Long-Living Budding Yeast Cell Subpopulation Induced by Ethanol/Acetate and Respiration

2019 ◽  
Vol 75 (8) ◽  
pp. 1448-1456 ◽  
Author(s):  
Young-Yon Kwon ◽  
Seung-Soo Kim ◽  
Han-Jun Lee ◽  
Seo-Hyeong Sheen ◽  
Kyoung Heon Kim ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Budding Yeast ◽  
Gradient Centrifugation ◽  
Carbon Sources ◽  
Cell Types ◽  
Glucose Sensing ◽  
Differentially Expressed ◽  
Cell Subpopulation ◽  
Genes Encoding ◽  
Cell Transcriptome

Abstract Budding yeast generate heterogeneous cells that can be separated into two distinctive cell types: short-living low-density and long-living high-density (HD) cells by density gradient centrifugation. We found that ethanol and acetate induce formation of HD cells, and mitochondrial respiration is required. From their transcriptomes and metabolomes, we found upregulated differentially expressed genes in HD cells involved in the RGT2/RGT1 glucose sensing pathway and its downstream genes encoding hexose transporters. For HD cells, we determined an abundance of various carbon sources including glucose, lactate, pyruvate, trehalose, mannitol, mannose, and galactose. Other upregulated differentially expressed genes in HD cells were involved in the TORC1–SCH9 signaling pathway and its downstream genes involved in cytoplasmic translation. We also measured an abundance of free amino acids in HD cells including valine, proline, isoleucine, and glutamine. These characteristics of the HD cell transcriptome and metabolome may be important conditions for maintaining a long-living phenotype.

scholarly journals Dynamics of Gene Expression Revealed by Comparison of Serial Analysis of Gene Expression Transcript Profiles from Yeast Grown on Two Different Carbon Sources

1999 ◽  
Vol 10 (6) ◽  
pp. 1859-1872 ◽  
Author(s):  
Arnoud J. Kal ◽  
Anton Jan van Zonneveld ◽  
Vladimir Benes ◽  
Marlene van den Berg ◽  
Marian Groot Koerkamp ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adaptive Response ◽  
Carbon Sources ◽  
Differentially Expressed ◽  
Mitochondrial Enzymes ◽  
A Genome ◽  
Redox Shuttles ◽  
Genes Encoding ◽  
Transcript Profiles ◽  
Yeast Genes

We describe a genome-wide characterization of mRNA transcript levels in yeast grown on the fatty acid oleate, determined using Serial Analysis of Gene Expression (SAGE). Comparison of this SAGE library with that reported for glucose grown cells revealed the dramatic adaptive response of yeast to a change in carbon source. A major fraction (>20%) of the 15,000 mRNA molecules in a yeast cell comprised differentially expressed transcripts, which were derived from only 2% of the total number of ∼6300 yeast genes. Most of the mRNAs that were differentially expressed code for enzymes or for other proteins participating in metabolism (e.g., metabolite transporters). In oleate-grown cells, this was exemplified by the huge increase of mRNAs encoding the peroxisomal β-oxidation enzymes required for degradation of fatty acids. The data provide evidence for the existence of redox shuttles across organellar membranes that involve peroxisomal, cytoplasmic, and mitochondrial enzymes. We also analyzed the mRNA profile of a mutant strain with deletions of the PIP2and OAF1 genes, encoding transcription factors required for induction of genes encoding peroxisomal proteins. Induction of genes under the immediate control of these factors was abolished; other genes were up-regulated, indicating an adaptive response to the changed metabolism imposed by the genetic impairment. We describe a statistical method for analysis of data obtained by SAGE.

scholarly journals Common and Distinctive Intercellular Communication Patterns in Human Obstructive and Nonobstructive Hypertrophic Cardiomyopathy

2021 ◽  
Author(s):  
Christina J. Codden ◽  
Michael T. Chin
Keyword(s):  
Extracellular Matrix ◽  
Hypertrophic Cardiomyopathy ◽  
Dendritic Cell ◽  
Differentially Expressed Genes ◽  
Intercellular Communication ◽  
Cell Communication ◽  
Cell Types ◽  
Ventricular Outflow Tract ◽  
Left Ventricular ◽  
Differentially Expressed

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder characterized by unexplained left ventricular hypertrophy, with or without left ventricular outflow tract (LVOT) obstruction. Single nuclei RNA-sequencing (snRNA-seq) of both obstructive and nonobstructive HCM patient samples have revealed alterations in communication between various cell types but a direct and integrated comparison between the two HCM phenotypes has not been reported. We performed a bioinformatic analysis of HCM snRNA-seq datasets from obstructive and nonobstructive patient samples to identify differentially expressed genes and distinctive patterns of intercellular communication. Differential gene expression analysis revealed 37 differentially expressed genes, predominantly in cardiomyocytes but also in other cell types, relevant to aging, muscle contraction, cell motility and the extracellular matrix. Intercellular communication was generally reduced in HCM, affecting the extracellular matrix, growth factor binding, integrin binding, PDGF binding and SMAD binding, but with increases in adenylate cyclase binding, calcium channel inhibitor activity, and serine-threonine kinase activity in nonobstructive HCM. Increases in neuron to leukocyte and dendritic cell communication, in fibroblast to leukocyte and dendritic cell communication and in endothelial cell communication to other cell types, largely through changes in expression of integrin-b1 and its cognate ligands, were also noted. These findings indicate both common and distinct physiological mechanisms affecting the pathogenesis of obstructive and nonobstructive HCM and provide opportunities for personalized management of different HCM phenotypes.

scholarly journals Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19

2020 ◽  
Author(s):  
Sudhir Ghandikota ◽  
Mihika Sharma ◽  
Anil G. Jegga
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Immune Cell ◽  
Gene Clusters ◽  
Cell Types ◽  
Host Protein ◽  
Differentially Expressed ◽  
Protein Interaction Data ◽  
Neuropsychiatric Diseases

ABSTRACTKnowledge about the molecular mechanisms driving COVID-19 pathophysiology and outcomes is still limited. To learn more about COVID-19 pathophysiology we performed secondary analyses of transcriptomic data from two in vitro (Calu-3 and Vero E6 cells) and one in vivo (Ad5-hACE2-sensitized mice) models of SARS-CoV-2 infection. We found 1467 conserved differentially expressed host genes (differentially expressed in at least two of the three model system transcriptomes compared) in SARS-CoV-2 infection. To find potential genetic factors associated with COVID-19, we analyzed these conserved differentially expressed genes using known human genotype-phenotype associations. Genome-wide association study enrichment analysis showed evidence of enrichment for GWA loci associated with platelet functions, blood pressure, body mass index, respiratory functions, and neurodegenerative and neuropsychiatric diseases, among others. Since human protein complexes are known to be directly related to viral infection, we combined and analyzed the conserved transcriptomic signature with SARS-CoV-2-host protein-protein interaction data and found more than 150 gene clusters. Of these, 29 clusters (with 5 or more genes in each cluster) had at least one gene encoding protein that interacts with SARS-CoV-2 proteome. These clusters were enriched for different cell types in lung including epithelial, endothelial, and immune cell types suggesting their pathophysiological relevancy to COVID-19. Finally, pathway analysis on the conserved differentially expressed genes and gene clusters showed alterations in several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals Cardiac endothelial cell transcriptome in neonatal, adult, and remodeling hearts

2019 ◽  
Vol 51 (6) ◽  
pp. 186-196 ◽  
Author(s):  
Zarha Vermeulen ◽  
Ligia Mateiu ◽  
Lindsey Dugaucquier ◽  
Gilles W. De Keulenaer ◽  
Vincent F. M. Segers
Keyword(s):  
Endothelial Cells ◽  
Endothelial Cell ◽  
Cardiac Remodeling ◽  
Cellular Response ◽  
Large Fraction ◽  
Cell Types ◽  
Differentially Expressed ◽  
Normal Adult ◽  
Limited Degree ◽  
Cell Transcriptome

Cardiac microvascular endothelial cells (CMVECs) are the most numerous cells in the myocardium and orchestrate cardiogenesis during development, regulate adult cardiac function, and modulate pathophysiology of heart failure. It has been shown that the transcriptome of CMVECs differs from other endothelial cell types, but transcriptomic changes in cardiac endothelial cells during cardiac maturation and cardiac remodeling have not been studied. CMVECs were isolated from rat hearts based on CD31 expression and were immediately processed for RNA sequencing. We compared gene expression levels from primary CMVECs of neonatal hearts, normal adult hearts, and infarcted hearts. Between neonatal and adult CMVECs, 6,838 genes were differentially expressed, indicating that CMVECs undergo a substantial transformation during postnatal cardiac growth. A large fraction of genes upregulated in neonatal CMVECs are part of mitosis pathways, whereas a large fraction of genes upregulated in adult CMVECs are part of cellular response, secretory, signaling, and cell adhesion pathways. Between CMVECs of normal adult hearts and infarcted hearts, 159 genes were differentially expressed. We found a limited degree of overlap (55 genes) between the differentially expressed genes in neonatal and infarcted-hearts. Of 46 significantly upregulated genes in the infarcted heart, 46% were also upregulated in neonatal hearts relative to sham. Of 113 significantly downregulated genes in the infarcted-hearts, 30% were also downregulated in neonatal hearts relative to sham. These data demonstrate that CMVECs undergo dramatic changes from neonatal to adult and more subtle changes between normal state and cardiac remodeling.

scholarly journals Divergence between motoneurons: gene expression profiling provides a molecular characterization of functionally discrete somatic and autonomic motoneurons

2006 ◽  
Vol 24 (3) ◽  
pp. 276-289 ◽  
Author(s):  
Dapeng Cui ◽  
Kimberly J. Dougherty ◽  
David W. Machacek ◽  
Michael Sawchuk ◽  
Shawn Hochman ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Cell Types ◽  
Differentially Expressed ◽  
Columnar Cell ◽  
Expression Change ◽  
Affymetrix Microarrays ◽  
Lateral Motor Column ◽  
Combinatorial Codes

Studies in the developing spinal cord suggest that different motoneuron (MN) cell types express very different genetic programs, but the degree to which adult programs differ is unknown. To compare genetic programs between adult MN columnar cell types, we used laser capture microdissection (LCM) and Affymetrix microarrays to create expression profiles for three columnar cell types: lateral and medial MNs from lumbar segments and sympathetic preganglionic motoneurons located in the thoracic intermediolateral nucleus. A comparison of the three expression profiles indicated that ∼7% (813/11,552) of the genes showed significant differences in their expression levels. The largest differences were observed between sympathetic preganglionic MNs and the lateral motor column, with 6% (706/11,552) of the genes being differentially expressed. Significant differences in expression were observed for 1.8% (207/11,552) of the genes when comparing sympathetic preganglionic MNs with the medial motor column. Lateral and medial MNs showed the least divergence, with 1.3% (150/11,552) of the genes being differentially expressed. These data indicate that the amount of divergence in expression profiles between identified columnar MNs does not strictly correlate with divergence of function as defined by innervation patterns (somatic/muscle vs. autonomic/viscera). Classification of the differentially expressed genes with regard to function showed that they underpin all fundamental cell systems and processes, although most differentially expressed genes encode proteins involved in signal transduction. Mining the expression profiles to examine transcription factors essential for MN development suggested that many of the same transcription factors participate in combinatorial codes in embryonic and adult neurons, but patterns of expression change significantly.

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Dustin J Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cost Constraints ◽  
Mouse Tissues

Abstract RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

scholarly journals Dissecting the Single-Cell Transcriptome Network in Superficial/Deep Tumor Tissues of Diffuse Gastric Cancer

2021 ◽  
Author(s):  
Yili Ren ◽  
Beibei Zhang ◽  
Chenkai Xu ◽  
Lei Zhang
Keyword(s):  
Gastric Cancer ◽  
Survival Analysis ◽  
Single Cell ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cell Subpopulation ◽  
Diffuse Gastric Cancer ◽  
Normal Tissues ◽  
Tumor Tissues

Abstract Background and purpose: Gastric cancer is a type of highly heterogeneous malignant tumor and the prognosis of gastric cancer is hard to be improved due to limited knowledge on the molecular mechanism of heterogeneity. Single-cell sequencing technology is recently widely used for the investigation of both inter-tumoral heterogeneity and intra-tumoral heterogeneity. The present study aims to explore the potential oncogene by analyzing the single-cell data in the GSE167297 dataset.Methods: The GSE167297 dataset was downloaded from the GEO database, followed by quality control to remove data with lower quality. The division on cell subtypes was determined by the characteristic marker expressed in each cell subpopulation. Wilcoxon rank-sum test was used to screen out differentially expressed genes. Survival analysis was performed to evaluate the prognostic value of G-protein subunit g 11 (GNG11) gene which was significantly overexpressed in deep tumor tissues of diffuse gastric cancer.Results: In both normal tissues and tumor tissues, subtypes of immune cells and stromal cells were identified, with a higher proportion of infiltrated macrophages observed in deep tumor tissues. EPCAM was found significantly highly expressed in a cell subpopulation from gastric tumor tissues. 515 differentially expressed genes (| log2FC | > 2 and FDR < 1e-5) were screened out between normal tissues and tumor tissues. 86 differentially expressed genes (| log2FC | > 1 and FDR < 0.01) were screened out between superficial and deep tumor tissues, in which GNG11 was most highly expressed in deep tumor tissues (mean expression value: 0.1247, FC value: 52.2109). Disease-specific survival analysis on GNG11 results showed that the HR [95%CI] in the constructed univariate Cox proportional risk model was 4.419 [1.399-13.96] and the P-value in the log-rank test was 0.0056.Conclusion: Differentially expression profiles were provided both extratumorally and intratumorally, indicating a higher infiltration of macrophages in deep tumor tissues. Additionally,GNG11 was screened out to be a significant risk factor in STAD patients.

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