Biology subjects

Mejias, J.

Publications and source records attributed to Mejias, J..

12 recordsLinked to original sources

bioRxiv2026

MIRA: an open source and user-friendly software to automate counting and sizing of fungal spores

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

plant biology↗

bioRxiv2026

A conserved root-knot nematode effector targets plant kinesin light chain related proteins to promote parasitism

Root-knot nematodes (RKNs) are obligate plant parasites that establish biotrophic interactions with a wide range of host plants, causing major crop losses worldwide. During infection, RKNs secrete effector proteins from their esophageal glands into root tissues via a stylet. These effectors target specific subcellular compartments and manipulate host cell functions to induce the formation of hypertrophied and multinucleated giant cells, which serve as feeding sites essential for nematode development. Here we describe a new RKN-specific effector, EFFECTOR17 (EFF17), conserved in the five most damaging RKN species: Meloidogyne incognita, M. enterolobii, M. arenaria, M. javanica, and M. hapla. In situ hybridization showed that EFF17 genes are specifically expressed in the esophageal glands of various RKN species. Silencing EFF17 in M. incognita reduced its reproduction on Nicotiana benthamiana, demonstrating a key role in parasitism. Yeast two-hybrid and split luciferase assays revealed that tomato and Arabidopsis KINESIN LIGHT CHAIN RELATED (KLCR)/CELLULOSE-MICROUBULE UNCOUPLING (CMU) proteins are host targets of MiEFF17. Arabidopsis klcr mutants developed significantly fewer galls and egg masses. Our findings propose that EFF17 manipulates KLCR function to promote parasitism.

plant biology↗

bioRxiv2026

The sugar-beet cyst nematode effector Hs2B11 targets the Arabidopsis serine protease inhibitor AtPR-6 to favor parasitism

Cyst nematodes secrete effector proteins to manipulate host cell biology and suppress immunity, yet the mechanisms underlying these interactions remain largely unexplored. In this study, we characterize the function of Hs2B11, a Heterodera schachtii effector that was previously shown to be expressed in the dorsal gland of sugar-beet cyst nematodes (BCN). Here, we report that in Arabidopsis thaliana Hs2B11 functions as an immune regulator that modulates the production of elicitor-induced oxidative species, likely to favor parasitism. To elucidate the molecular basis of this immune suppression, we performed a yeast-two-hybrid screen and identified the host serine protease inhibitor AtPR-6 as a direct interactor of Hs2B11. We show that AtPR-6 acts as a positive regulator of plant immunity; its expression is induced upon nematode infection and knock-out of AtPR-6 compromises oxidative species production leading to higher susceptibility to H. schachtii infection. Conversely, AtPR-6 overexpression enhances immune responses resulting in increased resistance to BCN infection. Detailed analysis of this interaction demonstrated that Hs2B11 interacts with AtPR-6 using its carboxyl-terminal domain. AlphaFold2 predicts that the C-terminal domain forms a beta-solenoid-like structure with a ladder of serine residues organized across one of its surfaces. We propose that using this interface, Hs2B11 targets AtPR-6 via molecular titration, preventing the inhibitor from regulating host proteases that control immune signaling. These findings highlight a counter-defense strategy where a nematode effector neutralizes a specific host protease inhibitor to subvert plant immunity.

plant biology↗

bioRxiv2026

Immune checkpoint inhibitors amplify type 2 immune mediated repair bypro-regenerative scaffolds

Extracellular matrix (ECM) scaffolds induce type 2 immunity to promote repair. Here, we show that immune cells recruited to ECM-treated murine muscle injuries and clinical soft tissue defects express immune checkpoints. Specifically, TH2 cells and regulatory T cells (Tregs) increase LAG3 expression, while macrophages express PDL2. TCR analysis and a triple-reporter strain for interleukin (IL)-13 and Treg fate-mapping suggest that Tregs in ECM-treated wounds transition into TH2-like exTregs that express LAG3. Immune checkpoint inhibition (ICI) significantly stimulated type 2 immunity in ECM-treated wounds, including increased TH2 cells, Treg transition to TH2-like exTregs, and pro-regenerative macrophages. Moreover, ICI enhanced muscle repair and reduced fibrosis in ECM-treated wounds. Collectively, these findings show Treg/TH2 plasticity in wound healing and introduce a novel ICI application to enhance immune-mediated regeneration.

bioengineering↗

bioRxiv2024

A novel toolbox of GATEWAY-compatible vectors for rapid functional gene analysis in soybean composite plants

The generation of transgenic plants is essential for plant biology research to investigate plant physiology, pathogen interactions and gene function. However, producing stable transgenic plants for plants such as soybean is a laborious and time-consuming process, which can impede research progress. Composite plants consisting of wild-type shoots and transgenic roots are an alternative method for generating transgenic plant tissues that can facilitate functional analysis of genes-of-interest involved in root development or root-microbe interactions. In this report, we introduce a novel set of GATEWAY-compatible vectors that enable a wide range of molecular biology uses in roots of soybean composite plants. These vectors incorporate in-frame epitope fusions of green fluorescent protein, 3x-HA, or miniTurbo-ID, which can be easily fused to a gene-of-interest using the GATEWAY cloning system. Moreover, these vectors allow for the identification of transgenic roots using either mCherry fluorescence or the RUBY marker. We demonstrate the functionality of these vectors by expressing subcellular markers in soybean, providing evidence of their effectiveness in generating protein fusions in composite soybean plants. Furthermore, we show how these vectors can be used for gene function analysis by expressing the bacterial effector, AvrPphB in composite roots, enabling the identification of soybean targets via immunoprecipitation followed by mass spectrometry. Additionally, we demonstrate the successful expression of stable miniTurbo-ID fusion proteins in composite roots. Overall, this new set of vectors is a powerful tool that can be used to assess subcellular localization and perform gene function analyses in soybean roots without the need to generate stable transgenic plants. Key MessageWe developed a set of GATEWAY vectors to accelerate gene function analysis in soybean composite plants to rapidly screen transgenic roots and investigate subcellular localization, protein-protein interactions, and root-pathogen interactions.

plant biology↗

bioRxiv2024

Nemacounter: A user-friendly software to accurately phenotype SCN cysts

The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plants root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.

plant biology↗

bioRxiv2024

The Soybean Cyst Nematode Effector Cysteine Protease 1 (CPR1) Targets a Mitochondrial Soybean Branched-Chain Amino Acid Aminotransferase (GmBCAT1) for Degradation

The soybean cyst nematode (SCN; Heterodera glycines) facilitates infection by secreting a repertoire of effector proteins into host cells to establish a permanent feeding site composed of a syncytium of root cells. Among the diverse proteins secreted by the nematode, we were specifically interested in identifying proteases to pursue our goal of engineering decoy substrates that elicit an immune response when cleaved by an SCN protease. We identified a cysteine protease that we named Cysteine Protease 1 (CPR1), which was predicted to be a secreted effector based on transcriptomic data obtained from SCN esophageal gland cells, presence of a signal peptide, and lack of transmembrane domains. CPR1 is conserved in all isolates of SCN sequenced to date, suggesting it is critical for virulence. Transient expression of CPR1 in Nicotiana benthamiana leaves suppressed cell death induced by a constitutively active nucleotide binding leucine-rich repeat protein, RPS5, indicating that CPR1 inhibits effector-triggered immunity. CPR1 localizes in part to the mitochondria when expressed in planta. Proximity-based labeling in transgenic soybean roots, co-immunoprecipitation, and cleavage assays identified a branched-chain amino acid aminotransferase from soybean (GmBCAT1) as a substrate of CPR1. Silencing of the CPR1 transcript in the nematode reduced penetration frequency in soybean roots while the expression of CPR1 in soybean roots enhanced susceptibility. Our data demonstrates that CPR1 is a conserved effector protease with a direct target in soybean roots, highlighting it as a promising candidate for decoy engineering.

plant biology↗

bioRxiv2024

The SUbventral-Gland master Regulator (SUGR) of nematode virulence

All pathogens must tailor their gene expression to their environment. Therefore, targeting host:parasite biology that regulates these changes in gene expression could open up routes to pathogen control. Here, we show that in the plant-parasitic nematode Heterodera schachtii, host signals (termed effectostimulins) within plant roots activate the master regulator sugr1. SUGR1, then, directly binds effector promoters, and orchestrates their production. Effector production, in turn, facilitates host entry, releasing more effectostimulins. These data show that gene expression during the very earliest stages of parasitism is defined by a feed forward loop for host entry. Importantly, we demonstrate that blocking SUGR1 blocks parasitism, underlining the SUGR1 signalling cascade as a valuable target for crop protection. Given that nematodes also parasitise humans and other animals, the potential impact is broad: disrupting effector production could, in principle, be applied to any pathogen that secrets effectors. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/576598v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@cebedaorg.highwire.dtl.DTLVardef@153fe87org.highwire.dtl.DTLVardef@16b6957org.highwire.dtl.DTLVardef@d0e920_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2023

A soybean rust effector protease suppresses host immunity and cleaves a 3-deoxy-7-phosphoheptulonate synthase

The devastating soybean rust (SBR) pathogen, Phakopsora pachyrhizi, encodes many secreted proteins, but only two have been functionally characterized for their roles in rust virulence. Here, we demonstrate that transient expression of P. pachyrhizi effector candidate 15 (PpEC15), an aspartic protease, leads to enhanced bacterial growth in planta, suppression of callose deposition, reduced expression of plant defense-related marker genes and suppression of pathogen-associated molecular pattern (PAMP)-induced reactive oxygen species (ROS). Stable expression of PpEC15 in soybean suppresses PAMP-induced ROS production and enhances bacterial growth, indicating that, collectively, PpEC15 suppresses host and non-host innate immune responses. Yeast-two-hybrid and proximity labeling identified putative PpEC15 interacting partners including a peptide-chain release factor (PCRF), a NAC83 (NAM, ATAF, and CUC) transcription factor, and a DAHP (3-deoxy-7-phosphoheptulonate) synthase. We further show that PpEC15 can cleave DAHP but does not cleave PCRF or NAC83. Virus-induced gene silencing of NAC83, PCRF and DAHP altered PAMP-induced ROS production and salicylic acid production, indicating that these proteins may be involved in immune signaling. Collectively, our data show that PpEC15 is conserved across P. pachyrhizi isolates and other economically important rust species and is involved in the suppression of plant basal defense responses. Understanding the role of PpEC15 in P. pachyrhizi virulence will provide a foundation for designing targeted intervention strategies to generate rust-resistant crops.

plant biology↗

bioRxiv2023

Predictive coding with spiking neurons and feedforward gist signalling

Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neural network features such as a non-linear, continuous, and clock-driven function approximator as basic unit of computation. Therefore, we have developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. While adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: 1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and 2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high basal firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature. Author summaryHow does the brain seamlessly perceive the world, in the midst of chaotic sensory barrage? Rather than passively relaying information that sensory organs pick up from the external world along the cortical hierarchy for a series of feature extractions, it actively gathers statistical regularities from sensory inputs to track causal relationships between physical properties of external objects and the body. In other words, the brains perceptual apparatus is constantly trying to make sense of the incoming streams of sensory input and represent the subjects current situation by building and maintaining internal models of the world and body. While this constructivist theme in understanding perception has been pervasive across multiple disciplines from philosophy to psychology to computer science, a comprehensive theory of brain function called predictive coding aims at unifying neural implementations of perception. In this study, we present a biologically plausible neural network for predictive coding that uses spiking neurons, Hebbian learning, and a feedforward visual pathway to perform perceptual inference and learning on images. Not only does the model show that predictive coding is well behaved under the biological constraint of spiking neurons, but it also provides deep learning and neuromorphic communities with novel paradigms of learning and computational architectures inspired by the natures most intelligent system, the brain.

neuroscience↗

bioRxiv2022

miR167-ARF8, an auxin-responsive module involved in the formation of root-knot nematode-induced galls in tomato

O_LIRoot-knot nematodes (RKN) from genus Meloidogyne induce the dedifferentiation of root vascular cells into giant multinucleate feeding cells. These feeding cells result from an extensive reprogramming of gene expression in targeted root cells, as shown by transcriptomic analyses of galls or giant cells from various plant species. C_LIO_LISmall non-coding RNAs, and messenger RNAs from tomato (Solanum lycopersicum) galls and uninfected roots were sequenced. De novo microRNA prediction in the tomato genome identified microRNAs expressed in galls and uninfected roots. Statistical analyses identified 174 miRNA genes differentially expressed in galls at 7 and/or 14 days post infection (dpi). C_LIO_LIIntegrative analyses combining small non-coding RNA and transcriptome datasets with the specific sequencing of cleaved transcripts identified miRNA targets in tomato galls. Functional analyses of promoter-GUS fusions and CRISPR-Cas9 mutants highlighted the role of the miR167-regulated transcription factor AUXIN RESPONSE FACTOR 8 (ARF8) in giant cell formation. C_LI

plant biology↗

bioRxiv2020

Silencing SmD1 in Solanaceae alters susceptibility to root-knot nematodes

Root-knot nematodes (RKNs) are among the most damaging pests of agricultural crops. Indeed, Meloidogyne is an extremely polyphagous genus of nematodes that can infect thousands of plant species. A few genes for resistance (R-genes) to RKNs suitable for use in crop breeding have been identified, and new virulent strains and species of nematode emerge rendering these R-genes ineffective. Effective parasitism is dependent on the secretion, by the RKN, of effectors targeting plant functions, which mediate the reprogramming of root cells into specialised feeding cells. These cells, the giant cells, are essential for RKN development and reproduction. The EFFECTOR 18 protein (EFF18) from M. incognita interacts with the spliceosomal protein SmD1 in Arabidopsis, disrupting its function in alternative splicing regulation and modulating the giant cell transcriptome. We show here that EFF18 is a conserved RKN-specific effector. We also show here that EFF18 effectors also target SmD1 in Nicotiana benthamiana and Solanum lycopersicum. The alteration of SmD1 expression by virus-induced gene silencing (VIGS) in Solanaceae affects giant cell formation and nematode development. Thus, SmD1 is a susceptibility gene and a promising target for the development of broad resistance, especially in Solanaceae, for the control of Meloidogyne spp.

plant biology↗