Biology subjects

Judge, K.

Publications and source records attributed to Judge, K..

6 recordsLinked to original sources

bioRxiv2026

Neuron-intrinsic and glial pathways regulate sensory cilia regeneration in adult C. elegans

Primary cilia are microtubule-based organelles that mediate cellular responses to environmental cues. Although cilia disassemble and reassemble upon cell cycle entry and exit, respectively, in dividing cells, it remains unclear whether postmitotic cells such as neurons can regenerate these structures in vivo following injury to restore function, and whether this process recapitulates developmental ciliogenesis. Here we show that a subset of sensory neuron cilia in adult C. elegans regrows following conditional truncation and restores neuronal functions. This regeneration is regulated by both cell-intrinsic and extrinsic mechanisms that are in part distinct from those employed during embryonic ciliogenesis. We find that the conserved ciliogenic DAF-19 RFX transcription factor is dispensable for cilia maintenance in the adult but is required for regeneration, in part via transcriptional upregulation of a subset of ciliary intraflagellar transport (IFT) genes. We further identify the DLK-1 dual leucine-zipper kinase and the CEBP-1 C/EBP transcription factor, previously implicated in axon regeneration, as necessary for efficient cilia regrowth but not for developmental ciliogenesis. We show that cebp-1 expression is induced during cilia truncation and regrowth and that this induction is also DAF-19-dependent. Finally, we show that cilia truncation and regeneration dynamics vary in a neuron type-specific manner and are modulated by signals from surrounding glia. Our results establish that the cilia of mature neurons can regenerate and recover functions in vivo and identify conserved pathways that regulate this process in adult animals.

cell biology↗

bioRxiv2022

Nanopore sequencing for real-time genomic surveillance of Plasmodium falciparum

Malaria is a global public health priority causing over 600,000 deaths annually, mostly young children living in Sub-Saharan Africa. Molecular surveillance can provide key information for malaria control, such as the prevalence and distribution of antimalarial drug resistance. However, genome sequencing capacity in endemic countries can be limited. Here, we have implemented an end-to-end workflow for P. falciparum genomic surveillance in Ghana using Oxford Nanopore Technologies, targeting antimalarial resistance markers and the leading vaccine antigen circumsporozoite protein (csp). The workflow was rapid, robust, accurate, affordable and straightforward to implement, and could be deployed using readily collected dried blood spot samples. We found that P. falciparum parasites in Ghana had become largely susceptible to chloroquine, with persistent sulfadoxine-pyrimethamine (SP) resistance, and no evidence of artemisinin resistance. Multiple Single Nucleotide Polymorphism (SNP) differences from the vaccine csp sequence were identified, though their significance is uncertain. This study demonstrates the potential utility and feasibility of malaria genomic surveillance in endemic settings using Nanopore sequencing.

genomics↗

bioRxiv2022

A manually curated annotation characterises genomic features of P. falciparum lncRNAs

BackgroundImportant regulation occurs at the level of transcription in Plasmodium falciparum and growing evidence suggests that these apicomplexan parasites have complex regulatory networks. Recent studies implicate long noncoding RNAs (lncRNAs) as transcriptional regulators in P. falciparum. However, due to limited research and the lack of necessary experimental tools, our understanding of their role in the malaria-causing parasite remains largely unelucidated. In this work, we address one of these limitations, the lack of an updated and improved lncRNA annotation in P. falciparum. ResultsWe generated long-read RNA sequencing data and integrated information extracted and curated from multiple sources to manually annotate lncRNAs. We identified 1522 novel lncRNAs and validated and refined 847 existing annotations. Utilising the collated datasets, we generated evidence-based ranking scores for each annotation and characterised the distinct genomic contexts and features of P. falciparum lncRNAs. Certain features indicated subsets with potential biological significance such as 25 lncRNAs containing multiple introns, 335 lncRNAs lacking mutations in piggyBac mutagenic studies and lncRNAs associated with specific biologic processes including two new types of lncRNAs found proximal to var genes. ConclusionsThe insights and the annotation presented in this study will serve as valuable tools for researchers seeking to understand the role of lncRNAs in parasite biology through both bioinformatics and experimental approaches.

genomics↗

bioRxiv2019

Mechanisms Generating Cancer Genome Complexity From A Single Cell Division Error

The chromosome breakage-fusion-bridge (BFB) cycle is a mutational process that produces gene amplification and genome instability. Signatures of BFB cycles can be observed in cancer genomes with chromothripsis, another catastrophic mutational process. Here, we explain this association by identifying a mutational cascade downstream of chromosome bridge formation that generates increasing amounts of chromothripsis. We uncover a new role for actomyosin forces in bridge breakage and mutagenesis. Chromothripsis then accumulates starting with aberrant interphase replication of bridge DNA, followed by an unexpected burst of mitotic DNA replication, generating extensive DNA damage. Bridge formation also disrupts the centromeric epigenetic mark, leading to micronucleus formation that itself promotes chromothripsis. We show that this mutational cascade generates the continuing evolution and sub-clonal heterogeneity characteristic of many human cancers.

cancer biology↗

bioRxiv2019

Complete assembly of Escherichia coli ST131 genomes using long reads demonstrates antibiotic resistance gene variation within diverse plasmid and chromosomal contexts

The incidence of infections caused by extraintestinal Escherichia coli (ExPEC) is rising globally, which is a major public health concern. ExPEC strains that are resistant to antimicrobials have been associated with excess mortality, prolonged hospital stays and higher healthcare costs. E. coli ST131 is a major ExPEC clonal group worldwide with variable plasmid composition, and has an array of genes enabling antimicrobial resistance (AMR). ST131 isolates frequently encode the AMR genes blaCTX-M-14/15/27, which are often rearranged, amplified and translocated by mobile genetic elements (MGEs). Short DNA reads do not fully resolve the architecture of repetitive elements on plasmids to allow MGE structures encoding blaCTX-M genes to be fully determined. Here, we performed long read sequencing to decipher the genome structures of six E. coli ST131 isolated from six patients. Most long read assemblies generated entire chromosomes and plasmids as single contigs, contrasting with more fragmented assemblies created with short reads alone. The long read assemblies highlighted diverse accessory genomes with blaCTX-M-15, blaCTX-M-14 and blaCTX-M-27 genes identified in three, one and one isolates, respectively. One sample had no blaCTX-M gene. Two samples had chromosomal blaCTX-M-14 and blaCTX-M-15 genes, and the latter was at three distinct locations, likely transposed by the adjacent MGEs: ISEcp1, IS903B and Tn2. This study showed that AMR genes exist in multiple different chromosomal and plasmid contexts even between closely-related isolates within a clonal group such as E. coli ST131. ImportanceDrug-resistant bacteria are a major cause of illness worldwide and a specific subtype called Escherichia coli ST131 cause a significant amount of these infections. ST131 become resistant to treatment by modifying their DNA and by transferring genes among one another via large packages of genes called plasmids, like a game of pass-the-parcel. Tackling infections more effectively requires a better understanding of what plasmids are being exchanged and their exact contents. To achieve this, we applied new high-resolution DNA sequencing technology to six ST131 samples from infected patients and compared the output to an existing approach. A combination of methods shows that drug-resistance genes on plasmids are highly mobile because they can jump into ST131s chromosomes. We found that the plasmids are very elastic and undergo extensive rearrangements even in closely related samples. This application of DNA sequencing technologies illustrates at a new level the highly dynamic nature of ST131 genomes.

genomics↗

bioRxiv2017

PlasmidTron: assembling the cause of phenotypes from NGS data

When defining bacterial populations through whole genome sequencing (WGS) the samples often have detailed associated metadata that relate to disease severity, antimicrobial resistance, or even rare biochemical traits. When comparing these bacterial populations, it is apparent that some of these phenotypes do not follow the phylogeny of the host i.e. they are genetically unlinked to the evolutionary history of the host bacterium. One possible explanation for this phenomenon is that the genes are moving independently between hosts and are likely associated with mobile genetic elements (MGE). However, identifying the element that is associated with these traits can be complex if the starting point is short read WGS data. With the increased use of next generation WGS in routine diagnostics, surveillance and epidemiology a vast amount of short read data is available and these types of associations are relatively unexplored. One way to address this would be to perform assembly de novo of the whole genome read data, including its MGEs. However, MGEs are often full of repeats and can lead to fragmented consensus sequences. Deciding which sequence is part of the chromosome, and which is part of a MGE can be ambiguous. We present PlasmidTron, which utilises the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographic information, to identify sequences that are likely to represent MGEs linked to the phenotype. Given a set of reads, categorised into cases (showing the phenotype) and controls (phylogenetically related but phenotypically negative), PlasmidTron can be used to assemble de novo reads from each sample linked by a phenotype. A k-mer based analysis is performed to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs. By utilising k-mers and only assembling a fraction of the raw reads, the method is fast and scalable to large datasets. This approach has been tested on plasmids, because of their contribution to important pathogen associated traits, such as AMR, hence the name, but there is no reason why this approach cannot be utilized for any MGE that can move independently through a bacterial population. PlasmidTron is written in Python 3 and available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron.\n\nDATA SUMMARYO_LISource code for PlasmidTron is available from Github under the open source licence GNU GPL 3; (url - https://goo.gl/ot6rT5)\nC_LIO_LISimulated raw reads files have been deposited in Figshare; (url - https://doi.org/10.6084/m9.figshare.5406355.vl)\nC_LIO_LISalmonella enterica serovar Weltevreden strain VNS10259 is available from GenBank; accession number GCA_001409135.\nC_LIO_LISalmonella enterica serovar Typhi strain BL60006 is available from GenBank; accession number GCA_900185485.\nC_LIO_LIAccession numbers for all of the Illumina datasets used in this paper are listed in the supplementary tables.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTPlasmidTron utilises the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographic information, to identify sequences that are likely to represent MGEs linked to the phenotype.

bioinformatics↗