Biology subjects

Atwell, S.

Publications and source records attributed to Atwell, S..

10 recordsLinked to original sources

bioRxiv2023

Engineered Antigen-Binding Fragments for Enhanced Crystallisation of Antibody:Antigen Complexes

The atomic-resolution structural information that X-ray crystallography can provide on the binding interface between a Fab and its cognate antigen is highly valuable for understanding the mechanism of interaction. However, many Fab:antigen complexes are recalcitrant to crystallisation, making the endeavour a significant effort with no guarantee of success. Consequently, there have been significant steps taken to increase the likelihood of Fab:antigen complex crystallisation by altering the Fab framework. In this investigation, we applied the surface entropy reduction strategy coupled with phage-display technology to identify a set of surface substitutions that improve the propensity of a human Fab framework to crystallise. In addition, we showed that combining these surface substitutions with previously reported Crystal Kappa and elbow substitutions results in a striking improvement in Fab and Fab:antigen complex crystallisability, revealing a synergistic relationship between these sets of substitutions. Through comprehensive Fab and Fab:antigen complex crystallisation screenings followed by structure determination and analysis, we defined the roles that each of these substitutions play in facilitating crystallisation and how they complement each other in the process.

biochemistry↗

bioRxiv2022

Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip

The dynamic chemical and architectural microenvironments of 3D stem cell cultures can be controlled by integration into a microfluidic chip. Massive parallelized 3D stem cell cultures for engineering in vitro human cell types require new imaging methods with high time and spatial resolution to fully exploit technological advances in cell culture. Here, we introduce a label-free deep learning method called Bright2Nuc to predict in silico nuclear staining in 3D from bright-field images obtained using traditional confocal microscopy. Bright2Nuc was trained and applied to several hundred 3D human induced pluripotent stem cell cultures differentiating towards definitive endoderm on a microfluidic platform. Combined with existing image analysis tools, Bright2Nuc segmented individual nuclei from bright-field images, quantified their morphological properties, predicted stem cell differentiation state, and tracked the cells over time. Our methods are available in an open-source pipeline that enables researchers to upscale 3D cell phenotyping in stem cell culture.

bioinformatics↗

bioRxiv2021

SHAPR - An AI approach to predict 3D cell shapes from 2D microscopic images

Reconstruction of shapes and sizes of three-dimensional (3D) objects from two-dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naive stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79.0% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.

bioinformatics↗

bioRxiv2020

Rapid and robust antibody Fab fragment crystallization utilizing edge-to-edge beta-sheet packing

Antibody therapeutics are one of the most important classes of drugs. Antibody structures have become an integral part of predicting the behavior of potential therapeutics, either directly or as the basis of modeling. Structures of Fab:antigen complexes have even greater value. While the crystallization and structure determination of Fabs is easy relative to many other protein classes, especially membrane proteins, broad screening and optimization of crystalline hits is still necessary. Through a comprehensive review of rabbit Fab crystal contacts and their incompatibility with human Fab structures, we identified a small secondary structural element from the rabbit light chain constant domain potentially responsible for hindering the crystallization of human Fabs. Upon replacing the human kappa constant domain FG loop (HQGLSSP) with the two residue shorter rabbit loop (QGTTS), we dramatically improved the crystallization of human Fabs and Fab:antigen complexes. Our design, which we call "Crystal Kappa", enables rapid crystallization of human fabs and fab complexes in a broad range of conditions, with less material in smaller screens or from dilute solutions.

immunology↗

bioRxiv2019

Pathogen genetic control of transcriptome variation in the Arabidopsis thaliana - Botrytis cinerea pathosystem

Disease symptoms arise from the interaction of the host and pathogen genomes. However, little is known about how genetic variation in the interaction modulates both organisms transcriptomes, especially in complex interactions like those between generalist pathogens and their plant hosts. To begin mapping how polygenic pathogen variation influences both organisms transcriptomes, we used the Botrytis cinerea - Arabidopsis thaliana pathosystem. We measured the co-transcriptome across a genetically diverse collection of 96 B. cinerea isolates infected on the Arabidopsis wildtype, Col-0. Using the B. cinerea genomic variation, we performed genome-wide association (GWA) for each of 23,947 measurable transcripts in the host, and 9,267 measurable transcripts in the pathogen. Unlike other eGWA studies, there was a relative absence of cis-eQTL that is likely explained by structural variants and allelic heterogeneity within the pathogens genome. This analysis identified mostly trans-eQTL in the pathogen with eQTL hotspots dispersed across the pathogen genome that altered the pathogens transcripts, the hosts transcripts, or both the pathogen and the host. Gene membership in the trans-eQTL hotspots suggests links to several known and many novel virulence mechanisms in the plant-pathogen interaction. Genes annotated to these hotspots provide potential targets for blocking manipulation of the host response by this ubiquitous generalist pathogen. This shows that genetic control over the co-transcriptome is polygenic, similar to the virulence outcome in the interaction of Botrytis cinerea on Arabidopsis thaliana.

plant biology↗

bioRxiv2018

A generalist pathogen view of plant evolution

Botrytis cinerea is a polyphagous fungal pathogen that causes necrotic disease on more than a thousand known hosts widely spread across the plant kingdom. While it is known that quantitative resistance in the host and quantitative virulence in the pathogen largely mediate this pathosystem, how this pathogen interacts with the extensive host diversity is unknown. Does this pathogen have quantitative virulence efficiency on all hosts or individual solutions for each host? To address this question, we generated an infectivity matrix of 98 strains of Botrytis cinerea on 90 genotypes representing eight host plants. This experimental infectivity matrix showed that the predominant sources of quantitative variation are between host species and among pathogen strains. Furthermore, the eight eudicot hosts interacted individually with Botrytis cinerea strains independently of the evolutionary relatedness between hosts. An additive quantitative model can explain the complexity of these interactions in which Botrytis host specificity and general virulence have distinct polygenic architectures.

plant biology↗

bioRxiv2018

Resequencing and association mapping of the generalist pathogen Botrytis cinerea

We performed whole genome resequencing of 84 field isolates of Botrytis cinerea, largely collected from a local set of plant species. Combined with 13 previously resequenced isolates sampled from diverse locations, this gave a collection of 97 isolates for studies of natural variation. Alignment to the reference sequence T4 and SNP detection provided further data for population genetics analysis including a mapping population for association studies. Although much of the genomic diversity was captured in the original 13 isolates, the additional genomes increased total diversity in the population by a third. Surprisingly, the same additional genomes increase mitochondrial diversity 2-fold. Across the population, LD was limited and decayed rapidly, reflecting frequent outcrossings. Effectively, this sampling strategy increased the level of genetic diversity available, whilst limiting the problem of population stratification and enabling GWAS of several phenotypes on common Arabidopsis plants affected in disease pathways. Overlap of results using all GWAS methods revealed numerous candidate genes / pathways that potentially contribute to its broad host range and offer conceivable pathogen decrease targets.

genomics↗

bioRxiv2018

Plant-Necrotroph Co-transcriptome Networks Illuminate a Metabolic Battlefield

A central goal of studying host-pathogen interaction research is to understand how the host and pathogen manipulate each other to promote their own fitness in a pathosystem. Co-transcriptomic approaches can simultaneously analyze dual transcriptomes during infection and provide a systematic map of the cross-kingdom communication between two species. Here we used the Arabidopsis-B. cinerea pathosystem to test how plant host and fungal pathogen interaction at the transcriptomic level during infection. We assessed the impact of natural genetic diversity in the pathogen and plant host by utilization of a collection of 96 isolates of B. cinerea infection on Arabidopsis wild-type and two mutants with jasmonate or salicylic acid compromised immunities. We identified ten B. cinerea gene co-expression networks (GCNs) that encode known or novel virulence mechanisms. We constructed a dual interaction network by combining four host-and ten pathogen-GCNs into a single network, which revealed potential connections between the fungal and plant GCNs involving both novel and conserved mechanisms. These co-transcriptome data shed lights on the potential mechanisms underlying host-pathogen interaction and illustrate the continued need for advancements of in planta analysis of dual-species dynamics.

systems biology↗

bioRxiv2018

Combining Digital Imaging and Genome Wide Association Mapping to Dissect Uncharacterized Traits in Plant/Pathogen Interactions

Plant resistance to generalist pathogens with broad host ranges, such as Botrytis cinerea, is typically quantitative and highly polygenic. Recent studies have begun to elucidate the molecular genetic basis underpinning plant-pathogen interactions using commonly measured traits including lesion size and/or pathogen biomass. Yet with the advent of digital imaging and phenomics, there are a large number of additional resistance traits available to study quantitative resistance. In this study, we used high-throughput digital imaging analysis to investigate previously uncharacterized visual traits of plant-pathogen interactions related disease resistance using the Arabidopsis thaliana/Botrytis cinerea pathosystem. Using a large collection of 75 visual traits collected from every lesion, we focused on lesion color, lesion shape, and lesion size, to test how these aspects of the interaction are genetically related. Using genome wide association (GWA) mapping in A. thaliana, we show that lesion color and shape are genetically separable traits associated with plant-disease resistance. Using defined mutants in 23 candidate genes from the GWA mapping, we could identify and show that novel loci associated with each different plant-pathogen interaction trait, which expands our understanding of the functional mechanisms driving plant disease resistance.\n\nSummaryDigital imaging allows the identification of genes controlling novel lesion traits.

plant biology↗

bioRxiv2018

Crop domestication and pathogen virulence: Interactions of tomato and Botrytis genetic diversity

Human selection during crop domestication alters numerous traits, including disease resistance. Studies of qualitative resistance to specialist pathogens typically find decreased resistance in domesticated crops in comparison to their wild relatives. However, less is known about how crop domestication affects quantitative interactions with generalist pathogens. To study how crop domestication impacts plant resistance to generalist pathogens, and correspondingly how this interacts with the pathogens genetics, we infected a collection of wild and domesticated tomato accessions with a genetically diverse population of the generalist pathogen Botrytis cinerea. We quantified variation in lesion size of 97 B. cinerea genotypes (isolates) on 6 domesticated Solanum lycopersicum and 6 wild S. pimpinellifolium genotypes. This showed that lesion size was significantly controlled by plant domestication, plant genetic variation, and the pathogens genotype. Overall, resistance was slightly elevated in the wild germplasm in comparison to domesticated tomato accessions. Genome-wide association (GWA) mapping in B. cinerea identified a highly polygenic collection of genes. This suggests that breeding against this pathogen would need to utilize a diversity of isolates to capture all possible mechanisms. Critically, we identified a discrete subset of B. cinerea genes where the allelic variation was linked to altered virulence against the wild versus domesticated tomato accessions. This indicates that this generalist pathogen already has the necessary allelic variation in place to handle the introgression of wild resistance mechanisms into the domesticated crop. Future studies are needed to assess how these observations extend to other domesticated crops and other generalist pathogens.

plant biology↗