Utilization of tissue-free minimal residual disease testing in colorectal cancer patients from Asia and Middle East.

Introduction: The presence of minimal residual disease (MRD) after curative-intent surgery for early-stage cancers is associated with disease recurrence. Circulating tumour deoxyribonucleic acid (ctDNA) has emerged as a promising biomarker for MRD assessment in patients with colorectal cancer (CRC) who have undergone surgery or completed adjuvant therapy. MRD tests are already available for use in clinics; however, treatment decisions following MRD results obtained in routine practice are infrequently described. Methods: In this observational study, we report on the real-world clinical use of Guardant Reveal, a validated tissue-free MRD assay, in the first 215 consecutive patients (279 samples) with CRC tested in Asia and the Middle East. Results: Overall, 22% of patients had ctDNA detected in their first MRD test, and the frequency of ctDNA positivity increased with increasing tumour stage. 132 samples were tested with an earlier version of Guardant Reveal, one that assessed both genomic and epigenomic features. An updated version of the assay assesses only ctDNA methylation data and was used for the remaining 147 samples. In patients with stage II CRC, 71% of tests were ordered within 12 weeks after tumour resection, while for patients with stage III disease, 69% of tests were ordered after completion of all curative-intent treatment. Discussion: Clinical cases utilizing tissue-free MRD assessment are described.

Different genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes characterize relapse and remission of multiple sclerosis: Focus on GNAS.

BACKGROUND: Relapsing-remitting multiple sclerosis (RRMS) is a most common form of multiple sclerosis in which periods of neurological worsening are followed by periods of clinical remission. RRMS relapses are caused by an acute autoimmune inflammatory process, which can occur in any area of the central nervous system. Although development of exacerbation cannot yet be accurately predicted, various external factors are known to affect its risk. These factors may trigger the pathological process through epigenetic mechanisms of gene expression regulation, first of all, through changes in DNA methylation. METHODS: In the present work, we for the first time analyzed genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes of the same RRMS patients in relapse and remission. The effects of the differential methylation on gene expression were studied using qPCR. RESULTS: We found 743 differentially methylated CpG positions (DMPs) in CD4+ cells and only 113 DMPs in CD14+ cells. They were mostly hypermethylated in RRMS relapse in both cell populations. However, the proportion of hypermethylated DMPs (as well as DMPs located within or in close proximity to CpG islands) was significantly higher in CD4+ T lymphocytes. In CD4+ and CD14+ cells we identified 469 and 67 DMP-containing genes, respectively; 25 of them were common for two cell populations. When we conducted a search for differentially methylated genomic regions (DMRs), we found a CD4+ specific DMR hypermethylated in RRMS relapse (adj. p = 0.03) within the imprinted GNAS locus. Total level of the protein-coding GNAS transcripts in CD4+ T cells decreased significantly in the row from healthy control to RRMS remission and then to RRMS relapse (adj. p = 3.1 × 10-7 and 0.011, respectively). CONCLUSION: Our findings suggest that the epigenetic mechanism of DNA methylation in immune cells contributes to the development of RRMS relapse. Further studies are now required to validate these results and shed light on the molecular mechanisms underlying the observed GNAS methylation and expression changes.

Methylation patterns at the adjacent CpG sites within enhancers are a part of cell identity.

BACKGROUND: It is generally accepted that methylation status of CpG sites spaced up to 50 bp apart is correlated, and accumulation of locally disordered methylation at adjacent CpG sites is involved in neoplastic transformation, acting in similar way as stochastic accumulation of mutations. RESULTS: We used EPIC microarray data from 596 samples, representing 12 healthy tissue and cell types, as well as 572 blood cancer specimens to analyze methylation status of adjacent CpG sites across human genome, and subsequently validated our findings with NGS and Sanger sequencing. Our analysis showed that there is a subset of the adjacent CpG sites in human genome, with cytosine at one CpG site methylated and the other devoid of methyl group. These loci map to enhancers that are targeted by families of transcription factors involved in cell differentiation. Moreover, our results suggest that the methylation at these loci differ between alleles within a cell, what allows for remarkable level of heterogeneity of methylation patterns. However, different types of specialized cells acquire only one specific and stable pattern of methylation at each of these loci and that pattern is to a large extent lost during neoplastic transformation. CONCLUSIONS: We identified a substantial number of adjacent CpG loci in human genome that display remarkably stable and cell type specific methylation pattern. The methylation pattern at these loci appears to reflect different methylation of alleles in cells. Furthermore, we showed that changes of methylation status at those loci are likely to be involved in regulation of the activity of enhancers and contribute to neoplastic transformation.

Epigenomic newborn screening for conditions with intellectual disability and autistic features in Australian newborns.

This study describes a protocol to assess a novel workflow called Epi-Genomic Newborn Screening (EpiGNs) on 100,000 infants from the state of Victoria, Australia. The workflow uses a first-tier screening approach called methylation-specific quantitative melt analysis (MS-QMA), followed by second and third tier testing including targeted methylation and copy number variation analyzes with droplet digital PCR, EpiTYPER system and low-coverage whole genome sequencing. EpiGNs utilizes only two 3.2 mm newborn blood spot punches to screen for genetic conditions, including fragile X syndrome, Prader-Willi syndrome, Angelman syndrome, Dup15q syndrome and sex chromosome aneuploidies. The program aims to: identify clinically actionable methylation screening thresholds for the first-tier screen and estimate prevalence for the conditions screened.

[Box: see text].

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Corrigendum: Homeobox regulator Wilms Tumour 1 is displaced by androgen receptor at cis-regulatory elements in the endometrium of PCOS patients.

[This corrects the article DOI: 10.3389/fendo.2024.1368494.].

Enhancer landscape of lung neuroendocrine tumors reveals regulatory and developmental signatures with potential theranostic implications.

Well-differentiated low-grade lung neuroendocrine tumors (lung carcinoids or LNETs) are histopathologically classified as typical and atypical LNETs, but each subtype is still heterogeneous at both the molecular level and its clinical manifestation. Here, we report genome-wide profiles of primary LNETs' cis-regulatory elements by H3K27ac ChIP-seq with matching RNA-seq profiles. Analysis of these regulatory landscapes revealed three regulatory subtypes, independent of the typical/atypical classification. We identified unique differentiation signals that delineate each subtype. The "proneuronal" subtype emerges under the influence of ASCL1, SOX4, and TCF4 transcription factors, embodying a pronounced proneuronal signature. The "luminal-like" subtype is characterized by gain of acetylation at markers of luminal cells and GATA2 activation and loss of LRP5 and OTP. The "HNF+" subtype is characterized by a robust enhancer landscape driven by HNF1A, HNF4A, and FOXA3, with notable acetylation and expression of FGF signaling genes, especially FGFR3 and FGFR4, pivotal components of the FGF pathway. Our findings not only deepen the understanding of LNETs' regulatory and developmental diversity but also spotlight the HNF+ subtype's reliance on FGFR signaling. We demonstrate that targeting this pathway with FGF inhibitors curtails tumor growth both in vitro and in xenograft models, unveiling a potential vulnerability and paving the way for targeted therapies. Overall, our work provides an important resource for studying LNETs to reveal regulatory networks, differentiation signals, and therapeutically relevant dependencies.

Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Improved inference of population histories by integrating genomic and epigenomic data.

With the availability of high-quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, for example bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites, or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to (1) improve the accuracy of demographic inference in recent times, (2) uncover past demographic events hidden to SNP-based inference methods, and (3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demographic inference in Arabidopsis thaliana using DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregating single methylated polymorphisms (SMPs) satisfy the modeling assumptions of the SMC framework, while differentially methylated regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post-glacial population expansion of the European A. thaliana population. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next-generation inference of evolutionary history by combining information from several genetic and epigenetic markers.

A Study of Small Intestinal Epigenomic Changes Induced by Royal Jelly.

This study explores the impact of royal jelly (RJ) on small intestinal epigenomic changes. RJ, produced by honeybees, is known for its effects on metabolic diseases. The hypothesis is that RJ induces epigenomic modifications in small intestinal epithelial cells, affecting gene expression and contributing to metabolic health. Male db/m and db/db mice were used to examine RJ's effects through mRNA sequencing and CUT&Tag methods. This study focused on histone modifications and gene expression changes, with statistical significance set at p < 0.05. RJ administration improved insulin sensitivity and lipid metabolism without affecting body weight. GO and KEGG pathway analyses showed significant enrichment in metabolic processes, cellular components, and molecular functions. RJ altered histone modifications, increasing H3K27me3 and decreasing H3K23Ac in genes associated with the G2M checkpoint. These genes, including Smc2, Mcm3, Ccnd1, Rasal2, Mcm6, and Mad2l1, are linked to cancer progression and metabolic regulation. RJ induces beneficial epigenomic changes in small intestinal epithelial cells, improving metabolic health and reducing cancer-associated gene expression. These findings highlight RJ's potential as a therapeutic agent for metabolic disorders. Further research is needed to fully understand the mechanisms behind these effects and their implications for human health.

Increasing Temporal Sensitivity of Omics Association Studies with Epigenome-Wide Distributed Lag Models.

Current methods for identifying temporal windows of effect for time-varying exposures in omics settings can control false discovery rates at the biomarker-level but cannot efficiently screen for timing-specific effects in high dimensions. Current approaches leverage separate models for site screening and identification of susceptible time windows, which miss associations that vary over time. We introduce the epigenome-wide distributed lag model (EWDLM), a novel approach that combines traditional false discovery rate methods with the distributed lag model (DLM) to screen for timing-specific effects in high dimensional settings. This is accomplished by marginalizing DLM effect estimates over time and correcting for multiple comparisons. In a simulation investigating timing-specific effects of ambient air pollution during pregnancy on DNA methylation across the epigenome at age 12 years, EWDLM achieved an increased sensitivity for associations limited to specific periods of time compared to traditional two-stage approaches. In a real-world EWDLM analysis, 353 CpG sites at which DNAm measured at age 12 was significantly associated with PM2.5 exposure during pregnancy were identified. EWDLM is a novel method that provides an efficient and sensitive way to screen epigenomic datasets for associations with exposures localized to specific time periods.

Integrated multi-omics analysis identifies features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. An integrated transcriptomic and epigenomic analysis assesses how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Resulting analysis identifies predictive markers of CPCs that give rise to high purity CM batches, including TTN, TRIM55, DGKI, MEF2C, MAB21L2, MYL7, LDB3, SLC7A11, and CALD1. Predictive models developed from these genes provide high accuracy in determining terminal CM purities at the CPC stage. Further, insights into mechanisms of batch failure and dominant non-CM cell types generated in failed batches are elucidated. Namely EMT, MAPK, and WNT signaling emerge as significant drivers of batch divergence, giving rise to off-target populations of fibroblasts/mural cells, skeletal myocytes, epicardial cells, and a non-CPC SLC7A11+ subpopulation. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness.

Nr2f1 enhancers have distinct functions in controlling Nr2f1 expression during cortical development.

There is evidence that transcription factor (TF) encoding genes, which temporally control development in multiple cell types, can have tens of enhancers that regulate their expression. The NR2F1 TF developmentally promotes caudal and ventral cortical regional fates. Here, we epigenomically compared the activity of Nr2f1's enhancers during mouse cortical development with their activity in a transgenic assay. We identified at least six that are likely to be important in prenatal cortical development, with three harboring de novo mutants identified in ASD individuals. We chose to study the function of two of the most robust enhancers by deleting them singly or together. We found that they have distinct and overlapping functions in driving Nr2f1's regional and laminar expression in the developing cortex. Thus, these two enhancers, probably in combination with the others that we defined epigenetically, precisely tune Nr2f1's regional, cell type, and temporal expression during corticogenesis.

Exploring the Missing link between vitamin D and autism spectrum disorder: Scientific evidence and new perspectives.

Aim: This study aims to address the key question of the causal relationship between serum levels of 25-hydroxyvitamin D (vitamin D) and autism spectrum disorders (ASD). Methods: Publicly available Genome-Wide Association Study (GWAS) datasets were used to conduct the bidirectional Two-sample MR analyses using methods including inverse-variance weighted (IVW), weighted median, MR-Egger regression, simple mode, MR-PRESSO test, Steiger filtering, and weighted mode, followed by BWMR for validation. Results: The MR analysis indicated that there was no causal relationship between Vitamin D as the exposure and ASD as the outcome in the positive direction of the MR analysis (IVW: OR = 0.984, 95 % CI: 0.821-1.18, P = 0.866). The subsequent BWMR validation stage yielded consistent results (OR = 0.984, 95 % CI 0.829-1.20, P = 0.994). Notably, in the reverse MR analysis with ASD as the exposure and Vitamin D as the outcome, the results suggested that the occurrence of ASD could lead to decreased Vitamin D levels (IVW: OR = 0.976, 95 % CI: 0.961-0.990, P = 0.000855), with BWMR findings in the validation stage confirming the discovery phase (OR = 0.975, 95 % CI: 0.958-0.991, P = 0.00297). For the positive MR analysis, no pleiotropy was detected in the instrumental variables. Similarly, no pleiotropy or heterogeneity was detected in the instrumental variables for the reverse MR analysis. Sensitivity analysis using the leave-one-out approach for both positive and reverse instrumental variables suggested that the MR analysis results were robust. Conclusion: Through the discovery and validation analysis process, we can confidently assert that there is no causative link between Vitamin D and ASD, and that supplementing Vitamin D is not expected to provide effective improvement for patients with ASD. Our study significantly advances a new perspective in ASD research and has a positive impact on medication guidance for patients with ASD.

Exploring the use of immunomethylomics in the characterization of depressed patients: A proof-of-concept study.

Alterations in DNA methylation and inflammation could represent valid biomarkers for the stratification of patients with major depressive disorder (MDD). This study explored the use of DNA-methylation based immunological cell-type profiles in the context of MDD and symptom severity over time. In 119 individuals with MDD, DNA-methylation was assessed on whole blood using the Illumina Infinium MethylationEPIC 850 k BeadChip. Quality control and data processing, as well as cell type estimation was conducted using the RnBeads package. The cell type composition was estimated using epigenome-wide DNA methylation signatures, applying the Houseman method, considering six cell types (neutrophils, natural killer cells (NK), B cells, CD4+ T cells, CD8+ T cells and monocytes). Two cytokines (IL-6 and IL-1ß) and hsCRP were quantified in serum. We performed a hierarchical cluster analysis on the six estimated cell-types and tested the differences between these clusters in relation to the two cytokines and hsCRP, depression severity at baseline, and after 6 weeks of treatment (celecoxib/placebo + vortioxetine). We performed a second cluster analysis with cell-types and cytokines combined. ANCOVA was used to test for differences across clusters. We applied the Bonferroni correction. After quality control, we included 113 participants. Two clusters were identified, cluster 1 was high in CD4+ cells and NK, cluster 2 was high in CD8+ T-cells and B-cells, with similar fractions of neutrophils and monocytes. The clusters were not associated with either of the two cytokines and hsCRP, or depression severity at baseline, but cluster 1 showed higher depression severity after 6 weeks, corrected for baseline (p = 0.0060). The second cluster analysis found similar results: cluster 1 was low in CD8+ T-cells, B-cells, and IL-1ß. Cluster 2 was low in CD4+ cells and natural killer cells. Neutrophils, monocytes, IL-6 and hsCRP were not different between the clusters. Participants in cluster 1 showed higher depression severity at baseline than cluster 2 (p = 0.034), but no difference in depression severity after 6 weeks. DNA-methylation based cell-type profiles may be valuable in the immunological characterization and stratification of patients with MDD. Future models should consider the inclusion of more cell-types and cytokines for better a prediction of treatment outcomes.

Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

This article introduces ESDA, a new analytic tool for integrating multiple data types to identify the most distinguishing features following an exposure. Using the ESDA, we were able to identify signatures of infectious diseases. The results of the study indicate that integration of multiple types of large datasets can be used to identify distinguishing features for infectious diseases. Understanding the changes from different exposures will enable development of diagnostic tests for infectious diseases that target responses from the patient. Using the ESDA, we will be able to build a database of human response signatures to different infections and simplify diagnostic testing in the future.

Mitochondrial stress-induced H4K12 hyperacetylation dysregulates transcription in Parkinson's disease.

Aberrant epigenetic modification has been implicated in the pathogenesis of Parkinson's disease (PD), which is characterized by the irreversible loss of dopaminergic (DAergic) neurons. However, the mechanistic landscape of histone acetylation (ac) in PD has yet to be fully explored. Herein, we mapped the proteomic acetylation profiling changes at core histones H4 and thus identified H4K12ac as a key epigenomic mark in dopaminergic neuronal cells as well as in MitoPark animal model of PD. Notably, the significantly elevated H4K12ac deposition in post-mortem PD brains highlights its clinical relevance to human PD. Increased histone acetyltransferase (HAT) activity and decreased histone deacetylase 2 (HDAC2) and HDAC4 were found in experimental PD cell models, suggesting the HAT/HDAC imbalance associated with mitochondrial stress. Following our delineation of the proteasome dysfunction that possibly contributes to H4K12ac deposition, we characterized the altered transcriptional profile and disease-associated pathways in the MitoPark mouse model of PD. Our study uncovers the axis of mitochondrial impairment-H4K12ac deposition-altered transcription/disease pathways as a neuroepigenetic mechanism underlying PD pathogenesis. These findings provide mechanistic information for the development of potential pharmacoepigenomic translational strategies for PD.

Single-cell multi-modal chromatin profiles revealing epigenetic regulations of cells in hepatocellular carcinoma.

BACKGROUND: Various epigenetic regulations systematically govern gene expression in cells involving various biological processes. Dysregulation of the epigenome leads to aberrant transcriptional programs and subsequently results in diseases, such as cancer. Therefore, comprehensive profiling epigenomics is essential for exploring the mechanisms underlying gene expression regulation during development and disease. METHODS: In this study, we developed single-cell chromatin proteins and accessibility tagmentation (scCPA-Tag), a multi-modal single-cell epigenetic profile capturing technique based on barcoded Tn5 transposases and a droplet microfluidics platform. scCPA-Tag enables the simultaneous capture of DNA profiles of histone modification and chromatin accessibility in the same cell. RESULTS: By applying scCPA-Tag to K562 cells and a hepatocellular carcinoma (HCC) sample, we found that the silence of several chromatin-accessible genes can be attributed to lysine-27-trimethylation of the histone H3 tail (H3K27me3) modification. We characterized the epigenetic features of the tumour cells and different immune cell types in the HCC tumour tissue by scCPA-Tag. Besides, a tumour cell subtype (C2) with more aggressive features was identified and characterized by high chromatin accessibility and a lower abundance of H3K27me3 on tumour-promoting genes. CONCLUSIONS: Our multi-modal scCPA-Tag provides a comprehensive approach for exploring the epigenetic landscapes of heterogeneous cell types and revealing the mechanisms of gene expression regulation during developmental and pathological processes at the single-cell level. HIGHLIGHTS: scCPA-Tag offers a highly efficient and high throughput technique to simultaneously profile histone modification and chromatin accessibility within a single cell. scCPA-Tag enables to uncover multiple epigenetic modification features of cellular compositions within tumor tissues. scCPA-Tag facilitates the exploration of the epigenetic landscapes of heterogeneous cell types and provides the mechanisms governing gene expression regulation.