Biology subjects

Rieber, L.

Publications and source records attributed to Rieber, L..

6 recordsLinked to original sources

bioRxiv2026

Structure-Led Exploration of the Metagenome Yields Novel RNA-Guided Nucleases with Broad PAM Diversity

Compact RNA-guided nucleases with favorable targeting properties are challenging to discover due to their low natural abundance. Here, we develop a structure-led search strategy -leveraging predicted protein folds and sequence-independent similarity metrics -to systematically identify extremely low-homology compact RNA-guided nucleases across vast metagenomic datasets with high computational efficiency. Homology clustering resolved these proteins into distinct groups, for which we performed comprehensive PAM profiling and evaluated editing efficiency in eukaryotic cells. This structure-guided discovery revealed a previously undiscovered landscape of compact nuclease subtypes that exhibit extensive protospacer-adjacent motif (PAM) diversity, expanding the targeting potential of compact editors. Comparative analysis across the novel RNA-guided nuclease families demonstrates that compact systems are not intrinsically limited to highly constrained PAMs but instead have a broad and previously unknown breadth of genome targeting capabilities, comparable to that of Cas9 and far exceeding common transposon- derived systems. Additionally, this search revealed that a compact transposon-associated motif (TAM) is a prerequisite for the emergence of a CRISPR-Cas system from ancestral transposons, before protein domain expansions increase the target length and specificity constraints. These results enrich the catalog of RNA-guided nuclease architectures and contribute validated compact genome editing tools with broad and diverse PAM recognition, which may have therapeutic applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/714800v2_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@19ccdc9org.highwire.dtl.DTLVardef@18a5027org.highwire.dtl.DTLVardef@1945836org.highwire.dtl.DTLVardef@2b5bae_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Machine Learning-Driven Optimization of Specific, Compact, and Efficient Base Editors via Single-Round Diversification

Base editing shows great potential in research and clinical applications. Current iterations of the deaminases used to create precise single-nucleotide changes via base editing exhibit undesirable effects, including off-targeting, off-base editing, and bystander editing. Current deaminases are derived from either larger eukaryotic deaminases, which exhibit high levels of Cas-independent DNA targeting, or from evolved variants of the smaller E. coli TadA protein (ecTadA), which exhibits off-base editing. To overcome the limitations inherent to using a single protein sequence for engineering, we diversified newly identified TadA orthologs by DNA shuffling to yield millions of training sequences for measuring base editor efficiency. We trained generative models on the performance data from the pools of variants and drew on information-theoretic insights to efficiently explore the sequence space to generate diverse and high-performing deaminases. From a single round of diversification, we created a small set of novel and specific cytosine and adenosine deaminases that were markedly distinct in sequence from published base editor deaminases. We found that our model created deaminases generally outperform those we identified through typical directed evolution. The novel compact deaminases identified here show high on-base activity, comparable to the leading published base editors, and with demonstrably lower off-base activity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/666807v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cd25c1org.highwire.dtl.DTLVardef@69ad98org.highwire.dtl.DTLVardef@412e4org.highwire.dtl.DTLVardef@1744100_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioengineering↗

bioRxiv2021

Synthetic genomic reconstitution reveals principles of mammalian Hox cluster regulation

Precise Hox gene expression is crucial for embryonic patterning. Intra-Hox transcription factor binding and distal enhancer elements have emerged as the major regulatory modes controlling Hox gene expression. However, quantifying their relative contributions has remained elusive. Here, we introduce synthetic regulatory reconstitution, a novel conceptual framework for studying gene regulation and apply it to the HoxA cluster. We synthesized and delivered variant rat HoxA clusters (130-170 kilobases each) to an ectopic location in the mouse genome. We find that a HoxA cluster lacking distal enhancers recapitulates correct patterns of chromatin remodeling and transcription in response to patterning signals, while distal enhancers are required for full transcriptional output. Synthetic regulatory reconstitution is a generalizable strategy to decipher the regulatory logic of gene expression in complex genomes. One-Sentence SummaryReconstitution of gene regulation using large DNA constructs unravels the regulatory logic of a developmental gene locus.

synthetic biology↗

bioRxiv2020

Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map

Few existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide Hi-C datasets are often displayed using "heatmap" matrices, but it is difficult to intuit from these heatmaps which biochemical activities are compartmentalized together. High-dimensional Hi-C data vectors can alternatively be projected onto three-dimensional space using dimensionality reduction techniques. The resulting three-dimensional structures can serve as scaffolds for projecting other forms of genomic information, thereby enabling the exploration of relationships between genome organization and various genome annotations. However, while three-dimensional models are contextually appropriate for chromatin interaction data, some analyses and visualizations may be more intuitively and conveniently performed in two-dimensional space. We present a novel approach to the visualization and analysis of chromatin organization based on the Self-Organizing Map (SOM). The SOM algorithm provides a two-dimensional manifold which adapts to represent the high dimensional chromatin interaction space. The resulting data structure can then be used to assess the relationships between regulatory genomic activities and chromatin interactions. For example, given a set of genomic coordinates corresponding to a given biochemical activity, the degree to which this activity is segregated or compartmentalized in chromatin interaction space can be intuitively visualized on the 2D SOM grid and quantified using Lorenz curve analysis. We demonstrate our approach for exploratory analysis of genome compartmentalization in a high-resolution Hi-C dataset from the human GM12878 cell line. Our SOM-based approach provides an intuitive visualization of the large-scale structure of Hi-C data and serves as a platform for integrative analyses of the relationships between various genomic activities and genome organization.

bioinformatics↗

bioRxiv2019

Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types

Cell-type-specific chromosome conformation is correlated with differential gene regulation. Broad compartmentalization into two compartments (A & B) is proposed to be the main driver of cell-specific chromosome organization. However it is unclear what fraction of chromosome conformation changes between cell types and conditions is independent of changes in compartmentalization and whether any such compartment-independent reorganization is functionally important. We developed MultiMDS to jointly infer and align 3D chromosomal structures, thereby enabling a quantitative comparison of locus-specific changes across Hi-C datasets. We compared Hi-C datasets from yeast, which lack compartmentalization, grown with and without galactose. These comparisons confirmed known relocalizations as well as identifying additional examples. We also compared mammalian datasets across a variety of cell lines. We found a consistent enrichment for changes along the A/B compartment (nuclear interior/nuclear periphery) axis, even when comparing the same cell type from different individuals. Despite the prevalence of compartment changes, we consistently find compartment-independent relocalizations of loci that are within the A compartment in both compared cell types. Some such intra-compartment relocalizations involve loci that display enhancer-associated histone marks in one cell type and polycomb-associated histone marks in the other. MultiMDS thus enables a new way to compare chromosome conformations across two Hi-C datasets. Availabilityhttps://github.com/seqcode/multimds

bioinformatics↗

bioRxiv2017

miniMDS: 3D structural inference from high-resolution Hi-C data

MotivationRecent experiments have provided Hi-C data at resolution as high as 1 Kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.\n\nResultsWe have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 Kbp).\n\nAvailabilityA Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS.

bioinformatics↗