Targeted gene expression profiling for accurate endometrial receptivity testing.

Expressional profiling of the endometrium enables the personalised timing of the window of implantation (WOI). This study presents and evaluates a novel analytical pipeline based on a TAC-seq (Targeted Allele Counting by sequencing) method for endometrial dating. The expressional profiles were clustered, and differential expression analysis was performed on the model development group, using 63 endometrial biopsies spanning over proliferative (PE, n = 18), early-secretory (ESE, n = 18), mid-secretory (MSE, n = 17) and late-secretory (LSE, n = 10) endometrial phases of the natural cycle. A quantitative predictor model was trained on the development group and validated on sequenced samples from healthy women, consisting of 52 paired samples taken from ESE and MSE phases and five LSE phase samples from 31 individuals. Finally, the developed test was applied to 44 MSE phase samples from a study group of patients diagnosed with recurrent implantation failure (RIF). In validation samples (n = 57), we detected displaced WOI in 1.8% of the samples from fertile women. In the RIF study group, we detected a significantly higher proportion of the samples with shifted WOI than in the validation set of samples from fertile women, 15.9% and 1.8% (p = 0.012), respectively. The developed model was evaluated with an average cross-validation accuracy of 98.8% and an accuracy of 98.2% in the validation group. The developed beREADY screening model enables sensitive and dynamic detection of selected transcriptome biomarkers, providing a quantitative and accurate prediction of endometrial receptivity status.

DUX4 is a multifunctional factor priming human embryonic genome activation.

Double homeobox 4 (DUX4) is expressed at the early pre-implantation stage in human embryos. Here we show that induced human DUX4 expression substantially alters the chromatin accessibility of non-coding DNA and activates thousands of newly identified transcribed enhancer-like regions, preferentially located within ERVL-MaLR repeat elements. CRISPR activation of transcribed enhancers by C-terminal DUX4 motifs results in the increased expression of target embryonic genome activation (EGA) genes ZSCAN4 and KHDC1P1. We show that DUX4 is markedly enriched in human zygotes, followed by intense nuclear DUX4 localization preceding and coinciding with minor EGA. DUX4 knockdown in human zygotes led to changes in the EGA transcriptome but did not terminate the embryos. We also show that the DUX4 protein interacts with the Mediator complex via the C-terminal KIX binding motif. Our findings contribute to the understanding of DUX4 as a regulator of the non-coding genome.

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples.

Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no prior comprehensive, head-to-head accuracy comparison of the various tools has been published. Here, we compared the outcome accuracies obtained for clinically validated samples with five commonly used computational NIPT aneuploidy analysis tools (WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq) across various sequencing depths (coverage) and fetal DNA fractions. The sample set included cases of fetal trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). We determined that all of the compared tools were considerably affected by lower sequencing depths, such that increasing proportions of undetected trisomy cases (false negatives) were observed as the sequencing depth decreased. We summarised our benchmarking results and highlighted the advantages and disadvantages of each computational NIPT software. To conclude, trisomy detection for lower coverage NIPT samples (e.g. 2.5M reads per sample) is technically possible but can, with some NIPT tools, produce troubling rates of inaccurate trisomy detection, especially in low-FF samples.

Small RNA expression and miRNA modification dynamics in human oocytes and early embryos.

Small noncoding RNAs (sRNAs) play important roles during the oocyte-to-embryo transition (OET), when the maternal phenotype is reprogrammed and the embryo genome is gradually activated. The transcriptional program driving early human development has been studied with the focus mainly on protein-coding RNAs, and expression dynamics of sRNAs remain largely unexplored. We profiled sRNAs in human oocytes and early embryos using an RNA-sequencing (RNA-seq) method suitable for low inputs of material. We show that OET in humans is temporally coupled with the transition from predominant expression of oocyte short piRNAs (os-piRNAs) in oocytes, to activation of microRNA (miRNA) expression in cleavage stage embryos. Additionally, 3' mono- and oligoadenylation of miRNAs is markedly increased in zygotes. We hypothesize that this may modulate the function or stability of maternal miRNAs, some of which are retained throughout the first cell divisions in embryos. This study is the first of its kind elucidating the dynamics of sRNA expression and miRNA modification along a continuous trajectory of early human development and provides a valuable data set for in-depth interpretative analyses.

Identification of fetal unmodified and 5-hydroxymethylated CG sites in maternal cell-free DNA for non-invasive prenatal testing.

BACKGROUND: Massively parallel sequencing of maternal cell-free DNA (cfDNA) is widely used to test fetal genetic abnormalities in non-invasive prenatal testing (NIPT). However, sequencing-based approaches are still of high cost. Building upon previous knowledge that placenta, the main source of fetal circulating DNA, is hypomethylated in comparison to maternal tissue counterparts of cfDNA, we propose that targeting either unmodified or 5-hydroxymethylated CG sites specifically enriches fetal genetic material and reduces numbers of required analytical sequencing reads thereby decreasing cost of a test. METHODS: We employed uTOPseq and hmTOP-seq approaches which combine covalent derivatization of unmodified or hydroxymethylated CG sites, respectively, with next generation sequencing, or quantitative real-time PCR. RESULTS: We detected increased 5-hydroxymethylcytosine (5hmC) levels in fetal chorionic villi (CV) tissue samples as compared with peripheral blood. Using our previously developed uTOP-seq and hmTOP-seq approaches we obtained whole-genome uCG and 5hmCG maps of 10 CV tissue and 38 cfDNA samples in total. Our results indicated that, in contrast to conventional whole genome sequencing, such epigenomic analysis highly specifically enriches fetal DNA fragments from maternal cfDNA. While both our approaches yielded 100% accuracy in detecting Down syndrome in fetuses, hmTOP-seq maintained such accuracy at ultra-low sequencing depths using only one million reads. We identified 2164 and 1589 placenta-specific differentially modified and 5-hydroxymethylated regions, respectively, in chromosome 21, as well as 3490 and 2002 Down syndrome-specific differentially modified and 5-hydroxymethylated regions, respectively, that can be used as biomarkers for identification of Down syndrome or other epigenetic diseases of a fetus. CONCLUSIONS: uTOP-seq and hmTOP-seq approaches provide a cost-efficient and sensitive epigenetic analysis of fetal abnormalities in maternal cfDNA. The results demonstrated that T21 fetuses contain a perturbed epigenome and also indicated that fetal cfDNA might originate from fetal tissues other than placental chorionic villi. Robust covalent derivatization followed by targeted analysis of fetal DNA by sequencing or qPCR presents an attractive strategy that could help achieve superior sensitivity and specificity in prenatal diagnostics.

Creating basis for introducing non-invasive prenatal testing in the Estonian public health setting.

OBJECTIVE: The study aimed to validate a whole-genome sequencing-based NIPT laboratory method and our recently developed NIPTmer aneuploidy detection software with the potential to integrate the pipeline into prenatal clinical care in Estonia. METHOD: In total, 424 maternal blood samples were included. Analysis pipeline involved cell-free DNA extraction, library preparation and massively parallel sequencing on Illumina platform. Aneuploidies were determined with NIPTmer software, which is based on counting pre-defined per-chromosome sets of unique k-mers from sequencing raw data. SeqFF was implemented to estimate cell-free fetal DNA (cffDNA) fraction. RESULTS: NIPTmer identified correctly all samples of non-mosaic trisomy 21 (T21, 15/15), T18 (9/9), T13 (4/4) and monosomy X (4/4) cases, with the 100% sensitivity. However, one mosaic T18 remained undetected. Six false-positive (FP) results were observed (FP rate of 1.5%, 6/398), including three for T18 (specificity 99.3%) and three for T13 (specificity 99.3%). The level of cffDNA of <4% was estimated in eight samples, including one sample with T13 and T18. Despite low cffDNA level, these two samples were determined as aneuploid. CONCLUSION: We believe that the developed NIPT method can successfully be used as a universal primary screening test in combination with ultrasound scan for the first trimester fetal examination.

Computational framework for targeted high-coverage sequencing based NIPT.

Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal trisomies. The majority of publicly available computational methods for sequencing-based NIPT analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable for targeted high-coverage sequencing data from cell-free DNA samples. Here, we present a novel computational framework for a targeted high-coverage sequencing-based NIPT analysis. The developed framework uses a hidden Markov model (HMM) in conjunction with a supplemental machine learning model, such as decision tree (DT) or support vector machine (SVM), to detect fetal trisomy and parental origin of additional fetal chromosomes. These models were developed using simulated datasets covering a wide range of biologically relevant scenarios with various chromosomal quantities, parental origins of extra chromosomes, fetal DNA fractions, and sequencing read depths. Developed models were tested on simulated and experimental targeted sequencing datasets. Consequently, we determined the functional feasibility and limitations of each proposed approach and demonstrated that read count-based HMM achieved the best overall classification accuracy of 0.89 for detecting fetal euploidies and trisomies on simulated dataset. Furthermore, we show that by using the DT and SVM on the HMM classification results, it was possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively. We demonstrate that read count and allelic ratio-based models can achieve a high accuracy (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently, existing commercial NIPT analysis requires at least 4% of fetal fraction, which can be possibly a challenge in case of early gestational age (<10 weeks) or high maternal body mass index (>35 kg/m2). More accurate detection can be achieved at higher sequencing depth using HMM in conjunction with supplemental models, which significantly improve the trisomy detection especially in borderline scenarios (e.g., very low fetal fraction) and enables to perform NIPT even earlier than 10 weeks of pregnancy.

TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting.

Targeted next-generation sequencing (NGS) methods have become essential in medical research and diagnostics. In addition to NGS sensitivity and high-throughput capacity, precise biomolecule counting based on unique molecular identifier (UMI) has potential to increase biomolecule detection accuracy. Although UMIs are widely used in basic research its introduction to clinical assays is still in progress. Here, we present a robust and cost-effective TAC-seq (Targeted Allele Counting by sequencing) method that uses UMIs to estimate the original molecule counts of mRNAs, microRNAs, and cell-free DNA. We applied TAC-seq in three different clinical applications and compared the results with standard NGS. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX, +21) genomic DNA. TAC-seq mRNA profiling showed identical clustering results to transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard NGS. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p = 7.6 × 10-4) excess of chromosome 21 molecules at 10% fetal fraction level. Based on three proof-of-principle applications we demonstrate that TAC-seq is an accurate and highly potential biomarker profiling method for advanced medical research and diagnostics.

NIPTmer: rapid k-mer-based software package for detection of fetal aneuploidies.

Non-invasive prenatal testing (NIPT) is a recent and rapidly evolving method for detecting genetic lesions, such as aneuploidies, of a fetus. However, there is a need for faster and cheaper laboratory and analysis methods to make NIPT more widely accessible. We have developed a novel software package for detection of fetal aneuploidies from next-generation low-coverage whole genome sequencing data. Our tool - NIPTmer - is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data, and applying linear regression model on the counts. Additionally, the filtering process used for k-mer list creation allows one to take into account the genetic variance in a specific sample, thus reducing the source of uncertainty. The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. NIPTmer has the potential to reduce significantly the time and complexity of NIPT post-sequencing analysis compared to mapping-based methods. For non-commercial users the software package is freely available at http://bioinfo.ut.ee/NIPTMer/ .