Biology subjects

Blanchette, M.

Publications and source records attributed to Blanchette, M..

16 recordsLinked to original sources

bioRxiv2026

Discovery and Targeting of a Cryptic Human Proteome

First-in-class therapeutics require first-in-class biology. Yet despite decades of genomic and proteomic cataloging, vast regions of the human transcriptome remain dark and their encoded proteins invisible. Here we present RyboCypher, an integrated RNA-sequencing and AI-assisted proteogenomics platform that systematically maps the RyboCypher-derived "dark" transcriptome to unannotated peptides, predicting and empirically identifying cryptic proteins across the uncharted genome. Applied to cancer cell lines, patient tumors, and matched healthy tissues, RyboCypher resolved [~]8.3 million dark RNA isoforms and [~]16 million candidate ORFs. Interrogating these against [~]0.5 billion MS/MS spectra from cellular proteomics, membrane proteomics, and immunopeptidomics datasets (comprising a total of >8,000 raw MS data files ([~]7TB of MS data), derived from 2,229 patient samples), we empirically identified [~]80,000 cryptic peptides ([~]10,000 cancer-associated or cancer-upregulated) at <1% FDR. Altogether, these datasets establish the CypherAtlas, a comprehensive proteogenomic atlas of an unreported proteome comprising thousands of novel proteins, including membrane proteins with targetable extracellular domains, and intracellular proteins accessible through antigen presentation. By linking dark-RNA transcripts, predicted proteins, and patient-level metadata across RyboDyns proprietary experimental data, CypherAtlas further provides the training substrate for multi-modal models such as DarkCypher, which is being developed to prioritize cryptic targets and to forecast their expression in new patient samples. As proof of therapeutic potential, we disclose evidence for a cancer-associated, cryptic protein expressed from the YBX1 locus, (cryptic YBX1; cYBX1) and demonstrate selective in vitro tumor cell killing through a cryptic peptide-MHC (pMHC) complex derived from this protein with a TCR-mimic (TCRm) antibody when formatted as antibody drug conjugates (ADCs). Together, RyboCypher and CypherAtlas establish the dark proteome as a vast and previously inaccessible reservoir of novel targetable biology, laying the foundation for the next generation of first-in-class therapeutics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/743124v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@104c385org.highwire.dtl.DTLVardef@1c065ccorg.highwire.dtl.DTLVardef@5b717forg.highwire.dtl.DTLVardef@1788694_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Genomic Flexibility Through Extrachromosomal Amplifications: A Leishmania Survival Strategy

Leishmania parasites modulate gene copy number through extrachromosomal DNA (ecDNA) amplification, enabling adaptation to environmental stress. Under drug pressure, both linear and circular ecDNA amplifications (amplicons) carrying resistance genes emerge. However, how these ecDNA structures form, diversify, and coexist remains poorly understood. Here, using experimental evolution and Oxford Nanopore long-read sequencing, we show that a single clonal population of drug resistant Leishmania produces a variety of linear and circular amplicons. As antimonial pressure increases, linear amplicons transition into circular forms, with high-stress conditions favoring circular amplicons carrying at least two copies of the resistance gene. Using the Nanopore long reads, we map recombination events driving linear and circular amplicon formation. Our model suggests that gene duplication in the amplicons originates from inter-chromatid homologous recombination, leading to an intermediate intra-chromosomal duplication, followed by a second homologous recombination event. Additionally, different Leishmania species exhibited distinct biases toward linear or circular amplification under identical drug conditions, suggesting species-specific adaptive strategies. Together, these findings define recombination-driven ecDNA dynamics as a central axis of genomic plasticity in Leishmania and underscore the potential for targeting ecDNA in therapeutic and diagnostic strategies against Leishmania and related pathogens.

microbiology↗

bioRxiv2025

Telomere-to-telomere assembly detects genomic diversity in Canadian strains of Borrelia burgdorferi

Borrelia burgdorferi, the bacteria causing Lyme disease, has a complex genome comprising a linear chromosome and a combination of linear and circular plasmids. The atypical hairpin structure at the telomere of linear replicons and the highly paralogous plasmids make the genome assembly challenging. We developed a genome assembly pipeline using Oxford Nanopore Technologies (ONT) long read and Illumina short read to overcome these challenges. Using ONT reads enabled us to completely assemble the hairpin telomeres of the linear replicons along with a novel lp28 subtype plasmid and the complete circular plasmids of nine B. burgdorferi strains from five geographical regions in Canada. Although these strains are highly similar across the conserved genomic regions, variability was observed predominantly at the right telomeric ends. Comparative analyses revealed that all nine strains carry a [~]2-10 kb right telomeric end identical to the linear plasmid lp28-1, which leads to variability in the telomere length and the gene content. Additionally, we observed diversity at the hairpin telomeric sequences of the linear chromosomes. Further analysis showed that the nine strains belong to seven ospC types and have diverse plasmid profile, highlighting the genomic diversity among the strains from the same geographical locations. Overall, these findings suggest that even the B. burgdorferi strains from close geographical locations can carry substantial genomic variation, especially at the telomeres and with respect to their plasmid content, emphasizing it to be a possible mechanism of rapid evolution within these Canadian strains.

genomics↗

bioRxiv2024

A universal tool for chromatin loop annotation in bulk and single-cell Hi-C data

Annotating chromatin loops is essential for understanding the 3D genomes role in gene regulation, but current methods struggle with low coverage, particularly in single-cell datasets. Chromatin loops are kilo-to mega-range structures that exhibit broader features, such as co-occurring loops, stripes, and domain boundaries along axial directions of Hi-C contact maps. However, existing tools primarily focus on detecting localized, highly-concentrated, interactions. Furthermore, the wide variety of available chromatin conformation datasets is rarely utilized in developing effective loop callers. Here, we present Polaris, a universal tool that integrates axial attention with a U-shaped backbone to accurately detect loops across different 3D genome assays. By leveraging extensive Hi-C contact maps in a pretrain-finetune paradigm, Polaris achieves consistent performance across various datasets. We compare Polaris against existing tools in loop annotation from both bulk and single-cell data and find that Polaris outperforms other programs across different cell types, species, sequencing depths, and assays.

bioinformatics↗

bioRxiv2024

Multi-ancestry polygenic risk scores using phylogenetic regularization

Accurately predicting phenotype using genotype across diverse ancestry groups remains a significant challenge in human genetics. Many state-of-the-art polygenic risk score models are known to have difficulty generalizing to genetic ancestries that are not well represented in their training set. To address this issue, we present a novel machine learning method for fitting genetic effect sizes across multiple ancestry groups simultaneously, while leveraging prior knowledge of the evolutionary relationships among them. We introduce DendroPRS, a machine learning model where SNP effect sizes are allowed to evolve along the branches of the phylogenetic tree capturing the relationship among populations. DendroPRS outperforms existing approaches at two important genotype-to-phenotype prediction tasks: expression QTL analysis and polygenic risk scores. We also demonstrate that our method can be useful for multiancestry modelling, both by fitting population-specific effect sizes and by more accurately accounting for covariate effects across groups. We additionally find a subset of genes where there is strong evidence that an ancestry-specific approach improves eQTL modelling.

bioinformatics↗

bioRxiv2023

H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs

Polycomb Repressive Complex 2 (PRC2)-mediated histone H3K27 tri-methylation (H3K27me3) recruits canonical PRC1 (cPRC1) to maintain heterochromatin. In early development, polycomb-regulated genes are connected through long-range 3D interactions which resolve upon differentiation. Here, we report that polycomb looping is controlled by H3K27me3 spreading and regulates target gene silencing and cell fate specification. Using glioma-derived H3 Lys-27-Met (H3K27M) mutations as tools to restrict H3K27me3 deposition, we show that H3K27me3 confinement concentrates the chromatin pool of cPRC1, resulting in heightened 3D interactions mirroring chromatin architecture of pluripotency, and stringent gene repression that maintains cells in progenitor states to facilitate tumor development. Conversely, H3K27me3 spread in pluripotent stem cells, following neural differentiation or loss of the H3K36 methyltransferase NSD1, dilutes cPRC1 concentration and dissolves polycomb loops. These results identify the regulatory principles and disease implications of polycomb looping and nominate histone modification-guided distribution of reader complexes as an important mechanism for nuclear compartment organization. Highlights{square} The confinement of H3K27me3 at PRC2 nucleation sites without its spreading correlates with increased 3D chromatin interactions. {square}The H3K27M oncohistone concentrates canonical PRC1 that anchors chromatin loop interactions in gliomas, silencing developmental programs. {square}Stem and progenitor cells require factors promoting H3K27me3 confinement, including H3K36me2, to maintain cPRC1 loop architecture. {square}The cPRC1-H3K27me3 interaction is a targetable driver of aberrant self-renewal in tumor cells.

molecular biology↗

bioRxiv2023

High-resolution CTCF footprinting reveals impact of chromatin state on cohesin extrusion dynamics

DNA looping is vital for establishing many enhancer-promoter interactions. While CTCF is known to anchor many cohesin-mediated loops, the looped chromatin fiber appears to predominantly exist in a poorly characterized actively extruding state. To better characterize extruding chromatin loop structures, we used CTCF MNase HiChIP data to determine both CTCF binding at high resolution and 3D contact information. Here we present FactorFinder, a tool that identifies CTCF binding sites at near base-pair resolution. We leverage this substantial advance in resolution to determine that the fully extruded (CTCF-CTCF) state is rare genome-wide with locus-specific variation from ~1-10%. We further investigate the impact of chromatin state on loop extrusion dynamics, and find that active enhancers and RNA Pol II impede cohesin extrusion, facilitating an enrichment of enhancer-promoter contacts in the partially extruded loop state. We propose a model of topological regulation whereby the transient, partially extruded states play active roles in transcription.

genomics↗

bioRxiv2023

Tracing cancer evolution and heterogeneity using Hi-C

Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples, which can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. We validated HiDENSEC with in silico and in vitro controls, and then characterized chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and characterizing these events yields insights into drivers of melanoma progression.

genomics↗

bioRxiv2020

SACSANN: identifying sequence-based determinants of chromosomal compartments

Genomic organization is critical for proper gene regulation and based on a hierarchical model, where chromosomes are segmented into megabase-sized, cell-type-specific transcriptionally active (A) and inactive (B) compartments. Here, we describe SACSANN, a machine learning pipeline consisting of stacked artificial neural networks that predicts compartment annotation solely from genomic sequence-based features such as predicted transcription factor binding sites and transposable elements. SACSANN provides accurate and cell-type specific compartment predictions, while identifying key genomic sequence determinants that associate with A/B compartments. Models are shown to be largely transferable across analogous human and mouse cell types. By enabling the study of chromosome compartmentalization in species for which no Hi-C data is available, SACSANN paves the way toward the study of 3D genome evolution. SACSANN is publicly available on GitHub: https://github.com/BlanchetteLab/SACSANN

bioinformatics↗

bioRxiv2019

Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals

All living things experience entropy, manifested as a loss of inherited genetic and epigenetic information over time. As budding yeast cells age, epigenetic changes result in a loss of cell identity and sterility, both hallmarks of yeast aging. In mammals, epigenetic information is also lost over time, but what causes it to be lost and whether it is a cause or a consequence of aging is not known. Here we show that the transient induction of genomic instability, in the form of a low number of non-mutagenic DNA breaks, accelerates many of the chromatin and tissue changes seen during aging, including the erosion of the epigenetic landscape, a loss of cellular identity, advancement of the DNA methylation clock and cellular senescence. These data support a model in which a loss of epigenetic information is a cause of aging in mammals.\n\nOne Sentence SummaryThe act of repairing DNA breaks induces chromatin reorganization and a loss of cell identity that may contribute to mammalian aging

molecular biology↗

bioRxiv2019

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodelling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed. However, they still present some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared to existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and emphasizes the role of transcription in the establishment of chromatin structure.

genomics↗

bioRxiv2018

Estimating DNA-DNA interaction frequency from Hi-C data at restriction-fragment resolution

Hi-C is a popular technique to map three-dimensional chromosome conformation by capturing the frequency of physical contacts between pairs of genomic regions in cell populations. Although the resolution of Hi-C data is in principle only limited by the size of restriction fragments (300 bp - 4 kb), stochastic noise caused by the limited sequencing coverage forces researchers to artificially reduce the resolution of Hi-C matrices by binning the genome into 5-100 kb regions, resulting in a loss of information and biological interpretability. Here, we present the Hi-C Interaction Frequency Inference (HIFI) algorithms, a family of computational approaches that takes advantage of dependencies between neighboring restriction fragments to estimate restriction-fragment resolution interaction frequency matrices from Hi-C data. HIFI is shown to be superior to existing fixed-binning and state-of-the-art approaches via cross-validation experiments on Hi-C data and comparisons to 5C data. It also greatly improves the delineation of enhancer-promoter contacts. Finally, the high resolution afforded by HIFI reveals a new role for active regulatory regions in structuring topologically associating domains (TADs) and subTADs. By operating upstream of many Hi-C data analysis tools (e.g., normalization tools, as well as loop, TAD, and compartment predictors), HIFI will be easily inserted into a number of Hi-C data analysis pipelines, enabling a variety of high-resolution genomic organization analyses.\n\nAvailabilitygithub.com/BlanchetteLab/HIFI

bioinformatics↗

bioRxiv2018

RobusTAD: A Tool for Robust Annotation ofTopologically Associating Domain Boundaries

MotivationTopologically Associating Domains (TADs) are chromatin structures that can be identified by analysis of Hi-C data. Tools currently available for TAD identification are sensitive to experimental conditions such as coverage, resolution and noise level.\n\nResultsHere, we present RobusTAD, a tool to score TAD boundaries in a manner that is robust to these parameters. In doing so, RobusTAD eases comparative analysis of TAD structures across multiple heterogeneous samples.\n\nAvailabilityRobusTAD is implemented in R and released under a GPL license. RobusTAD can be downloaded from https://github.com/rdali/RobusTAD and runs on any standard desktop computer.\n\nContactrola.dali@mail.mcgill.ca, blanchem@cs.mcgill.ca\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Structural variation detection by proximity ligation from FFPE tumor tissue

The clinical management and therapy of many solid tumor malignancies is dependent on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call \"Fix-C\", which is similar in concept to Hi-C. Fix-C enables structural variation detection from fresh and archival FFPE samples. We applied this method to 15 clinical adenocarcinoma and sarcoma specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with FISH assays - the current clinical gold standard. Additionally, we are able to identify novel structural variation undetected by other methods and recover long-range chromatin configuration information from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material, and inform the development of targeted molecular diagnostic assays for patient care.

genomics↗

bioRxiv2018

Efficient Homology Directed Repair by Cas9:DNA Localization and Cationic Polymeric Transfection in Mammalian Cells

Homology directed repair (HDR) induced by site specific DNA double strand breaks (DSB) with CRISPR/Cas9 is a precision gene editing approach that occurs at low frequency in comparison to indel forming non homologous end joining (NHEJ). In order to obtain high HDR percentages in mammalian cells, we engineered Cas9 protein fused to a high-affinity monoavidin domain to deliver biotinylated donor DNA to a DSB site. In addition, we used the cationic polymer, polyethylenimine, to deliver Cas9 RNP-donor DNA complex into the cell. Combining these strategies improved HDR percentages of up to 90% in three tested loci (CXCR4, EMX1, and TLR) in standard HEK293 cells. Our approach offers a cost effective, simple and broadly applicable gene editing method, thereby expanding the CRISPR/Cas9 genome editing toolbox.\n\nSummaryPrecision gene editing occurs at a low percentage in mammalian cells using Cas9. Colocalization of donor with Cas9MAV and PEI delivery raises HDR occurrence.

biochemistry↗

bioRxiv2017

An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures

Predicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in analysis of high-dimensional data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly-used two-step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype.\n\nIt is known that important exposure variables can alter correlation patterns between clusters of high-dimensional variables, i.e., alter network properties of the variables. However, it is not well understood whether such altered clustering is informative in prediction. Here, assuming there is a binary exposure with such network-altering effects, we explore whether use of exposure-dependent clustering relationships in dimension reduction can improve predictive modelling in a two-step framework. Hence, we propose a modelling framework called ECLUST to test this hypothesis, and evaluate its performance through extensive simulations.\n\nWith ECLUST, we found improved prediction and variable selection performance compared to methods that do not consider the environment in the clustering step, or to methods that use the original data as features. We further illustrate this modelling framework through the analysis of three data sets from very different fields, each with high dimensional data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package.

genomics↗