Lysine 117 residue is essential for the function of the hepatocyte nuclear factor 1α in pancreas
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE232242 | GEO | 2023/11/23
REPOSITORIES: GEO
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Project description:Hepatocyte nuclear factor 1α (HNF1α) play essential roles in controlling development and metabolism, its mutations are clearly linked to the occurrence of maturity-onset diabetes of the young (MODY3) in human. 117 lysine to glutamic acid (E117) mutation in the HNF1α gene was clinically associated with MOYD3, but no functional data on this variant is available. Here, we generated a knock-in mouse model in which the lysine 117 is replaced with glutamic acid in the endogenous murine HNF1α locus. The HNF1αE117/E117 mice reproduced HNF1-null phenotypes including dwarfism, hepatic dysfunction, renal Fanconi syndrome, diabetes and die from a progressive wasting syndrome, but no hyperphenylalaninemia. Moreover, HNF1αE117/E117 mice shared similar hepatic gene expression profiles with HNF1 deficient mice. In vitro experiments showed that HNF1 mutants in which the lysine 117 residue were replaced by glutamic acid, arginine or glutamine residues, respectively, had a significant decreased in overall transactivation and DNA binding capacity. K117 mutants had no effect on its protein stabilities and nuclear localization, but significantly blocked its own association. Our results clearly demonstrate that the lysine 117 residue is essential for the function of the HNF1a, and prove important to identify the molecular basis for diseases associated with defects in HNF1α We then performed gene expression profiling analysis using data obtained from RNA-seq of liver tissue of 3 WT mice and 3 KI mice.
2023-01-01 | GSE207758 | GEO
Project description:Lysine 117 residue is essential for the function of the hepatocyte nuclear factor 1α in pancreas
| PRJNA971476 | ENA
Project description:Although hepatocyte-nuclear-factor-1α (Hnf1α) is crucial for pancreas and liver functions, it is believed to play a limited functional role for intestinal epithelial functions. The aim of this study was to assess the consequences of abrogating Hnf1α on the maintenance of adult small intestinal epithelial functions. Methodology/Principal Findings An Hnf1α knockout mouse model was used. Assessment of histological abnormalities, crypt epithelial cell proliferation, epithelial barrier, glucose transport and signalling pathways were measured in these animals. Changes in global gene expression were also analyzed. Mice lacking Hnf1α displayed increased crypt proliferation and intestinalomegaly as well as a disturbance of intestinal epithelial cell lineages production during adult life. This phenotype was associated with a decrease of the mucosal barrier function and lumen-to-blood glucose delivery. The mammalian target of rapamycin (mTOR) signalling pathway was found to be overly activated in the small intestine of adult Hnf1α mutant mice. The intestinal epithelium of Hnf1α null mice displayed a reduction of the enteroendocrine cell population. An impact was also observed on proper Paneth cell differentiation with abnormalities in the granule exocytosis pathway. Conclusions/Significance Together, these results unravel a functional role for Hnf1α in regulating adult intestinal growth and sustaining the functions of intestinal epithelial cell lineages. HNF1alpha was knocked out. A total of 3 control and 3 mutant littermate individuals were sacrificed at 4 months of age. The jejunum was harvested and total RNA was isolated from each individual. Each RNA sample was independently used to generate probes to screen Affymetrix chips.
2011-01-01 | E-GEOD-23040 | biostudies-arrayexpress
Project description:Lysine 117 Residue is Essential for the Function of the Hepatocyte nuclear factor 1α
| PRJNA857016 | ENA
Project description:Although hepatocyte-nuclear-factor-1α (Hnf1α) is crucial for pancreas and liver functions, it is believed to play a limited functional role for intestinal epithelial functions. The aim of this study was to assess the consequences of abrogating Hnf1α on the maintenance of adult small intestinal epithelial functions. Methodology/Principal Findings An Hnf1α knockout mouse model was used. Assessment of histological abnormalities, crypt epithelial cell proliferation, epithelial barrier, glucose transport and signalling pathways were measured in these animals. Changes in global gene expression were also analyzed. Mice lacking Hnf1α displayed increased crypt proliferation and intestinalomegaly as well as a disturbance of intestinal epithelial cell lineages production during adult life. This phenotype was associated with a decrease of the mucosal barrier function and lumen-to-blood glucose delivery. The mammalian target of rapamycin (mTOR) signalling pathway was found to be overly activated in the small intestine of adult Hnf1α mutant mice. The intestinal epithelium of Hnf1α null mice displayed a reduction of the enteroendocrine cell population. An impact was also observed on proper Paneth cell differentiation with abnormalities in the granule exocytosis pathway. Conclusions/Significance Together, these results unravel a functional role for Hnf1α in regulating adult intestinal growth and sustaining the functions of intestinal epithelial cell lineages.
2011-01-01 | GSE23040 | GEO
Project description:To determine the role of HNF1α on long noncoding RNAs (lncRNAs) and the overall mechanisms in hepatocellular carcinoma, we analyzed the expression profiles of lncRNAs and mRNAs in huh7 cells with overexpression or knockdown of HNF1α. Expression profile showed that there were 339 mRNAs and 22 intergenic lncRNAs positively regulated by HNF1α for more than 2 times. The up-regulated genes separated samples by comparing lenti-HNF1α group with lenti-ctrl group, with overlap the down-regulated genes separated in HNF1α knockdown groups were identified as the genes positived regulated by HNF1α. Expression of seven genes from these 22 intergenic lncRNAs was quantified in the same RNA samples by real-time PCR, confirming the regulatory effect of HNF1α on lncRNAs as well as the microarray analysis pattern.
2017-08-26 | GSE103128 | GEO
Project description:Lysine crotonylation (Kcr) is involved in plenty of activities in the human body. Various technologies have been developed for Kcr prediction. Sequence-based features are typically adopted in existing methods, in which only linearly neighboring amino acid composition was considered. However, modified Kcr sites are neighbored by not only the linear-neighboring amino acid but also those spatially surrounding residues around the target site. In this paper, we have used residue-residue contact as a new feature for Kcr prediction, in which features encoded with not only linearly surrounding residues but also those spatially nearby the target site. Then, the spatial-surrounding residue was used as a new scheme for feature encoding for the first time, named residue-residue composition (RRC) and residue-residue pair composition (RRPC), which were used in supervised learning classification for Kcr prediction. As the result suggests, RRC and RRPC have achieved the best performance of RRC at an accuracy of 0.77 and an area under curve (AUC) value of 0.78, RRPC at an accuracy of 0.74, and an AUC value of 0.80. In order to show that the spatial feature is of a competitively high significance as other sequence-based features, feature selection was carried on those sequence-based features together with feature RRPC. In addition, different ranges of the surrounding amino acid compositions' radii were used for comparison of the performance. After result assessment, RRC and RRPC features have shown competitively outstanding performance as others or in some cases even around 0.20 higher in accuracy or 0.3 higher in AUC values compared with sequence-based features.
| S-EPMC8764140 | biostudies-literature
Project description:HetxHet breeding pairs for the Gtf2i/Gtf2ird1 double mutants and Gtf2ird1 were set up for timed breedings. E13.5 embyros of WT, HET, and HOM mutants (n=3 for each genotype and each cross), were used for RNA-seq. Similar breedings were done for ChIP-seq. The ChIP-seq WT controls were E13.5 embyros from WT FVB/ANTJ x FVB/ANTJ and compared to HOM Gtf2i/Gtf2ird1 double mutatns and HOM Gtf2ird1 single mutants. There were n=3 WT and n=3 hom Gtf2ird1 single mutants for Gtf2ird1 ChIP-seq. There were n=4 WT and n=4 hom Gtf2i/Gtf2ird1 double mutants for Gtf2i ChIP-seq. Each genotype had the sample matched input control along with ChIP sample.
2020-11-11 | GSE138234 | GEO
Project description:Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent years, out of which, lysine phosphoglycerylation has emerged as one of the recent developments. The traditional methods of identifying phosphoglycerylated residues, which are experimental procedures such as mass spectrometry, have shown to be time-consuming and cost-inefficient, despite the abundance of proteins being sequenced in this post-genomic era. Due to these drawbacks, computational techniques are being sought to establish an effective identification system of phosphoglycerylated lysine residues. The development of a predictor for phosphoglycerylation prediction is not a first, but it is necessary as the latest predictor falls short in adequately detecting phosphoglycerylated and non-phosphoglycerylated lysine residues. In this work, we introduce a new predictor named RAM-PGK, which uses sequence-based information relating to amino acid residues to predict phosphoglycerylated and non-phosphoglycerylated sites. A benchmark dataset was employed for this purpose, which contained experimentally identified phosphoglycerylated and non-phosphoglycerylated lysine residues. From the dataset, we extracted the residue adjacency matrix pertaining to each lysine residue in the protein sequences and converted them into feature vectors, which is used to build the phosphoglycerylation predictor. RAM-PGK, which is based on sequential features and support vector machine classifiers, has shown a noteworthy improvement in terms of performance in comparison to some of the recent prediction methods. The performance metrics of the RAM-PGK predictor are: 0.5741 sensitivity, 0.6436 specificity, 0.0531 precision, 0.6414 accuracy, and 0.0824 Mathews correlation coefficient.
| S-EPMC7766696 | biostudies-literature