Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

Metabolic characteristics of benign and malignant pulmonary nodules and establishment of invasive lung adenocarcinoma model by high-resolution mass spectrometry

BACKGROUND

Increasing pulmonary nodule presentations in lung adenocarcinoma patients reveal diagnostic limitations of CT-based invasiveness assessment. The critical unmet need lies in developing non-invasive biomarkers differentiating invasive adenocarcinoma from premalignant lesions and benign nodules, while characterizing metabolic trajectory from health to metastatic disease.

METHODS

Untargeted metabolomics analyzed plasma samples from 102 subjects stratified into four cohorts: confirmed adenocarcinoma (n = 35), benign nodules (n = 22), precursor lesions (n = 24), and healthy controls (n = 21). Multivariate analysis identified discriminative metabolites for constructing an infiltration prediction model.

RESULTS

Three diagnostic groups exhibited distinct metabolic profiles. Hexaethylene glycol, tetraethylene glycol, and Met-Thr showed stage-dependent concentration gradients. Progressive malignancy correlated with elevated levels of 41 metabolites. An eight-metabolite panel achieved AUC 0.933 (0.873-0.994) in distinguishing precursors from early malignancies, sustained through internal validation (AUC 0.934, 0.905-0.966).

CONCLUSIONS

Met-Thr depletion inversely correlates with malignancy progression, while eight-metabolite signatures demonstrate diagnostic potential for preoperative infiltration assessment in nodular adenocarcinoma.

Precision Medicine and the Human Proteome in Disease, Diagnostics and Translation: Current Status and Future Prospects

The human proteome-the entire collection of proteins expressed by the human genome-represents a dynamic and intricate landscape of biological function. Proteins are the workhorses of the body, driving processes from cellular communication to immune defense, and their alterations underpin many diseases. Understanding the proteome has become a cornerstone of modern biomedical research, offering insights into disease mechanisms, diagnostic tools, and personalized treatments through precision medicine. This commentary explores the current state of human proteome research; its applications in disease understanding, diagnostics, and therapeutic advancements; and the exciting prospects that lie ahead.

Plasma proteome analysis of rheumatic patients reveals differences in fingerprints based on cardiovascular history: a pilot study

The risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) is much higher than that in the general population. As its etiology is not fully understood, we performed a pilot study using a shotgun proteomic approach to investigate whether the plasma signature in RA patients with CVD might show an altered profile. Subjects with RA were compared to a group of RA patients with a previous cardiovascular event (CVE). The cohort consisted of an RA control group (n = 10) and a group (n = 10) of RA patients with a history of CVD. Samples were collected at least 6 months before the CVE and 3-6 months after the CVE. All subjects were matched to controls for age, sex, and medication use. Plasma depletion of the 14 most abundant proteins was followed by bottom-up shotgun proteomics analysis (LC‒MS/MS). Relative changes in protein/peptide abundance were investigated using classical statistical analyses with Perseus and XG-Boost machine learning to compare between groups and to determine the relative importance of identified proteins, respectively. Principal component analysis (PCA) revealed no difference in the global protein and peptide signatures between the control and CVE groups. A total of 150, 239 and 74 protein ID's showed in comparison between Post Event vs. controls, Event vs. no Event and Pre event vs. Post Event respectively a statistically difference in relative abundance (p < 0.05). Remarkedly a total of 236 proteins ID's showed a statistical significant difference in relative abundance in the PRE-Event group compared to the control group which could also be confirmed by XGboost machine learning. Here, we demonstrated potential differences in the plasma proteome signature of rheumatic patients with cardiovascular events. Interestingly, this signature may be present prior to CVE's. However the conclusions must be drawn with caution, since this is a pilot study and further investigation with larger cohorts is warranted to identify potential risk markers that may predict the relative risk of CVEs in rheumatic diseases.

Integrative proteogenomic analysis identifies COL6A3-derived endotrophin as a mediator of the effect of obesity on coronary artery disease

Obesity strongly increases the risk of cardiometabolic diseases, yet the underlying mediators of this relationship are not fully understood. Given that obesity strongly influences circulating protein levels, we investigated proteins mediating the effects of obesity on coronary artery disease, stroke and type 2 diabetes. By integrating two-step proteome-wide Mendelian randomization, colocalization, epigenomics and single-cell RNA sequencing, we identified five mediators and prioritized collagen type VI α3 (COL6A3). COL6A3 levels were strongly increased by body mass index and increased coronary artery disease risk. Notably, the carboxyl terminus product of COL6A3, endotrophin, drove this effect. COL6A3 was highly expressed in disease-relevant cell types and tissues. Finally, we found that body fat reduction could reduce plasma levels of COL6A3-derived endotrophin, indicating a tractable way to modify endotrophin levels. In summary, we provide actionable insights into how circulating proteins mediate the effects of obesity on cardiometabolic diseases and prioritize endotrophin as a potential therapeutic target.

Oxidised Apolipoprotein Peptidome Characterises Metabolic Dysfunction-Associated Steatotic Liver Disease

BACKGROUND

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) encompasses a spectrum of histological conditions ranging from simple steatosis to fibrosing steatohepatitis, and is a risk factor for cardiovascular diseases (CVD). While oxidised apolipoproteins A and B have been linked to obesity and CVD, the association between other oxidised apolipoproteins and MASLD is yet to be established. To fill this gap, we characterised the circulating serum peptidome of patients with MASLD.

METHODS

We studied the serum of 87 biopsy-confirmed MASLD patients and 20 age- and sex-matched control (CTRL) subjects. We first employed an untargeted LC-MS/MS peptidomics approach (9 CTRL, 32 MASLD) to identify key hits differentially modulated, and subsequently validated the most relevant findings through targeted peptidomics in an enlarged study population (87 MASLD and 20 CTRL).

RESULTS

Untargeted serum peptidomics identified several oxidised apolipoprotein peptide fragments, including ApoE and ApoC-III, significantly upregulated in MASLD compared to CTRL. Specifically focusing on the oxidative status of intact ApoC-III, studied through its major glycoforms (ApoC-III0, ApoC-IIIi and ApoC-IIIii), we observed a marked reduction in non-oxidised forms of these circulating peptides alongside substantially increased levels of their oxidised proteoforms in MASLD versus controls (but not within the disease stages). Oxidised ApoE and ApoC-III peptide fragments were also significantly correlated with obesity, insulin resistance, dyslipidaemia and transaminases, suggesting a potential link between circulating apolipoprotein oxidation and systemic/hepatic metabolic dysfunction.

CONCLUSION

Our data reveals a previously unreported oxidised apolipoprotein profile associated with MASLD. The functional and clinical implications of these findings warrant further mechanistic investigation.

Proteomics of neuropsychiatric disorders

The pathogenesis of neuropsychiatric disorders (NDs) remains largely unclear, hence there is a lack of objective and reliable biomarkers. Proteomics, as a powerful tool for disease biomarkers research, has been largely ignored in the field of NDs. This review summarizes recent research on the application of mass spectrometry-based proteomics in NDs. Proteins associated with NDs have been identified in various sample sources, including blood, urine, saliva, tear, cerebrospinal fluid, and brain tissue. These studies have preliminarily demonstrated the potential of proteomics in NDs and require comprehensive validation in multi-center, large-scale clinical cohorts. We also discuss the challenges and prospects of proteomics in the research of early diagnostic biomarkers for NDs. These findings may provide a foundation for developing proteomic-based diagnostics and advancing precision medicine in NDs.

Investigating causal relationships between plasma proteins and lung adenocarcinoma: result from proteomics and Mendelian randomization study

BACKGROUND

Plasma proteins contribute to the identification, diagnosis, and prognosis of human illnesses, which may be conducive to understanding the molecular mechanism and diagnosis of Lung adenocarcinoma (LUAD).

METHODS

We collected plasma samples from 28 healthy individuals (H) and 56 LUAD patients and analyzed them using LC-MS/MS-based proteomics to determine differential expression plasma proteins (DEPPs). Then, the DEPPs were subjected to a two-sample Mendelian randomization (MR) study based on an "Inverse variance weighted (IVW)" approach to investigate the causal relationships between DEPPs and LUAD. Logistic regression analysis was conducted to develop a diagnostic model for LUAD.

RESULTS

317 plasma proteins were found in proteomics, and 19 DEPPs were identified. The MR study revealed that IL20RB (odds ratio (OR) = 1.600, 95% Confidence Interval (CI) [1.098, 2.331], P = 0.014) and SAA2 (OR = 1.048, 95%CI [1.012, 1.081], P = 0.017) were highly related to LUAD. A diagnostic model was established with IL20RB and SAA2. The AUC of this diagnostic model was 0.858.

CONCLUSION

Plasma IL20RB and SAA2 levels were closely connected with LUAD.

Proteomic and metabolomic profiling of plasma uncovers immune responses in patients with Long COVID-19

Long COVID is an often-debilitating condition with severe, multisystem symptoms that can persist for weeks or months and increase the risk of various diseases. Currently, there is a lack of diagnostic tools for Long COVID in clinical practice. Therefore, this study utilizes plasma proteomics and metabolomics technologies to understand the molecular profile and pathophysiological mechanisms of Long COVID, providing clinical evidence for the development of potential biomarkers. This study included three age- and gender-matched cohorts: healthy controls (n = 18), COVID-19 recovered patients (n = 17), and Long COVID patients (n = 15). The proteomics results revealed significant differences in proteins between Long COVID-19 patients and COVID-19 recovered patients, with dysregulation mainly focused on pathways such as coagulation, platelets, complement cascade reactions, GPCR cell signal transduction, and substance transport, which can participate in regulating immune responses, inflammation, and tissue vascular repair. Metabolomics results showed that Long COVID patients and COVID-19 recovered patients have similar metabolic disorders, mainly involving dysregulation in lipid metabolites and fatty acid metabolism, such as glycerophospholipids, sphingolipid metabolism, and arachidonic acid metabolism processes. In summary, our study results indicate significant protein dysregulation and metabolic abnormalities in the plasma of Long COVID patients, leading to coagulation dysfunction, impaired energy metabolism, and chronic immune dysregulation, which are more pronounced than in COVID-19 recovered patients.

Clinical Peptidomics in Acute Leukemias: Current Advances and Future Perspectives

The study of circulating peptides in the blood offers significant opportunities for diagnosing, stratifying, and managing various diseases. With recent technological advances and the ongoing need to understand complex diseases such as acute leukemias (AL), peptidomic analysis of peripheral blood, especially serum and plasma, has become increasingly important for studying human biology and pathophysiology. Here, we provide insights and perspectives on technological developments and potential clinical applications using widely used peptidomic analysis methods. We discuss examples where peptidomics using serum or plasma has contributed to the understanding of AL. Specifically, we highlight the scarcity of peptidomic studies applied to AL and emphasize the importance of exploring this area, as the few published studies present promising results that can significantly contribute to precision medicine.

Non-coding RNAs (ncRNAs) as therapeutic targets and biomarkers in oligodendroglioma

Oligodendrogliomas (ODGs) are neuroepithelial tumors that need personalized treatment plans because of their unique molecular and histological features. Non-coding RNAs form an epigenetic class of molecules that act as the first steps in gene regulation. They consist of microRNAs, long non-coding RNAs, and circular RNAs. These molecules significantly participate in ODG pathogenesis by regulating ODG initiation, progression, and treatment response. This review is designated to analyze the literature and describe the genomic profile of ODGs, the complex actions of ncRNAs in ODGs pathogenesis and treatment, and their roles as appropriate biomarkers and as one of the precision mechanisms action targets, such as antisense oligonucleotides, small interfering RNAs, gene therapy vectors, peptide nucleic acids, and small molecule inhibitors. Overall, ncRNAs considerably alter the pathological spectrum of ODGs by influencing fundamental processes in tumor biology. Applying ncRNAs in a clinical context exhibits promise for enhanced diagnosis and individualized therapeutic interventions. Nevertheless, the delivery efficacy and potential adverse "off-target" sequels retain the main obstacles undermining clinical potential. Continuous research and technological advancements in ncRNAs offer new insights and promising prospects for revolutionizing oligodendroglioma care, leading to better, personalized treatment outcomes.

Automated multiplexed affinity-based enrichment of peptides for LC-MS/MS plasma proteomics

Plasma proteomics offers high potential for biomarker discovery, as plasma is collected through a minimally invasive procedure and constitutes the most complex human-derived proteome. However, the wide dynamic range poses a significant challenge. Here, we propose a semi-automated method based on the use of multiple single chain variable fragment antibodies, each enriching for peptides found in up to a few hundred proteins. This approach allows for the analysis of a complementary fraction compared to full proteome analysis. Proteins from pooled plasma were extracted and digested before testing the performance of 29 different antibodies with the aim of reproducibly maximizing peptide enrichment. Our results demonstrate the enrichment of 3662 peptides not detected in neat plasma or negative controls. Moreover, most antibodies were able to enrich for at least 155 peptides across different levels of abundance in plasma. To further reduce analysis time, a combination of antibodies was used in a multiplexed setting. Repeated sample analyses showed low coefficients of variation, and the method is flexible in terms of affinity binders. It does not impose drastic increases in instrument time, thus showing excellent potential for usage in large scale discovery projects.

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.

Feasibility of MALDI-MSI-Based Proteomics Using Bouin-Fixed Pathology Samples: Untapping the Goldmine of Nephropathology Archives

The application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts. A large proportion of the features (270, 68.9%) was detected in both BFPE and FFPE renal samples, demonstrating only limited variability in signal intensity (10.22-10.06%). Samples processed with either fixative were able to discriminate the principal parenchyma regions along with diverse renal substructures, such as glomeruli, tubules, and vessels. This was observed when performing an additional "stress test", showing comparable results in both BFPE and FFPE samples when the distribution of several amyloid fingerprint proteins was mapped. These results suggest the utility of BFPE tissue specimens in MSI-based nephropathology research, further widening their application in this field.

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins

Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.

Origins, Technological Advancement, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Huanglian Jiedu Wan intervened with "Shi-Re Shanghuo" syndrome through regulating immune balance mediated by biomarker succinate

With increasing stress in daily life and work, subhealth conditions induced by "Shi-Re Shanghuo" syndrome was gradually universal. "Huanglian Jiedu Wan" (HLJDW) was the first new syndrome Chinese medicine approved for the treatment of "Shi-Re Shanghuo" with promising clinical efficacy. Preliminary small-sample clinical studies have identified some notable biomarkers (succinate, 4-hydroxynonenal, etc.). However, the correlation and underlying mechanism between these biomarkers of HLJDW intervention on "Shi-Re Shanghuo" syndrome remained ambiguous. Therefore, this study was designed as a randomized, double-blind, multicenter, placebo-controlled Phase II clinical trial, employing integrated analysis techniques such as non-targeted and targeted metabolomics, salivary microbiota, proteomics, parallel peaction monitoring, molecular docking and surface plasmon resonance (SPR). The results of the correlation analysis indicated that HLJDW could mediate the balance between inflammation and immunity through succinate produced via host and microbial source to intervene "Shi-Re Shanghuo" syndrome. Further through the HIF1α/MMP9 pathway, succinate regulated downstream arachidonic acid metabolism, particularly the lipid peroxidation product 4-hydroxynonenal. Finally, an animal model of recurrent oral ulcers induced by "Shi-Re Shang Huo" was established and HLJDW was used for intervention, key essential indicators (succinate, glutamine, 4-hydroxynonenal, arachidonic acid metabolism) essential in the potential pathway HIF1α/MMP9 discovered in clinical practice were validated. The results were found to be consistent with our clinical findings. Taken together, succinate was observed as an important signal that triggered immune responses, which might serve as a key regulatory metabolic switch or marker of "Shi-Re Shanghuo" syndrome treated with HLJDW.

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

Therapeutic potential of natural antisense transcripts and various mechanisms involved for clinical applications and disease prevention

Antisense transcription, a prevalent occurrence in mammalian genomes, gives rise to natural antisense transcripts (NATs) as RNA molecules. These NATs serve as agents of diverse transcriptional and post-transcriptional regulatory mechanisms, playing crucial roles in various biological processes vital for cell function and immune response. However, when their normal functions are disrupted, they can contribute to human diseases. This comprehensive review aims to establish the molecular foundation linking NATs to the development of disorders like cancer, neurodegenerative conditions, and cardiovascular ailments. Additionally, we evaluate the potential of oligonucleotide-based therapies targeting NATs, presenting both their advantages and limitations, while also highlighting the latest advancements in this promising realm of clinical investigation.Abbreviations: NATs- Natural antisense transcripts, PRC1- Polycomb Repressive Complex 1, PRC2- Polycomb Repressive Complex 2, ADARs- Adenosine deaminases acting on RNA, BDNF-AS- Brain-derived neurotrophic factor antisense transcript, ASOs- Antisense oligonucleotides, SINEUPs- Inverted SINEB2 sequence-mediated upregulating molecules, PTBP1- Polypyrimidine tract binding protein-1, HNRNPK- heterogeneous nuclear ribonucleoprotein K, MAPT-AS1- microtubule-associated protein tau antisense 1, KCNQ1OT- (KCNQ1 opposite strand/antisense transcript 1, ERK- extracellular signal-regulated kinase 1, USP14- ubiquitin-specific protease 14, EGF- Epidermal growth factor, LSD1- Lysine Specific Demethylase 1, ANRIL- Antisense Noncoding RNA in the INK4 Locus, BWS- Beckwith-Wiedemann syndrome, VEGFA- Vascular Endothelial Growth component A.

Mass spectrometry as a lens into molecular human nutrition and health

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Proteomic Research of Extracellular Vesicles in Clinical Biofluid

Extracellular vesicles (EVs), the lipid bilayer membranous structures of particles, are produced and released from almost all cells, including eukaryotes and prokaryotes. The versatility of EVs has been investigated in various pathologies, including development, coagulation, inflammation, immune response modulation, and cell-cell communication. Proteomics technologies have revolutionized EV studies by enabling high-throughput analysis of their biomolecules to deliver comprehensive identification and quantification with rich structural information (PTMs, proteoforms). Extensive research has highlighted variations in EV cargo depending on vesicle size, origin, disease, and other features. This fact has sparked activities to use EVs for diagnosis and treatment to ultimately achieve clinical translation with recent endeavors summarized and critically reviewed in this publication. Notably, successful application and translation require a constant improvement of methods for sample preparation and analysis and their standardization, both of which are areas of active research. This review summarizes the characteristics, isolation, and identification approaches for EVs and the recent advances in EVs for clinical biofluid analysis to gain novel knowledge by employing proteomics. In addition, the current and predicted future challenges and technical barriers are also reviewed and discussed.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Peptidomics analysis of plasma in patients with ankylosing spondylitis

Background

This study aimed to explore the differential expression of peptides associated with ankylosing spondylitis (AS) patients, enabling identification of potential functional peptides to provide the basis for the novel intervention targets for AS.

Material and Methods

3 AS patients and 3 healthy volunteers were enrolled in this study. The expression profiles for peptides present in the plasma of AS patients and the healthy individual were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The physicochemical properties and biological functions of identified peptides were further analyzed by bioinformatics. The results of peptide identification were verified by cell viability analysis, using CCK8 and Edu staining assay, and the differential peptides relevant to the disease were screened.

Results

52 differential peptides were successfully identified using mass spectrometry. 44 peptides were up-regulated, while eight were down-regulated. FGA-peptide (sequences: DSGEGDFLAEGGGVRGPR), C4A-peptide (sequences: NGFKSHAL), and TUBB-peptide (sequences: ISEQFTAMFR) were screened out that could significantly promote the proliferation of fibroblasts in AS patients. Bioinformatics analysis showed these differentially expressed peptides might be associated with "MHC class I protein binding" and "pathogenic Escherichia coli infection" pathways, which might further affect the progression of AS.

Conclusion

This pilot study shows 3 differentially expressed peptides may have the potential function for the occurrence and development of AS, may provide novel insights into the underlying molecular mechanisms of AS based on peptide omics.

Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides

Nature may have the answer to many of our questions about human, animal, and environmental health. Natural bioactives, especially when harvested from sustainable plant and food sources, provide a plethora of molecular solutions to nutritionally actionable, chronic conditions. The spectrum of these conditions, such as metabolic, immune, and gastrointestinal disorders, has changed with prolonged human life span, which should be matched with an appropriately extended health span, which would in turn favour more sustainable health care: "adding years to life and adding life to years". To date, bioactive peptides have been undervalued and underexploited as food ingredients and drugs. The future of translational science on bioactive peptides-and natural bioactives in general-is being built on (a) systems-level rather than reductionist strategies for understanding their interdependent, and at times synergistic, functions; and (b) the leverage of artificial intelligence for prediction and discovery, thereby significantly reducing the time from idea and concept to finished solutions for consumers and patients. This new strategy follows the path from benefit definition via design to prediction and, eventually, validation and production.

The human host response to monkeypox infection: a proteomic case series study

The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID-19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID-19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID-19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein-panel assays in emerging infectious diseases.

Systems Biology Approach to Investigate Biomarkers, Boron-10 Carriers, and Mechanisms Useful for Improving Boron Neutron Capture Therapy

Systems biology approach, carried out with high-throughput omics technologies, has become a fundamental aspect of the study of complex diseases like cancer. It can molecularly characterize subjects, physiopathological conditions, and interactions, allowing a precise description, to reach personalized medicine. In particular, proteomics, typically performed with liquid chromatography coupled to mass spectrometry, is a powerful tool for systems biology, giving the possibility to perform diagnosis, patient stratification, and prediction of therapy effects. Boron Neutron Capture Therapy (BNCT) is a selective antitumoral radiotherapy based on a nuclear reaction that occurs when ¹⁰B atoms are irradiated by low-energy thermal neutrons, leading to cell death, thanks to the production of high-energy α particles. Since BNCT is recently becoming an important therapy for the treatment of different types of solid tumors such as gliomas, head and neck cancers, and others, it can take advantage of molecular investigation to improve the understanding of effects and mechanisms and so help its clinical applications. In this context, proteomics can provide a better understanding of mechanisms related to BNCT effect, identify potential biomarkers, and individuate differential responses by specific patients, stratifying responders and nonresponders. Another key aspect of BNCT is the study of new potential Boron-10 carriers to improve the selectivity of Boron delivery to tumors and proteomics can be important in this application, studying the effectiveness of new boron delivery agents, including protein-based carriers, also using computational studies that can investigate new molecules, such as boronated monoclonal antibodies, for improving BNCT.

Biomarkers in Neurodegenerative Diseases: Proteomics Spotlight on ALS and Parkinson’s Disease

Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) are both characterized by pathogenic protein aggregates that correlate with the progressive degeneration of neurons and the loss of behavioral functions. Both diseases lack biomarkers for diagnosis and treatment efficacy. Proteomics is an unbiased quantitative tool capable of the high throughput quantitation of thousands of proteins from minimal sample volumes. We review recent proteomic studies in human tissues, plasma, cerebrospinal fluid (CSF), and exosomes in ALS and PD that identify proteins with potential utility as biomarkers. Further, we review disease-related post-translational modifications in key proteins TDP43 in ALS and α-synuclein in PD studies, which may serve as biomarkers. We compare relative and absolute quantitative proteomic approaches in key biomarker studies in ALS and PD and discuss recent technological advancements which may identify suitable biomarkers for the early-diagnosis treatment efficacy of these diseases.