Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development

SummaryThe complex life cycle of malaria parasites requires well-orchestrated stage specific gene expression. In the vertebrate host the parasites grow and multiply by schizogony in two different environments: within erythrocytes and within hepatocytes. Whereas erythrocytic parasites are rather well-studied in this respect, relatively little is known about the exo-erythrocytic stages. In an attempt to fill this gap, we performed genome wide RNA-seq analyses of various exo-erythrocytic stages of Plasmodium berghei including sporozoites, samples from a time-course of liver stage development and detached cells, which contain infectious merozoites and represent the final step in exo-erythrocytic development. The analysis represents the completion of the transcriptome of the entire life cycle of P. berghei parasites with temporal detailed analysis of the liver stage allowing segmentation of the transcriptome across the progression of the life cycle. We have used these RNA-seq data from different developmental stages to cluster genes with similar expression profiles, in order to infer their functions. A comparison with published data of other parasite stages confirmed stage-specific gene expression and revealed numerous genes that are expressed differentially in blood and exo-erythrocytic stages. One of the most exo-erythrocytic stage-specific genes was PBANKA_1003900, which has previously been annotated as a “gametocyte specific protein”. The promoter of this gene drove high GFP expression in exo-erythrocytic stages, confirming its expression profile seen by RNA-seq. The comparative analysis of the genome wide mRNA expression profiles of erythrocytic and different exo-erythrocytic stages improves our understanding of gene regulation of Plasmodium parasites and can be used to model exo-erythrocytic stage metabolic networks and identify differences in metabolic processes during schizogony in erythrocytes and hepatocytes.

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Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development

Malaria Journal ◽

10.1186/s12936-019-2968-7 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 10

Author(s):

Reto Caldelari ◽

Sunil Dogga ◽

Marc W. Schmid ◽

Blandine Franke-Fayard ◽

Chris J. Janse ◽

...

Keyword(s):

Gene Expression ◽

Life Cycle ◽

Plasmodium Berghei ◽

Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Erythrocytic Stage ◽

Liver Stage ◽

Specific Gene Expression ◽

Genome Wide

Abstract Background The complex life cycle of malaria parasites requires well-orchestrated stage specific gene expression. In the vertebrate host the parasites grow and multiply by schizogony in two different environments: within erythrocytes and within hepatocytes. Whereas erythrocytic parasites are well-studied in this respect, relatively little is known about the exo-erythrocytic stages. Methods In an attempt to fill this gap, genome wide RNA-seq analyses of various exo-erythrocytic stages of Plasmodium berghei including sporozoites, samples from a time-course of liver stage development and detached cells were performed. These latter contain infectious merozoites and represent the final step in exo-erythrocytic development. Results The analysis represents the complete transcriptome of the entire life cycle of P. berghei parasites with temporal detailed analysis of the liver stage allowing comparison of gene expression across the progression of the life cycle. These RNA-seq data from different developmental stages were used to cluster genes with similar expression profiles, in order to infer their functions. A comparison with published data from other parasite stages confirmed stage-specific gene expression and revealed numerous genes that are expressed differentially in blood and exo-erythrocytic stages. One of the most exo-erythrocytic stage-specific genes was PBANKA_1003900, which has previously been annotated as a “gametocyte specific protein”. The promoter of this gene drove high GFP expression in exo-erythrocytic stages, confirming its expression profile seen by RNA-seq. Conclusions The comparative analysis of the genome wide mRNA expression profiles of erythrocytic and different exo-erythrocytic stages could be used to improve the understanding of gene regulation in Plasmodium parasites and can be used to model exo-erythrocytic stage metabolic networks toward the identification of differences in metabolic processes during schizogony in erythrocytes and hepatocytes.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Johan Gustafsson ◽

Felix Held ◽

Jonathan Robinson ◽

Elias Björnson ◽

Rebecka Jörnsten ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific ◽

Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v2 ◽

2020 ◽

Author(s):

Johan Gustafsson ◽

Felix Held ◽

Jonathan Robinson ◽

Elias Björnson ◽

Rebecka Jörnsten ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific ◽

Technical Factors

Abstract Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of applying cell type profiles derived from blood on mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

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A novel computational complete deconvolution method using RNA-seq data

10.1101/496596 ◽

2018 ◽

Cited By ~ 2

Author(s):

Kai Kang ◽

Qian Meng ◽

Igor Shats ◽

David M. Umbach ◽

Melissa Li ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Composition ◽

Type Composition ◽

Cell Type Specific

AbstractThe cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell type composition and compared its performance to the performance of two state-of-the art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-typespecificgene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue (MATLAB code is available at https://github.com/kkang7/CDSeq_011).

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Faculty Opinions recommendation of Ketamine and Imipramine Reverse Transcriptional Signatures of Susceptibility and Induce Resilience-Specific Gene Expression Profiles.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726695455.793533096 ◽

2017 ◽

Author(s):

E Ronald de Kloet ◽

Lisa van Weert ◽

Onno Meijer

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Specific Gene Expression

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Distinct CoREST complexes act in a cell-type-specific manner

Nucleic Acids Research ◽

10.1093/nar/gkz1050 ◽

2019 ◽

Author(s):

Igor Mačinković ◽

Ina Theofel ◽

Tim Hundertmark ◽

Kristina Kovač ◽

Stephan Awe ◽

...

Keyword(s):

Gene Expression ◽

Germ Line ◽

Protein Complexes ◽

Specific Gene ◽

S2 Cells ◽

Cell Type ◽

Specific Gene Expression ◽

Genome Wide ◽

A Cell ◽

Cell Type Specific

Abstract CoREST has been identified as a subunit of several protein complexes that generate transcriptionally repressive chromatin structures during development. However, a comprehensive analysis of the CoREST interactome has not been carried out. We use proteomic approaches to define the interactomes of two dCoREST isoforms, dCoREST-L and dCoREST-M, in Drosophila. We identify three distinct histone deacetylase complexes built around a common dCoREST/dRPD3 core: A dLSD1/dCoREST complex, the LINT complex and a dG9a/dCoREST complex. The latter two complexes can incorporate both dCoREST isoforms. By contrast, the dLSD1/dCoREST complex exclusively assembles with the dCoREST-L isoform. Genome-wide studies show that the three dCoREST complexes associate with chromatin predominantly at promoters. Transcriptome analyses in S2 cells and testes reveal that different cell lineages utilize distinct dCoREST complexes to maintain cell-type-specific gene expression programmes: In macrophage-like S2 cells, LINT represses germ line-related genes whereas other dCoREST complexes are largely dispensable. By contrast, in testes, the dLSD1/dCoREST complex prevents transcription of germ line-inappropriate genes and is essential for spermatogenesis and fertility, whereas depletion of other dCoREST complexes has no effect. Our study uncovers three distinct dCoREST complexes that function in a lineage-restricted fashion to repress specific sets of genes thereby maintaining cell-type-specific gene expression programmes.

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Establishment of Cell Lines That Exhibit Correct Ontogenic Stage-Specific Gene Expression Profiles From Tissues of Yeast Artificial Chromosome Transgenic Mice Using Chemically Induced Growth Signals

YAC Protocols ◽

10.1385/1-59745-158-4:163 ◽

2006 ◽

pp. 163-174

Author(s):

C. Anthony Blau ◽

Kenneth R. Peterson

Keyword(s):

Gene Expression ◽

Transgenic Mice ◽

Cell Lines ◽

Yeast Artificial Chromosome ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Artificial Chromosome ◽

Specific Gene ◽

Specific Gene Expression ◽

Chemically Induced

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Region-specific gene expression profiles in the left atria of patients with valvular atrial fibrillation

Heart Rhythm ◽

10.1016/j.hrthm.2012.11.013 ◽

2013 ◽

Vol 10 (3) ◽

pp. 383-391 ◽

Cited By ~ 28

Author(s):

Yung-Hsin Yeh ◽

Chi-Tai Kuo ◽

Yun-Shien Lee ◽

Yuan-Min Lin ◽

Stanley Nattel ◽

...

Keyword(s):

Gene Expression ◽

Atrial Fibrillation ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Specific Gene Expression

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Association of specific gene expression profiles with a pathologic complete response to neoadjuvant therapy in esophageal adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.4_suppl.51 ◽

2017 ◽

Vol 35 (4_suppl) ◽

pp. 51-51

Author(s):

Patrick James McLaren ◽

Anthony P Barnes ◽

Willy Z Terrell ◽

Gina M. Vaccaro ◽

Jack Wiedrick ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Cancer ◽

Neoadjuvant Therapy ◽

Esophageal Adenocarcinoma ◽

Expression Profiles ◽

Pathologic Complete Response ◽

Gene Expression Profiles ◽

Complete Response ◽

Specific Gene ◽

Specific Gene Expression

51 Background: Predicting prognosis in esophageal cancer remains an unrealized goal despite studies linking constellations of genes to therapeutic response. In this study, we analyzed specific predictor genes expressed in tumor specimens from our institutional repository. Our aim was to determine if specific gene expression profiles are associated with pathologic complete response (pCR) after neoadjuvant chemo-radiotherapy (CRT). Methods: We investigated eleven genes identified from prior studies (CCL28, SPARC, S100A2, SPRR3, SIRT2, NOV, PERP, PAPSS2, DCK, DKK3, ALDH1) that have significant association with esophageal cancer progression. Patients with esophageal adenocarcinoma treated with neoadjuvant CRT followed by esophagectomy at our institution between January 2011 and July 2015 were included. Quantitative real-time polymerase chain reaction was conducted on pre-treatment biopsy specimens to determine gene expression. Patients were classified into two groups: 1) pCR and, 2) no or poor response (NR) after CRT based on final pathology report. An omnibus test using Mahalanobis distance was applied to evaluate overall genetic expression differences between groups. Log-rank tests compared the differential expression of individual genes. Results: 29 patients (11 pCR and 18 NR) were analyzed. Overall, gene expression profiles were significantly different between pCR and NR patients (p < 0.01). In particular, CCL28 was over-expressed in pCR (Log-HR: 1.53, 95%CI: 0.46-2.59, p = 0.005), and DKK3-was under-expressed in pCR patients (Log-HR: -1.03 95%CI: -1.97, -0.10, p = 0.031). Conclusions: Esophageal adenocarcinoma patients with a pCR after neoadjuvant therapy have genetic profiles that are significantly different from typical NR profiles. In our population, the genes CCL28 and DKK3 are potential predictors of treatment response.

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