scholarly journals Strain-based and sex-biased differences in adrenal and pancreatic gene expression between KK/HlJ and C57BL/6 J mice

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Angela Inglis ◽  
Rosario Ubungen ◽  
Sarah Farooq ◽  
Princess Mata ◽  
Jennifer Thiam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Beta Cells ◽  
Small Animal ◽  
Secretory Vesicle ◽  
Serum Markers ◽  
Mouse Serum ◽  
Significance Level ◽  
Biologically Relevant ◽  
The Metabolic Syndrome ◽  
Insulin Tolerance

Abstract Background The ever-increasing prevalence of diabetes and associated comorbidities serves to highlight the necessity of biologically relevant small-animal models to investigate its etiology, pathology and treatment. Although the C57BL/6 J model is amongst the most widely used mouse model due to its susceptibility to diet-induced obesity (DIO), there are a number of limitations namely [1] that unambiguous fasting hyperglycemia can only be achieved via dietary manipulation and/or chemical ablation of the pancreatic beta cells. [2] Heterogeneity in the obesogenic effects of hypercaloric feeding has been noted, together with sex-dependent differences, with males being more responsive. The KK mouse strain has been used to study aspects of the metabolic syndrome and prediabetes. We recently conducted a study which characterized the differences in male and female glucocentric parameters between the KK/HlJ and C57BL/6 J strains as well as diabetes-related behavioral differences (Inglis et al. 2019). In the present study, we further characterize these models by examining strain- and sex-dependent differences in pancreatic and adrenal gene expression using Affymetrix microarray together with endocrine-associated serum analysis. Results In addition to strain-associated differences in insulin tolerance, we found significant elevations in KK/HlJ mouse serum leptin, insulin and aldosterone. Additionally, glucagon and corticosterone were elevated in female mice of both strains. Using 2-factor ANOVA and a significance level set at 0.05, we identified 10,269 pancreatic and 10,338 adrenal genes with an intensity cut-off of ≥2.0 for all 4 experimental groups. In the pancreas, gene expression upregulated in the KK/HlJ strain related to increased insulin secretory granule biofunction and pancreatic hyperplasia, whereas ontology of upregulated adrenal differentially expressed genes (DEGs) related to cell signaling and neurotransmission. We established a network of functionally related DEGs commonly upregulated in both endocrine tissues of KK/HlJ mice which included the genes coding for endocrine secretory vesicle biogenesis and regulation: PCSK2, PCSK1N, SCG5, PTPRN, CHGB and APLP1. We also identified genes with sex-biased expression common to both strains and tissues including the paternally expressed imprint gene neuronatin. Conclusion Our novel results have further characterized the commonalities and diversities of pancreatic and adrenal gene expression between the KK/HlJ and C57BL/6 J strains as well as differences in serum markers of endocrine physiology.

scholarly journals O18 Integrated molecular analysis of systemic sclerosis skin and blood shows significant differences between major autoantibody subgroups

Rheumatology ◽  
2021 ◽  
Vol 60 (Supplement_1) ◽  
Author(s):  
Kristina E Clark ◽  
Corrado Campochiaro ◽  
Eszter Csomor ◽  
Adam Taylor ◽  
Katherine Nevin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Sclerosis ◽  
Whole Blood ◽  
Serum Markers ◽  
Differentially Expressed ◽  
Longitudinal Change ◽  
Beta Catenin ◽  
Skin Score ◽  
Topoisomerase 1 ◽  
Organ Manifestations

Abstract Background/Aims  The major antinuclear autoantibodies of systemic sclerosis (SSc) associate with different skin score trajectories and risk of internal organ manifestations. To elucidate molecular differences between ANA-defined subgroups, we utilised the prospective BIOPSY cohort of well-characterised SSc patients. Methods  The prospectively collected BIOPSY cohort recruited 52 SSc patients (21 early dcSSc, 15 established dcSSc, 16 lcSSc) and 16 healthy controls (HC). 36 (69%) of the SSc patients are female. Mean disease duration in the early dcSSc cohort was 24 months (sd 12 months), and in established dcSSc was 11.3 years. ANA frequency in BIOPSY reflected the overall dcSSc population: anti-topoisomerase-1 (ATA) n = 14 (27%), anti-RNA pol III (ARA) n = 12 (23%) and other n = 26 (50%). Mean baseline skin score (MRSS) for early dcSSc was 21 (sd 11.2). At a group level mRSS peak was 21.9 (11.8) at 3 months and fell to 19.1(10.5) at 12 months. Serum biomarkers of ECM turnover and fibrosis were measured three monthly and genome-wide transcriptomic profiling of whole skin and whole blood performed by RNA-Seq. Statistical analysis used RStudio with ANOVA, and Tukey post-hoc test. Differential gene expression used the Bioconductor limma software, with standard thresholds for significance. Results  At baseline, there were differences in soluble markers between clinical SSc sugroups and HC but not for major ANA subgroups. However, we found clear differences in early dcSSc analysed by major ANA subset for longitudinal change in serum markers of fibrosis and in whole skin gene expression, suggesting a mechanistic basis for the distinct clinical phenotypes associated with hallmark ANAs. During follow-up, significant differences were observed in HA, TIMP1, and PIIINP at 6 and 12 months (p < 0.05), with stable levels in ATA+ patients compared to progressively increased levels in the other subgroups. There were 564 significantly differentially expressed genes in skin between early dcSSc and HC. Unsupervised clustering differentiated patients with ARA and ATA positivity with early dcSSc. 54 genes were significantly differentially expressed in skin between ATA and ARA patients. Whilst 179 genes were differentially expressed in whole blood between early dcSSc compared with HC, no genes could significantly differentiate ATA from ARA. Functional analysis using HALLMARK pathway analysis identified both shared pathways associated with SSc across ANA groups (e.g. TGF beta signaling, IL6 JAK STAT3 signalling, inflammatory response), and pathways only upregulated in patients with ATA (e.g. Wnt beta catenin signaling, Notch signaling), and ARA (e.g. interferon gamma response, adipogenesis). Conclusion  We have found significant differences in skin gene expression and longitudinal change in serum markers by autoantibody specificity in dcSSc. Our findings have implications for SSc pathogenesis and support stratification by ANA subgroup in clinical studies. Disclosure  K.E. Clark: None. C. Campochiaro: None. E. Csomor: Corporate appointments; employee of GSK. A. Taylor: Corporate appointments; employee of GSK. K. Nevin: Corporate appointments; employee of GSK. N. Galwey: Corporate appointments; employee of GSK. M.A. Morse: Corporate appointments; employee of GSK. V.H. Ong: None. E. Derrett-Smith: None. N. Wisniacki: Corporate appointments; employee of GSK. S. Flint: Corporate appointments; employee of GSK. C.P. Denton: Consultancies; Actelion, GlaxoSmithKline, Bayer, Sanofi, lnventiva, Boehringer Ingelheim, Roche, Bristol Myers Squibb, CSL Behring, UCB, Leadiant Biosciences, Corbus, Servier, Arxx Therapeutics.

scholarly journals Improvement of Cardiac Fibrosis Biomarkers through Inflammation Inhibition by Green Tea and Decaffeinated Light Roasted Green Coffee Extract Combination Administration in Metabolic Syndrome Rat Model

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1013
Author(s):  
Mifetika Lukitasari ◽  
Mohammad Saifur Rohman ◽  
Dwi Adi Nugroho ◽  
Nila Aisyah Wahyuni ◽  
Mukhamad Nur Kholis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Syndrome ◽  
Green Tea ◽  
Rat Model ◽  
Cardiac Fibrosis ◽  
Significant Risk ◽  
Green Coffee ◽  
The Metabolic Syndrome ◽  
Tnf Α ◽  
Tgf Β1

Background: Metabolic syndrome is a significant risk factor for cardiovascular diseases. Green tea and green coffee extracts, antioxidant and anti-inflammatory agents may participate in metabolic syndrome-induced cardiac fibrosis alleviation. However, the effect of combination of those extracts still needs exploration. Therefore, this study investigated the effect of green tea and decaffeinated light roasted green coffee extracts and their combination in metabolic syndrome-induced cardiac fibrosis rats. Methods: Metabolic syndrome rat model was i1nduced through high-fat high sucrose diets feeding for 8 weeks and injection of low dose streptozotocin at the 2nd week. The metabolic syndrome rats were divided into 4 experimental groups metabolic syndrome rats (MS); metabolic syndrome rats treated with 300 mg/ kg b.w green tea extract (GT); metabolic syndrome rats treated with 200 mg/ kg b.w decaffeinated light roasted green coffee extract (GC); metabolic syndrome rats treated with the combination of the two extracts (CE); and a normal control (NC) group was added. Angiotensin 2 level was analyzed by ELISA method. Gene expression of NF-κB, TNF-α, IL-6, Tgf-β1, Rac-1, and α-sma were analyzed by touchdown polymerase chain reaction methods. Results: Metabolic syndrome rats treated with green tea and decaffeinated light roasted green coffee significantly decreased angiotensin-2 serum level and cardiac inflammation and fibrosis gene expression level (NF-κB, TNF-α, IL-6, Tgf-β1, Rac-1, and α-sma). More significant alleviation was observed in the combination group. Conclusion: This study suggested that combination of green tea and decaffeinated light roasted green coffee extracts showed better improvement in metabolic syndrome-induced cardiac fibrosis rat model compared to that of single extract administration through inflammation inhibition

scholarly journals Glucose controls co-translation of structurally related mRNAs via the mTOR and eIF2 pathways in human pancreatic beta cells

2021 ◽  
Author(s):  
Manuel Bulfoni ◽  
Costas Bouyioukos ◽  
Albatoul Zakaria ◽  
Fabienne Nigon ◽  
Roberta Rapone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Beta Cell ◽  
Beta Cells ◽  
Pancreatic Beta Cells ◽  
Gene Expression Regulation ◽  
Human Cells ◽  
Rodent Models ◽  
Polysome Profiling ◽  
Acute Increase

ABSTRACTPancreatic beta cell response to glucose is critical for the maintenance of normoglycemia. A strong transcriptional response was classically described in rodent models but, interestingly, not in human cells. In this study, we exposed human pancreatic beta cells to an increased concentration of glucose and analysed at a global level the mRNAs steady state levels and their translationalability. Polysome profiling analysis showed an early acute increase in protein synthesis and a specific translation regulation of more than 400 mRNAs, independently of their transcriptional regulation. We clustered the co-regulated mRNAs according to their behaviour in translation in response to glucose and discovered common structural and sequence mRNA features. Among them mTOR- and eIF2-sensitive elements have a predominant role to increase mostly the translation of mRNAs encoding for proteins of the translational machinery. Furthermore, we show that mTOR and eIF2α pathways are independently regulated in response to glucose, participating to a translational reshaping to adapt beta cell metabolism. The early acute increase in the translation machinery components prepare the beta cell for further protein demand due to glucose-mediated metabolism changes.AUTHOR SUMMARYAdaptation and response to glucose of pancreatic beta cells is critical for the maintenance of normoglycemia. Its deregulation is associated to Diabetic Mellitus (DM), a significant public health concern worldwide with an increased incidence of morbidity and mortality. Despite extensive research in rodent models, gene expression regulation in response to glucose remains largely unexplored in human cells. In our work, we have tackled this question by exposing human EndoC-BH1 cells to high glucose concentration. Using polysome profiling, the gold standard technique to analyse cellular translation activity, we observed a global protein synthesis increase, independent from transcription activity. Among the specific differentially translated mRNAs, we found transcripts coding for ribosomal proteins, allowing the cell machinery to be engaged in a metabolic response to glucose. Therefore, the regulation in response to glucose occurs mainly at the translational level in human cells, and not at the transcriptional level as described in the classically used rodent models.Furthermore, by comparing the features of the differentially translated mRNAs, and classifying them according to their translational response, we show that the early response to glucose occurs through the coupling of mRNA structure and sequence features impacting translation and regulation of specific signalling pathways. Collectively, our results support a new paradigm of gene expression regulation on the translation level in human beta cells.

Gene expression of type I phospholipase A2in pancreatic beta cells

FEBS Letters ◽  
1991 ◽  
Vol 295 (1-3) ◽  
pp. 110-112 ◽  
Author(s):  
S. Metz ◽  
D. Holmes ◽  
R.P. Robertson ◽  
W. Leitner ◽  
B. Draznin
Keyword(s):  
Gene Expression ◽  
Beta Cells ◽  
Pancreatic Beta Cells ◽  
Type I

Online monitoring of stimulus-induced gene expression in pancreatic beta-cells

Diabetes ◽  
2001 ◽  
Vol 50 (Supplement 1) ◽  
pp. S15-S19 ◽  
Author(s):  
T. Moede ◽  
B. Leibiger ◽  
P. Berggren ◽  
I. B. Leibiger
Keyword(s):  
Gene Expression ◽  
Beta Cells ◽  
Pancreatic Beta Cells ◽  
Online Monitoring

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals Co-expression networks for plant biology: why and how

2019 ◽  
Vol 51 (10) ◽  
pp. 981-988 ◽  
Author(s):  
Xiaolan Rao ◽  
Richard A Dixon
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Expression Analysis ◽  
Expression Profiling ◽  
Recent Literature ◽  
Genomic Data ◽  
Relevant Information ◽  
Plant Biology ◽  
Biologically Relevant

Abstract Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.

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