Lung cancer is the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of these cases. Despite advancements in targeted therapies, early detection remains a significant challenge, highlighting the need for novel biomarkers. This study investigates the role of PIWI-interacting RNAs (piRNAs) in lung cancer, specifically focusing on their potential as oncofetal biomarkers in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two most common histological subtypes of NSCLC. We hypothesize that piRNAs exhibit oncofetal expression patterns and may contribute to lung cancer development. Through bioinformatics analysis, we identified distinct piRNA profiles in non-neoplastic, malignant, and fetal lung tissues. Among these, 37 piRNAs in LUAD and 46 piRNAs in LUSC displayed oncofetal expression, meaning they were present in tumor tissues but absent in adjacent normal lung tissue. These oncofetal piRNAs showed significant prognostic value in both LUAD and LUSC cohorts, with a specific signature of eight oncofetal piRNAs predicting high-risk patients in LUAD. We validated the robustness of this signature in a separate in-house cohort, which underscores its potential as a prognostic biomarker. Our findings suggest that oncofetal piRNAs could offer new diagnostic and therapeutic opportunities, particularly for early detection.

The human placenta is distinct from most organs due to its uniquely low-methylated genome. DNA methylation (DNAme) is particularly depleted in the placenta at partially methylated domains and on the inactive X chromosome (Xi) in XX samples. While Xi DNAme is known to be critical for X-chromosome inactivation (XCI) in other tissues, its role in the placenta remains unclear. Understanding X-linked DNAme variation in the placenta may provide insights into XCI and have implications for prenatal development and phenotypic sex differences. DNAme data were analyzed from over 350 human placental (chorionic villus) samples, along with samples from cord blood, amnion and chorion placental membranes, and fetal somatic tissues. We characterized X chromosome DNAme variation in the placenta relative to sample variables including cell composition, ancestry, maternal age, placental weight, and fetal birth weight, and compared these patterns to other tissues. We also evaluated the relationship between X-linked DNAme and previously reported XCI gene expression status in placenta. Our findings confirm that the placenta exhibits significant depletion of DNAme on the Xi compared to other tissues. Additionally, we observe that X chromosome DNAme profiles in the placenta are influenced by cell composition, particularly trophoblast proportion, with minimal DNAme variation across gestation. Notably, low promoter DNAme is observed at most genes on the Xi regardless of XCI status, challenging known associations in somatic tissues between low promoter DNAme and escape from XCI. This study provides evidence that the human placenta has a distinct Xi DNAme landscape, which may inform our understanding of sex differences during prenatal development. Future research should explore the mechanisms underlying the placenta's unique X-linked DNAme profile, and the factors involved in placental XCI maintenance.

The placenta mediates fetal growth, and its development and function are influenced by immune interactions at the maternal-fetal interface. The cysteine-cysteine chemotactic cytokine receptor type 5 (CCR5) gene codes for a pro-inflammatory protein receptor expressed in the placenta on syncytiotrophoblasts and Hofbauer cells. Associations of the placental-fetal genotype at CCR5 and birth outcomes have not been examined. Furthermore, influence of CCR5 polymorphisms on nearby DNA methylation in the placenta and in the context of infection is understudied. We assessed two functional polymorphisms in CCR5, a 32 base pair deletion (Δ32) in the open reading frame and an A/G promoter point mutation (rs1799987) in EPIC (n = 233) a cohort consisting of complicated and uncomplicated pregnancies ascertained in Vancouver BC and found that the variant alleles were associated with birth weight (p = 0.007 and p = 0.01 respectively). We validated the association of rs1799987 with birthweight (p = 0.003) in the published NICHD dataset of normative term births (n = 286). These variant associations were, however, not present in CARMA-Preg (n = 200) a cohort enriched for HIV-exposure. Interestingly, we found rs1799987 was associated with altered DNA methylation (DNAme) at multiple CpGs spanning over 275 kb, overlapping both the CCR2 and CCR5 genes. DNAme in this region was, however, not associated with birthweight. Further investigations are needed to validate the association of CCR5 variants with fetal growth. Such studies must consider the population structure and demographics, as well as the large haplotype blocks spanning this region, which make it difficult to assign a causal relationship to specific variants.

Background Exposure to prenatal maternal stress (PNMS) in utero has been associated with several adverse perinatal outcomes, such as pre-term birth and perturbed cognitive development. As the interface between the fetal and maternal compartments during pregnancy, the placenta has been postulated to play a role in this process. We hypothesized that placental DNA methylation (DNAme) may be altered in association with natural disaster-mediated PNMS. Methods Pooled placental samples from the Queensland Flood Study, or QF2011, cohort (n = 105) were processed for assessment of DNAme using the Illumina Infinium MethylationEPIC BeadChip array. Results Overall, we did not find significant associations between placental DNAme and several stress measurements using linear modelling (FDR<0.05 and Δβ>0.03). While we found that XX placentas had slightly higher predicted cytotrophoblast to syncytiotrophoblast cell ratios than XY placentas (p = 0.01), this difference in cell ratio was not associated with PNMS exposure. However, we did observe associations between placental epigenetic age acceleration and all three types of PNMS investigated (objective hardship (QFOSS) (p = 0.03), subjective distress (COSMOSS) (p = 0.03), and maternal cognitive appraisal (CONSEQ) (p = 0.0005) scores). Conclusion The lack of large global impacts of PNMS on placental DNAme possibly indicates that the placenta can buffer moderate levels of maternal stress during pregnancy. It remains unclear what the impact of increased placental epigenetic age acceleration is on fetal development or perinatal outcomes and will require further investigation.

Epigenetic modifications such as DNA methylation are both cell type and developmental age specific. Here, we show that the immunological maturation of blood cell types influences DNA methylation changes from naive cord blood to fully functional adult blood. Lymphoid cells in adult blood showed more variability than in cord blood suggesting an antigen-dependent maturation of DNA methylation in lymphoid cells throughout the lifespan. Fewer DNA methylation changes between cord and adult blood were observed in myeloid cells, particularly in monocytes, which demonstrated the least number of DNA methylation changes between cord and adult blood. We also noted differences in epigenetic ages by immune cell types within the same individuals, specifically in cord blood where monocytes were epigenetically oldest compared to the other cell types. In addition, we provide a publicly available resource to the community as an R Shiny web application to interactively explore epigenetic patterns between naive cord white blood cells and fully functional adult white blood cells for six immune cell types.

The human placenta is the composite of multiple cell types, each which contributes uniquely to placental function. Small non-coding RNAs (sncRNAs) are regulators of gene expression and can be cell-specific. The sncRNA transcriptome of individual placental cell types has not yet been investigated due to difficulties in their procurement and isolation. Using a custom sequencing method, we explored the expression of seven sncRNA species (miRNA, piRNA, rRNA, scaRNA, snRNA, snoRNA, tRNA) from whole chorionic villi and four major sample-matched FACS-sorted cell type (cytotrophoblast, stromal, endothelial, Hofbauer) samples from 9 first trimester and 17 term placentas. After normalization for technical variables, samples clustered primarily by cell type lineage. No sncRNAs were uniquely expressed by cell type, however, mean expression differed by cell type for 115 sncRNAs. Known placentally-expressed sncRNAs showed differing expression by cell type and trimester. Expression of few sncRNAs varied by sex. Lastly, sample-matched sncRNA expression and DNA methylation correlation was not significant, although high correlation (> R2 ± 0.6) was observed for some sncRNA-CpG pairs. This study represents the first exploration of the sncRNA transcriptome of bulk placental villi and placental cell types, informing about the expression and regulatory patterns underlying human placental development.

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 sexdependent 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.

Human trophoblast stem cells (hTSCs) and related trophoblast organoids are state-of-the-art culture systems that facilitate the study of trophoblast development and human placentation. Using single-cell transcriptomics, we evaluate how organoids derived from freshly isolated first-trimester trophoblasts or from established hTSC cell lines reproduce developmental cell trajectories and transcriptional regulatory processes defined in vivo. Although organoids from primary trophoblasts and hTSCs overall model trophoblast differentiation with accuracy, specific features related to trophoblast composition, trophoblast differentiation, and transcriptional drivers of trophoblast development show levels of misalignment. This is best illustrated by the identification of an expanded progenitor state in stem cell-derived organoids that is nearly absent in vivo and transcriptionally shares both villous cytotrophoblast and extravillous trophoblast characteristics. Together, this work provides a comprehensive resource that identifies strengths and limitations of current trophoblast organoid platforms.

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. 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, as well as over very long placental processing times. 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. 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. We further demonstrate the specific utility of epiphenotyping tools developed for use with placental DNAme data, and show that these variables (i) provide an independent check of clinically obtained data and (ii) provide a robust approach to compare variables across different datasets. Finally, we present a general framework for the processing and analysis of placental DNAme data, integrating the epiphenotype variables discussed here.

Background: A growing body of literature has reported molecular and histological changes in the human placenta in association with preeclampsia (PE). Placental DNA methylation (DNAme) and transcriptomic patterns have revealed molecular subgroups of PE that are associated with placental histopathology and clinical phenotypes of the disease. However, the clinical and molecular heterogeneity of PE both across and within subtypes complicates the study of this disease. PE is most strongly associated with placental pathology and adverse fetal and maternal outcomes when it develops early in pregnancy. We focused on placentae from pregnancies affected by preeclampsia that were delivered before 34 weeks of gestation to develop eoPred, a predictor of the DNAme signature associated with the placental phenotype of early-onset preeclampsia (EOPE).Results: Public data from 83 placental samples (HM450K), consisting of 42 EOPE and 41 normotensive preterm birth (nPTB) cases, was used to develop eoPred—a supervised model that relies on a highly discriminative 45 CpG DNAme signature of EOPE in the placenta. The performance of eoPred was assessed using cross-validation (AUC = 0.95) and tested in an independent validation cohort (n = 49, AUC = 0.725). A subset of fetal growth restriction (FGR) and late-PE cases showed a similar DNAme profile at the 45 predictive CpGs, consistent with the overlap in placental pathology between these conditions. The relationship between the EOPE probability generated by eoPred and various phenotypic variables was also assessed, revealing that it is associated with gestational age, and it is not driven by cell composition differences.Conclusion: eoPred relies on a 45-CpG DNAme signature to predict a homogeneous placental phenotype of EOPE in a discrete or continuous manner. Using this classifier should 1) aid in the study of placental insufficiency and improve the consistency of future placental DNAme studies of PE, 2) facilitate identifying the placental phenotype of EOPE in public data sets and 3) importantly, standardize the placental diagnosis of EOPE to allow better cross-cohort comparisons. Lastly, classification of cases with eoPred will be useful for investigating the relationship between placental pathology and genetic or environmental variables.

BACKGROUND:Many human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data. RESULTS:With Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters. CONCLUSION:While there may be no single "best" approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.

Genetic variation shapes placental development and function, which has long been known to impact fetal growth and pregnancy outcomes such as miscarriage or maternal pre-eclampsia. Early epidemiology studies provided evidence of a strong heritable component to these conditions with both maternal and fetal-placental genetic factors contributing. Subsequently, cytogenetic studies of the placenta and the advent of prenatal diagnosis to detect chromosomal abnormalities provided direct evidence of the importance of spontaneously arising genetic variation in the placenta, such as trisomy and uniparental disomy, drawing inferences that remain relevant to this day. Candidate gene approaches highlighted the role of genetic variation in genes influencing immune interactions at the maternal-fetal interface and angiogenic factors. More recently, the emergence of molecular techniques and in particular high-throughput technologies such as Single-Nucleotide Polymorphism (SNP) arrays, has facilitated the discovery of copy number variation and study of SNP associations with conditions related to placental insufficiency. This review integrates past and more recent knowledge to provide important insights into the role of placental function on fetal and perinatal health, as well as into the mechanisms leading to genetic variation during development.