scholarly journals Genome-wide gene expression changes in postpartum depression point towards an altered immune landscape

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Divya Mehta ◽  
Karen Grewen ◽  
Brenda Pearson ◽  
Shivangi Wani ◽  
Leanne Wallace ◽  
...  
Keyword(s):  
Gene Expression ◽  
Depressive Symptoms ◽  
Postpartum Depression ◽  
Expression Profiles ◽  
Depression Scale ◽  
Well Being ◽  
Health Concern ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Gene Expression

AbstractMaternal postpartum depression (PPD) is a significant public health concern due to the severe negative impact on maternal and child health and well-being. In this study, we aimed to identify genes associated with PPD. To do this, we investigated genome-wide gene expression profiles of pregnant women during their third trimester of pregnancy and tested the association of gene expression with perinatal depressive symptoms. A total of 137 women from a cohort from the University of North Carolina, USA were assessed. The main phenotypes analysed were Edinburgh Postnatal Depression Scale (EPDS) scores at 2 months postpartum and PPD (binary yes/no) based on an EPDS cutoff of 10. Illumina NextSeq500/550 transcriptomic sequencing from whole blood was analysed using the edgeR package. We identified 71 genes significantly associated with postpartum depression scores at 2 months, after correction for multiple testing at 5% FDR. These included several interesting candidates including TNFRSF17, previously reported to be significantly upregulated in women with PPD and MMP8, a matrix metalloproteinase gene, associated with depression in a genome-wide association study. Functional annotation of differentially expressed genes revealed an enrichment of immune response-related biological processes. Additional analysis of genes associated with changes in depressive symptoms from recruitment to 2 months postpartum identified 66 genes significant at an FDR of 5%. Of these genes, 33 genes were also associated with depressive symptoms at 2 months postpartum. Comparing the results with previous studies, we observed that 15.4% of genes associated with PPD in this study overlapped with 700 core maternal genes that showed significant gene expression changes across multiple brain regions (P = 7.9e-05) and 29–53% of the genes were also associated with estradiol changes in a pharmacological model of depression (P values range = 1.2e-4–2.1e-14). In conclusion, we identified novel genes and validated genes previously associated with oestrogen sensitivity in PPD. These results point towards the role of an altered immune transcriptomic landscape as a vulnerability factor for PPD.

Early predictive biomarkers for postpartum depression point to a role for estrogen receptor signaling

2014 ◽  
Vol 44 (11) ◽  
pp. 2309-2322 ◽  
Author(s):  
D. Mehta ◽  
D. J. Newport ◽  
G. Frishman ◽  
L. Kraus ◽  
M. Rex-Haffner ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Postpartum Depression ◽  
Expression Profiles ◽  
Predictive Biomarkers ◽  
Estrogen Signaling ◽  
Third Trimester ◽  
The Third ◽  
Genome Wide ◽  
A Genome

BackgroundPostpartum depression (PPD) affects approximately 13% of women and has a negative impact on mother and infant, hence reliable biological tests for early detection of PPD are essential. We aimed to identify robust predictive biomarkers for PPD using peripheral blood gene expression profiles in a hypothesis-free genome-wide study in a high-risk, longitudinal cohort.MethodWe performed a genome-wide association study in a longitudinal discovery cohort comprising 62 women with psychopathology. Gene expression and hormones were measured in the first and third pregnancy trimesters and early postpartum (201 samples). The replication cohort comprised 24 women with third pregnancy trimester gene expression measures. Gene expression was measured on Illumina-Human HT12 v4 microarrays. Plasma estradiol and estriol were measured. Statistical analysis was performed in R.ResultsWe identified 116 transcripts differentially expressed between the PPD and euthymic women during the third trimester that allowed prediction of PPD with an accuracy of 88% in both discovery and replication cohorts. Within these transcripts, significant enrichment of transcripts implicated that estrogen signaling was observed and such enrichment was also evident when analysing published gene expression data predicting PPD from a non-risk cohort. While plasma estrogen levels were not different across groups, women with PPD displayed an increased sensitivity to estrogen signaling, confirming the previously proposed hypothesis of increased sex-steroid sensitivity as a susceptibility factor for PPD.ConclusionsThese results suggest that PPD can be robustly predicted in currently euthymic women as early as the third trimester and these findings have implications for predictive testing of high-risk women and prevention and treatment for PPD.

scholarly journals DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Dana M. Lapato ◽  
Roxann Roberson-Nay ◽  
Robert M. Kirkpatrick ◽  
Bradley T. Webb ◽  
Timothy P. York ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Depressive Symptoms ◽  
Health Outcomes ◽  
Meta Analysis ◽  
Previous History ◽  
Depression Scale ◽  
Postpartum Depressive Symptoms ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Abstract Background Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology. Results Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression. Conclusions Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

scholarly journals DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Ieva Rauluseviciute ◽  
Finn Drabløs ◽  
Morten Beck Rye
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Dna Methylation ◽  
Epigenetic Regulation ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Expression Data ◽  
Dna Hypermethylation ◽  
Genome Wide ◽  
A Genome

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

Acquiring genome-wide gene expression profiles in Guthrie card blood spots using microarrays

2010 ◽  
Vol 61 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Sok Kean Khoo ◽  
Karl Dykema ◽  
Naga Manjari Vadlapatla ◽  
David LaHaie ◽  
Saul Valle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Guthrie Card ◽  
Blood Spots ◽  
Genome Wide ◽  
Genome Wide Gene Expression

scholarly journals Tissue-specific transcriptome profiling of the Arabidopsis thaliana inflorescence stem reveals local cellular signatures

2020 ◽  
Author(s):  
Dongbo Shi ◽  
Virginie Jouannet ◽  
Javier Agustí ◽  
Verena Kaul ◽  
Victor Levitsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Arabidopsis Thaliana ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Tissue Specific ◽  
Inflorescence Stem ◽  
Genome Wide ◽  
A Genome

AbstractGenome-wide gene expression maps with a high spatial resolution have substantially accelerated molecular plant science. However, the number of characterized tissues and growth stages is still small because of the limited accessibility of most tissues for protoplast isolation. Here, we provide gene expression profiles of the mature inflorescence stem of Arabidopsis thaliana covering a comprehensive set of distinct tissues. By combining fluorescence-activated nucleus sorting and laser-capture microdissection with next generation RNA sequencing, we characterize transcriptomes of xylem vessels, fibers, the proximal and the distal cambium, phloem, phloem cap, pith, starch sheath, and epidermis cells. Our analyses classify more than 15,000 genes as being differentially expressed among different stem tissues and reveal known and novel tissue-specific cellular signatures. By determining transcription factor binding regions enriched in promoter regions of differentially expressed genes, we furthermore provide candidates for tissue-specific transcriptional regulators. Our datasets predict expression profiles of an exceptional amount of genes and allow generating hypotheses toward the spatial organization of physiological processes. Moreover, we demonstrate that information on gene expression in a broad range of mature plant tissues can be established with high spatial resolution by nuclear mRNA profiling.One sentence summaryA genome-wide high-resolution gene expression map of the Arabidopsis inflorescence stem is established.

Genome‐wide gene expression in a pharmacological hormonal transition model and its relation to depressive symptoms

2019 ◽  
Vol 140 (1) ◽  
pp. 77-84 ◽  
Author(s):  
D. Mehta ◽  
M. Rex‐Haffner ◽  
H. B. Søndergaard ◽  
A. Pinborg ◽  
E. B. Binder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Depressive Symptoms ◽  
Transition Model ◽  
Genome Wide ◽  
Genome Wide Gene Expression

scholarly journals Trajectories of Postpartum Depression in Italian First-Time Fathers

2016 ◽  
Vol 11 (4) ◽  
pp. 880-887 ◽  
Author(s):  
Sara Molgora ◽  
Valentina Fenaroli ◽  
Matteo Malgaroli ◽  
Emanuela Saita
Keyword(s):  
Depressive Symptoms ◽  
Postpartum Depression ◽  
Edinburgh Postnatal Depression Scale ◽  
Depression Scale ◽  
Growth Mixture Modeling ◽  
Well Being ◽  
Mixture Modeling ◽  
Latent Growth ◽  
Growth Mixture ◽  
First Time

Paternal postpartum depression (PPD) has received little attention compared with maternal prenatal and postpartum depression, despite research reporting that paternal PPD concerns a substantial number of fathers. History of depression and antenatal depression have been identified as important PPD’s risk factors, underlining the continuity of depressive symptoms during the transition to parenthood. However, only few studies have focused on the evolution of depressive symptoms with longitudinal research design. The present study aims at analyzing the longitudinal trajectories of depressive symptoms from the third trimester of pregnancy to 1 year after childbirth. One hundred and twenty-six first-time fathers completed the Edinburgh Postnatal Depression Scale at four time points (7-8 months of pregnancy, 40 days, 5-6 months, and 12 months after childbirth). Data were analyzed throughout latent growth mixture modeling. Latent growth mixture modeling analysis indicated a three-class model as the optimal solution. The three-class solution included a trajectory of low, stable depressive symptoms across the four time points ( resilient, 52%); a trajectory of moderate, relatively stable depressive symptomatology ( distress, 37%); and a trajectory of emergent clinical depression following a pattern of high depressive symptoms ( emergent depression, 11%). This study allowed to identify different subpopulation within the sample, distinguishing among mental well-being, emotional distress, and high-risk conditions when—1 year after childbirth—fathers report the highest scores to the Edinburgh Postnatal Depression Scale. These results underline the importance to analyze fathers’ well-being over the time during the transition to fatherhood.

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