Biology subjects

Djordjevic, D.

Publications and source records attributed to Djordjevic, D..

5 recordsLinked to original sources

bioRxiv2024

Mapping atherogenesis mechanisms in smooth muscle cells by targeting genes linked to coronary artery disease

Recent genome-wide association studies (GWAS) have identified multiple vascular cell-expressed genes linked to coronary artery disease (CAD), suggesting that smooth muscle cells (SMCs) and SMC-derived metaplastic cells are promising targets for novel antiatherosclerosis therapies. However, the disease-promoting pathways of most GWAS-identified genes are unknown, hindering their translation into therapeutic targets. This study integrated public GWAS data for CAD and single-cell RNA sequencing (scRNA-seq) analyses of human atherosclerotic plaques to identify 20 GWAS risk genes with a putative mechanism of action in SMCs or SMC-derived cells. Gene perturbation experiments in SMCs coaxed to plaque-relevant phenotypes revealed that the selected risk genes, despite encoding very different types of proteins, regulated shared sets of genes associated with contractile functions, cell cycle pathways, NF{kappa}B, and type I interferon signaling. By integrating information about GWAS gene effect direction and a deep analysis of cholesterol- and stretch-induced gene modules in SMCs, we find evidence that cholesterol-induced signaling is a pro-atherogenic disease mechanism in SMCs that is upregulated by detrimental and downregulated by protective GWAS genes. Overall, our study identifies a set of candidate disease mechanisms in SMCs that are regulated by multiple GWAS genes across several SMC assays. Furthermore, it provides proof-of-concept for using GWAS gene effect directionality to predict the pathogenic effect of candidate disease mechanisms that can be extended to other GWAS genes and cell types in the future.

cell biology↗

bioRxiv2023

Kita crispants for systematic image-based genetic screens of complex traits in zebrafish larvae

With thousands of loci identified by genome-wide association studies for complex traits, there is a need for in vivo model systems that can reliably and quickly infer the role of large numbers of candidate genes. CRISPR/Cas9-based functional screens in F0 zebrafish represent such a system. However, negative controls used so far - including scrambled guide RNAs (gRNAs), inactivated Cas9, and sham injections - do not elicit the same cellular and organismal responses as mutagenesis by CRISPR/Cas9, and may fuel biased conclusions. Here, we show that targeting kita facilitates efficient optical pre-screening for successful mutagenesis, higher quality imaging data, and efficient classification of cases and controls. We identified and tested two gRNAs that target kita with similarly high mutagenic efficiency and effects on pigmentation, and are free from off-target effects or major effects on cardiometabolic traits. We propose several approaches that will result in valid, unbiased conclusions.

molecular biology↗

bioRxiv2018

CardiacProfileR: An R package for extraction and visualisation of heart rate profiles from wearable fitness trackers

A persons heart rate profile, which consists of resting heart rate, increase of heart rate upon exercise and recovery of heart rate after exercise, is traditionally measured by electrocardiography during a controlled exercise stress test. A heart rate profile is a useful clinical tool to identify individuals at risk of sudden death and other cardiovascular conditions. Nonetheless, conducting such exercise stress tests routinely is often inconvenient and logistically challenging for patients. The widespread availability of affordable wearable fitness trackers, such as Fitbit and Apple Watch, provides an exciting new means to collect longitudinal heart rate and physical activity data. We reason that by combining the heart rate and physical activity data from these devices, we can construct a persons heart rate profile. Here we present an open source R package CardiacProfileR for extraction, analysis and visualisation of heart rate dynamics during physical activities from data generated from common wearable heart rate monitors. This package represents a step towards quantitative deep phenotyping in humans. CardiacProfileR is available via an MIT license at https://github.com/VCCRI/CardiacProfileR.

bioinformatics↗

bioRxiv2018

C3: An R package for cross-species compendium-based cell-type identification

Cell type identification from an unknown sample can often be done by comparing its gene expression profile against a gene expression database containing profiles of a large number of cell-types. This type of compendium-based cell-type identification strategy is particularly successful for human and mouse samples because a large volume of data exists for these organisms. However, such rich data repositories often do not exist for most non-model organisms. This makes transcriptome-based sample classification in these species challenging. We propose to overcome this challenge by performing a cross-species compendium comparison. The key is to utilise a recently published cross-species gene set analysis (XGSA) framework to correct for biases that may arise due to potentially complex homologous gene mapping between two species. The framework is implemented as an open source R package called C3. We have evaluated the performance of C3 using a variety of public data in NCBI Gene Expression Omnibus. We also compared the functionality and performance of C3 against some similar gene expression profile matching tools. Our evaluation shows that C3 is a simple and effective method for cell type identification. C3 is available at https://github.com/VCCRI/C3.

bioinformatics↗

bioRxiv2017

GEOracle: Mining perturbation experiments using free text metadata in Gene Expression Omnibus

There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBIs Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEOs free text metadata to annotate the experimental type and sample type, this database remains di[ffi]cult to harness computationally without significant manual intervention.\n\nIn this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network.\n\nGEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.

bioinformatics↗