Alan P. Boyle

Human-specific long interspersed nuclear element-1 (L1HS) is an active and autonomous retrotransposon in the human genome. Changes in its transcription and transposition are known to affect cellular processes involved in development and aging, and diseases such as neurological disorders and cancer. To better understand natural variability in epigenetic patterns that affect L1HS regulation, we developed a targeted long-read method to simultaneously profile individual haplotypes for DNA methylation and chromatin accessibility across L1HS loci in a healthy human cell line trio. We show that the intronic L1HS in the ZNF638 gene consistently displays high chromatin accessibility and DNA hypomethylation with bidirectional transcription. Our approach also reveals additional intronic and intergenic L1HS copies with allele-specific chromatin accessibility and methylation, and instances of reduced promoter DNA methylation that does not correspond with increased chromatin accessibility. We also identify potential cases of non-Mendelian inheritance of DNA methylation patterns over a subset of L1HS promoters. Our method's high coverage over L1HS loci enables detection and profiling of loci that are missed even by long-read-based assemblies and enables more accurate inheritance tracing of L1HS insertions. Overall, our results offer new insights into the locus-specific regulation of both reference and non-reference L1HS within the human genome.

Summary: SEMPLR is an R package that predicts transcription factor binding and variant effects using SNP Effect Matrices (SEMs), providing efficient, genome-wide scoring, enrichment testing, and visualization tools for comprehensive analysis of regulatory sequences. Availability: Available on GitHub at https://github.com/grkenney/SEMPLR and on Bioconductor at https://bioconductor.org/packages/release/bioc/html/SEMPLR.html.

Sperm formation requires a radical chromatin reorganization, where nucleosomes are replaced by transition proteins (TNPs) and subsequently by protamines (PRM1 and PRM2). Although essential for fertility, the regulatory logic governing this exchange is unknown, but it’s presumed to be stochastic and unregulated. Using endogenously tagged PRM mouse models and stage-resolved, genome-wide profiling, we revise the order of histone-to-protamine exchange and show that the chromatin remodeling process is highly programmed. Imaging and biochemical experiments reveal a direct histone-to-PRM1 exchange, while TNPs appear after PRM1 but precede PRM2 incorporation. This temporal uncoupling of PRM1 and PRM2 incorporation coincides with dynamic, region-specific chromatin remodeling that is not governed by histone acetylation but is instructed by the three-dimensional nuclear architecture of round spermatids. Therefore, by integrating ATAC-seq, CUT&Tag, and Hi-C, we define a compartment-encoded “blueprint” that prescribes the assembly of the mature sperm epigenome and establishes a complex molecular hierarchy for the histone-to-protamine exchange.

Mapping the genomic locations and patterns of transcription factor binding sites (TFBS) is essential for understanding gene regulation and advancing treatments for diseases driven by DNA modifications, including epigenetic changes and sequence variants. Although several TFBS databases exist, no study has systematically benchmarked these databases across different sequencing technologies and computational algorithms. In this study, we addressed this gap by constructing a TFBS database that integrates all available ENCODE cell line ATAC-seq and Cistrome Data Browser ChIP-seq datasets, comprising 11.3 million human and 1.87 million mouse TFBS. We also integrated previously published TFBS resources (Factorbook, Unibind, RegulomeDB, and ENCODE_footprint) and found each contains a substantial fraction of unique TFBS predictions, highlighting significant discrepancies among existing resources. To assess the accuracy of the combined TFBS regions, we assembled ten independent genomic annotation datasets for evaluation and found that TFBS regions predicted by multiple databases are more likely to represent true and biologically meaningful binding sites. For each predicted TFBS region, we define two scores: the confidence score reflects prediction reliability, while the importance score represents biological functional relevance. Finally, we introduce TFBSpedia, a lightweight and efficient search engine that enables rapid retrieval of TFBS regions and comprehensive annotation information across the integrated databases.

Genomic variation between individuals is essential for understanding how differences in the genome sequence affect molecular and cellular processes. The Impact of Genomic Variation on Function (IGVF) Consortium aims to uncover the relationships among genomic variation, genome function, and phenotypes by combining experimental techniques, such as single-cell mapping and genomic perturbation assays, with computational approaches such as machine learning-based predictive modeling. The IGVF Data and Administrative Coordinating Centers collect, analyze, and disseminate data and results from across the consortium through an open-source platform called the IGVF Catalog. This resource includes, but is not limited to, data on the effects of coding variants on protein abundance and function, noncoding variants on enhancer activity (measured by MPRA or predicted computationally), and associations between variants and quantitative traits. All data are organized within a graph database comprising over 50 types of data collections with nearly 3 billion nodes and over 7.5 billion edges. The Catalog offers public API endpoints (https://api.catalogkg.igvf.org/) and a user-friendly interface for exploring, querying, and visualizing the data at https://catalog.igvf.org. We expect that this open-access platform will support the broader scientific community to advance our understanding of how genomic variation influences biology and disease.

Mobile element insertions (MEI) shape the human genome in both germline and somatic tissues. While inherited MEIs are well characterized, mapping somatic MEIs (sMEI) in non-cancer tissues remains challenging due to their low allelic fraction and repetitive nature. We established an integrative framework for sMEI analysis leveraging modern sequencing technologies and analytical innovations. We first benchmarked sMEI detection and demonstrated advantages of long-read and MEI-targeted sequencing for ultra-low-frequency events using a mixture of well-established cell lines. We then showed that haplotype phasing and donor-specific assemblies refine sMEI detection, effectively distinguishing from germline and false signals in in-silico tumor-normal mixtures. We further developed a source-tracing strategy based on internal sequence variation, expanding the catalogue of active source elements beyond traditional transduction-based methods. Applying this framework to donor tissues, we identified 18 rare somatic L1 insertions, revealing structural and source diversity. Our work provides a foundational framework and biological insight into sMEIs.

We present the genome sequence of Scytonema sp. PRP1, a cyanobacterium identified during single-cell sequencing library preparation of de-identified human brain samples associated with the Brain Somatic Mosaicism Network. Our 8,266,022 bp genome comprises 140 contigs, containing 6,765 protein-coding sequences, 37 tRNA genes, and 11 rRNA genes.

From fertilization onwards, the cells of the human body acquire variations in their DNA sequence, known as somatic mutations. These postzygotic mutations arise from intrinsic errors in DNA replication and repair, as well as from exposure to mutagens. Somatic mutations have been implicated in some diseases, but a fundamental understanding of the frequency, type and patterns of mutations across healthy human tissues has been limited. This is primarily due to the small proportion of cells harbouring specific somatic variants within an individual, making them more challenging to detect than inherited variants. Here we describe the Somatic Mosaicism across Human Tissues Network, which aims to create a reference catalogue of somatic mutations and their clonal patterns across 19 different tissue sites from 150 non-diseased donors and develop new technologies and computational tools to detect somatic mutations and assess their phenotypic consequences, including clonal expansions. This strategy enables a comprehensive examination of the mutational landscape across the human body, and provides a comparison baseline for somatic mutation in diseases. This will lead to a deep understanding of somatic mutations and clonal expansions across the lifespan, as well as their roles in health, in ageing and, by comparison, in diseases.

Recent advancements in sequencing technologies have yielded numerous long-read draft genomes, promising to enhance understanding of genomic variation. However, draft genomes are typically highly fragmented, posing challenges for functional genomics. We introduce GPatch, a tool that constructs chromosome-scale pseudoassemblies from fragmented drafts using alignments to a reference assembly. GPatch produces complete, accurate, gap-free assemblies preserving over 95% of nucleotides from human and non-human draft genomes. We show that GPatch pseudoassemblies can be used to construct Hi-C matrices, whereas fragmented draft assemblies cannot. Until complete genome assembly becomes routine, GPatch presents a necessary tool for maximizing the utility of draft genomes.

Prior infection elicits durable reprogramming in myeloid cells and their progenitors; however, the long-term consequences of this reprogramming are not well understood. We previously established a murine model of sepsis survival induced by cecal ligation and puncture which results in enhanced lung injury responses to lipopolysaccharide. In this model, classical monocytes from post-sepsis mice display persistently enhanced cytokine expression after lipopolysaccharide. To test the hypothesis that inflammatory reprogramming of monocytes mediates enhanced lung injury in post-sepsis mice, depletion and/or adoptive transfer was performed three weeks and three months after sepsis. Transcriptomic and epigenomic pathways associated with monocyte reprogramming and shifts in novel monocyte subsets were determined after sepsis in mice and humans. Monocytes from post-sepsis mice mediated enhanced LPS-induced lung injury and promoted neutrophil degranulation. Prior sepsis enhanced JAK-STAT signaling and AP-1 binding in monocytes, shifting toward the neutrophil-like monocyte subset and their progenitors. Similar neutrophil-like monocyte shifts were observed in adult sepsis patients and monocyte counts were predictive of 90-day mortality. We conclude that sepsis induces inflammatory memory affecting bone marrow progenitors and monocyte subsets predisposing to lung injury. These observations serve as a foundation for future investigations on neutrophil-like monocytes and inflammatory program interaction in tissue injury responses. Summary Sepsis shifts the immune program and ontogeny of monocytes and their progenitors contributing to long-term risk for new organ injury.