Biology subjects

Fenn, A.

Publications and source records attributed to Fenn, A..

9 recordsLinked to original sources

bioRxiv2026

Transcriptomic analysis of FER-RALF-LRX pathway mutants suggests constitutive gene expression defects contribute to powdery mildew resistance

The receptor kinase FERONIA (FER) perceives endogenous RAPID ALKALINIZATION FACTOR (RALF) peptides and regulates a plethora of plant physiological processes, including immunity. RALF peptides also bind to LEUCINE-RICH REPEAT EXTENSIN (LRX) proteins as structural components of the cell wall. We recently showed that the FER-RALF-LRX pathway supports colonization by the obligate biotrophic fungal pathogen Erysiphe cruciferarum (Ecr), a member of the powdery mildew species complex that infects Arabidopsis. Genetic disruption of the pathway primarily affects conidiation of the fungus, raising the question of effects on fungal nutrition. To get further insight into the underlying mechanisms, we performed RNA sequencing (RNAseq) to identify differential transcriptional responses of FER-RALF-LRX pathway mutants upon Ecr infection. Surprisingly, our results revealed that pathway disruption has a limited impact on the overall transcriptional changes upon fungal infection. However, consistent with previous reports, FER-RALF-LRX pathway mutants show changes in basal expression of a plethora of genes, mainly associated with cell wall metabolism, jasmonic acid signalling, amino acid biosynthesis and secondary metabolism. Many of these genes are regulated by Ecr infection across genotypes, too. This raises the question whether these are relevant pathway components for powdery mildew host establishment downstream of the FER-RALF-LRX module. In summary, our data reveals new insights into FER-RALF-LRX-dependent responses that may support host susceptibility to biotrophic plant pathogens.

plant biology↗

bioRxiv2026

The Limits of Cross-Species WGCNA: Library Imbalance and Signal Dilution Constrain Effector Gene Recovery in Dual-Organism RNA-seq

Dual-organism RNA sequencing (RNA-seq) experiments, in which the transcriptomes of a host and a microbe are sequenced simultaneously, are increasingly used to study plant-microbe interactions. A central analytical goal is identifying effector proteins and their host targets through gene co-expression. Weighted Gene Co-expression Network Analysis (WGCNA) is the dominant tool for gene co-expression analyses, yet its ability to recover interaction-interface genes from a merged dual-organism matrix has not been systematically characterised. Here we present a simulation framework using real gene models from Hordeum vulgare (barley) and Blumeria graminis f. sp. Hordei M.Liu & Hambl (powdery mildew) to evaluate single-network WGCNA across a gradient of plant-to-fungal library size ratios (1:1-20:1), three levels of co-expression signal strength, and three WGCNA network construction types (signed, unsigned, signed hybrid). We embed 20 model effector genes (bridge genes) driven by a mixed host-pathogen eigengene and evaluate recovery using four metrics aligned with the biological objective: cross-species hub rank, top-decile hub enrichment, bridge gene detection rate, and bridge co-separation (the fraction of effector-target pairs co-assigned to the same detected module). Across 225 simulation runs (15 conditions x 5 replicates x 3 network types), bridge genes are robustly identifiable as cross-species connectivity hubs (mean rank 0.92 versus 0.50 for module genes) but co-assignment of effector-target pairs to the same module fails in 41% of runs due to scale-free topology collapse. Signal strength (2 = 0.12) and library ratio (2 = 0.22) are the primary determinants of co-separation, while network type choice accounts for less than 2%. A read-depth bias systematically inflates pathogen gene hub ranks relative to host genes at high ratios. These results establish that the method can identify effector candidates as cross-species hubs under a broad range of conditions, but reliable co-assignment requires adequate pathogen read depth and strong co-expression signal--properties that experimental design, not analytical parameterisation, must provide.

systems biology↗

bioRxiv2026

Unequal requirement of KAI2 for AM symbiosis across angiosperms

Development of arbuscular mycorrhiza (AM), a symbiosis between plants and beneficial Glomeromycotan fungi, is largely under plant control. Several genes, required for AM development, are proposed to be regulated by the karrikin signalling module, comprising the alpha/beta hydrolase receptor KARRIKIN INSENSITIVE 2 (KAI2), the F-box protein MORE AXILLARY GROWTH2 (MAX2) and the transcriptional repressor SUPPRESSOR OF MAX2 1 (SMAX1), which is ubiquitylated for proteasomal degradation upon KAI2-ligand-induced binding to the KAI2-MAX2 complex. Rice and Brachypodium distachyon kai2 mutants are incapable of forming AM. Here, we show that in Lotus japonicus, Pisum sativum, and Nicotiana benthamiana, KAI2 only quantitatively affects AM development, indicating angiosperms vary in their requirement for KAI2-signalling to support AM. Comparative transcriptomics of L. japonicus and B. distachyon roots after treatment with fungal signalling molecules revealed some AM-relevant genes respond KAI2-independently in L. japonicus but not in B. distachyon. Consistently we obtained evidence for low-level degradation of SMAX1 in Ljkai2a,b observed through a ratiometric reporter for the SMAX1 degron (SMAX1D2). Further, we found an unexpected accumulation of SMAX1D2 in in response to AM even in wild type. Together, this suggests an unexpected role of SMAX1 accumulation in AM roots and that in AM symbiosis of L. japonicus, redundant mechanisms drive SMAX1 degradation and gene activation independently of KAI2.

plant biology↗

bioRxiv2026

Evolution as Active Geometry: The Geometric State Equation of the Tree of Life

Any process that generates information at a constant rate into a branching hierarchy must embed into hyperbolic space: exponentially growing lineages cannot pack into polynomial-growth Euclidean geometry. We derive a geometric state equation,{kappa} = (h ln 2/(n-1))2, relating the curvature{kappa} of the embedding manifold to the entropy rate h and dimension n, with zero adjustable parameters. Back-solving the dimension across every system tested--from decade-old viral outbreaks to 3.8-billion-year cellular lineages to domain-level species phylogenies--yields n = 2.00 {+/-} 0.05: evolution is two-dimensional. Curvature, by contrast, is scale-dependent. At the inter-domain scale, a neural encoder trained on 5,550 genomes with no phylogenetic supervision finds an optimal curvature range{kappa} {approx} 1.28-1.34, set by the Kolmogorov complexity profile of the biosphere; at the intra-domain scale, direct [H]2 embeddings of the complete GTDB bacterial (107,000 tips), archaeal (5,900 tips), and fungal (1,600 tips) species trees yield{kappa} = 3-16. Both regimes obey the state equation at the scale-appropriate entropy rate. Fifteen viral families trace the predicted curvature-entropy curve at Pearson r = 0.996; fifteen protein families confirm the predicted 3.1x curvature increase from a 4-letter to a 20-letter alphabet. The universal invariant is the dimension, not the curvature. The geometry of the tree of life is not a historical accident but a constraint imposed by the information capacity of the genetic code. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/710612v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@148fbf6org.highwire.dtl.DTLVardef@95f2e9org.highwire.dtl.DTLVardef@7ea052org.highwire.dtl.DTLVardef@14f8999_HPS_FORMAT_FIGEXP M_FIG C_FIG The tree of life embeds into 2D hyperbolic space with curvature determined by the geometric state equation{kappa} = (h ln 2)2. Top: Voronoi tessellation of 5,550 genome embeddings in the Poincare disk, colored by domain (Bacteria, Archaea, Eukarya). LUCA occupies the center; cell boundaries are hyperbolic geodesics (circular arcs orthogonal to the disk boundary). Bottom: The state equation predicts curvature from entropy alone across a 13-fold range--from the compressed inter-domain hierarchy ({kappa} {approx} 1.3) through domain-level species trees ({kappa} = 3-16)--and across both DNA and protein alphabets (3.1 x increase), with zero adjustable parameters. Fifteen viral families confirm the curvature-entropy curve at r = 0.996.

evolutionary biology↗

bioRxiv2024

A pangenome and pantranscriptome of hexaploid oat

Oat grain is a traditional human food rich in dietary fiber that contributes to improved human health. Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogs. Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales. In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here, we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oats along with an atlas of gene expression across six tissues of different developmental stages in 23 accessions. We describe the interplay of gene expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory up-regulation of the remaining homeologs, but this process is constrained by subgenome divergence. Chromosomal rearrangements have significantly impacted recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semidwarf mutant has risen to prominence in Australian elite varieties. The oat pangeome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.

genomics↗

bioRxiv2023

Air monitoring by nanopore sequencing

While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiomes composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment. We show that long-read sequencing can resolve species-level annotations and specific ecosystem functions through de novo metagenomic assemblies despite the low amount of fragmented DNA used as an input for nanopore sequencing. We then apply our pipeline to assess the diversity and variability of an urban air microbiome, using Barcelona, Spain, as an example; this randomized experiment gives first insights into the presence of highly stable location-specific air microbiomes within the citys boundaries, and showcases the robust microbial assessments that can be achieved through automatable, fast, and portable nanopore sequencing technology.

molecular biology↗

bioRxiv2023

Alternative splicing impacts microRNA regulation within coding regions

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and noncoding regions using six cancer data sets from The Cancer Genome Atlas (TCGA). First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

bioinformatics↗

bioRxiv2022

Systematic analysis of alternative splicing in time course data using Spycone

During disease progression or organism development, alternative splicing (AS) may lead to isoform switches (IS) that demonstrate similar temporal patterns and reflect the AS co-regulation of such genes. Tools for dynamic process analysis usually neglect AS. Here we propose Spycone (https://github.com/yollct/spycone), a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.

bioinformatics↗

bioRxiv2021

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with protein-protein and domain-domain interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

bioinformatics↗