Comparative analysis of expression profiles of chemokines, chemokine receptors, and components of signaling pathways mediated by chemokines in eight cell types during rat liver regeneration

Genome ◽  
2010 ◽  
Vol 53 (8) ◽  
pp. 608-618 ◽  
Author(s):  
Xiaoguang Chen ◽  
Cunshuan Xu ◽  
Fuchun Zhang ◽  
Ji Ma
Keyword(s):  
Liver Regeneration ◽  
Signaling Pathways ◽  
Expression Profiles ◽  
Gradient Centrifugation ◽  
Cell Types ◽  
Cellular Level ◽  
Pcr Analysis ◽  
Immunomagnetic Bead ◽  
Percoll Density Gradient Centrifugation ◽  
Chemokine Signaling

It has been documented that chemokines can positively regulate liver regeneration at the tissue level after partial hepatectomy. However, the precise mechanism of the effects of chemokines on regeneration at the cellular level remains poorly defined. In this study, 8 cell types from rat regenerating liver at 8 recovery time points after 2/3 hepatectomy were isolated and purified using Percoll density gradient centrifugation and immunomagnetic bead methods. The expression profiles of each cell type were monitored using a microarray. RT-PCR analysis was performed to validate the reliability of the microarray results. The results showed that, on the whole, the expression profiles of chemokine and receptor genes varied among different cell types; most genes involved in chemokine signaling pathways showed an increase in expression across the 8 liver cell types during liver regeneration. The implication of these genes in regeneration was analyzed by bioinformatics and systems biology methods. According to the microarray results and gene synergy, activation of chemokine signaling pathways at 24 h in biliary epithelial cells and at 2–12 h in dendritic cells may be triggered by CCL2–CCR2 and CCL7–CCR3, respectively; activation of Plc/Pkc and Pi3k/Akt pathways at 2–12 h in sinusoidal endothelial cells might be caused by CCL7–CCR1; and activation of the Src/Ptk, Src/Vav, and Plc/Pkc pathways at the priming stage may be related to the inductive effect of CCL7. These data suggest the potential relevance of the pro-inflammatory chemokines for liver regeneration at the cellular level.

Expression profiles uncover the correlation of OPN signaling pathways with rat liver regeneration at cellular level

2015 ◽  
Vol 39 (11) ◽  
pp. 1329-1340 ◽  
Author(s):  
Gaiping Wang ◽  
Xiaofang Li ◽  
Shasha Chen ◽  
Weiming Zhao ◽  
Jing Yang ◽  
...  
Keyword(s):  
Liver Regeneration ◽  
Signaling Pathways ◽  
Rat Liver ◽  
Expression Profiles ◽  
Cellular Level ◽  
Rat Liver Regeneration

Oxidative stress in isolated rat renal proximal and distal tubular cells

1990 ◽  
Vol 259 (2) ◽  
pp. F338-F347 ◽  
Author(s):  
L. H. Lash ◽  
J. J. Tokarz
Keyword(s):  
Rat Kidney ◽  
Gradient Centrifugation ◽  
Buthionine Sulfoximine ◽  
Oxidative Injury ◽  
Cell Types ◽  
Protective Role ◽  
Cortical Cells ◽  
Percoll Density Gradient Centrifugation ◽  
Tert Butyl Hydroperoxide ◽  
Percoll Density Gradient

Suspensions of purified proximal tubular (PT) and distal tubular (DT) cells were isolated from rat kidney cortical cells by Percoll density-gradient centrifugation and were used to investigate susceptibility of these regions of the nephron to oxidative injury. Exposure to tert-butyl hydroperoxide (tBH), menadione (MD), or H2O2 produced significantly greater cytotoxicity, as assessed by leakage of lactate dehydrogenase, in DT cells than in PT cells. The order of cytotoxic potency in both cell types was MD greater than tBH greater than H2O2. Preincubation of PT and DT cells with 5 mM glutathione (GSH) or 5 mM dithiothreitol delayed tBH-induced cytotoxicity, indicating a protective role of GSH. Addition of buthionine sulfoximine and acivicin with GSH, to inhibit GSH synthesis and degradation, eliminated the protective effect of GSH, indicating that protection by GSH in DT cells is not dependent on uptake of the intact tripeptide. Incubation of both PT and DT cells with tBH resulted in oxidation of GSH to glutathione disulfide. Activities of five detoxication enzymes were significantly higher in PT cells, indicating that a diminished ability to detoxify reactive metabolites may contribute to the higher intrinsic susceptibility of DT cells to oxidative injury.

Gene expression profiles predict the possible regulatory role of OPN-mediated signaling pathways in rat liver regeneration

Gene ◽  
2016 ◽  
Vol 576 (2) ◽  
pp. 782-790 ◽  
Author(s):  
Gaiping Wang ◽  
Shasha Chen ◽  
Congcong Zhao ◽  
Xiaofang Li ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Regeneration ◽  
Signaling Pathways ◽  
Rat Liver ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Regulatory Role ◽  
Rat Liver Regeneration

scholarly journals Update on the Mechanisms of Liver Regeneration

2017 ◽  
Vol 37 (02) ◽  
pp. 141-151 ◽  
Author(s):  
Morgan Preziosi ◽  
Satdarshan Monga
Keyword(s):  
Cell Proliferation ◽  
Liver Regeneration ◽  
Signaling Pathways ◽  
Surgical Resection ◽  
Molecular Basis ◽  
Cell Types ◽  
Hepatic Cell ◽  
Restoration Process

AbstractLiver possesses many critical functions such as synthesis, detoxification, and metabolism. It continually receives nutrient-rich blood from gut, which incidentally is also toxin-rich. That may be why liver is uniquely bestowed with a capacity to regenerate. A commonly studied procedure to understand the cellular and molecular basis of liver regeneration is that of surgical resection. Removal of two-thirds of the liver in rodents or patients instigates alterations in hepatic homeostasis, which are sensed by the deficient organ to drive the restoration process. Although the exact mechanisms that initiate regeneration are unknown, alterations in hemodynamics and metabolism have been suspected as important effectors. Key signaling pathways are activated that drive cell proliferation in various hepatic cell types through autocrine and paracrine mechanisms. Once the prehepatectomy mass is regained, the process of regeneration is adequately terminated. This review highlights recent discoveries in the cellular and molecular basis of liver regeneration.

scholarly journals SC2disease: a manually curated database of single-cell transcriptome for human diseases

2020 ◽  
Vol 49 (D1) ◽  
pp. D1413-D1419 ◽  
Author(s):  
Tianyi Zhao ◽  
Shuxuan Lyu ◽  
Guilin Lu ◽  
Liran Juan ◽  
Xi Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cellular Level ◽  
Human Diseases ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

scholarly journals Expression profiles uncover the relationship between erythropoietin and cell proliferation in rat hepatocytes after a partial hepatectomy

2014 ◽  
Vol 19 (3) ◽  
Author(s):  
Jihong Zhang ◽  
Yajuan Yang ◽  
Tingting He ◽  
Yunqing Liu ◽  
Yun Zhou ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Liver Regeneration ◽  
Signaling Pathway ◽  
Partial Hepatectomy ◽  
Rat Liver ◽  
Target Genes ◽  
Expression Profiles ◽  
Synergetic Effect ◽  
Rat Liver Regeneration ◽  
Percoll Density Gradient Centrifugation

AbstractErythropoietin (EPO) has a beneficial effect on hepatic cell proliferation during liver regeneration. However, the underlying mechanism has not yet been elucidated. To uncover the proliferation response of EPO in rat liver regeneration after partial hepatectomy (PH) at the cellular level, hepatocytes (HCs) were isolated using Percoll density gradient centrifugation. The genes of the EPO-mediated signaling pathway and the target genes of the transcription factor (TF) in the pathway were identified in a pathway and TF database search. Their expression profiles were then detected using Rat Genome 230 2.0 Microarray. The results indicated that the EPO-mediated signaling pathway is involved in 19 paths and that 124 genes participate, of which 32 showed significant changes and could be identified as liver regeneration-related genes. In addition, 443 targets regulated by the TFs of the pathway and 60 genes associated with cell proliferation were contained in the array. Subsequently, the synergetic effect of these genes in liver regeneration was analyzed using the E(t) mathematical model based on their expression profiles. The results demonstrated that the E(t) values of paths 3, 8, 12 and 14–17 were significantly strengthened in the progressing phase of liver regeneration through the RAS/MEK/ERK or PI3K/AκT pathways. The synergetic effect of the target genes, in parallel with target-related cell proliferation, was also enhanced 12–72 h after PH, suggesting a potential positive effect of EPO on HC proliferation during rat liver regeneration. These data imply that the EPO receptor may allow EPO to promote HC proliferation through paths 3, 8, 12 and 14–17, mediating the RAS/MEK/ERK and PI3K/AκT pathways in rat liver regeneration after PH.

Flow cytometric identification and isolation of hypertrophic type II pneumocytes after partial pneumonectomy

1989 ◽  
Vol 257 (3) ◽  
pp. C528-C536 ◽  
Author(s):  
B. D. Uhal ◽  
S. R. Rannels ◽  
D. E. Rannels
Keyword(s):  
Gradient Centrifugation ◽  
Type Ii Cells ◽  
Type Ii ◽  
Acridine Orange Staining ◽  
Flow Cytometric ◽  
Percoll Density Gradient Centrifugation ◽  
Type Ii Pneumocytes ◽  
The Mean ◽  
Cellular Dna ◽  
The Right

Type II pneumocytes were isolated by either Percoll density gradient centrifugation or by immunoglobulin G (IgG) panning from the lungs of normal rats and the right lung of rats subjected to left pneumonectomy. Cells were studied at 7- (pnx-7) and 15- (pnx-15) days postoperative, times during and after, respectively, rapid compensatory growth of the right lung. Acridine orange staining permitted resolution of type II cells from contaminants on the basis of high red fluorescence (greater than 590 nm). Simultaneous measurement of forward-angle light scatter (FALS) suggested a shift of pnx-7 cells toward greater size, which was reversed in pnx-15 cells. By Percoll gradient isolation, approximately 15% of pnx-7 cells analyzed were above the mean FALS of control cells. In contrast, approximately 30% of the pnx-7 cells isolated by IgG panning were above the mean FALS of corresponding control cells. Biochemical analyses of pnx-7 cells separated by cell sorting into "high FALS" and "low FALS" subgroups revealed that high FALS type II cells contained 50% more protein (P less than 0.05) and 140% more RNA (P less than 0.01) than low FALS cells, with no significant change in cellular DNA content. These data are consistent with previous studies of type II cells isolated from the lungs of pneumonectomized animals and confirm the presence of hypertrophic cells in these preparations. They provide a foundation from which to design further flow cytometric studies of the role of hypertrophic type II pneumocytes in compensatory lung growth.

scholarly journals In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kolja Becker ◽  
Holger Klein ◽  
Eric Simon ◽  
Coralie Viollet ◽  
Christian Haslinger ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Vision Loss ◽  
Ganglion Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Disease Stages ◽  
Post Transcriptional Regulation

AbstractDiabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP–PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

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