Abstract P146: Testicular Inflammation Is Associated With Immune Cell Infiltration And Lymphangiogenesis In L-NAME-induced Hypertension

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Shobana Navaneethabalakrishnan ◽  
Bethany L Goodlett ◽  
Brett M Mitchell

Hypertension (HTN) is associated with reduced fertility in men. Although numerous studies report that HTN disrupts hormonal balance in men, less is known about the direct effect of HTN on testes and how HTN influences testicular inflammation and lymphatics. We hypothesized that HTN increases testicular lymphatic vessel density and this is associated with immune cell infiltration and inflammation. Male mice (8 weeks old) were made hypertensive by providing them with L-arginine methyl ester hydrochloride (L-NAME) (0.5 mg/mL) in the drinking water for 3 weeks and control mice received tap water. Testes of hypertensive mice had a significant increase in gene expression of the lymphatic vessel markers Lyve-1 (17.7 ± 2.1 fold; p<0.05), Podoplanin (6.7 ± 1.2 fold; p<0.05), and Prox-1 (68.1 ± 10.6 fold; p<0.05), the lymphangiogenic growth factors VEGF-C (5.7 ± 1.1 fold; p<0.05), VEGF-D (2.2 ± 0.7 fold; p<0.05), and VEGF-A (5.8 ± 1.1 fold; p<0.05) and their receptors VEGFR-2 (8.0 ± 2.0 fold; p<0.05) and VEGFR-3 (25.4 ± 3.5 fold; p<0.05). There was also a significant increase in the expression of the pro-inflammatory cytokines TNF-a (24.0 ± 6.8 fold; p<0.05), IFN-g (17.5 ± 3.0 fold; p<0.05), IL-1b (4.2 ± 1.2 fold; p<0.05), IL-6 (24.8 ± 13.0 fold; p<0.05), and IL-17 (4.4 ± 0.4 fold; p<0.05). There were also increases in the lymphatic endothelial cell-derived immune cell trafficking chemokines CCL21 (7.8 ± 1.7 fold; p<0.05) and CCL19 (9.0 ± 4.1 fold; p<0.05) and their receptor CCR7 (9.6 ± 3.2 fold; p<0.05), as well as the cell adhesion molecule ICAM (6.2 ± 1.0 fold; p<0.05) in testes of hypertensive mice. Flow cytometry analysis revealed an increased accumulation of F4/80+ macrophages in the testes from hypertensive mice. Together, these data demonstrate that HTN induces inflammation-associated lymphangiogenesis in testes, in association with immune cell infiltration. It is possible that increasing testicular lymphatics may reduce inflammation and improve reproductive function in hypertensive men.

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Yunlong Yang ◽  
Dechun Geng ◽  
Yonghua Wu

Background. Osteoporosis is characterized by low bone mass, deterioration of bone tissue structure, and susceptibility to fracture. New and more suitable therapeutic targets need to be discovered. Methods. We collected osteoporosis-related datasets (GSE56815, GSE99624, and GSE63446). The methylation markers were obtained by differential analysis. Degree, DMNC, MCC, and MNC plug-ins were used to screen the important methylation markers in PPI network, then enrichment analysis was performed. ROC curve was used to evaluate the diagnostic effect of osteoporosis. In addition, we evaluated the difference in immune cell infiltration between osteoporotic patients and control by ssGSEA. Finally, differential miRNAs in osteoporosis were used to predict the regulators of key methylation markers. Results. A total of 2351 differentially expressed genes and 5246 differentially methylated positions were obtained between osteoporotic patients and controls. We identified 19 methylation markers by PPI network. They were mainly involved in biological functions and signaling pathways such as apoptosis and immune inflammation. HIST1H3G, MAP3K5, NOP2, OXA1L, and ZFPM2 with higher AUC values were considered key methylation markers. There were significant differences in immune cell infiltration between osteoporotic patients and controls, especially dendritic cells and natural killer cells. The correlation between MAP3K5 and immune cells was high, and its differential expression was also validated by other two datasets. In addition, NOP2 was predicted to be regulated by differentially expressed hsa-miR-3130-5p. Conclusion. Our efforts aim to provide new methylation markers as therapeutic targets for osteoporosis to better treat osteoporosis in the future.


Author(s):  
Wenshi Liu ◽  
Dongdong Zheng ◽  
Wenjing Lv ◽  
Ying Hua ◽  
Rong Huang ◽  
...  

IntroductionThis study aimed to identify novel differentially co-expressed genes and to investigate the features of immune cell infiltration in PAH.Material and methodsThe GSE113439 and GSE117261 datasets were acquired from the Gene Expression Omnibus database. And the differentially expressed genes between PAH and control groups were identified based on the GSE117261 dataset. Weighted Gene Co-Expression Network Analysis (WGCNA) was adopted to analyze the pre-processed data. Functional enrichment analysis was then carried out to explore the biological functions of these genes modules. The differentially co-expressed key genes modules were in-depth verified by GEO2R analysis. The immune infiltration in PAH was investigated by Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT).ResultsWGCNA analysis found 15 differentially co-expressed genes modules, amongst which module blue indicated that it exhibited the strongest positive link to PAH, whereas module green presented the strongest negative association with PAH. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the genes in module blue were largely enriched in Lysosome, Complement, and coagulation cascades, and others, while the genes in module green were primarily enriched in the Chemokine signaling pathway, Platelet activation, etc. Integrin subunit alpha M (ITGAM) was identified as the differentially co-expressed key gene. Immune infiltration analysis by CIBERSORT showed that the differences between PAH and control groups or between PAH subgroups.ConclusionsITGAM was considered a promising biomarker to discriminate PAH from the control. Obvious differences were observed in immune infiltration between patients with PAH and normal groups.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Maxime De Laere ◽  
Carmelita Sousa ◽  
Megha Meena ◽  
Roeland Buckinx ◽  
Jean-Pierre Timmermans ◽  
...  

Many neuroinflammatory diseases are characterized by massive immune cell infiltration into the central nervous system. Identifying the underlying mechanisms could aid in the development of therapeutic strategies specifically interfering with inflammatory cell trafficking. To achieve this, we implemented and validated a blood–brain barrier (BBB) model to study chemokine secretion, chemokine transport, and leukocyte trafficking in vitro. In a coculture model consisting of a human cerebral microvascular endothelial cell line and human astrocytes, proinflammatory stimulation downregulated the expression of tight junction proteins, while the expression of adhesion molecules and chemokines was upregulated. Moreover, chemokine transport across BBB cocultures was upregulated, as evidenced by a significantly increased concentration of the inflammatory chemokine CCL3 at the luminal side following proinflammatory stimulation. CCL3 transport occurred independently of the chemokine receptors CCR1 and CCR5, albeit that migrated cells displayed increased expression of CCR1 and CCR5. However, overall leukocyte transmigration was reduced in inflammatory conditions, although higher numbers of leukocytes adhered to activated endothelial cells. Altogether, our findings demonstrate that prominent barrier activation following proinflammatory stimulation is insufficient to drive immune cell recruitment, suggesting that additional traffic cues are crucial to mediate the increased immune cell infiltration seen in vivo during neuroinflammation.


2016 ◽  
Vol 186 (8) ◽  
pp. 2193-2203 ◽  
Author(s):  
Epameinondas Gousopoulos ◽  
Steven T. Proulx ◽  
Jeannette Scholl ◽  
Maja Uecker ◽  
Michael Detmar

2015 ◽  
Vol 53 (12) ◽  
Author(s):  
AB Widera ◽  
L Pütter ◽  
S Leserer ◽  
G Campos ◽  
K Rochlitz ◽  
...  

Author(s):  
Lu Yuan ◽  
Xixi Wu ◽  
Longshan Zhang ◽  
Mi Yang ◽  
Xiaoqing Wang ◽  
...  

AbstractPulmonary surfactant protein A1 (SFTPA1) is a member of the C-type lectin subfamily that plays a critical role in maintaining lung tissue homeostasis and the innate immune response. SFTPA1 disruption can cause several acute or chronic lung diseases, including lung cancer. However, little research has been performed to associate SFTPA1 with immune cell infiltration and the response to immunotherapy in lung cancer. The findings of our study describe the SFTPA1 expression profile in multiple databases and was validated in BALB/c mice, human tumor tissues, and paired normal tissues using an immunohistochemistry assay. High SFTPA1 mRNA expression was associated with a favorable prognosis through a survival analysis in lung adenocarcinoma (LUAD) samples from TCGA. Further GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that SFTPA1 was involved in the toll-like receptor signaling pathway. An immune infiltration analysis clarified that high SFTPA1 expression was associated with an increased number of M1 macrophages, CD8+ T cells, memory activated CD4+ T cells, regulatory T cells, as well as a reduced number of M2 macrophages. Our clinical data suggest that SFTPA1 may serve as a biomarker for predicting a favorable response to immunotherapy for patients with LUAD. Collectively, our study extends the expression profile and potential regulatory pathways of SFTPA1 and may provide a potential biomarker for establishing novel preventive and therapeutic strategies for lung adenocarcinoma.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander J. Dwyer ◽  
Jacob M. Ritz ◽  
Jason S. Mitchell ◽  
Tijana Martinov ◽  
Mohannad Alkhatib ◽  
...  

AbstractThe notion that T cell insulitis increases as type 1 diabetes (T1D) develops is unsurprising, however, the quantitative analysis of CD4+ and CD8+ T cells within the islet mass is complex and limited with standard approaches. Optical microscopy is an important and widely used method to evaluate immune cell infiltration into pancreatic islets of Langerhans for the study of disease progression or therapeutic efficacy in murine T1D. However, the accuracy of this approach is often limited by subjective and potentially biased qualitative assessment of immune cell subsets. In addition, attempts at quantitative measurements require significant time for manual analysis and often involve sophisticated and expensive imaging software. In this study, we developed and illustrate here a streamlined analytical strategy for the rapid, automated and unbiased investigation of islet area and immune cell infiltration within (insulitis) and around (peri-insulitis) pancreatic islets. To this end, we demonstrate swift and accurate detection of islet borders by modeling cross-sectional islet areas with convex polygons (convex hulls) surrounding islet-associated insulin-producing β cell and glucagon-producing α cell fluorescent signals. To accomplish this, we used a macro produced with the freeware software ImageJ equipped with the Fiji Is Just ImageJ (FIJI) image processing package. Our image analysis procedure allows for direct quantification and statistical determination of islet area and infiltration in a reproducible manner, with location-specific data that more accurately reflect islet areas as insulitis proceeds throughout T1D. Using this approach, we quantified the islet area infiltrated with CD4+ and CD8+ T cells allowing statistical comparison between different age groups of non-obese diabetic (NOD) mice progressing towards T1D. We found significantly more CD4+ and CD8+ T cells infiltrating the convex hull-defined islet mass of 13-week-old non-diabetic and 17-week-old diabetic NOD mice compared to 4-week-old NOD mice. We also determined a significant and measurable loss of islet mass in mice that developed T1D. This approach will be helpful for the location-dependent quantitative calculation of islet mass and cellular infiltration during T1D pathogenesis and can be combined with other markers of inflammation or activation in future studies.


Bioengineered ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 3410-3425
Author(s):  
Xiangzhou Tan ◽  
Linfeng Mao ◽  
Changhao Huang ◽  
Weimin Yang ◽  
Jianping Guo ◽  
...  

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