary components regulate cellular processes and signaling pathways. Meanwhile, metabolomics provides a detailed profile of low-molecular-weight compounds, including endogenous metabolites and diet-derived molecules, offering insights into the metabolic states that influence kidney function. Methods: We have conducted a narrative review of key papers from databases such as PubMed, Scopus, and Web of Science to explore the potential of proteomic and metabolomic analysis in identifying molecular signatures associated with diet in human and animal biological samples, such as blood plasma, urine, and in kidney tissues. These signatures help elucidate how specific foods, food groups, and overall dietary patterns may either contribute to or mitigate CKD risk. Results: Recent studies the impact of high-fat diets on protein expression involved in energy metabolism, inflammation, and fibrosis, identifying early biomarkers of kidney injury. Metabolic, including disruptions in in fatty acid metabolism, glucose regulation, and amino acid pathways, have been recognized as key indicators of CKD risk. Additionally, several studies explore specific metabolites found in biological fluids and renal tissue in response to protein-rich foods, assessing their potential roles in a progressive loss of kidney function. Emerging evidence also suggests that dietary interventions targeting the gut microbiota may help alleviate inflammation, oxidative stress, and toxin accumulation in chronic kidney disease. Notably, recent findings highlight metabolomic signatures linked to beneficial shifts in gut microbial metabolism, particularly in the context of prebiotic supplementation. Conclusions: By integrating proteomics and metabolomics, future research can refine precision nutrition strategies, helping mitigate CKD progression. Expanding large-scale studies and clinical trials will be essential in translating these molecular insights into actionable dietary guidelines.

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer's disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) and six variations of a deep neural network (DNN) model-the DL model in the H2O package. Least Absolute Shrinkage and Selection Operator (LASSO) selected 35 proteomic biomarkers from the pool. Based on grid search, the DNN model with an activation function of "Rectifier With Dropout" with 2 layers and 32 of 35 selected proteomic biomarkers revealed the best model with the highest accuracy of 0.995 and an F1 Score of 0.996, while among seven traditional ML methods, XGBoost was the best with an accuracy of 0.986 and an F1 Score of 0.985. Several biomarkers were correlated with the APOE-ε4 genotype, polygenic hazard score (PHS), and three clinical cerebrospinal fluid biomarkers (Aβ42, tTau, and pTau). Bioinformatics analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed several molecular functions and pathways associated with the selected biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, and regulation of lipid localization. The results showed that the DL model may represent a promising tool in the prediction of MCI. These plasma proteomic biomarkers may help with early diagnosis, prognostic risk stratification, and early treatment interventions for individuals at risk for MCI.

TIMS2Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, and metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can mitigate these issues by acquiring more discerning information at higher sensitivity levels. This is exemplified by the incorporation of ion mobility and parallel accumulation and serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data and search results from ProteoScape and many other search engines, including Sage and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.

EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data

BACKGROUND AND OBJECTIVES

Early breast cancer subtypes classification improves the survival rate as it facilitates prognosis of the patient. In literature this problem was prominently solved by various Machine Learning and Deep Learning techniques. However, these studies have three major shortcomings: huge Trainable Weight Parameters (TWP), suffer from low performance and class imbalance problem.

METHODS

This paper proposes a lightweight model named EpiBrCan-Lite for classifying breast cancer subtypes using DNA methylation data. This model encompasses three blocks namely Data Encoding, TransGRU, and Classification blocks. In Data Encoding block, the input features are encoded into equal sized chunks and then passed down to TransGRU block which is a modified version of traditional Transformer Encoder (TE). In TransGRU block, MLP module of traditional TE is replaced by GRU module, consisting of two GRU layers to reduce TWP and capture the long-range dependencies of input feature data. Furthermore, output of TransGRU block is passed to Classification block for classifying breast cancer into their subtypes.

RESULTS

The proposed model is validated using Accuracy, Precision, Recall, F1-score, FPR, and FNR metrics on TCGA breast cancer dataset. This dataset suffers from the class imbalance problem which is mitigated using Synthetic Minority Oversampling Technique (SMOTE). Experimentation results demonstrate that EpiBrCan-Lite model attained 95.85 % accuracy, 95.96 % recall, 95.85 % precision, 95.90 % F1-score, 1.03 % FPR, and 4.12 % FNR despite of utilizing only 1/1500 of TWP than other state-of-the-art models.

CONCLUSION

EpiBrCan-Lite model is efficiently classifying breast cancer subtypes, and being lightweight, it is suitable to be deployed on low computational powered devices.

Application of Human Plasma/Serum to Cell Culture In Vitro: A Translational Research Approach to Better Define Disease Mechanisms

In vitro cell culture experiments play an important role in medical research. Various cellular mechanisms and signaling pathways have been identified with in vitro experimental techniques. Unfortunately, the clinical and translational impact of these studies is often limited due to their inability to closely resemble physiological or pathophysiological milieus in cell culture and the use of unrealistic experimental conditions. Thus, further developments must be made to improve the translation of in vitro cell culture work. The application of human plasma or serum as a stimulus for cells, human or otherwise, is a relatively new approach that ultimately overcomes many of the in vitro limitations and provides a more physiologically relevant model. While this technique has been used for the investigation of various diseases and pharmacological mechanisms, discrepancies remain regarding the appropriate methodologies. This review provides insight into recent findings through the application of human plasma or serum as stimuli, as well as an analysis of methodological considerations and suggestions for future directions.

The effect of exercise and physical activity on skeletal muscle epigenetics and metabolic adaptations

Physical activity (PA) and exercise elicit adaptations and physiological responses in skeletal muscle, which are advantageous for preserving health and minimizing chronic illnesses. The complicated atmosphere of the exercise response can be attributed to hereditary and environmental variables. The primary cause of these adaptations and physiological responses is the transcriptional reactions that follow exercise, whether endurance- (ET) or resistance- training (RT). As a result, the essential metabolic and regulatory pathways and myogenic genes associated with skeletal muscle alter in response to acute and chronic exercise. Epigenetics is the study of the relationship between genetics and the environment. Exercise evokes signaling pathways that strongly alter myofiber metabolism and skeletal muscle physiological and contractile properties. Epigenetic modifications have recently come to light as essential regulators of exercise adaptations. Research has shown various epigenetic markers linked to PA and exercise. The most critical epigenetic alterations in gene transcription identified are DNA methylation and histone modifications, which are associated with the transcriptional response of skeletal muscle to exercise and facilitate the modification to exercise. Other changes in the epigenetic markers are starting to emerge as essential processes for gene transcription, including acetylation as a new epigenetic modification, mediated changes by methylation, phosphorylation, and micro-RNA (miRNA). This review briefly introduces PA and exercise and associated benefits, provides a summary of epigenetic modifications, and a fundamental review of skeletal muscle physiology. The objectives of this review are 1) to discuss exercise-induced adaptations related to epigenetics and 2) to examine the interaction between exercise metabolism and epigenetics.

From Nanozymes to Multi-Purpose Nanomaterials: The Potential of Metal-Organic Frameworks for Proteomics Applications

Metal-organic frameworks (MOFs) have the potential to revolutionize the biotechnological and medical landscapes due to their easily tunable crystalline porous structure. Herein, the study presents MOFs' potential impact on proteomics, unveiling the diverse roles MOFs can play to boost it. Although MOFs are excellent catalysts in other scientific disciplines, their role as catalysts in proteomics applications remains largely underexplored, despite protein cleavage being of crucial importance in proteomics protocols. Additionally, the study discusses evolving MOF materials that are tailored for proteomics, showcasing their structural diversity and functional advantages compared to other types of materials used for similar applications. MOFs can be developed to seamlessly integrate into proteomics workflows due to their tunable features, contributing to protein separation, peptide enrichment, and ionization for mass spectrometry. This review is meant as a guide to help bridge the gap between material scientists, engineers, and MOF chemists and on the other side researchers in biology or bioinformatics working in proteomics.

Identification of novel proteomic biomarkers for hypertension: a targeted approach for precision medicine

BACKGROUND

Hypertension is a critical public health issue worldwide. The identification of specific proteomic biomarkers in the Qatari population aims to advance personalized treatment strategies.

METHODS

We conducted proteomic profiling on 778 Qatari individuals using an aptamer-based SOMAscan platform to analyze 1,305 biomarkers. Statistical analysis involved two-way ANOVA and association analyses with FDR correction, alongside pathway and gene-set enrichment analyses using Reactome and DisGeNET databases.

RESULTS

The study identified 26 significant protein biomarkers associated with hypertension. Notably, QORL1 and BMP1 were identified as novel protein biomarkers. Enrichment analysis linked these biomarkers to critical pathways involved in vascular biology, immune system responses, and pathologies like arteriosclerosis and coronary artery disease. Correlation analyses highlighted robust interactions, particularly between QORL1 and various Apolipoprotein E isoforms, suggesting these biomarkers play pivotal roles in the molecular mechanisms underlying hypertension.

CONCLUSIONS

This research enhances our understanding of the molecular basis of hypertension in the Qatari population and supports the development of precision medicine approaches for treatment.

Unraveling diversity by isolating peptide sequences specific to distinct taxonomic groups

The identification of succinct, universal fingerprints that enable the characterization of individual taxonomies can reveal insights into trait development and can have widespread applications in pathogen diagnostics, human healthcare, ecology and the characterization of biomes. Here, we investigated the existence of peptide k-mer sequences that are exclusively present in a specific taxonomy and absent in every other taxonomic level, termed taxonomic quasi-primes. By analyzing proteomes across 24,073 species, we identified quasi-prime peptides specific to superkingdoms, kingdoms, and phyla, uncovering their taxonomic distributions and functional relevance. These peptides exhibit remarkable sequence uniqueness at six- and seven-amino-acid lengths, offering insights into evolutionary divergence and lineage-specific adaptations. Moreover, we show that human quasi-prime loci are more prone to harboring pathogenic variants, underscoring their functional significance. This study introduces taxonomic quasi-primes and offers insights into their contributions to proteomic diversity, evolutionary pathways, and functional adaptations across the tree of life, while emphasizing their potential impact on human health and disease.

Emerging opportunities for intact and native protein analysis using chemical proteomics

Chemical proteomics has advanced small molecule ligand discovery by providing insights into protein-ligand binding mechanism and enabling medicinal chemistry optimization of protein selectivity on a global scale. Mass spectrometry is the predominant analytical method for chemoproteomics, and various approaches have been deployed to investigate and target a rapidly growing number of protein classes and biological systems. Two methods, intact mass analysis (IMA) and top-down proteomics (TDMS), have gained interest in recent years due to advancements in high resolution mass spectrometry instrumentation. Both methods apply mass spectrometry analysis at the proteoform level, as opposed to the peptide level of bottom-up proteomics (BUMS), thus addressing some of the challenges of protein inference and incomplete information on modification stoichiometry. This Review covers recent research progress utilizing MS-based proteomics methods, discussing in detail the capabilities and opportunities for improvement of each method. Further, heightened attention is given to IMA and TDMS, highlighting these methods' strengths and considerations when utilized in chemoproteomic studies. Finally, we discuss the capabilities of native mass spectrometry (nMS) and ion mobility mass spectrometry (IM-MS) and how these methods can be used in chemoproteomics research to complement existing approaches to further advance the field of functional proteomics.

Deep learning: A game changer in drug design and development

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

Isolation of Biomolecules Using MXenes

Biomolecule isolation is a crucial process in diverse biomedical and biochemical applications, including diagnostics, therapeutics, research, and manufacturing. Recently, MXenes, a novel class of two-dimensional nanomaterials, have emerged as promising adsorbents for this purpose due to their unique physicochemical properties. These biocompatible and antibacterial nanomaterials feature a high aspect ratio, excellent conductivity, and versatile surface chemistry. This timely review explores the potential of MXenes for isolating a wide range of biomolecules, such as proteins, nucleic acids, and small molecules, while highlighting key future research trends and innovative applications poised to transform the field. This review provides an in-depth discussion of various synthesis methods and functionalization techniques that enhance the specificity and efficiency of MXenes in biomolecule isolation. In addition, the mechanisms by which MXenes interact with biomolecules are elucidated, offering insights into their selective adsorption and customized separation capabilities. This review also addresses recent advancements, identifies existing challenges, and examines emerging trends that may drive the next wave of innovation in this rapidly evolving area.

Temperature cycling between 4 °C and 37 °C could reduce Salmonella viability in low-moisture foods

Low-moisture foods (LMFs) have been linked to Salmonella transmission due to the remarkable resilience of Salmonella against desiccation, allowing its survival for extended periods. Being metabolically inactive, Salmonella in LMFs exhibit extraordinary resistance to inactivation treatments. This study proposes a novel strategy for mitigating Salmonella in LMF products through a temperature cycling (TC) approach. Alternating the temperature between 4 °C and 37 °C on a daily basis reduced the viability of S. Typhimurium air-dried on surfaces by >4 log after 6 days. TC also diminished Salmonella resistance to acidity and reduced its virulence. The mechanism was elucidated through an integrated analysis of transcriptomics and proteomics data. Specifically, transcriptomic data revealed elevated levels of protein synthesis alongside active energy metabolism. Proteomic analysis demonstrated that these protein activities were associated primarily with the heat shock protein response. Taken together, the principal mechanism by which TC exerts its inhibitory effect appears to be the repeated induction of heat shock protein synthesis within Salmonella, ultimately leading to energy depletion. Finally, the efficacy of TC was validated on representative LMF samples, including flour, protein powder, and mixed spices. The most notable effect was observed in the mixed spices, with a reduction of 2.7 ± 0.2 log after 6 days (P < 0.05). In conclusion, the TC approach demonstrated in this study provides valuable insights into the management of foodborne pathogens in LMFs.

Proteomic and serologic assessments of responses to mRNA-1273 and BNT162b2 vaccines in human recipient sera

Introduction

The first vaccines approved against SARS-CoV-2, mRNA-1273 and BNT162b2, utilized mRNA platforms. However, little is known about the proteomic markers and pathways associated with host immune responses to mRNA vaccination. In this proof-of-concept study, sera from male and female vaccine recipients were evaluated for proteomic and immunologic responses 1-month and 6-months following homologous third vaccination.

Methods

An aptamer-based (7,289 marker) proteomic assay coupled with traditional serology was leveraged to generate a comprehensive evaluation of systemic responsiveness in 64 and 68 healthy recipients of mRNA-1273 and BNT162b2 vaccines, respectively.

Results

Sera from female recipients of mRNA-1273 showed upregulated indicators of inflammatory and immunological responses at 1-month post-third vaccination, and sera from female recipients of BNT162b2 demonstrated upregulated negative regulators of RNA sensors at 1-month. Sera from male recipients of mRNA-1273 showed no significant upregulation of pathways at 1-month post-third vaccination, though there were multiple significantly upregulated proteomic markers. Sera from male recipients of BNT162b2 demonstrated upregulated markers of immune response to doublestranded RNA and cell-cycle G(2)/M transition at 1-month. Random Forest analysis of proteomic data from pre-third-dose sera identified 85 markers used to develop a model predictive of robust or weaker IgG responses and antibody levels to SARS-CoV-2 spike protein at 6-months following boost; no specific markers were individually predictive of 6-month IgG response. Thirty markers that contributed most to the model were associated with complement cascade and activation; IL-17, TNFR pro-apoptotic, and PI3K signaling; and cell cycle progression.

Discussion

These results demonstrate the utility of proteomics to evaluate correlates or predictors of serological responses to SARS-CoV-2 vaccination.

Reduced Dietary Protein Induces Changes in the Dental Proteome

Experimental studies have demonstrated that nutritional changes during development can result in phenotypic changes to mammalian cheek teeth. This developmental plasticity of tooth morphology is an example of phenotypic plasticity. Because tooth development occurs through complex interactions between manifold processes, there are many potential mechanisms which can contribute to a tooth's norm of reaction. Determining the identity of those mechanisms and the relative importance of each of them is one of the main challenges to understanding phenotypic plasticity. Quantitative proteomics combined with experimental studies allow for the identification of potential molecular contributors to a plastic response through quantification of expressed gene products. Here, we present the results of a quantitative proteomics analysis of mature upper first molars (M1s) in Mus musculus from a controlled feeding experiment. Pregnant and nursing mothers were fed either a low-dietary protein (10%) treatment diet or control (20%) diet. Expression of tooth-related proteins, immune system proteins, and actin-based myosin proteins were significantly altered in our low-dietary protein sample. The recovery of expression change in tooth development proteins was anticipated and consistent with previous proteomic studies. We also identified differential immune protein response along with systematic reduction in actin-based myosin protein expression, which are novel discoveries for tooth proteomics studies. We propose that studies which aim to elucidate specific mechanisms of molar phenotypic plasticity should prioritize investigations into the relationships between IGF regulation and tooth development and actin-based myosin expression and tooth development.

Research Highlights

A low-protein diet during development results in significantly altered protein expression for major dental building proteins, immune system proteins, and actin-based myosin proteins within Mus musculus .

Proteomic patterns associated with ketamine response in major depressive disorders

BACKGROUND

Major depressive disorder (MDD) is characterized by persistent feelings of sadness and loss of interest. Ketamine has been widely used to treat MDD owing to its rapid effect in relieving depressive symptoms. Importantly, not all patients respond to ketamine treatment. Identifying sub-populations who will benefit from ketamine, as well as those who may not, prior to treatment initiation, would significantly advance precision medicine in patients with MDD.

METHODS

Here, we used mass spectrometry-based plasma proteomics to analyze matched pre- and post-ketamine treatment samples from a cohort of 30 MDD patients whose treatment outcomes and demographic and clinical characteristics were considered.

RESULTS

Ketamine responders and non-responders were identified according to their individual outcomes after two weeks of treatment. We analyzed proteomic alterations in post-treatment samples from responders and non-responders and identified a collection of six proteins pivotal to the antidepressive effect of ketamine. Subsequent co-regulation analysis revealed that pathways related to immune response were involved in ketamine response. By comparing the proteomic profiles of samples from the same individuals at the pre- and post-treatment time points, dynamic proteomic rearrangements induced by ketamine revealed that immune-related processes were activated in association with its antidepressive effect. Furthermore, receiver operating characteristic curve analysis of pre-treatment samples revealed three proteins with strong predictive performance in determining the response of patients to ketamine before receiving treatment.

CONCLUSIONS

These findings provide valuable knowledge about ketamine response, which will ultimately lead to more personalized and effective treatments for patients.

TRIAL REGISTRATION

The study was registered in the Chinese Clinical Trials Registry (ChiCTR-OOC-17012239) on May 26, 2017.

Aqueous Proteomic and Metabolomic Profiles in Low-Energy vs High-Energy Femtosecond Laser-Assisted Cataract Surgery

Purpose

To investigate the aqueous proteomics and metabolomics in low-energy and high-energy femtosecond laser-assisted cataract surgery (FLACS).

Methods

In this prospective observational study, 72 patients were randomized to 3 groups: low-energy FLACS, high-energy FLACS, and conventional phacoemulsification (controls). Aqueous was collected after femtosecond laser treatment or at the beginning of surgery (controls). Proteomic analysis was conducted using a data-independent acquisition method, whereas aqueous metabolomics were analyzed with liquid chromatography-tandem mass spectrometry. Bioinformatics analyses were performed to integrate the results of proteomics and metabolomics.

Results

Compared with low-energy FLACS, significantly elevated aqueous hemoglobin subunit beta, G protein subunit beta, carbonic anhydrase 1, and asymmetric dimethylarginine were observed in high-energy FLACS, suggesting significantly greater oxidative stress, inflammation, immunity, metabolism, and mitochondrial fatty acids oxidation. Compared with controls, significantly increased aqueous proteins and metabolites related to immune and inflammation (beta-crystallin B1, hemoglobin subunit beta, putrescine, and spermine) and oxidative stress (heat shock proteins, peroxiredoxins, and long-chain acylcarnitines) were observed in FLACS. Joint pathway analysis revealed nicotinate/nicotinamide metabolism and riboflavin metabolism were significantly overexpressed in high-energy FLACS compared with low-energy FLACS, whereas the pentose phosphate pathway and glycolysis were the most significant pathways when comparing FLACS with controls.

Conclusions

FLACS induced higher immunological and inflammatory responses, oxidative stress reactions, and mitochondrial fatty acid oxidative stress compared with controls. These differential effects were more pronounced when a higher laser energy was used.

New molecular mechanisms to explain the neuroprotective effects of insulin-like growth factor II in a cellular model of Parkinson's disease

INTRODUCTION

One of the hallmarks of Parkinsońs Disease (PD) is oxidative distress, leading to mitochondrial dysfunction and neurodegeneration. Insulin-like growth factor II (IGF-II) has been proven to have antioxidant and neuroprotective effects in some neurodegenerative diseases, including PD. Consequently, there isgrowing interest in understanding the different mechanisms involved in the neuroprotective effect of this hormone.

OBJECTIVES

To clarify the mechanism of action of IGF-II involved in the protective effect of this hormone.

METHODS

The present study was carried out on a cellular model PD based on the incubation of dopaminergic cells (SN4741) in a culture with the toxic 1-methyl-4-phenylpyridinium (MPP+), in the presence of IGF-II. This model undertakes proteomic analyses in order to understand which molecular cell pathways might be involved in the neuroprotective effect of IGF-II. The most important proteins found in the proteomic study were tested by Western blot, colorimetric enzymatic activity assay and immunocytochemistry. Along with the proteomic study, mitochondrial morphology and function were also studied by transmission electron microscopy and oxygen consumption rate. The cell cycle was also analysed using 7AAd/BrdU staining, and flow cytometry.

RESULTS

The results obtained indicate that MPP+, MPP++IGF-II treatment and IGF-II, when compared to control, modified the expression of 197, 246 proteins and 207 respectively. Some of these proteins were found to be involved in mitochondrial structure and function, and cell cycle regulation. Including IGF-II in the incubation medium prevents the cell damage induced by MPP+, recovering mitochondrial function and cell cycle dysregulation, and thereby decreasing apoptosis.

CONCLUSION

IGF-II improves mitochondrial dynamics by promoting the association of Mitofilin with mitochondria, regaining function and redox homeostasis. It also rebalances the cell cycle, reducing the amount of apoptosis and cell death by the regulation of transcription factors, such as Checkpoint kinase 1.

[Non-invasive biomarkers for early diagnosis of Alzheimer's disease in bodily fluids]

One of the urgent problems of modern health care is Alzheimer's disease (AD) and its early diagnosis. This is due to the rapid global spread of AD, the lack of pathogenetic therapy, and the ability to stabilize the progression of cognitive impairment in the early stages of the disease. Currently, only an autopsy can confirm the diagnosis of AD with 100% reliability, and classical laboratory and instrumental methods of diagnosis verification are difficult to implement in routine clinical practice due to several limitations. That is why the study of new and available biomarkers identified in human bodily fluids is promising for the early diagnosis of AD. The review addresses the problem of AD verification using markers in human bodily fluids, which can be obtained in a non-invasive way. Potential biomarkers of AD in saliva, tear fluid, urine, and nasal secretion are reviewed, and their prognostic values as AD indicators in the early stage are evaluated.

Spotting targets with 2D-DIGE proteomics

Two-dimensional difference gel electrophoresis (2D-DIGE) has been a staple of protein studies for almost three decades since first described in 1997. Although the advent of omic technologies has greatly expanded protein research and discovery, 2D-DIGE has consistently been the mainstay in biomedical applications. Differential protein expression is a hallmark of many disease states and identification of these biomarkers can improve diagnosis, prognosis and treatment. In this review, we examine the use of 2D-DIGE in exploring the cellular environment in physiologic and pathophysiologic states. We highlight this technology in protein identification and quantification, functional modification and biochemical pathways of interest. 2D-DIGE remains a useful tool due low cost and high resolving power for comparative and quantitative purposes in assessing disease states and facilitating identification of unique and novel biomarkers.

Current Role and Potential of Triple Quadrupole Mass Spectrometry in Biomedical Research and Clinical Applications

Mass-spectrometry-based assays nowadays play an essential role in biomedical research and clinical applications. There are different types of commercial mass spectrometers on the market today, and triple quadrupole (QqQ) is one of the time-honored systems. Here, we overview the main areas of QqQ applications in biomedicine and assess the current level, evolution, and trends in the use of QqQ in these areas. Relevant data were extracted from the Scopus database using the specified terms and Boolean operators defined for each field of the QqQ application. We also discuss the recent advances in QqQ and QqQ-based analytical platforms, which promote the clinical application of these systems, and explain the indicated substantial increase in triple quadrupole use in biomedicine. The number of biomedical studies utilizing QqQ increased 2-3 times this decade. Triple quadrupole is most intensively used in the field of endocrine research and testing. On the contrary, the relative rate of immunoassay utilization-a major competitor of chromatography-mass spectrometry-decreased in this area as well as its use within Therapeutic drug monitoring (TDM) and forensic toxicology. Nowadays, the applications of high-resolution accurate mass (HRAM) mass spectrometers in the investigated areas represent only a small fraction of the total amount of research using mass spectrometry; however, their application substantially increased during the last decade in the untargeted search for new biomarkers.

Cell-specific spatial profiling of targeted protein expression to characterize the impact of intracortical microelectrode implantation on neuronal health

Intracortical microelectrode arrays (MEAs) can record neuronal activity and advance brain-computer interface (BCI) devices. Implantation of the invasive MEA kills local neurons, which has been documented using immunohistochemistry (IHC). Neuronal nuclear protein (NeuN), a protein that lines the nuclei of exclusively neuronal cells, has been used as a marker for neuronal health and survival for decades in neuroscience and neural engineering. NeuN staining is often used to describe the neuronal response to intracortical microelectrode array (MEA) implantation. However, IHC is semiquantitative, relying on intensity readings rather than directly counting expressed proteins. To supplement previous IHC studies, we evaluated the expression of proteins representing different aspects of neuronal structure or function: microtubule-associated protein 2 (MAP2), neurofilament light (NfL), synaptophysin (SYP), myelin basic protein (MBP), and oligodendrocyte transcription factor 2 (OLIG2) following a neural injury caused by intracortical MEA implantation. Together, these five proteins evaluate the cytoskeletal structure, neurotransmitter release, and myelination of neurons. To fully evaluate neuronal health in NeuN-positive (NeuN+) regions, we only quantified protein expression in NeuN+ regions, making this the first-ever cell-specific spatial profiling evaluation of targeted proteins by multiplex immunochemistry following MEA implantation. We performed our protein quantification along with NeuN IHC to compare the results of the two techniques directly. We found that NeuN immunohistochemical analysis does not show the same trends as MAP2, NfL, SYP, MBP, and OLIG2 expression. Further, we found that all five quantified proteins show a decreased expression pattern that aligns more with historic intracortical MEA recording performance.

Proteomics and Metabolomics in Varicocele-Associated Male Infertility: Advancing Precision Diagnostics and Therapy

Background/Objectives: Varicoceles are a common contributor to male infertility, significantly impacting male-factor infertility cases. Traditional diagnostic methods often lack the sensitivity to detect the molecular and cellular disruptions caused by varicoceles, limiting the development of effective, personalized treatments. This narrative review aims to explore the advancements in proteomics and metabolomics as innovative, non-invasive diagnostic tools for varicocele-associated male infertility and their potential in guiding personalized therapeutic strategies. Methods: A comprehensive literature search was conducted using databases such as PubMed, Scopus, and Web of Science up to October 2024. Studies focusing on the application of proteomic and metabolomic analyses in varicocele-associated male infertility were selected. The findings were critically analyzed to synthesize current knowledge and identify future research directions. Results: Proteomic analyses revealed differentially expressed proteins in the sperm and seminal plasma of varicocele patients, revealing disruptions in pathways related to oxidative stress, mitochondrial dysfunction, apoptosis, and energy metabolism. Key proteins such as heat shock proteins, mitochondrial enzymes, and apoptotic regulators were notably altered. Metabolomic profiling uncovered specific metabolites in seminal plasma-such as decreased levels of lysine, valine, and fructose-that correlate with impaired sperm function and fertility potential. The integration of proteomic and metabolomic data provides a comprehensive molecular fingerprint of varicocele-induced infertility, facilitating the identification of novel biomarkers for early diagnosis and the development of personalized therapeutic interventions. Conclusions: Advances in proteomics and metabolomics have significantly enhanced our understanding of the molecular mechanisms underlying varicocele-associated male infertility. These "omics" technologies hold great promise for improving diagnostic accuracy and personalizing treatment, ultimately leading to better outcomes for affected men. Future large-scale clinical trials and validations are essential to confirm these biomarkers and facilitate their integration into routine clinical practice.

Neoadjuvant immunotherapy for dMMR and pMMR colorectal cancers: therapeutic strategies and putative biomarkers of response

Approximately 15% of locally advanced colorectal cancers (CRC) have DNA mismatch repair deficiency (dMMR), resulting in high microsatellite instability and a high tumour mutational burden. These cancers are frequently sensitive to therapy with immune-checkpoint inhibitors (ICIs) in the metastatic setting. This sensitivity seems to be even more pronounced in locally advanced disease, and organ preservation has become a realistic aim in ongoing clinical trials involving patients with dMMR rectal cancer. By contrast, metastatic CRCs with proficient DNA mismatch repair (pMMR) are generally resistant to ICIs, although a proportion of locally advanced pMMR tumours seem to have a high degree of sensitivity to ICIs. In this Review, we describe the current and emerging clinical evidence supporting the use of neoadjuvant ICIs in patients with dMMR and pMMR CRC, and the potential advantages (based on a biological rationale) of such an approach. We discuss how neoadjuvant 'window-of-opportunity' trials are being leveraged to progress biomarker discovery and we provide an overview of potential predictive biomarkers of response to ICIs, exploring the challenges faced when evaluating such biomarkers in biopsy-derived samples. Lastly, we describe how these discoveries might be used to drive a rational approach to trialling novel immunotherapeutic strategies in patients with pMMR CRC, with the ultimate aim of disease eradication and the generation of long-term immunosurveillance.

Homology-based identification and structural analysis of Pangasius hypophthalmus Annexins and Serine proteases to search molecules for wound healing applications

Chronic wounds and burns are a worldwide healthcare problem that erodes patients' well-being and healthcare systems. This silent and costly epidemic requires new, cost-efficient solutions to improve patients' physical and economic welfare. Eschar-degrading vegetal and bacterial proteases have been utilized as a solution. However, these proteins are evolutionarily far from those present in human wound healing. Serine protease (SP) and annexin (ANX) proteins interact within the skin healing process. A homology-based identification pipeline can help in discovering selective human SP and ANX analogs in the epithelial tissue of the fast-healing species, Pangasius hypophthalmus. In the present work, we found 14 candidates for RT-PCR in P. hypophthalmus using homology inference. The genetically detected candidates were then structurally and sequentially analyzed to understand their possible relation to SPs and ANXs involved in human wound healing. A total of six TBLASTN/BLASTX candidates (four SPs and two ANXs) were detected in P. hypophthalmus skin. Structural analysis revealed that all SP candidates resembled human KLK4, KLK5, KLK6, and KLK8, whereas all ANX only resembled human ANXA4. Structure and sequence analysis revealed high conservation of ANX Ca2+ binding sites (GDXD) and SP catalytic triad (HDS) motifs. In addition, structural analysis revealed that SP substrate selectivity position 186 was the main difference between human KLK5 and P. hypophthalmus SPs. These findings may allow the proposal and testing of more selective formulations, broadening treatments beyond debridement.

Exploring Molecular Alterations in Breast Cancer Among Indian Women Using Label-Free Quantitative Serum Proteomics

The clinical data indicate that diverse parameters characterize breast cancer patients in India, including age at presentation, risk factors, outcomes, and behavior. Alarming incidence and mortality rates emphasize the crucial need for early screening measures to combat breast cancer-related deaths effectively. Quantitative proteomic approaches prove pivotal in predicting cancer prognosis, analyzing protein expression patterns tied to disease aggressiveness and metastatic potential, and facilitating conversant therapy selection. Thus, this study was envisioned with the goal of identifying protein markers associated with breast cancer in Indian women, which could potentially be developed as diagnostic tools and therapeutic targets in the future. Applying label-free proteomic quantitation method and statistical analysis, several differentially expressed proteins (DEPs) were identified in the serum of breast cancer patients compared to controls, including SBSN, ANG, PCOLCE, and WFDC3 (upregulated), and PFN1, FLNA, and DSG2 (downregulated). The expression of SBSN was also validated by western blotting. Statistical methods were employed to proteomic expression data, which highlighted the ability of DEPs to distinguish between breast cancer and control samples. Conclusively, this study recognizes prospective biomarkers for breast cancer among Indian women and highlights the requisite of in-depth functional studies to elucidate their precise roles in breast cancer development. We particularly emphasize on SBSN and PFN1, as these proteins were observed to be progressively overexpressed and under expressed, respectively, in breast cancer samples compared to control samples, ranging from early-stage to metastatic cases.

Decoding the Challenges: navigating Intact Peptide Mass Spectrometry-Based Analysis for Biological Applications

Quantitative analysis of peptides in biological fluids offers a high diagnostic and prognostic tool to reflect the pathophysiological condition of the patient. Recently, methods based on liquid chromatography coupled with mass spectrometry (LC-MS) for the quantitative determination of intact peptides have been replacing traditionally used ligand-binding assays, which suffer from cross-reactivity issues. The use of "top-down" analysis of peptides is rapidly increasing since it does not undergo incomplete or non-reproducible digestion like "bottom-up" approaches. However, the low abundance of peptides and their peculiar characteristics, as well as the complexity of biological fluids, make their quantification challenging. Herein, the analytical pitfalls that may be encountered during the development of an LC-MS method for the analysis of intact peptides in biological fluids are discussed. Challenges in the pre-analytical phase, stability after sampling and sample processing, significantly impact the accuracy of peptide quantification. Emerging techniques, such as microextractions, are becoming crucial for improved sample cleanup and enrichment of target analytes. A comparison between the roles of high-resolution and low-resolution mass spectrometry in the quantification of intact peptides, as well as the introduction of supercharging reagents to enhance ionization, will be discussed.

In Silico Analysis: Genome-Wide Identification, Characterization and Evolutionary Adaptations of Bone Morphogenetic Protein (BMP) Gene Family in Homo sapiens

We systematically analyzed BMP gene family in H. sapiens to elucidate genetic structure, phylogenetic relationships, adaptive evolution and tissue-specific expression pattern. Total of 13 BMPs genes were identified in the H. sapiens genome. Bone morphogenetic proteins (BMPs) are composed of a variable number of exons ranging from 2 to 21. They exhibit a molecular weight ranging from 31,081.81 to 82,899.61 Da. These proteins possess hydrophilic characteristics, display thermostability, and exhibit a pH range from acidic to basic. We identified four segmental and two tandem duplication events in BMP gene family of H. sapiens. All of the vertebrate species that were studied show the presence of BMPs 1, 2, 3, 4, 5, 6, 7, 8A, and 15, however only Homo sapiens demonstrated the presence of BMP9 and BMP11. The pathway and process enrichment analysis of BMPs genes showed that these were considerably enriched in positive regulation of pathway-restricted SMAD protein phosphorylation (92%) and cartilage development (77%) biological processes. These genes exhibited positive selection signals that were shown to be conserved across vertebrate lineages. The results showed that BMP2/3/5/6/8a/15 proteins underwent adaptive selection at many amino acid locations and increased positive selection was detected in TGF-β propeptide and TGF-β super family domains which were involved in dorso-ventral patterning, limb bud development. More over the expression pattern of BMP genes revealed that BMP1 and BMP5; BMP4 and BMP6 exhibited substantially identical expression patterns in all tissues while BMP10, BMP15, and BMP3 showed tissue-specific expression.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

The mTOR Pathway: A Common Link Between Alzheimer's Disease and Down Syndrome

Down syndrome (DS) is a chromosomal condition that causes many systemic dysregulations, leading to several possible age-related diseases including Alzheimer's disease (AD). This may be due to the triplication of the Amyloid precursor protein (APP) gene or other alterations in mechanistic pathways, such as the mTOR pathway. Impairments to upstream regulators of mTOR, such as insulin, PI3K/AKT, AMPK, and amino acid signaling, have been linked to amyloid beta plaques (Aβ) and neurofibrillary tangles (NFT), the most common AD pathologies. However, the mechanisms involved in the progression of pathology in human DS-related AD (DS-AD) are not fully investigated to date. Recent advancements in omics platforms are uncovering new insights into neurodegeneration. Genomics, spatial transcriptomics, proteomics, and metabolomics are novel methodologies that provide more data in greater detail than ever before; however, these methods have not been used to analyze the mTOR pathways in connection to DS-AD. Using these new techniques can unveil unexpected insights into pathological cellular mechanisms through an unbiased approach.

Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response

The intratumoral microbiome (TM) refers to the microorganisms in the tumor tissues, including bacteria, fungi, viruses, and so on, and is distinct from the gut microbiome and circulating microbiota. TM is strongly associated with tumorigenesis, progression, metastasis, and response to therapy. This paper highlights the current status of TM. Tract sources, adjacent normal tissue, circulatory system, and concomitant tumor co-metastasis are the main origin of TM. The advanced techniques in TM analysis are comprehensively summarized. Besides, TM is involved in tumor progression through several mechanisms, including DNA damage, activation of oncogenic signaling pathways (phosphoinositide 3-kinase [PI3K], signal transducer and activator of transcription [STAT], WNT/β-catenin, and extracellular regulated protein kinases [ERK]), influence of cytokines and induce inflammatory responses, and interaction with the tumor microenvironment (anti-tumor immunity, pro-tumor immunity, and microbial-derived metabolites). Moreover, promising directions of TM in tumor therapy include immunotherapy, chemotherapy, radiotherapy, the application of probiotics/prebiotics/synbiotics, fecal microbiome transplantation, engineered microbiota, phage therapy, and oncolytic virus therapy. The inherent challenges of clinical application are also summarized. This review provides a comprehensive landscape for analyzing TM, especially the TM-related mechanisms and TM-based treatment in cancer.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities' expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Proteomic Signatures of Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with COVID-19: A Narrative Review

BACKGROUND/OBJECTIVES

Multisystem Inflammatory Syndrome in Children (MIS-C) is a post-infectious complication of COVID-19. MIS-C has overlapping features with other pediatric inflammatory disorders including Kawasaki Disease (KD), Macrophage Activation Syndrome (MAS), Toxic Shock Syndrome and sepsis. The exact mechanisms responsible for the clinical overlap between MIS-C and these conditions remain unclear, and biomarkers that could distinguish MIS-C from its clinical mimics are lacking. This study aimed to provide an overview of how proteomic methods, like Mass Spectrometry (MS) and affinity-based proteomics, can offer a detailed understanding of pathophysiology and aid in the diagnosis and prognosis of MIS-C.

METHODS

A narrative review of relevant studies published up to July 2024 was conducted.

RESULTS

We identified 15 studies and summarized their key proteomic findings. These studies investigated the serum or plasma proteome of MIS-C patients using MS, Proximity Extension, or Aptamer-based assays. The studies associated the proteomic profile of MIS-C with laboratory and clinical parameters and/or compared it with that of other diseases including acute COVID-19, KD, MAS, pediatric rheumatic diseases, sepsis and myocarditis or pericarditis following COVID-19 mRNA immunization. Depending on the method and the control group, different proteins were increased or decreased in the MIS-C group. The limitations and challenges in MIS-C proteomic research are also discussed, and future research recommendations are provided.

CONCLUSIONS

Although proteomics appear to be a promising approach for understanding the pathogenesis and uncovering candidate biomarkers in MIS-C, proteomic studies are still needed to recognize and validate biomarkers that could accurately discriminate MIS-C from its clinical mimics.

From Proteomics to the Analysis of Single Protein Molecules

Limit of detection (LoD) is a term that is used to characterize the sensitivity of an analytical method. The existing limitation of the sensitivity of analysis using modern mass spectrometry methods has been experimentally shown to be a limiting factor in the application of proteomic technologies in medicine. This article proposes a concept of a new technology that will set a new vector of development in the development of systems for solving problems of medical diagnostics and deals with theoretical and practical aspects of creating a new technology for the detection of single biomacromolecules (in particular, proteins) in biological samples. Such technology should be based on the principle of signal registration similar to that used in a Geiger counter (also known as a Geiger-Müller counter or G-M counter), a device that automatically counts the number of ionizing particles that hit it. This counter is free from probabilistic components; it registers a signal if there is at least one target molecule in the analysis chamber. Predictive medical diagnostics require technology based on methods where sensitivity allows for the detection of single marker molecules in a biological sample volume of 1-10 µL, the smallest volume of biomaterial used in laboratory diagnostics. Creation of a detector with a sensitivity of 10-18 M would allow for the detection of one molecule in 1 µL of the sample, which fundamentally makes this approach analogous to a G-M counter for solutions. To date, bioanalytical methods are limited to a sensitivity of 10-12 M (which is approximately 1 million molecules per 1 μL), which is insufficient to capture the early stages of pathological processes.