Co-transcriptomic network analysis of host-parasite interactions reveals genes underpinning parasite fitness

Interactions between parasites and their host organisms generate continuous variation in infection outcomes. Although dual transcriptome studies have demonstrated extensive molecular dialogues between interacting species, it is still unclear how natural variation in host and parasite gene regulatory networks and coordination between them contribute to diverse disease phenotypes. Here, we re-contextualized co-transcriptomes from recombinant inbred lines of the plant-parasitic root-knot nematode Meloidogyne hapla infecting Medicago truncatula host plants with relative measures of infection intensity as a proxy for parasite load. We used infection intensity to estimate interaction gradients on gene expression by calculating its covariance with transcript levels. Quantitative analyses of interaction gradients for host and parasite transcriptomes identified major differences in development-related transcriptional programs along the infection continuum. In addition, integrated network analyses connected host cell wall remodeling with parasite protein production and secretion. Silencing of candidate root-knot nematode genes reduced host galling and parasite reproductive fitness, providing experimental support for our network-derived predictions. Together, our findings present an integrated systems genetics framework connecting naturally occurring genetic variation to quantitative disease phenotypes.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-29
DOI
https://doi.org/10.64898/2026.10.23.761410
Primary Topic
Nematode management and characterization studies
Type
preprint

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preprint

Co-transcriptomic network analysis of host-parasite interactions reveals genes underpinning parasite fitness

Simon Cornelis Groen, Anthony K. Nguyen, Tyler R. Inskeep, Erick Celis
2 links
bioRxiv (Cold Spring Harbor Laboratory)
Nematode management and characterization studies
preprint

Co-transcriptomic network analysis of host-parasite interactions reveals genes underpinning parasite fitness

Simon Cornelis Groen, Anthony K. Nguyen, Tyler R. Inskeep, Erick Celis
preprint en

Abstract

Interactions between parasites and their host organisms generate continuous variation in infection outcomes. Although dual transcriptome studies have demonstrated extensive molecular dialogues between interacting species, it is still unclear how natural variation in host and parasite gene regulatory networks and coordination between them contribute to diverse disease phenotypes. Here, we re-contextualized co-transcriptomes from recombinant inbred lines of the plant-parasitic root-knot nematode Meloidogyne hapla infecting Medicago truncatula host plants with relative measures of infection intensity as a proxy for parasite load. We used infection intensity to estimate interaction gradients on gene expression by calculating its covariance with transcript levels. Quantitative analyses of interaction gradients for host and parasite transcriptomes identified major differences in development-related transcriptional programs along the infection continuum. In addition, integrated network analyses connected host cell wall remodeling with parasite protein production and secretion. Silencing of candidate root-knot nematode genes reduced host galling and parasite reproductive fitness, providing experimental support for our network-derived predictions. Together, our findings present an integrated systems genetics framework connecting naturally occurring genetic variation to quantitative disease phenotypes.

bioRxiv (Cold Spring Harbor Laboratory)
University of California, Riverside (US)
National Science Foundation, National Institutes of Health, National Institute of General Medical Sciences
Zero hunger
Nematode management and characterization studies
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Co-transcriptomic network analysis of host-parasite interactions reveals genes underpinning parasite fitness — Simon Cornelis Groen, Anthony K. Nguyen, et al. · bioRxiv (Cold Spring Harbor Laboratory) (2026) | TGRS Research Map | TGRS