scholarly journals Mapping gastrointestinal gene expression patterns in wild primates and humans via fecal RNA-seq

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ashok Kumar Sharma ◽  
Barbora Pafčo ◽  
Klára Vlčková ◽  
Barbora Červená ◽  
Jakub Kreisinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rna Seq

scholarly journals Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

2017 ◽  
Author(s):  
Nisar Wani ◽  
Khalid Raza
Keyword(s):  
Gene Expression ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Dna Binding Proteins ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cellular Processes ◽  
Regulatory Effects

AbstractGene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

Differences in gene expression patterns, revealed by RNA-seq analysis, between various Sanfilippo and Morquio disease subtypes

Gene ◽  
2021 ◽  
pp. 146090
Author(s):  
Karolina Wiśniewska ◽  
Lidia Gaffke ◽  
Karolina Krzelowska ◽  
Grzegorz Węgrzyn ◽  
Karolina Pierzynowska
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Morquio Disease ◽  
Disease Subtypes

scholarly journals Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

2020 ◽  
Author(s):  
Kyungmin Ahn ◽  
Hironobu Fujiwara
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Functional Regression ◽  
Rna Seq ◽  
Temporal Gene Expression ◽  
Early Stages ◽  
The Future

Statement of withdrawalThe authors have withdrawn version 1 of this manuscript because a draft manuscript, which was still in the early stages of preparation and required major revisions including the replacement of the source RNA-seq datasets, was erroneously submitted. The authors do not wish this version to be cited as reference for this study. We will post a revised manuscript in the future. If you have any questions, please contact the corresponding author.

scholarly journals Zebrafish pituitary gene expression before and after sexual maturation

2014 ◽  
Vol 221 (3) ◽  
pp. 429-440 ◽  
Author(s):  
Wenxia He ◽  
Xiangyan Dai ◽  
Xiaowen Chen ◽  
Jiangyan He ◽  
Zhan Yin
Keyword(s):  
Gene Expression ◽  
Sexual Maturation ◽  
Sexual Maturity ◽  
Expression Patterns ◽  
Endocrine System ◽  
Global Gene Expression ◽  
Gene Expression Patterns ◽  
Pituitary Hormone ◽  
Rna Seq ◽  
Before And After

Sexual maturation and somatic growth cessation are associated with adolescent development, which is precisely controlled by interconnected neuroendocrine regulatory pathways in the endogenous endocrine system. The pituitary gland is one of the key regulators of the endocrine system. By analyzing the RNA sequencing (RNA-seq) transcriptome before and after sexual maturation, in this study, we characterized the global gene expression patterns in zebrafish pituitaries at 45 and 90 days post-fertilization (dpf). A total of 15 043 annotated genes were expressed in the pituitary tissue, 3072 of which were differentially expressed with a greater than or equal to twofold change between pituitaries at 45 and 90 dpf. In the pituitary transcriptome, the most abundant transcript was gh. The expression levels of gh remained high even after sexual maturation at 90 dpf. Among the eight major pituitary hormone genes, lhb was the only gene that exhibited a significant change in its expression levels between 45 and 90 dpf. Significant changes in the pituitary transcripts included genes involved in the regulation of immune responses, bone metabolism, and hormone secretion processes during the juvenile–sexual maturity transition. Real-time quantitative PCR analysis was carried out to verify the RNA-seq transcriptome results and demonstrated that the expression patterns of the eight major pituitary hormone genes did not exhibit a significant gender difference at 90 dpf. For the first time, we report the quantitative global gene expression patterns at the juvenile and sexual maturity stages. These expression patterns may account for the dynamic neuroendocrine regulation observed in body metabolism.

scholarly journals Genetic Subtypes of Human Pediatric ALLs Show Gene Expression Differences That Parallel Those Observed in Mouse B1 and B2 Progenitors, Suggesting Divergent Developmental Origins

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1741-1741
Author(s):  
Scott C. Kogan ◽  
Huimin Geng ◽  
Ritu Roy ◽  
Encarnacion Montecino-Rodriguez ◽  
Coline M Gaillard ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Developmental Origins ◽  
Rna Seq ◽  
Pediatric All

Abstract The presence in mice of progenitors that can give rise selectively to B-1 or B-2 B-cells raised the possibility that B-lymphoid neoplasms may have varied developmental origins. Prior work demonstrated that murine B-1 and B-2 progenitors of mice can both be transformed by the BCR-ABL1 oncogene and that leukemias derived from B-1 progenitors initiated more quickly than leukemias derived from B-2 progenitors (J Immunol. 2014;192:5171-8). We have recently profiled the gene expression patterns of murine B-1 and B-2 progenitors using RNA-seq and have applied the observed divergences in gene expression pattern to investigate whether varied human pediatric B-cell acute lymphoblastic leukemias (B-ALLs) might arise from human B-cell progenitors with characteristics paralleling either mouse B-1 or B-2 progenitors. Underlying these analyses is the concept that pediatric ALLs might arise from progenitors present during development that may not persist into adulthood. In order to assess our hypothesis that human pediatric B-ALLs have common features with either mouse B-1 progenitors or mouse B-2 progenitors, we examined whether gene expression differences observed in the mouse cells could be used to segregate human pediatric ALLs into B-1 progenitor-like or B-2 progenitor-like subsets. We used two different computational approaches to compare gene expression of mouse B-1 and B-2 progenitors to 126 human pediatric B-ALLs (St Jude ALLs: GSE26281, JCI 2013 123:3099-3111). In the first approach, we identified 327 genes that varied between mouse B-1 and B-2 progenitors derived from mice of varied ages (embryonic day 15, post-natal day 1, post-natal day 2, week 9 and week 11 animals). We were able to map 207 of these genes onto probes for human orthologs in the St Jude ALL gene expression microarray dataset. When multiple probes for a gene were present in the human array, the most variable probe was selected for analysis. The human ALLs were analyzed by unsupervised hierarchical clustering using these 207 genes. Of interest, when examining the first bifurcation of the unsupervised clustering diagram, ETV6-RUNX1 leukemias clustered in the same fork as the TCF3-PBX1 leukemias, whereas Hyperdiploid B-ALLs clustered in the same fork as most of the MLL leukemias. Further analysis identified 58 genes that could be used to score the human leukemias as B-1 progenitor-like and B-2 progenitor-like with a Z-score. Examination of the resulting scores showed that TCF3-PBX1 and ETV6-RUNX1 leukemias scored as the most similar to mouse B-1 progenitors, whereas hyperdiploid and MLL ALLs appeared predominantly B-2 like. In our second analytical approach, we identified 574 genes that showed at least a 2 fold difference and a p-value <0.001 in gene expression between the B-1 and B-2 progenitor subsets derived from embryonic day 15 and post-natal day 2 animals and could also be mapped onto the St. Jude ALL microarray data. From these 574 genes, we removed genes that were expressed at very low levels (at least one RNA-seq read count of 0), and selected genes previously identified as being expressed in B-cells by ImmGen (J Immunol. 2011 186:3047-57). This allowed us to identify 76 genes with increased expression in mouse B-1 progenitors and 77 genes with increased expression in mouse B-2 progenitors. Then we applied those 153 mouse B1- or B2-progenitor gene signatures in examining the expression in human ALLs using a Baysian predictor. Interestingly, this algorithm also classified human TCF3-PBX1 and ETV6-RUNX1 leukemias as B-1 progenitor-like and Hyperdiploid and MLL leukemias as B-2 progenitor-like. Using the same approach, we examined two additional human pediatric ALL datasets (COG P9906 ALLS: GSE28460, Blood 2010 116: 4874-4884 & COALL/DCOG ALLS: GSE13351, Lancet Oncol. 2009 10(2): 125-134). These added analyses supported our conclusion that different genetic sub-types of human pediatric ALL have gene expression patterns that parallel features of mouse B-1 or B-2 progenitors. Additional studies are underway to further assess whether human ALLs may be derived from B-1 like or B-2 like progenitors and to examine whether there may be a biological basis to use differences in cell of origin to inform future therapy. This work was supported by R21-CA173028 from the National Cancer Institute. Disclosures No relevant conflicts of interest to declare.

scholarly journals Single cell transcriptomics reveals a heterogeneous cellular response to BKV infection

2020 ◽  
Author(s):  
Ping An ◽  
Paul G. Cantalupo ◽  
Wenshan Zheng ◽  
Maria Teresa Sáenz-Robles ◽  
Alexis M. Duray ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Viral Mrna ◽  
Rna Seq ◽  
Cellular Gene ◽  
Viral Mrnas ◽  
Infected Cells ◽  
Low Levels

BKV is a human polyomavirus that is generally harmless but can cause devastating disease in immunosuppressed individuals. BKV infection of renal cells is a common problem for kidney transplant patients undergoing immunosuppressive therapy. In cultured primary human renal proximal tubule epithelial cells (RPTE), BKV undergoes a productive infection. The BKV-encoded large T antigen (LT) induces cell cycle entry, resulting in the upregulation of numerous genes associated with cell proliferation. Consistently, microarray and RNA-seq experiments performed in bulk infected cell populations identified several proliferation-related pathways that are upregulated by BKV. These studies revealed few genes that are downregulated. In this study, we analyzed viral and cellular transcripts in single mock or BKV-infected cells. We found that the levels of viral mRNAs vary widely among infected cells, resulting in different levels of LT and viral capsid protein expression. Cells expressing the highest levels of viral transcripts account for approximately 20% of the culture and have a gene expression pattern that is distinct from cells expressing lower levels of viral mRNAs. Surprisingly, cells expressing low levels of viral mRNA do not progress with time to high expression, suggesting that the two cellular responses are determined prior to or shortly following infection. Finally, comparison of cellular gene expression patterns of cells expressing high levels of viral mRNA with mock-infected cells, or with cells expressing low levels of viral mRNA, revealed previously unidentified pathways that are downregulated by BKV. Among these are pathways associated with drug metabolism and detoxification, TNF-signaling, energy metabolism, and translation. IMPORTANCE The outcome of viral infection is determined by the ability of the virus to redirect cellular systems towards progeny production countered by the ability of the cell to block these viral actions. Thus, an infected culture consists of thousands of cells, each fighting their own individual battle. Bulk measurements, such as PCR or RNA-seq, measure the average of these individual responses to infection. Single cell transcriptomics provides a window to the one-on-one battle between BKV and each cell. Our studies reveal that only a minority of infected cells are overwhelmed by the virus and produce large amounts of BKV mRNAs and proteins, while the infection appears to be restricted in the remaining cells. Correlation of viral transcript levels with cellular gene expression patterns reveals pathways manipulated by BKV that may play a role in limiting infection.

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