Source Of Biomarkers Research Articles

Methylation and expression of imprinted genes in circulating extracellular vesicles from women experiencing early onset preeclampsia

Preeclampsia (PE) is a pregnancy complication marked by high blood pressure, posing risk to maternal and fetal health. "Genomic imprinting", an epigenetic phenomenon regulated by DNA methylation at Differently Methylated Regions (DMR's), influences placental development. Research on circulating extracellular vesicles (EVs) in PE suggests them as potential source for early biomarkers, but methylation status of EV-DNA in Preeclampsia is not reported yet. This study examines the methylation and expression profile of imprinted genes - PEG10, PEG3, MEST, and DLK1 in circulating EVs of 1st and 3rd trimester control and early onset preeclampsia (EOPE) pregnant women (n=15) using pyrosequencing and qRT-PCR respectively. In 1st trimester, PEG3 was significantly hypermethylated, whereas no significant methylation changes were noted in PEG10 and MEST in EOPE. In 3rd trimester, significant hypomethylation in PEG10, PEG3 and IGDMR was observed whereas significant hypermethyaltion noted in MEST. mRNA expression of PEG10, PEG3 and DLK1 was not affected in circulating EVs of 1st trimester EOPE. However, in 3rd trimester significant increased expression in PEG10, PEG3 and DLK1 noted. MEST expression was reduced in 3rd trimester EOPE. No correlation was observed between average DNA methylation and gene expression in PEG10 and PEG3 in 1st trimester. However, in 3rd trimester, significant negative correlation was noted in PEG10 (r=-0.426, p=0.04), PEG3 (r=-0.496, p=0.01), MEST (r=-0.398, p=0.03) and DLK1 (r=-0.403, p=0.03). The results of our study strengthen the potential of circulating EVs from maternal serum as non-invasive indicators of placental pathophysiology, including preeclampsia.

The Role of Urinary Extracellular Vesicles in Kidney Cancer: Diagnostic and Therapeutic Potential

Renal cancer ranks as the 14th most common cancer globally, with renal cell carcinoma (RCC) being the primary variant, arising from renal tubular epithelial cells; clear cell RCC constitutes about 80% of cases. Despite their limitations, surgery and targeted therapy remain the mainstays of RCC treatment. Regardless of advancements in RCC research, substantial obstacles continue to exist, such as delayed diagnosis, advanced distant metastasis, and drug resistance. As urine is an easily accessible biofluid, the identification of EVs has paved the way for novel biomarker research. Urinary extracellular vesicles (uEVs) are a novel source of biomarkers with potential applications in cancer detection and management, utilizing a less invasive approach. New data indicate that uEVs are crucial in several areas of RCC, containing tumor development, metastasis, immune evasion, and response to drugs. These vesicles facilitate intercellular communication by transporting a variety of bioactive substances, including RNA, DNA, proteins, and lipids, and are released into the extracellular space by the majority of cell types. uEVs RNAs and proteins are presently being investigated for their possible application as diagnostic biomarkers for different types of kidney cancer. This review summarizes the most recent research examining the potential of uEVs cargo as a biomarker for the diagnosis, prognosis, and treatment of renal cancer.

Ultrasensitive Detection of Blood-Based Alzheimer's Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning.

Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in their early stages, leading to delayed treatment outcomes. Early diagnosis using blood derivatives as a source for biomarkers is particularly important for managing Alzheimer's disease (AD). This study introduces a novel approach for the precise and ultrasensitive detection of multiple core AD biomarkers (Aβ40, Aβ42, p-tau, and t-tau) using surface-enhanced Raman spectroscopy (SERS) combined with machine-learning algorithms. Our method employs an antibody-immobilized aluminum SERS substrate, which offers high precision, sensitivity, and accuracy. The platform achieves an impressive detection limit in the attomolar (aM) range and spans a wide dynamic range from aM to micromolar (μM) concentrations. This ultrasensitive and specific SERS immunoassay platform shows promise for identifying mild cognitive impairment (MCI), a potential precursor to AD, from blood plasma. Machine-learning algorithms applied to the spectral data enhance the differentiation of MCI from AD and healthy controls, yielding excellent sensitivity and specificity. Our integrated SERS-machine-learning approach, with its interpretability, advances AD research and underscores the effectiveness of a cost-efficient, easy-to-prepare Al-SERS substrate for clinical AD detection.

Single step capture and assessment of multiple plasma extracellular vesicle biomarkers in Alzheimer's disease detection.

Blood tests for Alzheimer's disease (AD) that measure biomarkers related to neuropathology have demonstrated to be useful, minimally-invasive ways to identify patients for screening into clinical trials. While some AD biomarkers can be detected in plasma, greater sensitivity is needed to make plasma AD tests more effective. Extracellular vesicles (EVs) in plasma carry AD-related biomarkers from the brain and could offer a concentrated source of brain-related biomarkers, though the methodological complexities involved in isolating plasma EVs have hampered its validation for clinical use. To explore the feasibility and effectiveness of developing blood tests for AD utilizing extracellular vesicle-bound protein biomarkers. We developed a simplified method for isolating EVs directly from plasma using an alternating current electrokinetic (ACE) microchip. No sample pretreatment steps were needed. Protein biomarkers on the EVs were detected by adding fluorescent antibodies to the plasma samples before capture by the chip. This allowed measurement of EV biomarker levels directly on the chip. AD or non-AD control plasma was measured for ten different AD-related biomarkers. EV-associated NCAM1, pTau231, α-synuclein, and TDP-43 levels were able to distinguish a group of 10 AD, 10 mild cognitive impairment (MCI), and 10 non-AD subjects. pTau231 was different between AD and non-AD (p = 0.0300) and α-synuclein differentiated AD from MCI (p = 0.0148). This study shows how ACE microfluidic chip technology can help differentiate AD and MCI patients from non-AD controls with clinical relevance. This work also highlights the important diagnostic role of plasma EV biomarkers in neurodegenerative disease.

BIOM-50. SERIAL INTRACRANIAL CEREBROSPINAL CELL-FREE DNA FOR DISEASE MONITORING IN PATIENTS WITH GLIOMAS

Abstract Improved response assessment methods are needed for patients with gliomas. Intracranial cerebrospinal fluid (CSF) is longitudinally accessible and provides an abundance source of candidate biomarkers, such as cell-free DNA (cfDNA), for disease monitoring. To evaluate the clinical feasibility of molecular profiling from longitudinal intracranial CSF, CSF was acquired from patients with CSF access devices, including Ommaya reservoirs and ventriculoperitoneal shunts. cfDNA was extracted and analyzed via PredicineCARE (Next Generation sequencing) or PredicineSCORE (low pass whole genome sequencing). Five patients (2 females, 3 males; median age: 40 years, range 32-64 years) underwent longitudinal intracranial CSF collection via Ommaya reservoirs (n=4) or VP shunt (n=1). In total, forty-eight CSF samples were obtained (median volume: 4.00 mL; 0.5-5 mL), with 39 samples (81.3%) yielding sufficient cfDNA for variant profiling. Our initial findings suggest that tumor fraction increased by 6.28x, 2.87x, and 8.31x with radiographic progression in three patients. Tumor fraction decreased by 0.08x and 0.04x in two patients with paired immediate pre-versus-post chemoradiation samples. Despite ongoing pseudoprogression in one patient, tumor fraction decreased to unmeasurable levels from a post-resection baseline of 0.26. Patient-specific tumor-associated variant allelic frequencies (VAFs), including TP53, PTEN, TERT, and CDKN2A/B, decreased within individual patients after resection and chemoradiation. In two patients with isocitrate dehydrogenase (IDH) mutant gliomas, decreasing IDH1 VAF after resection and chemoradiation correlated with decreased CSF D-2-hydroxyglutarate (D-2-HG) levels (0.64x and 0.62x, respectively, for the first patient, and 0.01x and 0.07x for the other patient). Both CSF IDH1 VAF and CSF D-2-HG increased a patient who had radiographic progression (2.56x and 9.21x, respectively). Moreover, CNB decreased below the limit of quantification during treatment and increased above the limit at progression. In conclusion, longitudinal CSF cfDNA can feasibly be obtained via CSF access devices in patients with gliomas during their disease course toward evaluating candidate monitoring biomarkers.

BIOM-52. THE DYNAMIC GLIOMA CSF PROTEOME: IMPACT OF LOCATION AND RESECTION FOR DISEASE MONITORING

Abstract While serial sampling of glioma tissue is rarely performed before recurrence, cerebrospinal fluid (CSF) can be acquired serially to evaluate candidate monitoring and pharmacodynamic biomarkers in patients with gliomas. However, the impact of key variables to consider in longitudinal CSF, including anatomical location and post-surgical changes, remains unknown. To that end, pre- versus post-resection intracranial CSF samples from patients with gliomas were obtained at early (1-16 days; n=20) or delayed (86-153 days; n=11) timepoints, as well as lumbar-versus-intracranial glioma CSF samples (n=14). Using aptamer-based proteomics, we identified significant differences in the glioma CSF proteome between lumbar, subarachnoid, and ventricular CSF. Indeed, intracranial CSF in paired lumbar/intracranial samples had nearly 1600 unique aptamers, regardless of which intracranial location was compared to the paired lumbar CSF, including in enrichment style analyses that evaluated the overlap between protein states. Importantly, resection had a significant, evolving longitudinal impact on the CSF proteome, with subsets of proteins associated with early versus delayed post-resection changes, many of which were enriched for inflammatory-related pathways. Interestingly, despite increased contamination from surgical debris, enrichment for plasma-associated proteins decreased with resection (FDR: 0.000), suggesting that a portion of the baseline glioma panel could be derived from plasma-originating analytes due to blood-brain barrier disruption. Despite these challenges, our data still revealed preliminary candidate monitoring biomarkers, such as brevican, that increased with progression (example patient: fold-change of 3.58x and 2.08x through lomustine and bevacizumab, respectively), but not pseudoprogression (Fold change less than 1.5). Serial intracranial CSF samples suggested the early potential for disease monitoring and evaluation of pharmacodynamic impact of targeted therapies, including bevacizumab and pembrolizumab. In conclusion, our available data to date suggest that the intracranial glioma CSF proteome provides an abundant and evolving source of candidate biomarkers to longitudinally discern the impact of resection and systemic treatments.

BIOM-36. DETECTION OF CIRCULATING TUMOR DNA (CTDNA) IN CEREBROSPINAL FLUID (CSF) IN PATIENTS WITH GLIOBLASTOMA TREATED IN PHASE I CLINICAL TRIAL

Abstract BACKGROUND As circulating tumor DNA (ctDNA) has been shown to be a source of tumor-specific biomarkers, liquid biopsy has emerged as a novel noninvasive tool in diagnosis, disease monitoring and treatment selection in several solid tumors. However, its use in brain tumors remains challenging because ctDNA is commonly not detected in blood. Thus, CSF (cerebrospinal fluid) has emerged as a potential source of ctDNA in the context of brain tumors. PATIENTS AND METHODS We prospectively evaluated the detection of ctDNA in CSF (CSF-ctDNA) +/- blood of patients with pre-treated MTAP-deleted glioblastoma, currently treated in a phase I clinical trial evaluating PRMT5 inhibitors. We did CSF samples at C2D15 and at progression, after patient’s oral and written consent. CSF was collected by lumbar puncture and analyzed with a panel of 35 genes including IDH1-2, TERT promoter and EGFR. Blood was collected, synchronously with CSF, by peripheral veinous punction and analyzed with the Foundation One Liquid CDx panel. RESULTS Between January and April 2024, five patients were included. All of them undergone initial surgical resection and received at least one previous systemic treatment associated with radiotherapy. At least one somatic mutation was identified in each patient, including EGFR amplification in 2/5 patients (40%). CSF was not available at baseline. One patient provided CSF only at progression. Four patients provided CSF at C2D15 on treatment, and among them, one patient was also collected at progression. At data cut-off, one patient was still on treatment. We identified a detectable level of CSF-ctDNA in only one patient with occipital tumor localization, with detection of EGFR amplification known at diagnosis. Synchronous blood analysis showed only clonal hematopoiesis variants. CONCLUSION We showed in this small cohort of patients that detection of CSF-ctDNA is technically feasible. The type of panel used might be optimized for this population especially to monitor patients as a potential surrogate biomarker for treatment response.

Fecal and Sputum Microbiota and Treatment Response in Patients with Mycobacterium abscessus Pulmonary Disease.

The microbiota is a potential source of biomarkers for clinical outcomes in chronic respiratory conditions, but its role in Mycobacterium abscessus pulmonary disease (PD) remains unexplored. We aimed to identify microbial signatures in fecal and sputum microbiotas associated with treatment response in patients with M. abscessus PD. We conducted a cohort study prospectively enrolling patients undergoing antibiotic treatment for M. abscessus PD. Fecal and sputum samples were collected before, and 2 weeks and 6 months after, treatment initiation. 16S rRNA amplicon sequencing approach was used to characterize the microbiotas. After categorizing patients as early treatment responders and non-responders based on sputum culture results at 2 weeks, we elucidated the differences in their diversity and composition of microbiotas. Among 32 patients enrolled, 27 patients (median 66 years, 85.2% women, 48.1% with subspecies abscessus) were included for microbiota analysis. Fifteen patients (55.6%) achieved negative conversion at 2 weeks, which was maintained in 14 (93.3%) after 6 months. Alpha-diversity of the fecal microbiota after 2 weeks of treatment decreased significantly in responders, but not in non-responders (P=0.029). Increased abundance of Eubacterium hallii group in baseline fecal microbiota was indicative of unresponsiveness to antibiotics, whereas an increase in Enterococcus in fecal microbiota at week 2 was linked with favorable response. Increased abundance of Burkholderia-Caballeronia-Paraburkholderia and Porphyromonas in baseline, and decreased abundance of Rothia in week 2 sputum microbiota were also associated with good treatment response. In M. abscessus PD, changes in microbial diversity and compositional signatures vary with treatment response.

Plasma proteomic signature of chronic psychosocial stress in mice

Chronic stress significantly impacts both physical and mental wellbeing, increasing risk of cardiovascular disease, immune dysregulation, and psychiatric conditions such as depression and anxiety disorders. The plasma proteome is a valuable source of biomarkers of health and disease, but the limited number of studies exploring the potential of the plasma proteome as a biomarker for stress-related disorders underscores the importance of further investigation of the effects of chronic stress on the plasma proteome. The aim of this study was to examine the effect of a 5-week chronic psychosocial stress paradigm on the plasma proteome in mice and to determine if any affected proteins correlated with stress-induced changes in behaviour and physiology, and thus might represent biomarkers of negative impacts of chronic stress. Using LC-MS/MS proteomic analysis, 38 proteins in the mouse plasma proteome were identified to be affected by chronic psychosocial stress. Functional analysis revealed that these proteins clustered into biological functions including inflammatory response, regulation of the immune response, complement and coagulation cascades, lipid metabolic process, and high-density lipoprotein particles Correlation analyses of the identified proteins with stress-induced behavioural or physiological changes stress revealed significant correlations between stress-induced anxiety-like behaviour and Phosphatidylinositol-glycan-specific phospholipase D, Complement C2, Epidermal growth factor receptor, Prosaposin, Actin-related protein 2/3 complex subunit 1B, Maltase-glucoamylase, Mannosyl-oligosaccharide 1,2-alpha-mannosidase IA and Fibrinogen-like protein 1. Chronic psychosocial stress blunted acute stress-induced corticosterone release, and this correlated with abundance of Pyrethroid hydrolase Ces2a; N-fatty-acyl-amino acid synthase/hydrolase Pm20d1, Mannosyl-oligosaccharide 1,2-alpha-mannosidase IA, Alpha-2-macroglobulin-P and L-selectin. Finally, stress-induced reductions in both brown and epididymal fat correlated with Phosphatidylinositol-glycan-specific phospholipase D, Complement C2, Epidermal growth factor receptor, Kininogen-1, Apolipoprotein M, Angiopoietin-related protein 3, Proprotein convertase subtilisin/kexin type 9, and Lipopolysaccharide-binding protein. These findings demonstrate that chronic psychosocial stress induces alterations in plasma proteins implicated in key biological processes and pathways related to stress response, immune function, and lipid metabolic regulation. Further investigation into these proteins may provide new avenues for identification of biomarkers or mediators of stress-induced pathology.

Efficient extraction of interstitial fluid using an ultrasonic-powered replaceable hexagram-shaped hydrogel microneedle patch for monitoring of dermal pharmacokinetics and psoriatic biomarkers

Monitoring biomarkers and pharmacokinetics is crucial for the precise diagnosis and treatment of chronic skin diseases. Dermal interstitial fluid (ISF) presents an attractive alternative source of biomarkers and drugs to blood; however, a simple, efficient, and comfortable extraction method is lacking. Here, we combine high-precision 3D printing and micromolding to develop an ultrasonic-powered, replaceable hexagram-shaped hydrogel microneedle (HMN) patch. Hexagram-shaped HMNs exhibit superior mechanical strength and faster ISF extraction rates compared to cone, pyramid, and octagram shapes. The patch, attached to a holder and integrated with a commercial ultrasonic device via a customized connector, allows easy replacement after use. With ultrasound assistance, the patch extracts over 15 μL, 6 μL, and 4 μL of ISF from porcine skin ex vivo, healthy mouse skin, and psoriatic mouse skin in vivo, respectively, within 1 min. The recovered ISF, obtained via centrifugation from HMNs, is used to monitor the dermal pharmacokinetics of drugs (e.g., methotrexate) and psoriasis-related biomarkers (e.g., interleukin-17 and –22) in mouse models, showing trends comparable to those detected via skin biopsy and blood draw. This ultrasonic-powered, replaceable hexagram-shaped HMN patch holds great promise for advancing the diagnosis, treatment, and research of skin diseases.

Screening of pathologically significant diagnostic biomarkers in tears of thyroid eye disease based on bioinformatic analysis and machine learning.

Lacrimal gland enlargement is a common pathological change in patients with thyroid eye disease (TED). Tear fluid has emerged as a new source of diagnostic biomarkers, but tear-based diagnostic biomarkers for TED with high efficacy are still lacking. We aim to investigate genes associated with TED-associated lacrimal gland lesions. Additionally, we seek to identify potential biomarkers for diagnosing TED in tear fluid. We obtained two expression profiling datasets related to TED lacrimal gland samples from the Gene Expression Omnibus (GEO). Subsequently, we combined the two separate datasets and conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on the obtained integrated dataset. The genes were employed for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The genes were intersected with the secretory proteins profile to get the potential proteins in the tear fluid. Machine learning techniques were then employed to identify optimal biomarkers and develop a diagnostic nomogram for predicting TED. Finally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted on screened hub genes to further elucidate their potential mechanisms in TED. In our analysis of the integrated TED dataset, we identified 2,918 key module genes and 157 differentially expressed genes and finally obtained 84 lacrimal-associated key genes. Enrichment analysis disclosed that these 84 genes primarily pertain to endoplasmic reticulum organization. After intersecting with the secretory proteins, 13 lacrimal gland-associated secretory protein genes (LaSGs) were identified. The results from machine learning indicated the substantial diagnostic value of dyslexia associated gene (KIAA0319) and peroxiredoxin4 (PRDX4) in TED-associated lacrimal gland lesions. The two hub genes were chosen as candidate biomarkers in tear fluid and employed to establish a diagnostic nomogram. Furthermore, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in the lacrimal gland of TED, with KIAA0319 and PRDX4 showing significant associations with infiltrating immune cells. We uncovered the distinct pathophysiology of TED-associated lacrimal gland enlargement compared to TED-associated orbital adipose tissue enlargement. We have demonstrated the endoplasmic reticulum-related pathways involved in TED-associated lacrimal gland lesions and established a diagnostic nomogram for TED utilizing KIAA0319 and PRDX4 through integrated bioinformatics analysis. This contribution offers novel insights for non-invasive, prospective diagnostic approaches in the context of TED.

SNORD3A: a new possible circulating biomarker of human heart failure

Abstract Background Heart failure (HF) is a complex clinical syndrome and the leading cause of mortality, morbidity and hospitalization in industrialized countries. Human cardiac biopsies are invaluable resources for identifying cardiac signaling pathways, however blood fractions and peripheral blood mononuclear cells (PBMCs) could be used as a source of surrogate biomarkers of signaling pathway activation in human heart failure. Purpose In the present study we aimed to identify novel circulating biomarkers mirroring human myocardial signaling in HF and to study the role played by SNORD3A in cardiomyocyte survival and death. Methods Cardiac left ventricle samples and PBLs from 8 HF patients undergoing heart transplantation and 2 control patients (CTRL) were analyzed by RNA sequencing (RNASeq) to identify transcripts that were regulated in both hearts and PBLs. Five identified transcripts, KCNQOT1, MIAT, SCRN1, MALAT1 and SNORD3A, were then validated by quantitative real-time PCR in PBMCs and in LVs. Next, we analyzed the expression of the Small Nucleolar RNA (snoRNA) SNORD3A in LVs and PBMCs from 8-week-old wild type C57BL/6 mice with pressure overload-induced HF by transverse aortic constriction (TAC). Sham-operated mice (sham) were used as controls. To further investigate the role of SNORD3A in cardiomyocyte function and survival, SNORD3A antisense oligonucleotides (SNOaso) and scramble control sequences (GFPaso) were synthesized and tested in cardiomyoblasts H9C2 cells under normoxic or hypoxic conditions to evaluate SNORD3A transcription levels, cell death and protein synthesis. Results SNORD3A expression was significantly increased in both LVs and PBMCs from HF patients compared to control subjects. Consistent with these results, SNORD3A levels were increased both in PBMCs and LVs from TAC mice compared to sham. In vitro hypoxia significantly increased SNORD3A expression levels in H9C2 cells, compared to normoxia. After SNOaso transfection, SNORD3A transcripts were downregulated, and they were further reduced following 3-hour-hypoxia. Depletion of SNORD3A levels increased cardiomyocyte death and reduced protein synthesis in H9C2 cells. Conclusions Our data suggest that SNORD3A might be involved in the regulation of cardiomyocyte survival in HF and could be useful for diagnostic and prognostic applications in HF.

Proteins sensing of plasma-derived extracellular vesicles in Alzheimer’s disease based on primer exchange reaction amplification strategy

Neuron-derived extracellular vesicles (EVs) in blood offer unique cellular and molecular information related to the onset and progression of Alzheimer’s disease (AD), and they are increasingly being investigated as sources of biomarkers. However, sensitively detecting AD-related EVs markers in blood remains a challenge. In this study, we employed a magnetic separation-assisted primer exchange reaction (MSPER) amplification strategy to achieve highly sensitive and convenient detection of CD63 and Aβ42 proteins on EVs (CD63+/Aβ42+ EVs). Specifically, CD63-expressing EVs are enriched using CD63 antibody-functionalized immune-magnetic beads (anti-CD63@IMBs). Subsequently, Aβ42 and CD63 aptamer are introduced to efficiently recognize CD63 and Aβ42 proteins on the EVs. Iterative primer exchange reaction (PER) concatemers, which create additional binding sites for fluorescence imagers, are then used to produce an amplified fluorescence signal. With this method, we achieved a limit of detection (LOD) of 7.2 × 103 particles/mL for sensitive detection of CD63+/Aβ42+ EVs. More importantly, this MSPER amplification strategy allows us to distinguish AD model mice from healthy controls with high accuracy. This method may provide an alternative for the early diagnosis and monitoring of AD through the detection of CD63+/Aβ42+ EVs.

Signature Proteins in Small Extracellular Vesicles of Granulocytes and CD4+ T-Cell Subpopulations Identified by Comparative Proteomic Analysis.

Several studies have described the proteomic profile of different immune cell types, but only a few have also analysed the content of their delivered small extracellular vesicles (sEVs). The aim of the present study was to compare the protein signature of sEVs delivered from granulocytes (i.e., neutrophils and eosinophils) and CD4+ T cells (i.e., TH1, TH2, and TH17) to identify potential biomarkers of the inflammatory profile in chronic inflammatory diseases. Qualitative (DDA) and quantitative (DIA-SWATH) analyses of in vitro-produced sEVs revealed proteome variations depending on the cell source. The main differences were found between granulocyte- and TH cell-derived sEVs, with a higher abundance of antimicrobial proteins (e.g., LCN2, LTF, MPO) in granulocyte-derived sEVs and an enrichment of ribosomal proteins (RPL and RPS proteins) in TH-derived sEVs. Additionally, we found differentially abundant proteins between neutrophil and eosinophil sEVs (e.g., ILF2, LTF, LCN2) and between sEVs from different TH subsets (e.g., ISG15, ITGA4, ITGB2, or NAMPT). A "proof-of-concept" assay was also performed, with TH2 biomarkers ITGA4 and ITGB2 displaying a differential abundance in sEVs from T2high and T2low asthma patients. Thus, our findings highlight the potential use of these sEVs as a source of biomarkers for diseases where the different immune cell subsets studied participate, particularly chronic inflammatory pathologies such as asthma or chronic obstructive pulmonary disease (COPD).

Impact of T Cell Ratios on Survival in Pleural Mesothelioma: Insights from Tumor Microenvironment Analysis

Background: Immunotherapy has significantly improved overall survival in patients with pleural mesothelioma, yet this benefit does not extend to those with the epithelioid subtype. Tumor growth is believed to be influenced by the immune response. This study aimed to analyze the tumor microenvironment to gain a better understanding of its influence on tumor growth. Methods: The tumor immune cell infiltration of 188 patients with pleural mesothelioma was characterized by multiplex immunofluorescence staining for CD3+ cells (CD3+), CD4+ cells (CD3+/CD4+), CD8+ cells (CD3+/CD8+), Treg (CD3+/CD4+/CD8-/CD163-/Foxp3+), PD1 cells (PD1+), and T helper cells (CD3+/CD4+/CD8-/CD163-/FoxP3-). The distribution of specific immune cells was correlated with clinical parameters. Results: A total of 188 patients with pleural mesothelioma (135 epithelioid, 9 sarcomatoid, 44 biphasic subtypes) were analyzed. The median age was 64.8 years. Overall survival was significantly longer in the epithelioid subtype than in the non-epithelioid subtype (p = 0.016). The presence of PD-L1 expression had a negative effect on overall survival (p = 0.041). A high ratio of CD4+ cells to regulatory T cells was associated with a significantly longer overall survival of more than 12 months (p = 0.015). The ratio of CD4+ cells to regulatory T cells retained its significant effect on overall survival in the multivariate analysis. Conclusions: Distinct differences in the T cell immune infiltrates in mesothelioma are strongly associated with overall survival. The tumor microenvironment could therefore serve as a source of prognostic biomarkers.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

482EMF Cerebral Microdialysis Fluid as a Source of Protein Biomarkers in a Porcine Model of Traumatic Brain Injury

482EMF Cerebral Microdialysis Fluid as a Source of Protein Biomarkers in a Porcine Model of Traumatic Brain Injury

Small RNAs in plasma extracellular vesicles define biomarkers of premanifest changes in Huntington's disease.

Despite the advances in the understanding of Huntington's disease (HD), there is a need for molecular biomarkers to categorize mutation carriers during the preclinical stage of the disease preceding functional decline. Small RNAs (sRNAs) are a promising source of biomarkers since their expression levels are highly sensitive to pathobiological processes. Here, using an optimized method for plasma extracellular vesicles (EVs) purification and an exhaustive analysis pipeline of sRNA sequencing data, we show that EV-sRNAs are downregulated early in mutation carriers and that this deregulation is associated with premanifest cognitive performance. Seven candidate sRNAs (tRF-Glu-CTC, tRF-Gly-GCC, miR-451a, miR-21-5p, miR-26a-5p, miR-27a-3p and let7a-5p) were validated in additional subjects, showing a significant diagnostic accuracy at premanifest stages. Of these, miR-21-5p was significantly decreased over time in a longitudinal study; and miR-21-5p and miR-26a-5p levels correlated with cognitive changes in the premanifest cohort. In summary, the present results suggest that deregulated plasma EV-sRNAs define an early biosignature in mutation carriers with specific species highlighting the progression and cognitive changes occurring at the premanifest stage.

Differentially expressed lncRNAs in SOD1G93A mice skeletal muscle: H19, Myhas and Neat1 as potential biomarkers in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a devastating neuromuscular disease characterized by progressive motor function and muscle mass loss. Despite extensive research in the field, the underlying causes of ALS remain incompletely understood, contributing to the absence of specific diagnostic and prognostic biomarkers and effective therapies. This study investigates the expression of long-non-coding RNAs (lncRNAs) in skeletal muscle as a potential source of biomarkers and therapeutic targets for the disease. The expression profiles of 12 lncRNAs, selected from the literature, were evaluated across different disease stages in tissue and muscle biopsies from the SOD1G93A transgenic mouse model of ALS. Nine out of the 12 lncRNAs were differentially expressed, with Pvt1, H19 and Neat1 showing notable increases in the symptomatic stages of the disease, and suggesting their potential as candidate biomarkers to support diagnosis and key players in muscle pathophysiology in ALS. Furthermore, the progression of Myhas and H19 RNA levels across disease stages correlated with longevity in the SOD1G93A animal model, effectively discriminating between long- and short-term survival individuals, thereby highlighting their potential as prognostic indicators. These findings underscore the involvement of lncRNAs, especially H19 and Myhas, in ALS pathophysiology, offering novel insights for diagnostic, prognostic and therapeutic targets.