scholarly journals Phylogeny and gene expression of the complete NITRATE TRANSPORTER 1/PEPTIDE TRANSPORTER FAMILY in Triticum aestivum

2020 ◽  
Vol 71 (15) ◽  
pp. 4531-4546
Author(s):  
Huadun Wang ◽  
Yongfang Wan ◽  
Peter Buchner ◽  
Robert King ◽  
Hongxiang Ma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Family ◽  
Expression Profiles ◽  
Peptide Transporter ◽  
Specific Gene ◽  
Rna Seq ◽  
Systematic Analysis ◽  
Whole Wheat ◽  
Transporter Family ◽  
Systematic Identification

Abstract NPF genes encode membrane transporters involved in the transport of a large variety of substrates including nitrate and peptides. The NPF gene family has been described for many plants, but the whole NPF gene family for wheat has not been completely identified. The release of the wheat reference genome has enabled the identification of the entire wheat NPF gene family. A systematic analysis of the whole wheat NPF gene family was performed, including responses of specific gene expression to development and nitrogen supply. A total of 331 NPF genes (113 homoeologous groups) have been identified in wheat. The chromosomal location of the NPF genes is unevenly distributed, with predominant occurrence in the long arms of the chromosomes. The phylogenetic analysis indicated that wheat NPF genes are closely clustered with Arabidopsis, Brachypodium, and rice orthologues, and subdivided into eight subfamilies. The expression profiles of wheat NPF genes were examined using RNA-seq data, and a subset of 44 NPF genes (homoeologous groups) with contrasting expression responses to nitrogen and/or development in different tissues were identified. The systematic identification of gene composition, chromosomal locations, evolutionary relationships, and expression profiles contributes to a better understanding of the roles of the wheat NPF genes and lays the foundation for further functional analysis in wheat.

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 SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Strategies for cellular deconvolution in human brain RNA sequencing data

2020 ◽  
Author(s):  
Olukayode A. Sosina ◽  
Matthew N Tran ◽  
Kristen R Maynard ◽  
Ran Tao ◽  
Margaret A. Taub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Size ◽  
Expression Profiles ◽  
Brain Regions ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Target User ◽  
Size Estimates

AbstractStatistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. Here we show that several existing deconvolution algorithms which estimate the RNA composition of homogenate tissue, relates to the amount of RNA attributable to each cell type, and not the cellular composition relating to the underlying fraction of cells. Incorporating “cell size” parameters into RNA-based deconvolution algorithms can successfully recover cellular fractions in homogenate brain RNA-seq data. We lastly show that using both cell sizes and cell type-specific gene expression profiles from brain regions other than the target/user-provided bulk tissue RNA-seq dataset consistently results in biased cell fractions. We report several independently constructed cell size estimates as a community resource and extend the MuSiC framework to accommodate these cell size estimates (https://github.com/xuranw/MuSiC/).

scholarly journals Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development

2019 ◽  
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

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.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development

2019 ◽  
Vol 18 (1) ◽  
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.

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

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 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.

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

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.

scholarly journals A novel computational complete deconvolution method using RNA-seq data

2018 ◽  
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).

scholarly journals Genome-Wide Profiling and Phylogenetic Analysis of the SWEET Sugar Transporter Gene Family in Walnut and Their Lack of Responsiveness to Xanthomonas arboricola pv. juglandis Infection

2020 ◽  
Vol 21 (4) ◽  
pp. 1251 ◽  
Author(s):  
Shijiao Jiang ◽  
Bipin Balan ◽  
Renata de A. B. Assis ◽  
Cintia H. D. Sagawa ◽  
Xueqin Wan ◽  
...  
Keyword(s):  
Gene Family ◽  
Stress Responses ◽  
Expression Profiles ◽  
Juglans Regia ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Transcription Activator ◽  
Sugar Transporters ◽  
Transporter Family ◽  
Xanthomonas Arboricola

Following photosynthesis, sucrose is translocated to sink organs, where it provides the primary source of carbon and energy to sustain plant growth and development. Sugar transporters from the SWEET (sugar will eventually be exported transporter) family are rate-limiting factors that mediate sucrose transport across concentration gradients, sustain yields, and participate in reproductive development, plant senescence, stress responses, as well as support plant–pathogen interaction, the focus of this study. We identified 25 SWEET genes in the walnut genome and distinguished each by its individual gene structure and pattern of expression in different walnut tissues. Their chromosomal locations, cis-acting motifs within their 5′ regulatory elements, and phylogenetic relationship patterns provided the first comprehensive analysis of the SWEET gene family of sugar transporters in walnut. This family is divided into four clades, the analysis of which suggests duplication and expansion of the SWEET gene family in Juglans regia. In addition, tissue-specific gene expression signatures suggest diverse possible functions for JrSWEET genes. Although these are commonly used by pathogens to harness sugar products from their plant hosts, little was known about their role during Xanthomonas arboricola pv. juglandis (Xaj) infection. We monitored the expression profiles of the JrSWEET genes in different tissues of “Chandler” walnuts when challenged with pathogen Xaj417 and concluded that SWEET-mediated sugar translocation from the host is not a trigger for walnut blight disease development. This may be directly related to the absence of type III secretion system-dependent transcription activator-like effectors (TALEs) in Xaj417, which suggests different strategies are employed by this pathogen to promote susceptibility to this major aboveground disease of walnuts.

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