Plasma Signatures Research Articles

Integrated plasma proteomics and myocardial tissue transcriptomics reveal elevated fibrosis markers in arrhythmogenic cardiomyopathy patients

Abstract Background Arrhythmogenic cardiomyopathy (ACM) is characterized by myocardial fibrofatty replacement and ventricular arrhythmias, with potential progression to heart failure. This study aims to identify ACM-specific biomarkers for improved prognosis and possible treatment strategies. Methods RNA sequencing and transcriptome analyses were performed in cardiomyocytes of 10 ACM patients, 10 dilated cardiomyopathy patients and 10 healthy controls. Analyses were performed using liquid chromatography and mass spectrometry. Additionally, plasma protein expression was assessed in 38 ACM patients and 24 healthy controls using the Proximity Extension Assay (Olink®). A standardized bicycle exercise test was performed in 11 ACM patients and controls and blood was obtained before and after exercise. Results Myocardial tissue transcriptomics identified several upregulated molecules associated with extracellular matrix formation and immune response in ACM. Plasma protein sequencing revealed upregulation of proteins involved in metabolic pathways, inflammation, and fibrosis. Integration of these analyses identified key soluble profibrotic markers such as Fibulin-3, Endostatin and C-X-C motif chemokine 10 (CXCL10) in plasma of ACM patients. After exercise, Fibulin-3 levels increased in ACM patients compared to controls. These findings were further validated using enzyme-linked immunosorbent assay (ELISA). Conclusion This comprehensive analysis revealed distinct proteomic and transcriptomic signatures in ACM patients' plasma and cardiomyocytes. The integrative analysis identified several upregulated profibrotic markers in ACM. Our Findings may advance diagnostic and prognostic strategies and offer potential therapeutic targets.

Plasma signature metabolites of dietary fat intake characterize associations with prevalent metabolic syndrome

Plasma signature metabolites of dietary fat intake characterize associations with prevalent metabolic syndrome

Novel plasma protein biomarkers: A time-dependent predictive model for Alzheimer's disease

BackgroundThe accurate prediction of Alzheimer's disease (AD) is crucial for the efficient management of its progression. The objective of this research was to construct a new risk predictive model utilizing novel plasma protein biomarkers for predicting AD incidence in the future and analyze their potential biological correlation with AD incidence. MethodsA cohort of 440 participants aged 60 years and older from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal cohort was utilized. The baseline plasma proteomics data was employed to conduct Cox regression, LASSO regression, and cross-validation to identify plasma protein signatures predictive of AD risk. Subsequently, a multivariable Cox proportional hazards model based on these signatures was constructed. The performance of the risk prediction model was evaluated using time-dependent receiver operating characteristic (t-ROC) curves and Kaplan-Meier curves. Additionally, we analyzed the correlations between protein signature expression in plasma and predicted AD risk, the time of AD onset, the expression of protein signatures in cerebrospinal fluid (CSF), the expression of CSF and plasma biomarkers, and APOE ε4 genotypes. Colocalization and Mendelian randomization analyses was conducted to investigate the association between protein features and AD risk. GEO database was utilized to analyze the differential expression of protein features in the blood and brain of AD patients. ResultsWe identified seven protein signatures (APOE, CGA, CRP, CCL26, CCL20, NRCAM, and PYY) that independently predicted AD incidence in the future. The risk prediction model demonstrated area under the ROC curve (AUC) values of 0.77, 0.76, and 0.77 for predicting AD incidence at 4, 6, and 8 years, respectively. Furthermore, the model remained stable in the range of the 3rd to the 12th year (ROC ≥ 0.74). The low-risk group, as defined by the model, exhibited a significantly later AD onset compared to the high-risk group (P < 0.0001). Moreover, all protein signatures exhibited significant correlations with AD risk (P < 0.001) and the time of AD onset (P < 0.01). There was no strong correlation between the protein expression levels in plasma and CSF, as well as AD CSF biomarkers. APOE, CGA, and CRP exhibited significantly lower expression levels in APOE ε4 positive individuals (P < 0.05). Additionally, colocalization analysis reveals a significant association between AD and SNP loci in APOE. Mendelian randomization analysis shows a negative correlation between NRCAM and AD risk. Transcriptomic analysis indicates a significant downregulation of NRCAM and PYY in the peripheral blood of AD patients (P < 0.01), while APOE, CGA, and NRCAM are significantly downregulated in the brains of AD patients (P < 0.0001). ConclusionOur research has successfully identified protein signatures in plasma as potential risk biomarkers that can independently predict AD onset in the future. Notably, this risk prediction model has demonstrated commendable predictive performance and stability over time. These findings underscore the promising utility of plasma protein signatures in dynamically predicting the risk of AD, thereby facilitating early screening and intervention strategies.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Novel eicosanoid signature in plasma provides diagnostic for metabolic dysfunction-associated steatotic liver disease

There is a clinical need for a simple test implementable at the primary point of care to identify individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) in the population. Blood plasma samples from adult patients with varying phenotypes of MASLD were used to identify a minimal set of lipid analytes reflective of underlying histologically confirmed MASLD. Samples were obtained from the NIDDK Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) NAFLD Database prospective cohort study (MASLD group; N = 301). Samples of control subjects were obtained from cohort studies at the University of California San Diego (control group; N = 48). Plasma samples were utilized for targeted quantitation of circulating eicosanoids, related bioactive metabolites, and polyunsaturated fatty acids by ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) lipidomics analysis. Bioinformatic approaches were used to discover a panel of bioactive lipids that can be used as a diagnostic tool to identify MASLD. The final panel of fifteen lipid metabolites consists of 12 eicosanoid metabolites and 3 free fatty acids that were identified to be predictive for MASLD by multivariate area under the receiver operating characteristics curve (AUROC) analysis. The panel was highly predictive for MASLD with an AUROC of 0.999 (95% CI = 0.986–1.0) with only one control misclassified. A validation study confirmed the resulting MASLD LIPIDOMICS SCORE, which may require a larger-scale prospective study to optimize. This predictive model should guide the development of a non-invasive “point-of-care” test to identify MASLD patients requiring further evaluation for the presence of metabolic dysfunction-associated steatohepatitis.

A Multiplexed Quantitative Proteomics Approach to the Human Plasma Protein Signature.

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Lipidomic Signature of Plasma and Synovial Fluid in Patients with Osteoarthritis: Putative Biomarkers Determined by UHPLC-QTOF-ESI+MS.

Osteoarthritis (OA) is a prevalent joint condition causing pain and disability, especially in the elderly. Currently, OA diagnosis relies on clinical data and imaging, but recent interest in metabolomics suggests that early biochemical changes in biofluids, particularly synovial fluid (SF), could enable an earlier diagnosis and understanding of the disease. In this regard, we conducted a lipidomics study in 33 plasma and SF samples from OA patients and 20 OA-free controls to assess the diagnostic value of various lipid metabolites, using UHPLC-QTOF-ESI+MS. In plasma samples, 25 metabolites had area-under-the-curve (AUC) values higher than 0.9, suggesting a very good diagnostic potential for phosphatidic acid PA (16:0/16:0), PA (34:0), phosphatidylethanolamine PE (34:2), glucosylceramide, phosphatidylcholine PC (32:1), and other metabolites while in SF 20, metabolites had AUC values higher than 0.8, the vast majority belonging to lipid metabolism as well. Although the results align with the previous literature, larger cohort studies are necessary to confirm the diagnostic value of the lipid metabolites.

Nucleotide sugars correlate with leukocyte telomere length as part of a dyskeratosis congenita metabolomic plasma signature.

Not available.

The Food Sources in Western Diets Modulate Obesity Development, Insulin Sensitivity, and the Plasma and Cecal Metabolome in Mice.

Dietary constituents modulate development of obesity and type 2 diabetes. The metabolic impact from different food sources in western diets (WD) on obesity development is not fully elucidated. This study aims to identify dietary sources that differentially affect obesity development and the metabolic processes involved. Mice were fed isocaloric WDs with protein and fat from different food groups, including egg and dairy, terrestrial meat, game meat, marine, vegetarian, and a mixture of all. This study evaluates development of obesity, glucose tolerance, insulin sensitivity, and plasma and cecal metabolome. WD based on marine or vegetarian food sources protects male mice from obesity development and insulin resistance, whereas meat-based diets promote obesity. The intake of different food sources induces marked differences in the lipid-related plasma metabolome, particularly impacting phosphatidylcholines. Fifty-nine lipid-related plasma metabolites are positively associated with adiposity and a distinct cecal metabolome is found in mice fed a marine diet. This study demonstrates differences in obesity development between the food groups. Diet specific metabolomic signatures in plasma and cecum associated with adiposity, where a marine based diet modulates the level of plasma and cecal phosphatidylcholines in addition to preventing obesity development.

Saliva, Plasma, and Multifluid Metabolomic Signatures of Periodontal Disease, Type 2 Diabetes Progression, and Markers of Glycemia and Dyslipidemia Among Puerto Rican Adults With Overweight and Obesity.

Evidence from cohort studies indicates a bidirectional relationship between periodontal disease and type 2 diabetes (T2D), but the underlying mechanisms remain unknown. In this study, we aimed to (1) identify saliva, plasma, and multifluid metabolomic signatures associated with periodontal disease and (2) determine if these signatures predict T2D progression and cardiometabolic biomarkers at year 3. We included participants from the SOALS (San Juan Overweight Adult Longitudinal Study) (n=911). Metabolites from saliva (k=635) and plasma (k=1051) were quantified using liquid chromatography-mass spectrometry. We applied elastic net regression with 10-fold cross-validation to identify baseline metabolomic signatures of periodontal disease. Multivariable Cox proportional hazards regression and linear regression were used to evaluate the association with T2D progression and biomarker concentrations. Metabolomic profilesincluded highly weighted metabolites related to lysine and pyrimidine metabolism. Periodontal disease or its 3 metabolomic signatures were not associated with T2D progression in 3 years. Prospectively, 1-SD increments in the multifluid and saliva metabolomic signatures were associated with higher low-density lipoprotein (multifluid: 12.9±5.70, P=0.02; saliva: 13.3±5.11, P=0.009). A 1-SD increment in the plasma metabolomic signature was also associated with Homeostatic Model Assessment for Insulin Resistance (2.67±1.14, P=0.02) and triglyceride (0.52±0.18, P=0.002). Although metabolomic signatures of periodontal disease could not predict T2D progression, they were associated with low-density lipoprotein, triglyceride, and Homeostatic Model Assessment for Insulin Resistance levels at year 3.

Lipoprotein Lipidomics as a Frontier in Non-Alcoholic Fatty Liver Disease Biomarker Discovery.

Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease characterized by the build-up of fat in the liver of individuals in the absence of alcohol consumption. This condition has become a burden in modern societies aggravated by the lack of appropriate predictive biomarkers (other than liver biopsy). To better understand this disease and to find appropriate biomarkers, a new technology has emerged in the last two decades with the ability to explore the unmapped role of lipids in this disease: lipidomics. This technology, based on the combination of chromatography and mass spectrometry, has been extensively used to explore the lipid metabolism of NAFLD. In this review, we aim to summarize the knowledge gained through lipidomics assays exploring tissues, plasma, and lipoproteins from individuals with NAFLD. Our goal is to identify common features and active pathways that could facilitate the finding of a reliable biomarker from this field. The most frequent observation was a variable decrease (1-9%) in polyunsaturated fatty acids in phospholipids and non-esterified fatty acids in NAFLD patients, both in plasma and liver. Additionally, a reduction in phosphatidylcholines is a common feature in the liver. Due to the scarcity of studies, further research is needed to properly detect lipoprotein, plasma, and tissue lipid signatures of NAFLD etiologies, and NAFLD subtypes, and to define the relevance of this technology in disease management strategies in the push toward personalized medicine.

Targeted Quantitative Plasma Metabolomics Identifies Metabolite Signatures that Distinguish Heart Failure with Reduced and Preserved Ejection Fraction.

Two general phenotypes of heart failure (HF) are recognized: HF with reduced ejection fraction (HFrEF) and with preserved EF (HFpEF). To develop HF disease phenotype-specific approaches to define and guide treatment, distinguishing biomarkers are needed. The goal of this study was to utilize quantitative metabolomics on a large, diverse population to replicate and extend existing knowledge of the plasma metabolic signatures in human HF. Quantitative, targeted LC/MS plasma metabolomics was conducted on 787 samples collected by the Penn Medicine BioBank from subjects with HFrEF (n=219), HFpEF (n=357), and matched non-failing Controls (n=211). A total of 90 metabolites were analyzed, comprising 28 amino acids, 8 organic acids, and 54 acylcarnitines. 733 of these samples were also processed via an OLINK protein panel for proteomic profiling. Consistent with previous studies, unsaturated forms of medium/long chain acylcarnitines were elevated in the HFrEF group to a greater extent than the HFpEF group compared to Controls. A number of amino acid derivatives, including 1- and 3-methylhistidine, homocitrulline, and symmetric (SDMA) and asymmetric (ADMA) dimethylarginine were elevated in HF, with ADMA elevated uniquely in HFpEF. Plasma branched-chain amino acids (BCAA) were not different across the groups; however, short-chain acylcarnitine species indicative of BCAA catabolism were significantly elevated in both HF groups. The ketone body 3-hydroxybutyrate (3-HBA) and its metabolite C4-OH carnitine were uniquely elevated in the HFrEF group. Linear regression models demonstrated a significant correlation between plasma 3-HBA and NT-proBNP in both forms of HF, stronger in HFrEF. These results identify plasma signatures that are shared as well as potentially distinguish between HFrEF and HFpEF. Metabolite markers for ketogenic metabolic reprogramming in extra-cardiac tissues were identified as unique signatures in the HFrEF group, possibly related to the lipolytic action of increased levels of BNP. Future studies will be necessary to further validate these metabolites as HF biosignatures that may guide phenotype-specific therapeutics and provide insight into the systemic metabolic responses to HFpEF and HFrEF.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study.

Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles.

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements. Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy. DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Formation of a Magnetic Cloud from the Merging of Two Successive Coronal Mass Ejections

On 2022 March 28 two successive coronal mass ejections (CMEs) were observed by multiple spacecraft and resulted in a magnetic cloud (MC) at 1 au. We investigate the propagation and interaction properties of the two CMEs correlated with the MC using coordinated multipoint remote sensing and in situ observations from Solar Orbiter, STEREO-A, Solar and Heliospheric Observatory, and Wind. The first CME was triggered by a filament eruption with a high inclination angle. Roughly 9 hr later, the second CME originating from the same active region erupted with a smaller tilt angle and faster speed compared to the first one. The second CME overtook the preceding CME and formed a merged front at approximately 75 R ☉, which developed into a complex ejecta at 1 au. The descending speed and low proton temperature inside the complex ejecta suggest that the two CMEs have fully merged before reaching 1 au, leading them to begin expanding rather than compressing against each other. The complex ejecta appears to have the magnetic field and plasma signatures of an MC, although there is a discontinuity in the magnetic field implying previous interactions. The cross section of the complex ejecta, reconstructed from in situ data using a Grad–Shafranov technique, exhibits a right-handed flux rope structure. These results highlight that an MC-like complex ejecta lacking interaction features could arise from the complete merging of two CMEs.

Validation of EUHFORIA cone and spheromak coronal mass ejection models

We present validation results for calculations of arrival times and geomagnetic impact of coronal mass ejections (CMEs) using the cone and spheromak CME models implemented in EUropean Heliospheric FORecasting Information Asset (EUHFORIA). Validating numerical models is crucial for ensuring their accuracy and performance with respect to real data. We compared CME plasma and magnetic field signatures measured in situ by satellites at the L1 point with the simulation output of EUHFORIA. The validation of this model was carried out using two datasets in order to ensure a comprehensive evaluation. The first dataset focuses on 16 CMEs that arrived at Earth, offering specific insights into the model's accuracy in predicting arrival time and geomagnetic impact. Meanwhile, the second dataset encompasses all CMEs observed over eight months within Solar Cycle 24, regardless of whether or not they arrived at Earth, covering periods of both solar minimum and maximum activity. This second dataset enables a more comprehensive evaluation of the model's predictive precision in term of CME arrivals and misses. Our results show that EUHFORIA provides good estimates in terms of arrival times, with root mean square error (RMSE) values of 9 hours. Regarding the number of correctly predicted ICME arrivals and misses, we find a 75&lt;!PCT!&gt; probability of detection in a 12 hour time window and 100&lt;!PCT!&gt; probability of detection in a 24 hour time window. The geomagnetic impact forecasts measured by the $K_p$ index provide different degrees of accuracy ranging from 31&lt;!PCT!&gt; to 69&lt;!PCT!&gt;. These results validate the use of cone and spheromak CMEs for real-time space weather forecasting.

Epigenomic signatures of sarcomatoid differentiation to guide the treatment of renal cell carcinoma

Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and a heightened response to immune checkpoint inhibitors (ICIs). Two major barriers to improving outcomes for sRCC are the limited understanding of its gene regulatory programs and the low diagnostic yield of tumor biopsies due to spatial heterogeneity. Herein, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries from tissue and plasma samples from 50 patients with RCC and healthy volunteers. By profiling histone modifications and DNA methylation, we identified highly recurrent epigenomic reprogramming enriched in sRCC. Furthermore, CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs, and FOSL1 expression was associated with the response to ICIs in RCC in two randomized clinical trials. Finally, we established a blood-based diagnostic approach using detectable sRCC epigenomic signatures in patient plasma, providing a framework for discovering epigenomic correlates of tumor histology via liquid biopsy.

Plasma and synovial fluid extracellular vesicles display altered microRNA profiles in horses with naturally occurring post-traumatic osteoarthritis: an exploratory study.

The objective of this study was to characterize extracellular vesicles (EVs) in plasma and synovial fluid obtained from horses with and without naturally occurring post-traumatic osteoarthritis (PTOA). EVs were isolated from plasma and synovial fluid from horses with (n = 6) and without (n = 6) PTOA. Plasma and synovial fluid EVs were characterized with respect to quantity, size, and surface markers. Small RNA sequencing was performed, and differentially expressed microRNAs (miRNAs) underwent bioinformatic analysis to identify putative targets and to explore potential associations with specific biological processes. Plasma and synovial fluid samples from horses with PTOA had a significantly higher proportion of exosomes and a lower proportion of microvesicles compared to horses without PTOA. Small RNA sequencing revealed several differentially expressed miRNAs, including miR-144, miR-219-3p, and miR-199a-3l in plasma and miR-199a-3p, miR-214, and miR-9094 in synovial fluid EVs. Bioinformatics analysis of the differentially expressed miRNAs highlighted their potential role in fibrosis, differentiation of chondrocytes, apoptosis, and inflammation pathways in PTOA. We have identified dynamic molecular changes in the small noncoding signatures of plasma and synovial fluid EVs in horses with naturally occurring PTOA. These findings could serve to identify promising biomarkers in the pathogenesis of PTOA, to facilitate the development of targeted therapies, and to aid in establishing appropriate translational models of PTOA.