PEREGRINE: A genome-wide prediction of enhancer to gene relationships supported by experimental evidence

ATAC-clock: An aging clock based on chromatin accessibility

The establishment of aging clocks highlighted the strong link between changes in DNA methylation and aging. Yet, it is not known if other epigenetic features could be used to predict age accurately. Furthermore, previous studies have observed a lack of effect of age-related changes in DNA methylation on gene expression, putting the interpretability of DNA methylation-based aging clocks into question. In this study, we explore the use of chromatin accessibility to construct aging clocks. We collected blood from 159 human donors and generated chromatin accessibility, transcriptomic, and cell composition data. We investigated how chromatin accessibility changes during aging and constructed a novel aging clock with a median absolute error of 5.27 years. The changes in chromatin accessibility used by the clock were strongly related to transcriptomic alterations, aiding clock interpretation. We additionally show that our chromatin accessibility clock performs significantly better than a transcriptomic clock trained on matched samples. In conclusion, we demonstrate that the clock relies on cell-intrinsic chromatin accessibility alterations rather than changes in cell composition. Further, we present a new approach to construct epigenetic aging clocks based on chromatin accessibility, which bear a direct link to age-related transcriptional alterations, but which allow for more accurate age predictions than transcriptomic clocks.

Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes

Pacific Biosciences (PacBio) HiFi sequencing technology generates long reads (>10 kbp) with very high accuracy (<0.01% sequencing error). Although several de novo assembly tools are available for HiFi reads, there are no comprehensive studies on the evaluation of these assemblers. We evaluated the performance of 11 de novo HiFi assemblers on (1) real data for three eukaryotic genomes; (2) 34 synthetic data sets with different ploidy, sequencing coverage levels, heterozygosity rates, and sequencing error rates; (3) one real metagenomic data set; and (4) five synthetic metagenomic data sets with different composition abundance and heterozygosity rates. The 11 assemblers were evaluated using quality assessment tool (QUAST) and benchmarking universal single-copy ortholog (BUSCO). We also used several additional criteria, namely, completion rate, single-copy completion rate, duplicated completion rate, average proportion of largest category, average distance difference, quality value, run-time, and memory utilization. Results show that hifiasm and hifiasm-meta should be the first choice for assembling eukaryotic genomes and metagenomes with HiFi data. We performed a comprehensive benchmarking study of commonly used assemblers on complex eukaryotic genomes and metagenomes. Our study will help the research community to choose the most appropriate assembler for their data and identify possible improvements in assembly algorithms.

Molecular Atlas of HER2+ Breast Cancer Cells Treated with Endogenous Ligands: Temporal Insights into Mechanisms of Trastuzumab Resistance

Trastuzumab therapy in HER2+ breast cancer patients has mixed success owing to acquired resistance to therapy. A detailed understanding of downstream molecular cascades resulting from trastuzumab resistance is yet to emerge. In this study, we investigate the cellular mechanisms underlying acquired resistance using trastuzumab-sensitive and -resistant cancer cells (BT474 and BT474R) treated with endogenous ligands EGF and HRG across time. We probe early receptor organization through microscopy and signaling events through multiomics measurements and assess the bioenergetic state through mitochondrial measurements. Integrative analyses of our measurements reveal significant alterations in EGF-treated BT474 HER2 membrane dynamics and robust downstream activation of PI3K/AKT/mTORC1 signaling. EGF-treated BT474R shows a sustained interferon-independent activation of the IRF1/STAT1 cascade, potentially contributing to trastuzumab resistance. Both cell lines exhibit temporally divergent metabolic demands and HIF1A-mediated stress responses. BT474R demonstrates inherently increased mitochondrial activity. HRG treatment in BT474R leads to a pronounced reduction in AR expression, affecting downstream lipid metabolism with implications for treatment response. Our results provide novel insights into mechanistic changes underlying ligand treatment in BT474 and BT474R and emphasize the pivotal role of endogenous ligands. These results can serve as a framework for furthering the understanding of trastuzumab resistance, with therapeutic implications for women with acquired resistance.

Atlas of nascent RNA transcripts reveals enhancer to gene linkages

Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.

PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships

The vast majority of disease-associated variants identified in genome-wide association studies map to enhancers, powerful regulatory elements which orchestrate the recruitment of transcriptional complexes to their target genes' promoters to upregulate transcription in a cell type- and timing-dependent manner. These variants have implicated thousands of enhancers in many common genetic diseases, including nearly all cancers. However, the etiology of most of these diseases remains unknown because the regulatory target genes of the vast majority of enhancers are unknown. Thus, identifying the target genes of as many enhancers as possible is crucial for learning how enhancer regulatory activities function and contribute to disease. Based on experimental results curated from scientific publications coupled with machine learning methods, we developed a cell type-specific score predictive of an enhancer targeting a gene. We computed the score genome-wide for every possible cis enhancer-gene pair and validated its predictive ability in four widely used cell lines. Using a pooled final model trained across multiple cell types, all possible gene-enhancer regulatory links in cis (~17 M) were scored and added to the publicly available PEREGRINE database ( www.peregrineproj.org ). These scores provide a quantitative framework for the enhancer-gene regulatory prediction that can be incorporated into downstream statistical analyses.

Multifaceted regulation of enhancers in cancer

Enhancer is one kind of cis-elements regulating gene transcription, whose activity is tightly controlled by epigenetic enzymes and histone modifications. Active enhancers are classified into typical enhancers, super-enhancers and over-active enhancers, according to the enrichment and location of histone modifications. Epigenetic factors control the level of histone modifications on enhancers to determine their activity, such as histone methyltransferases and acetylases. Transcription factors, cofactors and mediators co-operate together and are required for enhancer functions. In turn, abnormalities in these trans-acting factors affect enhancer activity. Recent studies have revealed enhancer dysregulation as one of the important features for cancer. Variations in enhancer regions and mutations of enhancer regulatory genes are frequently observed in cancer cells, and altering the activity of onco-enhancers is able to repress oncogene expression, and suppress tumorigenesis and metastasis. Here we summarize the recent discoveries about enhancer regulation in cancer and discuss their potential application in diagnosis and treatment.

Annotation Query (AnnoQ): an integrated and interactive platform for large-scale genetic variant annotation

The Annotation Query (AnnoQ) (http://annoq.org/) is designed to provide comprehensive and up-to-date functional annotations for human genetic variants. The system is supported by an annotation database with ∼39 million human variants from the Haplotype Reference Consortium (HRC) pre-annotated with sequence feature annotations by WGSA and functional annotations to Gene Ontology (GO) and pathways in PANTHER. The database operates on an optimized Elasticsearch framework to support real-time complex searches. This implementation enables users to annotate data with the most up-to-date functional annotations via simple queries instead of setting up individual tools. A web interface allows users to interactively browse the annotations, annotate variants and search variant data. Its easy-to-use interface and search capabilities are well-suited for scientists with fewer bioinformatics skills such as bench scientists and statisticians. AnnoQ also has an API for users to access and annotate the data programmatically. Packages for programming languages, such as the R package, are available for users to embed the annotation queries in their scripts. AnnoQ serves researchers with a wide range of backgrounds and research interests as an integrated annotation platform.

PANTHER: Making genome-scale phylogenetics accessible to all

Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must be performed carefully in order to avoid systematic errors and artifacts. Protein Analysis THrough Evolutionary Relationships (PANTHER; http://pantherdb.org) is a publicly available, user-focused knowledgebase that stores the results of an extensive phylogenetic reconstruction pipeline that includes computational and manual processes and quality control steps. First, fully reconciled phylogenetic trees (including ancestral protein sequences) are reconstructed for a set of "reference" protein sequences obtained from fully sequenced genomes of organisms across the tree of life. Second, the resulting phylogenetic trees are manually reviewed and annotated with function evolution events: inferred gains and losses of protein function along branches of the phylogenetic tree. Here, we describe in detail the current contents of PANTHER, how those contents are generated, and how they can be used in a variety of applications. The PANTHER knowledgebase can be downloaded or accessed via an extensive API. In addition, PANTHER provides software tools to facilitate the application of the knowledgebase to common protein sequence analysis tasks: exploring an annotated genome by gene function; performing "enrichment analysis" of lists of genes; annotating a single sequence or large batch of sequences by homology; and assessing the likelihood that a genetic variant at a particular site in a protein will have deleterious effects.

Comprehensive epigenomic profiling of human alveolar epithelial differentiation identifies key epigenetic states and transcription factor co-regulatory networks for maintenance of distal lung identity

BACKGROUND

Disruption of alveolar epithelial cell (AEC) differentiation is implicated in distal lung diseases such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and lung adenocarcinoma that impact morbidity and mortality worldwide. Elucidating underlying disease pathogenesis requires a mechanistic molecular understanding of AEC differentiation. Previous studies have focused on changes of individual transcription factors, and to date no study has comprehensively characterized the dynamic, global epigenomic alterations that facilitate this critical differentiation process in humans.

RESULTS

We comprehensively profiled the epigenomic states of human AECs during type 2 to type 1-like cell differentiation, including the methylome and chromatin functional domains, and integrated this with transcriptome-wide RNA expression data. Enhancer regions were drastically altered during AEC differentiation. Transcription factor binding analysis within enhancer regions revealed diverse interactive networks with enrichment for many transcription factors, including NKX2-1 and FOXA family members, as well as transcription factors with less well characterized roles in AEC differentiation, such as members of the MEF2, TEAD, and AP1 families. Additionally, associations among transcription factors changed during differentiation, implicating a complex network of heterotrimeric complex switching in driving differentiation. Integration of AEC enhancer states with the catalog of enhancer elements in the Roadmap Epigenomics Mapping Consortium and Encyclopedia of DNA Elements (ENCODE) revealed that AECs have similar epigenomic structures to other profiled epithelial cell types, including human mammary epithelial cells (HMECs), with NKX2-1 serving as a distinguishing feature of distal lung differentiation.

CONCLUSIONS

Enhancer regions are hotspots of epigenomic alteration that regulate AEC differentiation. Furthermore, the differentiation process is regulated by dynamic networks of transcription factors acting in concert, rather than individually. These findings provide a roadmap for understanding the relationship between disruption of the epigenetic state during AEC differentiation and development of lung diseases that may be therapeutically amenable.

A multi-omic single-cell landscape of human gynecologic malignancies

Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.