Discovery Of Potential Biomarkers Research Articles

Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery

In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population-level features separately, inevitably neglecting the multi-level characteristics of brain disorders. To address this issue, we propose an end-to-end multi-graph neural network model called KMGCN. This model simultaneously leverages individual- and population-level features for brain network analysis. At the individual level, we construct multi-graph using both knowledge-driven and data-driven approaches. Knowledge-driven refers to constructing a knowledge graph based on prior knowledge, while data-driven involves learning a data graph from the data itself. At the population level, we construct multi-graph using both imaging and phenotypic data. Additionally, we devise a pooling method tailored for brain networks, capable of selecting brain regions that impact brain disorders. We evaluate the performance of our model on two large datasets, ADNI and ABIDE, and experimental results demonstrate that it achieves state-of-the-art performance, with 86.87% classification accuracy for ADNI and 86.40% for ABIDE, accompanied by around 10% improvements in all evaluation metrics compared to the state-of-the-art models. Additionally, the biomarkers identified by our model align well with recent neuroscience research, indicating the effectiveness of our model in brain network analysis and potential biomarker discovery. The code is available at https://github.com/GN-gjh/KMGCN.

A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies.

This study paves the way for the creation of innovative therapeutic strategies, aimed at subverting resistance to immune checkpoint blockade (ICB) therapies in metastatic melanoma patients. By unraveling the specific molecular mechanisms underlying resistance, scientists can design effective alternative treatments that target pathways such as pathways associated with cell cycle dysregulation and c-MYC signaling. Furthermore, through the application of advanced immune monitoring techniques such as NanoString Digital Spatial Profiling (DSP) and Cyclic Immunofluorescence (CyCIF), this study has significantly enriched our understanding of the tumor microenvironment. This enhanced characterization facilitates the discovery of potential biomarkers that may forecast a patient's response to ICB treatment. Ultimately, these advancements could potentially refine patient outcomes and foster the development of more personalized cancer treatments in the future.

Potential Biomarker Discovery with Auto-Encoder from 16s Sequence Data in Inflammatory Bowel Disease (IBD)

Bir habitattaki fonksiyonel ve ekolojik dengeye (homeostasis) sahip mikrobiyal komünitelere mikrobiyota denir. Mikrobiyom ise bu komüniteyi oluşturan toplam genetik materyal ve bu genetik materyallerin çevre ile etkileşimine verilen isimdir. Mikrobiyotamız ile simbiyotik bir ilişki içinde olduğumuz yapılan çalışmalarla gösterilmiştir. Yeni nesil DNA dizileme teknolojilerinin yaygınlaşması ve hesaplama kabiliyeti yüksek bilgisayarların gelişmesi ile insan mikrobiyomunu ve sağlığa etkilerini keşfetmeye yönelik çalışmalar artmıştır. Yakın zamandaki araştırmalar birçok hastalıkla, kişinin mikrobiyom profilinin ilişkili olduğunu göstermiştir. Hastalık durumunda tedavinin yöntemini değiştirebilecek nitelikteki mikrobiyom çalışmaları, yüksek potansiyelli translasyonel çıktıları öncelikli alan haline gelmiştir. Ancak oldukça karmaşık olan bu verinin içerisinde hastalığın tanı ya da tedavisinde kullanılabilecek yüksek doğrulukta özniteliklerin bulunması oldukça zordur. Derin öğrenme teknikleri ise çeşitli çalışmalarda özellikle sınıflandırma alanında karmaşık verilerde ilham verici başarılar elde etmektedir. Oto-kodlama (AE) tekniklerinin ortaya çıkışı ise özellik seçme görevi için tasarlanmış bir sinir ağı mimarisidir. Bizim çalışmamızda veriyi yeniden temsil etmede önemli olarak görülen öznitelikler bir oto-kodlayıcısı tarafından belirlenmiş ve sadece belirlenen bu özniteliklerin gruplarda görülme sıklığına bakılarak İBH hastaları ve sağlıklı kontroller XGBoost algoritmasıyla %88.89 doğruluk değeri ile başarılı bir şekilde sınıflandırılmıştır. Önerilen yöntemle İBH hastalığını temsil eden mikrobiyol türler hastalığın tanısı için muhtemel biyobelirteçleri oluşturduğu düşünülmektedir.

Effects of healthy aging on tongue-jaw kinematics during feeding behavior in rhesus macaques.

Several age-related oral health problems have been associated with neurodegenerative diseases such as Alzheimer's Disease (AD), yet how oromotor dysfunction in healthy aging differ from those found in pathological aging is still unknown. This is partly because changes in the cortical and biomechanical ("neuromechanical") control of oromotor behavior in healthy aging are poorly understood. To this end, we investigated the natural feeding behavior of young and aged rhesus macaques (Macaca mulatta) to understand the age-related differences in tongue and jaw kinematics. We tracked tongue and jaw movements in 3D using high-resolution biplanar videoradiography and X-ray Reconstruction of Moving Morphology (XROMM). Older subjects exhibited a reduced stereotypy in tongue movements during chews and a greater lag in tongue movements relative to jaw movements compared to younger subjects. Overall, our findings reveal age-related changes in tongue and jaw kinematics, which may indicate impaired tongue-jaw coordination. Our results have important implications for the discovery of potential neuromechanical biomarkers for early diagnosis of AD.

Proteomic Profile of Endometrial Cancer: A Scoping Review.

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Three-gene signature revealing the dynamics of lymphocyte infiltration in subchondral bone during osteoarthritis progression

Osteoarthritis (OA), a degenerative joint disorder, has an unclear immune infiltration mechanism in subchondral bone (SCB). Thus, this study aims to discern immune infiltration variations in SCB between early- and late-stages of OA and identify pertinent biomarkers. Utilizing the GSE515188 bulk-seq profile from the Gene Expression Omnibus database, we performed single-sample gene-set enrichment analysis alongside weighted gene co-expression network analysis to identify key cells and immune-related genes (IRGs) involved in SCB at both stages. At the meanwhile, differentially expressed genes (DEGs) were identified in the same dataset and intersected with IRGs to find IR-DEGs. Protein-protein interaction network and enrichment analyses and further gene filtering using LASSO regression led to the discovery of potential biomarkers, which were then validated by ROC curve analysis, single-cell RNA sequencing, qRT-PCR, western blot and immunofluorescence. ScRNA-seq analysis using GSE196678, qRT-PCR, western blot and immunofluorescence results confirmed the upregulation of their expression levels in early-stage OA SCB samples. Our comprehensive analysis revealed lymphocytes infiltration as a major feature in early OA SCB. A total of 13 IR-DEGs were identified, showing significant enrichment in T- or B-cell activation pathways. Three of them (CD247, POU2AF1, and TNFRSF13B) were selected via the LASSO regression analysis, and results from the ROC curve analyses indicated the diagnostic efficacy of these 3 genes as biomarkers. These findings may aid in investigating the mechanisms of SCB immune infiltration in OA, stratifying OA progression, and identifying relevant therapeutic targets.

Biomarkers in diabetes mellitus: Advancements, challenges, and future perspectives

Diabetes mellitus (DM) represents a multifaceted metabolic disorder characterized by hyperglycemia and dysregulated metabolism, stemming from a myriad of factors including insulin insufficiency and impaired glucose utilization. Its complications span a broad spectrum, encompassing vascular diseases and neurological impairments. While some risk factors are beyond control, such as age and genetics, others like diet and exercise offer avenues for risk mitigation. This review delineates the distinct pathogenic processes of the two primary forms of DM, type 1 and type 2, emphasizing their differential etiologies and epidemiological trends. Biomarkers play a pivotal role in the diagnosis and management of DM, offering insights into disease progression, beta-cell function, and therapeutic responses. Traditional biomarkers, alongside predictive markers like autoantibodies, facilitate early detection and intervention strategies. Moreover, advancements in high-throughput "-omics" technologies have unraveled the molecular intricacies underlying disease progression, heralding the discovery of potential serum protein biomarkers in type 1 DM and unveiling promising candidates such as unmethylated insulin DNA and dysregulated microRNAs. In type 2 DM, traditional biomarkers like fructosamine, glycated albumin, and 1,5-anhydroglucitol offer valuable tools for glycemic control assessment, with emerging biomarkers like zinc-alpha-2-glycoprotein holding promise for diabetic nephropathy detection. Despite challenges, ongoing research endeavors hold promise for refining these biomarkers and enhancing diabetes management strategies, thus improving patient outcomes.

Application of angiogenesis-related genes associated with immune infiltration in the molecular typing and diagnosis of acute myocardial infarction.

Angiogenesis has been discovered to be a critical factor in developing tumors and ischemic diseases. However, the role of angiogenesis-related genes (ARGs) in acute myocardial infarction (AMI) remains unclear. The GSE66360 dataset was used as the training cohort, and the GSE48060 dataset was used as the external validation cohort. The random forest (RF) algorithm was used to identify the signature genes. Consensus clustering analysis was used to identify robust molecular clusters associated with angiogenesis. The ssGSEA was used to analyze the correlation between ARGs and immune cell infiltration. In addition, we constructed miRNA-gene, transcription factor network, and targeted drug network of signature genes. RT-qPCR was used to verify the expression levels of signature genes. Seven signature ARGs were identified based on the RF algorithm. Receiver operating characteristic curves confirmed the classification accuracy of the risk predictive model based on signature ARGs (area under the curve [AUC] = 0.9596 in the training cohort and AUC = 0.7773 in the external validation cohort). Subsequently, the ARG clusters were identified by consensus clustering. Cluster B had a more generalized high expression of ARGs and was significantly associated with immune infiltration. The miRNA and transcription factor network provided new ideas for finding potential upstream targets and biomarkers. Finally, the results of RT-qPCR were consistent with the bioinformatics analysis, further validating our results. Angiogenesis is closely related to AMI, and characterizing the angiogenic features of patients with AMI can help to risk-stratify patients and provide personalized treatment.

Analysis of the effect of early subcutaneous specific immunotherapy on the levels of dust mite allergen-specific antibodies and polyunsaturated fatty acid metabolism

Objective: To investigate the effects of subcutaneous immunotherapy (SCIT) on patients' immune markers and metabolic levels in the early stage of allergen treatment, and to gain insight into the role of SCIT in regulating immune responses and metabolic levels, so as to provide reference data for the further discovery of potential biomarkers. Methods: A longitudinal study was used to include 40 subjects who underwent SCIT with dust mite allergens in the Department of Pediatrics of the First Affiliated Hospital of Guangzhou Medical University between November 2017 and February 2022, including 20 subjects each of single mite subcutaneous immunotherapy (SM-SCIT) and double mite subcutaneous immunotherapy (DM-SCIT). In this study, levels of dust mite allergen-specific antibodies and polyunsaturated fatty acid metabolism were measured before and 12 months after treatment, while pulmonary function tests were performed. The therapeutic effects of the patients were followed up by visual analogue scale (VAS), asthma control test (ACT) and total medication scores (TMS). The results were statistically analyzed using t-test and Mann-Whitney U-test. Results: After 12 months of treatment with SCIT, both groups showed a significant decrease in total VAS score (SM-SCIT:Z=-2.298, P<0.05; DM-SCIT:Z=-3.411, P<0.001); total ACT score (SM-SCIT:Z=-2.054, P<0.05; DM-SCIT:Z=-2.014, P<0.05) and total medication scores (SM-SCIT:Z=-3.799, P<0.000 1; DM-SCIT:Z=-3.474, P<0.001) were significantly higher, in addition to significantly higher MMEF75/25 values in the DM-SCIT group (t=-2.253, P<0.05). There was no significant change in sIgE in the SM-SCIT group (P>0.05), and the sIgG4 levels of the Der p, Der f, p 1, p 2, f 2, and p 21 fractions were significantly elevated (Z=-2.651, -3.771, -2.949, -2.912, -2.725, -2.128, and -3.285, respectively, all P<0.05); The sIgE of Der p 2, f 2, p 7 and p 23 fractions(Z=-2.651, -3.771, -2.949, -2.912, -2.725, -2.128, -3.285, all P<0.05) and the sIgG4 levels of the Der p, Der f, p 1, p 2, f 1, f 2, p 10, p 21 and p 23 fractions (Z=-3.808, -3.845, -3.061, -2.688, -2.464, -3.211, -2.371, -2.091, -2.427, all P<0.05) of the DM-SCIT group were significantly elevated. Metabolomics analysis showed that arachidonic acid, docosahexaenoic acid, docosapentaenoic acid, eicosapentaenoic acid, 5, 9, 12-octadecatrienoic acid, 5(S)-hydroxylated eicosatetraenoic acid, and dihomo-gamma-linolenic acid were significantly elevated at the beginning of the treatment period after SM-SCIT treatment (Z of -2.191, -2.497, -1.988, -2.090, -2.19, -2.803, -2.073, all P<0.05); 5(S)-hydroxylated eicosatetraenoic acid showed elevated and alpha-linolenic acid, eicosadienoic acid, and eicosapentaenoic acid were significantly decreased in the DM-SCIT group after treatment (Z=-1.988, -2.090, -2.497, -1.988, respectively, all P<0.05). Correlation analysis showed that arachidonic acid was significantly negatively correlated with changes in dust mite-specific IgG4 (r=-0.499, P<0.05), and that alpha-linolenic acid, 5, 9, 12-octadecatrienoic acid, and eicosapentaenoic acid were positively correlated with the ΔsIgG4 of the dust mite der p 2 (r=0.451, 0.420, 0.474, respectively; all P<0.05). Conclusion: Significant changes in allergen-specific antibody levels and polyunsaturated fatty acid metabolism levels occur during SCIT, and the two may interact and influence each other.

Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkers.

During pregnancy, there is a link between disruption of maternal immune tolerance and preeclampsia, but the molecular mechanisms that regulate maternal and fetal immune tolerance remain unclear. This study employs bioinformatics to identify new markers related to placental immune tolerance and explore their potential role in predicting preeclampsia. Analyzing preeclampsia-related gene expression profiles in the Gene Expression Omnibus (GEO) dataset reveals 211 differentially expressed genes (DEGs) in the placenta, mainly influencing immune cell differentiation and response pathways. Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia. Validation using the GSE4707 dataset confirmed the diagnostic and predictive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, while ELISA showed their correlation with immune tolerance factors associated with placental immune tolerance. As a result of this study, identifies potential biomarkers associated with placental immunity and contributes to understanding the molecular mechanism of preeclampsia.

Isolation and quantification of L1CAM-positive extracellular vesicles on a chip as a potential biomarker for Parkinson's Disease.

Extracellular vesicles (EVs) carry disease-specific molecular profiles, demonstrating massive potential in biomarker discovery. In this study, we developed an integrated biochip platform, termed EVID-biochip (EVs identification and detection biochip), which integrates in situ electrochemical protein detection with on-chip antifouling-immunomagnetic beads modified with CD81 antibodies and zwitterion molecules, enabling efficient isolation and detection of neuronal EVs. The capability of the EVID-biochip to isolate common EVs and detect neuronal EVs associated with Parkinson's disease in human serum is successfully demonstrated, using the transmembrane protein L1-cell adhesion molecule (L1CAM) as a target biomarker. The EVID-biochip exhibited high efficiency and specificity for the detection of L1CAM with a sensitivity of 1pg/mL. Based on the validation of 76 human serum samples, for the first time, this study discovered that the level of L1CAM/neuronal EV particles in serum could serve as a reliable indicator to distinguish Parkinson's disease from control groups with AUC=0.973. EVID-biochip represents a reliable and rapid liquid biopsy platform for the analysis of complex biofluids offering EVs isolation and detection in a single chip, requiring a small sample volume (300µL) and an assay time of 1.5h. This approach has the potential to advance the diagnosis and biomarker discovery of various neurological disorders and other diseases.

WITHDRAWN: Research on Traditional Chinese Medicine Syndrome Animal Models and Multi-omics Biomarkers in Type II Diabetes: A Review

IntroductionDiabetes, a chronic metabolic disease marked by hyperglycemia, predominantly manifests as Type 2 Diabetes Mellitus (T2DM), representing over 90% of diabetes cases with intricate pathogenesis and varied pathological attributes. Drawing upon Traditional Chinese Medicine (TCM) principles, this article outlines the methodology and evaluation criteria for integrating TCM syndromes into animal models for T2DM while comprehensively examining T2DM pathogenic mechanisms and physiological characteristics from various omics perspectives to pinpoint essential biomarkers. MethodsResearchers conducted comprehensive searches using databases such as Web of Science, Google Scholar, Baidu Scholar and CNKI. Selection criteria were based on content relevance and publication date, resulting in the inclusion of 223 articles, including Chinese and English journal papers, master's and doctoral theses. This comprehensive review aims to highlight the importance of TCM in research related to T2DM, as well as its compatibility with modern technology, providing guidance for future development of targeted drugs and clinical treatment for T2DM. ResultsThis article, based on the fundamental theories of Traditional Chinese Medicine, summarized the methodology and evaluation criteria for constructing 6 types of T2DM animal models: Blood Stasis Syndrome, Kidney Yin Deficiency Syndrome, Spleen Deficiency Syndrome, Yang Deficiency Syndrome, Qi and Yin Deficiency Syndrome, and Yin and Yang Deficiency Syndrome. It comprehensively analyzed the latest research progress on the pathogenic mechanisms and physiological characteristics of T2DM from the perspectives of genomics, proteomics, transcriptomics, metabolomics, and gut microbiomics, providing crucial insights for the discovery of potential biomarkers for T2DM. DiscussionsThe broad use of multi-omics tech aids in T2DM pathogenesis understanding. For comprehensive insight, analyzing genes, proteins, metabolites and microbiota is vital. As a metabolic disorder, T2DM has a prolonged development process, necessitating the selection of different biomarkers for various stages of the disease progression and long-term monitoring of the overall changes in key biomarkers. The development of a “disease-syndrome” animal model is pivotal for the advancement of targeted drug therapies. The crux lies in the accuracy, repeatability, and stability of the model and its assessment criteria. Future investigations should focus on multi-omics analyses across diverse T2DM syndromes to facilitate personalized treatment strategies.

Extracellular vescicles in psoriasis: from pathogenesis to possible roles in therapy.

Psoriasis is a chronic inflammatory disease affecting skin and joints characterized by a chronically altered immune and inflammatory response. Several factors occur from the onset to the development of this disease due to different types of cells spatially and temporally localized in the affected area, such as, keratinocytes, macrophages, neutrophils and T helper lymphocytes. This scenario leads to the chronic release of high levels of inflammatory mediators (i.e., IL-17, IL-23, IL-22, TNF-α, S100 proteins, Defensins) and lastly parakeratosis and thickening of the stratum spinosum. Extracellular vesicles (EVs) are small double membraned biological nanoparticles that are secreted by all cell types and classified, based on dimension and biogenesis, into exosomes, microvesicles and apoptotic bodies. Their role as vessels for long range molecular signals renders them key elements in the pathogenesis of psoriasis, as well as innovative platforms for potential biomarker discovery and delivery of fine-tuned anti-inflammatory therapies. In this review, the role of EVs in the pathogenesis of psoriasis and the modulation of cellular microenvironment has been summarized. The biotechnological implementation of EVs for therapy and research for new biomarkers has been also discussed.

Метаболомное профилирование пациентов с ревматоидным артритом (пилотное исследование)

Background. Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by progressive joint damage and leading to early disability in patients. The main purpose of its treatment is to alleviate the pain caused by the disease, delay the development of the disease, reduce the morbidity rate, and improve the quality of life of patients. At present the specificity and sensitivity of RA diagnosis methods are not convincing. In recent decades, research has focused on pathogenesis and the discovery of potential biomarkers using new and high-precision methods, in particular metabolomics. Metabolomics is an science studying low molecular weight compounds (metabolites) involved in biochemical processes and being the end products of metabolism. Metabolites reflect the current state of the humans body and can therefore serve as perspective biomarkers. Aim. To study the metabolomic profile of patients with RA without therapy in order to search for potential biomarkers for diagnosis and evaluation of therapy. Material and methods. The main study group consisted of 14 patients with a newly diagnosed RA – de novo RA. 16 healthy volunteers formed the control group. Metabolites in blood plasma were studied using ultra-performance liquid chromatography in combination with a triple quadrupole analyzer. A total of 93 metabolites were analyzed in de novo RA patients and healthy controls. In the group of RA patients the relationship of these metabolites with DAS28 activity, CRP, ESR, the presence of RF and ACCP were analyzed. Results. The most significant metabolites that play an important role in the pathogenesis of RA were identified: leucine/isoleucine, tyrosine, lysine, valine, phenylalanine, proline, ornithine, glutamine aspartate and long-chain acylcarnitines (C14, C14-OH, C16-1, C18). We found a correlation between DAS28 and leucine (p=0.03) and proline (p=0.05) levels. An inverse correlation was established between ACCP and glutamine (p=0,041) and a direct correlation between ACCP and proline (p=0.039), an inverse correlation between RF and phenylalanine (p=0.0491) and histidine (p=0.04). Conclusion. Metabolomics provides promising opportunities for further research in RA as it allows to identify metabolites most associated with disease, which can improve the accuracy of RA diagnosis and serve as targets for therapy

A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats

Cardiac hypertrophy (CH) is one of the stages in the occurrence and development of severe cardiovascular diseases, and exploring its biomarkers is beneficial for delaying the progression of severe cardiovascular diseases. In this research, we established a comprehensive and highly efficient pseudotargeted metabolomics method, which demonstrated a superior capacity to identify differential metabolites when compared to traditionaluntargeted metabolomics. The intra/inter-day precision and reproducibility results proved the method is reliable and precise. The established method was then applied to seek the potential differentiated metabolic biomarkers of cardiac hypertrophy (CH) rats, and oxylipins, phosphorylcholine (PC), lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), Krebs cycle intermediates, carnitines, amino acids, and bile acids were disclosed to be the possible differentiate components. Their metabolic pathway analysis revealed that the potential metabolic alterations in CH rats were mainly associated with phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and tyrosine metabolism. In sum, this research provided a comprehensiveand reliable LC-MS/MS MRM platform for pseudo-targeted metabolomics investigation of disease condition, and some interesting potential biomarkers were disclosed for CH, which merit further exploration in the future.

Host-Microbiome Crosstalk in Chronic Wound Healing.

The pathogenesis of chronic wounds (CW) involves a multifaceted interplay of biochemical, immunological, hematological, and microbiological interactions. Biofilm development is a significant virulence trait which enhances microbial survival and pathogenicity and has various implications on the development and management of CW. Biofilms induce a prolonged suboptimal inflammation in the wound microenvironment, associated with delayed healing. The composition of wound fluid (WF) adds more complexity to the subject, with proven pro-inflammatory properties and an intricate crosstalk among cytokines, chemokines, microRNAs, proteases, growth factors, and ECM components. One approach to achieve information on the mechanisms of disease progression and therapeutic response is the use of multiple high-throughput 'OMIC' modalities (genomic, proteomic, lipidomic, metabolomic assays), facilitating the discovery of potential biomarkers for wound healing, which may represent a breakthrough in this field and a major help in addressing delayed wound healing. In this review article, we aim to summarize the current progress achieved in host-microbiome crosstalk in the spectrum of CW healing and highlight future innovative strategies to boost the host immune response against infections, focusing on the interaction between pathogens and their hosts (for instance, by harnessing microorganisms like probiotics), which may serve as the prospective advancement of vaccines and treatments against infections.

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

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

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 &lt; 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 &lt; 0.05). LTB4 level significantly increased in the COPD group than in the control group (p &lt; 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 &lt; 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 &amp;gt; 2, adj. p-value &amp;lt; 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.