CD11b mediates hypertensive cardiac remodeling by regulating macrophage infiltration and polarization

PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach

Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.

Multi-platform omics sequencing dissects the atlas of plasma-derived exosomes in rats with or without depression-like behavior after traumatic spinal cord injury

BACKGROUND

Exosomes can penetrate the blood-brain barrier for material exchange between the peripheral and central nervous systems. Differences in exosome contents could explain the susceptibility of different individuals to depression-like behavior after traumatic spinal cord injury (TSCI).

METHODS

Hierarchical clustering was used to integrate multiple depression-related behavioral outcomes in sham and TSCI rats and ultimately identify non-depressed and depressed rats. The difference in plasma exosome contents between non-depressed and depressed rats after TSCI was assessed in 15 random subjects by performing plasma exosome transcriptomics, mass spectroscope-based proteomics, and non-targeted metabolomics analyses.

RESULTS

The results revealed that about 27.6% of the rats developed depression-like behavior after TSCI. Totally, 10 differential metabolites, 81 differentially expressed proteins (DEPs), 373 differentially expressed genes (DEGs), and 55 differentially expressed miRNAs (DEmiRNAs) were identified between non-depressed TSCI and sham rats. Meanwhile, 37 differential metabolites, 499 DEPs, 1361 DEGs, and 89 DEmiRNAs were identified between depressed and non-depressed TSCI rats. Enrichment analysis showed that the progression of depression-like behavior after TSCI may be related to amino acid metabolism disorder and dysfunction of multiple signaling pathways, including endocytosis, lipid and atherosclerosis, toll-like receptor, TNF, and PI3K-Akt pathway.

CONCLUSION

Overall, our study systematically revealed for the first time the differences in plasma exosome contents between non-depressed and depressed rats after TSCI, which will help broaden our understanding of the complex molecular mechanisms involved in brain functional recombination after TSCI.

ANGPTL3 accelerates atherosclerotic progression via direct regulation of M1 macrophage activation in plaque

INTRODUCTION

The N-terminal domain of angiopoietin-like protein 3 (ANGPTL3) inhibits lipoprotein lipase activity. Its C-terminal fibrinogen-like (FBN) domain is a ligand of macrophage integrin αvβ3.

OBJECTIVES

ANGPTL3 might home to plaque where it directly regulates macrophage function via integrin αvβ3 for atherosclerosis progression.

METHODS

Ldlr-/- mice on a high-fat diet and ApoE-/- mice on a chow diet were received adeno-associated virus (AAV)-mediated Angptl3 gene transfer and followed up for 12 weeks. ApoE-/- mice were injected AAV containing FLAG-tagged Angptl3 cDNA for tracing. Atherosclerotic features were compared between Angptl3-/-ApoE-/- mice and ApoE-/- littermates. THP-1 cells were exposed to 0 or 50 μg/ml ANGPTL3 FBN domain for 24 h to evaluate Toll-like receptor (TLR)4 expression using western blot analysis and circulating cytokine and chemokine profiles by the MILLIPLEX MAP assay. Phospho-proteomic profile was established in ANGPTL3-treated macrophages. Integrin β3 deficient THP-1 cells were obtained by sgRNAs targeting RGD sequence using Lentivirus-Cas9 system.

RESULTS

Angptl3 overexpression increased atherosclerotic progression and CD68+ macrophages in plaque (p < 0.05 for all). By immunostaining, FLAG+ cells were identified in plaque of gene transferred ApoE-/- mice. Fluorescent immunostaining detected co-localisation of Angptl3 and CD68 in plaque macrophages. Phospho-proteomic analysis revealed that Angptl3 induced phosphorylation of proteins that were involved in the IL-17 signalling pathway in THP-1 cells. In vitro, ANGPTL3 treatment increased the production of interleukin (IL)-1β and tumour necrosis factor-α in THP-1 cells (p < 0.05 for both). Exposure of ANGPTL3 to THP-1 cells induced Akt phosphorylation which was weakened in integrin β3 deficient ones. ANGPTL3 elevated TLR4 expression via Akt phosphorylation. In response to lipopolysaccharide, nuclear factor-κB activity was 2.2-fold higher in THP-1 cells pre-treated with ANGPTL3 than in untreated cells (p < 0.05).

CONCLUSIONS

Targeting ANGPTL3 could yield a dual benefit of lowering lipid levels in the blood and suppressing macrophage activation in plaque.

Counter-regulatory RAS peptides: new therapy targets for inflammation and fibrotic diseases?

The renin-angiotensin system (RAS) is an important cascade of enzymes and peptides that regulates blood pressure, volume, and electrolytes. Within this complex system of reactions, its counter-regulatory axis has attracted attention, which has been associated with the pathophysiology of inflammatory and fibrotic diseases. This review article analyzes the impact of different components of the counter-regulatory axis of the RAS on different pathologies. Of these peptides, Angiotensin-(1-7), angiotensin-(1-9) and alamandine have been evaluated in a wide variety of in vitro and in vivo studies, where not only they counteract the actions of the classical axis, but also exhibit independent anti-inflammatory and fibrotic actions when binding to specific receptors, mainly in heart, kidney, and lung. Other functional peptides are also addressed, which despite no reports associated with inflammation and fibrosis to date were found, they could represent a potential target of study. Furthermore, the association of agonists of the counter-regulatory axis is analyzed, highlighting their contribution to the modulation of the inflammatory response counteracting the development of fibrotic events. This article shows an overview of the importance of the RAS in the resolution of inflammatory and fibrotic diseases, offering an understanding of the individual components as potential treatments.

Mac-1 deficiency ameliorates pressure overloaded heart failure through inhibiting macrophage polarization and activation

Persistent pressure overload commonly leads to pathological cardiac hypertrophy and remodeling, ultimately leading to heart failure (HF). Cardiac remodeling is associated with the involvement of immune cells and the inflammatory response in pathogenesis. The macrophage-1 antigen (Mac-1) is specifically expressed on leukocytes and regulates their migration and polarization. Nonetheless, the involvement of Mac-1 in cardiac remodeling and HF caused by pressure overload has not been determined. The Mac-1-knockout (KO) and wild-type (WT) mice were subjected to transverse aortic constriction (TAC) for 6 weeks. Echocardiography and pressure-volume loop assessments were used to evaluate cardiac function, and cardiac remodeling and macrophage infiltration and polarization were estimated by histopathology and molecular techniques. The findings of our study demonstrated that Mac-1 expression was markedly increased in hearts subjected to TAC treatment. Moreover, compared with WT mice, Mac-1-KO mice exhibited dramatically ameliorated TAC-induced cardiac dysfunction, hypertrophy, fibrosis, oxidative stress and apoptosis. The potential positive impacts may be linked to the inhibition of macrophage infiltration and M1 polarization via reductions in NF-kB and STAT1 expression and upregulation of STAT6. In conclusion, this research reveals a new function of Mac-1 deficiency in reducing pathological cardiac remodeling and HF caused by pressure overload. Additionally, inhibiting Mac-1 could be a potential treatment option for patients with HF in a clinical setting.

ITGAM-mediated macrophages contribute to basement membrane damage in diabetic nephropathy and atherosclerosis

BACKGROUND

Diabetic nephropathy (DN) and atherosclerosis (AS) are prevalent and severe complications associated with diabetes, exhibiting lesions in the basement membrane, an essential component found within the glomerulus, tubules, and arteries. These lesions contribute significantly to the progression of both diseases, however, the precise underlying mechanisms, as well as any potential shared pathogenic processes between them, remain elusive.

METHODS

Our study analyzed transcriptomic profiles from DN and AS patients, sourced from the Gene Expression Omnibus database. A combination of integrated bioinformatics approaches and machine learning models were deployed to identify crucial genes connected to basement membrane lesions in both conditions. The role of integrin subunit alpha M (ITGAM) was further explored using immune infiltration analysis and genetic correlation studies. Single-cell sequencing analysis was employed to delineate the expression of ITGAM across different cell types within DN and AS tissues.

RESULTS

Our analyses identified ITGAM as a key gene involved in basement membrane alterations and revealed its primary expression within macrophages in both DN and AS. ITGAM was significantly correlated with tissue immune infiltration within these diseases. Furthermore, the expression of genes encoding core components of the basement membrane was influenced by the expression level of ITGAM.

CONCLUSION

Our findings suggest that macrophages may contribute to basement membrane lesions in DN and AS through the action of ITGAM. Moreover, therapeutic strategies that target ITGAM may offer potential avenues to mitigate basement membrane lesions in these two diabetes-related complications.

PerSEveML: A Web-Based Tool to Identify Persistent Biomarker Structure for Rare Events Using Integrative Machine Learning Approach

Omics datasets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these datasets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there remains a limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach1, we introduce PerSEveML, an interactive web-based that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.