Epigenomic Data Research Articles

Abstract A118: Chromatin accessibility profiling of human pancreatic tumors reveals epigenetic features of malignancy and rapid recurrence

Abstract Pancreatic ductal adenocarcinoma (PDAC) disease progression involves complex cell state transitions in both malignant and non-malignant populations within the tumor microenvironment. To uncover PDAC cell states and epigenetic features associated with clinical outcome, we studied 42 PDAC samples (35 primary tumors and 7 metastases) from 41 patients with single cell ATAC-seq, along with 10 sample-matched single cell RNA-seq profiles. Our work uncovered thousands of chromatin regions with altered accessibility in malignant cells relative to normal ductal and acinar cells. Numerous epigenetic changes were universal to all PDAC clusters regardless of patient-of-origin and occurred proximal to genes involved in glycolysis, regulation of MYC, hypoxic response, extracellular matrix remodeling, and repression of transposable elements. Topic modeling of high dimensional chromatin data revealed networks of co-accessible cis-regulatory regions active in malignant cells, as well as networks specific to basal-like and classical PDAC molecular subtypes. Furthermore, our epigenomic data also suggest alternate paths of lineage infidelity across the classical-basal continuum: while basal-like PDAC cells acquire chromatin accessibility mimicking that of skin or mammary epithelial tissue, several classical subtype tumors featured a subpopulation of malignant cells with increased accessibility of neural-associated chromatin regions genome-wide. Within the immune compartment, we also identified a cis-regulatory network with increased activity in T-cells sampled from rapidly recurring PDAC tumors. Motif analysis of this network revealed enrichment for SMAD and FOXP1 motifs, suggesting widespread modification of T-cells by TGF-β. To our knowledge, our work is the largest single-cell resolution study of chromatin state in human PDAC to date, revealing key epigenetic features associated with PDAC subtype and clinical outcome. Citation Format: Kevin MacPherson, Jason M. Link, Patrick Worth, Brett Sheppard, Andrew Fields, Colin Daniel, Andrew Nishida, Carl Pelz, Trent Waugh, Ethan Agritelle, John Muschler, Andrew Adey, Rosalie Sears. Chromatin accessibility profiling of human pancreatic tumors reveals epigenetic features of malignancy and rapid recurrence [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A118.

Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022.

Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.

SMARTdb: An Integrated Database for Exploring Single-cell Multi-omics Data of Reproductive Medicine.

Single-cell multi-omics sequencing has greatly accelerated reproductive research in recent years, and the data are continually growing. However, utilizing these data resources is challenging for wet-lab researchers. A comprehensive platform for exploring single-cell multi-omics data related to reproduction is urgently needed. Here, we introduce the single-cell multi-omics atlas of reproduction (SMARTdb), an integrative and user-friendly platform for exploring molecular dynamics of reproductive development, aging, and disease, which covers multi-omics, multi-species, and multi-stage data. We curated and analyzed single-cell transcriptomic and epigenomic data of over 2.0 million cells from 6 species across the entire lifespan. A series of powerful functionalities are provided, such as "Query gene expression", "DIY expression plot", "DNA methylation plot", and "Epigenome browser". With SMARTdb, we found that the male germ cell-specific expression pattern of RPL39L and RPL10L is conserved between human and other model animals. Moreover, DNA hypomethylation and open chromatin may collectively regulate the specific expression pattern of RPL39L in both male and female germ cells. In summary, SMARTdb is a powerful platform for convenient data mining and gaining novel insights into reproductive development, aging, and disease. SMARTdb is publicly available at https://smart-db.cn.

Quantitative transcriptomic and epigenomic data analysis: a primer.

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

Trackplot: a fast and lightweight R script for epigenomic enrichment plots.

BigWig files serve as essential inputs in epigenomic data visualization. However, current R packages for visualizing these files are limited, slow, and burdened by numerous dependencies. We introducetrackplot, a minimal R script designed for the rapid generation of integrative genomics viewer (IGV) style track plots, profile plots, and heatmaps from bigWig files. This script offers speed, owing to its reliance onbwtool, resulting in performance gains ofseveral magnitudescompared to equivalentpackages.The script is lightweight, requiring only thedata.tableandbwtoolpackages as primary dependencies. Notably, the plots are generated in base R graphics,eliminatingthe need for additional packages.trackplotqueries the University of California Santa Cruz (UCSC) genome browser for gene models thereby enhancing the reproducibility of analyses. The script extends its support to general transfer format (GTF) further enhancing its versatility. This tool addresses the gaps in existing bigWig visualization approaches by offering speed, simplicity, and minimal dependencies, thereby presenting a valuable asset to researchers in the fields of epigenomics. trackplot is implemented in R is made available under MIT license at https://github.com/PoisonAlien/trackplot.

A multimodal approach identifies lactate as a central feature of right ventricular failure that is detectable in human plasma.

In PAH metabolic abnormalities in multiple pathways are well-recognized features of right ventricular dysfunction, however, prior work has focused mainly on the use of a single "omic" modality to describe a single deranged pathway. We integrated metabolomic and epigenomic data using transcriptomics in failing and non-failing RVs from a rodent model to provide novel mechanistic insight and translated these findings to accessible human specimens by correlation with plasma from PAH patients. Study was conducted in a doxycycline-inducible BMPR2 mutant mouse model of RV failure. Plasma was collected from controls and PAH patients. Transcriptomic and metabolomic analyses were done on mouse RV tissue and human plasma. For mouse RV, we layered metabolomic and transcriptomic data for multiple metabolic pathways and compared our findings with metabolomic and transcriptomic data obtained for human plasma. We confirmed our key findings in cultured cardiomyocyte cells with BMPR2 mutation. In failing mouse RVs, (1) in the glycolysis pathway, glucose is converted to lactate via aerobic glycolysis, but may also be utilized for glycogen, fatty acid, and nucleic acid synthesis, (2) in the fatty acid pathway, FAs are accumulated in the cytoplasm because the transfer of FAs to mitochondria is reduced, however, the ß-oxidation pathway is likely to be functional. (3) the TCA cycle is altered at multiple checkpoints and accumulates citrate, and the glutaminolysis pathway is not activated. In PAH patients, plasma metabolic and transcriptomic data indicated that unlike in the failing BMPR2 mutant RV, expression of genes and metabolites measured for the glycolysis pathway, FA pathway, TCA cycle, and glutaminolysis pathway were increased. Lactate was the only metabolite that was increased both in RV and circulation. We confirmed using a stable isotope of lactate that cultured cardiomyocytes with mutant BMPR2 show a modest increase in endogenous lactate, suggesting a possibility of an increase in lactate production by cardiomyocytes in failing BMPR2 mutant RV. In the failing RV with mutant BMPR2, lactate is produced by RV cardiomyocytes and may be secreted out, thereby increasing lactate in circulation. Lactate can potentially serve as a marker of RV dysfunction in PAH, which warrants investigation.

DNA methylation profiling deciphers three EMT subtypes with distinct prognoses and therapeutic vulnerabilities in breast cancer.

Background: Epithelial-mesenchymal transition (EMT), deemed a pivotal hallmark of tumours, is intricately regulated by DNA methylation and encompasses multiple states along tumour progression. The potential mechanisms that drive the intrinsic heterogeneity of breast cancer (BC) via EMT transformation have not been identified, presenting a significant obstacle in clinical diagnosis and treatment. Methods: A total of 7,602 patients have been included in this study. We leveraged integrated multiomics data (epigenomic, genomic, and transcriptomic data) to delineate the comprehensive landscape of EMT in BC. Subsequently, a subtyping classifier was developed through a machine learning framework proposed by us. Results: We classified the BC samples into three methylation-driven EMT subtypes with distinct features, namely, C1 (the mammary duct development subtype with TP53 activation), C2 (the immune infiltration subtype with high TP53 mutation), and C3 (the ERBB2 amplification subtype with an unfavorable prognosis). Specifically, patients with the C1 subtype might respond to endocrine therapy or the p53-MDM2 antagonist nutlin-3. Patients with the C2 subtype might benefit from combined therapeutic regimens involving radiotherapy, PARP inhibitors, and immune checkpoint blockade therapy. Patients with the C3 subtype might benefit from anti-HER2 agents such as lapatinib. Notably, to increase the clinical applicability of the EMT subtypes, we devised a 96-gene panel-based classifier via a machine learning framework. Conclusions: Our study identified three methylation-driven EMT subtypes with distinct prognoses and biological traits to capture heterogeneity in BC and provided a rationale for the use of this classification as a powerful tool for developing new strategies for clinical trials.

Bottom-up data integration in polymer models of chromatin organisation

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, bottom-up frameworks which allow the incorporation of the sequence-specific nature of biochemistry in a unified model of 3D chromatin structure remain scarce. Here we propose SEMPER (Sequence Enhanced Magnetic PolymER), a novel stochastic polymer model which naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. We introduce a novel approximate Bayesian algorithm to quantify a posteriori the relative importance of various factors, including the polymeric nature of DNA, in determining chromatin epigenetic state, thus providing a transparent way to generate biological hypotheses. While accurate prediction of contact frequencies (a problem already extensively studied in the literature) is not our main aim, as a byproduct of the inference procedure and without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and nontrivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data and opens the way to a new class of more systematic approaches to interpreting epigenomic data.

StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution.

Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time. We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency. Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells. scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.

Stressful life events, social support, and epigenetic aging in the Women's Health Initiative.

Elevated psychosocial stress has been linked with accelerated biological aging, including composite DNA methylation (DNAm) markers that predict aging-related outcomes ("epigenetic age"). However, no study has examined whether stressful life events (SLEs) are associated with epigenetic age acceleration in postmenopausal women, an aging population characterized by increased stress burden and disease risk. We leveraged the Women's Health Initiative, a large muti-ancestry cohort of postmenopausal women with available psychosocial stress measures over the past year and epigenomic data. SLEs and social support were ascertained via self-report questionnaires. Whole blood DNAm array (450 K) data were used to calculate five DNAm-based predictors of chronological age, health span and life span, and telomere length (HorvathAge, HannumAge, PhenoAge, GrimAge, DNAmTL). After controlling for potential confounders, higher SLE burden was significantly associated with accelerated epigenetic aging, as measured by GrimAge (β: 0.34, 95% CI: 0.08, 0.59) and DNAmTL (β: -0.016, 95% CI: -0.028, -0.004). Exploratory analyses showed that SLEs-GrimAge associations were stronger in Black women as compared to other races/ethnicities and in those with lower social support levels. In women with lower social support, SLEs-DNAmTL associations showed opposite association in Hispanic women as compared to other race/ethnicity groups. Our findings suggest that elevated stress burden is associated with accelerated epigenetic aging in postmenopausal women. Lower social support and/or self-reported race/ethnicity may modify the association of stress with epigenetic age acceleration. These findings advance understanding of how stress may contribute to aging-related outcomes and have important implications for disease prevention and treatment in aging women.

Epidermal Growth Factor Receptor and Double-Imprinted Nanoparticles for Targeted Cancer Drug Delivery - A Review

The Epidermal Growth Factor Receptor (EGFR) plays a crucial role in maintaining tissue balance, but in conditions like tumors or lung and skin cancer, it acts as a driver for cancer development. Nanoparticles are effective tools for delivering medications to tumor cells while minimizing drug impact on healthy cells. A potent therapeutic approach involves using nanoparticles with tumor-specific ligands for cancer treatment. This review study focuses on double-imprinted nanoparticles, synthesized to target the estrogen alpha cancer cell receptor's linear epitope and loaded with an anti-cancer drug. The challenge in cancer therapy is delivering drugs specifically to cancer cells without causing adverse effects on healthy cells. Advanced high-throughput technologies generate genomic and epigenomic data, and Support Vector Machine (SVM) classification in cancer genomics identifies new indicators, drug targets, and cancer transport genetics. The EGFR-SVM approach is designed to enhance the selectivity and reduce the toxicity of cytotoxic drugs by targeting cancer cells more specifically. Targeted treatment involves interfering with specific proteins that promote tumor growth and spread, and the study demonstrates a molecularly targeted drug delivery method for efficiently and selectively targeting anticancer drugs to tumor cells overexpressing EGFR. This approach has the potential to improve the therapeutic effectiveness of existing anticancer drugs.

Whole-genome sequencing reveals the molecular implications of the stepwise progression of lung adenocarcinoma

The mechanism underlying the development of tumors, particularly at early stages, still remains mostly elusive. Here, we report whole-genome long and short read sequencing analysis of 76 lung cancers, focusing on very early-stage lung adenocarcinomas such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma. The obtained data is further integrated with bulk and spatial transcriptomic data and epigenomic data. These analyses reveal key events in lung carcinogenesis. Minimal somatic mutations in pivotal driver mutations and essential proliferative factors are the only detectable somatic mutations in the very early-stage of AIS. These initial events are followed by copy number changes and global DNA hypomethylation. Particularly, drastic changes are initiated at the later AIS stage, i.e., in Noguchi type B tumors, wherein cancer cells are exposed to the surrounding microenvironment. This study sheds light on the pathogenesis of lung adenocarcinoma from integrated pathological and molecular viewpoints.

Churros: a Docker-based pipeline for large-scale epigenomic analysis.

The epigenome, which reflects the modifications on chromatin or DNA sequences, provides crucial insight into gene expression regulation and cellular activity. With the continuous accumulation of epigenomic datasets such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) data, there is a great demand for a streamlined pipeline to consistently process them, especially for large-dataset comparisons involving hundreds of samples. Here, we present Churros, an end-to-end epigenomic analysis pipeline that is environmentally independent and optimized for handling large-scale data. We successfully demonstrated the effectiveness of Churros by analyzing large-scale ChIP-seq datasets with the hg38 or Telomere-to-Telomere (T2T) human reference genome. We found that applying T2T to the typical analysis workflow has important impacts on read mapping, quality checks, and peak calling. We also introduced a useful feature to study context-specific epigenomic landscapes. Churros will contribute a comprehensive and unified resource for analyzing large-scale epigenomic data.

Core promoterome of barley embryo

Precise localization and dissection of gene promoters are key to understanding transcriptional gene regulation and to successful bioengineering applications. The core RNA polymerase II initiation machinery is highly conserved among eukaryotes, leading to a general expectation of equivalent underlying mechanisms. Still, less is known about promoters in the plant kingdom. In this study, we employed cap analysis of gene expression (CAGE) at three embryonic developmental stages in barley to accurately map, annotate, and quantify transcription initiation events. Unsupervised discovery of de novo sequence clusters grouped promoters based on characteristic initiator and position-specific core-promoter motifs. This grouping was complemented by the annotation of transcription factor binding site (TFBS) motifs. Integration with genome-wide epigenomic data sets and gene ontology (GO) enrichment analysis further delineated the chromatin environments and functional roles of genes associated with distinct promoter categories. The TATA-box presence governs all features explored, supporting the general model of two separate genomic regulatory environments. We describe the extent and implications of alternative transcription initiation events, including those that are specific to developmental stages, which can affect the protein sequence or the presence of regions that regulate translation. The generated promoterome dataset provides a valuable genomic resource for enhancing the functional annotation of the barley genome. It also offers insights into the transcriptional regulation of individual genes and presents opportunities for the informed manipulation of promoter architecture, with the aim of enhancing traits of agronomic importance.

Efforts toward discovering inhibitors of the SYK SH2–FCER1γ interaction as potential Alzheimer’s disease chemical probes and therapeutics

AbstractBackgroundSpleen associated tyrosine kinase (SYK) plays a potential role in several neurodegenerative conditions. To date, a few pharmacological inhibitors have been developed, targeting the ATP‐binding kinase domain, and some of these inhibitors have been reported to show beneficial effects on the pathophysiology of Alzheimer’s (PMC: 9179326).In 2015, the Accelerating Medicines Partnership: Alzheimer’s Disease (AMP‐AD) Knowledge Portal analyzed and identified SYK and the FC gamma receptor (FCεR1γ) as targets for Alzheimer’s Disease (AD), through multidimensional human “omic” (genomic, epigenomic, RNAseq, and proteomic) data (PMID: 26853544). Thus, the interaction between FCER1G p‐ITAM and SYK tandem SH2 domain was selected for further target validation and small molecule hit discovery efforts through the portfolio of novel targets to treat the Alzheimer’s disease via the Target Enablement to Accelerate Therapy Development for Alzheimer’s Disease (TREAT‐AD) program were initiated. As part of the TREAT‐AD effort, we aim to provide experimental validation of a SYK–FCεR1γ interaction inhibitor for AD and begin drug discovery campaigns to identify novel inhibitors of this interaction, with the goal of developing an in vivo chemical probe.MethodUsing purified SYK tandem‐SH2 (tSH2) domain protein, a time‐resolved fluorescence energy transfer (TR‐FRET) based assay platform was developed with a fluoroprobe‐labeled phospho‐ITAM peptide of FCεR1γ. This assay was miniaturized for ultra‐high‐throughput screening (uHTS) of 100K compounds in parallel with a DNA encoded library (DEL) screening approach (Ref: X‐Chem). The hits from these screening methods were evaluated in secondary assays in an attempt to validate the initial hits.ResultWe have previous crystallography data of the SYK tSH2 domains bound to phospho‐ITAM peptides and have established a TR‐FRET based assay to discover and characterize inhibitors of the interaction between the FCεR1γ peptide and SYK tSH2 domains. Several thio‐uric acids hit compounds have been identified from uHTS. Most of them demonstrate potency in the TR‐FRET based assay of <10 µM. Although, orthogonal experimental validation is still under process.ConclusionThrough the TREAT‐AD program, we have explored the SYK–FCεR1γ interaction and demonstrated novel screening strategies, which can be further exploited to generate novel inhibitors of this interaction.

An intronic LINE-1 regulates IFNAR1 expression in human immune cells

BackgroundDespite their origins as selfish parasitic sequences, some transposons in the human genome have been co-opted to serve as regulatory elements, contributing to the evolution of transcriptional networks. Most well-characterized examples of transposon-derived regulatory elements derive from endogenous retroviruses (ERVs), due to the intrinsic regulatory activity of proviral long terminal repeat regions. However, one subclass of transposable elements, the Long Interspersed Nuclear Elements (LINEs), have been largely overlooked in the search for functional regulatory transposons, and considered to be broadly epigenetically repressed.ResultsWe examined the chromatin state of LINEs by analyzing epigenomic data from human immune cells. Many LINEs are marked by the repressive H3K9me3 modification, but a subset exhibits evidence of enhancer activity in human immune cells despite also showing evidence of epigenetic repression. We hypothesized that these competing forces of repressive and activating epigenetic marks might lead to inducible enhancer activity. We investigated a specific L1M2a element located within the first intron of Interferon Alpha/Beta Receptor 1 (IFNAR1). This element shows epigenetic signatures of B cell-specific enhancer activity, despite being repressed by the Human Silencing Hub (HUSH) complex. CRISPR deletion of the element in B lymphoblastoid cells revealed that the element acts as an enhancer that regulates both steady state and interferon-inducible expression of IFNAR1.ConclusionsOur study experimentally demonstrates that an L1M2a element was co-opted to function as an interferon-inducible enhancer of IFNAR1, creating a feedback loop wherein IFNAR1 is transcriptionally upregulated by interferon signaling. This finding suggests that other LINEs may exhibit cryptic cell type-specific or context-dependent enhancer activity. LINEs have received less attention than ERVs in the effort to understand the contribution of transposons to the regulatory landscape of cellular genomes, but these are likely important, lineage-specific players in the rapid evolution of immune system regulatory networks and deserve further study.

Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes.

This review aims to elucidate the transformative impact and potential of machine learning (ML) in the diagnosis, prognosis, and clinical management of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). It further aims to bridge the gap between current advances of ML and their practical application in these diseases. Recent advances in ML have revolutionized prognostication, diagnosis, and treatment of MDS and AML. ML algorithms have proven effective in predicting disease progression, optimizing treatment responses, and in the stratification of patient groups. Particularly, the use of ML in genomic and epigenomic data analysis has unveiled novel insights into the molecular heterogeneity of MDS and AML, leading to better-informed therapeutic strategies. Furthermore, deep learning techniques have shown promise in analyzing complex patterns in bone marrow biopsy images, providing a potential pathway towards early and accurate diagnosis. While still in the nascent stages, ML applications in MDS and AML signify a paradigm shift towards precision medicine. The integration of ML with traditional clinical practices could potentially enhance diagnostic accuracy, refine risk stratification, and improve therapeutic approaches. However, challenges related to data privacy, standardization, and algorithm interpretability must be addressed to realize the full potential of ML in this field. Future research should focus on the development of robust, transparent ML models and their ethical implementation in clinical settings.

Statistical learning quantifies transposable element-mediated cis-regulation

BackgroundTransposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryogenesis to innate immune responses. Because of their repetitive nature, TEs have the potential to form CRE platforms enabling the coordinated and genome-wide regulation of protein-coding genes by only a handful of trans-acting transcription factors (TFs).ResultsHere, we directly test this hypothesis through mathematical modeling and demonstrate that differences in expression at protein-coding genes alone are sufficient to estimate the magnitude and significance of TE-contributed cis-regulatory activities, even in contexts where TE-derived transcription fails to do so. We leverage hundreds of overexpression experiments and estimate that, overall, gene expression is influenced by TE-embedded CREs situated within approximately 500 kb of promoters. Focusing on the cis-regulatory potential of TEs within the gene regulatory network of human embryonic stem cells, we find that pluripotency-specific and evolutionarily young TE subfamilies can be reactivated by TFs involved in post-implantation embryogenesis. Finally, we show that TE subfamilies can be split into truly regulatorily active versus inactive fractions based on additional information such as matched epigenomic data, observing that TF binding may better predict TE cis-regulatory activity than differences in histone marks.ConclusionOur results suggest that TE-embedded CREs contribute to gene regulation during and beyond gastrulation. On a methodological level, we provide a statistical tool that infers TE-dependent cis-regulation from RNA-seq data alone, thus facilitating the study of TEs in the next-generation sequencing era.

Prevalently carried HLA allele associates with inflammation and poor prognosis in HF patients via co-stimulatory, adhesion and MHC-II enhanceosome regulation

Abstract Background Heart failure with reduced ejection fraction (HFrEF) is associated with impaired inflammation resolution, adverse remodeling, restricted function, and poor prognosis. Furthermore, immune cells play crucial roles in pathological remodeling in cardiovascular diseases. Thus identifying subsets of patients with unique inflammatory profiles may aid in better risk stratification and precision therapies for chronic HFrEF patients with altered immune profiles. Methods and Results A pilot study using scRNA-seq of circulating immune cells (n=16) showed strong T cell activation in HFrEF patients, which was validated via flow cytometry in HFrEF patients and healthy controls (n=180 patients) with a shift into the memory/effector phenotype (p<0.0001). Thus, HFrEF patients may have unique MHC signatures promoting aberrant T cell activation, leading to chronic inflammation and pathology. We investigated antigen-presenting cells (APCs) that activate CD4+ T cells via MHC-II and costimulatory molecules. Indeed, we found MHC-II and costimulatory molecule signatures associated with HFrEF and T cell activation, such as HLA-DRB5 (DRB5) and ICAM-1, indicating both T cell activation potential and a pro-inflammatory status. To study whether HLA-DRB5 selectively promotes immune dysregulation, circulating monocytes from healthy DRB5 carriers and non-carriers were investigated using scRNA-seq showing enhanced antigen presentation, co-stimulatory and inflammatory molecule expression (e.g. HLA-DRA, ICAM-2, JAK2). ScRNA-seq confirmed HLA-DRB5 regulation of MHC-II, inflammatory and adhesion (e.g. ITGAM and ITGB1) properties by silencing HLA-DRB5 in THP1 cells. In silico analysis of monocyte epigenome data predicted unique transcription factors (e.g. ERG) controlling of HLA-DRB5 via regulatory elements suggesting a role in selective MHC-II molecule activation. To gain insights as to whether HLA carrier status might associate with prognosis, we performed HLA sequencing on age and gender matched HFrEF patients (n=93). During follow up of this cohort we identified HLA-DRB5*0101, which belongs to the DR2-supertype, as a predictor for death (p<0.05). We hypothesized whether HLA-DRB5 expression could impact T cell infiltration into the heart. Thus, we stimulated naïve T cells with pre-treated THP1-derived DCs (stress v. no stress cardiac organoid medium) and found HLA-DRB5 silencing reduced the infiltration of T cells into unstressed cardiac organoids compared to control group. Conclusions This is the first study to provide mechanistic insights for how HLA-DRB5 regulates innate and adaptive immunity in the context of heart failure, while also showing corresponding changes in T cell activation, infiltration capacity and prognosis in HFrEF patients. These data provide novel insights for risk stratification of heart failure patients and novel immunological interventions which may improve prognosis in HFrEF patients.

Bone Marrow Microenvironment and Hematopoietic Cell Types Are Differentially Affected By Transcriptomic Quality Control Filtering Metrics in Single Cell Data

A crucial step prior to analysis of single cell transcriptomic data includes filtering out low-quality (e.g., dying) cells and artifacts (e.g., empty droplets). Choosing appropriate quality control parameters leads to correct cell assignment, so that the data for downstream analysis is high-quality and is representative of all processed cell types. These metrics include high and low cut-offs for mitochondrial content, total number of genes, number of different genes, and ribosomal content per cell. They have been found to vary based on cell and tissue type, as well biological variables such as age and disease, and technical variables such as sequencing technology. The aim of this study was to investigate the variation of these quality control metrics in a data set that included stem, progenitor, and differentiated cell types of the hematopoietic, mesenchymal, and endothelial lineages. Furthermore, we set out to determine whether applying standard cut-offs may differentially affect different cell types. We calculated individual filtering parameters for each cell type using the R package ddqc (Subramanian et al. Genome Biol. 2022). Thus, any biological variability based on mitochondrial, ribosomal, and gene counts was accounted for. Notably, distribution of measurements for each of the above quality control metrics varied by cluster, and certain cell-type means were adjacent to commonly used cut-off values. For instance, we observed higher content of mitochondrial genes in osteoblasts, sinusoidal endothelial cells, a subset of adipocyte-primed mesenchymal cells (MSPC), as well as hematopoietic progenitor cells, pre-B, and B cells. On the other hand, subsets of erythroblasts, neutrophils, as well as arteriolar endothelial cells had the lowest mitochondrial content. In addition, lower total gene counts and lower gene complexity were found in adipocyte-primed MSPCs, neutrophils, and megakaryocyte-erythrocyte progenitors, while higher counts were found in granulocyte-monocyte progenitor cells, megakaryocyte/erythroid-primed multipotent progenitor cells and subsets of erythroblasts. Applying universal cutoff values to this data set would greatly reduce, and some cases, eliminate cell types that have high mitochondrial content or lower gene counts and complexity, while leaving those cell types on the opposite spectrum unaffected. This would also skew proportions of cells in downstream analyses, thus affecting biological interpretation of the results. Overall, our results demonstrate biological variability of the quality control metric distributions in a single cell transcriptomic data set that includes diverse bone marrow niche and hematopoietic cell types. This reveals novel biological information regarding distinct cell types as well as cautions that applying standard filtering paraments on such a data set would eliminate some cell types and bias downstream analysis for others. Optimization of filtering criteria will be important when broadening single cell analysis to multiplexed approaches incorporating transcriptome, genome, epigenome, and proteome-level data.