Discovery Of Potential Biomarkers Research Articles

Correlation between Volatile Organic Compounds (VOC) with Leukotriene B4 and Eosinophil Counts in Chronic Obstructive Pulmonary Disease Patients

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disorder characterized by increasing Leukotriene B4 (LTB4) and eosinophil counts. Volatile organic compounds (VOCs) have shown promise as non-invasive biomarkers, reflecting COPD pathophysiology. Identifying specific VOCs associated with increased LTB4 and eosinophil counts could lead to the discovery of potential biomarkers for COPD severity or progression. AIMS: This study aims to investigate the correlation between VOCs and leukotriene B4 (LTB4) levels, as well as eosinophil counts counts in COPD patients. METHOD: Using an observational-analytic method with a case-control approach, 20 COPD patients and 20 controls were enrolled from the respiratory outpatient department of Dr. Saiful Anwar General Hospital, Malang. VOC levels were measured using a breath analyzer, while LTB4 levels were determined through enzyme-linked immunosorbent assays. Spearman’s correlation tests examined associations between VOCs, LTB4, eosinophil counts, and comorbidity, with Mann-Whitney tests comparing results against the control group. Data significance was set at p < 0.05. RESULT: There were 40 COPD patients and 40 controls in this study. There were significant differences between VOCs in the COPD group and the control group (p < 0.05). LTB4 level significantly increased in the COPD group than in the control group (p < 0.001), and there was no difference in the eosinophil level. There was a correlation between LTB4 and VOC level of C2H5OH in COPD patients (p = 0.009; r = 0.410). There was no correlation between eosinophil counts and VOCs (p = 0.939). The level of VOCs was significantly different between patients with only COPD and patients with COPD and comorbid lung cancer (p < 0.05). CONCLUSION: There is a correlation between VOC and LTB4 in COPD patients.

Abstract 2653: Integrative analysis of proteomic alterations in human PBMC’s treated by HPK1 inhibitor FB849 reveals enriched pathways from various immune cells leading to potential novel biomarker discovery

Abstract HPK1, also known as MAP4K1, is a critical signaling kinase molecule as a negative feedback loop in various immune cells. Despite the advancement of multiple clinical programs for therapeutic inhibition of HPK1, there is an urgent need to understand HPK1 biology in genome-wide signaling networks not only from lymphocytes but also from monocytes. FB849 is designed as a potent and highly selective inhibitor of HPK1 and is currently in phase I clinical trial with advanced cancer patients (NCT05761223). We aim to generate proteomic and phosphoproteomic profiles to reveal modes of action of FB849 in the broad spectrum of cancer immunity. Human PBMCs were treated with FB849 under conditions with or without CD3/CD28 stimulation. Utilizing an antibody array comprising 1,930 antibodies, the expression of 1,438 distinct proteins was assessed. The antibody arrays in human PBMCs identified a total of 85 and 33 phospho-proteins showing differential expression in response to FB849 treatment compared to the control in the presence or absence of CD3/CD28 stimulations, respectively (fold change > 2, adj. p-value < 0.01). KEGG pathway enrichment analysis demonstrated that the differentially expressed proteins by FB849 in the presence of CD3/CD28 stimulation were significantly enriched in immune-related biological processes, including 'pathways in cancer', 'PI3K-Akt signaling pathway', 'Focal adhesion', 'cytokine-cytokine receptor interaction', 'MAPK signaling pathway', 'JAK-STAT signaling pathway', and 'Th1 and Th2 cell differentiation'. Reactome biology pathway analysis also showed significant enrichment in ‘immune system’, ‘homeostasis’, and ‘cytokine signaling in immune cells’. In addition, we performed LC-MS/MS-based phosphoproteomics in PBMC samples pooled from CT26 mouse syngeneic models treated with 25mg/kg and 50mg/kg of FB849 and its derivative. The proteomics identified 1239 unique phosphorylated proteins and 915 distinct phosphorylation proteins, while 68 and 39 phosphorylated proteins were identified as differentially expressed proteins in FB849 or its derivative treated groups compared with control groups, respectively. The several phosphoproteins exhibited consistent decreases or increases in both FB849 and its derivative. Together, this work reveals genome-wide alterations in the proteomic and phosphoproteomic landscape by FB849 in both mouse PBMC’s and human PBMC’s with clear pathway enrichment potentially from various immune cell lineages. Further exploration of the differential expression of each protein and/or phosphoproteins especially in T cell-, B cell-, and monocyte-enriched biomarkers which link to HPK1-relevant signal transduction is needed to find out their clinical implication and to highlight new understanding of HPK1 signaling network in cancer immunity. Citation Format: Seungmook Lim, Sooryeonhwa Jin, A Yeong Park, Hyekyoung Kim, Seongkon Kim, Jamie Jae Eun Kim, Jinhwa Lee. Integrative analysis of proteomic alterations in human PBMC’s treated by HPK1 inhibitor FB849 reveals enriched pathways from various immune cells leading to potential novel biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2653.

Abstract 7659: AI-driven hypothesis-free analysis for biomarker discovery: Introducing the Biomarkers Navigator Framework

Abstract The pivotal role of biomarkers in personalizing patient treatment and guiding drug development is well-recognized, with the ultimate goal of enhancing therapeutic efficacy and patient quality of life by minimizing adverse events. Their development, however, is challenged by strict regulations and high costs, leading to sparse and varied data collected over extended periods and through diverse technologies. These variations complicate the harmonization and analysis of the data. Traditional methods struggle to navigate these complexities, further obscuring biomarker identification. For instance, PDL-1, approved as a biomarker by the FDA, has its effectiveness debated in several studies. To address these issues, we introduce Biomarkers Navigator, a flexible and automated analysis framework engineered explicitly for the robust identification of biomarkers. This framework is designed to aggregate, standardize, and store expansive biomarker information, ensuring efficient and traceable knowledge retrieval. By pre-calculating each potential biomarker, Biomarkers Navigator enables immediate and comprehensive meta-analyses, facilitating rapid decision-making tailored to the level of risk acceptance. Large Language Models (LLMs) further enhance the framework's capability to uncover signatures of response and resistance. LLM integration has accelerated the discovery of new potential biomarkers for hematological malignancies by combining prior knowledge and previously obscured associations. Our approach demonstrates the feasibility of large-scale hypothesis-free analysis and substantiates the utility of AI in unveiling hidden biological connections within clinical data. Our findings underscore the potential of AI-augmented frameworks in revolutionizing biomarker discovery and advancing personalized medicine. Citation Format: Ioannis Kagiampakis, Mishka Gidwani, Iaroslav Korkodinov, Georgiy Ponomarev, Etai Jacob. AI-driven hypothesis-free analysis for biomarker discovery: Introducing the Biomarkers Navigator Framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7659.

Abstract 4979: Genomic profiling of early-onset metastatic colorectal cancer based on tumor sample location

Abstract Background: Here, we describe analyses towards identifying and comparing genetic variations between the primary and metastatic sites in early-onset metastatic colorectal cancer patients. Methods: We selected patients with early-onset metastatic colorectal cancer (under 50 years old) using data from the American Association for Cancer Research Project GENIE (version 14.1). We used the Fisher exact test to compare genetic mutations between the primary tumor and metastatic sites, complemented by the Benjamini-Hochberg adjustment method. Results: In a total of 1,791 patients with early-onset metastatic colorectal cancer, 27.1% had tumor samples from metastatic sites, while the majority, 72.9%, were from the primary tumor location. The most common mutations found were in the TP53 gene (77.6%), followed by APC (76.0%), KRAS (42.4%), and PIK3CA (19.0%). Notably, the metastatic samples had a significantly higher occurrence of SMAD4 mutations compared to their primary site counterparts (20.8% vs. 14.0%, p<0.001, q<0.001). Conversely, the primary tumor samples exhibited higher frequencies of TCF7L2 and ARID1A mutations (16.9% vs. 10.6%, p<0.001, q<0.001, and 13.0% vs. 6.4%, p<0.001, q<0.001, respectively). Conclusion: Determining the specific genes responsible for the formation of metastatic tumors is crucial for a deeper understanding of the disease's underlying biology in early-onset metastatic colorectal cancer. Such genomic details are crucial to pave the way to the discovery of potential biomarkers for diagnosis and the development of new cancer therapeutics. Citation Format: Muhammet Ozer, Suleyman Y. Goksu, Busra Bacik Goksu, Nina N. Sanford, Amy Jones, Nilesh Verma, Syed Kazmi. Genomic profiling of early-onset metastatic colorectal cancer based on tumor sample location [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4979.

DYT-THAP1: exploring gene expression in fibroblasts for potential biomarker discovery.

Dystonia due to pathogenic variants in the THAP1 gene (DYT-THAP1) shows variable expressivity and reduced penetrance of ~ 50%. Since THAP1 encodes a transcription factor, modifiers influencing this variability likely operate at the gene expression level. This study aimed to assess the transferability of differentially expressed genes (DEGs) in neuronal cells related to pathogenic variants in the THAP1 gene, which were previously identified by transcriptome analyses. For this, we performed quantitative (qPCR) and Digital PCR (dPCR) in cultured fibroblasts. RNA was extracted from THAP1 manifesting (MMCs) and non-manifesting mutation carriers (NMCs) as well as from healthy controls. The expression profiles of ten of 14 known neuronal DEGs demonstrated differences in fibroblasts between these three groups. This included transcription factors and targets (ATF4, CLN3, EIF2A, RRM1, YY1), genes involved in G protein-coupled receptor signaling (BDKRB2, LPAR1), and a gene linked to apoptosis and DNA replication/repair (CRADD), which all showed higher expression levels in MMCs and NMCs than in controls. Moreover, the analysis of genes linked to neurological disorders (STXBP1, TOR1A) unveiled differences in expression patterns between MMCs and controls. Notably, the genes CUEDC2, DRD4, ECH1, and SIX2 were not statistically significantly differentially expressed in fibroblast cultures. With > 70% of the tested genes being DEGs also in fibroblasts, fibroblasts seem to be a suitable model for DYT-THAP1 research despite some restrictions. Furthermore, at least some of these DEGs may potentially also serve as biomarkers of DYT-THAP1 and influence its penetrance and expressivity.

Comprehensive profiling of amino acids and derivatives in biological samples: A robust UHPLC-MS/MS method for investigating acute lung injury

The severe respiratory dysfunctions associated with acute lung injury (ALI) and its sequelae have a high morbidity and mortality rate, are multifactorial, and lack a viable treatment. Considering the critical function that amino acids and derivatives play in the genesis of illnesses and the regulation of metabolic processes, monitoring the levels of metabolites associated with amino acids in biological matrices is necessary and interesting to study their pathological mechanisms. Exploring the dynamics of amino acids and derivatives level and searching for biomarkers provides improved clinical ideas for the diagnosis and treatment of ALI. Therefore, we developed an ultra-high-performance liquid chromatography-electrospray tandem mass spectrometry (UHPLC-MS/MS) method that can simultaneously determine the amino acid and derivatives metabolic levels to study amino acid profiles in different biological samples to facilitate clinical research of ALI. In this study, 48 amino acids and derivatives, including neurotransmitters, polyamines, purines, and other types, were quantified simultaneously in a fast, high-throughput, sensitive, and reliable manner within a 15-minute run time without derivatization. No relevant studies have been reported to quantify these 48 amino acid metabolites in three biological samples simultaneously. Satisfactory linearity (R > 0.995), inter-day and intra-day accuracy (85.17–112.67 % and 85.29–111.60 %, respectively), inter-day and intra-day precision (RSD < 13.80 % and RSD < 12.01 %, respectively), matrix effects (81.00 %–118.00 %), recovery (85.09 %–114.65 %) and stability (RSD < 14.72 %) were all demonstrated by the optimized method's successful validation for all analytes. In addition, the suggested method was effectively implemented in plasma, urine, and lung tissue from normal mice and mice with ALI, with the aim of finding potential biomarkers associated with ALI. Potential biomarkers were screened through multivariate statistical analysis and volcanic map analysis, and the changes of markers in ALI were again identified through heat map analysis and correlation analysis with biochemical indicators, which provided ideas and references for subsequent mechanism studies. Here, the technique created in this work offers a quick and dependable way to perform an integrated analysis of amino acids in a variety of biological materials, which can provide research ideas for understanding the physiopathological state of various diseases.

Abstract 2245 Discovery and functional characterization of potential biomarkers for wild-type EGFR lung adenocarcinoma via super-SILAC-based quantitative tissue proteomic analysis

Abstract 2245 Discovery and functional characterization of potential biomarkers for wild-type EGFR lung adenocarcinoma via super-SILAC-based quantitative tissue proteomic analysis

Temporal changes in ewe vaginal microbiota throughout gestation.

Numerous factors are known to influence reproductive efficiency in ewes, but few studies have investigated the potential role of vaginal microbiota in sheep reproductive success. The objective of this study was to thoroughly characterize the ewe vaginal microbiota throughout the course of pregnancy. Vaginal samples were collected from 31 pregnant Hampshire and Hampshire X Suffolk crossbred ewes on a weekly basis from pre-breeding to pregnancy testing and then biweekly until just after lambing. To characterize the vaginal microbial communities, DNA was extracted and 16S rRNA gene Illumina MiSeq amplicon sequencing was performed. Alpha diversity metrics indicated an increase in species richness, evenness, and overall diversity throughout gestation. Distinct shifts in the bacterial communities were observed during gestation and were segregated into three periods: early gestation, a transitional period and mid/late gestation. During early gestation, Actinobacillus, Histophilus, and unclassified Leptotrichiaceae were found in greater relative abundance. During the transitional period, a population shift occurred characterized by increasing relative abundance of Streptococcus and Staphylococcus. During mid/late gestation, Staphylococcus, Streptococcus, and Ureaplasma had the greatest relative abundance. These shifts in the microbial population throughout the ewe's gestation are likely related to hormonal changes triggered by the growing conceptus, specifically increasing blood concentration of progesterone. The transitional period shift in vaginal microbial communities potentially aligns with the placental take-over of progesterone production from the corpus luteum at approximately day 50 after conception (gestational week 7). Understanding the observed variability of the vaginal microbiota throughout pregnancy will allow for future comparison of ewes that did not become pregnant or had abnormal pregnancies, which could lead to the discovery of potential bacterial biomarkers for pregnancy outcome; this understanding could also lead to development of probiotics to improve sheep reproductive success.

Serum metabolism characteristics of patients with myocardial injury after noncardiac surgery explored by the untargeted metabolomics approach

BackgroundMyocardial injury after noncardiac surgery (MINS) is one of the most common complications associated with postoperative adverse cardiovascular outcomes and mortality. However, MINS often fails to be timely diagnosed due to the absence of clinical symptoms and limited diagnostic methods. The metabolomic analysis might be an efficient way to discover new biomarkers of MINS. Characterizing the metabolomic features of MINS patients may provide new insight into the diagnosis of MINS.MethodsIn this study, serum samples from 20 matched patients with or without MINS (n = 10 per group) were subjected to untargeted metabolomics analysis to investigate comprehensive metabolic information. Differential metabolites were identified, and the enriched metabolic pathway was determined based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.ResultsA comprehensive analysis revealed 124 distinct metabolites, predominantly encompassing lipids, amino acids and other compounds. The observed modifications in metabolic pathways in patients with or without MINS showed significant clustering in cholesterol metabolism, aldosterone synthesis and secretion, primary bile acid biosynthesis, as well as cysteine and methionine metabolism. Four specific metabolites (taurocholic acid, L-pyroglutamic acid, taurochenodeoxycholic acid, and pyridoxamine) exhibited promising potential as biomarkers for prognosticating MINS.ConclusionsThis study contributes valuable insights into the metabolomic features of MINS and the discovery of potential biomarkers which may help the early diagnosis of MINS. The identified metabolites and altered pathways offer valuable insights into the molecular underpinnings of MINS, paving the way for improved diagnostic approaches and potential intervention strategies.

Plausible prediction of renoprotective effects of sodium-glucose cotransporter-2 inhibitors in patients with chronic kidney diseases.

This narrative review was conducted due to uncertainty in predicting the beneficial impact of sodium-glucose cotransporter-2 (SGLT2) inhibitors on a dip of estimated glomerular filtration rate (eGFR), regardless of albuminuria presence, with the aim of elucidating plausible predictors of kidney function outcome among patients treated with SGLT2 inhibitors. The PubMed and Web of Science databases were searched in May 2023 for relevant articles published in English between 2013 and 2023. A total of 25 full-length scientific publications (comprising 11 large randomized trials and two cohort studies) were included for analysis. The majority of studies demonstrated a limited value of conventional biomarkers, such as initial decline in eGFR, a trajectory of eGFR during SGLT2 inhibitor administration, and urine albumin-to-creatinine ratio (UACR), in prediction of renoprotection. Included studies showed that the tendency to decreased eGFR, UACR, hemoglobin, glycosylated hemoglobin, lipid profile, serum uric acid, inflammatory biomarkers and natriuretic peptides did not predict clinical outcomes in groups without heart failure (HF) treated with SGLT2 inhibitors. In HF groups, biomarkers of inflammation, kidney injury, oxidative stress, mitochondrial dysfunction, ketogenesis, energy metabolism, and adipose tissue dysfunction (adropin and irisin), were detected with the aim of finding potential biomarkers. Biomarkers of adipose tissue dysfunction and inflammation may be promising for predicting SGLT2 inhibitor benefit compared with N-terminal pro-B-type natriuretic peptide and energy metabolism indicators.

Discovery of potential biomarkers for osteoporosis using LC/GC-MS metabolomic methods.

For early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC-MS in a Chinese elderly population to find possible diagnostic biomarkers. A total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC-MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways. We screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women. These findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals.

Identification of urine biomarkers predictive of prolonged QTc interval in multidrug-resistant tuberculosis patients treated with bedaquiline.

The most frequent adverse event associated with bedaquiline (BDQ) is the QTc interval prolongation; however, there was no biomarkers that could be used to predict the occurrence of QTc prolongation in BDQ-treated patients. In this study, we employed the ultra-high performance liquid chromatography-MS/MS (UHPLC-MS/MS) to generate metabolic profiling for the discovery of potential predictive urine biomarkers of QTc prolongation in these patients. Untargeted metabolomic technique was used to concentrate the differential metabolic pathway, and targeted metabolomic technique was subsequently performed to identify predictive biomarkers for QTc prolongation. A total of 45 rifampicin-resistant TB (RR-TB) and multidrug-resistant TB (MDR-TB) patients were enrolled in our study, including 15 RR/MDR-TB patients with QTc interval prolongation (QIP) and 30 RR/MDR-TB patients with QTc interval un-prolongations (QIU). Untargeted technique revealed that the lipid metabolism was the most differential metabolic pathway between two groups. Further targeted technique identified four differential metabolites, including betaine, LPE (18:2), LPE (20:3), and LPE (20:4). The combined analysis of metabolisms revealed that the combined use of LPE (20:3) and LPE (20:4) had the best performance for predicting the occurrence of QTc prolongation in TB patients, yielding a sensitivity of 87.4% and a specificity of 78.5%. In addition, with the progression of BDQ treatment, the LPEs exhibited persistent difference in the BDQ-treated TB patients experiencing QTc interval prolongation. In conclusion, our data demonstrate that the combined use of LPE (20:3) and LPE (20:4) yields promising performance for predicting the occurrence of QTc interval prolongation in BDQ-treated patients.

Discovery and Validation of Potential Serum Biomarkers for Heart Failure by Untargeted Metabolomics

Detection of biomarkers was extremely important for the early diagnosis, prognosis, and therapy optimization of diseases. The purpose of this study was to investigate the differences in serum metabolites between patients with heart failure (HF) and healthy control (HC) and to diagnose HF qualitatively. In this study, serum samples from 83 patients with HF and 35 HCs were used as the research subjects for untargeted metabolomic analysis using ultraperformance liquid chromatography combined with quadrupole‐time of flight mass spectrometry (UPLC‐QTOF/MS) technology. Potential biomarkers were screened and validated using the orthogonal partial least squares discriminant analysis (OPLS‐DA), random forest (RF), binary logistic regression (BLR), and receiver operating characteristic (ROC) analysis. The results indicated that a total of 43 metabolites were considered as differentially expressed metabolites (DEMs). Among these DEMs, glycodeoxycholate was identified as a specific biomarker of HF. A ROC curve analysis for HC versus HF discrimination showed an area under the ROC curve (AUC) of 0.9853 (95% CI: 0.9859–1.0000), a sensitivity of 95%, and a specificity of 100%. Hence, glycodeoxycholate might serve as a potential biomarker for HF. Furthermore, the amino acid metabolism was screened as the most significantly altered pathway in patients with HF. By identifying serum biomarkers and analyzing metabolic pathways, our study provided opportunities to enhance the understanding of the pathogenesis and early diagnosis of HF.

Omics to Unveil Diabetes Mellitus Pathogenesis and Biomarkers: Focus on Proteomics, Lipidomics, and Metabolomics.

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, resulting from either body's inability to produce or effectively utilize insulin. There are several types of DM, but the most common are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM). DM is a complex disease and a global health concern, and the current clinical markers, such as fasting glucose, are helpful in the diagnosis of DM, but are not specific and sensitive, especially when measured on the beginning of the pathogenesis. Therefore, there is a pressing need to discover new early biomarkers that can provide an early diagnosis. Omics is an important field for the discovery of potential new biomarkers, especially proteomics, metabolomics, and lipidomics, where techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance are utilized to identify novel DM biomarkers and their pathways. In this review, we report papers that applied omics in the context of DM to identify new markers and their relationship with this disease, with the aim of elucidating new diagnostic techniques for the main types of DM.

SINGLE-CELL AND SPATIAL OMICS FOR MAPPING CELLULAR SENESCENCE IN HEALTH, AGING AND DISEASE

Abstract NIH SenNet consortium aims to dissect the heterogeneity of senescent cells (SnCs) and map their impact on the microenvironment at a single cell resolution and in the spatial tissue context, which requires the implementation of an array of omics technologies to comprehensively identify, characterize, and spatially profile SnCs across tissues in humans and mice. These technologies are broadly categorized into two groups –single cell omics and spatial mapping. To achieve single cell resolution and overcome the scarcity of SnCs, high-throughput single-cell and single-nucleus transcriptomic techniques have become a mainstay tool for surveying tens of thousands of cells to identify transcriptional signatures in rare cell populations, enabling discovery of potential new SnC biomarkers. Novel single cell mass spectrometry methods are developed for unbiased discovery of proteomic signatures of SnCs. A hallmark of SnCs is the senescence-associated secretory phenotype (SASP), which requires the use of proteomics, secretomics, metabolomics and lipidomics, especially SASP-associated extracellular vesicles, for comprehensive characterization of SAPS. High resolution molecular and cellular imaging of gene expression (e.g., MERFISH) or protein markers (e.g., CODEX) is critical for the study of SnCs in the large-scale tissue context. NGS-based spatial omics sequencing is poised to bridge the gap to realize both genome scale and cellular resolution in mapping SnCs in tissue. Novel technologies such as Seq-Scope and Pixel-Seq developed within SenNet further enabled subcellular resolution. SenNet investigators also developed spatially resolved epigenome and multi-omics sequencing techniques to link transcriptional or proteomic phenotype of SnCs to epigenetic mechanism. Further integration with high-resolution imaging makes spatial omics the crucial linchpin in connecting mechanistic underpinnings and molecular signatures with morphological features and spatial distribution. All these are critical for the construction of a map of SnCs and associated niches in the native tissue environment implicated in human health, aging, and disease, which is one of the main goals of the SenNet consortium.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200μl of plasma, in just 30min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

Th17 Cell-Related Gene Biomarkers in Osteoporosis: Comprehensive Bioinformatics Analysis and In Vivo Validation.

The interaction between the bone and immune systems has a major role in osteoporosis regulation. However, the infiltration of T helper 17 (Th17) cells and their associated genes in osteoporosis remains unclear. The GSE35959 dataset was obtained from the Gene Expression Omnibus (GEO) database, and the Immune Cell Abundance Identifier (ImmuCellAI) program was used to evaluate the abundance of 24 immune cell types, including Th17 cells. Differential analysis and relevance analysis were performed to identify differentially expressed Th17 cell-related genes (DETh17RGs) in osteoporosis. The potential functions of DETh17RGs were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment. Hub DETh17RGs were obtained through comprehensive analysis using Weighted Gene Co-Expression Network Analysis (WGCNA) and the CytoHubba plug-in algorithm. The expression levels of hub genes were validated using additional osteoporosis datasets. Additionally, the transcript levels of Hub genes in a mice model of osteoporosis were examined using quantitative PCR (qPCR). 464 DETh17RGs were identified in this study, with 421 genes showing positive associations and 43 genes showing negative associations. Among these, seven genes (CD44, TGFB1, ACTN4, ARHGDIA, ESR1, TLN1, FLNA) were considered as Hub DETh17RGs. The qPCR transcript levels of hub DETh17RGs in a mice model of osteoporosis exhibited consistent expression trends with the bioinformatics analysis. This research enhances our understanding of the molecular mechanisms involving Th17 cells in the development of osteoporosis and contributes to the discovery of potential biomarkers.

Mass spectrometry–based proteomics technology in pancreatic cancer research

Pancreatic ductal adenocarcinoma (PDAC) has become a significant health concern with increasing incidence and mortality rates over the past few decades. Researchers have turned their attention to cutting-edge mass spectrometry (MS) technology due to its high-throughput and accurate detection capacity, which plays a vital role in understanding the mechanisms and discovering biomarkers for pancreatic diseases. In this review, we comprehensively investigate various methodologies of quantitative and qualitative proteomics MS technologies, alongside bioinformatical platforms employed in pancreatic cancer research. The integration of these optimized approaches provides novel insights into the molecular mechanisms underlying tumorigenesis and disease progression, ultimately facilitating the discovery of potential diagnostic, prognostic biomarkers, and therapeutic targets. The robust MS-based strategy shows promise in paving the way for early diagnosis and personalized medicine for pancreatic cancer patients.

Discovery of potential plasma protein biomarkers for ventricular fibrillation due to first ST-elevation myocardial infarction via proteomics

Abstract Background Although most sudden cardiac deaths are a consequence of ventricular fibrillation (VF) subsequent to myocardial infarction, the underlying biological mechanisms of VF in the setting of ischemia are largely unknown. We used an unbiased mass-spectrometry (MS)-based proteomic approach to improve our understanding of the molecular VF mechanisms. Purpose We aimed to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI). Methods We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n=110) and without VF (control, n=120) before guided catheter insertion for primary percutaneous coronary intervention. Using MS-based proteomics on plasma samples collected within 24h of symptom onset, we investigated the plasma proteome. After quality control, one patient was excluded and the difference in the quantitative proteome composition between cases and controls were analysed using t-tests, and further corrected for multiple comparison using the Benjamini-Hochberg procedure (q-value). Cardiac enzymes and anterior infarct location were used in a subset as a proxy for large myocardial damage. Results In 229 STEMI patients (72% men, median age 62 years (interquartile range (IQR): 54-70)), a total of 401 unique proteins were identified with a median of 257 proteins (IQR: 244-281) quantified per patient. A total of 26 proteins were associated with VF, these proteins were involved in several biological processes. The most frequent processes were blood coagulation, hemostasis and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 (ACTBL2, FC 2.25, p&amp;lt;0.001, q=0.023) and coagulation factor XIII-A (F13A1, FC 1.48, p&amp;lt;0.001, q=0.023). The most up-regulated protein was ACTBL2, and the second most up-regulated protein was actin alpha 2 (ACTA2, FC 1.72, p=0.001, q=0.073). None of the three proteins were correlated with cardiac enzymes or anterior infarct location. Conclusion VF due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1). None of these proteins were correlated with infarct size which could reflect novel underlying molecular VF mechanisms.

Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry

ObjectiveThis study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR).MethodThis study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25–40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models.ResultsA total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively.ConclusionThe urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.