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Chan, P. P.

Publications and source records attributed to Chan, P. P..

12 recordsLinked to original sources

A multimodal precision oncology platform integrating genomics,single-cell transcriptomics, and functional drug screening expandstherapeutic options in pediatric brain tumors

Precision oncology for pediatric brain tumors mainly relies on clinically validated, targetable genomic alterations, which are often sparse, yield few matched therapies, and do not guide chemotherapy selection. We developed a tiered framework in which deep whole-genome sequencing and single-cell RNA sequencing extend targeted-therapy selection, with ex vivo functional drug screening supporting selection of both targeted therapies and chemotherapies. We applied it to 17 children with malignant CNS tumors. The approach expanded recommendations beyond genomics: functional screening provided every patient with at least one high-confidence targeted-therapy or chemotherapy nomination, while single-cell RNA sequencing identified an average of 37 druggable gene targets per patient, producing a fourfold increase in high-confidence targeted-therapy recommendations. In an exploratory analysis, functional classifications were directionally consistent with outcomes in 8 of 10 evaluable cases, although combination treatments and radiation precluded causal attribution. Single-cell profiling also identified subclone-specific vulnerabilities, supporting prospective evaluation of the platform's feasibility and predictive utility.

cancer biology↗

The diploid reference genome of a human embryonic stem cell line

Advances in DNA sequencing and assembly technologies are spurring a shift from haploid reference genomes to sample-specific diploid assemblies. Here, we generated the first telomere-to-telomere (T2T) diploid reference for the widely used human embryonic stem cell (hESC) line, H9 (WAe009-A). This haplotype-resolved assembly is highly accurate with comprehensive annotation of genes, segmental duplications, methylation, and chromatin conformation. Pangenomic and phased-locus inference point to H9s mixed ancestry with a predominant European component. H9-specific genomic features include near-perfect telomeres [~]1.65-fold longer than other T2T assemblies, consistent with telomerase activity during pluripotency; chromosome 17 inversions that can predispose offspring to neurological syndromes; and expansions of ncRNA clusters, with overall genomic stability maintained despite extensive culturing. Mapping multi-omic datasets to the genome, we demonstrate the power of this resource for allele-specific, high-precision transcriptomic, genetic, and epigenetic analyses, with far-reaching implications for human development and disease.

genomics↗

Diverse expression and modification of tRNAs and tRNA-derived RNAs in 19 mouse tissues

Transfer RNAs (tRNAs) and their derived small RNAs (tDRs) vary in abundance and processing across mammalian tissues. However, these differences and the regulatory mechanisms controlling them are poorly understood. Dysregulation of tRNAs has increasingly been implicated as a critical factor in disease progression; thus, a deeper understanding of the homeostatic variance across somatic tissues is vital. In this study, we applied Ordered Two-Template Relay sequencing (OTTR-seq) to examine small RNAs in 19 tissues from adult mice, quantifying mature tRNAs and tDRs at single-transcript resolution and inferring select base-modification signatures from characteristic reverse-transcriptase-mediated misincorporation. We observed tissue-biased expression of many unique transcripts and identified tDRs with greater tissue specificity than their parent tRNAs. Differences between tRNAs with the same anticodon but containing variations elsewhere in the sequence (isodecoders) suggest diverse regulatory processes across tissues that alter modification, translation functionality, stability, and tDR synthesis. These data provide an isodecoder-resolved reference for tissue-biased tRNA and tDR regulation in mice and form a framework for exploring disease-associated remodeling of the tRNA pool.

genomics↗

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

R2DT: a comprehensive platform for visualising RNA secondary structure

RNA secondary (2D) structure visualisation is an essential tool for understanding RNA function. R2DT is a software package designed to visualise RNA 2D structures in consistent, recognisable, and reproducible layouts. The latest release, R2DT 2.0, introduces multiple significant features, including the ability to display position-specific information, such as single nucleotide polymorphisms (SNPs) or SHAPE reactivities. It also offers a new template-free mode allowing visualisation of RNAs without pre-existing templates, alongside a constrained folding mode and support for animated visualisations. Users can interactively modify R2DT diagrams, either manually or using natural language prompts, to generate new templates or create publication-quality images. Additionally, R2DT features faster performance, an expanded template library, and a growing collection of compatible tools and utilities. Already integrated into multiple biological databases, R2DT has evolved into a comprehensive platform for RNA 2D visualisation, accessible at https://r2dt.bio.

bioinformatics↗

A Simple and Scalable Zebrafish Model of Sonic Hedgehog Medulloblastoma

Medulloblastoma (MB) is the most common malignant brain tumor in children and is stratified into three major subgroups. The Sonic hedgehog (SHH) subgroup represents [~]30% of all MB cases and has significant survival disparity depending upon TP53 status. Here, we describe the first zebrafish model of SHH MB using CRISPR to mutate ptch1, the primary genetic driver in human SHH MB. These tumors rapidly arise adjacent to the valvula cerebelli and resemble human SHH MB by histology and comparative genomics. In addition, ptch1-deficient MB tumors with loss of tp53 have aggressive tumor histology and significantly worse survival outcomes, comparable to human patients. The simplicity and scalability of the ptch1 MB model makes it highly amenable to CRISPR-based genome editing screens to identify genes required for SHH MB tumor formation in vivo, and here we identify the grk3 kinase as one such target.

cancer biology↗

tRNA Analysis of eXpression (tRAX): A tool for integrating analysis of tRNAs, tRNA-derived small RNAs, and tRNA modifications

Recent high-throughput sequencing protocols have facilitated increased accuracy in measurements of transfer tRNAs (tRNAs) and tRNA-derived small RNAs (tDRs) from biological samples. However, commonly used RNA-seq analysis pipelines overlook special considerations given the unique features of tRNA metabolism. We present tRAX (tRNA Analysis of eXpression), a user-friendly analytic package for streamlined processing and graphic presentation of small-RNA sequencing data. Here, we apply it to both tRNAs and tDRs from mouse tissues to illustrate the extensive analysis and visualization features. Biologically compelling results demonstrate tRAX as an effective and accessible tool for in-depth characterization of tRNA and tDR transcriptomes.

bioinformatics↗

A Standardized Ontology for Naming tRNA-derived RNAs Based on Molecular Origin

Small RNAs processed from transfer RNA are recognized to have regulatory functions distinct from protein synthesis. This rapidly advancing field has led to a constellation of transcript nomenclatures. Building upon the accepted tRNA naming system and linking tRNA-derived small RNAs to their molecular sources, we propose an improved nomenclature. Facilitated by the tDRnamer system, it will promote inter-study comparisons leveraging a biologically-rooted nomenclature for this emerging class of intriguing regulators.

bioinformatics↗

Distinct stress-dependent signatures of cellular and extracellular tRNA-derived small RNAs (tDRs)

The cellular response to stress is an important determinant of disease pathogenesis. Uncovering the molecular fingerprints of distinct stress responses may yield novel biomarkers for different diseases, and potentially identify key signaling pathways important for disease progression. tRNAs and tRNA-derived small RNAs (tDRs) comprise one of the most abundant RNA species in cells and have been associated with cellular stress responses. The presence of RNA modifications on tDRs has been an obstacle for accurately identifying tDRs with conventional small RNA sequencing. Here, we use AlkB-facilitated methylation sequencing (ARM-seq) to uncover a comprehensive landscape of cellular and extracellular tDR expression in a variety of human and rat cells during common stress responses, including nutritional deprivation, hypoxia, and oxidative stress. We found that extracellular tDRs have a distinct fragmentation signature with a predominant length of 31-33 nts and a highly specific termination position when compared with intracellular tDRs. Importantly, we found these signatures are better discriminators of different cellular stress responses compared to extracellular miRNAs. Distinct extracellular tDR signatures for each profiled stressor are elucidated in four different types of cells. This distinct extracellular tDR fragmentation pattern is also noted in plasma extracellular RNAs from patients on cardiopulmonary bypass. The observed overlap of these patient tDR signatures with the signatures of nutritional deprivation and oxidative stress in our cellular models provides preliminary in vivo corroboration of our findings and demonstrates the potential to establish novel extracellular tDR biomarkers in human disease models.

cell biology↗

R2DT: computational framework for template-based RNA secondary structure visualisation across non-coding RNA types

Non-coding RNAs (ncRNA) are essential for all life, and the functions of many ncRNAs depend on their secondary (2D) and tertiary (3D) structure. Despite proliferation of 2D visualisation software, there is a lack of methods for automatically generating 2D representations in consistent, reproducible, and recognisable layouts, making them difficult to construct, compare and analyse. Here we present R2DT, a comprehensive method for visualising a wide range of RNA structures in standardised layouts. R2DT is based on a library of 3,632 templates representing the majority of known structured RNAs, from small RNAs to the large subunit ribosomal RNA. R2DT has been applied to ncRNA sequences from the RNAcentral database and produced >13 million diagrams, creating the worlds largest RNA 2D structure dataset. The software is freely available at https://github.com/rnacentral/R2DT and a web server is found at https://rnacentral.org/r2dt.

bioinformatics↗

tRNAscan-SE 2.0: Improved Detection and Functional Classification of Transfer RNA Genes

tRNAscan-SE has been widely used for transfer RNA (tRNA) gene prediction for over twenty years, developed just as the first genomes were decoded. With the massive increase in quantity and phylogenetic diversity of genomes, the accurate detection and functional prediction of tRNAs has become more challenging. Utilizing a vastly larger training set, we created nearly one hundred specialized isotype-and clade-specific models, greatly improving tRNAscan-SEs ability to identify and classify both typical and atypical tRNAs. We employ a new comparative multi-model strategy where predicted tRNAs are scored against a full set of isotype-specific covariance models, allowing functional prediction based on both the anticodon and the highest-scoring isotype model. Comparative model scoring has also enhanced the programs ability to detect tRNA-derived SINEs and other likely pseudogenes. For the first time, tRNAscan-SE also includes fast and highly accurate detection of mitochondrial tRNAs using newly developed models. Overall, tRNA detection sensitivity and specificity is improved for all isotypes, particularly those utilizing specialized models for selenocysteine and the three subtypes of tRNA genes encoding a CAU anticodon. These enhancements will provide researchers with more accurate and detailed tRNA annotation for a wider variety of tRNAs, and may direct attention to tRNAs with novel traits.

bioinformatics↗

Functionally redundant forms of extended-spectrum beta-lactamases and aminoglycoside modifying enzymes drive the evolution of two distinct multidrug resistance gene clusters in clinical populations of EXPEC

We evaluate the distribution of genetic markers for antibiotic resistance in 276 genomic sequences of Extraintestinal Pathogenic E. coli from two hospitals on the U.S. West coast. Plasmid-borne genes encoding drug-inactivating enzymes dominate the distribution of aminoglycoside and {beta}-lactam resistance markers. These genes can be assigned based on their distribution to two mutually exclusive complementarity groups (CGs: CG1 and CG2) with each displaying genetic linkage and minimal functional overlap. CG1 includes genes encoding OXA-1 and AAC(6)-Ib-cr, frequently also CTX-M-15, and sometimes AAC(3)-IIe.2, a variant of AAC(3)-IIe; CG2 includes AAC(3)-IId tightly linked to TEM-1, and occasionally also to genes encoding CTX-M-14-like {beta}-lactamases. This binary distribution of aminoglycoside and {beta}-lactamase resistance genes suggests a convergence between two different evolutionary solutions, and results in a ubiquitous functional redundancy in the clinical populations. CG1 and CG2 are largely carried in IncF plasmids, of which we distinguish seven classes based on Rpt-A1 sequence homology. Both CG1 and CG2 genes are found in two different IncF plasmid classes, demonstrating their pervasive mobility across plasmid backbones. Different CG genes and IncF plasmid classes are found in a wide range of MLSTs, highlighting the prevalence of horizontal gene transfer. We also identify at least five clonally expanding MLSTs, which represent high-risk clones: ST131, ST95, ST73, ST127, and ST69. The identification of clonally-expanding types, the discovery of CGs that are ubiquitously spread in diverse clinical strains, and the functional redundancy that these two groups represent have significant implications for monitoring and controlling the spread of resistance.

microbiology↗