Biology subjects

Hanlon, V. C. T.

Publications and source records attributed to Hanlon, V. C. T..

4 recordsLinked to original sources

bioRxiv2026

3D printing and deep learning enable holistic and dynamic analyses of tens of thousands of parasites infecting hundreds of genotypes

Host-parasite interactions are dynamic systems, where parasites usually outnumber hosts by one or more orders of magnitude. However, our understanding is often limited by the assessment of parts of the host, at arbitrary time points, and/or aggregate parasite responses. Here we combined custom-built 3D-printed hardware and deep-learning-based algorithms to enable holistic (i.e. all infecting individuals on the whole plant), spatio-temporal, and parasite-centric analyses of plant-parasitism by nematodes from timelapse videos of infection over months. In so doing, we tracked the dynamic growth and development of all individual parasites, at the organismal level, for thousands of hosts across hundreds of genotypes of Arabidopsis thaliana. Categorising traits into the static (i.e. in an acquired image at a given time point) and dynamic (i.e. phenotypic changes over time), we revealed a greater extent of host-genetic control of parasite traits, and new physiological limits of the species under these conditions. Using this capability, we identify Quantitative Trait Loci (QTL) in the host plant associated with 18 phenotypic traits in the parasite as a resource for the community. Finally, we leverage the large and diverse dataset to understand fundamental features of the parasite, independent of host genotype, revealing aspects of the life cycle which are pseudo-deterministic as well as local, deleterious interactions between co-infecting parasites. Given that plant-parasitic nematodes cause an estimated $100 billion in agricultural damages per year, these insights are contextualised in a global challenge driven by plant-parasitic nematodes.

pathology↗

bioRxiv2025

The host range paradox of Meloidogyne incognita: a physiological and transcriptomic analysis of nine susceptible interactions across six plant orders

The plant-parasitic nematode Meloidogyne incognita is the pathogen with the broadest host range among all known biotrophic interactions. This species is also the single most damaging of a group of agriculturally important plant-parasites, which together are estimated to contribute to losses in excess of $170 billion/year to world agriculture. Understanding how M. incognita is able to infect representatives from most orders of flowering plants, covering more than 3000 species, addresses a fundamentally important question of how pathogens adapt to their host-environment, and may inform control of a pathogen which threatens global food security. Here, we analyse the plant-nematode infection phenotype, and cross-kingdom transcriptome, of nine interactions across six orders of flowering plants at 25 days post infection. At this stage, majority of nematodes found within roots were immature and mature females (49.2% - 91.8%). Our data show that the phylogenetic distribution of hosts does not explain the phenotypic distribution of parasitism. Interestingly, though, M. incognita do have distinct transcriptional responses to different groups of hosts, but in a pattern which is independent of host phylogenetic. Three distinct nematode "transcriptional programmes" - Group 1, 2, and 3 - are evident, and we find that effectors are neither uniformly deployed across hosts, nor across groups of hosts. Importantly, we show that this differential deployment of effectors can have profound consequences for host specificity. Finally, we show that there is essentially no widespread core gall transcriptome at 25 days post infection, prompting the proposal of a model best described as "all roads lead to Rome".

plant biology↗

bioRxiv2024

A familial, telomere-to-telomere reference for human de novo mutation and recombination from a four-generation pedigree

Using five complementary short- and long-read sequencing technologies, we phased and assembled >95% of each diploid human genome in a four-generation, 28-member family (CEPH 1463) allowing us to systematically assess de novo mutations (DNMs) and recombination. From this family, we estimate an average of 192 DNMs per generation, including 75.5 de novo single-nucleotide variants (SNVs), 7.4 non-tandem repeat indels, 79.6 de novo indels or structural variants (SVs) originating from tandem repeats, 7.7 centromeric de novo SVs and SNVs, and 12.4 de novo Y chromosome events per generation. STRs and VNTRs are the most mutable with 32 loci exhibiting recurrent mutation through the generations. We accurately assemble 288 centromeres and six Y chromosomes across the generations, documenting de novo SVs, and demonstrate that the DNM rate varies by an order of magnitude depending on repeat content, length, and sequence identity. We show a strong paternal bias (75-81%) for all forms of germline DNM, yet we estimate that 17% of de novo SNVs are postzygotic in origin with no paternal bias. We place all this variation in the context of a high-resolution recombination map ([~]3.5 kbp breakpoint resolution). We observe a strong maternal recombination bias (1.36 maternal:paternal ratio) with a consistent reduction in the number of crossovers with increasing paternal (r=0.85) and maternal (r=0.65) age. However, we observe no correlation between meiotic crossover locations and de novo SVs, arguing against non-allelic homologous recombination as a predominant mechanism. The use of multiple orthogonal technologies, near-telomere-to-telomere phased genome assemblies, and a multi-generation family to assess transmission has created the most comprehensive, publicly available "truth set" of all classes of genomic variants. The resource can be used to test and benchmark new algorithms and technologies to understand the most fundamental processes underlying human genetic variation.

genomics↗

bioRxiv2022

Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds. The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for indels. Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.

genomics↗