Biology subjects

Arguello-Miranda, O.

Publications and source records attributed to Arguello-Miranda, O..

8 recordsLinked to original sources

bioRxiv2025

A co-evolved peptide-GPCR system senses host entry to drive fungal infection

A successful infection requires pathogens to recognize the specific host environment in order to reprogram their physiology accordingly. One major way in which eukaryotic cells sense their surroundings is via G-Protein Coupled Receptors (GPCRs), which share a seven-transmembrane architecture and G-protein-mediated downstream signaling. While mammalian GPCRs are well-characterized and represent important drug targets, their fungal counterparts remain poorly understood. In the corn pathogen Ustilago maydis, we now uncover a GPCR-based mechanism that allows the fungus to scout the host environment to sense whether it has entered into the plant tissue. During infection, the fungus secretes the protein Pit2, which is cleaved by host apoplastic-cysteine proteases, releasing a peptide ligand hidden within Pit2. This ligand activates the fungal GPCR Gpe1 strongly promoting fungal proliferation after initial host penetration. Comparative analyses reveal conservation of the Gpe1/Pit2 system, with co-evolutionary signatures preserving receptor-ligand specificity. Furthermore, this GPCR system recognizing hidden peptide ligands shows conceptual similarities to the fungal pheromone mating system, without sharing sequence similarity. Our findings reveal a co-evolved mechanism between fungus and host that encodes environmental context into a protein scaffold, establishing a novel paradigm for host-dependent signaling with implications for inter-organismic communication.

microbiology↗

bioRxiv2025

Generative frame interpolation enhances tracking of biological objects in time-lapse microscopy

Object tracking in microscopy videos is crucial for understanding biological processes. While existing methods often require fine-tuning tracking algorithms to fit the image dataset, here we explored an alternative paradigm: augmenting the image time-lapse dataset to fit the tracking algorithm. To test this approach, we evaluated whether generative video frame interpolation can augment the temporal resolution of time-lapse microscopy and facilitate object tracking in multiple biological contexts. We systematically compared the capacity of Latent Diffusion Model for Video Frame Interpolation (LDMVFI), Real-time Intermediate Flow Estimation (RIFE), Compression-Driven Frame Interpolation (CDFI), and Frame Interpolation for Large Motion (FILM) to generate synthetic microscopy images derived from interpolating real images. Our testing image time series ranged from fluorescently labeled nuclei to bacteria, yeast, cancer cells, and organoids. We showed that the off-the-shelf frame interpolation algorithms produced bio-realistic image interpolation even without dataset-specific retraining, as judged by high structural image similarity and the capacity to produce segmentations that closely resemble results from real images. Using a simple tracking algorithm based on mask overlap, we confirmed that frame interpolation significantly improved tracking across several datasets without requiring extensive parameter tuning and capturing complex trajectories that were difficult to resolve in the original image time series. Taken together, our findings highlight the potential of generative frame interpolation to improve tracking in time-lapse microscopy across diverse scenarios, suggesting that a generalist tracking algorithm for microscopy could be developed by combining deep learning segmentation models with generative frame interpolation.

cell biology↗

bioRxiv2024

Mixed DAMP/MAMP oligosaccharides promote both growth and defense against fungal pathogens of cucumber

Plants recognize a variety of environmental molecules, thereby triggering appropriate responses to biotic or abiotic stresses. Substances containing microbes-associated molecular patterns (MAMPs) and damage-associated molecular patterns (DAMPs) are representative inducers of pathogen resistance and damage repair, thus treatment of healthy plants with such substances can pre-activate plant immunity and cell repair functions. In this study, the effects of DAMP/MAMP oligosaccharides mixture (Oligo-Mix) derived from plant cell wall (cello-oligosaccharide and xylo-oligosaccharide), and fungal cell wall (chitin-oligosaccharide) were examined in cucumber. Treatment of cucumber with Oligo-Mix promoted root germination and plant growth, along with increased chlorophyll contents in the leaves. Oligo-Mix treatment also induced typical defense responses such as MAP kinase activation and callose deposition in leaves. Pretreatment of Oligo-Mix enhanced disease resistance of cucumber leaves against pathogenic fungi Podosphaera xanthii (powdery mildew) and Colletotrichum orbiculare (anthracnose). Oligo-Mix treatment increased the induction of hypersensitive cell death around the infection site of pathogens, which inhibited further infection and the conidial formation of pathogens on the cucumber leaves. RNA-seq analysis revealed that Oligo-Mix treatment upregulated genes associated with plant structural reinforcement, responses to abiotic stresses and plant defense. These results suggested that Oligo-Mix has beneficial effects on growth and disease resistance in cucumber, making it a promising biostimulant for agricultural application.

plant biology↗

bioRxiv2024

Measuring regulatory network inheritance in dividing yeast cells using ordinary differential equations

Quantifying the inheritance of protein regulation during asymmetric cell division remains a challenge due to the complexity of these systems and the lack of a formal mathematical definition. We introduce ODEinherit, a new statistical framework leveraging ordinary differential equations (ODEs) to measure how much a mother cells regulatory network is passed on to its daughters, addressing this gap. ODEin-herit first estimates cell-specific regulatory networks through ODE systems, incorporating novel adjustments for non-oscillatory trajectories. Then, inheritance is quantified by evaluating how well a mothers regulatory network explains its daughters trajectories. We demonstrate that precise quantification of this inheritance relies on pruning and adjustment for the network density. We benchmark ODEinherit on simulated data and apply it to live-cell, time-lapse microscopy data, where we track the expression dynamics of six proteins across 85 dividing S. cerevisiae cells over eight hours. Our results reveal substantial heterogeneity in inheritance rates among mother-daughter pairs, paving the way for applications in cellular stress response and cell-fate prediction studies across generations.

cell biology↗

bioRxiv2024

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

cell biology↗

bioRxiv2023

Multi-signal regulation of the GSK-3β homolog Rim11 governs meiosis entry in yeast

Starvation of budding yeast diploid cells induces the cell-fate program that drives meiosis and spore formation. Transcription activation of early meiotic genes (EMGs) requires the transcription activator Ime1, its DNA-binding partner Ume6, and GSK-3{beta} kinase Rim11. Phosphorylation of Ume6 by Rim11 is key for EMG activation. We report that Rim11 integrates multiple input signals to control Ume6 phosphorylation and EMG transcription. Under nutrient-rich conditions PKA represses Rim11 to low levels while TORC1 keeps Rim11 localized to the cytoplasm. Inhibiting PKA and TORC1 induces Rim11 expression and nuclear localization. Remarkably, nuclear Rim11 is required, but not sufficient, for Rim11-dependent Ume6 phosphorylation. Additionally, Ime1 is an essential anchor protein for phosphorylating Ume6. Subsequently, Ume6-Ime1 coactivator complexes form that drive EMG transcription. Our results demonstrate how varied signalling inputs (PKA/TORC1/Ime1) integrated by Rim11 determine EMG expression and entry into meiosis. We propose that the signalling-regulatory network described here generates robustness in cell-fate control.

molecular biology↗

bioRxiv2021

Cdc14 activates autophagy to regulate spindle pole body dynamics during meiosis

Autophagy, a conserved eukaryotic lysosomal degradation pathway that responds to environmental and cellular cues, is regulated by multiple signaling pathways that oversee cell survival, growth, and proliferation. In budding yeast, autophagy plays an essential role in meiotic exit, although the molecular mechanisms underlying its regulation and cargo selection remain unknown. In this study, we found that autophagy is maintained during meiosis and is upregulated at anaphase I and anaphase II. In addition, we found that cells with higher levels of autophagy during meiosis I and II completed meiosis faster, and that genetically activated autophagy machinery increased sporulation efficiency. Strikingly, our data revealed that Cdc14, a highly conserved phosphatase that counteracts Cdc28 (CDK1), is a meiosis-specific autophagy regulator. At anaphase I and anaphase II, Cdc14 was activated and released from the nucleolus into the cytoplasm, where it dephosphorylated Atg13 to stimulate Atg1 kinase activity and thus autophagy. Importantly, the meiosis-specific spindle pole body (SPB, the yeast centrosome) component (Spo74) was sensitized to autophagy-mediated degradation at anaphase II, upon its dephosphorylation by Cdc14. Together, our findings reveal a meiosis-tailored mechanism of Cdc14 that spatiotemporally guides meiotic autophagy activity to control SPB dynamics.

cell biology↗

bioRxiv2021

Cell cycle-independent integration of stress signals promotes Non-G1/G0 cellular quiescence

Cellular quiescence is a non-proliferative state required for cell survival under stress and during development. In most quiescent cells, proliferation is stopped in a reversible state of low Cdk1 kinase activity; in many organisms, however, quiescent states with high Cdk1 activity can also be established through still uncharacterized stress or developmental mechanisms. Here, we used a microfluidics approach coupled to phenotypic classification by machine learning to identify stress pathways associated with starvation-triggered high-Cdk1 quiescent states in Saccharomyces cerevisiae. We found that low- and high-Cdk1 quiescent states shared a core of stress-associated processes, such as autophagy, protein aggregation, and mitochondrial upregulation, but differed in the nuclear accumulation of the stress transcription factors Xbp1, Gln3, and Sfp1. The decision between low- or high-Cdk1 quiescence was controlled by cell cycle-independent accumulation of Xbp1, which acted as a time-delayed integrator of the duration of stress stimuli. Our results show how cell cycle-independent stress-activated factors promote cellular quiescence outside of G1/G0.

cell biology↗