scholarly journals Multi-species transcriptome meta-analysis of the response to retinoic acid in vertebrates and comparative analysis of the effects of retinol and retinoic acid on gene expression in LMH cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Clemens Falker-Gieske ◽  
Andrea Mott ◽  
Sören Franzenburg ◽  
Jens Tetens
Keyword(s):  
Gene Expression ◽  
Retinoic Acid ◽  
Cellular Response ◽  
Homo Sapiens ◽  
Meta Analysis ◽  
Early Response ◽  
Rna Seq ◽  
Transcriptomic Response ◽  
Time Points ◽  
Lmh Cells

Abstract Background Retinol (RO) and its active metabolite retinoic acid (RA) are major regulators of gene expression in vertebrates and influence various processes like organ development, cell differentiation, and immune response. To characterize a general transcriptomic response to RA-exposure in vertebrates, independent of species- and tissue-specific effects, four publicly available RNA-Seq datasets from Homo sapiens, Mus musculus, and Xenopus laevis were analyzed. To increase species and cell-type diversity we generated RNA-seq data with chicken hepatocellular carcinoma (LMH) cells. Additionally, we compared the response of LMH cells to RA and RO at different time points. Results By conducting a transcriptome meta-analysis, we identified three retinoic acid response core clusters (RARCCs) consisting of 27 interacting proteins, seven of which have not been associated with retinoids yet. Comparison of the transcriptional response of LMH cells to RO and RA exposure at different time points led to the identification of non-coding RNAs (ncRNAs) that are only differentially expressed (DE) during the early response. Conclusions We propose that these RARCCs stand on top of a common regulatory RA hierarchy among vertebrates. Based on the protein sets included in these clusters we were able to identify an RA-response cluster, a control center type cluster, and a cluster that directs cell proliferation. Concerning the comparison of the cellular response to RA and RO we conclude that ncRNAs play an underestimated role in retinoid-mediated gene regulation.

scholarly journals Publicly available transcriptomes provide the opportunity for dual RNA-Seq meta analysis in Plasmodium infection

2019 ◽  
Author(s):  
Parnika Mukherjee ◽  
Gaétan Burgio ◽  
Emanuel Heitlinger
Keyword(s):  
Gene Expression ◽  
Species Interactions ◽  
Homo Sapiens ◽  
Meta Analysis ◽  
Model Systems ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Regulatory Pathways ◽  
Or Gene ◽  
Host Parasite

AbstractDual RNA-Seq is the simultaneous analysis of host and parasite transcriptomes. It can potentially identify host-parasite interactions by correlated gene expression. Co-expression might highlight interlinked signalling, metabolic or gene regulatory pathways in addition to potentially physically interacting proteins. Numerous studies on malaria focus on one organism – either the host or the parasite – and the other is considered contaminant. Here we assess the applicability of a meta-analysis approach for dual RNA-Seq.We screened malaria transcriptome experiments for gene expression data from both Plasmodium and its host. Out of 171 malaria studies in Homo sapiens, Macaca mulatta and Mus musculus, we identified 63 studies with the potential to provide host and parasite data. While 16 studies (1950 total samples) explicitly aimed to generate dual RNA-Seq data, 47 (1398 samples) had an original focus on either the host or the parasite. We show that a total of up to 727 samples from blood and liver studies are suitable for dual RNA-Seq analysis. As a proof-of-principle, we conceive and apply a method for meta-analysis linking host-parasite systems via orthologs. Our approach recovered broad processes known to be interlinked between host and parasites in malaria in addition to individual associations between host and parasite proteins. We suggest these for further experimental investigation.We argue that the multitude of variations in experimental conditions found in the selected studies should help narrow down a conserved core of cross-species interactions. In the future, detailed analyses building on the datasets and concepts conceived here, conserved sets of core interacting pathways and co-regulated genes across study systems might be identified. This might also provide the opportunity to gauge the applicability of model systems for different pathways in malaria studies.

scholarly journals Transcriptomal dissection of soybean circadian rhythmicity in two geographically, phenotypically and genetically distinct cultivars

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanlei Yue ◽  
Ze Jiang ◽  
Enoch Sapey ◽  
Tingting Wu ◽  
Shi Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Circadian Clock ◽  
Chromosome Structure ◽  
Clock Genes ◽  
Expression Profiles ◽  
Circadian Rhythmicity ◽  
Rna Seq ◽  
Night Time ◽  
Time Points ◽  
Circadian Clock Genes

Abstract Background In soybean, some circadian clock genes have been identified as loci for maturity traits. However, the effects of these genes on soybean circadian rhythmicity and their impacts on maturity are unclear. Results We used two geographically, phenotypically and genetically distinct cultivars, conventional juvenile Zhonghuang 24 (with functional J/GmELF3a, a homolog of the circadian clock indispensable component EARLY FLOWERING 3) and long juvenile Huaxia 3 (with dysfunctional j/Gmelf3a) to dissect the soybean circadian clock with time-series transcriptomal RNA-Seq analysis of unifoliate leaves on a day scale. The results showed that several known circadian clock components, including RVE1, GI, LUX and TOC1, phase differently in soybean than in Arabidopsis, demonstrating that the soybean circadian clock is obviously different from the canonical model in Arabidopsis. In contrast to the observation that ELF3 dysfunction results in clock arrhythmia in Arabidopsis, the circadian clock is conserved in soybean regardless of the functional status of J/GmELF3a. Soybean exhibits a circadian rhythmicity in both gene expression and alternative splicing. Genes can be grouped into six clusters, C1-C6, with different expression profiles. Many more genes are grouped into the night clusters (C4-C6) than in the day cluster (C2), showing that night is essential for gene expression and regulation. Moreover, soybean chromosomes are activated with a circadian rhythmicity, indicating that high-order chromosome structure might impact circadian rhythmicity. Interestingly, night time points were clustered in one group, while day time points were separated into two groups, morning and afternoon, demonstrating that morning and afternoon are representative of different environments for soybean growth and development. However, no genes were consistently differentially expressed over different time-points, indicating that it is necessary to perform a circadian rhythmicity analysis to more thoroughly dissect the function of a gene. Moreover, the analysis of the circadian rhythmicity of the GmFT family showed that GmELF3a might phase- and amplitude-modulate the GmFT family to regulate the juvenility and maturity traits of soybean. Conclusions These results and the resultant RNA-seq data should be helpful in understanding the soybean circadian clock and elucidating the connection between the circadian clock and soybean maturity.

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1487
Author(s):  
Marie Lataretu ◽  
Martin Hölzer
Keyword(s):  
Gene Expression ◽  
Homo Sapiens ◽  
Standard Technique ◽  
Common Species ◽  
Rna Seq ◽  
Rna Molecules ◽  
Gene Filtering ◽  
Differential Gene ◽  
High Level ◽  
Very High

RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data’s computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies. Here, we present a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus. We evaluated the DEG detection functionality while using qRT-PCR data serving as a reference and observed a very high correlation of the logarithmized gene expression fold changes.

scholarly journals RNA-Seq-Based Analysis of Cortisol-Induced Differential Gene Expression Associated with Piscirickettsia salmonis Infection in Rainbow Trout (Oncorhynchus mykiss) Myotubes

Animals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 2399
Author(s):  
Rodrigo Zuloaga ◽  
Phillip Dettleff ◽  
Macarena Bastias-Molina ◽  
Claudio Meneses ◽  
Claudia Altamirano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Proliferation ◽  
Rainbow Trout ◽  
Enrichment Analysis ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Transcriptomic Response ◽  
Bacterial Gene ◽  
Intensive Farming ◽  
Piscirickettsia Salmonis

Salmonid rickettsial septicemia (SRS) is the major infectious disease of the Chilean salmonid aquaculture industry caused by Piscirickettsia salmonis. Intensive farming conditions generate stress and increased susceptibility to diseases, being skeletal muscle mainly affected. However, the interplay between pathogen infection and stress in muscle is poorly understood. In this study, we perform an RNA-seq analysis on rainbow trout myotubes that are pretreated for 3 h with cortisol (100 ng/mL) and then infected with P. salmonis strain LF-89 for 8 h (MOI 50). Twelve libraries are constructed from RNA samples (n = 3 per group) and sequenced on Illumina HiSeq 4000. A total of 704,979,454 high-quality reads are obtained, with 70.25% mapped against the reference genome. In silico DETs include 175 total genes—124 are upregulated and 51 are downregulated. GO enrichment analysis reveals highly impacted biological processes related to apoptosis, negative regulation of cell proliferation, and innate immune response. These results are validated by RT-qPCR of nine candidate transcripts. Furthermore, cortisol pretreatment significantly stimulated bacterial gene expression of ahpC and 23s compared to infection. In conclusion, for the first time, we describe a transcriptomic response of trout myotubes infected with P. salmonis by inducing apoptosis, downregulating cell proliferation, and intrinsic immune-like response that is differentially regulated by cortisol.

Meta-analysis of mammary RNA seq datasets reveals the molecular understanding of bovine lactation biology

Genome ◽  
2019 ◽  
Vol 62 (7) ◽  
pp. 489-501
Author(s):  
Periyasamy Vijayakumar ◽  
Sanniyasi Bakyaraj ◽  
Arunasalam Singaravadivelan ◽  
Thangavelu Vasanthakumar ◽  
Ramalingam Suresh
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Milk Production ◽  
Meta Analysis ◽  
Mammary Tissue ◽  
Secretory Cells ◽  
Molecular Pathways ◽  
Production Traits ◽  
Rna Seq ◽  
Milk Production Traits

A better understanding of the biology of lactation, both in terms of gene expression and the identification of candidate genes for the production of milk and its components, is made possible by recent advances in RNA seq technology. The purpose of this study was to understand the synthesis of milk components and the molecular pathways involved, as well as to identify candidate genes for milk production traits within whole mammary transcriptomic datasets. We performed a meta-analysis of publically available RNA seq transcriptome datasets of mammary tissue/milk somatic cells. In total, 11 562 genes were commonly identified from all RNA seq based mammary gland transcriptomes. Functional annotation of commonly expressed genes revealed the molecular processes that contribute to the synthesis of fats, proteins, and lactose in mammary secretory cells and the molecular pathways responsible for milk synthesis. In addition, we identified several candidate genes responsible for milk production traits and constructed a gene regulatory network for RNA seq data. In conclusion, this study provides a basic understanding of the lactation biology of cows at the gene expression level.

scholarly journals Network analysis links genome-wide phenotypic and transcriptional stress responses in a bacterial pathogen with a large pan-genome

2016 ◽  
Author(s):  
Paul A. Jensen ◽  
Zeyu Zhu ◽  
Tim van Opijnen
Keyword(s):  
Gene Expression ◽  
Stress Responses ◽  
Meta Analysis ◽  
Bacterial Pathogen ◽  
Metabolic Model ◽  
Genetic Network ◽  
Rna Seq ◽  
Genome Wide ◽  
A Genome ◽  
Response To Stress

ABSTRACTBackgroundBacteria modulate subcellular processes to handle stressful environments. Genome-wide profiling of gene expression (RNA-Seq) and fitness (Tn-Seq) allows two views of the same genetic network underlying these responses. However, it remains unclear how they combine, enabling a bacterium to overcome a perturbation.ResultsHere we generate RNA-Seq and Tn-Seq profiles in three strains of S. pneumoniae in response to stress defined by different levels of nutrient depletion. These profiles show that genes that change their expression and/or become phenotypically important come from a diverse set of functional categories, and genes that are phenotypically important tend to be highly expressed. Surprisingly, we find that expression and fitness changes rarely occur on the same gene, which we confirmed by over 140 validation experiments. To rationalize these unexpected results we built the first genome-scale metabolic model of S. pneumoniae showing that differential expression and phenotypic importance actually correlate between nearest neighbors, although they are distinctly partitioned into small subnetworks. Moreover, a meta-analysis of 234 S. pneumoniae gene expression studies reveals that essential genes and phenotypically important subnetworks rarely change expression, indicating that they are shielded from transcriptional fluctuations and that a clear distinction exists between transcriptional and phenotypic response networks.ConclusionsWe present a genome-wide computational/experimental approach that contextualizes changes that occur on transcriptomic and phenomic levels in response to stress. Importantly, this highlights the need to connect disparate response networks, for instance in antibiotic target identification, where preferred targets are phenotypically important genes that would be overlooked by transcriptomic analyses alone.

scholarly journals Comparing alternative pipelines for cross-platform microarray gene expression data integration with RNA-seq data in breast cancer

2016 ◽  
Author(s):  
Alina Frolova ◽  
Vladyslav Bondarenko ◽  
Maria Obolenska
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Data Integration ◽  
Gene Expression Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Expression Data ◽  
Rna Seq ◽  
Microarray Gene Expression ◽  
Cross Platform

AbstractBackgroundAccording to major public repositories statistics an overwhelming majority of the existing and newly uploaded data originates from microarray experiments. Unfortunately, the potential of this data to bring new insights is limited by the effects of individual study-specific biases due to small number of biological samples. Increasing sample size by direct microarray data integration increases the statistical power to obtain a more precise estimate of gene expression in a population of individuals resulting in lower false discovery rates. However, despite numerous recommendations for gene expression data integration, there is a lack of a systematic comparison of different processing approaches aimed to asses microarray platforms diversity and ambiguous probesets to genes correspondence, leading to low number of studies applying integration.ResultsHere, we investigated five different approaches of the microarrays data processing in comparison with RNA-seq data on breast cancer samples. We aimed to evaluate different probesets annotations as well as different procedures of choosing between probesets mapped to the same gene. We show that pipelines rankings are mostly preserved across Affymetrix and Illumina platforms. BrainArray approach based on updated annotation and redesigned probesets definition and choosing probeset with the maximum average signal across the samples have best correlation with RNA-seq, while averaging probesets signals as well as scoring the quality of probes sequences mapping to the transcripts of the targeted gene have worse correlation. Finally, randomly selecting probeset among probesets mapped to the same gene significantly decreases the correlation with RNA-seq.ConclusionWe show that methods, which rely on actual probesets signal intensities, are advantageous to methods considering biological characteristics of the probes sequences only and that cross-platform integration of datasets improves correlation with the RNA-seq data. We consider the results obtained in this paper contributive to the integrative analysis as a worthwhile alternative to the classical meta-analysis of the multiple gene expression datasets.

scholarly journals Meta-Analysis of Transcriptomic Variation in T cell Populations Reveals Novel Signatures of Gene Expression and Splicing

2019 ◽  
Author(s):  
Caleb M. Radens ◽  
Davia Blake ◽  
Paul Jewell ◽  
Yoseph Barash ◽  
Kristen W. Lynch
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Previous Analysis ◽  
Meta Analysis ◽  
Treg Cells ◽  
T Cell Subsets ◽  
Cell Populations ◽  
Rna Seq ◽  
Master Regulators ◽  
Splicing Patterns

SummaryDistinct T cell subtypes are typically defined by the expression of distinct gene repertoires. However, there is variability between studies regarding the markers used to define each T cell subtype. Moreover, previous analysis of gene expression in T cell subsets has largely focused on gene expression rather than alternative splicing. Here we take a meta-analysis approach, comparing eleven independent RNA-Seq studies of human Th1, Th2, Th17 and/or Treg cells to identify transcriptomic features that correlate consistently with subtype. We find that known master-regulators are consistently enriched in the appropriate subtype, however, cytokines and other genes often used as markers are more variable. Importantly, we also identify previously unknown transcriptomic markers that consistently differentiate between subsets, including a few Treg-specific splicing patterns. Together this work highlights the heterogeneity in gene expression between isolates of the same subtype, but also suggests additional markers that can be used to define functional groupings.

scholarly journals Fast and interpretable alternative splicing and differential gene-level expression analysis using transcriptome segmentation with Yanagi

2018 ◽  
Author(s):  
Mohamed K Gunady ◽  
Stephen M Mount ◽  
Héctor Corrada Bravo
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Expression Analysis ◽  
Homo Sapiens ◽  
Small Subset ◽  
Rna Seq ◽  
Multiple Transcripts ◽  
Alignment Algorithms ◽  
Gene Level ◽  
Comparable Performance

AbstractIntroduction:Analysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, besides, the annotated transcripts are often a small subset of the possible transcripts of a gene. Here we describe Yanagi, a tool for segmenting transcriptome to create a library of maximal L-disjoint segments from a complete transcriptome annotation. That segment library preserves all transcriptome substrings of length L and transcripts structural relationships while eliminating unnecessary sequence duplications.Contributions:In this paper, we formalize the concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens and provide gene-level visualization of the segments for better interpretability. Then we demonstrate the use of segments-level quantification into gene expression and alternative splicing analysis. The notion of transcript segmentation as introduced here and implemented in Yanagi opens the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of RNA-seq analyses.Conclusion:Using segment library rather than the standard transcriptome succeeds in significantly reducing ambigious alignments where reads are multimapped to several sequences in the reference. That allowed avoiding the quantification step required by standard kmer-based pipelines for gene expression analysis. Moreover, using segment counts as statistics for alternative splicing analysis enables achieving comparable performance to counting-based approaches (e.g. rMATS) while rather using fast and lighthweight pseudo alignment.

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