Biology subjects

Pucic-Bakovic, M.

Publications and source records attributed to Pucic-Bakovic, M..

6 recordsLinked to original sources

bioRxiv2026

Reg3β removes aged neutrophils after myocardial infarction

AO_SCPLOWBSTRACTC_SCPLOWRecruitment and resolution of immune cell accumulation after myocardial infarction (MI) is critical for effective wound healing and prevention of maladaptive remodeling. Numerous signals are known to recruit neutrophils, critical drivers of inflammation, but knowledge about signals containing and resolving their accumulation is limited. We discovered that Regenerating-islet derived protein 3 beta (REG3{beta}) limits the persistence of neutrophils after MI, thereby promoting resolution of inflammation. REG3{beta} selectively binds to aged and hyperactive neutrophils and induces rapid cell death, enabling clearance via macrophage-mediated efferocytosis. Selective binding of REG3{beta} is achieved by interaction with paucimannosylated proteins that translocate from azurophile granules to the plasma membrane in an activation- and age-dependent manner. Endocytotic uptake and accumulation of REG3{beta} in lysosomes initiates programmed cell death of neutrophils via lyosomal membrane permeabilization and release of cathepsins. Our work establishes REG3{beta} as a local immune checkpoint essential for neutrophil resolution and cardiac repair.

immunology↗

bioRxiv2024

Disruption of the epigenetic regulator BAP1 drives chromatin remodeling leading to the emergence of cells with breast cancer stem cell properties and aberrant glycosylation.

BackgroundEpigenetic regulator genes play critical roles in controlling cell identity and are frequently disrupted in breast cancers, suggesting a key driver role in this disease and its associated phenotypes. However, specific epigenetic drivers (epidrivers) of mammary cell plasticity and their mechanistic contributions to this phenotype are poorly characterized. MethodsTo identify potential epidrivers of the emergence of mesenchymal breast cancer stem cell-like phenotypes in non-tumorigenic mammary cells, we employed a CRISPR/Cas9 loss-of-function screening strategy targeting epigenetic regulator genes. This approach was followed by an in-depth validation and characterization of epigenomic, transcriptomic, proteomic and phenotypic changes resulting from the disruption of the putative epidriver gene BAP1. ResultsOur investigation revealed that loss of the histone deubiquitinase BAP1 impacts cellular processes associated with breast cancer cell plasticity such as epithelial-to-mesenchymal transition (EMT) and actin cytoskeleton organization. In addition, we unveiled that BAP1 loss resulted in an overall less permissive chromatin and downregulated gene expression, impacting programs that control cellular glycosylation and leading to decreased glycan abundance and complexity. BAP1 rescue restored the expression of several deregulated genes in a catalytic activity-dependent manner, suggesting that BAP1-mediated cell identity and glycosylation regulation are largely dependent on its histone deubiquitinase activity. ConclusionsOverall, our results point to BAP1 disruption as a driver of mammary cell plasticity and reveal a novel role of BAP1 as an epigenetic regulator of cellular glycosylation.

cancer biology↗

bioRxiv2019

Systematic evaluation of normalization methods for glycomics data based on performance of network inference

Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, we here quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms (LC-ESI-MS, UHPLC-FLD and MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the Probabilistic Quotient method followed by log-transformation, irrespective of the measurement platform.

bioinformatics↗

bioRxiv2019

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are commonly used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the significance of the underlying correlation coefficients. A statistical cutoff, however, is not guaranteed to capture biological reality, and heavily depends on dataset properties such as sample size. We here propose an alternative, innovative approach to address the problem of network reconstruction. Specifically, we developed a cutoff selection algorithm that maximizes the agreement to a given ground truth. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. The optimal network outperforms networks obtained with statistical cutoffs and is robust with respect to sample size. Importantly, we can show that even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach on an untargeted metabolomics and a transcriptomics dataset from The Cancer Genome Atlas (TCGA). For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for the optimization. Overall, this paper shows that using prior information for correlation network inference is superior to using regular statistical cutoffs, even if the prior information is incomplete or partially inaccurate.

bioinformatics↗

bioRxiv2019

Global variability of the human IgG glycome

Immunoglobulin G (IgG) is the most abundant serum antibody and is a key determinant of the humoral immune response. Its structural characteristics and effector functions are modulated through the attachment of various sugar moieties called glycans. IgG N-glycome patterns change with the age of individual and in different diseases. Variability of IgG glycosylation within a population is well studied and is affected by a combination of genetic and environmental factors. However, global inter-population differences in IgG glycosylation have never been properly addressed. Here we present population-specific N-glycosylation patterns of whole IgG, analysed in 5 different populations totalling 10,482 IgG glycomes, and of IgGs fragment crystallisable region (Fc), analysed in 2,530 samples from 27 populations sampled across the world. We observed that country of residence associates with many N-glycan features and is a strong predictor of monogalactosylation variability. IgG galactosylation also strongly correlated with the development level of a country, defined by United Nations health and socioeconomic development indicators. We found that subjects from developing countries had low IgG galactosylation levels, characteristic for inflammation and ageing. Our results suggest that citizens of developing countries may be exposed to country-specific environmental factors that can cause low-grade chronic inflammation and the apparent increase in biological age.

biochemistry↗

bioRxiv2018

Defining the genetic control of human blood plasma N-glycome using genome-wide association study

Glycosylation is a common post-translational modification of proteins. It is known, that glycans are directly involved in the pathophysiology of every major disease. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here, we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people. We discovered and replicated twelve loci. This allowed us to demonstrate a clear overlap in genetic control between total plasma and IgG glycosylation. Majority of loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3, and MGAT5). We, however, also found loci that are likely to reflect other, more complex, aspects of plasma glycosylation process. Functional genomic annotation suggested the role of DERL3, which potentially highlights the role of glycoprotein degradation pathway, and such transcription factor as IKZF1.

genetics↗