scholarly journals Dissection of full-length transcriptome and metabolome of Dichocarpum (Ranunculaceae): implications in evolution of specialized metabolism of Ranunculales medicinal plants

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12428
Author(s):  
Da-Cheng Hao ◽  
Pei Li ◽  
Pei-Gen Xiao ◽  
Chun-Nian He
Keyword(s):  
Gene Expression ◽  
High Throughput Sequencing ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Folk Medicine ◽  
Full Length ◽  
Metabolic Phenotype ◽  
Cytochrome P450 Monooxygenases ◽  
Phylogenomic Analysis ◽  
Specialized Metabolites

Several main families of Ranunculales are rich in alkaloids and other medicinal compounds; many species of these families are used in traditional and folk medicine. Dichocarpum is a representative medicinal genus of Ranunculaceae, but the genetic basis of its metabolic phenotype has not been investigated, which hinders its sustainable conservation and utilization. We use the third-generation high-throughput sequencing and metabolomic techniques to decipher the full-length transcriptomes and metabolomes of five Dichocarpum species endemic in China, and 71,598 non-redundant full-length transcripts were obtained, many of which are involved in defense, stress response and immunity, especially those participating in the biosynthesis of specialized metabolites such as benzylisoquinoline alkaloids (BIAs). Twenty-seven orthologs extracted from trancriptome datasets were concatenated to reconstruct the phylogenetic tree, which was verified by the clustering analysis based on the metabolomic profile and agreed with the Pearson correlation between gene expression patterns of Dichocarpum species. The phylogenomic analysis of phytometabolite biosynthesis genes, e.g., (S)-norcoclaurine synthase, methyltransferases, cytochrome p450 monooxygenases, berberine bridge enzyme and (S)-tetrahydroprotoberberine oxidase, revealed the evolutionary trajectories leading to the chemodiversity, especially that of protoberberine type, aporphine type and bis-BIA abundant in Dichocarpum and related genera. The biosynthesis pathways of these BIAs are proposed based on full-length transcriptomes and metabolomes of Dichocarpum. Within Ranunculales, the gene duplications are common, and a unique whole genome duplication is possible in Dichocarpum. The extensive correlations between metabolite content and gene expression support the co-evolution of various genes essential for the production of different specialized metabolites. Our study provides insights into the transcriptomic and metabolomic landscapes of Dichocarpum, which will assist further studies on genomics and application of Ranunculales plants.

The Relationships Among Spatiotemporal Gene Expression, Histology, and Biomechanics Following Full-Length Injury in the Murine Patellar Tendon

2011 ◽  
Author(s):  
Nathaniel A. Dyment ◽  
Namdar Kazemi ◽  
Lindsey E. Aschbacher-Smith ◽  
Nicolas J. Barthelery ◽  
Keith Kenter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Patellar Tendon ◽  
Expression Patterns ◽  
Tendon Healing ◽  
Full Length ◽  
Treatment Modalities ◽  
Type I ◽  
Biomechanical Assessment ◽  
Tendon Tissue Engineering ◽  
Natural Healing

Tendon and ligament injuries present a considerable socioeconomic impact as close to 50% of the 32 million musculoskeletal injuries in the US per year include these structures [1]. The inadequate healing in these tissues requires novel treatment modalities. Improving tendon tissue engineering dictates that we better understand the process of natural adult tendon healing. Type-I (Col1) and Type-II (Col2) collagens are important structural proteins in tendon as Col1 is the main collagen type found in the tendon midsubstance while Col2 is expressed at the insertion into bone during development, growth, and healing [2–3]. Expression of Col1 and Col2 has typically been analyzed via qPCR, western blotting, and immunohistochemistry (IHC) during healing. However, the temporal expression of these genes is still poorly understood on a cell-by-cell basis. Our lab has previously studied patellar tendon (PT) healing in NZW rabbits [4]. While the NZW rabbit allows for controlled injuries and accurate biomechanical assessment of healing, it lacks the genetic power that is offered in the mouse. Therefore, pOBCo13.6GFPtpz (Col1) and pCol2ECFP (Col2) double transgenic (DT) reporter mice were created to track spatiotemporal gene expression. Thus, the objectives of this study were to monitor changes in: 1) spatiotemporal Col1 and Col2 gene expression patterns, 2) tissue morphology, and 3) healing biomechanics following a full-length, central PT injury in Col1/Col2 DT mice and to compare these natural healing results to contralateral surgical shams and normal PT in age-matched controls.

scholarly journals Gene Expression Profiling in Liver of Mice Fed a High-Fat Diet Supplemented With Watermelon Flesh, Arginine, or Citrulline Shows Similar Pattern Changes

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 303-303
Author(s):  
Mikayla Chen ◽  
Neil Shay
Keyword(s):  
Gene Expression ◽  
High Fat Diet ◽  
Expression Patterns ◽  
Cytochrome P450 Monooxygenases ◽  
Mrna Levels ◽  
Male Mice ◽  
High Fat ◽  
Total Rna ◽  
P450 Monooxygenases ◽  
Close Relationship

Abstract Objectives Watermelon is a nutrient-dense fruit known to contain high levels or arginine and citrulline; these two compounds may influence the nitric oxide pathway, vasodilation, and thus be hypotensive. We tested the hypothesis that when C57BL/6J male mice fed a high-fat diet (HF) had additions to the HF diet of either watermelon flesh (WF), arginine (ARG) or citrulline (CIT), changes in gene expression patterns would occur vs. those seen in HF. Further, we hypothesize that patterns of expression seen in WF, ARG, and CIT groups would be somewhat similar based on increased dietary levels of ARG and CIT in all three groups. Methods Following prior work (Becraft et al.; 2018, Egea et al. 2020), groups of mice were provided either a low-fat diet (LF, 10% kcal fat), high-fat diet (HF, 45% kcal fat), HF plus Watermelon Flesh (WF), HF plus 1% (w/w) arginine (ARG) or 1% (w/w citrulline (CIT) for 10 weeks. Watermelon flesh was provided at 10% of total energy. After ten weeks, animals were euthanized, and liver total RNA was isolated using Trizol. Total RNA was then used for gene expression analysis (N = 4 per group) using Clariom S microarrays and TAC analysis software (ThermoFisher). Results Mice fed WF, ARG, and CIT had several shared canonical pathways of gene expression, including eicosanoid metabolism via cytochrome P450 monooxygenases and exercise-induced circadian rhythm (All P < 0.05). Intake of WF and ARG significantly up-regulated both Cyp2c9 and Cyp2c38 mRNA levels (P < 0.05). The Bst2 gene was significantly down-regulated in all three groups compared to HF mice (P < 0.05). The Cyp2b9 gene was upregulated ∼10.7 fold in WF, and > 1000-fold in ARG mice (P < 0.05). Conclusions We demonstrated that when added to a HF diet, WF, ARG, and CIT all produced a change in hepatic gene expression in male mice. Possibly due to the close relationship of ARG and CIT metabolism, and high content of ARG and CIT in WF, expression patterns observed in all three groups demonstrated a high degree of similarity. Several genes, including Cyp2c9, Cyp2c38, and Elvol5 were up-regulated; these genes may be involved in modifying steroids and arachidonic acid and other long-chain fatty acids. Funding Sources National Watermelon Promotion Board.

scholarly journals Tissue-specificity of gene expression diverges slowly between orthologs, and rapidly between paralogs

2016 ◽  
Author(s):  
Nadezda Kryuchkova-Mostacci ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Tissue Specificity ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Divergence Time ◽  
Gene Copy ◽  
Strong Increase ◽  
Small Scale ◽  
Ontology Term ◽  
Tissue Specific

AbstractThe ortholog conjecture implies that functional similarity between orthologous genes is higher than between paralogs. It has been supported using levels of expression and Gene Ontology term analysis, although the evidence was rather weak and there were also conflicting reports. In this study on 12 species we provide strong evidence of high conservation in tissue-specificity between orthologs, in contrast to low conservation between within-species paralogs. This allows us to shed a new light on the evolution of gene expression patterns. While there have been several studies of the correlation of expression between species, little is known about the evolution of tissue-specificity itself. Ortholog tissue-specificity is strongly conserved between all tetrapod species, with the lowest Pearson correlation between mouse and frog at r = 0.66. Tissue-specificity correlation decreases strongly with divergence time. Paralogs in human show much lower conservation, even for recent Primate-specific paralogs. When both paralogs from ancient whole genome duplication tissue-specific paralogs are tissue-specific, it is often to different tissues, while other tissue-specific paralogs are mostly specific to the same tissue. The same patterns are observed using human or mouse as focal species, and are robust to choices of datasets and of thresholds. Our results support the following model of evolution: in the absence of duplication, tissue-specificity evolves slowly, and tissue-specific genes do not change their main tissue of expression; after small-scale duplication the less expressed paralog loses the ancestral specificity, leading to an immediate difference between paralogs; over time, both paralogs become more broadly expressed, but remain poorly correlated. Finally, there is a small number of paralog pairs which stay tissue-specific with the same main tissue of expression, for at least 300 million years.Author summaryFrom specific examples, it has been assumed by comparative biologists that the same gene in different species has the same function, whereas duplication of a gene inside one species to create several copies allows them to acquire different functions. Yet this model was little tested until recently, and then has proven harder than expected to confirm. One of the problems is defining “function” in a way which can be easily studied. We introduce a new way of considering function: how specific is the activity (“expression”) of a gene? Genes which are specific to certain tissues have functions related to these tissues, whereas genes which are broadly active over many or all tissues have more general functions for the organism. We find that this “tissue-specificity” evolves very slowly in the absence of duplication, while immediately after duplication the new gene copy differs. This shows that indeed duplication leads to a strong increase in the evolution of new functions.

scholarly journals A re-assessment of gene-tag classification approaches for describing var gene expression patterns during human Plasmodium falciparum malaria parasite infections

2017 ◽  
Vol 2 ◽  
pp. 86 ◽  
Author(s):  
George Githinji ◽  
Peter C. Bull
Keyword(s):  
Gene Expression ◽  
Plasmodium Falciparum ◽  
Severe Malaria ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Full Length ◽  
Endothelial Protein C Receptor ◽  
Gene Sequences ◽  
Sequence Features ◽  
Var Gene

PfEMP1 are variant parasite antigens that are inserted on the surface of Plasmodium falciparum infected erythrocytes (IE). Through interactions with various host molecules, PfEMP1 mediate IE sequestration in tissues and play a key role in the pathology of severe malaria. PfEMP1 is encoded by a diverse multi-gene family called var. Previous studies have shown that that expression of specific subsets of var genes are associated with low levels of host immunity and severe malaria. However, in most clinical studies to date, full-length var gene sequences were unavailable and various approaches have been used to make comparisons between var gene expression profiles in different parasite isolates using limited information. Several studies have relied on the classification of a 300 – 500 base-pair “DBLα tag” region in the DBLα domain located at the 5’ end of most var genes. We assessed the relationship between various DBLα tag classification methods, and sequence features that are only fully assessable through full-length var gene sequences. We compared these different sequence features in full-length var gene from six fully sequenced laboratory isolates. These comparisons show that despite a long history of recombination, DBLα sequence tag classification can provide functional information on important features of full-length var genes. Notably, a specific subset of DBLα tags previously defined as “group A-like” is associated with CIDRα1 domains proposed to bind to endothelial protein C receptor. This analysis helps to bring together different sources of data that have been used to assess var gene expression in clinical parasite isolates.

scholarly journals A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Simon Haile ◽  
Richard D. Corbett ◽  
Veronique G. LeBlanc ◽  
Lisa Wei ◽  
Stephen Pleasance ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Single Cells ◽  
Cell Types ◽  
Full Length ◽  
Sequencing Data ◽  
Total Rna ◽  
Specific Protocol

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

scholarly journals Macrophage phenotype and bioenergetics are controlled by oxidized phospholipids identified in lean and obese adipose tissue

2018 ◽  
Vol 115 (27) ◽  
pp. E6254-E6263 ◽  
Author(s):  
Vlad Serbulea ◽  
Clint M. Upchurch ◽  
Michael S. Schappe ◽  
Paxton Voigt ◽  
Dory E. DeWeese ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Stromal Vascular Fraction ◽  
Full Length ◽  
Metabolic Phenotype ◽  
Flux Analysis ◽  
Macrophage Phenotype ◽  
Oxidized Phospholipids ◽  
Obese Mice

Adipose tissue macrophages (ATMs) adapt their metabolic phenotype either to maintain lean tissue homeostasis or drive inflammation and insulin resistance in obesity. However, the factors in the adipose tissue microenvironment that control ATM phenotypic polarization and bioenergetics remain unknown. We have recently shown that oxidized phospholipids (OxPL) uniquely regulate gene expression and cellular metabolism in Mox macrophages, but the presence of the Mox phenotype in adipose tissue has not been reported. Here we show, using extracellular flux analysis, that ATMs isolated from lean mice are metabolically inhibited. We identify a unique population of CX3CR1neg/F4/80low ATMs that resemble the Mox (Txnrd1+HO1+) phenotype to be the predominant ATM phenotype in lean adipose tissue. In contrast, ATMs isolated from obese mice had characteristics typical of the M1/M2 (CD11c+CD206+) phenotype with highly activated bioenergetics. Quantifying individual OxPL species in the stromal vascular fraction of murine adipose tissue, using targeted liquid chromatography-mass spectrometry, revealed that high fat diet-induced adipose tissue expansion led to a disproportional increase in full-length over truncated OxPL species. In vitro studies showed that macrophages respond to truncated OxPL species by suppressing bioenergetics and up-regulating antioxidant programs, mimicking the Mox phenotype of ATMs isolated from lean mice. Conversely, full-length OxPL species induce proinflammatory gene expression and an activated bioenergetic profile that mimics ATMs isolated from obese mice. Together, these data identify a redox-regulatory Mox macrophage phenotype to be predominant in lean adipose tissue and demonstrate that individual OxPL species that accumulate in adipose tissue instruct ATMs to adapt their phenotype and bioenergetic profile to either maintain redox homeostasis or to promote inflammation.

scholarly journals ScLRTC: Imputation for Single-Cell RNA-Seq Data Via Low-Rank Tensor Completion

2021 ◽  
Author(s):  
Zhong Li ◽  
Xiutao Pan ◽  
Shengwei Qin ◽  
Minzhe Yu ◽  
Hang Hu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Low Rank ◽  
Tensor Completion ◽  
Rank Tensor ◽  
Cell Expression

Abstract Background: With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results: In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories.Conclusions: a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

scholarly journals Gene induction and repression during terminal erythropoiesis are mediated by distinct epigenetic changes

Blood ◽  
2011 ◽  
Vol 118 (16) ◽  
pp. e128-e138 ◽  
Author(s):  
Piu Wong ◽  
Shilpa M. Hattangadi ◽  
Albert W. Cheng ◽  
Garrett M. Frampton ◽  
Richard A. Young ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Polymerase Ii ◽  
Histone Modifications ◽  
High Throughput Sequencing ◽  
Expression Patterns ◽  
Erythroid Differentiation ◽  
Mrna Levels ◽  
Epigenetic Changes ◽  
Erythroid Progenitors ◽  
Pol Ii

Abstract It is unclear how epigenetic changes regulate the induction of erythroid-specific genes during terminal erythropoiesis. Here we use global mRNA sequencing (mRNA-seq) and chromatin immunoprecipitation coupled to high-throughput sequencing (CHIP-seq) to investigate the changes that occur in mRNA levels, RNA polymerase II (Pol II) occupancy, and multiple posttranslational histone modifications when erythroid progenitors differentiate into late erythroblasts. Among genes induced during this developmental transition, there was an increase in the occupancy of Pol II, the activation marks H3K4me2, H3K4me3, H3K9Ac, and H4K16Ac, and the elongation methylation mark H3K79me2. In contrast, genes that were repressed during differentiation showed relative decreases in H3K79me2 levels yet had levels of Pol II binding and active histone marks similar to those in erythroid progenitors. We also found that relative changes in histone modification levels, in particular, H3K79me2 and H4K16ac, were most predictive of gene expression patterns. Our results suggest that in terminal erythropoiesis both promoter and elongation-associated marks contribute to the induction of erythroid genes, whereas gene repression is marked by changes in histone modifications mediating Pol II elongation. Our data map the epigenetic landscape of terminal erythropoiesis and suggest that control of transcription elongation regulates gene expression during terminal erythroid differentiation.

scholarly journals ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiutao Pan ◽  
Zhong Li ◽  
Shengwei Qin ◽  
Minzhe Yu ◽  
Hang Hu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Low Rank ◽  
Tensor Completion ◽  
Rank Tensor ◽  
Cell Expression

Abstract Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

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