Identification and validation of hub genes involved in foam cell formation and atherosclerosis development via bioinformatics

Background

Foam cells play crucial roles in all phases of atherosclerosis. However, until now, the specific mechanisms by which these foam cells contribute to atherosclerosis remain unclear. We aimed to identify novel foam cell biomarkers and interventional targets for atherosclerosis, characterizing their potential mechanisms in the progression of atherosclerosis.

Methods

Microarray data of atherosclerosis and foam cells were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expression genes (DEGs) were screened using the "LIMMA" package in R software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were both carried out. Hub genes were found in Cytoscape after a protein-protein interaction (PPI) enrichment analysis was carried out. Validation of important genes in the GSE41571 dataset, cellular assays, and tissue samples.

Results

A total of 407 DEGs in atherosclerosis and 219 DEGs in foam cells were identified, and the DEGs in atherosclerosis were mainly involved in cell proliferation and differentiation. CSF1R and PLAUR were identified as common hub genes and validated in GSE41571. In addition, we also found that the expression of CSF1R and PLAUR gradually increased with the accumulation of lipids and disease progression in cell and tissue experiments.

Conclusion

CSF1R and PLAUR are key hub genes of foam cells and may play an important role in the biological process of atherosclerosis. These results advance our understanding of the mechanism behind atherosclerosis and potential therapeutic targets for future development.

Simulating intervertebral disc cell behaviour within 3D multifactorial environments

MOTIVATION

Low back pain is responsible for more global disability than any other condition. Its incidence is closely related to intervertebral disc (IVD) failure, which is likely caused by an accumulation of microtrauma within the IVD. Crucial factors in microtrauma development are not entirely known yet, probably because their exploration in vivo or in vitro remains tremendously challenging. In silico modelling is, therefore, definitively appealing, and shall include approaches to integrate influences of multiple cell stimuli at the microscale. Accordingly, this study introduces a hybrid Agent-based (AB) model in IVD research and exploits network modelling solutions in systems biology to mimic the cellular behaviour of Nucleus Pulposus cells exposed to a 3D multifactorial biochemical environment, based on mathematical integrations of existing experimental knowledge. Cellular activity reflected by mRNA expression of Aggrecan, Collagen type I, Collagen type II, MMP-3 and ADAMTS were calculated for inflamed and non-inflamed cells. mRNA expression over long periods of time is additionally determined including cell viability estimations. Model predictions were eventually validated with independent experimental data.

RESULTS

As it combines experimental data to simulate cell behaviour exposed to a multifactorial environment, the present methodology was able to reproduce cell death within 3 days under glucose deprivation and a 50% decrease in cell viability after 7 days in an acidic environment. Cellular mRNA expression under non-inflamed conditions simulated a quantifiable catabolic shift under an adverse cell environment, and model predictions of mRNA expression of inflamed cells provide new explanation possibilities for unexpected results achieved in experimental research.

AVAILABILITYAND IMPLEMENTATION

The AB model as well as used mathematical functions were built with open source software. Final functions implemented in the AB model and complete AB model parameters are provided as Supplementary Material. Experimental input and validation data were provided through referenced, published papers. The code corresponding to the model can be shared upon request and shall be reused after proper training.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of genes and underlying mechanisms involved in foam cells formation and atherosclerosis development

Background

Foam cells (FCs) play crucial roles in the process of all stages of atherosclerosis. Smooth muscle cells (SMCs) and macrophages are the major sources of FCs. This study aimed to identify the common molecular mechanism in these two types of FCs.

Methods

GSE28829, GSE43292, GSE68021, and GSE54666 were included to identify the differentially expressed genes (DEGs) associated with FCs derived from SMCs and macrophages. Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by using the DAVID database. The co-regulated genes associated with the two origins of FCs were validated (GSE9874), and their expression in vulnerable atherosclerosis plaques (GSE120521 and GSE41571) was assessed.

Results

A total of 432 genes associated with FCs derived from SMCs (SMC-FCs) and 81 genes associated with FCs derived from macrophages (M-FCs) were identified, and they were mainly involved in lipid metabolism, inflammation, cell cycle/apoptosis. Furthermore, three co-regulated genes associated with FCs were identified: GLRX, RNF13, and ABCA1. These three common genes showed an increased tendency in unstable or ruptured plaques, although in some cases, no statistically significant difference was found.

Conclusions

DEGs related to FCs derived from SMCs and macrophages have contributed to the understanding of the molecular mechanism underlying the formation of FCs and atherosclerosis. GLRX, RNF13, and ABCA1 might be potential targets for atherosclerosis treatment.

IRF-1 contributes to the pathological phenotype of VSMCs during atherogenesis by increasing CCL19 transcription

Atherosclerosis (AS) is a chronic inflammatory disease that mainly involves the large and middle arteries, but the specific mechanism is not precise. Chemokine ligand 19 (CCL19) has been reported highly expressed in peripheral blood of patients with atherosclerosis, but its role lacks explicit data. By ELISA assay and immunohistochemical (IHC) analysis, we found that the CCL19 was significantly up-regulated in AS. Therefore, we tried to clarify whether CCL19 expression was related to the progression of AS. QRT-PCR and western blot demonstrated that overexpression of CCL19 promoted the secretion of inflammatory factors and the deposition of the extracellular matrix, and facilitated the proliferation and migration of VSMCS. Besides, knockdown of CCL19 reduced the inflammation, collagen secretion, proliferation and migration of VSMCS induced by PGDF-BB. The results of database analysis, chromatin immunoprecipitation (ChIP) and luciferase assay showed that interferon regulatory factor 1 (IRF-1) activated the expression of CCL19 at the transcriptional level. Importantly, silencing IRF-1 inhibited atherosclerosis in high-fat-fed mice, inhibited the proliferation and migration of VSMCS, and down-regulated the expression of CCL19. Summing up, the results demonstrated that IRF-1 contributed to the pathological phenotype of VSMCs during atherogenesis by increasing CCL19 transcription.

Using deep reinforcement learning to speed up collective cell migration

BACKGROUND

Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration.

RESULTS

Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems.

CONCLUSION

Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

Deep reinforcement learning of cell movement in the early stage of C.elegans embryogenesis

Motivation

Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images.

Results

We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C.elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore the large datasets generated by live imaging.

Availability and implementation

Source code is available at https://github.com/zwang84/drl4cellmovement.

Supplementary information

Supplementary data are available at Bioinformatics online.

An agent-based model of leukocyte transendothelial migration during atherogenesis

A vast amount of work has been dedicated to the effects of hemodynamics and cytokines on leukocyte adhesion and trans-endothelial migration (TEM) and subsequent accumulation of leukocyte-derived foam cells in the artery wall. However, a comprehensive mechanobiological model to capture these spatiotemporal events and predict the growth and remodeling of an atherosclerotic artery is still lacking. Here, we present a multiscale model of leukocyte TEM and plaque evolution in the left anterior descending (LAD) coronary artery. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). In this computational framework, the ABM implements the diffusion kinetics of key biological proteins, namely Low Density Lipoprotein (LDL), Tissue Necrosis Factor alpha (TNF-α), Interlukin-10 (IL-10) and Interlukin-1 beta (IL-1β), to predict chemotactic driven leukocyte migration into and within the artery wall. The ABM also considers wall shear stress (WSS) dependent leukocyte TEM and compensatory arterial remodeling obeying Glagov's phenomenon. Interestingly, using fully developed steady blood flow does not result in a representative number of leukocyte TEM as compared to pulsatile flow, whereas passing WSS at peak systole of the pulsatile flow waveform does. Moreover, using the model, we have found leukocyte TEM increases monotonically with decreases in luminal volume. At critical plaque shapes the WSS changes rapidly resulting in sudden increases in leukocyte TEM suggesting lumen volumes that will give rise to rapid plaque growth rates if left untreated. Overall this multi-scale and multi-physics approach appropriately captures and integrates the spatiotemporal events occurring at the cellular level in order to predict leukocyte transmigration and plaque evolution.