Soil, rhizosphere, and root microbiome in kiwifruit vine decline, an emerging multifactorial disease

Abstract

Kiwifruit vine decline syndrome (KVDS) is characterized by severe root system impairment, which leads to irreversible wilting of the canopy. Plants usually collapse rapidly from the appearance of the first aboveground symptoms, without recovery even in the following seasons. The syndrome has been negatively impacting kiwifruit yield in different areas of Italy, the main producing European country, since its first outbreak in 2012. To date, a unique, common causal factor has yet to be found, and the syndrome is referred to as multifactorial. In this article, we investigated the whole biotic community (fungi, bacteria, and oomycetes) associated with the development of KVDS in three different belowground matrices/compartments (soil, rhizosphere, and root). Sampling was performed at both healthy and affected sites located in the main kiwifruit-producing area of Northwestern Italy. To address the multifactorial nature of the syndrome and to investigate the potential roles of abiotic factors in shaping these communities, a physicochemical analysis of soils was also performed. This study investigates the associations among taxonomic groups composing the microbiome and also between biotic and abiotic factors. Dysbiosis was considered as a driving event in shaping KVDS microbial communities. The results obtained from this study highlight the role of the oomycete genus Phytopythium, which resulted predominantly in the oomycete community composition of diseased matrices, though it was also present in healthy ones. Both bacterial and fungal communities resulted in a high richness of genera and were highly correlated to the sampling site and matrix, underlining the importance of multiple location sampling both geographically and spatially. The rhizosphere community associated with KVDS was driven by a dysbiotic process. In addition, analysis of the association network in the diseased rhizosphere revealed the presence of potential cross-kingdom competition for plant-derived carbon between saprobes, oomycetes, and bacteria.

Keywords: Phytopythium; dysbiosis; kiwifruit vine decline syndrome; metabarcoding; microbiome; multifactorial disease; next-generation sequencing.

Figure 1

Sampling site map. Green pinpoints refer to healthy sites, whereas red pinpoints refer to diseased ones.

Figure 2

Box and whisker plot of Shannon Index (A), number of observed features (B), and Pielou evenness (C) measured across matrix and health status for bacteria. Whiskers extend to ±1.5 interquartile range. The presence of statistically significant differences was assessed by means of a Kruskal–Wallis test, followed by a Dunn post-hoc test with Benjamini–Hochberg p-value correction. Value for H₀ rejection was set at 0.05.

Figure 3

Box and whisker plot of Shannon Index (A), number of observed features (B), and Pielou evenness (C) measured across matrix and health status for fungi. Whiskers extend to ±1.5 interquartile range. The presence of statistically significant differences was assessed by means of a Kruskal–Wallis test, followed by a Dunn post-hoc test with Benjamini–Hochberg p-value correction. Value for H₀ rejection was set at 0.05.

Figure 4

Box and whisker plot of Shannon Index (A), number of observed features (B), and Pielou evenness (C) measured across matrix and health status for oomycetes. Whiskers extend to ±1.5 interquartile range. The presence of statistically significant differences was assessed by means of a Kruskal–Wallis test, followed by a Dunn post-hoc test with Benjamini–Hochberg p-value correction. Value for H₀ rejection was set at 0.05.

Figure 5

Non-metric multidimensional scaling (NMDS) plot of Aitchison distances between bacterial samples (A), fungal samples (B), and oomycetes (C). Colors indicate different matrix/health status combination, while shape and ellipsoids are associated with the sampling site.

Figure 6

Relative abundances (%) of bacteria, fungi, and oomycetes in soil (A), rhizosphere (B), and root (C) between healthy and diseased orchards. Only taxa with a relative abundance higher than 1% in either condition were considered.

Figure 7

Relative abundance of Phytopythium in healthy and diseased plants for root, rhizosphere, and soil samples. For each comparison, the Bonferroni-adjusted p-value is provided, as returned by the ALDEx2 analysis.

Figure 8

(A,B) Association network of Phytopythium in microbial communities associated with kiwi plant rhizosphere (A) and soil (B). Edge color represents association type (co-presence of co-exclusion), whereas node color refers to their taxonomic kingdom. Node size is proportional to the number of connections (i.e., degree).

Figure 9

Interaction network of the rhizosphere microbiome and soil meta-variables, filtered to underline interactions between Phytopythium, interacting taxa and meta-variables. Orange nodes are associated with Phytopythium; light green nodes are the first-degree neighbor nodes of Phytopythium, i.e., have direct interaction with Phytopythium; blue nodes are associated with soil meta-variables; dark green nodes are associated with the health status of communities; and finally, the purple nodes are bridge nodes, which are defined as nodes sitting on the shortest path between Phytopythium and soil meta-variables. Edges are colored based on interaction, which can be either co-presence (green) or co-exclusion (red). Edge thickness is directly proportional to the strength of interaction.