scholarly journals 141 A nutrigenomics approach using RNA sequencing technology to study nutrient-gene interactions in agricultural animals

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 135-135
Author(s):  
Shengfa F Liao ◽  
Shamimul Hasan ◽  
Jean M Feugang
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Gene Interactions ◽  
Animal Nutrition ◽  
Rna Seq ◽  
Bottom Line ◽  
Holistic View ◽  
Nutrition Research ◽  
Dietary Nutrients ◽  
A Genome

Abstract Animal life essentially is a set of gene expression processes. Thorough understanding of these processes driven by dietary nutrients and other environmental factors can be regarded as a bottom line of modern advanced animal nutrition research for improving animal growth, development, health, production, and reproduction performance. Nutrigenomics, a genome-wide approach using the knowledge and techniques obtained from the disciplines of genomics (including transcriptomics) and molecular biology, is to study the effects of dietary nutrients on cellular gene expression, cellular metabolic responses and, ultimately, the phenotypic changes of a living organism. Transcriptomics can be applied to investigate animal tissue transcriptome at a defined physiological or nutritional state, which provides a holistic view of the intracellular expression of RNA, especially mRNA. As a novel, promising transcriptomics approach, RNA sequencing (RNA-Seq) technology can monitor all-gene expressions simultaneously in response to dietary intervention. The principle and history of RNA-Seq technology will be briefly reviewed, and the three principal steps of this methodology, including the laboratory analysis of tissue samples, the bioinformatics analysis of the generated sequence data, and the subsequent biological interpretation of the data, will be described. The application of RNA-Seq technology in different areas of animal nutrition research, which include maternal nutrition, feeding strategy and gut microbiota, will be summarized. Lastly, the application of RNA-Seq technology in swine science and nutrition research will also be discussed. In short, to further improve animal feeding or production efficiency, RNA-Seq technology holds a great potential to be employed to explore the new insights into better understanding of nutrient-gene interactions in agricultural animals, and it is expected that the application of this cutting-edge technology in animal nutrition research will continue to grow in the foreseeable future. This research was supported in part by a USDA-NIFA Multistate Project (No. 1007691).

scholarly journals A Nutrigenomics Approach Using RNA Sequencing Technology to Study Nutrient–Gene Interactions in Agricultural Animals

2019 ◽  
Vol 3 (8) ◽  
Author(s):  
M Shamimul Hasan ◽  
Jean M Feugang ◽  
Shengfa F Liao
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Dietary Intervention ◽  
Gene Interactions ◽  
Animal Nutrition ◽  
Rna Seq ◽  
Bottom Line ◽  
Holistic View ◽  
Nutrition Research ◽  
Dietary Nutrients

ABSTRACT Thorough understanding of animal gene expression driven by dietary nutrients can be regarded as a bottom line of advanced animal nutrition research. Nutrigenomics (including transcriptomics) studies the effects of dietary nutrients on cellular gene expression and, ultimately, phenotypic changes in living organisms. Transcriptomics can be applied to investigate animal tissue transcriptomes at a defined nutritional state, which can provide a holistic view of intracellular RNA expression. As a novel transcriptomics approach, RNA sequencing (RNA-Seq) technology can monitor all gene expressions simultaneously in response to dietary intervention. The principle and history of RNA-Seq are briefly reviewed, and its 3 principal steps are described in this article. Application of RNA-Seq in different areas of animal nutrition research is summarized. Lastly, the application of RNA-Seq in swine science and nutrition is also reviewed. In short, RNA-Seq holds significant potential to be employed for better understanding the nutrient–gene interactions in agricultural animals.

scholarly journals 319 Application and Practices of RNA Sequencing for Understanding Transcriptional Regulation of Gene Expression by Dietary Nutrients or Feed Additives in Swine

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 55-55
Author(s):  
Shengfa F Liao ◽  
M Shamimul Hasan
Keyword(s):  
Gene Expression ◽  
Best Practices ◽  
Rna Sequencing ◽  
Regulation Of Gene Expression ◽  
Feed Additives ◽  
Dietary Fatty Acids ◽  
Research Approach ◽  
Rna Seq ◽  
Dietary Nutrients ◽  
The Impact

Abstract In life science, RNA sequencing (RNA-seq) technique is a state-of-the-art research approach for tissue or cell transcriptome analyses. In recent years, RNA-seq has been applied to profile the gene expression in response to dietary nutrients or feed additives to gain thorough understanding of the complex nutrient-gene interactions in agricultural animals. In this presentation, we will selectively review the application of RNA-seq technique in nutrigenomics studies in swine. Such studies have investigated the impact of various sources and quantities of dietary fatty acids, protein (including alternative protein), energy, probiotics, and plant-derived bioactive compounds on the gene expression in major metabolic tissues, such as liver, muscle, and adipose. Although the RNA-seq methodology is a powerful quantitative tool for transcriptomics analysis, it still has various technical challenges and pitfalls throughout its practice steps that include experiment design, sample collection, sample laboratory analysis, data statistical and bioinformatic analyses, and data interpretation. Currently, many options are available for use in some steps, but a thorough understanding of each option is critical for making right decisions and avoiding getting into inconclusive results. Therefore, this presentation will also provide an overview on the “best practices” for applying RNA-seq technique in swine nutrigenomics studies, which include the aspects of appropriately designing experiments, collecting samples, and analyzing the data in order to have confidence in the results obtained from this approach. In short, the aims of this presentation are to provide some basic guidelines for researchers new in the field and to promote a discussion of standardization or “best practices” of RNA-seq methodology for animal nutrigenomics studies.

scholarly journals Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11875
Author(s):  
Tomoko Matsuda
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Time Course ◽  
Sequence Data ◽  
Specific Gene ◽  
Time Interval ◽  
Short Time Interval ◽  
Rna Seq ◽  
Lysis Buffer ◽  
Rna Protection

Large volumes of high-throughput sequencing data have been submitted to the Sequencing Read Archive (SRA). The lack of experimental metadata associated with the data makes reuse and understanding data quality very difficult. In the case of RNA sequencing (RNA-Seq), which reveals the presence and quantity of RNA in a biological sample at any moment, it is necessary to consider that gene expression responds over a short time interval (several seconds to a few minutes) in many organisms. Therefore, to isolate RNA that accurately reflects the transcriptome at the point of harvest, raw biological samples should be processed by freezing in liquid nitrogen, immersing in RNA stabilization reagent or lysing and homogenizing in RNA lysis buffer containing guanidine thiocyanate as soon as possible. As the number of samples handled simultaneously increases, the time until the RNA is protected can increase. Here, to evaluate the effect of different lag times in RNA protection on RNA-Seq data, we harvested CHO-S cells after 3, 5, 6, and 7 days of cultivation, added RNA lysis buffer in a time course of 15, 30, 45, and 60 min after harvest, and conducted RNA-Seq. These RNA samples showed high RNA integrity number (RIN) values indicating non-degraded RNA, and sequence data from libraries prepared with these RNA samples was of high quality according to FastQC. We observed that, at the same cultivation day, global trends of gene expression were similar across the time course of addition of RNA lysis buffer; however, the expression of some genes was significantly different between the time-course samples of the same cultivation day; most of these differentially expressed genes were related to apoptosis. We conclude that the time lag between sample harvest and RNA protection influences gene expression of specific genes. It is, therefore, necessary to know not only RIN values of RNA and the quality of the sequence data but also how the experiment was performed when acquiring RNA-Seq data from the database.

scholarly journals On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

2020 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Dynamic Range ◽  
Substantial Reduction ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Sample Pooling ◽  
Pooling Strategies ◽  
The Cost

Abstract Background: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. Results: The data generating model in pooled experiments is defined mathematically to evaluate the the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. Conclusion: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

scholarly journals DOP23 Single-cell RNA sequencing identifies an important role for class I histone-deacetylase enzymes in intestinal myofibroblasts from patients with Crohn’s Disease strictures

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S062-S062
Author(s):  
A Lewis ◽  
B Pan-Castillo ◽  
G Berti ◽  
C Felice ◽  
H Gordon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Line ◽  
Rna Sequencing ◽  
Histone Deacetylase ◽  
Chromatin Accessibility ◽  
Collagen I ◽  
Class I ◽  
Rna Seq ◽  
Single Cell Rna Sequencing

Abstract Background Histone-deacetylase (HDAC) enzymes are a broad class of ubiquitously expressed enzymes that modulate histone acetylation, chromatin accessibility and gene expression. In models of Inflammatory bowel disease (IBD), HDAC inhibitors, such as Valproic acid (VPA) are proven anti-inflammatory agents and evidence suggests that they also inhibit fibrosis in non-intestinal organs. However, the role of HDAC enzymes in stricturing Crohn’s disease (CD) has not been characterised; this is key to understanding the molecular mechanism and developing novel therapies. Methods To evaluate HDAC expression in the intestine of SCD patients, we performed unbiased single-cell RNA sequencing (sc-RNA-seq) of over 10,000 cells isolated from full-thickness surgical resection specimens of non-SCD (NSCD; n=2) and SCD intestine (n=3). Approximately, 1000 fibroblasts were identified for further analysis, including a distinct cluster of myofibroblasts. Changes in gene expression were compared between myofibroblasts and other resident intestinal fibroblasts using the sc-RNA-seq analysis pipeline in Partek. Changes in HDAC expression and markers of HDAC activity (H3K27ac) were confirmed by immunohistochemistry in FFPE tissue from patient matched NSCD and SCD intestine (n=14 pairs). The function of HDACs in intestinal fibroblasts in the CCD-18co cell line and primary CD myofibroblast cultures (n=16 cultures) was assessed using VPA, a class I HDAC inhibitor. Cells were analysed using a variety of molecular techniques including ATAC-seq, gene expression arrays, qPCR, western blot and immunofluorescent protein analysis. Results Class I HDAC (HDAC1, p= 2.11E-11; HDAC2, p= 4.28E-11; HDAC3, p= 1.60E-07; and HDAC8, p= 2.67E-03) expression was increased in myofibroblasts compared to other intestinal fibroblasts subtypes. IHC also showed an increase in the percentage of stromal HDAC2 positive cells, coupled with a decrease in the percentage of H3K27ac positive cells, in the mucosa overlying SCD intestine relative to matched NSCD areas. In the CCD-18co cell line and primary myofibroblast cultures, VPA reduced chromatin accessibility at Collagen-I gene promoters and suppressed their transcription. VPA also inhibited TGFB-induced up-regulation of Collagen-I, in part by inhibiting TGFB1|1/SMAD4 signalling. TGFB1|1 was identified as a mesenchymal specific target of VPA and siRNA knockdown of TGFB1|1 was sufficient suppress TGFB-induced up-regulation of Collagen-I. Conclusion In SCD patients, class I HDAC expression is increased in myofibroblasts. Class I HDACs inhibitors impair TGFB-signalling and inhibit Collagen-I expression. Selective targeting of TGFB1|1 offers the opportunity to increase treatment specificity by selectively targeting meschenymal cells.

scholarly journals RNA sequencing data: hitchhiker's guide to expression analysis

2018 ◽  
Author(s):  
Koen Van Den Berge ◽  
Katharina Hembach ◽  
Charlotte Soneson ◽  
Simone Tiberi ◽  
Lieven Clement ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Large Scale ◽  
Science Studies ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Types ◽  
The Past ◽  
Long Read ◽  
Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

scholarly journals Transcriptome diversity is a systematic source of bias in RNA-sequencing data

2021 ◽  
Author(s):  
Pablo E. García-Nieto ◽  
Ban Wang ◽  
Hunter B. Fraser
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Systematic Bias ◽  
Simple Explanation ◽  
Rna Seq ◽  
Sequencing Data ◽  
Biological Variables ◽  
Systematic Effects ◽  
Standard Practices ◽  
Transcriptome Diversity

ABSTRACTBackgroundRNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to detect and remove artifactual signals. Several factors such as sex, age, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER) has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors.ResultsHere we show that transcriptome diversity – a simple metric based on Shannon entropy – explains a large portion of variability in gene expression, and is a major factor detected by PEER. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. This prevalent confounding factor provides a simple explanation for a major source of systematic biases in gene expression estimates.ConclusionsOur results show that transcriptome diversity is a metric that captures a systematic bias in RNA-seq and is the strongest known factor encoded in PEER covariates.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

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.

Gene Expression Profiling Of Multiple Myeloma Samples Using RNA Sequencing Identifies Frequent Rearrangements Of FCHSD2

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 534-534
Author(s):  
Venkata D Yellapantula ◽  
Christopher Murray ◽  
Winnie Liang ◽  
Daniel Auclair ◽  
Joan Levy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Genome Sequencing ◽  
Expression Profiling ◽  
Fusion Transcript ◽  
Rna Seq ◽  
Gene Index

Abstract The Multiple Myeloma Research Consortium (MMRC) has characterized over 300 hundred patient samples using a variety of platforms as part of the Multiple Myeloma Genomics Initiative (MMGI). Part of this large study includes a subset of 84 patients that were screened for somatic mutations using whole genome sequencing (WGS) or whole exome sequencing (WES) in combination with mRNA sequencing. This represents one of the first cohorts of myeloma patients with matched genome and transcriptome sequencing results. Given the historic value of microarray based gene expression profiling (GEP), this cohort provides the unique opportunity to compare gene expression measurements from the two platforms as Affymetrix U133Plus2.0 based GEP was performed on 42 of these samples. As part of the MMGI study, the Broad Institute has completed the genome sequencing, using WGS and WES, for 213 patients. A frequently mutated list of 9 genes including NRAS, KRAS, TP53, PNRC1, MAGED1, FAM46C, DIS3, CCND1 and ALOX12B were identified initially. Given the potential for RNAseq data to be used to define gene expression levels and to identify mutations in expressed genes we tested the feasibility of mutation calling on RNAseq alone. We independently called mutations on the entire transcriptome of the 84 patients and used a filtering method to eliminate likely germline variants in the absence of a matched normal control. We looked for point mutation concordance between, the calls identified by RNA-Seq alone and the previously reported variants through exome sequencing in the 9 frequently mutated genes. Out of the 66 SNV’s identified by these criteria using WGS or WES sequencing, 55(84%) were detected using RNA-Seq. Of the remaining 11 loci, 7(10%) were not detectably expressed and in 4(6%) cases the mutation was not detectable even though there was ample coverage. It is unclear if the last 6% represent false positives in the genome calls or the preferential expression of the wild-type allele. To interrogate the utility of RNAseq based GEP in myeloma we independently recapitulated many of common GEP measurements. First we independently used the 84 samples to define cutoffs for the implementation of the TC classification method. We compared our independent assignment of the 42 samples with matched gene expression array data, to their existing microarray assignments. This resulted in 40/42 (95%) samples being classified 40(95%) into identical TC classes. The two discordant samples MMRC0312 and MMRC0387 classified as TC class “none” by expression arrays were classified as other classes by RNAseq. MMRC0312 exhibited high CCND3 expression using RNA-Seq and was assigned to ‘6p21’ class. MMRC0387 exhibited elevated CCND1 expression and was classified as ‘D1’ using RNAseq. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq. One additional advantage of RNAseq over microarray based gene expression measurements is the potential to detect fusion transcripts. We have applied fusion transcript detection to this cohort of patients and 69 human myeloma cell lines, which were also screened by RNAseq and WES as part of the MMGI study. The most common fusion transcript detected is the @IGH-MMSET fusion characteristic of t(4;14). The next most common fusion we identified appears to be a promoter replacement event were the highly expressed gene, FCHSD2, is fused to multiple partners including known myeloma related genes, MMSET and MYC, and previously unreported genes in myeloma, CARNS1 and NCF2. Additional structural rearrangements involving FCHSD2 are also predicted based on the high frequency of copy number abnormalities encompassing the 5′ region of this gene as detected by comparative genomic hybridization in the MMGI study. This study should provide the basis for the migration of myeloma based gene expression profiling from microarrays to RNA sequencing based approaches. In the future RNA sequencing has the potential to provide novel classification schemes that leverage the multitude of measurements that can be made from this single assay. Disclosures: Levy: MMRC: Employment.

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