Biology subjects

MacLean, D.

Publications and source records attributed to MacLean, D..

12 recordsLinked to original sources

bioRxiv2026

AI-guided discovery of atypical protein assemblies

Artificial intelligence (AI) systems such as AlphaFold have transformed structural biology by enabling accurate prediction of protein structures. However, their capacity to uncover new classes of macromolecular assemblies remains largely untapped. We developed the Structural Novelty Index (SNI), a quantitative framework for identifying protein complexes that diverge from canonical architectures. As one implementation of SNI, we developed SNINRC-Hexa, to identify unconventional resistosomes formed by nucleotide-binding, leucine-rich repeat immune receptors (NLRs). We used it to analyze AlphaFold 3 models of 637 non-redundant NRC proteins from 346 genomes representing 85 plant species. This analysis identified candidates with predicted architectures distinct from the canonical hexameric resistosomes of NRC proteins. Biochemical purification and negative-stain transmission electron microscopy of NRC7 orthologs from multiple species supported the SNI prediction and revealed an unexpected undecameric (11-mer) assembly. Our results establish SNI as a scalable approach for discovering atypical protein complexes.

bioinformatics↗

bioRxiv2025

Integrating and refining the predictions of ensembled plant effector detection programs using machine learning

Effectors are pathogen proteins that facilitate infection by manipulating plant immunity. Computational programs have been developed that identify effectors from sequence data. Many of these programs use internal models that have unavoidable biases due to their training processes and the diverse nature of effector sequence, function and phylogeny. Each programs ability to predict effectors across a broad range of plant pathogens is therefore limited. We hypothesised that a meta-predictor constructed using machine learning (ML) approaches could integrate predictions from multiple programs and improve our ability to predict effectors more accurately from bacteria, fungi, and oomycetes. We trained a range of classifiers using classical ML approaches and deep neural networks (DNNs), then selected eight: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and five DNNs for evaluation. The training, test and validation data were carefully curated from effector and non-effector annotated sequence derived from the training and sample data of six programs: EffectorP 3.0, deepredeff, WideEffHunter, EffectorO, EffectiveT3, T3SEpp. The models were tested against existing programs on a test dataset, and we observed better performance from our models. The best-performing model was a DNN (Model_2) that balanced improved sensitivity with specificity across the three taxa. We observed using SHAP that all the features contributed to the output of Model_2, which might be the reason for its superior performance. The DNN was developed into a package, fimep, to allow easy use of our model.

bioinformatics↗

bioRxiv2025

Synchronous spatio-temporal control of autophagy and organelle trafficking is necessary for appressorium-mediated plant infection by Magnaporthe oryzae

The blast fungus Magnaporthe oryzae infects plants using a specialised infection structure called an appressorium that generates physical force to break the rice leaf cuticle. Appressorium development follows a cell cycle-controlled morphogenetic program, requiring autophagy-associated cell death of the fungal spore from which the infection cell develops. How proliferative growth of the fungus is regulated at the same time as programmed cell death, however, is unknown. In this study, we provide evidence that each cell of the conidium undergoes a separate developmental program, which is necessary for plant infection. Using quantitative live-cell imaging, we monitored trafficking of ten organelle types during appressorium morphogenesis in a wild-type M. oryzae strain and isogenic {Delta}atg8 autophagic mutant. High-resolution microscopy using a photoactivatable green fluorescent protein revealed that organelle trafficking occurs from a single conidium cell into the appressorium, while the remaining two cells undergo autophagy. Organelle inheritance operates independently of cell cycle checkpoints but is always associated with spore germination. We furthermore defined the temporal sequence of organelle movement and de novo organelle biogenesis in the incipient appressorium using photoconvertible fluorescent localisation microscopy. Taken together, our study reveals how synchronous spatiotemporal control of autophagy and organelle trafficking is necessary for rice blast infection.

molecular biology↗

bioRxiv2024

Multipartite complexity of the lichen symbiosis revealed by metagenome and transcriptome analysis of Xanthoria parietina

Lichens are composite symbiotic associations of fungi, algae, and bacteria that result in large, anatomically complex organisms adapted to many of the worlds most challenging environments. How such intricate, self-replicating lichen architectures develop from simple microbial components remains unknown because of their recalcitrance to experimental manipulation. Here we report a metagenomic and metatranscriptomic analysis of the lichen Xanthoria parietina at different developmental stages. We identified 168 genomes of symbionts and lichen-associated microbes within a lichen thallus, including representatives of green algae, three different classes of fungi, and 14 bacterial phyla. By analyzing occurrence of individual species across lichen thalli from diverse environments, we defined both substrate-specific and core microbial components of the lichen. Meta-transcriptomic analysis of the principal fungal symbiont from three different developmental stages of a lichen, compared to axenically grown fungus, revealed differential gene expression profiles indicative of lichen-specific transporter functions, specific cell signalling, transcriptional regulation and secondary metabolic capacity. Putative immunity-related proteins and lichen-specific structurally conserved secreted proteins resembling fungal pathogen effectors were also identified, consistent with a role for immunity modulation in lichen morphogenesis.

bioinformatics↗

bioRxiv2023

The phosphorylation landscape of infection-related development by the rice blast fungus

Many of the worlds most devastating crop diseases are caused by fungal pathogens which elaborate specialized infection structures to invade plant tissue. Here we present a quantitative mass spectrometry-based phosphoproteomic analysis of infection-related development by the rice blast fungus Magnaporthe oryzae, which threatens global food security. We mapped 8,005 phosphosites on 2,062 fungal proteins, revealing major re-wiring of phosphorylation-based signaling cascades during fungal infection. Comparing phosphosite conservation across 41 fungal species reveals phosphorylation signatures specifically associated with biotrophic and hemibiotrophic fungal infection. We then used parallel reaction monitoring to identify phosphoproteins directly regulated by the Pmk1 MAP kinase that controls plant infection by M. oryzae. We define 33 substrates of Pmk1 and show that Pmk1-dependent phosphorylation of a newly identified regulator, Vts1, is required for rice blast disease. Defining the phosphorylation landscape of infection therefore identifies potential therapeutic interventions for control of plant diseases.

plant biology↗

bioRxiv2023

Innate immune receptor C5aR1 regulates cancer cell fate and can be targeted to improve radiotherapy in tumours with immunosuppressive microenvironments

An immunosuppressive microenvironment causes poor tumour T-cell infiltration and is associated with reduced patient overall survival in colorectal cancer. How to improve treatment responses in these tumours is still a challenge. Using an integrated screening approach to identify cancer-specific vulnerabilities, we identify complement receptor C5aR1 as a druggable target which when inhibited improves radiotherapy even in tumours displaying immunosuppressive features and poor CD8+ T-cell infiltration. While C5aR1 is well-known for its role in the immune compartment, we find that C5aR1 is also robustly expressed on malignant epithelial cells, highlighting potential tumour-cell specific functions. C5aR1 targeting results in increased NF-{kappa}B-dependent apoptosis specifically in tumours and not normal tissues; indicating that in malignant cells, C5aR1 primarily regulates cell fate. Collectively, these data reveal that increased complement gene expression is part of the stress response mounted by irradiated tumours and that targeting C5aR1 can improve radiotherapy even in tumours displaying immunosuppressive features.

cancer biology↗

bioRxiv2021

An improved assembly of the Albugo candida Ac2V genome reveals the expansion of the "CCG" class of effectors

Albugo candida is an obligate oomycete pathogen that infects many plants in the Brassicaceae family. We re-sequenced the genome of isolate Ac2V using PacBio long reads and constructed an assembly augmented by Illumina reads. The Ac2VPB genome assembly is 10% larger and more contiguous compared to a previous version. Our annotation of the new assembly, aided by RNASeq information, revealed a dramatic 250% expansion (40 to 110) in the CHxC effector class, which we redefined as "CCG" based on motif analysis. This class of effectors consist of arrays of phylogenetically related paralogs residing in gene sparse regions, and shows signatures of positive selection and presence/absence polymorphism. This work provides a resource that allows the dissection of the genomic components underlying A. candida adaptation and particularly the role of CCG effectors in virulence and avirulence on different hosts.

plant biology↗

bioRxiv2020

Chromatin accessibility landscapes activated by cell surface and intracellular immune receptors

Activation of cell Surface and Intracellular Receptor-Mediated Immunity (SRMI and IRMI) results in rapid transcriptional reprogramming that underpins disease resistance. However, the mechanisms by which SRMI and IRMI lead to transcriptional changes are not clear. Here, we combine RNA-seq and ATAC-seq to define changes in gene expression and chromatin accessibility; both SRMI and IRMI increase chromatin accessibility at induced defense genes. Analysis of ATAC-seq and RNA-seq data combined with publicly available information on transcription factor DNA-binding motifs enabled comparison of individual gene regulatory networks activated by SRMI and IRMI, and by both. These results and analyses reveal overlapping and conserved transcriptional regulatory mechanism between the two immune systems.

plant biology↗

bioRxiv2019

High-resolution Expression Profiling of Selected Gene Sets during Plant Immune Activation

Sequence capture followed by next-generation sequencing has broad applications in cost-effective exploration of biological processes at high resolution [1, 2]. Genome-wide RNA sequencing (RNA-seq) over a time course can reveal the dynamics of differential gene expression. However, in many cases, only a limited set of genes are of interest, and are repeatedly used as markers for certain biological processes. Sequence capture can help generate high-resolution quantitative datasets to assess changes in abundance of selected genes. We previously used sequence capture to accelerate Resistance gene cloning [1, 3, 4], investigate immune receptor gene diversity [5] and investigate pathogen diversity and evolution [6, 7].\n\nThe plant immune system involves detection of pathogens via both cell-surface and intracellular receptors. Both receptor classes can induce transcriptional reprogramming that elevates disease resistance [8]. To assess differential gene expression during plant immunity, we developed and deployed quantitative sequence capture (CAP-I). We designed and synthesized biotinylated single-strand RNA bait libraries targeted to a subset of defense genes, and generated sequence capture data from 99 RNA-seq libraries. We built a data processing pipeline to quantify the RNA-CAP-I-seq data, and visualize differential gene expression. Sequence capture in combination with quantitative RNA-seq enabled cost-effective assessment of the expression profile of a specified subset of genes. Quantitative sequence capture is not limited to RNA-seq or any specific organism and can potentially be incorporated into automated platforms for high-throughput sequencing.

plant biology↗

bioRxiv2019

A convolutonal neural network for predicting transcriptional regulators of genes in Arabidopsis transcriptome data reveals classification based on positive regulatory interactions

Gene Regulatory networks that control gene expression are widely studied yet the interactions that make them up are difficult to predict from high throughput data. Deep Learning methods such as convolutional neural networks can perform surprisingly good classifications on a variety of data types and the matrix-like gene expression profiles would seem to be ideal input data for deep learning approaches. In this short study I compiled training sets of expression data using the Arabidopsis AtGenExpress global stress expression data set and known transcription factor-target interactions from the Arabidopsis PLACE database. I built and optimised convolutional neural networks with a best model providing 95 % accuracy of classification on a held-out validation set. Investigation of the activations within this model revealed that classification was based on positive correlation of expression profiles in short sections. This result shows that a convolutional neural network can be used to make classifications and reveal the basis of those calssifications for gene expression data sets, indicating that a convolutional neural network is a useful and interpretable tool for exploratory classification of biological data. The final model is available for download and as a web application.

bioinformatics↗

bioRxiv2017

The ash dieback invasion of Europe was founded by two individuals from a native population with huge adaptive potential

Accelerating international trade and climate change make pathogen spread an increasing concern. Hymenoscyphus fraxineus, the causal agent of ash dieback is one such pathogen, moving across continents and hosts from Asian to European ash. Most European common ash (Fraxinus excelsior) trees are highly susceptible to H. fraxineus although a small minority (~5%) evidently have partial resistance to dieback. We have assembled and annotated a draft of the H. fraxineus genome which approaches chromosome scale. Pathogen genetic diversity across Europe, and in Japan, reveals a tight bottleneck into Europe, though a signal of adaptive diversity remains in key host interaction genes (effectors). We find that the European population was founded by two divergent haploid individuals. Divergence between these haplotypes represents the 'shadow' of a large source population and subsequent introduction would greatly increase adaptive potential and the pathogen's threat. Thus, EU wide biological security measures remain an important part of the strategy to manage this disease.

genomics↗

bioRxiv2017

An Oomycete Effector Protein Induces Shade Avoidance In Arabidopsis And Attenuates Salicylate Signaling By Binding To Host Proteins Of The RADICAL-INDUCED CELL DEATH1 Family

The oomycete pathogen Hyaloperonospora arabidopsidis (Hpa) causes downy mildew disease on Arabidopsis. During infection, Hpa like other biotrophic pathogens, suppresses activation of plant innate immunity by translocating effector proteins into host cells. Some of these effectors localize to the host cell nucleus where they may manipulate transcriptional reprogramming of plant defense genes. Here we report that the nuclear-localized Hpa effector HaRxL106, when expressed in Arabidopsis, induces shade avoidance and attenuates the transcriptional response to the defense signaling molecule salicylic acid. HaRxL106 interacts with RADICAL-INDUCED CELL DEATH1 (RCD1) and loss of RCD1 function renders Arabidopsis resilient against HaRxL106-mediated suppression of immunity. To further characterize the molecular functions of RCD1 we solved a crystal structure of RCD1s Poly-(ADP-ribose)-Polymerase (PARP) domain and, based on non-conservation of amino acids constituting the active site of canonical PARPs, conclude that RCD1 has no PARP activity. We report that RCD1-type proteins are phosphorylated and identified histone-modifying Mut9-like kinases (MLKs) as RCD1-interacting proteins. A mlk1,3,4 triple mutant exhibits stronger SA-induced defense marker gene expression compared to wild-type plants. Our data suggest that HaRxL106 suppresses Arabidopsis innate immunity by manipulating the function(s) of RCD1 in the host cell nucleus and point towards a role of RCD1 as a transcriptional co-regulator that integrates signals from light and pathogen sensors.

plant biology↗