Search

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups.Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients.Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Summary: During pre-implantation stages of mammalian development, maternally stored material promotes both the erasure of the sperm and oocyte epigenetic profiles and is responsible for concomitant genome activation. Here, we have utilized single-cell methylome and transcriptome sequencing (scM&T-seq) to quantify both mRNA expression and DNA methylation in oocytes and a developmental series of human embryos at single-cell resolution. We fully characterize embryonic genome activation and maternal transcript degradation and map key epigenetic reprogramming events in developmentally high-quality embryos. By comparing these signatures with early embryos that have undergone spontaneous cleavage-stage arrest, as determined by time-lapse imaging, we identify embryos that fail to appropriately activate their genomes or undergo epigenetic reprogramming. Our results indicate that a failure to successfully accomplish these essential milestones impedes the developmental potential of pre-implantation embryos and is likely to have important implications, similar to aneuploidy, for the success of assisted reproductive cycles.

Abstract Background Genome-wide studies have begun to link subtle variations in both allelic DNA methylation and parent-of-origin genetic effects with early development. Numerous reports have highlighted that the placenta plays a critical role in coordinating fetal growth, with many key functions regulated by genomic imprinting. With the recent description of wide-spread polymorphic placenta-specific imprinting, the molecular mechanisms leading to this curious polymorphic epigenetic phenomenon is unknown, as is their involvement in pregnancies complications. Results Profiling of 35 ubiquitous and 112 placenta-specific imprinted differentially methylated regions (DMRs) using high-density methylation arrays and pyrosequencing revealed isolated aberrant methylation at ubiquitous DMRs as well as abundant hypomethylation at placenta-specific DMRs. Analysis of the underlying chromatin state revealed that the polymorphic nature is not only evident at the level of allelic methylation, but DMRs can also adopt an unusual epigenetic signature where the underlying histones are biallelically enrichment of H3K4 methylation, a modification normally mutually exclusive with DNA methylation. Quantitative expression analysis in placenta identified two genes, GPR1-AS1 and ZDBF2, that were differentially expressed between IUGRs and control samples after adjusting for clinical factors, revealing coordinated deregulation at the chromosome 2q33 imprinted locus. Conclusions DNA methylation is less stable at placenta-specific imprinted DMRs compared to ubiquitous DMRs and contributes to privileged state of the placenta epigenome. IUGR-associated expression differences were identified for several imprinted transcripts independent of allelic methylation. Further work is required to determine if these differences are the cause IUGR or reflect unique adaption by the placenta to developmental stresses.

DNA methylation (5-methylcytosine, 5 mC) is involved in many cellular processes and is an epigenetic mechanism primarily associated with transcriptional repression. The recent discovery that 5 mC can be oxidized to 5-hydromethylcytosine (5hmC) by TET proteins has revealed the “sixth base” of DNA and provides additional complexity to what was originally thought to be a stable repressive mark. However, our knowledge of the genome-wide distribution of 5hmC in different tissues is currently limited. Here, we sought to define loci enriched for 5hmC in the placenta genome by combining oxidative bisulphite (oxBS) treatment with high-density Illumina Infinium HumanMethylation450 methylation arrays and to compare our results with those obtained in brain. Despite identifying over 17,000 high-confidence CpG sites with consistent 5hmC enrichment, the distribution of this modification in placenta is relatively sparse when compared to cerebellum and frontal cortex. Supported by validation using allelic T4 β-glucosyltransferase assays we identify 5hmC at numerous imprinted loci, often overlapping regions associated with parent-of-origin allelic 5 mC in both placenta and brain samples. Furthermore, we observe tissue-specific monoallelic enrichment of 5hmC overlapping large clusters of imprinted snoRNAs-miRNAs processed from long noncoding RNAs (lncRNAs) within the DLK1-DIO3 cluster on chromosome 14 and SNRPN-UBE3A domain on chromosome 15. Enrichment is observed solely on the transcribed alleles suggesting 5hmC is positively associated with transcription at these loci. Our study provides an extensive description of the 5hmC/5 mC landscape in placenta with our data available at www.humanimprints.net, which represents the most comprehensive resource for exploring the epigenetic profiles associated with human imprinted genes.

Altered genomic imprinting is frequently reported in cancer. Here, the authors analyze copy number and methylation in cancer cell lines and primary tumors to show that imprinted methylation profiles represent the accumulation of copy number alteration, rather than epigenetic alterations.

Abstract In peach, the flat phenotype is caused by a partially dominant allele in heterozygosis (Ss), fruits from homozygous trees (SS) abort a few weeks after fruit setting. Previous research has identified a SSR marker (UDP98–412) highly associated with the trait, found suitable for marker assisted selection (MAS). Here we report a ∼10 Kb deletion affecting the gene PRUPE.6G281100, 400 Kb upstream of UDP98-412, co-segregating with the trait. This gene is a leucine-rich repeat receptor-like kinase (LRR-RLK) orthologous to the Brassinosteroid insensitive 1-associated receptor kinase 1 (BAK1) group. PCR markers suitable for MAS confirmed its strong association with the trait in a collection of 246 cultivars. They were used to evaluate the DNA from a round fruit derived from a somatic mutation of the flat variety ‘UFO-4’, revealing that the mutation affected the flat associated allele (S). Protein BLAST alignment identified significant hits with genes involved in different biological processes. Best protein hit occurred with AtRLP12, which may functionally complement CLAVATA2, a key regulator that controls the stem cell population size. RT-PCR analysis revealed the absence of transcription of the partially deleted allele. The data support PRUPE.6G281100 as a candidate gene for flat shape in peach.