Biology subjects

Loo, C. E.

Publications and source records attributed to Loo, C. E..

4 recordsLinked to original sources

bioRxiv2025

Mapping Protein Occupancy on DNA with an Unnatural Cytosine Modification in Bio-orthogonal Contexts

The epigenome provides a dynamic layer of gene regulatory control above the static genetic sequence. DNA base modifications are key epigenetic regulators, predominantly found within CpG contexts in mammalian genomes. Working in tandem with these DNA modifications, chromatin-associated proteins and transcription factors further control gene expression. Given the interplay of these factors, concurrent mapping of DNA base modifications with protein-DNA occupancy can greatly aid in interpreting the epigenome. Existing multi-modal mapping methods include the use of DNA methyltransferases to mark accessible, protein-unbound DNA in non-CpG contexts. However, such approaches can either confound readouts with native DNA modifications or constrain users to third-generation sequencing approaches. To circumvent these limitations, we explored the possibility of introducing an unnatural DNA base modification, 5-carboxymethylcytosine, as an alternative label for protein occupancy. Here, we report our efforts to rationally engineer non-CpG-specific DNA methyltransferases to take on neomorphic DNA carboxymethyltransferase (CxMTase) activities. We find that DNA carboxymethylation of cytosines in GpC contexts shows broad compatibility with the most widely used epigenetic detection methods and can be used to reliably report on protein occupancy states. Using this approach, we reveal the single-molecule binding patterns of LexA, a master repressor in the bacterial DNA damage (SOS) response, at its self-regulated and endogenously methylated promoter. We thus show that unnatural DNA modifications in bio-orthogonal contexts can uncover biological insights and potentiate new approaches to multimodal epigenetic profiling. TOC GRAPHIC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/690163v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1817a46org.highwire.dtl.DTLVardef@182ca09org.highwire.dtl.DTLVardef@19e3572org.highwire.dtl.DTLVardef@1c6e1a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Sparse Sequencing permits accurate and efficient quantification of genome-wide cytosine modification levels

5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) regulate gene expression and exhibit dynamic levels during development and disease. While high-depth, base-resolution studies offer the most detailed view of epigenetic landscapes, many open questions are answered by surveying changes in 5mC/5hmC levels across larger cohorts. Nonetheless, current global quantification methods, including mass spectrometry, are typically limited in accessibility, accuracy, or throughput. Here, to evaluate the viability of low-coverage sequencing as an alternative, we first computationally downsampled deeply sequenced data to resolve the three-way relationship between sequencing coverage, modification levels, and measurement error. This relationship allowed us to develop a facile online tool for error calculation and to define experimental targets: <0.24% genome coverage can quantify 5mC and low-abundance 5hmC with minimal and predictable errors (<5%). Importantly, in direct comparisons, low-depth sequencing (Sparse-Seq) demonstrated high accuracy and less variability than mass spectrometry, while distinctively preserving genomic context. Applied serially to developing mouse brains, Sparse-Seq revealed an earlier emergence of 5hmCpG compared to 5mCpH and uncovered previously overlooked, genomic feature-specific epigenetic changes. This work establishes a rigorous foundation for employing Sparse-Seq as a highly accessible approach for 5mC/5hmC quantification, enabling economical first-pass analysis of epigenetic landscapes suited for large cohort studies and new hypothesis generation.

genomics↗

bioRxiv2024

Critical assessment of nanopore sequencing for the detection of multiple forms of DNA modifications

While nanopore sequencing is increasingly used for mapping DNA modifications, it is important to recognize associated false-positive calls, as they can mislead biological interpretations. To assist biologists and methods developers, we describe a framework, modFDR, for rigorous evaluation that emphasizes the use of the false discovery rate with rationally designed negative controls capturing both general background and confounding modifications. Our critical assessment across multiple DNA modifications shows that while nanopore sequencing performs reliably for high-abundance modifications--including 5-methylcytosine (5mC) at CpG sites in mammalian cells and 5-hydroxymethylcytosine (5hmC) in mammalian brain cells--it produces a substantial fraction of false-positive detections for low-abundance modifications, such as 5mC at CpH sites, 5hmC, and N6-methyldeoxyadenine (6mA) in most mammalian cell types. Although newer models improve certain aspects, systematic false positives remain, and we further observe elevated false negatives for 5mCpG when benchmarked against orthogonal enzymatic methods. This study highlights the urgent need to incorporate modFDR into future methods development, evaluation, and biological studies, and advocates prioritizing nanopore sequencing for mapping abundant rather than rare modifications in biomedical applications.

genomics↗

bioRxiv2023

Low-input and single-cell methods for Infinium DNA methylation BeadChips

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and [~]25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signals influence on the DNA methylation level readings. The modified detection p-values calculation achieved higher sensitivities for low-input datasets and was validated in over 100,000 public datasets with diverse methylation profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

bioinformatics↗