scholarly journals Landscape of GPCR Expression along the Mouse Nephron

2021 ◽  
Author(s):  
Brian G Poll ◽  
Lihe Chen ◽  
Chung-Lin Chou ◽  
Viswanathan Raghuram ◽  
Mark A. Knepper
Keyword(s):  
Renal Tubule ◽  
Mass Spectrometry Data ◽  
Expression Data ◽  
Rna Seq ◽  
Online Data ◽  
Specific Expression ◽  
Protein Mass ◽  
Protein Mass Spectrometry ◽  
Cell Type Specific Expression ◽  
Unbiased Gene

Kidney transport and other renal functions are regulated by multiple G protein-coupled receptors (GPCRs) expressed along the renal tubule. The rapid, recent appearance of comprehensive unbiased gene expression data in the various renal tubule segments, chiefly RNA-seq and protein mass spectrometry data, has provided a means of identifying patterns of GPCR expression along the renal tubule. To allow for comprehensive mapping, we first curated a comprehensive list of GPCRs in the genomes of mice, rats, and humans (https://hpcwebapps.cit.nih.gov/ESBL/Database/GPCRs/), using multiple online data sources. We used this list to mine segment-specific and cell-type specific expression data from RNA-seq studies in microdissected mouse tubule segments to identify GPCRs that are selectively expressed in discrete tubule segments. Comparisons of these mapped mouse GPCRs with other omics datasets as well as functional data from isolated perfused tubule and micro-puncture studies confirms patterns of expression for well-known receptors and identifies poorly studied GPCRs that are likely to play roles in regulation of renal tubule function. Thus, we provide data resources for GPCR expression across the renal tubule, highlighting both well-known GPCRs and understudied receptors in order to provide guidance for future studies.

scholarly journals Computational and Statistical Analysis of Protein Mass Spectrometry Data

2012 ◽  
Vol 8 (1) ◽  
pp. e1002296 ◽  
Author(s):  
William Stafford Noble ◽  
Michael J. MacCoss
Keyword(s):  
Mass Spectrometry ◽  
Statistical Analysis ◽  
Mass Spectrometry Data ◽  
Protein Mass ◽  
Protein Mass Spectrometry

scholarly journals Genome-Wide Identification and Analysis of Cell Cycle Genes in Birch

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 120
Author(s):  
Yijie Li ◽  
Song Chen ◽  
Yuhang Liu ◽  
Haijiao Huang
Keyword(s):  
Cell Cycle ◽  
Growth And Development ◽  
Betula Pendula ◽  
Cell Cycle Gene ◽  
Expression Data ◽  
Rna Seq ◽  
Specific Expression ◽  
Cell Cycles ◽  
Cell Cycle Genes ◽  
Genome Wide

Research Highlights: This study identified the cell cycle genes in birch that likely play important roles during the plant’s growth and development. This analysis provides a basis for understanding the regulatory mechanism of various cell cycles in Betula pendula Roth. Background and Objectives: The cell cycle factors not only influence cell cycles progression together, but also regulate accretion, division, and differentiation of cells, and then regulate growth and development of the plant. In this study, we identified the putative cell cycle genes in the B. pendula genome, based on the annotated cell cycle genes in Arabidopsis thaliana (L.) Heynh. It can be used as a basis for further functional research. Materials and Methods: RNA-seq technology was used to determine the transcription abundance of all cell cycle genes in xylem, roots, leaves, and floral tissues. Results: We identified 59 cell cycle gene models in the genome of B. pendula, with 17 highly expression genes among them. These genes were BpCDKA.1, BpCDKB1.1, BpCDKB2.1, BpCKS1.2, BpCYCB1.1, BpCYCB1.2, BpCYCB2.1, BpCYCD3.1, BpCYCD3.5, BpDEL1, BpDpa2, BpE2Fa, BpE2Fb, BpKRP1, BpKRP2, BpRb1, and BpWEE1. Conclusions: By combining phylogenetic analysis and tissue-specific expression data, we identified 17 core cell cycle genes in the Betulapendula genome.

scholarly journals Single Cell Expression Data Reveal Human Genes that Escape X-Chromosome Inactivation

2016 ◽  
Author(s):  
Kerem Wainer-Katsir ◽  
Michal Linial
Keyword(s):  
X Chromosome ◽  
Direct Method ◽  
Single Cells ◽  
X Chromosome Inactivation ◽  
Chromosome Inactivation ◽  
P Value ◽  
Expression Data ◽  
Rna Seq ◽  
Specific Expression ◽  
Biallelic Expression

ABSTRACTSex chromosomes pose an inherent genetic imbalance between genders. In mammals, one of the female’s X-chromosomes undergoes inactivation (Xi). Indirect measurements estimate that about 20% of Xi genes completely or partially escape inactivation. The identity of these escapee genes and their propensity to escape inactivation remain unsolved. A direct method for identifying escapees was applied by quantifying differential allelic expression from single cells. RNA-Seq fragments were assigned to informative SNPs which were labeled by the appropriate parental haplotype. This method was applied for measuring allelic specific expression from Chromosome-X (ChrX) and an autosomal chromosome as a control. We applied the protocol for measuring biallelic expression from ChrX to 104 primary fibroblasts. Out of 215 genes that were considered, only 13 genes (6%) were associated with biallelic expression. The sensitivity of escapees' identification was increased by combining SNP mapping for parental diploid genomes together with RNA-Seq from clonal single cells (25 lymphoblasts). Using complementary protocols, referred to as strict and relaxed, we confidently identified 25 and 31escapee genes, respectively. When pooled versions of 30 and 100 cells were used, <50% of these genes were revealed. We assessed the generality of our protocols in view of an escapee catalog compiled from indirect methods. The overlap between the escapee catalog and the genes’ list from this study is statistically significant (P-value of E-07). We conclude that single cells’ expression data are instrumental for studying X-inactivation with an improved sensitivity. Finally, our results support the emerging notion of the non-deterministic nature of genes that escape X-chromosome inactivation.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

scholarly journals Specific ACE2 Expression in Cholangiocytes May Cause Liver Damage After 2019-nCoV Infection

2020 ◽  
Author(s):  
Xiaoqiang Chai ◽  
Longfei Hu ◽  
Yan Zhang ◽  
Weiyu Han ◽  
Zhou Lu ◽  
...  
Keyword(s):  
Liver Damage ◽  
Cell Receptor ◽  
Rna Seq ◽  
Drug Induced ◽  
Specific Expression ◽  
Host Cell Receptor ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Hepatocyte Damage ◽  
Healthy Liver

AbstractA newly identified coronavirus, 2019-nCoV, has been posing significant threats to public health since December 2019. ACE2, the host cell receptor for severe acute respiratory syndrome coronavirus (SARS), has recently been demonstrated in mediating 2019-nCoV infection. Interestingly, besides the respiratory system, substantial proportion of SARS and 2019-nCoV patients showed signs of various degrees of liver damage, the mechanism and implication of which have not yet been determined. Here, we performed an unbiased evaluation of cell type specific expression of ACE2 in healthy liver tissues using single cell RNA-seq data of two independent cohorts, and identified specific expression in cholangiocytes. The results indicated that virus might directly bind to ACE2 positive cholangiocytes but not necessarily hepatocytes. This finding suggested the liver abnormalities of SARS and 2019-nCoV patients may not be due to hepatocyte damage, but cholangiocyte dysfunction and other causes such as drug induced and systemic inflammatory response induced liver injury. Our findings indicate that special care of liver dysfunction should be installed in treating 2019-nCoV patients during the hospitalization and shortly after cure.

scholarly journals Parallel analysis of Arabidopsis circadian clock mutants reveals different scales of transcriptome and proteome regulation

Open Biology ◽  
2017 ◽  
Vol 7 (3) ◽  
pp. 160333 ◽  
Author(s):  
Alexander Graf ◽  
Diana Coman ◽  
R. Glen Uhrig ◽  
Sean Walsh ◽  
Anna Flis ◽  
...  
Keyword(s):  
Circadian Clock ◽  
Mass Spectrometry Data ◽  
Starch Degradation ◽  
Growth And Survival ◽  
Protein Mass ◽  
Protein Levels ◽  
Protein Mass Spectrometry ◽  
Future System ◽  
Clock Mutant ◽  
Genes Encoding

The circadian clock regulates physiological processes central to growth and survival. To date, most plant circadian clock studies have relied on diurnal transcriptome changes to elucidate molecular connections between the circadian clock and observable phenotypes in wild-type plants. Here, we have integrated RNA-sequencing and protein mass spectrometry data to comparatively analyse the lhycca1 , prr7prr9 , gi and toc1 circadian clock mutant rosette at the end of day and end of night. Each mutant affects specific sets of genes and proteins, suggesting that the circadian clock regulation is modular. Furthermore, each circadian clock mutant maintains its own dynamically fluctuating transcriptome and proteome profile specific to subcellular compartments. Most of the measured protein levels do not correlate with changes in their corresponding transcripts. Transcripts and proteins that have coordinated changes in abundance are enriched for carbohydrate- and cold-responsive genes. Transcriptome changes in all four circadian clock mutants also affect genes encoding starch degradation enzymes, transcription factors and protein kinases. The comprehensive transcriptome and proteome datasets demonstrate that future system-driven research of the circadian clock requires multi-level experimental approaches. Our work also shows that further work is needed to elucidate the roles of post-translational modifications and protein degradation in the regulation of clock-related processes.

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