scholarly journals Interferon-regulated genetic programs and JAK/STAT pathway activate the intronic promoter of the short ACE2 isoform in renal proximal tubules

2021 ◽  
Author(s):  
Jakub Jankowski ◽  
Hye Kyung Lee ◽  
Julia Wilflingseder ◽  
Lothar Hennighausen
Keyword(s):  
Gene Expression ◽  
Proximal Tubule ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Regulatory Elements ◽  
Rna Seq ◽  
Angiotensin Converting Enzyme 2 ◽  
Genome Wide ◽  
Cytokine Stimulation

SummaryRecently, a short, interferon-inducible isoform of Angiotensin-Converting Enzyme 2 (ACE2), dACE2 was identified. ACE2 is a SARS-Cov-2 receptor and changes in its renal expression have been linked to several human nephropathies. These changes were never analyzed in context of dACE2, as its expression was not investigated in the kidney. We used Human Primary Proximal Tubule (HPPT) cells to show genome-wide gene expression patterns after cytokine stimulation, with emphasis on the ACE2/dACE2 locus. Putative regulatory elements controlling dACE2 expression were identified using ChIP-seq and RNA-seq. qRT-PCR differentiating between ACE2 and dACE2 revealed 300- and 600-fold upregulation of dACE2 by IFNα and IFNβ, respectively, while full length ACE2 expression was almost unchanged. JAK inhibitor ruxolitinib ablated STAT1 and dACE2 expression after interferon treatment. Finally, with RNA-seq, we identified a set of genes, largely immune-related, induced by cytokine treatment. These gene expression profiles provide new insights into cytokine response of proximal tubule cells.

scholarly journals RNA-Seq Mouse Brain Regions Expression Data Analysis: Focus on ApoE Functional Network

2017 ◽  
Vol 14 (3) ◽  
Author(s):  
Vladimir N. Babenko ◽  
Dmitry A. Smagin ◽  
Natalia N. Kudryavtseva
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Close Association ◽  
Gene Expression Profiles ◽  
Brain Regions ◽  
Genomic Region ◽  
Specific Marker ◽  
Rna Seq ◽  
Neighboring Gene ◽  
Genome Wide

AbstractApoE expression status was proved to be a highly specific marker of energy metabolism rate in the brain. Along with its neighbor, Translocase of Outer Mitochondrial Membrane 40 kDa (TOMM40) which is involved in mitochondrial metabolism, the corresponding genomic region constitutes the neuroenergetic hotspot. Using RNA-Seq data from a murine model of chronic stress a significant positive expression coordination of seven neighboring genes in ApoE locus in five brain regions was observed. ApoE maintains one of the highest absolute expression values genome-wide, implying that ApoE can be the driver of the neighboring gene expression alteration observed under stressful loads. Notably, we revealed the highly statistically significant increase of ApoE expression in the hypothalamus of chronically aggressive (FDR < 0.007) and defeated (FDR < 0.001) mice compared to the control. Correlation analysis revealed a close association of ApoE and proopiomelanocortin (Pomc) gene expression profiles implying the putative neuroendocrine stress response background of ApoE expression elevation therein.

scholarly journals RNAlater and flash freezing storage methods nonrandomly influence observed gene expression in RNAseq experiments

2018 ◽  
Author(s):  
Courtney N. Passow ◽  
Thomas J. Y. Kono ◽  
Bethany A. Stahl ◽  
James B. Jaggard ◽  
Alex C. Keene ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liquid Nitrogen ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gc Content ◽  
Gene Expression Profiles ◽  
Expression Studies ◽  
Genome Wide ◽  
Differential Gene ◽  
Genome Wide Gene Expression

AbstractRNA-sequencing is a popular next-generation sequencing technique for assaying genome-wide gene expression profiles. Nonetheless, it is susceptible to biases that are introduced by sample handling prior gene expression measurements. Two of the most common methods for preserving samples in both field-based and laboratory conditions are submersion in RNAlater and flash freezing in liquid nitrogen. Flash freezing in liquid nitrogen can be impractical, particularly for field collections. RNAlater is a solution for stabilizing tissue for longer-term storage as it rapidly permeates tissue to protect cellular RNA. In this study, we assessed genome-wide expression patterns in 30 day old fry collected from the same brood at the same time point that were flash-frozen in liquid nitrogen and stored at −80°C or submerged and stored in RNAlater at room temperature, simulating conditions of fieldwork. We show that sample storage is a significant factor influencing observed differential gene expression. In particular, genes with elevated GC content exhibit higher observed expression levels in liquid nitrogen flash-freezing relative to RNAlater-storage. Further, genes with higher expression in RNAlater relative to liquid nitrogen experience disproportionate enrichment for functional categories, many of which are involved in RNA processing. This suggests that RNAlater may elicit a physiological response that has the potential to bias biological interpretations of expression studies. The biases introduced to observed gene expression arising from mimicking many field-based studies are substantial and should not be ignored.

scholarly journals Identifying Transcriptomic Correlates of Histology using Deep Learning

2020 ◽  
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

AbstractLinking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Identifying transcriptomic correlates of histology using deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242858
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Chromatin occupancy analysis reveals genome-wide GATA factor switching during hematopoiesis

Blood ◽  
2012 ◽  
Vol 119 (16) ◽  
pp. 3724-3733 ◽  
Author(s):  
Louis C. Doré ◽  
Timothy M. Chlon ◽  
Christopher D. Brown ◽  
Kevin P. White ◽  
John D. Crispino
Keyword(s):  
Gene Expression ◽  
Transcriptional Activation ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Regulatory Elements ◽  
Gata Factor ◽  
Related Factors ◽  
Genome Wide ◽  
A Genome

Abstract There are many examples of transcription factor families whose members control gene expression profiles of diverse cell types. However, the mechanism by which closely related factors occupy distinct regulatory elements and impart lineage specificity is largely undefined. Here we demonstrate on a genome wide scale that the hematopoietic GATA factors GATA-1 and GATA-2 bind overlapping sets of genes, often at distinct sites, as a means to differentially regulate target gene expression and to regulate the balance between proliferation and differentiation. We also reveal that the GATA switch, which entails a chromatin occupancy exchange between GATA2 and GATA1 in the course of differentiation, operates on more than one-third of GATA1 bound genes. The switch is equally likely to lead to transcriptional activation or repression; and in general, GATA1 and GATA2 act oppositely on switch target genes. In addition, we show that genomic regions co-occupied by GATA2 and the ETS factor ETS1 are strongly enriched for regions marked by H3K4me3 and occupied by Pol II. Finally, by comparing GATA1 occupancy in erythroid cells and megakaryocytes, we find that the presence of ETS factor motifs is a major discriminator of megakaryocyte versus red cell specification.

scholarly journals Genome-Wide Identification of Soybean ABC Transporters Relate to Aluminum Toxicity

2021 ◽  
Vol 22 (12) ◽  
pp. 6556
Author(s):  
Junjun Huang ◽  
Xiaoyu Li ◽  
Xin Chen ◽  
Yaru Guo ◽  
Weihong Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abc Transporters ◽  
Developmental Stages ◽  
Aluminum Toxicity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Purifying Selection ◽  
Biotic Stresses ◽  
Genome Wide ◽  
New Germplasm

ATP-binding cassette (ABC) transporter proteins are a gene super-family in plants and play vital roles in growth, development, and response to abiotic and biotic stresses. The ABC transporters have been identified in crop plants such as rice and buckwheat, but little is known about them in soybean. Soybean is an important oil crop and is one of the five major crops in the world. In this study, 255 ABC genes that putatively encode ABC transporters were identified from soybean through bioinformatics and then categorized into eight subfamilies, including 7 ABCAs, 52 ABCBs, 48 ABCCs, 5 ABCDs, 1 ABCEs, 10 ABCFs, 111 ABCGs, and 21 ABCIs. Their phylogenetic relationships, gene structure, and gene expression profiles were characterized. Segmental duplication was the main reason for the expansion of the GmABC genes. Ka/Ks analysis suggested that intense purifying selection was accompanied by the evolution of GmABC genes. The genome-wide collinearity of soybean with other species showed that GmABCs were relatively conserved and that collinear ABCs between species may have originated from the same ancestor. Gene expression analysis of GmABCs revealed the distinct expression pattern in different tissues and diverse developmental stages. The candidate genes GmABCB23, GmABCB25, GmABCB48, GmABCB52, GmABCI1, GmABCI5, and GmABCI13 were responsive to Al toxicity. This work on the GmABC gene family provides useful information for future studies on ABC transporters in soybean and potential targets for the cultivation of new germplasm resources of aluminum-tolerant soybean.

scholarly journals Genome-wide characterization of MATE gene family and expression profiles in response to abiotic stresses in rice (Oryza sativa)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhixuan Du ◽  
Qitao Su ◽  
Zheng Wu ◽  
Zhou Huang ◽  
Jianzhong Bao ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Gene Family ◽  
Tandem Repeats ◽  
Expression Profiles ◽  
Functional Divergence ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Genome Wide ◽  
Comprehensive Information ◽  
Family Gene

AbstractMultidrug and toxic compound extrusion (MATE) proteins are involved in many physiological functions of plant growth and development. Although an increasing number of MATE proteins have been identified, the understanding of MATE proteins is still very limited in rice. In this study, 46 MATE proteins were identified from the rice (Oryza sativa) genome by homology searches and domain prediction. The rice MATE family was divided into four subfamilies based on the phylogenetic tree. Tandem repeats and fragment replication contribute to the expansion of the rice MATE gene family. Gene structure and cis-regulatory elements reveal the potential functions of MATE genes. Analysis of gene expression showed that most of MATE genes were constitutively expressed and the expression patterns of genes in different tissues were analyzed using RNA-seq. Furthermore, qRT-PCR-based analysis showed differential expression patterns in response to salt and drought stress. The analysis results of this study provide comprehensive information on the MATE gene family in rice and will aid in understanding the functional divergence of MATE genes.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

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