Biology subjects

King, I.

Publications and source records attributed to King, I..

12 recordsLinked to original sources

bioRxiv2026

Structure of the Type I-F3 CAST holo integration complex reveals licensing mechanisms during RNA-guided DNA integration

Precise genomic integration of large DNA cargo remains a central challenge in genome engineering. In this context, CRISPR-associated transposases (CASTs) offer a promising solution by coupling RNA-guided DNA recognition to transposon-mediated integration. Type I-F3 CASTs exhibit exceptional targeting fidelity, making them a leading focus for biotechnology and gene therapy applications. Yet, despite extensive characterization, the structural basis for RNA-guided DNA integration of type I-F3 CASTs has remained elusive. Here, using cryo-EM and functional assays, we report the structural licensing mechanism of the Vibrio cholerae CAST (VchCAST) holo integration complex (HIC). Our structures reveal that sequential conformational licensing events govern selective activation of the integration machinery at the on-target site. Different components, including Cascade-TniQ, TnsC, and TnsA/B, undergo conformational changes upon assembly into the HIC, and the catalytically competent state is stabilized only within the fully assembled complex. Finally, comparison with existing CAST integration complex structures reveals a set of conserved architectural features, suggesting a common molecular basis for RNA-guided transposition across CAST diversity.

biochemistry↗

bioRxiv2025

Origin and Correlates of Viral Rebound in SIV-Infected Rhesus Macaques Following ART Discontinuation

The vast majority of persons living with HIV-1 who discontinue antiretroviral therapy (ART) demonstrate viral rebound, but the tissue-level events that lead to rebound viremia are poorly understood. Here we report the origin, dynamics, and correlates of viral rebound in 16 rhesus macaques (RMs) infected with molecularly barcoded SIVmac239M, treated with ART for 70 weeks, and necropsied on day 12 after ART discontinuation. Barcode analysis of plasma following ART discontinuation identified 1 to 38 rebounding barcode-defined viral lineages per animal, with 1 to 4 rebounding lineages contributing to first measurable rebound viremia. Analysis of barcode viral RNA (vRNA) expression in necropsy tissues revealed presumptive anatomic origin sites for 56 of 175 total rebounding viral lineages, with significant enrichment in the gastrointestinal (GI) tract and GI-associated lymph nodes. Daily transcriptomic and proteomic profiling in peripheral blood following ART discontinuation showed upregulation of pathways related to T cell signaling, cytokine responses, and cellular metabolism prior to detectable rebound viremia. These data suggest that viral rebound following ART discontinuation is initiated by local tissue replication of a limited number of clonal lineages, followed by systemic expansion of the initial rebounding lineages and serial initiation of replication of multiple additional clonal lineages. These findings provide mechanistic insights into the processes that result in viral rebound following ART discontinuation and will contribute to next generation HIV-1 cure strategies.

microbiology↗

bioRxiv2025

cfDecon: Accurate and Interpretable methylation-based cell type deconvolution for cell-free DNA

Cell-free DNA (cfDNA) analysis has emerged as a powerful tool for noninvasive diagnostics and monitoring of aberrant methylation. However, computational deconvolution of cfDNA remains challenging due to the complexity of methylation data, the diverse cellular compositions in cfDNA mixtures, and the limited interpretability of current methods. We present cfDecon, an efficient deep-learning framework for cfDNA deconvolution at the resolution of individual reads. cfDecon employs a multichannel autoencoder core module and an iterative refinement process to estimate cell-type proportions and generate condition-aware cell-type-specific methylation profiles. We rigorously evaluate cfDecon through comprehensive simulation experiments, including scenarios with normal cellular compositions, rare cell types, and unknown cell types, where cfDecon consistently outperforms previous state-of-the-art methods in deconvolution. Using strict data separation and careful control for potential data leakage, cfDecon shows scalability and applicability to large-scale studies by raising Lins Concordance Correlation Coefficient (CCC) over 33% on an atlas-level reference dataset. We further validate cfDecons utility in disease diagnosis on two clinical datasets, Amyotrophic Lateral Sclerosis (ALS) and Hepatocellular Carcinoma (HCC). cfDecon raises the performance of disease detection from 0.53 to 0.79 compared to existing methods on the ALS dataset and 0.55 to 0.77 on the HCC dataset. The capacity of cfDecon to predict condition-specific DNA methylation patterns across cell types offers a valuable framework for studying differentially methylated CpGs. Furthermore, integrating cfDecon with enrichment analysis facilitates the exploration of functional variations in CpGs across different conditions. cfDecon represents a significant advancement in cfDNA analysis, offering improved accuracy, interpretability, and potential for personalized medicine applications. The codebase is available at https://github.com/Susanxuan/cfDecon.

bioinformatics↗

bioRxiv2024

Chromosome-level Genome Assembly and Annotation of Petunia hybrida

Petunia hybrida is the worlds most popular garden plant and is regarded as a supermodel for studying the biology associated with the Asterid clade, the largest of the two major groups of flowering plants. Unlike other Solanaceae, petunia has a base chromosome number of seven, not 12. This along with recombination suppression has previously hindered efforts to assemble its genome to chromosome level. Here we achieve a chromosome-level assembly for P. hybrida using a combination of short-read and long-read sequencing, optical mapping (Bionano) and Hi-C technologies. The resulting assembly spans 1253.6 Mb with a BUSCO score of 99.8%. A total of 35,089 genes were predicted and of those 29,655 were functionally annotated. Syntenic regions between petunia, tomato and pepper were identified, highlighting rearrangements that have occurred since their divergence indicating that the 12 chromosomes of Solanaceae did not originate from whole genome duplication of an ancestral species with seven chromosomes like petunia. This chromosome-level assembly will significantly enhance trait mapping efficiency in petunia and serve as a valuable resource for functional genomic studies in this key plant model.

genomics↗

bioRxiv2022

Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions

Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FMs effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field.

bioinformatics↗

bioRxiv2022

Identification of Fusarium Head Blight resistance in Triticum timopheevii accessions and characterisation of wheat-T. timopheevii introgression lines for enhanced resistance.

A diverse panel of wheat wild relative species was screened for resistance to Fusarium head blight (FHB) by spray inoculation. The great majority of species and accessions were susceptible or highly susceptible to FHB. Accessions of Triticum timopheevii (P95-99.1-1), Agropyron desertorum (9439957) and Elymus vaillantianus (531552) were highly resistant to FHB while additional accessions of T. timopheevii were found to be susceptible to FHB. A combination of spray and point inoculation assessments over two consecutive seasons indicated that the resistance in accession P95-99.1-1 was due to enhanced resistance to initial infection of the fungus (type 1 resistance), and not to reduction in spread (type 2 resistance). A panel of wheat-T. timopheevii (accession P95-99.1-1) introgression lines was screened for FHB resistance over two consecutive seasons using spray inoculation. Most introgression lines were similar in susceptibility to FHB as the wheat recipient (Paragon) but substitution of the terminal portion of chromosome 3BS of wheat with a similar-sized portion of 3G of T. timopheevii significantly enhanced FHB resistance in the wheat background.

plant biology↗

bioRxiv2022

Introgression of the Triticum timopheevii genome into wheat detected by chromosome-specific KASP markers

Triticum timopheevii (2n=28, AtAtGG) is a tetraploid wild relative species with great potential to increase the genetic diversity of hexaploid wheat Triticum aestivum (2n=42, AABBDD) for various important agronomic traits. A breeding scheme that propagated advanced backcrossed populations of wheat-T. timopheevii introgression lines through further backcrossing and self-fertilisation resulted in the generation of 99 introgression lines (ILs) that carried 309 homozygous segments from the At and G subgenomes of T. timopheevii. These introgressions contained 89 and 74 unique segments from the At and G subgenomes, respectively. These overlapping segments covered 98.9% of the T. timopheevii genome that has now been introgressed into bread wheat cv. Paragon including the entirety of all T. timopheevii chromosomes via varying sized segments except for chromosomes 3At, 4G and 6G. Homozygous ILs contained between one and eight of these introgressions with an average of three per introgression line. These homozygous introgressions were detected through the development of a set of 480 chromosome-specific Kompetitive allele specific PCR (KASP) markers that are well-distributed across the wheat genome. Of these, 149 were developed in this study based on single nucleotide polymorphisms (SNPs) discovered through whole genome sequencing of T. timopheevii. A majority of these KASP markers were also found to be T. timopheevii subgenome specific with 182 detecting At subgenome and 275 detecting G subgenome segments. These markers showed that 98% of the At segments had recombined with the A genome of wheat and 74% of the G genome segments had recombined with the B genome of wheat with the rest recombining with the D genome of wheat. These results were validated through multi-colour in situ hybridisation analysis. Together these homozygous wheat-T. timopheevii ILs and chromosome-specific KASP markers provide an invaluable resource to wheat breeders for trait discovery to combat biotic and abiotic stress factors affecting wheat production due to climate change.

plant biology↗

bioRxiv2021

A novel approach to develop wheat chromosome-specific KASP markers for detecting Amblyopyrum muticum segments in doubled haploid introgression lines

Many wild relative species are being used in pre-breeding programmes to increase the genetic diversity of wheat. Genotyping tools such as single nucleotide polymorphism (SNP)-based arrays and molecular markers have been widely used to characterise wheat-wild relative introgression lines. However, due to the polyploid nature of the recipient wheat genome, it is difficult to develop SNP-based KASP markers that are codominant to track the introgressions from the wild species. Previous attempts to develop KASP markers have involved both exome- and PCR-amplicon-based sequencing of the wild species. But chromosome-specific KASPs assays have been hindered by homoeologous SNPs within the wheat genome. This study involved whole genome sequencing of the diploid wheat wild relative Amblyopyrum muticum and development of a SNP discovery pipeline that generated [~]38,000 SNPs in single-copy wheat genome sequences. New assays were designed to increase the density of Am. muticum polymorphic KASP markers. With a goal of one marker per 60 Mbp, 335 new KASP assays were validated as functional. Together with assays validated in previous studies, 498 well distributed chromosome-specific markers were used to recharacterize previously genotyped wheat-Am. muticum doubled haploid (DH) introgression lines. The chromosome specific nature of the KASP markers allowed clarification of which wheat chromosomes were involved with recombination events or substituted with Am. muticum chromosomes and the higher density of markers allowed detection of new small introgressions in these DH lines. Key MessageA novel methodology to generate chromosome-specific SNPs between wheat and its wild relative Amblyopyrum muticum and their use in the development of KASP markers to genotype wheat-Am. muticum introgression lines.

genetics↗

bioRxiv2021

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool to reveal the complex biological diversity and heterogeneity among cell populations. However, the technical noise and bias of the technology still have negative impacts on the downstream analysis. Here, we present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. In the task, the deep learning model learns to pull together the representations of similar cells while pushing apart distinct cells, without manual labeling. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43,695 single cells from peripheral blood mononuclear cells. Further experiments to process a million-scale single-cell dataset demonstrate the scalability of CLEAR. This scalable method generates effective scRNA-seq data representation while eliminating technical noise, and it will serve as a general computational framework for single-cell data analysis.

bioinformatics↗

bioRxiv2020

Prediction of protein mutational free energy: benchmark and sampling improvements increase classification accuracy

Software to predict the change in protein stability upon point mutation is a valuable tool for a number of biotechnological and scientific problems. To facilitate the development of such software and provide easy access to the available experimental data, the ProTherm database was created. Biases in the methods and types of information collected has led to disparity in the types of mutations for which experimental data is available. For example, mutations to alanine are hugely overrepresented whereas those involving charged residues, especially from one charged residue to another, are underrepresented. ProTherm subsets created as benchmark sets that do not account for this often underrepresented certain mutational types. This issue introduces systematic biases into previously published protocols ability to accurately predict the change in folding energy on these classes of mutations. To resolve this issue, we have generated a new benchmark set with these problems corrected. We have then used the benchmark set to test a number of improvements to the point mutation energetics tools in the Rosetta software suite.

bioengineering↗

bioRxiv2020

Development of Wheat-Aegilops caudata Introgression Lines and Their Characterisation Using Genome-Specific KASP Markers

Aegilops caudata L. [syn. Ae. markgrafii (Greuter) Hammer], a diploid wild relative of wheat (2n = 2x = 14, CC), is an important source for new genetic variation for wheat improvement due to a variety of disease resistance factors along with tolerance for various abiotic stresses. Its practical utilisation in wheat improvement can be facilitated through the generation of genome-wide introgressions leading to a variety of different wheat-Ae. caudata recombinant lines. In this study, we report the generation of nine such wheat-Ae. caudata recombinant lines which were characterized using wheat genome-specific KASP (Kompetitive Allele Specific PCR) markers and multi-colour genomic in situ hybridization (mcGISH). Of these, six lines have stable homozygous introgressions from Ae. caudata and will be used for future trait analysis. Through a combination of molecular and cytological analysis of all the recombinant lines, we were able to physically map 182 KASP markers onto the seven Ae. caudata chromosomes, of which 155 were polymorphic specifically with only one wheat subgenome. Comparative analysis of the physical positions of these markers in the Ae. caudata and wheat genomes confirmed that the former had chromosomal rearrangements with respect to wheat, as previously reported. These wheat-Ae. caudata recombinant lines and KASP markers provide a useful genetic resource for wheat improvement with the latter having a wider impact as a tool for detection of introgressions from other Aegilops species into wheat.

genetics↗

bioRxiv2016

Environmental DNA Barcode Sequence Capture: Targeted, PCR-free Sequence Capture for Biodiversity Analysis from Bulk Environmental Samples

Environmental DNA analysis using PCR amplified marker genes has been a key application of high-throughput sequencing (HTS). However, PCR bias is a major drawback to gain accurate qualitative and quantitative biodiversity data. We developed a PCR-free approach using enrichment baits for species-specific mitochondrial cytochrome c oxidase 1(COI) DNA barcodes. The sequence capture was tested on species-rich bulk terrestrial and aquatic benthic samples. Hybridization capture recovered an average of 6 and 4.7 more arthropod orders than amplicon sequencing for terrestrial and benthic samples, respectively. For the terrestrial sample, the four most abundant arthropod orders comprised 94.0% of the sample biomass. These same four orders comprised 95.5% and 97.5% of the COI sequences recovered by amplification and capture, respectively. Hybridization capture recovered three arthropod orders that were detected by biomass analysis, but not by amplicon sequencing and two other insect orders that were not detected by either biomass or amplicon methods. These results indicate the advantage of using sequence capture for a more accurate analysis of biodiversity in bulk environmental samples. The protocol can be easily customized to other DNA barcode markers or gene regions of interest for a wide range of taxa or for a specific target group.

genomics↗