Biology subjects

Vega-Arreguin, J. C.

Publications and source records attributed to Vega-Arreguin, J. C..

3 recordsLinked to original sources

bioRxiv2025

Phytophthora root rot induces compositional and functional changes in avocado rhizosphere bacterial communities

Understanding how plant pathogens modulate the rhizosphere microbiota is essential to integrated disease management. Here, we assessed the compositional and functional shifts in the avocado rhizosphere bacteriome induced by Phytophthora cinnamomi to elucidate the microbial functions modulated by the infection and identify taxa potentially recruited by the plant as a defense response. Through metabarcoding with metatranscriptomics, we showed that Phytophthora root rot (PRR) induced compositional shifts in bacterial communities, leading to the enrichment of members of MND1, RB41 and Nitrospira. Functional analysis showed that this enrichment may be due to the release of nutrients following root rot, as carbohydrate metabolism was stimulated in rhizobacterial communities of infected trees. We detected evidence of a cry-for-help strategy by the infected plant, as the most active genera in the rhizosphere of PRR-symptomatic trees up-regulated genes associated with stress response and cell signaling, suggesting that they were recruited to mitigate the adverse effects of infection. Our findings highlight the need to combine compositional and functional microbiome data to differentiate between taxa attracted by nutrient release and those actively recruited by the plant. The interactions of the latter with the pathogen should be further studied, as they may constitute promising biocontrol agents.

microbiology↗

bioRxiv2025

SIREN: Suite for Intelligent RNAi Design and Evaluation of Nucleotide Sequences

MotivationRNA interference (RNAi) is a powerful tool for gene silencing across biological research, therapeutics, and agriculture. While siRNA design has benefited from advances in thermodynamic modeling and machine learning, comprehensive tools for designing long double-stranded RNAs (dsRNAs) with minimized off-target effects remain limited. ResultsHere, we present SIREN, an open-source Python pipeline designed to streamline RNAi construct design. SIREN integrates siRNA generation, thermodynamically-informed off-target prediction, scoring of dsRNA candidates based on cumulative off-target effects, and primer design for in vitro synthesis. It accepts user-defined transcriptomes for context-specific analysis and provides adjustable sensitivity settings balancing accuracy and computational demands. Benchmarking with plant, oomycete, and human transcriptomes demonstrates SIRENs efficient scalability and the practical utility of medium sensitivity, recovering over 75% of high-sensitivity targets with significantly reduced computing times. Experimental validation in Phytophthora capsici confirms that SIREN effectively identifies highly specific RNAi constructs with no detectable off-target phenotypes in host plants. Availability and implementationSIREN is implemented in Python 3 and available under an open-source license at https://github.com/pablovargasmejia/SIREN; installer via PyPI: https://pypi.org/project/siren-rnai/.

bioinformatics↗

bioRxiv2025

Host-Specific Transcriptional Responses of Phytophthora capsici During Early Crown Infection in Cucurbitaceous and Solanaceous Plants

Phytophthora capsici is a destructive, broad-host-range oomycete responsible for substantial losses in global agriculture. While most transcriptomic studies have focused on host responses, the mechanisms by which generalist pathogens dynamically adapt their infection programs to diverse plant species remain poorly understood. Here, we present a comparative transcriptomic analysis of P. capsici during early-stage crown infection in four taxonomically and immunologically distinct hosts, Cucumis sativus, Cucumis melo, Capsicum annuum (CM334), and Solanum lycopersicum, via RNA-seq and multiphoton microscopy. Focusing on crown infections, the natural entry point for the pathogen, we reveal host-specific transcriptional programs that underpin differential infection strategies and outcomes. Our data show that P. capsici exhibits tightly regulated, host-dependent deployment of key virulence factors, including RxLR, NLP, and CRN, and elicitin effectors and reprograms its metabolism to exploit host-specific nutritional environments. In rapidly necrotizing hosts such as tomato, the pathogen induces glycolytic and fatty acid pathways while repressing immunogenic effectors. In contrast, cucurbits support prolonged biotrophic colonization, accompanied by the upregulation of carbohydrate metabolism and membrane transport genes. In the partially resistant chili pepper CM334, P. capsici shows signs of metabolic stress, cell wall remodeling, and effector repression, which is consistent with failed invasion. Functional validation via RNAi-mediated silencing of selected effectors revealed distinct roles in modulating virulence and host necrosis, confirming the functional relevance of the transcriptomic profiles. Co-expression network analysis uncovered discrete transcriptional modules associated with tissue-specific colonization, nutrient acquisition, and immune evasion. These results reveal how a generalist soil-borne pathogen finely tunes its gene expression in response to host-specific constraints, revealing conserved and host-specific transcriptional strategies that drive infection success or failure. This work provides mechanistic insight into adaptive virulence and expands our understanding of host-pathogen compatibility in eukaryotic microbes.

molecular biology↗