Global gene expression changes of amniotic fluid cell free RNA according to fetal development

Jang, Ji Hyon; Jung, Yong Wook; Shim, Sung Han; Sin, Yun Jeong; Lee, Kyung Jin; Shim, Sung Shin; Ahn, Eun Hee; Hyun, Dong Choon

doi:10.1016/j.ejogrb.2017.07.017

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…AF cfRNA were considered a biomarker source for abnormal fetal development. And the gene expression level in AF cfRNA could be in uenced by fetal status (such as fetal sex [2], gestation age [2,[5][6][7], genetic disorders [1,[8][9][10][11], and developmental disorders [12][13][14][15]). Therefore, AF cfRNA was proposed as a mean to monitoring fetal development in real-time.…”

Section: Discussionmentioning

confidence: 99%

“…The sources of AF cfRNA are not only limited to cells in amniotic uid [4]. The AF cfRNA could be in uenced by fetal sex [2], gestation age [2,[5][6][7], genetic disorders [1,[8][9][10][11], developmental disorders [12][13][14][15], maternal obesity [16], and preeclampsia [17]. Therefore analysis of the transcriptome for the AF cfRNA may provide feasible markers for organ development and fetal malformation [18].…”

Section: Introductionmentioning

confidence: 99%

“…Tissue-speci c gene analysis is based on public gene expression data in human tissues, such as BioGPS [19], Genomics Institute of the Novartis Research Foundation [20], DFLAT [21], and Gene Atlas [20]. According to the results of tissue-speci c gene expression analysis [1,2,6,7,14,22,23], the genes derived multiple tissues (brain, liver, kidney, lung, blood, ovary, pancreas, tongue, adrenal gland, thyroid, etc) are detected in AF cfRNA.…”

Section: Introductionmentioning

confidence: 99%

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Annotation of Gene Alteration in Amniotic Fluid Cell Free RNA Transcriptome via Weighted Gene Co-expression Network Analysis

Yang

Yin

Liu

et al. 2020

Preprint

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BackgroundHuman amniotic fluid (AF) cells are commonly used in prenatal diagnosis. The AF cell-free mRNA (cfRNA) derived from necrotic or apoptotic cells of fetus may provide feasible marker of organ development and fetal malformation. Analysis of gene co-expression network will help to annotate AF cfRNA gene variations in fetal diseases.MethodsDatasets of amniotic fluid free RNA were downloaded from the Gene Expression Omnibus database. Co-expressed modules based on normal fetus AF cfRNA transcriptome were established via Weighted Gene Co-expression Network Analysis. The relationship between modules and tissues were set up via tissue-specific gene expression analysis of genes in modules. Differential expressed genes in Down syndrome (DS), Edwards syndrome (ES), and Turner syndrome (TS) were analyzed via linear models implemented in the limma package. Gene Ontology (GO) analysis of modular specific differential expressed genes for these three syndrome fetus was performed separately. Based on relationship of GO terms, GO graph of all enriched GO terms were constructed. The associated enriched GO terms in GO graph were considered as the same subset. Numbers of GO terms of the same subset with diseases and modules were calculated.ResultsA total of 22 co-expressed modules were constructed. Most of co-expressed module eigengenes showed no correlation with gestation weeks. Probe sets with higher expression values showed significant clustering tendencies. Dominant tissues and modules were different for different modules and tissues respectively through analysis of tissue-specific genes distribution in modules. The total numbers of enriched GO terms in six major modules were related to diseases severity for ES, DS, and TS. Disease-specific modules for ES, DS, and TS were detected in relation to biological processes through GO analysis. Differential expressed genes in disease-specific module were identified for DS, ES, and TS.ConclusionsThe gene co-expression network for functional classification provides a potential tool for annotating AF cfRNA variations in fetal diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Annotation of Gene Alteration in Amniotic Fluid Cell Free RNA Transcriptome via Weighted Gene Co-expression Network Analysis

Yang

Yin

Liu

et al. 2020

Preprint

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“…For comparative analysis, we used gene expression datasets from mid-trimester euploid AF samples that were produced in our previous studies. [8] We calculated appropriate sample size with the model that is provided by the department of bioinformatics and computational biology of MD Anderson Cancer Center https://bioinformatics.mdanderson.org/main/SampleSizes:Overview#Sample_Sizes). Using this model, we set the number of differentially expressed genes (DEGs): 2000; acceptable number of false positives:1; desired fold differences: 3; desired power 0.8; standard deviation 0.7.…”

Section: Methodsmentioning

confidence: 99%

Global gene expression analysis of cell-free RNA in amniotic fluid from women destined to develop preeclampsia

Jung

Shim

et al. 2019

Medicine

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Preeclampsia (PE) is a disorder specific to pregnancy characterized by new-onset hypertension and proteinuria after 20 weeks of gestation. There is no definite treatment for PE except delivery of the placenta. The purpose of this study was to elucidate the biological pathways involved in the development of PE and to discover a novel biomarker for PE by performing global gene expression analysis of amniotic fluid cell-free RNA.The participants were recruited from the Department of Obstetrics and Gynecology of CHA Gangnam Medical Center (Seoul, Korea) between March 2014 and February 2015. Eight samples were collected from 8 subjects at second trimester who were later diagnosed with PE. From the amniotic fluid samples, cell-free RNA extraction was performed and gene expression was analyzed using the GeneChip PrimeView Array. Transcriptome data previously analyzed by our group from 9 euploid mid-trimester amniotic fluid samples were used as the control for comparative analysis. Functional analysis of the probe sets was performed using the online Database for Annotation, Visualization, and Integrated Discovery (DAVID) toolkit 6.7.We identified 1841 differentially expressed genes (DEGs) between the PE group and the control. Of these, 1557 genes were upregulated in the PE group, while 284 genes were upregulated in the control. The functional annotation of DEGs identified specific enriched functions such as “transport,” “signal transduction,” and “stress response.” Functional annotation clustering with enriched genes in the PE group revealed that translation-related genes, cell–cell adhesion genes, and immune-related genes were enriched. KEGG pathway analysis showed that several biological pathways, including the ribosome pathway and various immune pathways, were dysregulated. Several genes, including RPS29, IGF-2, and UBC, were significantly upregulated in PE, up to tenfold.This study provides the first genome-wide expression analysis of amniotic fluid cell-free RNA in PE. The results showed that gene expression involving the ribosome pathway and immunologic pathways are dysregulated in PE. Our results will aid in understanding the underlying pathogenesis of PE.

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“…Amniotic uid (AF) is the uid that surrounds the fetus in the amniotic cavity, which plays a key role in protecting and providing nutrients to the fetus. Numerous studies have shown that AF contains DNA, RNA, and metabolites that have vital biological functions in fetal development, which can be used to identify any fetal developmental abnormalities [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Amniotic Fluid Metabolic Profile With Fetal Gender, Maternal Age, and Gestational Age

Sun

Zhang

et al. 2021

Preprint

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Background Amniotic fluid (AF) provides vital information on fetal development, which is also valuable in identifying fetal abnormalities during pregnancy. However, the relationship between the metabolic profile of AF in the second trimester of a normal pregnancy with several maternal-fetal parameters remains poorly understood, which therefore limits its application in clinical practice. The aim of this study was to explore the association between the metabolic profile of AF with fetal gender, maternal age, and gestational age using an untargeted metabolomics method. Methods A total of 114 AF samples of normal pregnancy were selected. Clinical data on fetal gender, maternal age, and gestational week of these samples were collected. Samples were analyzed by gas chromatography/time-of-flight-mass spectrometry (GC-TOF/MS). Principal component analysis(PCA), orthogonal partial least square discrimination analysis༈OPLS-DA༉or partial least square discrimination analysis (PLS-DA) were conducted to compare metabolic profiles, and differential metabolites were obtained by univariate analysis. Results Both PCA and OPLS-DA demonstrated no significant separation trend between the metabolic profiles of male and female fetuses, and there were only 7 differential metabolites. When the association between the maternal age on AF metabolic profile was explored, both PCA and PLS-DA revealed that the maternal age in the range of 21 to 40 years had no significant effect on the metabolic profile of AF, and only four different metabolites were found. There was no significant difference in the metabolic profiles of AF from fetuses of 17–22 weeks, and 23 differential metabolites were found. Conclusions In the scope of our study, there was no significant correlation between the AF metabolic profile and the fetal gender, maternal age and gestational age of a small range. Nevertheless, few metabolites appeared differentially expressed.

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Global gene expression changes of amniotic fluid cell free RNA according to fetal development

Cited by 7 publications

References 14 publications

Annotation of Gene Alteration in Amniotic Fluid Cell Free RNA Transcriptome via Weighted Gene Co-expression Network Analysis

Annotation of Gene Alteration in Amniotic Fluid Cell Free RNA Transcriptome via Weighted Gene Co-expression Network Analysis

Global gene expression analysis of cell-free RNA in amniotic fluid from women destined to develop preeclampsia

Relationship Between Amniotic Fluid Metabolic Profile With Fetal Gender, Maternal Age, and Gestational Age

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