Biology subjects

Maric, I.

Publications and source records attributed to Maric, I..

5 recordsLinked to original sources

bioRxiv2024

Comprehensive Phenotyping of Extracellular Vesicles in Blood of Healthy Humans - Insights into Cellular Origin and Biological Variability

Despite immense interest in biomarker applications of extracellular vesicles (EVs) from blood, our understanding of their physiological population in healthy humans remains limited. Using imaging and multiplex bead-based flow cytometry, we comprehensively quantified circulating EVs with respect to their cellular origin in a large cohort of healthy blood donors. We assessed coefficients of variations to characterise their biological variability and explored demographic, clinical, and lifestyle factors contributing to this variability. Cell-specific circulating EV subsets show a wide range of concentrations, which do not directly reflect concentrations of blood cells, indicating diverse patterns of EV subset release and/or uptake, even for EVs originating from the same cell type. Interestingly, tetraspanin+ circulating EVs largely originate from platelets and to a lesser extent from lymphocytes. PCA and association analyses demonstrate high biological inter-individual variability in circulating EVs across healthy humans, which can be only partly explained by the influence of sex, menopausal status, age and smoking on specific circulating EV and/or tetraspanin+ circulating EV subsets. No global influence of the explored subjects factors on circulating EVs was detected. Our findings provide the first comprehensive, quantitative data towards the cell-origin atlas of blood EVs, with important implications in the clinical use of EVs as biomarkers of disease.

molecular biology↗

bioRxiv2022

Genomics of PDGFR-rearranged hypereosinophilic syndrome

A subset of patients with hypereosinophilia have a clonal hematologic neoplasm. These neoplastic hypereosinophilic syndromes (HES) can harbor somatic DNA rearrangements involving a PDGF receptor gene, PDGFRA or PGDFRB, most frequently FIP1L1::PDGFRA. Patients with PDGFR-rearranged HES are overwhelmingly males, and the hypereosinophilia responds robustly to the multi-kinase inhibitor imatinib. Unbiased genomics of PDGFR-rearranged HES have not been comprehensively studied. Here, we compared whole genome sequencing of eosinophil DNA from patients with PDGFR-rearranged HES at diagnosis (tumor) versus matched "normal" DNA. Other than the PDGFR rearrangement itself, we detected no recurrent coding somatic point mutations, insertions/deletions, or DNA copy number alterations in the neoplastic eosinophils; instead, there were additional somatic events private to each tumor. The tumors had a linear clonal structure with the PDGFR rearrangement as the initial driver event. We mapped the breakpoints of a novel IQGAP2::PDGFRB::UVRAG intra- and inter-chromosomal fusion event in one patient. Non-coding analysis found no recurrent abnormalities, including in eosinophilic leukemia-associated promoters or enhancers. The sex chromosomes showed no recurrent alterations to provide an obvious explanation for the diseases extreme male bias. We conclude that neoplastic HES has relatively simple genomics and that the only unambiguous recurrent driver event is the PDGFR rearrangement itself. Key PointsO_LIPDGFR-rearranged hypereosinophilic neoplasms have simple, linear genetic structure with the rearrangement as the likely initiating event C_LIO_LIThere are no obvious recurrent additional or sex-biased genomic events that cooperate with PDGFR rearrangement to drive the neoplasm C_LI

genomics↗

bioRxiv2021

Oncogenic RAS commandeers amino acid sensing machinery to aberrantly activate mTORC1 in multiple myeloma

Oncogenic mutations within the RAS pathway are common in multiple myeloma (MM), an incurable malignancy of plasma cells. However, the mechanisms of pathogenic RAS signaling in this disease remain enigmatic and difficult to inhibit therapeutically. We employed an unbiased proteogenomic approach to dissect RAS signaling in MM by combining genome-wide CRISPR-Cas9 screening with quantitative mass spectrometry focused on RAS biology. We discovered that mutant isoforms of RAS organized a signaling complex with the amino acid transporter, SLC3A2, and MTOR on endolysosomes, which directly activated mTORC1 by co-opting amino acid sensing pathways. MM tumors with high expression of mTORC1-dependent genes were more aggressive and enriched in RAS mutations, and we detected interactions between RAS and MTOR in MM patient tumors harboring mutant RAS isoforms. Inhibition of RAS-dependent mTORC1 activity synergized with MEK and ERK inhibitors to quench pathogenic RAS signaling in MM cells. This study redefines the RAS pathway in MM and provides a mechanistic and rational basis to target this novel mode of RAS signaling.

cancer biology↗

bioRxiv2020

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics↗

bioRxiv2018

Changes in pregnancy-related serum biomarkers early in gestation are associated with later development of preeclampsia

BackgroundPlacental protein expression plays a crucial biological role during normal and complicated pregnancies. We hypothesized that: (1) circulating pregnancy-associated, placenta-related protein levels throughout gestation reflect the uncomplicated, full-term temporal progression of human gestation, and effectively estimates gestational ages (GAs); (2) pregnancies with underlying placental pathology, such as preeclampsia (PE), are associated with disruptions in this GA estimation in early gestation; (3) malfunctions of this GA estimation can be employed to identify impending PE. In addition, to explore the underlying biology and PE etiology, we set to compare protein gestational patterns of human and mouse, using pregnant heme oxygenase-1 (HO-1) heterozygote (Het) mice, a mouse model reflecting PE-like symptoms.\n\nMethodsSerum levels of circulating placenta-related proteins - leptin (LEP), chorionic somatomammotropin hormone like 1 (CSHL1), elabela (ELA), activin A, soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF)- were quantified by ELISA in blood serially collected throughout human pregnancies (20 normal subjects with 66 samples, and 20 PE subjects with 61 samples). Linear multivariate analysis of the targeted serological protein levels was performed to estimate the normal GA. Logarithmic transformed mean-squared errors of GA estimations were used to identify impending PE. Then the human gestational protein patterns were compared to those in the pregnant HO-1 mice.\n\nResultsAn elastic net (EN)-based gestational dating model was developed (R2 = 0.76) and validated (R2 = 0.61) using the serum levels of the 6 proteins at various GAs from women with normal uncomplicated pregnancies (n = 10 for training and n = 6 for validation). In pregnancies complicated by PE (n = 14), the EN model was not (R2 = -0.17) associated with GA at sampling in PE. Statistically significant deviations from the normal GA EN model estimations were observed in PE-associated pregnancies between GAs of 16-30 weeks (P = 0.01). The EN model developed with 5 proteins (ELA excluded due to the lack of robustness of the mouse ELA essay) performed similarly on normal human (R2 = 0.68) and WT mouse (R2 = 0.85) pregnancies. Disruptions of this model were observed in both human PE-associated (human: R2 = 0.27) and mouse HO-1 Het (mouse: R2 = 0.30) pregnancies. LEP out performed sFlt-1 and PlGF in differentiating impending PE at early human and late mouse gestations.\n\nConclusionsAs revealed in both human and mouse GA EN analyses, temporal serological placenta-related protein patterns are tightly regulated throughout normal human pregnancies and can be significantly disrupted in pathologic PE states. LEP changes earlier during gestation than the well-established late GA PE biomarkers (sFlt-1 and PlGF). Our HO-1 Het mouse analysis provides direct evidence of the causative action of HO-1 deficiency in LEP upregulation in a PE-like murine model. Therefore, longitudinal analyses of pregnancy-related protein patterns in sera, may not only help in the exploration of underlying PE pathophysiology but also provide better clinical utility in PE assessment.

molecular biology↗