# Evolutionary Trajectories in Plant-Associated Pseudomonas and Xanthomonas Strains

Marco Scortichini, Dawn Arnold, Olivier Pruvost, Marie-Agnès Jacques, Adriana J. Bernal

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Marco Scortichini, Dawn Arnold, Olivier Pruvost, Marie-Agnès Jacques, Adriana J. Bernal. Evolutionary Trajectories in Plant-Associated Pseudomonas and Xanthomonas Strains. Frontiers Media SA, 170 p., 2020, Frontiers in Microbiology and Frontiers in Plant Science, 978-2-88963-700-3. 10.3389/978-2-88963-700-3. hal-03315723

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EVOLUTIONARY TRAJECTORIES IN
PLANT-ASSOCIATED PSEUDOMONAS
AND XANTHOMONAS STRAINS

EDITED BY: Marco Scortichini, Dawn Arnold, Olivier Pruvost,
Marie-Agnès Jacques and Adriana J. Bernal

PUBLISHED IN: Frontiers in Microbiology and Frontiers in Plant Science

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ISSN 1664-8714

ISBN 978-2-88963-700-3

DOI 10.3389/978-2-88963-700-3

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EVOLUTIONARY TRAJECTORIES IN
PLANT-ASSOCIATED PSEUDOMONAS
AND XANTHOMONAS STRAINS

Topic Editors:

**Marco Scortichini**, Council for Agricultural and Economics Research (CREA), Italy

**Dawn Arnold**, University of the West of England, United Kingdom

**Olivier Pruvost**, UMR Peuplement Végétaux et Bio-agresseurs en Milieu Tropical (CIRAD), France

**Marie-Agnès Jacques**, INRA Centre Angers-Nantes Pays de la Loire, France

**Adriana J. Bernal**, University of Los Andes, Colombia

The strict relationships between bacteria and plants represent one of the major facets of terrestrial ecology. Depending on the type of interaction and amount of metabolic advantage one organism can obtain from such relationships, these are classified as mutualistic, commensal or parasitic interactions. Within this context, Pseudomonas and Xanthomonas are bacterial genera with a worldwide spread, capable of establishing all of the above mentioned interactions with plants. Therefore, they represent good models for studying different lifestyles and, accordingly, deciphering distinct evolutionary trajectories followed by different lineages of a single genus to infect and/or to establish a mutualistic relationships with the plant. Some members of these two genera are regulated pests that are recognized as economically major threats for their host crop(s) both in temperate and tropical environments.

Some Pseudomonas and Xanthomonas are key examples of different lifestyles (i.e., mesophyll or vessel-colonizing pathogens, epiphytic pathogens, plant growth-promoting rhizobacteria, non-pathogenic strains of recognized pathogenic species, etc). Refining our knowledge on the ecology and epidemiology of these bacterial groups, as well as deciphering their evolutionary dynamics are keys for understanding their contrasting lifestyles and consequently improving plant disease control. At the same time, insights on the activation of different plant defense mechanisms as challenged by the different repertoires of virulence factors displayed by pseudomonads and xanthomonads, would yield new achievements to reduce the threats they pose to cultivated and wild plant species.

This Research Topic focuses on microbial and evolutionary ecology of plant associated Pseudomonas and Xanthomonas, as well as the genomic and molecular diversity of lineages and the virulence and fitness features involved in the interaction with the host-plant. Most of the literature available for this Research Topic has been performed for strains isolated in temperate zones. In line with the long-recognized high social and environmental impact of pests and pathogens in tropical countries, we have welcomed submissions of studies covering such situations for these areas. This Research Topic gathers high-quality contributions (Original Research, Methods, Protocols, Hypothesis & Theory, Reviews, Mini Reviews, Focused Reviews) and in order to promote complementary and original research approaches to improve our
knowledge on pseudomonads and xanthomonads-host interactions and their control,
it benefited from the scientific communities currently working on Pseudomonas and
Xanthomonas such as the teams dealing with the Pseudomonas syringae species
complex and the French Network on Xanthomonads (FNX).

**Citation:** Scortichini, M., Arnold, D., Pruvost, O., Jacques, M.-A., Bernal, A. J., eds. (2020). Evolutionary Trajectories in Plant-Associated *Pseudomonas* and *Xanthomonas* Strains. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-700-3
Table of Contents

**06** *Genetic and Phenotypic Characterization of Indole-Producing Isolates of Pseudomonas syringae pv. actinidiae Obtained From Chilean Kiwifruit Orchards*

Oriana Flores, Camila Prince, Mauricio Nuñez, Alejandro Vallejos, Claudia Mardones, Carolina Yañez, Ximena Besoain and Roberto Bastías

**18** *Plant Microbiome and its Link to Plant Health: Host Species, Organs and Pseudomonas syringae pv. actinidiae Infection Shaping Bacterial Phyllosphere Communities of Kiwifruit Plants*

Witoon Purahong, Luigi Orrù, Irene Donati, Giorgia Perpetuini, Antonio Cellini, Antonella Lamontanara, Vania Michelotti, Gianni Tacconi and Francesco Spinelli

**34** *A Strain of an Emerging Indian Xanthomonas oryzae pv. oryzae Pathotype Defeats the Rice Bacterial Blight Resistance Gene xa13 Without Inducing a Clade III SWEET Gene and is Nearly Identical to a Recent Thai Isolate*

Sara C. D. Carpenter, Prashant Mishra, Chandrika Ghoshal, Prasanta K. Dash, Li Wang, Samriti Midha, Gouri S. Laha, Jagjeet S. Lore, Wichai Kositratana, Nagendra K. Singh, Kuldeep Singh, Prabhu B. Patil, Ricardo Oliva, Sujin Patarapuwadol, Adam J. Bogdanove and Rhitu Rai

**45** *Inference of Convergent Gene Acquisition Among Pseudomonas syringae Strains Isolated From Watermelon, Cantaloupe, and Squash*

Eric A. Newberry, Mohamed Ebrahim, Sujan Timilsina, Nevena Zlatković, Aleksa Obradović, Carolee T. Bull, Erica M. Goss, Jose C. Huguet-Tapia, Mathews L. Paret, Jeffrey B. Jones and Neha Potnis

**63** *Corrigendum: Inference of Convergent Gene Acquisition Among Pseudomonas syringae Strains Isolated From Watermelon, Cantaloupe, and Squash*

Eric A. Newberry, Mohamed Ebrahim, Sujan Timilsina, Nevena Zlatković, Aleksa Obradović, Carolee T. Bull, Erica M. Goss, Jose C. Huguet-Tapia, Mathews L. Paret, Jeffrey B. Jones and Neha Potnis

**64** *Multiple Recombination Events Drive the Current Genetic Structure of Xanthomonas perforans in Florida*

Sujan Timilsina, Juliana A. Pereira-Martin, Gerald V. Minsavage, Fernanda Iruegas-Bocardo, Peter Abrahamian, Neha Potnis, Bryan Kolaczkowski, Gary E. Vallad, Erica M. Goss and Jeffrey B. Jones

**77** *Molecular Evolution of Pseudomonas syringae Type III Secreted Effector Proteins*

Marcus M. Dillon, Renan N.D. Almeida, Bradley Laflamme, Alexandre Martel, Bevan S. Weir, Darrell Desveaux and David S. Guttman

**95** *Fitness Features Involved in the Biocontrol Interaction of Pseudomonas chlororaphis With Host Plants: The Case Study of PcPCL1606*

Eva Arrebola, Sandra Tienda, Carmen Vida, Antonio de Vicente and Francisco M. Cazorla
**103** *Xanthomonas citri* pv. *viticola* *Affecting Grapevine in Brazil: Emergence of a Successful Monomorphic Pathogen*

Marisa A. S. V. Ferreira, Sophie Bonneau, Martial Briand, Sophie Cesbron, Perrine Portier, Armelle Darrasse, Marco A. S. Gama, Maria Angélica G. Barbosa, Rosa de L. R. Mariano, Elineide B. Souza and Marie-Agnès Jacques

**121** *A Pathovar of Xanthomonas oryzae Infecting Wild Grasses Provides Insight Into the Evolution of Pathogenicity in Rice Agroecosystems*

Jillian M. Lang, Alvaro L. Pérez-Quintero, Ralf Koebnik, Elysa DuCharme, Soungalo Sarra, Hinda Doucoure, Ibrahim Keita, Janet Ziegle, Jonathan M. Jacobs, Ricardo Oliva, Ousmane Koita, Boris Szurek, Valérie Verdier and Jan E. Leach

**136** *Pseudomonas syringae* pv. *syringae* Associated With Mango Trees, a Particular Pathogen Within the "Hedgepodge" of the Pseudomonas syringae Complex

José A. Gutiérrez-Barranquero, Francisco M. Cazorla and Antonio de Vicente

**156** *Analyses of Seven New Genomes of Xanthomonas citri* pv. *aurantifolii* Strains, Causative Agents of Citrus Canker B and C, Show a Reduced Repertoire of Pathogenicity-Related Genes

Natasha Peixoto Fonseca, José S. L. Patané, Alessandro M. Varani, Érica Barbosa Felestrino, Washington Luiz Caneschi, Angélica Bianchini Sanchez, Isabella Ferreira Cordeiro, Camila Gracyelle de Carvalho Lemes, Renata de Almeida Barbosa Assis, Camila Carrião Machado Garcia, José Belasque Jr., Joaquim Martins Jr., Agda Paula Facincani, Rafael Marini Ferreira, Fabrício José Jaciani, Nalvo Franco de Almeida, Jesus Aparecido Ferro, Leandro Marcio Moreira and João C. Setubal
<figure><img src="image_4.png" /></figure>

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# Genetic and Phenotypic Characterization of Indole-Producing Isolates of *Pseudomonas syringae* pv. *actinidiae* Obtained From Chilean Kiwifruit Orchards

Oriana Flores¹, Camila Prince¹, Mauricio Nuñez¹, Alejandro Vallejos²,
Claudia Mardones², Carolina Yañez¹, Ximena Besoain³ and Roberto Bastías¹*

¹ Laboratorio de Microbiología, Instituto de Biología, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile, ² Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile, ³ Laboratorio de Fitopatología, Escuela de Agronomía, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

OPEN ACCESS

**Edited by:**
Marco Scortichini,
Consiglio per la Ricerca in Agricoltura
e l'Analisi dell'Economia
Agraria (CREA), Italy

**Reviewed by:**
Stefania Tegli,
Università degli Studi di Firenze, Italy
David John Studholme,
University of Exeter, United Kingdom

*Correspondence:
Roberto Bastías
roberto.bastías@pucv.cl

**Specialty section:**
This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Microbiology

Received: 02 April 2018

Accepted: 30 July 2018

Published: 22 August 2018

**Citation:**
Flores O, Prince C, Nuñez M,
Vallejos A, Mardones C, Yañez C,
Besoain X and Bastías R (2018)
Genetic and Phenotypic
Characterization of Indole-Producing
Isolates of Pseudomonas syringae pv.
actinidiae Obtained From Chilean
Kiwifruit Orchards.
Front. Microbiol. 9:1907.
doi: 10.3389/fmicb.2018.01907

In recent years, Chilean kiwifruit production has been affected by the phytopathogen *Pseudomonas syringae* pv. *actinidiae* (Psa), which has caused losses to the industry. In this study, we report the genotypic and phenotypic characterization of 18 Psa isolates obtained from Chilean kiwifruits orchards between 2012 and 2016 from different geographic origins. Genetic analysis by multilocus sequence analysis (MLSA) using four housekeeping genes (*gyrB*, *rpoD*, *gltA*, and *gapA*) and the identification of type III effector genes suggest that the Chilean Psa isolates belong to the Psa Biovar 3 cluster. All of the isolates were highly homogenous in regard to their phenotypic characteristics. None of the isolates were able to form biofilms over solid plastic surfaces. However, all of the isolates formed cellular aggregates in the air-liquid interface. All of the isolates, except for Psa 889, demonstrated swimming motility, while only isolate Psa 510 demonstrated swarming motility. The biochemical profiles of the isolates revealed differences in 22% of the tests in at least one Psa isolate when analyzed with the BIOLOG system. Interestingly, all of the isolates were able to produce indole using a tryptophan-dependent pathway. PCR analysis revealed the presence of the genes *aldA/aldB* and *iaaL/matE*, which are associated with the production of indole-3-acetic acid (IAA) and indole-3-acetyl-3-L-lysine (IAA-Lys), respectively, in *P. syringae*. In addition, IAA was detected in the cell free supernatant of a representative Chilean Psa strain. This work represents the most extensive analysis in terms of the time and geographic origin of Chilean Psa isolates. To our knowledge, this is the first report of Psa being able to produce IAA. Further studies are needed to determine the potential role of IAA in the virulence of Psa during kiwifruit infections and whether this feature is observed in other Psa biovars.

**Keywords:** *Pseudomonas syringae* pv. *actinidiae*, Psa Biovar 3 (Psa-V), MLSA, kiwifruit, IAA, IAA production, indoleacetic acid lysine, IAA-L
# INTRODUCTION

Pseudomonas syringae pv. actinidiae (Psa) is the causal agent of bacterial canker in Actinidia deliciosa and Actinidia chinensis that has caused severe losses in all of the major areas of kiwifruit cultivation, including Italy, China, New Zealand, and Chile (Scortichini et al., 2012; Ferrante and Scortichini, 2015). This bacterium infects host plants by entering natural openings and wounds, moving inside the plant, and promoting the appearance of necrotic leaf spots, red exudate production, and canker and necrosis in the trunk. In the late stages of the infection, the plants wilt and desiccate which leads to the death of the kiwifruit vine (Vanneste et al., 2012; Cellini et al., 2014). Since its identification in Japan in 1984, successive outbreaks of Psa have been observed worldwide, and therefore it is now considered to be a pandemic phytopathogen (Scortichini et al., 2012; McCann et al., 2017). Comparative analysis using multilocus sequence analysis (MLSA), the detection of type III secretion system effector genes and phytotoxins (phaseolotoxin or coronatine) in Psa isolates from different geographic origins have revealed the existence of five clusters of biovars (Marcelletti et al., 2011; Ciarroni et al., 2015; Ferrante and Scortichini, 2015; Fujikawa and Sawada, 2016; McCann et al., 2017): biovar 1, comprising Japanese strains which are able to produce phaseolotoxin; biovar 2, including only South Korean strains which produce coronatine; biovar 3 or Psa-V, which includes the most virulent strains that are characterized by not producing phytotoxins and were first isolated in Italy (2008–2009) and have been subsequently reported to cause outbreaks in different countries (Butler et al., 2013; Ciarroni et al., 2015; Cunty et al., 2015a); biovar 4, contain strains with low virulence and was recently proposed to be a new pathovar called P. syringae pv. actinidifoliorum (Psaf) (Abelleira et al., 2015; Cunty et al., 2015b); and finally, biovar 5 with Japanese strains isolated in 2012 which do not produce phytotoxins. Recently, a potential new biovar was described in Japan, which produces both phaseolotoxin and coronatine (Fujikawa and Sawada, 2016).

The genetic analysis of the Psa biovars described a set of genes that participate in distinct phases of kiwifruit infection and niche colonization, both outside and inside of the host plant. These genes are related to bacterial motility, biofilm formation, copper and antibiotic resistance, siderophore production, and the degradation of lignin (Marcelletti et al., 2011; Scortichini et al., 2012; Ghods et al., 2015; Gao et al., 2016; Colombi et al., 2017; Patel et al., 2017). However, the mechanisms that determine infection and the interactions between Psa with the kiwifruit plant remain unknown. The production of the phytohormone indole-3-acetic acid (IAA) is another virulence factor that has been described in Pseudomonas savastanoi and P. syringae pathovars. This compound can perturb the regulation of the hormone balance in the plant and increase its susceptibility to infection (Glickmann et al., 1998; Cerboneschi et al., 2016). IAA production using the indole-3-acetamide (IAM) pathway is the most common mechanism in phytopathogenic bacteria, including P. syringae, and has mostly been characterized in P. savastanoi pv. savastanoi (Psav) (Baltrus et al., 2011;

Aragón et al., 2014) where IAA biosynthesis begins from L-tryptophan (Trp) and involves the activity of the enzymes tryptophan-2-monooxygenase (IaaM) and IAM hydrolase (IaaH) encoded by the *iaaM* and *iaaH* genes, respectively. However, in other *P. syringae* pathovars, the IAA production involves other genes that lack homology to *iaaM* and *iaaH* (Glickmann et al., 1998), and recently aldehyde dehydrogenase family proteins encoded by genes *aldA* and *aldB*, were associated with IAA synthesis in *P. syringae* pv. *tomato* (McClerklin et al., 2018). For instance, *P. savastanoi* pv. *nerii* can conjugate IAA to the amino acid lysine producing indole-3-acetyl-3-L-lysine (IAA-Lys) due to the action of the enzyme IAA-Lys ligase encoded by the *iaaL* gene (Cerboneschi et al., 2016). This gene has been found in several *P. syringae* pathovars where it is arranged in synteny with the gene *matE*, which encodes a putative MATE family transporter, and has been implicated in the fitness and virulence of *P. syringae* pv. *tomato* (*Pst*) in tomato plants (Glickmann et al., 1998; Castillo-Lizardo et al., 2015).

Pseudomonas syringae pv. actinidiae was first reported in Chile in 2010 following its isolation from kiwifruit orchards in the Maule Region, and since 2011, it has been considered to be a pest under the official control of the Agricultural and Livestock Service (SAG) of the Government of Chile (McCann et al., 2013). Previous studies included classifying the first Chilean Psa isolates in biovar 3 together with strains from China, Europe, and New Zealand (Butler et al., 2013; McCann et al., 2013; Cunty et al., 2015a). However, the scope of these studies was limited by the number of Chilean strains. In this study, we report the genotypic and phenotypic characterization of Chilean Psa isolates obtained between 2012 and 2016 from the regions that accumulate more than 80% of the Psa-positive orchards in Chile. In addition, we show the first evidence of Psa strains producing IAA.

# MATERIALS AND METHODS

## Bacterial Strains and Culture Conditions

Chilean Psa isolates are listed in Table 1 and were obtained from the SAG from kiwi orchards of different geographic areas in the central-south of Chile in 2012, 2013, and 2016. *P. syringae* pv. *tomato* DC3000 was provided by Dr. Paula Salinas of the Universidad Santo Tomás (Santiago, Chile). *Escherichia coli* DH5α, *E. coli* K12, *Pseudomonas aeruginosa* PAO1, *Azospirillum brasilense* SP7, *Salmonella bongori* X9617, and *Cupriavidus metallidurans* CH34 were obtained from the bacterial collection of the Laboratory of Microbiology of the Pontificia Universidad Católica de Valparaíso (PUCV). *Pseudomonas antarctica* S63 (Vásquez-Ponce et al., 2018) was provided by Dr. Jorge Olivares from the PUCV. The bacteria were grown at 25°C in Luria-Bertani (LB) medium except when another medium is specified. Growth curve were performed in 96 multi-well plates at 25°C during 30 h in a microplate spectrophotometer Infinite® M200 NanoQuant (TECAN). Optical density (OD600 nm) was determined each 30 min. All curves were performed in biological triplicates.
<table><caption>**TABLE 1** | Chilean Psa isolates used in this work.</caption><thead><tr><th>Isolate*</th><th>Place of collection (coordinates)</th><th>Year</th></tr></thead><tbody><tr><td>Psa 743</td><td>Linares, Maule (35°52'54.4" S 71°35'23.4" W)</td><td>2012</td></tr><tr><td>Psa 889</td><td>Retiro, Maule (35°54'47.4" S 71°44'20.3" W)</td><td>2012</td></tr><tr><td>Psa 817</td><td>Chillán, Bio Bio (36°40'43.6" S 71°54'13.7" W)</td><td>2012</td></tr><tr><td>Psa 381</td><td>Molina, Maule (35°07'40.2" S 71°15'05.1" W)</td><td>2013</td></tr><tr><td>Psa 510</td><td>Retiro, Maule (36°08'53.3" S 71°44'54.3" W)</td><td>2013</td></tr><tr><td>Psa 771</td><td>Retiro, Maule (36°01'28.5" S 71°44'40.0" W)</td><td>2013</td></tr><tr><td>Psa 784</td><td>Retiro, Maule (36°01'16.9" S 71°44'18.1" W)</td><td>2013</td></tr><tr><td>Psa 394</td><td>Colbún, Maule (35°43'17.5" S 71°28'56.4" W)</td><td>2013</td></tr><tr><td>Psa 387</td><td>Yerbas Buenas, Maule (35°59'12.0" S 71°34'45.2" W)</td><td>2013</td></tr><tr><td>Psa 882</td><td>Molina, Maule (35°07'40.2" S 71°15'05.1" W)</td><td>2013</td></tr><tr><td>Psa 144</td><td>Molina, Maule (35°07'39.8" S 71°15'03.2" W)</td><td>2013</td></tr><tr><td>Psa 598</td><td>San Carlos, Bio Bio (36°30'18.6" S 71°51'50.2" W)</td><td>2013</td></tr><tr><td>Psa 386</td><td>San Ignacio, Bio Bio (36°52'59.9" S 72°08'07.0" W)</td><td>2013</td></tr><tr><td>Psa 159</td><td>San Ignacio, Bio Bio (36°48'38.6" S 72°06'12.6" W)</td><td>2013</td></tr><tr><td>Psa 189</td><td>San Ignacio, Bio Bio (36°49'25.2" S 72°06'06.6" W)</td><td>2013</td></tr><tr><td>Psa 129</td><td>San Ignacio, Bio Bio (36°48'38.6" S 72°06'12.6" W)</td><td>2013</td></tr><tr><td>Psa 137</td><td>San Nicolás, Bio Bio (36°32'39.1" S 72°10'16.6" W)</td><td>2015</td></tr><tr><td>Psa 233</td><td>Molina, Maule (35°03'09.5" S 71°14'57.1" W)</td><td>2016</td></tr></tbody></table>

## Molecular Identification and Characterization of the Psa Isolates

Pseudomonas syringae pv. actinidiae strain molecular identification was performed using RG-PCR and duplex-PCR as previously described (Rees-George et al., 2010; Gallelli et al., 2011). For RG-PCR, specific primers were used to amplify the internal transcribed spacer (ITS) between the 16S and 23S rRNA sequences, and for duplex-PCR, specific primers against ompP1 (Outer Membrane Protein P1) and avrD1 (effector) genes were used. All 18 isolates amplified produced bands of the expected size (Supplementary Figure S1). In addition, the identity of these isolates was also confirmed by partial 16S rDNA sequences. For genomic DNA isolation, the bacteria were grown in LB media for 16 h until the stationary phase. Total genomic DNA was extracted using a Wizard® Genomic DNA Purification Kit (Promega) according to the manufacturer's instructions. The DNA concentration was determined using MaestroNano MN-913 (Maestrogen, Inc.). For the molecular identification of the type III effector genes, the reference genome of Psa ICMP 18884 biovar 3 strain (GenBank accession number: NZ_CP011972.2) (Templeton et al., 2015) and contigs of the Chilean Psa genomes, ICMP 19439 (ANJM00000000.1) and ICMP 19455 (ANJK00000000.1), available in GenBank (NCBI) were used to design specific primers for the PCRs. Comparative sequence analysis was performed using the Geneious R11 software (Kearse et al., 2012). The amplicons of effector genes obtained from strain Psa 743 were purified using an E.Z.N.A.® Cycle Pure Kit (Omega Bio-Tek, Inc.) and sequenced using the Sanger method by Macrogen, Inc. (South Korea). The quality and assembly of the sequences were analyzed using Geneious R11 software, which were compared with the NCBI database using BLASTN and BLASTX to identify the genes. Primers

and annealing temperatures used in the PCRs are listed in **Supplementary Table S1**. In all cases, PCR was performed on a SureCycler 8800 Thermal Cycler (Agilent Technologies) using SapphireAmp Fast PCR Master Mix (Takara Bio) according to the manufacturer's instructions. PCR products were separated using electrophoresis in agarose gel (1.5% agarose in 1x buffer TAE) stained with GelRed™ (Biotium), and the bands were visualized under UV light. PCRs were performed in triplicate. The genomic DNA of *P. syringae* pv. *tomato* DC3000 and *E. coli* DH5α were used as the control reactions. The sequences of the effector genes of a selected strain (Psa 743) were deposited in GenBank (NCBI), and the accession numbers are listed in **Supplementary Table S2**.

## Phylogenetic Analysis by MLSA

The gapA, gltA, gyrB, and rpoD genes, encoding glyceraldehyde-3-phosphate dehydrogenase, citrate synthase, DNA gyrase B, and sigma factor 70, respectively, were amplified from the genomic DNA of Psa isolates using the primers listed in **Supplementary Table S1** as previously described (Ferrante and Scortichini, 2010). PCR was performed in triplicate using a SureCycler 8800 Thermal Cycler (Agilent Technologies) with GoTaq G2 Flexi polymerase (Fermentas) according to the manufacturer's instructions. The PCR products were visualized using electrophoresis in agarose gels and purified using an E.Z.N.A.® Gel Extraction Kit (Omega Bio-Tek, Inc.). The automated sequencing of the amplicons was performed by Macrogen, Inc. (South Korea), and the sequences were analyzed using the Geneious R9 software package (Biomatters Limited) (Kearse et al., 2012). The nucleotide sequences of the gapA, gltA, gyr, and rpoD genes of Chilean Psa strains were added to GenBank (NCBI) and are listed in **Supplementary Table S3**. The sequences of other Psa biovars available in GenBank (NCBI) were included in the analysis and are listed in **Supplementary Table S4**. In addition, sequences of *P. syringae* pv. *tomato* strain DC3000 were included: gapA (AE016853.1:1415258-1416259), cts (AE016853.1:2414332-2415621), gyrB (AE016853.1:4147-6564), and rpoD (AE016853.1:588846-590696) (Buell et al., 2003). The sequences of each locus were aligned using the CLUSTALW included in the MEGA7 software (Kumar et al., 2016). A dendrogram from four-locus concatenated sequences was generated using neighbor-joining (UPGMA) and 1,000 bootstrap iterations.

## Biochemical Characterization

The bacteria were streaked out from a -80°C stock onto LB plates and incubated at 25°C for 48 h. Biochemical patterns were determined using the Biolog GEN III MicroPlate™ system (Biolog™, United States) according to the manufacturer's instructions. BIOLOG plates were read in an Infinite M200 PRO plate reader, TECAN. Reactions were considered positive if the OD$_{590}$ nm was greater than 50% of the positive control (~0.7). Reactions indistinguishable from the negative control and with an OD$_{590}$ nm below 25% of the positive control (~0.35) were considered to be negative. Reactions between these two parameters were considered borderline.

<footnote>*Accession number of the nucleotide sequences of the gapA, gltA, gyr, and rpoD genes added to GenBank (NCBI) are included in **Supplementary Table S3**.</footnote>
## Determination of Streptomycin and Copper Susceptibility

The copper and streptomycin susceptibility was determined using the broth microdilution method (Biebl and Pfennig, 1978; Mergeay et al., 1985). Bacterial strains were grown in Tris minimal (for the copper assay) or Mueller-Hinton (for the streptomycin assay) media during 18 h, and the optical density at 600 nm (OD<sub>600 nm</sub>) was adjusted to 0.7. For the copper susceptibility assays, 10 µL of each bacterial culture were inoculated in Tris minimal agar media (1.5% agar) supplemented with the corresponding copper sulfate concentration (0, 75, 100, 125, 150, 175, 200, 225, 250, 275, and 300 µg/mL). To assess the streptomycin susceptibility, bacterial strains were inoculated in Mueller-Hinton agar media supplemented with the corresponding antibiotic concentration (0, 3.9, 7.8, 15.7, 31.25, 62.5, 125, 250, 500, 1,000, and 2,000 µg/mL). Plates were incubated for 5 days at 25°C, and the bacterial growth was observed. *C. metallidurans* CH34 and *P. antarctica* S63 were used as experimental controls (von Rozycki and Nies, 2009; Vásquez-Ponce et al., 2018). All experiments were performed in biological and technical triplicates.

## Biofilm Production

Microtiter plate biofilm production was performed and adapted as previously described (Merritt et al., 2011; O'Toole, 2011; Ueda and Saneoka, 2015). Briefly, overnight bacterial cultures were adjusted to an optical density of 0.1 (OD<sub>600 nm</sub>) and diluted 10-fold. Aliquots (100 µL) of the dilution were added to each well (96-well microtiter plates), and the plates were incubated for 7 days at 25°C. After incubation, the liquid supernatant was removed and the plates were washed with distilled water. The wells were stained with 0.1% violet crystal solution, and the biofilm was solubilized with a 30% acetic acid solution. The biofilm production was quantified spectrophotometrically (550 nm) in a Tecan Infinite M200® microplate reader. For the air-liquid interface biofilm assay, 1 mL of the bacterial dilution was added to each well (12-well plates), and the plates were incubated at 25°C for 96 h. Surface biofilm formation was monitored and photo documented every 24 h. All of the experiments were performed in biological and technical triplicates, and *P. aeruginosa* PAO1 was used as the positive control (Ghafoor et al., 2011).

## Bacterial Motility Assay

Motility assays were adapted for the Psa assays as described by Hosseinidoust et al. (2013). Swimming motility assays were performed by inoculating 2 µL of stationary-phase bacterial culture (OD<sub>600 nm</sub>~1.3) into the center of 0.3% LB agar plates. Swarming motility assays were performed utilizing the same procedure except that 0.5% LB agar plates were used. The zone sizes were measured after incubation at 30°C for 72 h. The assays were performed in biological and technical triplicates. *E. coli* K12 was used as the experimental control (Swiecicki et al., 2013). Statistical analysis was performed using one-way ANOVA and Dunnett's multiple comparison test with *p* ≤ 0.05.

## Indole Production and Identification of IAA Pathway Genes

The indole production was determined using Salkowski's method as previously described (Mazzola and White, 1994; Mohite, 2013). Briefly, each strain was grown in LB media supplemented with Trp (2 g/L) and incubated at 25°C for 24 h. After incubation, the bacterial density was measured (OD<sub>600 nm</sub>), and the cultures were centrifuged at 10,000 rpm for 10 min. Cell-free supernatants were mixed with 0.5 mL of Salkowski's reagent (12 g of FeCl<sub>3</sub> per liter in 7.9 M H<sub>2</sub>SO<sub>4</sub>). The mixture was incubated for 30 min at room temperature in the dark, and the absorbance at 530 nm was determined. The concentration of indole in each sample was determined using a standard curve of indoleacetic acid (Sigma) (0–30 µg/mL) (Supplementary Figure S3). IAA concentrations were normalized to the cell density. *A. brasilense* SP7 (Bar and Okon, 1993) and *S. bongori* X9617 (De La Rosa Fraile et al., 1980) strains were used as experimental positive and negative controls, respectively. All of the analyses were performed in biological and technical triplicates. Statistical analysis was performed using one-way ANOVA and Dunnett's multiple comparison test with *p* ≤ 0.05. The detection of *iaaL*, *matE*, *iaaH*, *iaaM*, *aldA*, and *aldB* genes in the Chilean Psa isolates was performed using specific primers designed on the basis of conserved regions from the sequences of different *P. syringae* pathovars (Supplementary Table S5). The primers designed are listed in Supplementary Table S1. PCRs were performed on a SureCycler 8800 Thermal Cycler (Agilent Technologies) using a SapphireAmp Fast PCR Master Mix (Takara Bio) according to the manufacturer's instructions. The PCR conditions were as follows: 5 min at 95°C, followed by 35 cycles of 30 s at 95°C, 30 s at the annealing temperature (Supplementary Table S1), 2 min at 72°C, and a final elongation step of 5 min at 72°. Sanger automated sequencing of the amplicons from Psa 743, Psa 598, and Psa 889 was performed by Macrogen, Inc. (South Korea). The sequences were compared with those in the NCBI database using BLASTN and BLASTX for gene identification. The sequences obtained were deposited in GenBank (NCBI), and the accession numbers are listed in Supplementary Table S2.

## LC-ESI-MS/MS Analysis

To detect IAA, Psa strain 743 was grown in minimal media (4.5 g/L KH<sub>2</sub>PO<sub>4</sub>, 10.5 g/L K<sub>2</sub>HPO<sub>4</sub>, 1 g/L (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, and 0.5 g/L sodium citrate) supplemented with Trp (2 g/L) and incubated at 25°C for 72 h. After incubation, the bacterial density was measured (OD<sub>600 nm</sub>), and the cultures were centrifuged at 10,000 rpm for 10 min. The supernatant was filtered (0.22 µm). Methanol and acetic acid were added to the cell-free supernatant at a final concentration of 10 and 0.05%, respectively, and then filtered through a PVDF filter (0.22 µm). At the end, the sample was subjected to LC-ESI-MS/MS analysis using indoleacetic acid and lysine (Sigma) as standards. The analysis was performed using a Shimadzu Nexera HPLC system coupled to a 3200Q TRAP mass spectrometer equipped with a turbo ion spray interface (Applied
Biosystems/MDS Sciex, ON, Canada). A Kinetex C18 core shell column (150 mm × 4.6 mm i.d.; 2.6 µm particle size; Kinetex, Phenomenex) protected by a C18 UHPLC Ultra column guard (0.5 µm Porosity × 4, 6 mm. i.d., Phenomenex, United States) was used. The elution gradient was adapted from Matsuda et al. (2005) and consisted of a mixture of methanol:water containing 0.05% acetic acid (methanol gradient: 10–90% in 13 min; 95% from 13.1 to 28 min) at a flow rate of 0.4 mL/min and a column temperature of 30°C. MS was conducted in the positive ion mode during the following conditions: curtain gas (CUR), 10 psi; collision activated dissociation (CAD), medium; ion spray voltage (IS), 4500 V; nebulizer gas (Gas1), 60 psi; turbo gas (Gas2), 40 psi; temperature (TEM), 400°C. The detection was performed using multiple reaction monitoring (MRM). The data obtained were processed using Analyst 1.3 software (Applied Biosystems).

# RESULTS

## Phylogenetic Analysis and Molecular Characterization of the Chilean Psa Isolates

The 18 Chilean Psa isolates used in this study were collected from kiwi plants with canker disease symptoms by the SAG. These isolates were obtained between 2012 and 2016 from orchards in central-south Chile (Bío Bío and Maule Regions) that is the site of the vast majority of kiwifruit production in the country (Oficina de Estudios y Políticas Agrarias [ODEPA], 2018) and accumulates more than 50% of the Psa-infected orchards in Chile (Figure 1 and Table 1). All of the isolates were confirmed as Psa strains by PCR using different sets of primers (see section “Materials and Methods”).

The first Chilean Psa isolates had been previously assigned to the biovar 3 group (Butler et al., 2013; McCann et al., 2013; Cunty et al., 2015a). An MLSA using the housekeeping genes *gyrB* (DNA gyrase B), *rpoD* (sigma factor 70), *gltA* (citrate synthase), and *gapA* (glyceraldehyde-3-phosphate dehydrogenase) showed that the genes sequenced have 100% identity with the corresponding genes in different Psa strains belonging to biovar 3, including Chilean strains obtained in 2010. The phylogenetic analysis including other Psa strains shows a clear clustering of different biovars except for biovar 2 and 5 that are grouped together (Figure 2). The results show that all the Chilean Psa isolates group together with the other Psa biovar 3 isolates, confirming the findings of previous studies. These results were also confirmed by the PCR detection of the 16 type III effector genes that have been described in Psa biovar 3 strains (McCann et al., 2013; Ferrante and Scortichini, 2015). Type III effector genes were detected in all of the Chilean Psa strains, including those encoded in plasmid DNA in Psa biovar 3. The identity of these genes was confirmed by sequencing the amplicons of Psa strain 743 as a representative of the other Chilean Psa strains (Supplementary Table S2). These results also suggest that no new biovars have been introduced to Chile during this period.

## Phenotypic Characterization of the Psa Isolates

Different features implicated in the fitness and virulence of Psa were evaluated in the 18 Chilean isolates. None of the strains showed differences in their growth parameters (data not shown). However, their biochemical profile determined using the Biolog GEN III MicroPlate revealed differences in 22% of the different tests in at least one of the 18 strains (Supplementary Table S6). All of the strains were able to use different carbon sources such as D-glucose, D-mannose, D-galactose, glycerol, D-mannitol, L-arginine, L-serine, acetic acid, and citric acid. However, they varied in their ability to use sucrose, D-fructose, inosine, L-glutamic acid, and formic acid. Alternatively, all of the strains were resistant to antibiotics such as rifamycin SV, lincomycin, and vancomycin, while they were sensitive to minocycline and troleandomycin and showed variable sensitivity to aztreonam, nalidixic acid, and fusidic acid. Despite these differences, all of the strains were identified as *P. syringae* pathovars according to the Biolog GEN III database (version 2.8). Interestingly, all of the isolates were susceptible to copper (MIC 75 µg/mL Cu²⁺) and streptomycin (MIC 3.9 µg/mL), suggesting that no resistance has developed in these strains despite the use of copper compounds as antimicrobials in the Chilean kiwifruit industry.

Biofilm production has been proposed to be an important virulence factor in *P. syringae* (Ghods et al., 2015; Ueda and Saneoka, 2015). Therefore, the ability to produce biofilm was evaluated in the different Chilean Psa isolates. The results showed that none were able to produce biofilm over an abiotic surface. However, they do produce a thin layer of biofilm (pellicle) in the air-liquid interface. Initially a thin layer of cells was observed in the center of static cultures after 24 h of incubation, turning to a fully grown biofilm after 96 h (Supplementary Figure S2). Swimming and swarming motility was also evaluated among the different Psa isolates. The results show that all of the isolates exhibit swimming motility except for strain Psa 889 which shows a significant reduced displacement in comparison to the other strains (*p* < 0.05). In contrast, none of the strains except for Psa 510 demonstrated swarming motility under the experimental conditions (*p* < 0.05) (Figure 3). These results show that the Chilean Psa strains demonstrate a high phenotypic homogeneity with specific differences in particular strains.

## Indole Production in the Psa Isolates

Indole-3-acetic acid production has been described in different *P. syringae* pathovars and *P. savastanoi* (Glickmann et al., 1998; Cerboneschi et al., 2016) but not in Psa. It is produced mostly from Trp via IAM by enzymes encoded in the genes *iaaM* and *iaaH*. Therefore, all 18 isolates were evaluated for their ability to produce IAA (Glickmann and Dessaux, 1995). The results show that all of the Chilean Psa isolates can produce indole at different concentrations (Figure 4A). In addition, some of the Chilean Psa isolates (Psa 882 and Psa 394) produce indole concentrations similar to those of *A. brasilense* (63 µg/mL IAA) that produces exceptionally large amounts of IAA (Bar and Okon, 1993). In all cases, indole was produced only in the presence of Trp, suggesting that, as observed in other *P. syringae*,
<figure><img src="image_5.png" /><figcaption>**FIGURE 1** | Geographic localization of the Chilean Psa strains. The map shows the localization of the Maule and Bio Bio regions. The red spots represent the exact location from where the Chilean Psa strains were isolated.</figcaption></figure>

this amino acid is the precursor of IAA synthesis in Psa. IAA production was also confirmed in the Chilean Psa strain 743 using LC-ESI-MS/MS analysis, showing a strong signal for IAA in the supernatant of the Psa 743 cell-free cultures (Supplementary Figure S4). The *iaaM* and *iaaH* genes were not detected in the Chilean Psa isolates using PCR and specific primers, suggesting an alternative route of synthesis exists in these strains. Recently, a novel IAA synthesis pathway was reported in *P. syringae* pv. *tomato* DC3000 (Pst), which involves the participation of an indole-3-acetaldehyde dehydrogenase encoded by the gene *aldA* and its homolog, *aldB* (McClerklin et al., 2018). Comparative analysis by BLASTN showed 95 and 97% identity between the *aldA* and *aldB* genes, and an aldehyde dehydrogenase sequence (GenBank accession number: CP011972.2: 149109–150602) and a carnitine dehydratase/3-oxoadipate enol-lactonase sequences (GenBank accession number: CP011972.2: 3182732–3184213) were encoded in the Biovar 3 Psa strain ICMP 18884. PCR with specific primers revealed that the *aldA* and *aldB* genes were also detected in all of the Chilean Psa strains, suggesting that they are likely to be responsible for the synthetic route of IAA. The identity of genes *aldA* and *aldB* was confirmed in strains Psa 889, Psa 743, and Psa 598 using Sanger sequencing (Supplementary Table S2).

It has also been reported that IAA can be conjugated to the amino acid lysine to produce IAA-Lys by the enzymatic activity of the *iaaL* gene product (Glickmann et al., 1998; Castillo-Lizardo

et al., 2015; Cerboneschi et al., 2016). Our analysis detected the presence of the genes *iaaL* and *matE* in all of the Chilean Psa isolates (Figure 4B), which are in tandem in the Hrp regulon and are associated with IAA-Lys production. However, IAA-Lys production was not detected using the LC-ESI-MS/MS analysis. The identity of the *iaaL* and *matE* genes was also confirmed using Sanger sequencing in strains Psa 889, Psa 743, and Psa 598 (Supplementary Table S3). Taken together, these results strongly suggest that the Chilean Psa isolates produce IAA using a Trp-dependent pathway.

DISCUSSION

Genetic Analysis of the Chilean Psa
Isolates

Pseudomonas syringae pv. actinidiae was first isolated in Chile in 2010, and since then, it has been considered to be a quarantine pest under the official control of the SAG of Chile. The 18 Chilean Psa isolates included in this study were obtained as part of the monitoring program established by the SAG. They were isolated from the central south region of Chile, which is the zone that accumulates the majority of Psa infections reported in the country (Servicio Agrícola y Ganadero [SAG], 2018). These strains were obtained between the years 2012 and 2016, representing the most extended study performed on Psa
<figure><img src="image_6.png" /></figure>

in Chile. All of these strains were identified by the SAG and then confirmed by the standard molecular techniques used with this pathovar (Rees-George et al., 2010; Gallelli et al., 2011). As reported previously, the use of specific primers for the

ITS amplification was not specific to Psa and also amplified a fragment from *P. syringae* (Vanneste, 2013). Therefore, a duplex-PCR analysis was necessary to positively identify the Psa isolates.
A

<figure><img src="image_7.png" /></figure>

B

<figure><img src="image_8.png" /></figure>

**FIGURE 3** | Motility of the Chilean Psa isolates. (A) Swimming or swarming motility of different Chilean Psa strains. (B) Representative image of selected strains. Swimming or swarming movement was determined at 72 h post-inoculation measuring the diameter of displacement. The names of each strain are shown.

A

<figure><img src="image_9.png" /></figure>

B

<figure><img src="image_10.png" /></figure>

**FIGURE 4** | Indole production and detection of *iaaL/matE* genes in the Chilean Psa isolates. (A) Indole produced by the Chilean Psa isolates. Bacterial strains are organized according their level of indole production, and letters (a–d) show significant differences (p ≤ 0.05). The calibration curve with IAA (Sigma) is shown in **Supplementary Figure S3**. (B) PCR detection of genes *iaaL* (top) and *matE* (bottom). Molecular markers: 1 kb and 100 bp. Control (-): Reaction without DNA. *A. brasilense* (Ab) and *S. bongori* (Sb) were used for the positive and negative controls, respectively.

The MLSA confirmed that the Chilean Psa isolates belong to biovar 3. In this case, four housekeeping genes were used (*gyrB*, *rpoD*, *gltA*, and *gapA*), which seems to be sufficient to discriminate between biovar 3 and the other biovars; however, it is not sufficient to distinguish between biovars 2

and 5, which according to previous research, are very closely related (Fujikawa and Sawada, 2016). This phylogenetic analysis included sequences from several Psa strains with different biovars and origins, including some older Chilean strains that were also grouped in biovar 3. This suggests that this "hypervirulent"
group (Ciarroni et al., 2015) is the only found in Chile, and no other biovar has entered or emerged. The conclusions of this study are consistent with previous research in which the Chilean Psa isolates were classified in the Psa Biovar 3 cluster using different approaches: REP-PCR fingerprinting, MLVA (multiple locus variable number of tandem repeats analysis) assay and MLST (Ferrante and Scortichini, 2010, 2015; Vanneste et al., 2010; Ciarroni et al., 2015; Biondi et al., 2017). Genomic analyses of the Chilean Psa strains suggest that they originated from China forming a sub-group in biovar 3 (Butler et al., 2013; Ciarroni et al., 2015).

Nearly 50 putative effector genes have been identified in Psa and are found in most of the biovars (McCann et al., 2013; Ferrante and Scortichini, 2015; Fujikawa and Sawada, 2016). Sixteen type III effector genes, among others, were identified in all of the Chilean Psa isolates, including genes that were reported in conjugative DNA plasmids in other biovar 3 Psa strains (*hopAV1* and *hopAU1*). The emergence of resistant strains as an evolutionary response to the use of antimicrobial compounds was observed in countries affected by recent outbreaks of Psa biovar 3 strains (Han et al., 2004; Vanneste, 2013; Colombi et al., 2017).

## Phenotypic Features of the Chilean Psa Isolates

The results of this study show a high phenotypic homogeneity. However, it is still possible to observe differences between specific features and specific strains. For instance, the biochemical profile shows differences between the various Chilean Psa strains (**Supplementary Table S6**). These differences are related to carbon source utilization and chemical susceptibility assays. Moura et al. (2015) reported similar results with different Psa isolates from Portugal. Using the BIOLOG system, they observed differences in the ability to use at least 12 different carbon sources among the Portuguese strains. Interestingly, both the Chilean and Portuguese strains varied in their ability to use methyl pyruvate, bromo-succinic acid, and acetoacetic acid as carbon sources showing that variations in the biochemical repertory are not exclusive to the Chilean strains. Both groups of strains are susceptible to minocycline, lithium chloride, and sodium butyrate. The Chilean Psa strains are also resistant to antibiotics not used in agriculture such as rifamycin SV or vancomycin. However, curiously they were susceptible to streptomycin (MIC 3.9 µg/mL) that, in the past, has been authorized for use to control Psa infections in Chile. This suggests that no resistance has evolved among the Chilean Psa strains, in contrast to what has been reported by others where Psa strains can have a MIC for streptomycin greater than 2,000 µg/mL (Cameron and Sarojini, 2014). A similar situation has been observed for copper resistance in which other studies have reported Psa strains with a MIC from 100 µg/mL to more than 1,000 µg/mL (Cameron and Sarojini, 2014), while the Chilean strains have a MIC of 75 µg/mL. The absence of resistance among the Chilean Psa strains could be due to multiple factors such as low selective pressures from the environment or low plasticity in the Psa genome of these strains. However, is not possible to disregard the existence of resistant Chilean Psa strains in the environment. Our results do not show

a clear correlation between these differences in the biochemical profiles and the origin or isolation year of the strains, but it would be interesting to determine if these differences have any relevance for fitness or niche colonization in the natural environment of Psa.

All of the Chilean isolates demonstrate a similar range of swimming motility (**Figure 3**) with strain Psa 889 being the only exception that lacks motility. In contrast, none of the Chilean Psa strains show swarming motility, except for strain Psa 510 that demonstrates a slightly but significantly greater amount of displacement than the other strains. The differences observed between strains Psa 889 and Psa 510 are probably related to alterations in their flagella, since no differences were observed in the growth of any of the strains according to our analysis (**Supplementary Figure S5**). Flagellar motility is an important virulence factor that allows the infection of plants through natural openings on their tissue surfaces (Ichinose et al., 2013). Therefore, it remains to be determined if these differences in strains Psa 889 and Psa 510 are correlated with alterations in their virulence.

Psa infections are very persistent, and once they are detected in a region, it is very difficult or even impossible to eradicate the bacteria (Vanneste, 2017). This persistence could be related to the ability to endure environmental conditions through biofilm formation (Danhorn and Fuqua, 2007; Renzi et al., 2012). It has been reported that Psa can form biofilm (Ghods et al., 2015). However, our analysis showed that the Chilean Psa strains are not able to form biofilms over abiotic solid surfaces. This and other differences observed between the Chilean Psa strains and the other Psa are probably related to the unique clonal origin of the Psa strains present in Chile (Butler et al., 2013). However, the low affinity to form biofilms over solid surfaces has been observed in the *P. syringae* pathovars (Ueda and Saneoka, 2015). Therefore, it seems that biofilm formation is not a hallmark of this species. Interestingly the Chilean strains do form a thin layer of cells at the air-liquid interface in liquid cultures. This phenomenon has been described for other *Pseudomonas* species where an air-liquid interface would represent a favorable environment due to the oxygen access enabling a more rapid rate of growth (Constantin, 2009; Ueda and Saneoka, 2015). All of these results confirm the high degree of homogeneity among the different Chilean Psa strains. Further studies are needed to determine if the differences between the Chilean strains affect the colonization and infection of the kiwifruit plants.

## Indole Production in Psa Isolates

Several phytopathogens, including *P. syringae* pathovars, produce auxins that can alter the host's physiology and promote plant susceptibility to infection (Glickmann et al., 1998; Cerboneschi et al., 2016). To our knowledge, this is the first report showing that Psa can produce indole using a Trp-dependent pathway. All of the Chilean Psa strains evaluated produce indole, some of them at levels similar to *A. brasilense*, which is a plant growth promoting bacterium (Masciarelli et al., 2013). The common route for IAA production in *P. syringae* pathovars is via the IAM pathway using the enzymes IaaM and IaaH. This pathway has been studied in *P. syringae* pv. *syringae* (Pss) and Psav (Glickmann et al., 1998;
Baltrus et al., 2011; Aragón et al., 2014; Cerboneschi et al., 2016), and the only related report in Psa is from a strain isolated in 1984 belonging to biovar 1 which has putative ORFs of an IAM pathway (Baltrus et al., 2011). The Chilean Psa strains have the genes *aldA* and *aldB* which are associated with an alternative synthesis route of IAA recently found in *P. syringae* pv. *tomato* (McClerklin et al., 2018). Therefore, this is the most probable pathway in the Chilean Psa strains. Interestingly, bioinformatics analysis revealed that the genes *iaaH* and *iaaM*, associated with the common synthesis route of IAA, are only found in the Psa strains from biovar 4, which are now considered to be a new pathovar designated *P. syringae* pv. *actinidifoliorum* that is characterized by low virulence in kiwifruit plants (Abelleira et al., 2015). In this regard, the presence of the IAM pathway represents another distinctive feature differing the former biovar 4 from the other Psa biovars.

*Pseudomonas syringae* pv. *tomato* and other species, such as *P. savastanoi* pv. *nerii*, also produce the enzyme IAA-lysine ligase, encoded by the *iaaL* gene, which is responsible for IAA-Lys production (Glickmann et al., 1998; Castillo-Lizardo et al., 2015; Cerboneschi et al., 2016). In the *P. syringae* pv. *tomato* (*Pst*) genome, *iaaL* is found in synteny with the *matE* gene that encodes a multidrug transporter of the MatE family. The analysis of the Chilean Psa strains revealed that all of the strains contain the genes *iaaL* and *matE*. A bioinformatic analysis showed that the *iaaL* gene was first annotated as a pre-protein translocase subunit Tim44 in several *P. syringae* pathovars; however, later it was annotated as an indoleacetate-lysine ligase gene in *P. syringae* pv. *tomato* (Castillo-Lizardo et al., 2015). According to this analysis, the *matE* and *iaaL* genes are conserved in Psa Biovar 1, 2, 3, and 5 strains with near 100% identity in their amino acid sequences (Supplementary Figure S6). There are reports on the importance of IAA production, and IAA-Lys in particular, in the virulence of *P. syringae*. For instance, mutations in the IAM pathway of Pss affect its growth in *Phaseolus vulgaris* (Mazzola and White, 1994), and the deletion of the *aldA*, *aldB*, *iaaL*, or *matE* genes in *P. syringae* pv. *tomato* result in a reduction in fitness, colonization, and virulence in infected tomato plants (Castillo-Lizardo et al., 2015; McCllerklin et al., 2018). In addition, studies on the IAA-Lys effect on plants suggest that IAA conjugation can modulate hormone action and suppress the immune response (Romano et al., 1991). Our results show that the Chilean Psa strains produce IAA. However, we were not able to demonstrate IAA-Lys production. Despite this, the presence of the genes *iaaL* and *matE* in the Chilean and other Psa strains, including different biovars, raise the possibility that this compound could be produced in conditions other than those evaluated in this study. To date, the exact mechanism of action of IAA and IAA-Lys in the virulence of *P. syringae* species is not totally understood. The results presented here show that the Chilean Psa

## REFERENCES

Abelleira, A., Ares, A., Aguin, O., Peñalver, J., Morente, M. C., López, M. M., et al. (2015). Detection and characterization of *Pseudomonas syringae* pv. *actinidifoliorum* in kiwifruit in Spain. *J. Appl. Microbiol.* 119, 1659-1671. doi: 10.1111/jam.12968

strains produce IAA, but it is unknown if this feature is shared with other Psa strains of biovar 3 and other biovars. The results represent the starting point to determine the mechanisms and regulation of IAA production (and possibly IAA-Lys) in Psa and its participation during infection in kiwifruits plants.

## CONCLUSION

The results of this study confirm that the Chilean Psa isolates belong to biovar 3. The isolates exhibit high homogeneity with phenotypic differences in specific isolates. This study is also the first report of Psa strains producing IAA using a Trp-dependent pathway. Several reports suggest that this compound may be related to virulence in *P. syringae* pathovars. Therefore, it would be interesting to determine whether this feature plays a role during bacterial canker in kiwi plants and to evaluate whether this is a common characteristic in different biovars of this pathovar.

## AUTHOR CONTRIBUTIONS

OF, CY, XB, and RB conceived and designed the study, and analyzed the results. OF, CP, and MN performed the experiments. AV and CM performed the LC-ESI-MS/MS analysis. OF and RB wrote the manuscript. All authors reviewed and approved the final manuscript.

## FUNDING

This work was financially supported by CONICYT grants FONDEF/II Concurso IDeA en Dos Etapas ID15I10032 and FONDECYT Postdoctorado 2017 No. 3170567.

## ACKNOWLEDGMENTS

The authors wish to acknowledge the Agricultural and Livestock Service (SAG) for facilitating the Psa isolate collection, the Chilean Kiwifruit Committee for support assistance, and Dr. Paula Salinas and Dr. Jorge Olivares for providing bacterial strains.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2018.01907/full#supplementary-material

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Flores, Prince, Nuñez, Vallejos, Mardones, Yañez, Besoain and Bastias. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Plant Microbiome and Its Link to Plant Health: Host Species, Organs and *Pseudomonas syringae* pv. *actinidiae* Infection Shaping Bacterial Phyllosphere Communities of Kiwifruit Plants

OPEN ACCESS

**Edited by:**
Marco Scortichini,
Consiglio per la Ricerca in Agricoltura
e l'Analisi dell'Economia Agraria
(CREA), Italy

**Reviewed by:**
Vardis Ntoukakis,
University of Warwick,
United Kingdom
Dawn Arnold,
University of the West of England,
United Kingdom
Brian H. Kvitko,
University of Georgia, United States

*Correspondence:
Francesco Spinelli
francesco.spinelli3@unibo.it

† These authors have contributed
equally to this work

**Specialty section:**
This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Plant Science

**Received:** 06 June 2018
**Accepted:** 05 October 2018
**Published:** 07 November 2018

**Citation:**
Purahong W, Orrù L, Donati I, Perpetuini G, Cellini A, Lamontanara A, Michelotti V, Tacconi G and Spinelli F (2018) Plant Microbiome and Its Link to Plant Health: Host Species, Organs and Pseudomonas syringae pv. actinidiae Infection Shaping Bacterial Phyllosphere Communities of Kiwifruit Plants. *Front. Plant Sci.* 9:1563. doi: 10.3389/fpls.2018.01563

Witoon Purahong¹†, Luigi Orrù²†, Irene Donati³, Giorgia Perpetuini³, Antonio Cellini³, Antonella Lamontanara², Vania Michelotti², Gianni Tacconi² and Francesco Spinelli³*

¹ Department of Soil Ecology, Helmholtz Center for Environmental Research - UFZ, Halle, Germany, ² CREA Research Centre for Genomics and Bioinformatics – Fiorenzuola d’Arda, Italy, ³ Department of Agricultural and Food Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy

*Pseudomonas syringae* pv. *actinidiae* (Psa) is the causal agent of the bacterial canker, the most devastating disease of kiwifruit vines. Before entering the host tissues, this pathogen has an epiphytic growth phase on kiwifruit flowers and leaves, thus the ecological interactions within epiphytic bacterial community may greatly influence the onset of the infection process. The bacterial community associated to the two most important cultivated kiwifruit species, *Actinidia chinensis* and *Actinidia deliciosa*, was described both on flowers and leaves using Illumina massive parallel sequencing of the V3 and V4 variable regions of the 16S rRNA gene. In addition, the effect of plant infection by Psa on the epiphytic bacterial community structure and biodiversity was investigated. Psa infection affected the phyllosphere microbiome structures in both species, however, its impact was more pronounced on *A. deliciosa* leaves, where a drastic drop in microbial biodiversity was observed. Furthermore, we also showed that Psa was always present in syndemic association with *Pseudomonas syringae* pv. *syringae* and *Pseudomonas viridiflava*, two other kiwifruit pathogens, suggesting the establishment of a pathogenic consortium leading to a higher pathogenesis capacity. Finally, the analyses of the dynamics of bacterial populations provided useful information for the screening and selection of potential biocontrol agents against Psa.

**Keywords:** *Actinidia chinensis*, *Actinidia deliciosa*, epiphytic community, metagenome, bacterial biocoenosis, biocontrol, bacterial canker

## INTRODUCTION

The plant microbiome plays a crucial role in plant health and productivity and, thus, has received significant attention in recent years (Turner et al., 2013). The main focuses of the plant microbiome studies are devoted to model plants, such as *Arabidopsis thaliana*, as well as important economic crop species including barley (*Hordeum vulgare*), corn (*Zea mays*), rice (*Oryza sativa*), soybean
(Glycine max), wheat (Triticum aestivum), whereas less attention is given to fruit crops and tree species (Busby et al., 2017). Plant microbiomes are shaped by both plant-related (i.e., genotype, organ, species, health status etc.) and environmental factors (i.e., management, land use and climate) (Bringel and Couée, 2015). Although plant health status is reported in some studies to be reflected or linked to its microbiome (Berendsen et al., 2012; Turner et al., 2013; Berg et al., 2014), this aspect is actually still unclear and requires further empirical evidence. Thus, in fruit crop species, it is still uncertain how infectious diseases alter the microbiome of the infected organs.

Pseudomonas syringae pv. actinidiae (Psa) is the causal agent of the bacterial canker of kiwifruit, which is the major threat to kiwifruit production worldwide (Scortichini et al., 2012; Vanneste, 2012). The pathogen can infect both *Actinidia chinensis* and *A. deliciosa* plants, the two most important commercial species (Donati et al., 2014). So far, no resistant genotype has been found, but, generally, *A. deliciosa* varieties are considered less susceptible than the ones belonging to *A. chinensis* (Spinelli et al., 2011). Before infecting the plant, the pathogen grows on the epiphytic surfaces of *Actinidia* flowers and leaves. After this epiphytic phase, infection occurs via natural opening such stomata on leaves or stylar tissues on flowers (Donati et al., 2018), or via natural wounds, such as broken trichomes (Spinelli et al., 2011). Once Psa enters the host tissues, the infection rapidly becomes systemic, leading to the death of the host plant (Renzi et al., 2012; Scortichini et al., 2012; Donati et al., 2014). Therefore, the understanding of Psa interactions with the phyllosphere microbial community could provide essential information for developing innovative, effective and long-lasting control strategies. To date, no sustainable and completely effective control methods have been developed for this disease, and control mainly relies on the use of copper formulates (Collina et al., 2016; Scortichini, 2016). However, the increasing concerns about the environmental risks caused by the widespread use of xenobiotic pesticides led institutions, such as the European Commission, to develop regulations to restrict their use (Commission Regulation [EC], 2008. 396/2005/EC, 149/2008/EC). A sustainable and environment-friendly alternative to chemical pesticides for controlling disease in the phyllosphere is the use of biological control agents (BCAs) (Wicaksono et al., 2018). Indeed, the phyllosphere represents an ecological niche with pivotal agricultural and biological significance (Whipps et al., 2008; Vorholt, 2012), and the bacterial epiphytic community can positively impact plant health, physiology and environmental fitness (Kim et al., 2011; Vorholt, 2012; Dees et al., 2015). Several epiphytic bacterial species isolated from the phyllosphere have been reported to be strong competitors against plant pathogens, thus acting as BCAs (Volksch and May, 2001). In addition, to the direct competition for limited space and nutrients, some BCAs can also inhibit pathogen growth by secreting antimicrobial compounds (e.g., *Pantoea agglomerans*, *Lactobacillus plantarum*), or interfering with the pathogen signalling system (Volksch and May, 2001). Finally, other epiphytic bacteria are known to exert a plant growth-promoting activity and induce natural plant resistance against pathogens

(Ryu et al., 2003; Ottesen et al., 2013; Rastogi et al., 2013). For the control of Psa, strains of *Pseudomonas fluorescens*, *Bacillus subtilis*, *Bacillus amyloliquefaciens* and, more recently, *Lactobacillus plantarum* have been tested as possible BCAs (Gould et al., 2015; Collina et al., 2016; Yakhin et al., 2017).

Screening and selection of new BCAs has been mainly focused on the identification of single bacterial species effective in contrasting a specific pathogen. However, under natural conditions, bacteria live in communities regulated by interspecies signalling (Ryan and Dow, 2008) and, thus, the modern approach to enhance plant growth and health is to elucidate the effect of small microbial consortia against pathogens or on plant host resistance induction (Sarma et al., 2015). Several studies highlighted that, in comparison to the use of single beneficial species, the application of microbial consortia may improve efficacy, reliability and consistency of the growth and health promotion under a wider range of environmental conditions (Stockwell et al., 2011). In this view, the beneficial effect on plant health is the result of the combined and synergic interaction of multiple bacterial species each with specific positive effect (Kim et al., 2011; Sarma et al., 2015).

Understanding the dynamics and evolution of the bacterial community on the phyllosphere may also provide crucial information on other factors influencing Psa infection process. In fact, symbiotic interactions among different microbial species, leading to a pathogenic consortium, may increase disease incidence and development (Lamichhane and Venturi, 2015). Growing evidence highlighted that pathogens do not operate independently, but their virulence is mediated by their interaction with other pathogens (Singer, 2010; Lamichhane and Venturi, 2015). This phenomenon has led researchers to develop the idea of pathobiome, i.e., a community in which pathogens participate in complex interactions with their biotic environment (Vayssier-Taussat et al., 2014). The importance of the interactions among pathogens is well recognised in human health (Singer, 2010). For example, in the medical field, the term syndemic indicates the synergistic interactions among diseases (Singer and Clair, 2003). Even though some cases of bacterial pathogens co-occurrence has been described in plants, the impact of pathogens interactions on plants diseases has received far less attention (Küdela et al., 2010; Lamichhane and Venturi, 2015). In kiwifruit, *Pseudomonas* pathogens, such as *P. syringae* pv. *syringae* and *P. viridiflava*, often occur together but their interaction is still unclear (Balestra et al., 2008; Petriccione et al., 2017).

The main aims of this study were (i) to investigate the bacterial phyllosphere communities on leaves and flowers of two species of kiwifruit species (*A. deliciosa* cv. Hayward and *A. chinensis* cv. Hort16A) using Illumina sequencing of the V3 and V4 variable regions of the 16S ribosomal gene; (ii) to verify the relative importance of plant species, organ and Psa infection in shaping bacterial phyllosphere communities; (iii) to quantify (by quantitative real-time polymerase chain reaction, qPCR) the abundance in different plant organs of *Pseudomonas* pathogens (i.e., Psa, *P. syringae* pv. *syringae* and *P. viridiflava*) and BCAs (i.e., *Lactobacillus plantarum*, *Pantoea agglomerans*, *Bacillus subtilis*, *B. amyloliquefaciens*, and *P. fluorescens*) in
relation to plant health status. The experimental approach allowed us to highlight the possible contribute of the species-specific microbiome on the different susceptibility of *A. deliciosa* cv. Hayward and *A. chinensis* cv. Hort16A to Psa.

# MATERIALS AND METHODS

## Sample Collection

Leaves and flowers were collected from *A. deliciosa* cv. Hayward and *A. chinensis* cv. Hort16A plants grown in commercial orchards located in Faenza region (Emilia Romagna, Italy). In those orchards, the average disease incidence in the previous season were 8 and 21% in Hayward and Hort16A, respectively. At shoot emergence and beginning of blooming, an extensive screening was performed to discriminate uninfected from infected plants. For this purpose, 10 flowers and 10 leaves per each plant were sampled and Psa contamination was assessed according to Gallelli et al. (2014). The study of the microbial community in the phyllosphere was performed separately on uninfected from infected plants. For this purpose, sampling was performed at full blooming for both kiwifruit species. Standard orchard pruning, fertilisation and irrigation were applied, and no chemical or biological pesticides were applied in the 6 months preceding sampling. Moreover, the orchards were naturally pollinated, with no assisted pollen application. Leaves were sampled in groups of 10 per plant, randomly chosen either in uninfected or infected vines. To minimise the effect of leaf position and age, only the third fully expanded leaf from each shoot of the same age was collected. Flowers were sampled at another dehiscence in groups of 5 per plant, randomly selected either in uninfected or infected vines. Each leaf or flower sample was washed with 10 or 15 ml of sterile MgSO₄ 10 mM solution, respectively. To concentrate the bacterial load, the same washing solution was used for all samples inside a specific group. To extract all bacteria associated with phyllosphere, washing was carried out for 15 min under gentle agitation (100 rpm) at 4°C temperature to avoid mechanical tissue damage and bacterial multiplication. To verify the efficacy of the washing process, each washed leaf or flower was transferred in a new sterile solution of MgSO₄ and processed again as previously described. This washing solution was successively plated on LB-agar medium. Finally, the grouping of the different samples according to the presence of absence of Psa was confirmed by homogenising each group of leaves or flowers in a batch and processing according to Gallelli et al. (2014).

## DNA Extraction and Sequencing

Libraries were prepared using the Illumina (San Diego, CA, United States) 16S metagenomic sequencing library preparation protocol, which allows the sequencing of the variable V3 and V4 regions of the 16S rRNA gene. Briefly, washing solutions were pelleted by centrifugation at 20,000 × g for 20 min at 4°C, then the supernatant was discarded. Pellets were joined, immediately frozen in liquid nitrogen and stored at -80°C. From frozen pellets, genomic DNA was extracted and purified using NucleoSpin® soil kit (Macherey-Nagel GmbH & Co. KG,

Düren, Germany) following manufacturer instruction. After determining its concentration and purity by spectrophotometer, the extracted genomic DNA was used as template for V3–V4 regions amplification with 16S Amplicon PCR Forward = 5' TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACG GGNGGCWGCAG and Reverse = 5' GTCTCGTGGGCTCGGA GATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC primers following the PCR protocol suggested by Illumina.

PCR products were purified using the Agentcourt® AMPure® XP Beads (Beckman Coulter Company, Brea, CA, United States). The quality of the final products was assessed using a Bioanalyzer 2100 (Agilent Technologies, Waldbronn, Germany) and quantified with Qubit® fluorometer (Thermo Fisher Scientific, Waltham, MA, United States) following manufacturer protocol. The amplicons were coupled to dual indices and Illumina sequencing adaptors attaching using the Nextera XT Index Kit (Illumina Inc., San Diego, CA, United States), pooled in equal proportions and sequenced paired-end in an Illumina MiSeq (Illumina Inc., San Diego, CA, United States) at IGA Technology Services (Udine, Italy). To prevent focusing and phasing problems due to the sequencing of “low diversity” libraries such as 16S amplicons, 30% PhiX genome was spiked in the pooled library.

## Bioinformatic Analysis

Raw reads were first processed with Trimmomatic (Bolger et al., 2014) to remove low-quality reads using a sliding window of 5 bp length with an average phred score ≥ 20. Sequences shorter than 100 bases were discarded. The 16S rRNA sequences were analysed using the Mothur software package version 1.35.1 (Schloss et al., 2009). The paired-end reads were assembled and aligned to the SILVA 16S rRNA sequences database (Pruesse et al., 2007). Sequences were de-noised to remove sequencing error with the command “pre.cluster” and chimeric sequences were removed using the Uchime algorithm (Edgar et al., 2011) implemented in Mothur. Sequences were clustered into OTUs at 96% sequence identity using the nearest neighbour clustering methods. The sequences were classified using the references Ribosomal Database Project database (RDP) provided in Mothur. OTUs that were singletons and doubletons were removed. The samples were normalised to 6,886 sequences each (the size of the smallest sample) to ensure that the analysis was not influenced by differential sequencing depths. The bacterial 16S rRNA gene Illumina sequencing data are deposited in the NCBI BioProject library (Accession: PRJNA472855, ID: 472855). The bacterial taxonomic table (with bacterial relative abundance data) are given in **Supplementary Table S1**.

## qPCR Analysis

The primer sets used in this study are listed in **Table 1**. New primer sets were designed based on *L. plantarum* WCFS1 and *P. syringae* pv. *actinidiae* RC3 sequences available at the National Centre for Biotechnology Information (NCBI<fnref n="1" />). Appropriate primers were designed using the online programme Primer3Plus<fnref n="2" />

<footnote n="1">¹http://www.ncbi.nlm.nih.gov</footnote>

<footnote n="2">²http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi/</footnote>
<table><caption>TABLE 1 | Primers used in this study to reveal the presence of the indicated bacterial species.</caption><thead><tr><td>Species</td><td>Forward</td><td>Reverse</td><td>Reference</td></tr></thead><tbody><tr><td>P. syringae pv. actinidiae</td><td>ACACCGCCCGTCACACCA</td><td>GTTCCCCTACGGCTCCT</td><td>This study</td></tr><tr><td>P. syringae pv. syringae</td><td>TCCTTATCGATCTGCAACTGGCGA</td><td>ATGGTTGCCTGCAGTTCATTC</td><td>Najafi Pour and Taghavi, 2011</td></tr><tr><td>P. viridiflava</td><td>GTAGGTGGTTTGTTAAGTTGAA</td><td>GTAGGTGGTTTTGTTAAGTTGAA</td><td>Alimi et al., 2011</td></tr><tr><td>P. fluorescens</td><td>TGCATTCAAAACTGACTG</td><td>AATCACACCGTGGTAACCG</td><td>Scarpellini et al., 2004</td></tr><tr><td>L. plantarum</td><td>TTTGAGTGAGTGGCGAACTG</td><td>CGAAGCCATCTTTCAAGCTC</td><td>This study</td></tr><tr><td>B. subtilis</td><td>AATGACCGTGCTCCATCTGTAA</td><td>TTCCGATCTTTAACGGATTGCT</td><td>Rotolo et al., 2016</td></tr><tr><td>B. amyloquefaciens</td><td>CCGGCGAAATCAAAATAATGAC</td><td>GGCAGGATCATACGGGAGAA</td><td>Rotolo et al., 2016</td></tr><tr><td>P. agglomerans</td><td>ACGGTGCGTTCCGCAATA</td><td>GGCGCCGGGAAAACATAC</td><td>Braun-Kiewnick et al., 2012</td></tr></tbody></table>

(Untergasser et al., 2012). The BLAST search software (Basic Alignment Search Tool<fnref n="3" />) was used to cheque the specificity of each primer set. Properties of each primer were verified by Oligo analyser 3.1. Primer specificity was validated by melt curve analysis and end point PCR performed with the same protocol adopted for qPCR (see below), using AmpliTaq Gold® 360 enzyme and Master Mix (Thermo Fisher Scientific, Waltham, MA, United States).

qPCR analyses were performed using Sybr Green fast master mix chemistry (Applied Biosystem, Foster City, CA, United States) in a 96 well spectrofluorometric thermal cycler StepOnePlus® (Thermo Fisher Scientific, Waltham, MA, United States). DNA concentration was adjusted to 100 ng. All reactions were performed in triplicate, with the following thermal profile: 1 cycle at 50°C (2 min), 1 cycle at 95°C (10 min), 40 cycles of 95°C (15 s) and 60°C (30 s). The temperature was raised by 0.3°C every 10 s from 63 to 95°C to obtain the melting temperature. To quantify the bacterial titre of the samples, standard curves were generated for each bacterial species tested plotting cycle threshold (Ct) values versus bacterial cell titre, as measured by plating 10-fold dilutions of the same sample on LB-agar medium (Lyons et al., 2000). Upon verification by genome blast, the average number of detector gene copies per genome was assumed to be 1.0 for each species.

## Meta-Analysis of Bacterial Association

The correlation among natural epiphytic populations of Psa and other bacteria (*P. syringae* pv. *syringae*, *P. viridiflava*, *P. fluorescens*, *Pantoea agglomerans/vagans*, and *Lactobacillus* spp.) was evaluated based on data obtained between 2012 and 2016, relating to *A. deliciosa* cv. Hayward samples collected in the same area and season as the samplings for metagenomic analysis. Each sample was singularly washed in 10 ml MgSO₄ 10 mM sterile solution. Bacterial quantification was performed on the wash by qPCR as described above.

## Statistical Analysis

To assess the coverage of the sequencing depth, individual rarefaction analysis was performed for each sample using the “diversity” function in PAST 3.0 (Hammer et al., 2001). Alpha diversity indices (Pielou, Inverse Simpson and

Shannon) were analysed after normalisation using Mothur. Similarity Percentages (SIMPER) analysis using PAST was used to calculate the average dissimilarity and to obtain the identity and relative abundances of the bacterial taxa that contributed most of the observed pair-wise variation in the bacterial community composition due to different kiwifruit species (healthy plants), organs (healthy plants) and pathological status (healthy vs. diseased plants). Principal component analysis (PCA) based on correlation matrix was carried out in PAST to display the clusterisation of samples according to the variance in qPCR population analysis.

Multiple regression was performed on bacterial populations to test their association with Psa, using Statistica ver. 7.0 (Statsoft, Inc., Tulsa, OK, United States). The analysis was restricted to samples positive to Psa, and the data were transformed to Log₁₀ before elaboration. Statistical significance was assumed for *P* < 0.05.

# RESULTS

## Description of the Epiphytic Bacterial Microbiome of Kiwifruit Plant

In this study, the leaf- and flower-associated microbiota of two kiwifruit species, *A. deliciosa* and *A. chinensis*, were analysed. After the normalisation step at 6,886 sequences per sample, a total of 1,050 bacterial OTUs were retrieved. The rarefaction curves were close to saturation, suggesting that the OTUs recovered in this study nearly represented the whole bacterial genetic diversity (Supplementary Figure S1). The OTUs were assigned to 16 phyla, and 220 different genera (Supplementary Table S1). *Proteobacteria* was the most abundant phylum representing about 77.4% of the total contigs, followed by *Firmicutes* (10.7%), *Actinobacteria* (6.1%), and *Bacteroidetes* (3.5%) (Figure 1). At an OTU level, the most abundant OTUs were identified as *Pseudomonas* OTU 00001 (31.5%), two unclassified genera from the *Enterobacteriaceae* family, OTU 00002 and OTU 00004, representing together about 15.3% of the total sequences, *Sphingomonas* OTU 00003 (5.8%) and *Massilia* OTU 00005 (4.9%). The first 10 most abundant OTUs accounted for approximately 70% of total sequences (Figure 1 and Supplementary Table S1).

<footnote n="3">³http://www.ebi.ac.uk/blastall/nucleotide.html</footnote>
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

**FIGURE 1** | Bacterial community composition at the phylum (A) and genus level (B). In the stacked bar plot are reported the most abundant phyla and genera. In the sample codes, Ad, *Actinidia chinensis* var. HORT16A; Ad, *Actinidia deliciosa* var. Hayward; D, diseased; H, healthy; F, flower; L, leaf.

## Species-Specificity of Epiphytic Bacterial Microbiome

Bacterial community was shaped by the species of kiwifruit plants (Figure 2). In fact, the overall average dissimilarity between leaves or flowers of the two species was 78.27 and 63.26%, respectively. In leaves, two unclassified genera (OTU 00004 and 00011) belonging to *Enterobacteriaceae* accounted for about 23% of the dissimilarity. The genus *Pseudomonas* (OTU 00001) also accounted approximately for 20% of the dissimilarity, being 13 times more abundant on *A. deliciosa* than on *A. chinensis* leaves.

On flowers, *Enterobacteriaceae* (OTU 00002) accounted for approximately 27% of dissimilarity followed by the genus *Sphingomonas* (8.4%, OTU 00003). The genera *Arthrobacter* (OTU 00013) and *Bacillus* (OTU 00009) accounted for 5 and 4% of dissimilarity, respectively. *Pseudomonas* (OTU 00001) accounted only for the 2.7% of dissimilarity.

The influence of kiwifruit plant species was confirmed also by the three biodiversity indices determined: Shannon (*H*'), Inverse Simpson (1/*D*') and Pielou (*J*'). A higher biodiversity was observed in *A. chinensis* leaves (*H*' = 4.02; 1/*D*' = 11.93; *J*' = 0.65) than in *A. deliciosa* ones (*H*' = 2.99; 1/*D*' = 7.16; *J*' = 0.54). Similarly, higher values of all the considered indices were found for *A. chinensis* (*H*' = 4.13; 1/*D*' = 30.14; *J*' = 0.70) than for *A. deliciosa* (*H*' = 2.76; 1/*D*' = 5.73; *J*' = 0.49) flowers (Table 2).

## Organ-Specific Epiphytic Bacterial Microbiome

In each kiwifruit species, flowers and leaves harboured a distinct bacterial microbiome. In fact, in *A. chinensis* the overall average dissimilarity between leaves and flowers was 71.16%, while for *A. deliciosa* it was 58.55% (Figure 3). In *A. chinensis*, an unclassified genus (OTU 00004) belonging to *Enterobacteriaceae*
### Comparison by species, same organ

**Overall average dissimilarity:**
78.27%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th><th>A. chinensis</th><th>A. deliciosa</th></tr></thead><tbody><tr><td>Pseudomonas_Otu00001</td><td>19.55</td><td>2.54</td><td>33.1</td></tr><tr><td>Enterobacteriaceae_Otu00004</td><td>16.28</td><td>26.8</td><td>1.29</td></tr><tr><td>Enterobacteriaceae_Otu00011</td><td>6.80</td><td>0.0436</td><td>10.7</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>4.30</td><td>0.479</td><td>7.2</td></tr><tr><td>Massilia_Otu00005</td><td>3.65</td><td>1.22</td><td>6.94</td></tr><tr><td>Acinetobacter_Otu00008</td><td>3.22</td><td>0.029</td><td>5.07</td></tr><tr><td>Finegoldia_Otu00015</td><td>3.11</td><td>4.98</td><td>0.116</td></tr><tr><td>Streptococcus_Otu00014</td><td>2.90</td><td>4.89</td><td>0.349</td></tr><tr><td>Bacillus_Otu00009</td><td>2.77</td><td>0.116</td><td>4.46</td></tr><tr><td>Faecalibacterium_Otu00021</td><td>1.95</td><td>3.05</td><td>0</td></tr></tbody></table>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

**Overall average dissimilarity:**
63.26%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th><th>A. chinensis</th><th>A. deliciosa</th></tr></thead><tbody><tr><td>Enterobacteriaceae_Otu00002</td><td>26.66</td><td>2.96</td><td>36.7</td></tr><tr><td>Sphingomonas_Otu00003</td><td>8.38</td><td>2.4</td><td>13</td></tr><tr><td>Arthrobacter_Otu00013</td><td>5.01</td><td>6.94</td><td>0.61</td></tr><tr><td>Bacillus_Otu00009</td><td>4.37</td><td>7.42</td><td>1.89</td></tr><tr><td>Bradyrhizobiaceae_Otu00025</td><td>2.85</td><td>3.63</td><td>0.029</td></tr><tr><td>Phenylobacterium_Otu00020</td><td>2.69</td><td>3.43</td><td>0.029</td></tr><tr><td>Pseudomonas_Otu00001</td><td>2.69</td><td>7.26</td><td>10.7</td></tr><tr><td>Enterobacteriaceae_Otu00004</td><td>2.61</td><td>3.33</td><td>0.029</td></tr><tr><td>Sediminibacterium_Otu00028</td><td>2.40</td><td>3.05</td><td>0.0145</td></tr><tr><td>Enterobacteriaceae_Otu00011</td><td>2.34</td><td>3.22</td><td>0.261</td></tr></tbody></table>

**FIGURE 2** | Overall average dissimilarity between the same organs, leaves and flowers, of two kiwifruit species, *A. chinensis* and *A. deliciosa*. For each organ, the numbers of OTUs specific to one of the two species, or present in both of them, are indicated in the Venn diagrams. Top ten OTUs which contribute most to the overall average dissimilarity and their relative abundances are shown. The colour scale indicates the abundance ranking of the relative OTU: highest (red), mid-point with 50% percentile (yellow), lowest (green).

### Comparison by organ, within species

**Overall average dissimilarity:**
71.16%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th><th>A. chinensis</th><th>A. chinensis</th></tr></thead><tbody><tr><td>Enterobacteriaceae_Otu00004</td><td>16.48</td><td>26.8</td><td>3.33</td></tr><tr><td>Bacillus_Otu00009</td><td>5.13</td><td>0.116</td><td>7.42</td></tr><tr><td>Arthrobacter_Otu00013</td><td>4.38</td><td>0.712</td><td>6.94</td></tr><tr><td>Massilia_Otu00005</td><td>3.69</td><td>1.22</td><td>6.48</td></tr><tr><td>Pseudomonas_Otu00001</td><td>3.32</td><td>2.54</td><td>7.26</td></tr><tr><td>Finegoldia_Otu00015</td><td>3.21</td><td>4.98</td><td>0.407</td></tr><tr><td>Streptococcus_Otu00014</td><td>2.98</td><td>4.89</td><td>0.653</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>2.82</td><td>0.479</td><td>4.49</td></tr><tr><td>Bradyrhizobiaceae_Otu00025</td><td>2.52</td><td>0.0436</td><td>3.63</td></tr><tr><td>Acinetobacter_Otu00008</td><td>2.33</td><td>0.029</td><td>3.34</td></tr></tbody></table>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

**Overall average dissimilarity:**
58.55%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th><th>A. deliciosa</th><th>A. deliciosa</th></tr></thead><tbody><tr><td>Enterobacteriaceae_Otu00002</td><td>28.98</td><td>2.76</td><td>36.7</td></tr><tr><td>Pseudomonas_Otu00001</td><td>19.20</td><td>33.1</td><td>10.7</td></tr><tr><td>Enterobacteriaceae_Otu00011</td><td>8.90</td><td>10.7</td><td>0.261</td></tr><tr><td>Sphingomonas_Otu00003</td><td>7.43</td><td>4.3</td><td>13</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>3.47</td><td>7.2</td><td>3.14</td></tr><tr><td>Acinetobacter_Otu00008</td><td>2.46</td><td>5.07</td><td>2.19</td></tr><tr><td>Bacillus_Otu00009</td><td>2.20</td><td>4.46</td><td>1.89</td></tr><tr><td>Massilia_Otu00005</td><td>1.94</td><td>6.94</td><td>9.21</td></tr><tr><td>Methylobacterium_Otu00019</td><td>1.58</td><td>0.319</td><td>2.16</td></tr><tr><td>Pedobacter_Otu00031</td><td>1.23</td><td>1.48</td><td>0.0436</td></tr></tbody></table>

**FIGURE 3** | Overall average dissimilarity between leaves and flower of the same kiwifruit species, *A. chinensis* or *A. deliciosa*. For each species, the numbers of OTUs specific to one of the two organs, or present in both of them, are indicated in the Venn diagrams. Top ten OTUs which contribute most to the overall average dissimilarity and their relative abundances are shown. The colour scale indicates the abundance ranking of the relative OTU: highest (red), mid-point with 50% percentile (yellow), lowest (green).

accounted for 16.48% of dissimilarity followed by the genus *Bacillus* (OTU 00009, 5.13%). On the other hand, in *A. deliciosa* OTU 00002 belonging to *Enterobacteriaceae* accounted for about 29% of the dissimilarity being 13 times more abundant on flowers, while the genus *Pseudomonas* (OTU 00001) accounted for approximately 19% of dissimilarity and it was more abundant on leaves than flowers (Figure 3).

These data are in agreement with the bio diversity indices trend. *A. chinensis* hosted a more biodiverse epiphytic bacterial community ($H' = 4.13; 1/D' = 30.14; J' = 0.70$) than leaves ($H' = 4.02; 1/D' = 11.93; J' = 0.65$). On the other hand, in *A. deliciosa*, the bacterial community presented a higher complexity in leaves ($H' = 2.99; 1/D' = 7.16; J' = 0.54$) than flowers ($H' = 2.76; 1/D' = 5.73; J' = 0.49$) (Table 2).

<table><caption>**TABLE 2** | Summary of diversity estimate for each sample.</caption><thead><tr><th>Sample</th><th>Total OTUs</th><th>Shannon index (H')</th><th>Simpson reciprocal index (1/D')</th><th>Pielou index (J')</th></tr></thead><tbody><tr><td>Ac DF</td><td>173</td><td>1.61</td><td>1.96</td><td>0.31</td></tr><tr><td>Ac HF</td><td>363</td><td>4.13</td><td>30.14</td><td>0.70</td></tr><tr><td>Ad DF</td><td>411</td><td>3.49</td><td>10.84</td><td>0.58</td></tr><tr><td>Ad HF</td><td>294</td><td>2.76</td><td>5.73</td><td>0.49</td></tr><tr><td>Ac DL</td><td>246</td><td>2.95</td><td>8.09</td><td>0.54</td></tr><tr><td>Ac HL</td><td>492</td><td>4.02</td><td>11.93</td><td>0.65</td></tr><tr><td>Ad DL</td><td>59</td><td>0.57</td><td>1.23</td><td>0.14</td></tr><tr><td>Ad HL</td><td>256</td><td>2.99</td><td>7.16</td><td>0.54</td></tr></tbody></table>

Ac, *Actinidia chinensis* var. HORT16A; Ad, *Actinidia deliciosa* var. Hayward; D, diseased; H, healthy; F, flower; L, leaf. For each sample, 6,886 sequences were analysed.
## Effect of Psa Infection on the Epiphytic Bacterial Microbiome

A detailed comparison about bacterial community changes related to Psa infection showed that the overall average dissimilarity between leaves and flower of healthy and infected plants ranged from 79.10 to 66.70% in *A. chinensis* and from 64.45 to 35.41% in *A. deliciosa* (Figures 4A,B).

In *A. chinensis*, the substantial increase in the genus *Pseudomonas* (OTU 00001) in infected leaves and flowers contributed 8.25 and 47.86%, respectively (Figure 4A). A similar result was observed in *A. deliciosa* were the genus *Pseudomonas* (OTU 00001) increased up to three and two times in leaves and flowers, respectively (Figure 4B).

A reduction in diversity of the bacterial community was observed after Psa infection, with the only exception of *A. deliciosa* flowers, as indicated by the biodiversity indices (Table 2). Psa infection caused a marked drop in population evenness and biodiversity in infected *A. chinensis* (flowers and leaves) and *A. deliciosa* (leaves only), with the dominance of few genera, mainly *Pseudomonas* (OTU 00001).

The Venn diagrams in Figure 5 describe the distributions of unique and shared OTUs in healthy and diseased plants in the two species and tissues analysed. Psa infection had the strongest impact on the bacterial community of *A. deliciosa* leaves compared to the other conditions analysed, only 37 OTUs

being shared between healthy and diseased leaves. Furthermore, on infected leaves of *A. deliciosa* we identified some specific OTUs belonging to *Oxalobacteraceae* (OTU 00043, OTU 00186), *Haemophilus* (OTU 00076), *Moraxellaceae* (OTU 00323) that were not present in healthy *A. deliciosa* leaves (Supplementary Table S1). Finally, a considerable number of high abundance OTUs disappeared or showed a dramatic reduction on leaves from infected plants (Supplementary Table S1). A similar trend was observed in *A. chinensis* flowers and leaves. Also in this case, healthy plants were characterised by the presence of characteristic OTUs absent in diseased ones (e.g., *Epilithonimonas* OTU 00016, *Porphyromonas* OTU 00110, *Bacteroides* OTU 00111).

On the other hand, in *A. deliciosa* flowers some OTUs were more abundant or only present in diseased samples (e.g., *Pseudomonas* OTU 00001, *Propionibacterium* OTU 00012, *Weissella* OTU 00029) (Supplementary Table S1).

## Abundance and Dynamic of *Pseudomonas* spp. Pathogens and Putative Biocontrol Bacterial Agents in Relation to Plant Pathological Status

Quantitative PCR was applied to detect the occurrence, in relation to plant pathological status, of two other kiwifruit pathogenic bacteria: *P. syringae* pv. *syringae* (Pss) and

### A Comparison by disease status, *A. chinensis*

Overall average dissimilarity: 79.10%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>Enterobacteriaceae_Otu00004</td><td>16.73</td></tr><tr><td>Enterobacteriaceae_Otu00002</td><td>15.29</td></tr><tr><td>Pseudomonas_Otu00001</td><td>8.25</td></tr><tr><td>Acinetobacter_Otu00008</td><td>4.72</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>3.81</td></tr><tr><td>Sphingomonas_Otu00003</td><td>3.29</td></tr><tr><td>Finegoldia_Otu00015</td><td>3.08</td></tr><tr><td>Streptococcus_Otu00014</td><td>2.91</td></tr><tr><td>Massilia_Otu00005</td><td>2.46</td></tr><tr><td>Faecalibacterium_Otu00021</td><td>1.93</td></tr></tbody></table>

<figure><img src="image_18.png" /></figure>

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>A. chinensis H</td><td>26.8</td></tr><tr><td>A. chinensis D</td><td>0.305</td></tr><tr><td>134 OTUs</td><td>3.63</td></tr><tr><td>112 OTUs</td><td>27.8</td></tr><tr><td>112 OTUs</td><td>15.6</td></tr><tr><td>0.029</td><td>7.49</td></tr><tr><td>0.479</td><td>6.51</td></tr><tr><td>2.99</td><td>8.19</td></tr><tr><td>4.98</td><td>0.116</td></tr><tr><td>4.89</td><td>0.29</td></tr><tr><td>1.22</td><td>5.11</td></tr><tr><td>3.05</td><td>0</td></tr></tbody></table>

Overall average dissimilarity: 66.70%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>Pseudomonas_Otu00001</td><td>47.86</td></tr><tr><td>Bacillus_Otu00009</td><td>5.27</td></tr><tr><td>Arthrobacter_Otu00013</td><td>4.62</td></tr><tr><td>Massilia_Otu00005</td><td>3.88</td></tr><tr><td>Propionibacterium_Otu00012</td><td>2.37</td></tr><tr><td>Enterobacteriaceae_Otu00011</td><td>2.35</td></tr><tr><td>Acinetobacter_Otu00008</td><td>2.22</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>1.66</td></tr><tr><td>Bradyrhizobiaceae_Otu00025</td><td>1.24</td></tr><tr><td>Aquabacterium_Otu00034</td><td>1.19</td></tr></tbody></table>

<figure><img src="image_19.png" /></figure>

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>A. chinensis H</td><td>7.26</td></tr><tr><td>A. chinensis D</td><td>71.1</td></tr><tr><td>124 OTUs</td><td>7.42</td></tr><tr><td>49 OTUs</td><td>0.392</td></tr><tr><td>6.94</td><td>0.784</td></tr><tr><td>6.48</td><td>1.31</td></tr><tr><td>3.46</td><td>0.29</td></tr><tr><td>3.22</td><td>0.0871</td></tr><tr><td>3.34</td><td>0.378</td></tr><tr><td>4.49</td><td>2.28</td></tr><tr><td>3.63</td><td>1.98</td></tr><tr><td>2.03</td><td>0.45</td></tr></tbody></table>

### B Comparison by disease status, *A. deliciosa*

Overall average dissimilarity: 64.45%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>Pseudomonas_Otu00001</td><td>44.19</td></tr><tr><td>Enterobacteriaceae_Otu00011</td><td>8.29</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>5.43</td></tr><tr><td>Massilia_Otu00005</td><td>4.75</td></tr><tr><td>Acinetobacter_Otu00008</td><td>3.85</td></tr><tr><td>Bacillus_Otu00009</td><td>3.44</td></tr><tr><td>Sphingomonas_Otu00003</td><td>3.05</td></tr><tr><td>Duganella_Otu00007</td><td>2.57</td></tr><tr><td>Enterobacteriaceae_Otu00002</td><td>2.12</td></tr><tr><td>Oxalobacteraceae_Otu00010</td><td>1.30</td></tr></tbody></table>

<figure><img src="image_20.png" /></figure>

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>A. deliciosa H</td><td>33.1</td></tr><tr><td>A. deliciosa D</td><td>90.1</td></tr><tr><td>112 OTUs</td><td>10.7</td></tr><tr><td>112 OTUs</td><td>0</td></tr><tr><td>6.94</td><td>0.203</td></tr><tr><td>5.07</td><td>0.102</td></tr><tr><td>4.46</td><td>0.029</td></tr><tr><td>4.3</td><td>0.363</td></tr><tr><td>0.029</td><td>3.34</td></tr><tr><td>2.76</td><td>0.029</td></tr><tr><td>0.0436</td><td>1.71</td></tr></tbody></table>

Overall average dissimilarity: 35.41%

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>Enterobacteriaceae_Otu00002</td><td>36.20</td></tr><tr><td>Pseudomonas_Otu00001</td><td>15.71</td></tr><tr><td>Weissella_Otu00029</td><td>6.05</td></tr><tr><td>Propionibacterium_Otu00012</td><td>1.87</td></tr><tr><td>unclassified_Otu00070</td><td>1.85</td></tr><tr><td>Sphingomonas_Otu00003</td><td>1.85</td></tr><tr><td>Methylobacterium_Otu00019</td><td>1.78</td></tr><tr><td>Sphingomonadaceae_Otu00006</td><td>1.15</td></tr><tr><td>Massilia_Otu00005</td><td>1.10</td></tr><tr><td>Staphylococcus_Otu00024</td><td>0.70</td></tr></tbody></table>

<figure><img src="image_21.png" /></figure>

<table><thead><tr><th>Taxon</th><th>Contrib. %</th></tr></thead><tbody><tr><td>A. deliciosa H</td><td>36.7</td></tr><tr><td>A. deliciosa D</td><td>11.1</td></tr><tr><td>184 OTUs</td><td>10.7</td></tr><tr><td>227 OTUs</td><td>21.8</td></tr><tr><td>0.0145</td><td>4.3</td></tr><tr><td>1</td><td>2.32</td></tr><tr><td>0.0726</td><td>1.38</td></tr><tr><td>13</td><td>14.3</td></tr><tr><td>2.16</td><td>0.9</td></tr><tr><td>3.14</td><td>2.32</td></tr><tr><td>9.21</td><td>8.41</td></tr><tr><td>0.16</td><td>0.653</td></tr></tbody></table>

**FIGURE 4** | Overall average dissimilarity between leaves and flower of healthy and infected *A. chinensis* plants (A) and *A. deliciosa* (B) (H, healthy; D, diseased, infected with *Pseudomonas syringae* pv. *actinidiae*). For each combination of species and organ, the numbers of OTUs specific to H or D samples, or present in both of them, are indicated in the Venn diagrams. Top ten OTUs which contribute most to the overall average dissimilarity and their relative abundances are shown. The colour scale indicates the abundance ranking of the relative OTU: highest (red), mid-point with 50% percentile (yellow), lowest (green).
<figure><img src="image_22.png" /><figcaption>**FIGURE 5** | Venn diagrams showing the unique and shared OTUs between healthy and diseased tissue. (A) *A. chinensis* diseased and healthy leaves and flowers. (B) *A. deliciosa* diseased and healthy leaves and flowers.</figcaption></figure>

*P. viridiflava* (Pv) and bacterial species with potential biocontrol activity, such as *P. agglomerans/vagans*, *L. plantarum*, *B. subtilis*, *B. amyloliquefaciens*, and *P. fluorescens* (Choudhary and Johri, 2009; Savitha et al., 2013; Bonaterra et al., 2014; Dutkiewicz et al., 2016; Sharifazizi et al., 2017). Novel primer sets were developed for *L. plantarum* and Psa. Melting curves analysis (Supplementary Figure S1) revealed the specificity of designed primers: a unique peak was observed, suggesting the specificity of the amplification, i.e., each primer pair amplified a unique locus targeted on the genome.

In Psa-infected plants, all the three pathogens were present, although Pss and Pv populations were generally lower than Psa (Figure 6). Psa and Pss/Pv were associated in 62.5% of flowers. In non-infected samples, none of the pathogens was detected, with the only exception of a small amount of Pss on the leaves of *A. chinensis* (Figure 6). PCA analysis revealed that healthy

plants clustered together. In particular, diseased flowers of both species were mainly characterised by presence of Pv, while Psa and Pss were mainly associated to diseased leaves (Figure 7). A significant correlation was found between epiphytic Psa and Pss/Pv populations, for values lower than 10⁹ bacterial cells per flower (Figures 7B,C).

Regarding the bacterial species with potential biocontrol activity (*P. agglomerans/vagans*, *L. plantarum*, *B. subtilis*, *B. amyloliquefaciens*, and *P. fluorescens*), all of them were found in the healthy plant samples, while only *L. plantarum* appeared also in the corresponding infected organs, and *P. fluorescens* was present in diseased leaves, but not flowers (Figure 8). *B. amyloliquefaciens* appeared in some diseased samples, without connection to the plant species or organ. The other species were not present in diseased samples. *P. agglomerans/vagans* and *P. fluorescens* population sizes were inversely correlated with Psa, for pathogen populations lower than 10⁵ and 10⁶ bacterial cells per gramme of tissue, respectively. No significant correlation was found with *Lactobacillus* spp. Principal component analysis showed that diseased plants were well differentiated from healthy ones and that the presences of *L. plantarum* and *P. agglomerans/vagans* were mostly distinctive of *A. chinensis* and *A. deliciosa*, respectively (Figure 9).

DISCUSSION

Microbial Biodiversity in *Actinidia*
Phyllosphere

The phyllosphere supports complex microbial populations, and the phyllosphere microbiota can promote plant growth or exhibit biocontrol against various plant pathogens (Thapa et al., 2017). The abundance and spatial distribution of phyllosphere microbiota is to a large extent influenced by environmental factors, but host plant genotype also plays a key role (Bringel and Couée, 2015). Indeed, in previous research, a core community of 31 bacterial species, amounting to 99.8% of total sequences, was found on kiwifruit pollen samples regardless of the different geographical origins and year of collection (Kim M.J. et al., 2018). The present study provides a comprehensive description of the epiphytic bacterial communities on flowers and leaves of *A. chinensis* and *A. deliciosa*, the two main kiwifruit commercial species, and highlights their variability in relation to Psa infection. The differences in the microbiota structures were investigated also through the determination of three biodiversity indices. Shannon and Inverse Simpson indices were used to extrapolate the total richness from the observed OTUs. The former is the most widely used index based on species richness and is sensitive to changes in rare species, while the latter is preferred over other measures of alpha-diversity because it accounts for evenness in addition to the number of species. Finally, Pielou index provides information on species evenness, ranging from 0 to 1, with 1 representing perfect evenness and 0 complete dominance. In kiwifruit phyllosphere, *Proteobacteria*, *Firmicutes*, *Actinobacteria*, and *Bacteroidetes* were the most abundant phyla. These phyla are considered as phyllosphere-associated generalists and have been found to be the most abundant phyla in the
<figure><img src="image_23.png" /><figcaption>**FIGURE 6** | Abundance in each sample (mean ± standard error, n = 5) of *Pseudomonas syringae* pv. *actinidiae* (Psa), *Pseudomonas syringae* pv. *syringae* (Pss), and *Pseudomonas viridiflava* (Pv) determined by qPCR.</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**FIGURE 7** | Association of *Pseudomonas syringae* pv. *actinidiae* (Psa) with *P. syringae* pv. *syringae* (Pss) and *P. viridiflava* (Pv). (A) PCA ordination (with biplot function) showing the distribution of samples from healthy and infected plants on the basis of pathogens quantification by qPCR. (B) Linear regression between Psa and Pss populations. (C) Linear regression between Psa and Pv populations in infected flowers. In (B,C), the data sets were restricted to samples presenting a Psa population lower than $10^9$ bacterial cells flower$^{-1}$.</figcaption></figure>

phyllosphere of several plant species (Bulgarelli et al., 2013; Bringel and Couée, 2015; Kim M.J. et al., 2018). The most frequent genera were *Pseudomonas*, *Sphingomonas*, and *Massilia*.

Their presence has been reported also by other authors in other host plants (López-Velasco et al., 2011; Bodenhause et al., 2013; Bogas et al., 2015). The predominant genus was *Pseudomonas*.
<figure><img src="image_25.png" /><figcaption>**FIGURE 8** | Abundances (mean ± standard error, n = 5) of L. plantarum, P. agglomerans, B. subtilis, B. amyloliquefaciens, and P. fluorescens determined by qPCR.</figcaption></figure>

A

Component 2

<figure><img src="image_26.png" /><figcaption>**FIGURE 9** | Association of *Pseudomonas syringae* pv. *actinidiae* (Psa) with *Pantoea agglomerans/vagans*, *P. fluorescens*, *Lactobacillus plantarum*, and *Bacillus spp.*

**(A)** PCA ordination (with biplot function) showing the distribution of samples from healthy and infected plants on the basis of BCAs quantification.

**(B)** Linear regression between Psa and *Pantoea agglomerans/vagans* populations.

**(C)** Linear regression between Psa and *P. fluorescens* populations.

**(D)** Scatterplot representing Psa and *Lactobacillus* spp. populations on leaf samples. In (B–D), the data sets were restricted to samples presenting a Psa population lower than 10⁶ bacterial cells g⁻¹ tissue.</figcaption></figure>
In general, pseudomonads colonise plant surfaces, and many strains harbour interesting potential biocontrol actions (Thapa et al., 2017). Some species, such as *P. putida*, are known for their phosphate solubilisation ability and IAA production (Adesemoye et al., 2008; Gholami et al., 2009; Ahemad and Khan, 2012; Ahemad and Kibret, 2014). Strains of *P. fluorescens*, *P. aeruginosa*, *P. asplenii*, and *P. protegens* are also used as biocontrol agents against different pathogens (Krishnamurthy and Gnanamanickam, 1998; Akter et al., 2016; Michavila et al., 2017).

The *Actinobacteria* class also represents a reservoir of potential BCAs. Members of this phylum are well known for their ability to produce secondary metabolites with application in the agricultural, pharmaceutical and medical industries (Himaman et al., 2016). Several studies proposed their use as BCAs (El-Tarabily et al., 2000; Kunoh, 2002; Cao et al., 2005; Prapagdee et al., 2008; Mingma et al., 2014). They play key roles as plant growth promoters, disease resistance inducers and drought tolerance stimulators (Himaman et al., 2016).

The genus *Sphingomonas* generally acts as a plant-protective genus by suppressing disease symptoms and decreasing pathogen growth (Kim D. et al., 2018). Innerebner et al. (2011) showed that the inoculation with a *Sphingomonas* sp. strain reduced the population size of the plant pathogens *Pseudomonas syringae* pv. *tomato* DC3000 and *Xanthomonas campestris* pv. *campestris* LMG 568 on *Arabidopsis* leaves. The genus *Massilia* belongs to the family of *Oxalobacteraceae*. The presence of *Massilia* spp. was reported in the phyllosphere of different plants, including lettuce and apple (Bassas-Galia et al., 2012; Rastogi et al., 2012; Yashiro and McManus, 2012).

## Host-Specific Bacterial Communities

Both on flowers and on leaves, the epiphytic bacterial community differed according to kiwifruit species. The main taxa contributing to differences were *Enterobacteriaceae*, *Pseudomonas*, *Acinetobacter*, and *Sphingomonadaceae* on leaves, and *Sphingomonas*, *Arthrobacter*, *Bacillus*, and *Bradyrhizobiaceae* in flowers. The detection of this last genus has been reported also in the phyllosphere of spinach, rice, and tobacco, providing evidence for vertical transmission of bacteria from seed to the phyllosphere (Chi et al., 2005; Li et al., 2010; Lopez-Velasco et al., 2013).

Data obtained here are in agreement with other studies showing that different plants genotypes of the same species can host different bacterial communities (De Costa et al., 2006; Vorholt, 2012). The observed differences could be related to anatomical differences of leaves and flowers of the two kiwifruit species. In fact, bacteria are not uniformly distributed across leaf surfaces: instead, they form scattered microcolonies in proximity of trichomes, stomata, epidermal cell wall junctions and grooves along veins (L window and Brandl, 2003; Vorholt, 2012), where water and nutrients are most available (Kinkel, 1997; Leveau and L window, 2001; Monier and L window, 2004; Vorholt, 2012). One of the most considerable anatomical differences between *A. deliciosa* and *A. chinensis* is related with trichome structure and density. In *A. chinensis*, the leaves show a higher trichome density compared to *A. deliciosa* cultivars (He et al., 2000; Spinelli et al., 2011).

Moreover, trichomes in *A. chinensis* are characterised by a higher central peduncle (He et al., 2000). These differences in trichomes abundance may affect the microbial community composition and structure.

## Organ-Specific Bacterial Communities

Differences in the microbial community were also observed in different organs of the same plant species. *A. chinensis* leaves were populated mostly by *Enterobacteriaceae* and *Pseudomonas*, while on flowers, *Pseudomonas* and *Bacillus* were the most abundant genera, and a higher overall biodiversity was observed. Contrastingly, in *A. deliciosa*, the influence of organs on microbial composition was less evident than in *A. chinensis*, as *Pseudomonas* and *Enterobacteriaceae* were the dominant groups on both leaves and flowers. Organ-specific pathogenic consortia were observed in Psa-infected host. In fact, Pss, which primarily induces leaf spot symptoms (Petriccione et al., 2017), was more abundantly found on Psa-infected leaves, while Pv, responsible for blossom blight disease (Balestra et al., 2008) was more closely associated to Psa-infected flowers.

Since flowers are composed by different tissues (stigmas, styles, anthers, ovariums, nectarhodes), each of them providing a favourable and unique environment for the resident microbial community (Howpage et al., 1998; Spinelli et al., 2005; Aleklett et al., 2014), a higher biodiversity on flowers than on leaves could be expected. However, some flower parts may be less conducive to bacterial epiphytes than leaves. In fact, Junker et al. (2011) observed a lower biodiversity on petals than leaves of *Lotus corniculatus* and *Saponaria officinalis*. In this perspective, the relative organisation, proportion, and chemical features of *Actinidia* spp. flower parts may be evoked to explain differences in bacterial colonisation. Although sharing the same basic structure, *A. chinensis* and *A. deliciosa* flowers also show evident morphological differences. *A. deliciosa* flowers present a higher number of styles and stamens, and the perianth and androecium are closer to gynoecium, resulting in the nectar cup being more protected than in *A. chinensis* (Harvey and Fraser, 1988; Huang, 2014). It is likely that this last evidence could pose an obstacle to the formation of a highly diversified community, as observed on *A. deliciosa* flowers compared to leaves. Moreover, the production of volatile compounds may further act as a selective agent on the epiphytic microflora (Junker et al., 2011). The sesquiterpene α-farnesene, for instance, was found to play a role in plant defence (Huelin and Murray, 1966; Pare and Tumlinson, 1999; Yang et al., 2011), acting as a feeding deterrent to insects (Aharoni et al., 2003) and exhibiting toxicity to bacteria (Chorianopoulos et al., 2004) and fungi (Terzi et al., 2007). This compound is a major constituent of the flower odour bouquet of *A. deliciosa*, while it is not or scarcely emitted by *A. chinensis* (Tatsuka et al., 1990; Crowhurst et al., 2008; Nieuwenhuizen et al., 2009; Green et al., 2012).

## Antagonistic and Synergic Relationships of Psa With Other Microbes

*Pseudomonas syringae* pv. *actinidiae* infection had several effects on the diversity and taxonomic structure of the bacterial
communities with the only exception of *A. deliciosa* flowers. The mechanism by which Psa antagonises indigenous bacteria, determining its dominance on *A. chinensis* and *A. deliciosa* leaves and the disappearance of most of the dominant microbial species, is probably based on competition for limiting nutrient resources, since a mechanism based on antibiosis would not be affected by the plant host. In this view, a specialised pathogen such as Psa should be able to outcompete, in the *Actinidia* phyllosphere, other non-specialised residents. The extensive screening of plant material collected from infected orchards for 4 years confirmed a negative correlation between Psa and *P. agglomerans/vagans* or *P. fluorescens* populations when the population of these two bacteria ranges between 10⁴ and 10⁶. Thus, even though in highly infected leaves, Psa overwhelms other bacterial competitors, in early stages of Psa epiphytic growth the competition with *P. agglomerans/vagans* or *P. fluorescens* may prevent the reach of the infection threshold (approximately 10⁵ Psa cells per gramme of tissue) (Donati et al., 2018). On the other hand, no antagonism could be observed between Psa and *Lactobacillus* spp., speculatively suggesting that bacteria of the latter group are poorly affected by Psa competition. In comparison to leaves or *A. chinensis* flowers, Psa exerts a weak antagonism toward the other residents in *A. deliciosa* flowers (Figure 1B and Table 2). Thus, it may be concluded that this niche is not highly conducive for Psa, or that other bacteria are as specialised and adapted to the floral niche as Psa. This observation, together with the higher relative abundance of Psa on *A. chinensis* flowers, could also contribute in explaining the higher susceptibility of these flowers in comparison with *A. deliciosa* ones (Donati et al., 2018), in spite of the similar Psa population sizes that can be attained on the two species (Figure 6).

Quantitative real-time polymerase chain reaction analysis showed that Psa formed a syndemic association with Pss and Pv. In fact, Psa-infected flowers also harboured detectable Pss and Pv populations in 62.5% of the cases. It is reasonable to hypothesise that these three species form a consortium which may compete more effectively in the epiphytic niche (Buonaurio et al., 2015). When occurring in association, Psa and Pss have been shown to infect the host plant more efficiently (Petriccione et al., 2017). Such observations pose interesting implications for the control of bacterial canker of kiwifruit. For instance, treatments aimed at reducing Pss and/or Pv population may be envisaged to limit Psa pathogenicity. In addition, hacking the signalling network among different pathogen species may be a strategy to repress their virulence in field conditions. Many Gramme-negative, plant-associated bacteria pathogens have been reported to regulate their virulence by N-acyl-homoserine lactones (AHLs) (Ma et al., 2013). AHL-quorum sensing model includes AHL synthase, which belongs to the LuxI-protein family, and AHL receptors/transcriptional regulators (LuxR) (Papenfort and Bassler, 2016). Psa does not produce AHLs and a complete LuxI/R system is absent. However, it possesses three putative LuxR solos (Patel et al., 2014). Two of them respond to exogenous AHLs, while the third is most likely involved in interkingdom signalling (Patel et al., 2014). The presence of three LuxR solos is rather unusual as most commonly proteobacteria possess only one, therefore they could represent an evolutionary advantage for

Psa favouring the communication with other epiphytic bacteria (Patel et al., 2014).

Finally, inside this bacterial consortium, horizontal gene transfer may be facilitated, thus allowing a faster adaptation to environmental changes and stresses. In fact, the accessory genome of the *P. syringae* complex is characterised by genomic islands and various mobile elements, such as insertion sequences (IS elements), transposons, plasmids and integrative conjugative elements (ICEs) (Butler et al., 2013). These mobile elements include genes related to the ecological fitness and virulence, such as toxin production (Murillo et al., 2011), copper and antibiotic resistance (Butler et al., 2013; Colombi et al., 2017), siderophore production and phenolics degradation (Scortichini et al., 2012). In this sense, the possibility to characterise the “phyllobiome” through the application of high throughput, next generation sequencing technologies allows to strategically select BCAs highly specialised for the host’s most sensible pathways of infection. Among the putative BCAs studied in this work, for instance, only *L. plantarum* colonised both infected and healthy flowers and leaves in the two *Actinidia* species, while *B. amyloliquefaciens* could be occasionally found in infected tissues, although without relation to plant organ or species. Furthermore, coupling the complementary information acquired by next generation sequencing technologies and metanalysis of population association, allowed to identify other BCA candidates, such as *P. agglomerans/vagans* or *P. fluorescens*. The ability of tested BCAs to persist in these organs in spite of Psa infection could be related with the ability of members of this species to exert a strong antagonistic effect against the other microbes inhabiting the same niche throughout the release of a wide array of anti-microbial compounds opening new possibilities for its exploitation as BCAs.

## CONCLUSION

The data obtained in this work highlighted for the first time the impact of Psa on bacterial communities associated to kiwifruit plants. The results here reported provide new insight on how Psa influences the microbial communities associated with the leaf and the flower in kiwifruit. The complex interactions between host, environment, and microbes play a role in determine the outcome of the infection process and in defining niches important for the resident bacteria. The main causes underlying these changes remain unclear, and a better understanding of the process will require a greater interdisciplinary effort, as well as an integrative approach to detect the triggers of disease outbreak in kiwifruits, to develop more sustainable strategies for the control of the bacterial canker disease in kiwifruit.

## AUTHOR CONTRIBUTIONS

FS conceived the experiment and supervised the work. ID contributed to design of the experiments and performed all the sampling and the classical microbiological and pathological analysis. AC and ID performed DNA extraction, qPCR and bacterial identification. LO, AL, and WP analysed the next generation sequencing data. FS, LO, ID, and GP drafted the
manuscript. All authors critically contributed to the review of the
manuscript and discussion of the data.

FUNDING

The work was funded by the European Union's Seventh
Framework Programme for research, technological development
and demonstration under grant agreement no. 613678 (Dropsa
- Strategies to develop effective, innovative and practical
approaches to protect major European fruit crops from pests and
pathogens).

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ACKNOWLEDGMENTS

We thank Dr. Simona Nardozza and the Photography Team of Plant & Food Research, New Zealand for the photographs of *Actinidia deliciosa* and *Actinidia chinensis* flowers and leaves.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Purahong, Orrù, Donati, Perpetuini, Cellini, Lamontanara, Michelotti, Tacconi and Spinelli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_27.png" /></figure>

# A Strain of an Emerging Indian Xanthomonas oryzae pv. oryzae Pathotype Defeats the Rice Bacterial Blight Resistance Gene xa13 Without Inducing a Clade III SWEET Gene and Is Nearly Identical to a Recent Thai Isolate

OPEN ACCESS

**Edited by:**
Adriana J. Bernal,
University of Los Andes, Colombia

**Reviewed by:**
Brian H. Kvitko,
University of Georgia, United States
Neha Potnis,
Auburn University, United States

**\*Correspondence:**

Adam J. Bogdanove
ajb7@cornell.edu
Rhitu Rai
rhiturai@nrcpb.org

† These authors have contributed equally to this work

**‡Present Address:**

Samriti Midha,
Institute of Infection and Global
Health, University of Liverpool,
Liverpool, United Kingdom

**Specialty section:**

This article was submitted to Plant Microbe Interactions, a section of the journal Frontiers in Microbiology

Received: 09 August 2018

Accepted: 23 October 2018

Published: 13 November 2018

**Citation:**

Carpenter SCD, Mishra P, Ghoshal C, Dash PK, Wang L, Midha S, Laha GS, Lore JS, Kositratana W, Singh NK, Singh K, Patil PB, Oliva R, Patarapuwadol S, Bogdanove AJ and Rai R (2018) A Strain of an Emerging Indian Xanthomonas oryzae pv. oryzae Pathotype Defeats the Rice Bacterial Blight Resistance Gene xa13 Without Inducing a Clade III SWEET Gene and Is Nearly Identical to a Recent Thai Isolate. Front. Microbiol. 9:2703. doi: 10.3389/fmicb.2018.02703

Sara C. D. Carpenter<fnref n="1" />†, Prashant Mishra²†, Chandrika Ghoshal², Prasanta K. Dash², Li Wang<fnref n="1" />, Samriti Midha<fnref n="3" />†, Gouri S. Laha⁴, Jagjeet S. Lore⁵, Wichai Kositratana⁶, Nagendra K. Singh², Kuldeep Singh⁷, Prabhu B. Patil<fnref n="3" />, Ricardo Oliva⁸, Sujin Patarapuwadol⁶, Adam J. Bogdanove<fnref n="1" />* and Rhitu Rai²*

The rice bacterial blight pathogen Xanthomonas oryzae pv. oryzae (Xoo) injects transcription activator-like effectors (TALEs) that bind and activate host "susceptibility" (S) genes important for disease. Clade III SWEET genes are major S genes for bacterial blight. The resistance genes xa5, which reduces TALE activity generally, and xa13, a SWEET11 allele not recognized by the cognate TALE, have been effectively deployed. However, strains that defeat both resistance genes individually were recently reported in India and Thailand. To gain insight into the mechanism(s), we completely sequenced the genome of one such strain from each country and examined the encoded TALEs. Strikingly, the two strains are clones, sharing nearly identical TALE repertoires, including a TALE known to activate SWEET11 strongly enough to be effective even when diminished by xa5. We next investigated SWEET gene induction by the Indian strain. The Indian strain induced no clade III SWEET in plants harboring xa13, indicating a pathogen adaptation that relieves dependence on these genes for susceptibility. The findings open a door to mechanistic understanding of the role SWEET genes play in susceptibility and illustrate the importance of complete genome sequence-based monitoring of Xoo populations in developing varieties with effective disease resistance.

**Keywords:** bacterial blight of rice, SMRT sequencing, transcription activator-like effectors (TALEs), susceptibility genes, SWEET genes

<footnote n="1">¹ Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States, ² Plant Pathogen Interaction, National Research Centre on Plant Biotechnology (ICAR), New Delhi, India,</footnote>

<footnote n="3">³ Bacterial Genomics and Evolution Laboratory, Institute of Microbial Technology (CSIR), Chandigarh, India, ⁴ Department of Plant Pathology, Indian Institute of Rice Research (ICAR), Hyderabad, India, ⁵ Department of Plant Pathology, Punjab Agricultural University, Ludhiana, India, ⁶ Department of Plant Pathology, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Nakhon Pathom, Thailand, ⁷ National Bureau of Plant Genetic Resources (ICAR), New Delhi, India, ⁸ Rice Breeding Platform, International Rice Research Institute, Los Banos, Philippines</footnote>
# INTRODUCTION

Xanthomonas oryzae pv. oryzae (Xoo), causes bacterial blight of rice, a yield-reducing disease widespread in Asia and Africa (Nino-Liu et al., 2006). Xoo relies on type III secreted, transcription activator-like effectors (TALEs) that directly activate specific host genes, called “susceptibility” (S) genes, which contribute to disease development (Hutin et al., 2015). A TALE finds its DNA target by virtue of a central repeat region (CRR) in the protein composed of nearly identical, direct repeats of 33–35 amino acid residues. Residues at the 12th and 13th positions in each repeat, together the “repeat-variable diresidue” (RVD), correspond to a single nucleotide in the effector binding element (EBE) in the DNA in a contiguous, code-like fashion such that the number and composition of RVDs predict the sequence of the EBE (Boch et al., 2009; Moscou and Bogdanove, 2009). The first residue of each RVD plays a stabilizing role and the second is the base-specifying residue. Characterized Xoo strains harbor 9 to nearly 20 different TALE-encoding (tal) genes, of which only one or two may encode a major virulence factor (Yang and White, 2004; Bogdanove et al., 2011; Hutin et al., 2015). All strains examined to date activate one of three members of clade III of the SWEET sucrose transporter gene family in rice (SWEET11, SWEET12, and SWEET14). These genes are major S genes, targeted by diverse TALEs from different strains (Hutin et al., 2015). In an experimental context, each of the other two members of SWEET clade III (SWEET12 and SWEET15), and no other SWEET genes tested, also functioned as a major S gene (Antony et al., 2010; Streubel et al., 2013). SWEET activation apparently leads to sucrose export into the xylem vessels, facilitating Xoo proliferation and symptom development by an as yet uncharacterized mechanism.

Host resistance is the most effective means of controlling rice bacterial blight. To date, 42 bacterial blight resistance genes, called Xa genes, have been identified from cultivated and wild rice species (Hutin et al., 2015; Kim et al., 2015; Busungu et al., 2016). The functions of most of the dozen or so that have been cloned and characterized relate to TALEs, and several are recessive. All but one of these recessive genes are alleles of a SWEET gene with a mutation at the EBE that prevents binding and activation by the cognate TALE, conferring resistance through reduced susceptibility. For example, *xa13* is a variant of *SWEET11* that lacks the PthXo1 EBE in its promoter and thereby confers resistance to strains that depend on PthXo1 (Chu et al., 2006). A strain can overcome *xa13* if it expresses a TALE (such as PthXo2, PthXo3, AvrXa7, or TalC) that activates an alternate clade III SWEET gene (Zhou et al., 2015). The recessive bacterial blight resistance gene that is not a SWEET allele, *xa5*, acts more broadly. It is an allele of the general transcription factor subunit gene *TFIIAY5*. The protein encoded by the dominant allele is an apparent contact point between TALEs and the transcriptional machinery. The product of *xa5* harbors a single amino acid substitution that interferes with its interaction with TALEs and thereby reduces activation of their targets (Iyer-Pascuzzi et al., 2008; Huang et al., 2016). Interestingly, strains carrying PthXo1 are compatible with *xa5*. This compatibility is postulated to be due to the unusually strong activation of *SWEET11* by PthXo1,

which even diminished in the *xa5* background is apparently high enough to render the plant susceptible (Huang et al., 2016).

The *xa5* and *xa13* genes have been widely deployed, both singly and in combination (Jeung et al., 2006; Sundaram et al., 2008, 2009; Shanti et al., 2010). Their effectiveness, however, has varied in different rice growing countries. In India, which is the second largest producer of rice behind China and has a highly diverse Xoo population (Midha et al., 2017), *xa13* has historically been effective, whereas xa5-compatible Xoo isolates can be found throughout the country (Figure 1A; Mishra et al., 2013; Yugander et al., 2017). In contrast, in Thailand, another major rice producer, *xa13*-breaking stains are common while *xa5* has largely remained effective (Figure 1B) (Wonglom et al., 2015). Recently, strains compatible with either *R* gene have been reported in each country (Lore et al., 2011; Mishra et al., 2013; Wonglom et al., 2015; Yugander et al., 2017). To gain insight into the mechanism(s) by which such strains overcome *xa5* and *xa13*, we completely sequenced and compared the genomes and encoded TALE repertoires of one such strain from each country, IX-280 from India (Yugander et al., 2017) and SK2-3 from Thailand (Wonglom et al., 2015), to each other and to those of other sequenced Xoo strains. Further, we examined the ability of the Indian strain to activate SWEET gene expression in rice genotypes harboring *xa5*, *xa13*, or both genes.

# MATERIALS AND METHODS

## Genomic DNA Extraction and Sequencing

DNA for complete-genome sequencing was isolated using the protocol described by Booher et al. (2015) with the following two modifications: after overnight culture and centrifugation, extracellular polysaccharide was removed by washing the bacterial pellet 7–8 times with NE buffer (0.15 M NaCl, 50 mM

<figure><img src="image_28.png" /><figcaption>**FIGURE 1** | Distribution of Xoo strains compatible with rice varieties IRBB5 (*xa5*, white), IRBB13 (*xa13*, gray), or both IRBB5 and IRBB13 (*xa5* and *xa13* individually, black) in (A) India and (B) Thailand.</figcaption></figure>
EDTA), and after cell lysis, DNA was extracted four times with phenol/chloroform and once with chloroform/isoamyl alcohol. For each strain, 4–7 µg of genomic DNA was used to prepare a 20 kb library and each library was sequenced by SMRT technology to >150X genome coverage using P6-C4 chemistry (Pacific Biosciences, Menlo Park, CA USA), as described (Booher et al., 2015).

## Genome Sequence Assembly

De novo assembly of the sequence reads was performed using HGAP v.2.0 (HGAP2) and HGAP v. 3.0 (HGAP3) (Chin et al., 2013) as described (Booher et al., 2015). Since TALE encoding (*tal*) genes are often clustered and their repetitive sequences can lead to misassembly even using long-read technology, *tal* gene containing regions were separately assembled using the PBX toolkit, a pipeline that uses long, *tal* gene sequence-containing seed reads to assemble *tal* clusters with more accuracy (Booher et al., 2015). Length cutoff settings used for these seed reads were 16 kb (pbx16000), 12 kb (pbx12000), or 10 kb (pbx10000). After HGAP and PBX assemblies were completed, the HGAP assemblies with the fewest unitigs and the majority of the *tal* gene sequences found by PBX were chosen for manual closure and finishing.

## Genome Finishing, Assembly Verification, and Annotation

To finish the genomes, the circular assemblies were polished twice more with Quiver and then checked for structural variants and misassemblies using PBHoney (English et al., 2014). The *tal* gene repertoires were verified by consensus with the local *tal* assemblies made with PBX and by Southern blots of genomic DNA digested with either *Bam*HI or *Sph*I, or with *Bam*HI and *Eco*RI, and probed with the *tal* gene specific probe pZWavrXa7 (Yang and White, 2004). To confirm the absence of plasmids smaller than 20 kb that could have been excluded during library preparation, total DNA was prepared and examined by agarose gel electrophoresis as described, using *Xanthomonas campestris* pv. vesicatoria 85–10, which has four plasmids, as a positive control (Booher et al., 2015). After finishing and assembly verification, genomes were annotated using the NCBI Prokaryotic Genome Annotation Pipeline (Tatusova et al., 2016), and *tal* gene annotations were manually corrected.

## Genomic Comparisons

For structural comparison, complete genomes were aligned using progressiveMauve (Darling et al., 2010) in the MegAlign Pro module of the DNAStar Suite (Lasergene 13.0.0.357) with default settings. For phylogenetic analysis, complete and draft genomes were aligned using Mauve v2.3.1 (Darling et al., 2004), and core alignment was used to infer phylogeny using PhyML v3.1 (Guindon et al., 2010). The core alignment and maximum likelihood tree was further subjected to ClonalFrameML (Didelot and Wilson, 2015) analysis with 100 bootstrap replicates to refine the phylogeny considering the impact of recombination. The ClonalFrameML tree was visualized using iTOL v3 (Letunic and Bork, 2016).

## TALE Analysis and Target Prediction

All *tal* gene sequences were extracted using the PBX exporter (Booher et al., 2015) or AnnoTALE (Grau et al., 2016). Orthology of IX-280 and SK2-3 TALEs to previously sequenced TALEs was determined using FuncTAL (Pérez-Quintero et al., 2015) and AnnoTALE (Grau et al., 2016). RVD or amino acid sequence was used as input for FuncTAL, and DNA sequence for AnnoTALE. AnnoTALE class builder files used to assign TALEs to families were downloaded on July 1, 2017. The results from the two tools were consistent. Targets of IX-280 and SK2-3 TALEs of interest were predicted using the TALE-NT 2.0 Target Finder tool (Doyle et al., 2012) and TalGetter (Grau et al., 2013). Predictions were made for both forward and reverse strands of promoter sequences, defined as the 1,000 bp upstream of a transcriptional start site to the translational start site for TALgetter, and 1,000 bp upstream of the translational start site for TALE-NT 2.0, and using MSU Rice Genome Annotation Project Release 7 (http://rice.plantbiology.msu.edu/). Default settings were used for Target Finder (upstream base of binding site = T, score cutoff = 3.0, Doyle et al. scoring matrix) and for TALgetter (standard model, p = 0.000001, upstream/downstream offset = 0).

## Bacterial and Plant Growth Conditions and Disease and Gene Expression Assays

Plants were grown in a growth chamber maintained at 28°C and 85% relative humidity with a photoperiod of 12 h. The bacterium was cultured at 28°C on modified Wakimoto agar medium. For the disease assay, bacterial cells were resuspended in sterile water at an OD₆₀₀ of 0.2 and clip-inoculated (Kauffman, 1973) to fully expanded leaves of 40–45 day-old plants. Lesions were photographed 14 days later. For gene expression assays, bacterial cells were resuspended in sterile water at an OD₆₀₀ of 0.5 and infiltrated into leaves of 3-week-old plants using a needleless syringe. Water was used for mock inoculation as a control. The inoculated portions of leaves were harvested 24 h later, and total RNA was extracted using the PureLink™ RNA Mini kit (Invitrogen, Carlsbad, California, USA) following the manufacturer's instructions. RNA was further treated with DNase (Invitrogen) to remove genomic DNA contamination. Quality and quantity of RNA were analyzed by 1.0% agarose gel electrophoresis and spectrophotometry using a Nanodrop (Thermo Scientific, Waltham, Massachusetts, USA). cDNA was generated from 1 µg purified RNA using the Superscript™ Vilo™ cDNA synthesis kit (Invitrogen) with random primers. Quantitative real time PCR (qPCR) was performed on a Light cycler® 480 Instrument II (Roche Molecular Diagnostics, Santa Barbara, California, USA). About 250 ng of cDNA was used for each qPCR reaction with gene specific primers (Supplementary Table S1). Each gene was tested with three biological replicates, with three technical replicates each. The average threshold cycle (Ct) was used to determine the fold change of gene expression. The expression of each gene was normalized to the expression of the 18S rRNA gene. The 2⁻ΔΔCt method was used for relative quantification (Livak and Schmittgen, 2001).
# RESULTS

## Assembly of the Complete IX-280 and SK2-3 Genomes

Single Molecule Real-Time (SMRT) DNA sequence data for IX-280 assembled using either HGAP2 or HGAP3 (see Methods) resulted into two contigs, corresponding to a chromosome and a 43 kb plasmid. We named the plasmid pXOO43. The HGAP2 assembly, though it yielded an intact, self-complementary chromosomal contig, collapsed one cluster of four *tal* genes into three, indicated by a coverage spike in that cluster. A comparison of the ends of the misassembled cluster to pbx12000 and pbx16000 assemblies generated using the PBX toolkit (Booher et al., 2015) showed overlap with several that included an intact cluster of four *tal* genes. We chose a pbx16000 contig assembled using settings of 3,000 kb read overlap and 97% read identity to replace the misassembled cluster in the HGAP2 assembly. We also verified the presence of the cluster of four *tal* genes in the raw sequence of IX-280. To further confirm our final assembly, we obtained additional long reads from a separate DNA preparation of the same isolate and reassembled with HGAP3 using all available reads; the resulting HGAP3 assembly was consistent with the manually corrected HGAP2 assembly.

HGAP2 and HGAP3 assemblies of SK2-3 yielded a single chromosomal contig, but each terminated at a partial cluster of four *tal* genes. The intact cluster was present in pbx10000 assemblies. We selected a contig assembled using settings of 3,000 kb read overlap and 97% read identity to replace the broken cluster in the HGAP2 assembly and manually closed the genome.

The quality-control tool PBHoney (English et al., 2014) indicated no major inversions, deletions, or duplications in the assemblies. The proportion of mapped reads to post-filtered reads was 94.9% for IX-280 and 92% for SK2-3. Coverage graphs for the final assemblies showed no unusual peaks or dips that might indicate collapsed or expanded genomic repeats. PBX results were consistent with *tal* gene sequences extracted from the genomes, as were Southern blots hybridized with a *tal* gene-specific probe (Supplementary Figure S1). Separate DNA extraction and gel electrophoresis for both strains confirmed the absence of any small plasmids that might have been missed by SMRT sequencing (not shown).

## Comparison of the IX-280 and SK2-3 Genomes

The IX-280 plasmid pXOO43 has not been found in other *Xanthomonas* genomes, but some regions have a high degree of nucleotide identity with regions of pXAC64 from *Xanthomonas citri* ssp. citri (Da Silva et al., 2002). There are no predicted type III effector genes on the plasmid, but it harbors a cluster of genes annotated as type VI secretion genes. Associated with this cluster is an apparent operon containing *pemK*, encoding a toxin in a toxin/antitoxin system (Agarwal et al., 2007), and a gene encoding a protein of the XF1863 family, hypothesized to function as its antitoxin (Makarova et al., 2009). None of the pXOO43 content is found in the SK2-3 genome.

The IX-280 and SK2-3 chromosomes are entirely syntenous (Figure 2A, Supplementary Figure S2), including the *tal* genes,

which show no duplications, deletions, or rearrangements in one genome relative to the other (Figure 2B). To determine how the genome structure of IX-280 and SK2-3 compares with that of other *Xoo* strains, we aligned the genomes with those of select strains for which complete genome sequences have been published. The complete *Xoo* genomes published to date sort into three East Asian lineages and a more distant African lineage (Quibod et al., 2016). We included representatives of each: Philippines strain PXO71 and Japanese strain MAFF311018 representing lineage PX-A, Philippines strain PXO86 representing lineage PX-B, Philippines strain PXO99A representing lineage PX-C, and the African strain AXO1947. The alignment shows no relationship between geographic area of isolation and genome arrangement (Figure 2A). Like IX-280 and SK2-3, the genome structures of PXO71 (Philippines) and MAFF311018 (Japan) are similar to one another, despite the strains being from different countries. In contrast, PXO86, PXO71, and PXO99A, all from the Philippines, have undergone genomic rearrangements relative to one another. The genome structure of the African strain, AXO1947, is distinct from those of the other *Xoo* strains, showing some of the genomic variability encompassed by the species. Though there are areas of similarity, the genomic arrangement of IX-280 and SK2-3 is not shared by any of the other strains.

## IX-280 and SK2-3 Belong to a Highly Clonal Lineage

The striking genomic similarity of IX-280 and SK2-3 despite their geographic separation led us to explore their relatedness with other *Xoo* strains more broadly. Using all published complete Asian *Xoo* genomes and draft (short-read derived) genome sequences of 100 Indian *Xoo* strains previously subjected to phylogenetic analysis (Midha et al., 2017), we generated a phylogenetic tree using regions not affected by recombination. The previous phylogenetic analysis of the 100 Indian strains had revealed five lineages (Midha et al., 2017). Both IX-280 and SK2-3 map to the youngest and a highly clonal lineage, L-I (Figure 3). Of the strains examined, SK2-3 is the only non-Indian strain in this lineage.

## The TALE Repertoires Suggest Possible Mechanisms of *xa5* and *xa13* Defeat

The TALE repertoires of IX-280 and SK2-3 each consist of 15 TALEs and two truncTALEs, which are TALE variants with shortened N- and C-termini that can function as suppressors of resistance mediated by certain non-executor *R* genes (Ji et al., 2016; Read et al., 2016); each strain also harbors a *tal* pseudogene (Figure 4). The RVD sequence of each IX-280 TALE and truncTALE is identical to that of its counterpart in SK2-3, except for the truncTALE Tal2b, of which repeats 10-15 are missing in SK2-3. Since truncTALEs do not bind DNA and a specific RVD sequence is not critical to their function (Read et al., 2016), this difference in Tal2b between the two strains is likely functionally irrelevant.

Tal1c of both strains is an ortholog of PthXo1 (Figure 4), which likely explains the ability of each strain to overcome *xa5*.
<figure><img src="image_29.png" /></figure>

**FIGURE 2** | Synteny between IX-280 and SK2-3 genomes and comparison of their tal genes. (A) Progressive Mauve alignment of the chromosomes of IX-280 and SK2-3 and other representative Xoo strains. (B) Map of the tal genes in IX-280 and SK2-3. Black arrows represent full-length tal genes, gray arrows truncTALE genes, and white arrows tal pseudogenes. Solid lines connect tal genes with >99% nucleotide identity and identical RVD sequence, and dotted lines connect less similar but clearly orthologous genes.

PthXo1 in IX-280 and SK2-3 differs from PthXo1 in PXO99A at one RVD, but the base-specifying residue of that RVD is the same (Supplementary Figure S3). Notably, an ortholog of PthXo7, the PXO99A TALE that induces *TFIIAγ1*, is also present in both strains (Tal7). Compatibility with *xa5* had been postulated to be due to activation of the paralog *TFIIAγ1* by PthXo7 (Sugio et al., 2007), but it was recently shown that only *TFIIAγ5*, and not *TFIIAγ1*, interacts *in planta* with tested TALEs (Yuan et al., 2016).

TALEs that could enable defeat of *xa13* are less apparent. IX-280 and SK2-3 have no ortholog of known, major virulence factors such as PthXo2, which drives expression of *SWEET13* (also called *Os12N3* or *Xa25*), or PthXo3, TalC, Tal5, or AvrXa7, which activate *SWEET14* (Yang et al., 2006; Antony et al., 2010; Liu et al., 2011; Streubel et al., 2013; Wang et al., 2015). In all, eleven of the 15 TALEs of IX-280 and SK2-3 (including Tal1c and Tal7) are apparent orthologs of TALEs found in PXO99A, which does not overcome *xa13* (Figure 4, Supplementary Figure S3). Of these, six are identical to their PXO99A counterpart and the others have from one to several differences in RVD sequence. The gene encoding Tal4b of IX-280 and SK2-3 has an ortholog in PXO99A that is pseudogenized by a frameshift early in the coding sequence. We reason that the *xa13*-compatibility of IX-280 and SK2-3 is conferred by one or more of the TALEs with no apparent, intact ortholog in PXO99A (i.e., Tal1b, Tal1a, Tal4b, and Tal5c) or with a difference in RVD sequence relative to the PXO99A counterpart (i.e., Tal1c, Tal5b, Tal6b, Tal6c, Tal6d, and Tal7).

## Predicted Targets of Possible xa13-Breaking TALEs

Toward identifying the basis for IX-280 and SK2-3 compatibility with *xa13*, we generated lists of candidate target genes in rice (cv. Nipponbare) for their Tal1a, Tal1b, Tal4b, and Tal5c, which are either not found in PXO99A or differ by more than 6 RVDs from the most similar TALE in PXO99A (see Materials and Methods). Tal1a contains several instances of RVDs NN and NS, which have dual and lax specificity, respectively, so EBEs were predicted in most promoters. Among the candidates for Tal1b was a *SWEET* gene, *SWEET2b*, but *SWEET2b* was shown previously not to function as an S gene (Streubel et al., 2013). Another was a putative sulfate transporter gene, *OsSULTR3;3* (*Os04g55800.1*). The distinct putative sulfate transporter gene *OsSULTR3;6* is an S gene for bacterial leaf streak caused by *X. oryzae* pv. oryzicola (Cernadas et al., 2014), but whether sulfate transporters might confer susceptibility in bacterial blight is unknown; heterologous expression of an *OsSULTR3;6*-inducing TALE in the TALE-deficient strain X11-5A did not increase the extent of bacterial blight caused by this strain (Verdier et al., 2012). For Tal4b, EBEs were predicted in the promoters of three *SWEET* genes, *SWEET1b* (clade I and shown not to function as an S gene by Streubel et al., 2013), *SWEET7e* (clade II and not tested in that study), and *SWEET14*, within 350 bp of the transcriptional start sites (TSS). For Tal5c, EBEs were predicted in the promoters of *SWEET15* within 100 bp of the TSS, *SWEET13* within 250 bp, and *SWEET12* within 50 bp.
<figure><img src="image_30.png" /></figure>

## IX-280 Compatibility With *xa5* Is Associated With Induction of *SWEET11* but IX-280 Induces No Clade III *SWEET* in Compatible *xa13* Plants

To determine the mechanism by which these strains overcome *xa13*, we focused on the clade III *SWEET* genes. We inoculated IX-280 to rice cultivar IR24, which harbors neither *xa5* nor *xa13*, and near isogenic cultivars IRBB5 (*xa5*), IRBB13 (*xa13*), and IRBB53 (*xa5* and *xa13*). Each of these cultivars except IRBB53 is susceptible to IX-280 (Figure 5A; Yugander et al., 2017). We hypothesized that IX-280, by virtue of its PthXo1 ortholog Tal1c, induces *SWEET11* strongly in IR24 and sufficiently in IRBB5, and that for compatibility in IRBB13 it induces another *SWEET* gene or the *xa13* allele of *SWEET11* by virtue of some other TALE. Further, we hypothesized that induction of the alternate *SWEET* gene is not as strong as that of *SWEET11*, such that when diminished by *xa5* it is insufficient for susceptibility, explaining incompatibility with the combined *xa5* and *xa13* rice genotype IRBB53. We first compared expression of *SWEET11* across each

of the cultivars, using quantitative RT-PCR of RNA harvested from leaf tissue 24 h after inoculation. It was induced to 799-fold in IR24, to 553-fold in IRBB5, and not at all in IRBB13 or IRBB53, relative to mock (water) inoculation (Figure 5B). For reference we also examined expression of the bZIP transcription factor gene *TFX1* and the *TFIIA*γ 5 paralog *TFIIA*γ 1. These are targets of PXO99A TALEs PthXo6 and PthXo7; these TALEs contribute moderately to virulence (Sugio et al., 2007) and an ortholog of each (Tal3c and Tal7, respectively) is present in IX-280 and SK2-3. In IR24 and IRBB13 each of the transcription factor genes was moderately induced (20 to 35-fold) in IX-280-inoculated leaves relative to mock (Figure 5B). This induction provides evidence that Tal3c and Tal7 are delivered and functional, and that the single RVD difference between PthXo7 and Tal7 does not impact targeting of *TFIIA*γ 1. In IRBB5 and IRBB53, *TFX1* and *TFIIA*γ 1 induction was reduced to just 3 to 5-fold relative to mock (Figure 5B). This result is consistent with the observation that the *xa5* allele reduces generally the ability of TALEs to induce their targets (Yuan et al., 2016). Next, we assayed the ability of IX-280 inoculated to IRBB13 plants to induce any of the other