scholarly journals Genomic imbalance determines positive and negative modulation of gene expression in diploid maize

2021 ◽  
Author(s):  
Xiaowen Shi ◽  
Hua Yang ◽  
Chen Chen ◽  
Jie Hou ◽  
Katherine M Hanson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Dosage ◽  
Leaf Tissue ◽  
Inverse Correlation ◽  
Rna Seq ◽  
Gene Dosage Effect ◽  
Genomic Imbalance ◽  
Common Effect ◽  
Modulation Of Gene Expression ◽  
The Impact

Abstract Genomic imbalance caused by changing the dosage of individual chromosomes (aneuploidy) has a more detrimental effect than varying the dosage of complete sets of chromosomes (ploidy). We examined the impact of both increased and decreased dosage of fifteen distal and one interstitial chromosomal regions via RNA-seq of maize (Zea mays) mature leaf tissue to reveal new aspects of genomic imbalance. The results indicate that significant changes in gene expression in aneuploids occur both on the varied chromosome (cis) and the remainder of the genome (trans), with a wider spread of modulation compared with the whole-ploidy series of haploid to tetraploid. In general, cis genes in aneuploids range from a gene-dosage effect to dosage compensation, whereas for trans genes the most common effect is an inverse correlation in that expression is modulated towards the opposite direction of the varied chromosomal dosage, although positive modulations also occur. Furthermore, this analysis revealed the existence of increased and decreased effects in which expression of many genes under genome imbalance are modulated towards the same direction regardless of increased or decreased chromosomal dosage, which is predicted from kinetic considerations of multicomponent molecular interactions. The findings provide novel insights into understanding mechanistic aspects of gene regulation.

Impact of Trisomy 8 on Expression of Genes Located on Chromosome 8 in Different AML Subgroups.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2034-2034
Author(s):  
Claudia Schoch ◽  
Wolfgang Kern ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Wolfgang Hiddemann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Dosage ◽  
Gene Expression Pattern ◽  
Chromosome 8 ◽  
Support Vector ◽  
Trisomy 8 ◽  
Gene Dosage Effect ◽  
Class Prediction ◽  
Expression Of Genes ◽  
The Impact

Abstract Trisomy 8 is the most frequently observed trisomy in acute myeloid leukemia (AML). It occurs as a sole karyotype abnormality or in addition to other chromosome aberrations. It was the aim of this study to analyze the impact of trisomy 8 on the expression of genes located on chromosome 8 in different AML subgroups. Therefore, gene expression analyses were performed in a total of 567 AML cases using Affymetrix U133A+B oligonucleotide microarrays. The following 14 subgroups were analyzed: +8 sole (n=19), +8 within a complex aberrant karyotype (n=11), +8 with t(15;17) (n=7), +8 and inv(16) (n=3), +8 with t(8;21) (n=3), +8 and 11q23/MLL (n=8), and +8 with other abnormalities (n=10). These were compared to 200 AML with normal karyotype and the following subgroups without trisomy 8: complex aberrant karyotype (n=73), t(15;17) (n=36), inv(16) (n=46), t(8;21) (n=37), 11q23/MLL (n=37), and other abnormalities (n=77). In total 1188 probe sets cover sequences located on chromosome 8 representing 580 genes. A significant higher mean expression of all genes located on chromosome 8 was observed in subgroups with +8 in comparison to their respective control groups (for all comparisons, p<0.05). Significantly higher expressed genes in groups with +8 in comparison to the respective groups without +8 were identified in all comparisons. The number of identified genes ranged from 40 in 11q23/MLL to 326 in trisomy 8 sole vs. normal. There was no common gene significantly overexpressed in all comparisons. Three genes (TRAM1, CHPPR, MGC40214) showed a significantly higher expression in 5 out of 7 comparisons. Between 19 and 107 genes with an exclusive overexpression in trisomy 8 cases in only one subtype comparison were identified. In addition, we performed class prediction using support vector machines (SVM) including all probe sets on the arrays. In one approach all 14 different subgroups were analyzed as one class each. Only 3 out of 61 cases with trisomy 8 were assigned into their correct subclass, while 40 cases were assigned to their corresponding genetic subclass without trisomy 8. In a second approach only two classes were defined: all cases with trisomy 8 combined vs. all cases without trisomy 8. Only 26 out of 61 (42.6%) with trisomy 8 were identified correctly underlining the fact that no distinct gene expression pattern is associated with trisomy 8 in general. Performing SVM only with genes located on chromosome 8 did not improve the correct assignment of cases with trisomy 8 overall. Only cases with trisomy 8 sole were correctly predicted in 58% as compared to 11% in SVM using all genes. In conclusion, overall the gain of chromosome 8 leads to a higher expression of genes located on chromosome 8. However, no consistent pattern of genes was identified which shows a higher expression in all AML subtypes with trisomy 8. This data suggest that the higher expression of genes located on chromosome 8 only in part is directly related to a gene dosage effect. Trisomy 8 may rather provide a platform for a higher expression of chromosome 8 genes which are specifically upregulated by accompanying genetic abnormalities in the respective AML subtypes. Therefore, trisomy 8 does not seem to be an abnormality determining specific disease characteristics such as the well known primary aberrations (t(8;21), inv(16), t(15;17), MLL/11q23) but rather a disease modulating secondary event in addition to primary cytogenetic or moleculargenetic aberrations.

scholarly journals Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Li Tong ◽  
◽  
Po-Yen Wu ◽  
John H. Phan ◽  
Hamid R. Hassazadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Disease Outcome ◽  
Rna Seq ◽  
Next Generation Sequencing Technology ◽  
Normalization Methods ◽  
The Us ◽  
Sequencing Quality ◽  
Improved Accuracy ◽  
The Impact

Abstract To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

scholarly journals Spaceflight Induces Novel Regulatory Responses in Arabidopsis Seedling as Revealed by Combined Proteomic and Transcriptomic Analyses

2020 ◽  
Author(s):  
Colin Peter Singer Kruse ◽  
Alexander D Meyers ◽  
Proma Basu ◽  
Sarahann Hutchinson ◽  
Darron R Luesse ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Wall ◽  
Membrane Proteins ◽  
Plant Growth ◽  
Gene Transcription ◽  
Space Station ◽  
Growth Efficiency ◽  
Rna Seq ◽  
Plastid Gene ◽  
The Impact

Abstract Background: Understanding of gravity sensing and response is critical to long-term human habitation in space and can provide new advantages for terrestrial agriculture. To this end, the altered gene expression profile induced by microgravity has been repeatedly queried by microarray and RNA-seq experiments to understand gravitropism. However, the quantification of altered protein abundance in space has been minimally investigated. Results: Proteomic (iTRAQ-labelled LC-MS/MS) and transcriptomic (RNA-seq) analyses simultaneously quantified protein and transcript differential expression of three-day old, etiolated Arabidopsis thaliana seedlings grown aboard the International Space Station along with their ground control counterparts. Protein extracts were fractionated to isolate soluble and membrane proteins and analyzed to detect differentially phosphorylated peptides. In total, 968 RNAs, 107 soluble proteins, and 103 membrane proteins were identified as differentially expressed. In addition, the proteomic analyses identified 16 differential phosphorylation events. Proteomic data delivered novel insights and simultaneously provided new context to previously made observations of gene expression in microgravity. There is a sweeping shift in post-transcriptional mechanisms of gene regulation including RNA-decapping protein DCP5, the splicing factors GRP7 and GRP8, and AGO4,. These data also indicate AHA2 and FERONIA as well as CESA1 and SHOU4 as central to the cell wall adaptations seen in spaceflight. Patterns of tubulin-a 1, 3,4 and 6 phosphorylation further reveal an interaction of microtubule and redox homeostasis that mirrors osmotic response signaling elements. The absence of gravity also results in a seemingly wasteful dysregulation of plastid gene transcription. Conclusions: The datasets gathered from Arabidopsis seedlings exposed to microgravity revealed marked impacts on post-transcriptional regulation, cell wall synthesis, redox/microtubule dynamics, and plastid gene transcription. The impact of post-transcriptional regulatory alterations represents an unstudied element of the plant microgravity response with the potential to significantly impact plant growth efficiency and beyond. What’s more, addressing the effects of microgravity on AHA2, CESA1, and alpha tubulins has the potential to enhance cytoskeletal organization and cell wall composition, thereby enhancing biomass production and growth in microgravity. Finally, understanding and manipulating the dysregulation of plastid gene transcription has further potential to address the goal of enhancing plant growth in the stressful conditions of microgravity.

Gene Expression Profiling of Hyperdiploid Multiple Reveal Complex Gene Dosage Effects and an mRNA Translation/Protein Synthesis Signature.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1538-1538
Author(s):  
Wee-Joo Chng ◽  
Scott Van Wier ◽  
Gregory Ahmann ◽  
Tammy Price-Troska ◽  
Kim Henderson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Differentially Expressed Genes ◽  
Gene Dosage ◽  
Gene Copy Number ◽  
Mrna Translation ◽  
Differentially Expressed ◽  
Gene Copy ◽  
Gene Dosage Effect ◽  
Expression Of Genes

Abstract Hyperdiploid MM (H-MM), characterized by recurrent trisomies constitute about 50% of MM, yet very little is known about its pathogenesis and oncogenic mechanisms. Studies in leukemia and solid tumors have shown gene dosage effect of aneuploidy on gene expression. To determine the possible gene dosage effect and deregulated cellular program in H-MM we undertook a gene expression study of CD138-enriched plasma-cell RNA from 53 hyperdiploid and 37 non-hyperdiploid MM (NH-MM) patients using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). Gene expression data was analyzed using GeneSpring 7 (Agilent Technologies, Palo Alto, CA). Genes differentially expressed between H-MM and NH-MM were obtained by t-test (p&lt;0.01). The majority of the differentially expressed genes (57%) were under-expressed in H-MM. Genes located on the commonly trisomic chromosomes were mostly (but not always) over-expressed in H-MM and constitute 76% of over-expressed genes. Chromosome 1 contained the most differentially expressed genes (17%) followed by chromosome 12 (9%), and 19 (8%). To examine the relationship of gene copy number to gene expression, we examined the expression of genes on chromosomes 9 and 15 in subjects with 2 copies (15 normal control and 20 NH-MM) and 3 copies (12 H-MM) of each chromosome as detected by interphase FISH. We then derived a ratio of the mean expression of each gene on these chromosomes between patients with 3 copies and 2 copies of the chromosome. If a simple relationship exists between gene expression and gene copy number, one would expect the ratio of expression of most genes on these two chromosomes to be about 1.5 in H-MM compared to NH-MM. However, many genes have ratios either higher than 2 or lower than 0.5. Furthermore, when the heterogeneity of cells with underlying trisomies is taken into consideration by correcting the ratio for the number of cells with trisomies, the actual ratio is always lower than the expected ratio. When the expression of genes on a chromosome was compressed to a median value, this value was always higher in the trisomic chromosomes for H-MM compared to NH-MM. The data suggests that although gene dosage influence gene expression, the relationship is complex and some genes are more gene dosage dependent than others. Amongst the differentially expressed genes with known function, 33% are involved in mRNA translation/protein synthesis. Of note, 37 of the top 100 differentially expressed genes are involved in these processes. In particular, 60 ribosomal protein (RP) genes are significantly (p&lt;0.05) upregulated in H-MM. This signature in H-MM is not associated with increase proliferation as measured by PCLI. This predominant signature suggests that deregulated protein synthesis may be important for the biology of H-MM. Many of these RPs are involved in the synthesis of product of oncogenic pathways (e.g. MYC, NF-KB pathways) and may mediate the growth and survival of tumor cells. It is therefore possible that these tumor cells may be sensitive to the disruption of mRNA translation/protein synthesis. Targeting the mTOR pathway with rapamycin may therefore be useful for treatment of H-MM.

scholarly journals Transcriptional Profiling Reveals Strong Impact of Major Molecular Disease Subgroups and Mixed Epistasis in Chronic Lymphocytic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1742-1742
Author(s):  
Thorsten Zenz ◽  
Almut Luetge ◽  
Junyan Lu ◽  
Huellein Jennifer ◽  
Sascha Dietrich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Copy Number ◽  
Epistatic Interaction ◽  
Gene Dosage ◽  
Copy Number Variants ◽  
Gene Mutations ◽  
Gene Dosage Effect ◽  
Mutation Status ◽  
Trisomy 12

While recurrent mutations in CLL have been extensively catalogued, how driver mutations affect disease phenotypes remains incompletely understood. To address this, we performed RNA sequencing on 184 CLL patient samples and linked gene expression changes to molecular subgroups, gene mutations and copy number variants. Library preparation was performed according to the Illumina TruSeq RNA sample preparation v2 protocol. Samples were paired-end sequenced and two to three samples were multiplexed per lane on Illumina HiSeq 2000, Illumina HiSeq3000/4000 or Illumina HiSeqX machines. Raw RNA-seq reads were demultiplexed and quality control was performed using FastQC version 0.11.5. Internal trimming with STAR version 2.5.2a was used to remove adapters before mapping. Mapping was performed using STAR version 2.5.2a against the Ensembl human reference genome release 75 (Homo sapiens GRCh37.75). STAR was run in default mode with internal adapter trimming using the clip3pAdapterSeq option. Mapped reads were summarized into counts using htseq-count version 0.9.0 with default parameters and union mode. Thus, only fragments unambiguously overlapping with one gene were counted. The count data were then imported into R (version 3.4) for subsequent analysis. We identified robust and previously unknown gene expression signatures associated with recurrent copy number variants (including trisomy 12, del11q22.3, del17p13, del18p12 and gain8q24), gene mutations (TP53, BRAF and SF3B1) and the mutation status of the immunoglobulin heavy-chain variable region (IGHV). The most profound gene expression changes were associated with IGHV, methylation groups and trisomy 12. We found evidence for a significant influence of CNVs beyond the gene dosage effect. In line with these observations, unsupervised clustering showed that these major biological subgroups form distinct clusters and are discernible by unsupervised clustering (IGHV, methylation groups and trisomy 12). We found 3275 genes significantly differentially expressed between M-CLL and U-CLL after adjustment for multiple testing using the method of Benjamini and Hochberg for FDR = 1% . In total 9.5 % of variance within gene expression was associated with the IGHV status. These data suggest a much larger impact on transcriptional changes than previously detected (Ferreira et al. 2014), a finding much more in line with the key impact of IGHV on clinical course and biology of disease. We found distinct expression pattern of up- and downregulated genes for trisomy 12 samples. Even though many upregulated genes are located on chromosome 12, the majority of differentially expressed genes are indeed distributed among the other chromosomes and cannot be therefore not be ascribed to a simple gene dosage effect. To investigate the role of genetic interactions, we tested the collaborative effect on gene expression phenotypes. We investigated epistatic gene expression changes for IGHV status and trisomy 12. Epistasis was defined as a non-linear effect on gene expression between sample with both variants co-occuring and the single variants alone. In total 893 genes showed specific expression pattern in a combined genotype (padj<0.1). These expression changes differed from the expected change by simple combination of the single variant's effects. We observed different ways of epistatic interaction and clustered genes by them. In total, we identified five cluster of genes representing different ways of mixed epistasis as inversion down, suppression, different degrees of buffering and inversion up. To further investigate this interaction we used enrichment tests for genes in the different mixed epistasis cluster. We found genes upregulated in trisomy12 U-CLL sample, but suppressed in M-CLL trisomy12 samples were enriched in Wnt beta catenin and Notch signaling. In summary, our study provides a comprehensive reference data set for gene expression in CLL. We show that IGHV mutation status, recurrent gene mutations and CNVs drive gene expression in a previously underappreciated fashion. This includes epistatic interaction between trisomy 12 and IGHV. Using a novel way to describe coordinated changes we can group genes into sets related to buffering, inversion and suppression. Disclosures Sellner: Takeda: Employment.

scholarly journals Generation of guard cell RNA-seq transcriptomes during progressive drought and recovery using an adapted INTACT protocol for Arabidopsis thaliana shoot tissue

2021 ◽  
Author(s):  
Anna van Weringh ◽  
Asher Pasha ◽  
Eddi Esteban ◽  
Paul J. Gamueda ◽  
Nicholas J. Provart
Keyword(s):  
Gene Expression ◽  
Arabidopsis Thaliana ◽  
Drought Stress ◽  
Guard Cell ◽  
Crop Production ◽  
Leaf Tissue ◽  
Cell Types ◽  
Severe Drought ◽  
Data Sets ◽  
Rna Seq

Drought is an important environmental stress that limits crop production. Guard cells (GC) act to control the rate of water loss. To better understand how GCs change their gene expression during a progressive drought we generated guard cell-specific RNA-seq transcriptomes during mild, moderate, and severe drought stress. We additionally sampled re-watered plants that had experienced severe drought stress. These transcriptomes were generated using the INTACT system to capture the RNA from GC nuclei. We optimized the INTACT protocol for Arabidopsis thaliana leaf tissue, incorporating fixation to preserve RNA during nuclear isolation. To be able to identify gene expression changes unique to GCs, we additionally generated transcriptomes from all cell types, using a 35S viral promoter to capture the nuclei of all cell types in leaves. These data sets highlight shared and unique gene expression changes between GCs and the bulk leaf tissue. The timing of gene expression changes is different between GCs and other cell types: we found that only GCs had detectable gene expression changes at the earliest drought time point. The drought responsive GC and leaf RNA-seq transcriptomes are available in the Arabidopsis ePlant at the Bio-Analytic Resource for Plant Biology website.

scholarly journals Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Kayla A. Johnson ◽  
Arjun Krishnan
Keyword(s):  
Gene Expression ◽  
Expression Data ◽  
Rna Seq ◽  
Functional Relationships ◽  
Gene Coexpression ◽  
Transformation Methods ◽  
Network Transformation ◽  
Almost All ◽  
Coexpression Networks ◽  
The Impact

Abstract Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

Association of novel rare coding variants with juvenile idiopathic arthritis

2021 ◽  
pp. annrheumdis-2020-218359
Author(s):  
Xinyi Meng ◽  
Xiaoyuan Hou ◽  
Ping Wang ◽  
Joseph T Glessner ◽  
Hui-Qi Qu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Juvenile Idiopathic Arthritis ◽  
Autoimmune Diseases ◽  
Rare Variants ◽  
Variant Calling ◽  
Rna Seq ◽  
Common Variants ◽  
Association Analyses ◽  
Coding Variants ◽  
The Impact

ObjectiveJuvenile idiopathic arthritis (JIA) is the most common type of arthritis among children, but a few studies have investigated the contribution of rare variants to JIA. In this study, we aimed to identify rare coding variants associated with JIA for the genome-wide landscape.MethodsWe established a rare variant calling and filtering pipeline and performed rare coding variant and gene-based association analyses on three RNA-seq datasets composed of 228 JIA patients in the Gene Expression Omnibus against different sets of controls, and further conducted replication in our whole-exome sequencing (WES) data of 56 JIA patients. Then we conducted differential gene expression analysis and assessed the impact of recurrent functional coding variants on gene expression and signalling pathway.ResultsBy the RNA-seq data, we identified variants in two genes reported in literature as JIA causal variants, as well as additional 63 recurrent rare coding variants seen only in JIA patients. Among the 44 recurrent rare variants found in polyarticular patients, 10 were replicated by our WES of patients with the same JIA subtype. Several genes with recurrent functional rare coding variants have also common variants associated with autoimmune diseases. We observed immune pathways enriched for the genes with rare coding variants and differentially expressed genes.ConclusionThis study elucidated a novel landscape of recurrent rare coding variants in JIA patients and uncovered significant associations with JIA at the gene pathway level. The convergence of common variants and rare variants for autoimmune diseases is also highlighted in this study.

scholarly journals The Oviductal Extracellular Vesicles’ RNA Cargo Regulates the Bovine Embryonic Transcriptome

2020 ◽  
Vol 21 (4) ◽  
pp. 1303 ◽  
Author(s):  
Stefan Bauersachs ◽  
Pascal Mermillod ◽  
Carmen Almiñana
Keyword(s):  
Gene Expression ◽  
Extracellular Vesicles ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
High Throughput Analysis ◽  
Positive Effects ◽  
Early Embryos ◽  
And Control ◽  
The Impact

Oviductal extracellular vesicles (oEVs) are emerging as key players in the gamete/embryo–oviduct interactions that contribute to successful pregnancy. Various positive effects of oEVs on gametes and early embryos have been found in vitro. To determine whether these effects are associated with changes of embryonic gene expression, the transcriptomes of embryos supplemented with bovine fresh (FeEVs) or frozen (FoEVs) oEVs during in vitro culture compared to controls without oEVs were analyzed by low-input RNA sequencing. Analysis of RNA-seq data revealed 221 differentially expressed genes (DEGs) between FoEV treatment and control, 67 DEGs for FeEV and FoEV treatments, and minor differences between FeEV treatment and control (28 DEGs). An integrative analysis of mRNAs and miRNAs contained in oEVs obtained in a previous study with embryonic mRNA alterations pointed to direct effects of oEV cargo on embryos (1) by increasing the concentration of delivered transcripts; (2) by translating delivered mRNAs to proteins that regulate embryonic gene expression; and (3) by oEV-derived miRNAs which downregulate embryonic mRNAs or modify gene expression in other ways. Our study provided the first high-throughput analysis of the embryonic transcriptome regulated by oEVs, increasing our knowledge on the impact of oEVs on the embryo and revealing the oEV RNA components that potentially regulate embryonic development.

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