The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

Low oxygen enhances trophoblast column growth by potentiating differentiation of the extravillous lineage and promoting LOX activity.

Early placental development and the establishment of the invasive trophoblast lineage take place within a low oxygen environment. However, conflicting and inconsistent findings have obscured the role of oxygen in regulating invasive trophoblast differentiation. In this study, the effect of hypoxic, normoxic and atmospheric oxygen on invasive extravillous pathway progression was examined using a human placental explant model. Here, we show that exposure to low oxygen enhances extravillous column outgrowth and promotes the expression of genes that align with extravillous trophoblast (EVT) lineage commitment. By contrast, supra-physiological atmospheric levels of oxygen promote trophoblast proliferation while simultaneously stalling EVT progression. Low oxygen-induced EVT differentiation coincided with elevated transcriptomic levels of lysyl oxidase (LOX) in trophoblast anchoring columns, in which functional experiments established a role for LOX activity in promoting EVT column outgrowth. The findings of this work support a role for low oxygen in potentiating the differentiation of trophoblasts along the extravillous pathway. In addition, these findings generate insight into new molecular processes controlled by oxygen during early placental development.

Cell-specific characterization of the placental methylome.

BACKGROUND: DNA methylation (DNAm) profiling has emerged as a powerful tool for characterizing the placental methylome. However, previous studies have focused primarily on whole placental tissue, which is a mixture of epigenetically distinct cell populations. Here, we present the first methylome-wide analysis of first trimester (n = 9) and term (n = 19) human placental samples of four cell populations: trophoblasts, Hofbauer cells, endothelial cells, and stromal cells, using the Illumina EPIC methylation array, which quantifies DNAm at > 850,000 CpGs. RESULTS: The most distinct DNAm profiles were those of placental trophoblasts, which are central to many pregnancy-essential functions, and Hofbauer cells, which are a rare fetal-derived macrophage population. Cell-specific DNAm occurs at functionally-relevant genes, including genes associated with placental development and preeclampsia. Known placental-specific methylation marks, such as those associated with genomic imprinting, repetitive element hypomethylation, and placental partially methylated domains, were found to be more pronounced in trophoblasts and often absent in Hofbauer cells. Lastly, we characterize the cell composition and cell-specific DNAm dynamics across gestation. CONCLUSIONS: Our results provide a comprehensive analysis of DNAm in human placental cell types from first trimester and term pregnancies. This data will serve as a useful DNAm reference for future placental studies, and we provide access to this data via download from GEO (GSE159526), through interactive exploration from the web browser ( https://robinsonlab.shinyapps.io/Placental_Methylome_Browser/ ), and through the R package planet, which allows estimation of cell composition directly from placental DNAm data.

Confined placental mosaicism involving multiple de novo copy number variants associated with fetal growth restriction: A case report.

The presence of multiple large (>1 Mb) copy number variants (CNVs) in non-malignant tissue is rare in human genetics. We present a liveborn male with a birth weight below the first percentile associated with placental mosaicism involving eight 2.4-3.9 Mb de novo duplications. We found that the duplications likely co-localized to the same cells, were mosaic in the placenta, and impacted maternal and paternal chromosomes. In addition, 27.4 Mb and 240 genes were duplicated in affected cells, including candidate placental genes KISS1 and REN. We ruled out involvement of homologous recombination-based mechanisms or an altered epigenome in generating the CNVs. This case highlights the diversity of genetic abnormalities in the human placenta and the gaps in our knowledge of how such errors arise.

Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions.

Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.

The tidyomics ecosystem: Enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

The application of epiphenotyping approaches to DNA methylation array studies of the human placenta.

Background : Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, "epiphenotyping" approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied. Results : Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, but reassuringly were robust to placental processing time. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component. Conclusions : This work confirms that cohort, array (technical) batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. Further, we demonstrate that estimating epiphenotype variables from the DNAme data itself, when possible, provides both an independent check of clinically-obtained data and can provide a robust approach to compare variables across different datasets.

Appropriateness of transthoracic echocardiography in preoperative elective joint arthroplasty patients.

Elective joint arthroplasty is a commonly performed procedure with postoperative cardiovascular complications occurring in up to 3% of elderly patients. Preoperative cardiac evaluation, including transthoracic echocardiography, may improve risk stratification and optimise perioperative outcomes in patients having non-cardiac surgery.This study aimed to investigate the frequency, indications, appropriateness and consequences of preoperative transthoracic echocardiography in elective joint arthroplasty patients. A one-year retrospective audit was conducted for patients who had elective joint arthroplasties performed at St Vincent's Hospital Melbourne. Patient demographics, transthoracic echocardiography indication, time between transthoracic echocardiography being ordered, performed and its impact on date of surgery were obtained via database and manual chart review. Appropriateness of transthoracic echocardiography was determined in accordance with international guidelines. This study analysed 609 elective joint arthroplasties. Of these, 116 (19%) already had a recent transthoracic echocardiography. Of the remaining 493 patients, 192 (39%) received a resting transthoracic echocardiography. Only 92 (48%) of the transthoracic echocardiography's ordered were deemed appropriate. Transthoracic echocardiography resulted in a significant delay of 31 days in time to surgery.This study indicates that almost 40% of elective joint arthroplasty patients with no recent echocardiogram are having a resting transthoracic echocardiography as part of their preoperative assessment. In 52% of cases, these are not clearly appropriate and result in delays to surgery.

Analysis of commonly expressed genes between first trimester fetal heart and placenta cell types in the context of congenital heart disease.

Congenital heart disease (CHD) is often associated with fetal growth abnormalities. During the first trimester of pregnancy, the heart and placenta develop concurrently, and share key developmental pathways. It is hypothesized that defective morphogenesis of either organ is synergistically linked. However, many studies determined to understand the mechanisms behind CHD overlook the contribution of the placenta. In this study, we aimed to identify commonly expressed genes between first trimester heart and placenta cells using two publicly available single cell sequencing databases. Using a systematic computational approach, we identified 328 commonly expressed genes between heart and placenta endothelial cells and enrichment in pathways including Vasculature Development (GO:0001944, FDR 2.90E-30), and Angiogenesis (GO:0001525, FDR 1.18E-27). We also found, in comparison with fetal heart endothelial cells, 197 commonly expressed genes with placenta extravillous trophoblasts, 128 with cytotrophoblasts and 80 with syncytiotrophoblasts, and included genes such as FLT1, GATA2, ENG and CDH5. Finally, comparison of first trimester cardiomyocytes and placenta cytotrophoblasts revealed 53 commonly expressed genes and enrichment in biological processes integral to cellular function including Cellular Respiration (GO:0045333; FDR 5.05E-08), Ion Transport (GO:0006811; FDR 2.08E-02), and Oxidation-Reduction Process (GO:0055114; FDR 1.58E-07). Overall, our results identify specific genes and cellular pathways common between first trimester fetal heart and placenta cells which if disrupted may concurrently contribute to the developmental perturbations resulting in CHD.

A cross-cohort analysis of autosomal DNA methylation sex differences in the term placenta.

BACKGROUND: Human placental DNA methylation (DNAme) data is a valuable resource for studying sex differences during gestation, as DNAme profiles after delivery reflect the cumulative effects of gene expression patterns and exposures across gestation. Here, we present an analysis of sex differences in autosomal DNAme in the uncomplicated term placenta (n = 343) using the Illumina 450K array. RESULTS: At a false discovery rate < 0.05 and a mean sex difference in DNAme beta value of > 0.10, we identified 162 autosomal CpG sites that were differentially methylated by sex and replicated in an independent cohort of samples (n = 293). Several of these differentially methylated CpG sites were part of larger correlated regions of sex differential DNAme. Although global DNAme levels did not differ by sex, the majority of significantly differentially methylated CpGs were more highly methylated in male placentae, the opposite of what is seen in differential methylation analyses of somatic tissues. Patterns of autosomal DNAme at these 162 CpGs were significantly associated with maternal age (in males) and newborn birthweight standard deviation (in females). CONCLUSIONS: Our results provide a comprehensive analysis of sex differences in autosomal DNAme in the term human placenta. We report a list of high-confidence autosomal sex-associated differentially methylated CpGs and identify several key features of these loci that suggest their relevance to sex differences observed in normative and complicated pregnancies.

Recurrent ovarian steroid cell tumour not otherwise specified: A case report.

Steroid cell tumors not otherwise specified are one of the rare virilizing ovarian tumors. Most of the tumors are benign. This case report illustrates the challenge in managing steroid cell tumor not otherwise specified, which starts from determining its malignant potential, surveillance, and adjuvant treatment option.

Considerations when processing and interpreting genomics data of the placenta.

The application of genomic approaches to placental research has opened exciting new avenues to help us understand basic biological properties of the placenta, improve prenatal screening/diagnosis, and measure effects of in utero exposures on child health outcomes. In the last decade, such large-scale genomic data (including epigenomics and transcriptomics) have become more easily accessible to researchers from many disciplines due to the increasing ease of obtaining such data and the rapidly evolving computational tools available for analysis. While the potential of large-scale studies has been widely promoted, less attention has been given to some of the challenges associated with processing and interpreting such data. We hereby share some of our experiences in assessing data quality, reproducibility, and interpretation in the context of genome-wide studies of the placenta, with the aim to improve future studies. There is rarely a single "best" approach, as that can depend on the study question and sample cohort. However, being consistent, thoroughly assessing potential confounders in the data, and communicating key variables in the methods section of the manuscript are critically important to help researchers to collaborate and build on each other's work.

Accurate ethnicity prediction from placental DNA methylation data.

BACKGROUND: The influence of genetics on variation in DNA methylation (DNAme) is well documented. Yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches to address confounding by population stratification using DNAme data may not generalize to populations or tissues outside those in which they were developed. To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PlaNET (Placental DNAme Elastic Net Ethnicity Tool), using five cohorts with Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples that is also compatible with the newer EPIC platform. RESULTS: Data from 509 placental samples were used to develop PlaNET and show that it accurately predicts (accuracy = 0.938, kappa = 0.823) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), and produces ethnicity probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP arrays (> 2.5 million SNP) and ancestry informative markers (n = 50 SNPs). PlaNET's ethnicity classification relies on 1860 HM450K microarray sites, and over half of these were linked to nearby genetic polymorphisms (n = 955). Our placental-optimized method outperforms existing approaches in assessing population stratification in placental samples from individuals of Asian, African, and Caucasian ethnicities. CONCLUSION: PlaNET provides an improved approach to address population stratification in placental DNAme association studies. The method can be applied to predict ethnicity as a discrete or continuous variable and will be especially useful when self-reported ethnicity information is missing and genotyping markers are unavailable.

Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels.

The human pan-tissue epigenetic clock is widely used for estimating age across the entire lifespan, but it does not lend itself well to estimating gestational age (GA) based on placental DNAm methylation (DNAm) data. We replicate previous findings demonstrating a strong correlation between GA and genome-wide DNAm changes. Using substantially more DNAm arrays (n=1,102 in the training set) than a previous study, we present three new placental epigenetic clocks: 1) a robust placental clock (RPC) which is unaffected by common pregnancy complications (e.g., gestational diabetes, preeclampsia), and 2) a control placental clock (CPC) constructed using placental samples from pregnancies without known placental pathology, and 3) a refined RPC for uncomplicated term pregnancies. These placental clocks are highly accurate estimators of GA based on placental tissue; e.g., predicted GA based on RPC is highly correlated with actual GA (r>0.95 in test data, median error less than one week). We show that epigenetic clocks derived from cord blood or other tissues do not accurately estimate GA in placental samples. While fundamentally different from Horvath's pan-tissue epigenetic clock, placental clocks closely track fetal age during development and may have interesting applications.

IFPA meeting 2018 workshop report I: Reproduction and placentation among ocean-living species; placental imaging; epigenetics and extracellular vesicles in pregnancy.

Workshops are an important part of the IFPA annual meeting as they allow for discussion of specialized topics. At IFPA meeting 2018 there were nine themed workshops, four of which are summarized in this report. These workshops discussed new knowledge and technological innovations in the following areas of research: 1) viviparity in ocean-living species; 2) placental imaging; 3) epigenetics; and 4) extracellular vesicles in pregnancy.