Controlling the Meloidogyne Disease Complex in Ugandan Tomatoes

Adrian Wolfgang BSc

Controlling the Meloidogyne disease complex in Ugandan tomatoes

MASTER’S THESIS

To achieve the university degree of

Master of Science

Master degree program: Ecology and Evolutionary Biology

Submitted to

Karl-Franzens University Graz

Supervisor Priv.-Doz. Mag. Dr.rer.nat. Günther Raspotnig Institute of Biology

Graz, April 2018

In cooperation with:

❆✩✩✪✫❆✬✪✭

AFFIDAVIT

Ich erkläre ehrenwörtlich, dass ich die vorliegende Arbeit selbständig und ohne fremde Hilfe verfasst, andere als die angegebenen Quellen nicht benutzt und die den Quellen wörtlich oder inhaltlich entnommenen Stellen als solche kenntlich gemacht habe. Die Arbeit wurde bisher in gleicher oder ähnlicher Form keiner anderen inländischen oder ausländischen Prüfungsbehörde vorgelegt und auch noch nicht veröffentlicht. Die vorliegende Fassung entspricht der eingereichten elektronischen Version.

I declare that I have authored this thesis independently, that I have not used other than the declared sources/resources, and that I have explicitly indicated all material which has been quoted either literally or by content from the sources used. The text document uploaded to UNIGRAZonline is identical to the present master’s thesis.

______Date Signature

Table of contents

Table of contents ...... 4

0.1 Abstract ...... 6

0.2 Zusammenfassung ...... 7

1.0 Introduction ...... 8

1.1 Present situation of Agriculture in Uganda ...... 8

1.2 State and challenges of tomato farming in Uganda ...... 8

1.3 Root-knot nematodes - Meloidogyne spp...... 9

1.4 The impact of root-knot nematodes on host plants ...... 11

1.5 Plant health and microbiome ...... 12

1.6 Volatile organic compounds (VOCs) of bacteria as RKN control ...... 12

1.7 Aims of this study ...... 13

2.0 Materials and methods ...... 14

2.1 Sampling ...... 15

2.2 Bacterial isolates and DNA extraction ...... 16

2.3 DNA preparation for Amplicon analysis ...... 16

2.4 Amplicon analysis ...... 17

2.5 Bacterial antagonistic activity against fungal pathogens ...... 18

2.6 Screening for nVOCs-producing strains ...... 20

2.7 GC-MS of nVOC-producing bacteria ...... 21

2.8 Testing nematicdal effect of single VOCs ...... 22

2.9 Breeding of root-knot nematodes ...... 22

2.10 J2 larvae extraction ...... 23

2.11 Molecular and morphological determination of RKNs ...... 24

3.0 Results ...... 25

3.1 Physical soil composition ...... 25

3.2 Cultivable bacteria from tomato root endosphere ...... 25

3.3 Abundant bacterial community in soil and rhizosphere ...... 26

3.4 Bacterial diversity of the tropical RKN disease complex ...... 27

3.5 Differences between infected and non-infected tomato plant roots ...... 30

3.6 Antifungal isolates ...... 31

3.7 Nematicidal volatile producing strains ...... 33

3.8 Chemistry of bacterial volatiles ...... 34

3.9 Abundance of nematicidal bacterial genera in tomato plants ...... 36

3.10 Testing single compounds for nematicidal activity ...... 37

3.11 Abundant nematode species ...... 38

4.0 Discussion ...... 40

4.1 The microbiome of tomato roots in Uganda ...... 40

4.2 Genera with RKN-controlling properties within Ugandan tomatoes ...... 40

4.3 Bacterial community shift in tomato roots due to RKN infection ...... 41

4.4 Potential of nVOCs-producing bacterial strains ...... 43

4.5 Nematicidal volatiles ...... 45

4.6 Managing the root-knot nematode disease complex with BCAs ...... 46

4.7 Determination problems of abundant RKN species ...... 47

4.8 Future prospects ...... 47

5.0 References ...... 49

6.0 Acknowledgements: ...... 58

7.0 Appendix ...... 59

8.0 Supplementary data ...... 62

0.1 Abstract Root-knot nematodes (RKNs, Meloidogyne spp.) are one of the major polyphagous pests in tropical climates. Their effect on crops includes a change in the physiological state of the root system that increases the severity of fungal infections. For effective crop protection, management methods that control both fungi and RKNs are crucial. This study focuses on the influence of RKN infections on the root endosphere microbiome and bacteria that may be suitable for biocontrol by the production of nematicidal volatile organic compounds (nVOCs). RKNs were extracted from roots of tomato plants originating from two sites in Uganda: an open field with virgin soil in Luwero and a RKN breeding bed at the IITA research center in Namulonge. RKNs were identified using morphological and molecular techniques as M. javanica and M. incognita. Bacterial strains were isolated from the rhizosphere, healthy and diseased endosphere of 20 different tomato plants. In addition, a 16S rDNA amplicon analysis of the microbiome in soil, rhizosphere, healthy and diseased roots was carried out. A set of 260 randomly selected bacterial strains were tested whether they produce nVOCs. Furthermore, all bacterial strains were tested in dual cultures for antagonistic activity towards Botrytis cinerea, Fusarium verticilloides, F. oxysporum, Sclerotium rolfsii and Verticillium dahliae. Six nVOC-producing strains from the genera Pseudomonas, Comamonas and Variovorax could be detected. Their relative abundance was highest in rhizosphere (Pseudomonas spp.: 22.01%; Comamonas spp.: 0.34%) or diseased root endosphere (Variovorax spp.: 0.97%) VOCs of nematicidal strains were analyzed using GC-MS and present alkenes and different pyrazines were tested as single compounds for their nematicidal activity. Pyrazines with nematicidal properties could be found. Five fungal antagonists were identified as Bacillus amyloliquefaciens, B. methylotrophicus and B. velezensis. Bacillus spp. reach their highest relative abundance (4.1%) in non-infected root parts. Antagonists of V. dahliae were only found in rhizosphere isolates; they were less efficient against the other fungal pathogens and vice versa. Microbiome composition of diseased roots does not differ qualitatively but quantitatively from healthy roots. Pasteuriaceae and Rhizobiaceae are more abundant while Enterobacteriaceae and Burkholderiaceae are less abundant in diseased roots. Since bacterial antagonists of V. dahliae, the other tested fungal pathogens and RKN differ, biocontrol of RKNs should focus on bacterial control consortia.

0.2 Zusammenfassung Wurzelgallennematoden (RKNs, Meloidogyne spp.) sind wichtige Nutzpflanzenschädlinge in tropischen Klimaten. RKN-Infektionen verändern den physiologischen Zustand des Wirtswurzelsystems, was Infektionswahrscheinlichkeit und Symptomstärke von Pilzinfektionen erhöht. Ziel dieser Arbeit ist die Beschreibung des tropischen Krankheitskomplexes auf Mikrobiomebene und Screening für in der Biokontrolle einsetzbare Bakterien. RKNs und Bakterienstämme wurden aus Tomatenwurzeln zweier Standorte in Uganda isoliert: ein freies Feld mit Rohboden in Luwero und ein RKN-Zuchtbeet am IITA- Forschungszentrum in Namulonge. RKNs wurden mit morphologischen und molekularen Techniken als M. javanica und M. incognita identifiziert. Bakterienstämme wurden aus der Rhizosphäre, der gesunden und der erkrankten Endosphäre von insgesamt 20 Tomatenpflanzen isoliert. Eine 16S rDNA Amplikonanalyse des Mikrobioms in Böden, Rhizosphären, gesunden und erkrankten Wurzeln wurde durchgeführt. Insgesamt 260 zufällig ausgewählte Bakterienstämme wurden auf die Produktion von nVOCs und in Dualkulturen auf antagonistische Aktivität gegen Botrytis cinerea, Fusarium verticilloides, F. oxysporum, Sclerotium rolfsii und Verticillium dahliae getestet. Sechs nVOC-produzierende Stämme aus den Gattungen Pseudomonas, Comamonas und Variovorax konnten nachgewiesen werden. Ihre relative Häufigkeit war am höchsten in der Rhizosphäre (Pseudomonas spp.: 22,01%; Comamonas spp.: 0,34%) oder der erkrankten Wurzelendosphäre (Variovorax spp.: 0,97%). VOCs nematizider Stämme wurden mittels GC-MS analysiert. Nachgewiesene Alkene sowie eine Auswahl verschiedener Pyrazine wurden als Reinsubstanz auf nematizide Aktivität getestet. Zwei der getesteten Pyrazine wiesen nematizide Eigenschaften auf. Pilzantagonisten wurden als Bacillus amyloliquefaciens, B. methylotrophicus und B. velezensis identifiziert. Bacillus spp. erreichen ihre höchste relative Häufigkeit (4,1%) in nicht infizierten Wurzelteilen. Antagonisten von V. dahliae wurden nur in Rhizosphärenisolaten gefunden, sie waren weniger wirksam gegen die anderen Pilzpathogene und umgekehrt. Das Mikrobiom erkrankter Wurzeln unterscheidet sich nicht qualitativ, aber quantitativ von dem gesunder Wurzeln. Pasteuriaceae und Rhizobiaceae sind höherabundant, während Enterobacteriaceae und Burkholderiaceae in erkrankten Wurzeln niederabundanter sind. Da sich bakterielle Antagonisten von V. dahliae, den anderen getesteten Pilzpathogenen und RKNs unterscheiden, sollte sich die biologische Kontrolle von RKNs auf bakterielle Kontrollkonsortien (BCCs) konzentrieren.

1.0 Introduction

1.1 Present situation of Agriculture in Uganda In 2016 the number of undernourished people worldwide rose again to a new maximum, reaching a total of 815 million people (FAO 2017). The majority of these people are living in tropical countries, although harboring 60% of the arable land (De Waele & Elsen, 2007). Uganda has 37.7 Mio inhabitants and an exploding urbanization rate. About 72% of the Ugandan labor force is working in the agricultural or forestry sector, which contribute to 24.9% of the gross domestic product (UBoS, 2017). However, 70% of the farmers are smallholders owning less than two hectares of land (Jayne et al., 2003). The growing human population, competition for land and the increasing urbanization has led to a change towards more intensified farming systems, establishing horticultures and monocultures with new varieties, crop protection aids and inorganic fertilizers (Achterbosch et al., 2005). Changes in farming and the creation of simplified agro-ecosystems gave rise to several agricultural pests. The present knowledge on distribution patterns and presence of fungal, bacterial and animal pests in Uganda is very scarce (Sekamatte & Okwakol, 2007).

1.2 State and challenges of tomato farming in Uganda Agricultural research and public attention in East and Southern Africa mainly focus on “cash crops” that are mainly destined for export, for instance cotton, maize, sugar cane and tobacco (Talwana et al., 2016; UBoS, 2017). Despite tomatoes do not belong to the main cultivated crops in Uganda, for smallholders they are considered as a source of food security, responsible for their main income and the most important vegetable crop for domestic market (Ssekyewa 2006). Therefore tomato yield and quality are considered a top priority for vegetable production (Goldman & Kathleen 2002). Phytopathogenic nematodes (PPNs) of the genus Meloidogyne are one of the major problems in tomato cultivation. Meloidogyne sp., the so-called root-knot nematodes (RKN) are the main pest affecting Ugandan tomato (Bafokuzara 1996, Otipa et al., 2009), causing average annual yield losses of 20.6% on tomatoes (Sikora et al., 2003).

1.3 Root-knot nematodes - Meloidogyne spp. The genus Meloidogyne comprises mainly polyphagous, mainly parthenogenetic and highly adapted obligate sedentary plant parasites. They affect over 5500 host plants, which includes plant species from nearly every extant plant family known (Trudgill & Blok, 2001). Currently, RKN are regarded to form a single family Meloidogynidae with about 100 species (Karssen et al., 2013), 22 of which confirmed in Africa (Onkendi et al., 2014). The most devastating species in the tropics are M. incognita, M. javanica and M. arenaria. Due to high inter- and intraspecific morphological variation (Eisenback et al., 1981), molecular methods for determination are crucial for clear identification. Mainly due to these molecular methods half of all species have been described in the last three decades (De Ley et al., 2002).

The life cycle of RKNs includes four larval stages, whereas the second larva (J2) is the mobile infective stage (Fig. 1). When establishing a feeding site, both J2 and host plant undergo morphological and physiological changes. Secretions of the sub-ventral and dorsal pharyngeal glands induce the formation of so-called “giant cells”. Giant cells are phloem or parenchymatic host cells that switch to acytokinetic mitosis: the plant cell nucleus divides without generating new cell walls. Acytokinetic mitosis results in syncytial cells, which have high metabolic activity and act as a metabolic nutrient sink for the host plant. These giant cells stay in a physiological juvenile stage, in which nutrients from the leaves are transported to the infected sites to nourish the infected cells (reviewed by Gheysen and Fenoll 2002; Kyndt et al. 2013). Surrounding plant tissue undergoes hypertrophy and hyperplasia, resulting in the typical gall formations (Fig. 2A).

There are several possibilities for RKN control including logistic (e.g. crop rotation), physical (e.g. soil solarization), chemical (e.g. nematicides), genetic (e.g. GMOs) and biological (e.g. bacterial nematode antagonists) approaches, targeting different life stages of RKN (Fig. 1).

Fig. 1 (next page): Life cycle and targets for control methods of the tropical Meloidogyne-incognita group. J1: first-stage juvenile within egg-shell; J2: sexually undifferentiated second-stage juvenile (infective stage); sJ2: swollen J2 before second moult at infection site, sexes start to differentiate; J3: third-stage juvenile within second moult (non-feeding); J4: fourth-stage juvenile within second and third moult (non-feeding); adults: males vermiform, females enlarge and may become pyriform and produce a gelatinous matrix, in which eggs are extruded. ①: maturation of eggs and hatching of juveniles; ②: Migration through soil; ③: Attraction to roots; ④: Penetration of the root ⑤: Migration through plant tissue; ⑥: Induction of feeding site (giant cells); ⑦: Maturation within the plant; ⑧: egg production. After Karssen et al. 2013, Cottage & Urwin 2013)

Fig. 1: Life cycle of Meloidogyne spp. Detailed description on previous page.

A B

Fig. 2: A: Symptoms of root-knot nematodes (Meloidogyne spp.) on tomato root (Solanum lycopersicum); B: Tomato fruit from Ugandan field, covered in pesticide residuals (Photo: G. Berg) However, each management method has its disadvantages and is usually not able to totally reduce already established RKN populations (Viaene et al., 2013; Sikora et al., 2003).

1.4 The impact of root-knot nematodes on host plants The above-ground symptoms of RKN-infections resemble those of other plant diseases and include wilting, signs of nutritional deficiencies like leaf chlorosis, reduced yield, reduced shoot and root growth (Noling, 2009). Shorter roots lead to water stress, decreased nutrient uptake and a higher possibility to be uprooted. Photosynthetic products are actively transported to the infection site and are thus not available for the host plant (Karssen et al., 2013). Most smallholders assign RKN-caused damages and yield losses to insects like ants or termites (e.g. Gold et al., 1991; De Waele & Elsen, 2007; Talwana et al., 2016).The inability to assign the unspecific above-ground symptoms of a RKN infection leads to a massive overuse of fertilizers and biocides (Fig. 2B). As a result, tomatoes are the crops with the most frequently used pesticides in Uganda (Karungi et al., 2011). Excessive and frequent pesticide applications as well as inappropriate handling without environmental and pest monitoring may increase risks to human health as well as severe damage on the existing ecological system.

The devastating effects of RKNs on the host not only arouse from nutrient losses, but also from increased severity of other pathogen infections; bacterial wilts and fungal infection are an additional problem in Ugandan agriculture (Onkendi et al., 2014; Sekamatte & Okwakol,

2007; Tusiime, 2014). Nematode-fungi interactions often lead to synergistic yield losses (Atkinson 1892; Back et al 2002). First, the mechanical penetrations of the roots lead to openings that facilitate the invasion of the host root for other pathogens. Secondly, it also increases the leakage of plant metabolites into the rhizosphere. The higher concentration of metabolites affects the surrounding microorganisms (Van Gundy et al., 1977; Tian et al., 2015). Either the metabolic compounds or the physiologically changed root tissue at the RKN infection site seems to be a more favorable substrate for some bacteria and some pathogenic fungi. There is evidence that the root diffusates from RKN infected plants stimulate Fusarium and suppress Fusarium-antagonistic actinomycetes. (Karssen et al., 2013). The combination of RKNs, fungal and bacterial pathogens as disease-complex is responsible for the severe crop failures.

1.5 Plant health and microbiome Plant-associated microbes are of special importance for plant health. Like in animals, the performance of plants is connected to their interactions with bacteria and fungi, which may be detrimental or beneficial for their individual development. Microbes inhabit every plant organ (Ottesen et al., 2013); seed-inhabiting microbes are vertically transferred to the next plant generation (Rybakova et al., 2017). Thus, plants have to be seen as holobionts that are exposed to the effects of endophytes in every life-stage (Berg et al., 2014).

Some plant-associated bacteria are competitive to soil-borne pathogens, occupy potential free niches, induce stress resistance in plants, mineralize and transport nutrients, leading to increased growth, yield and quality of crops. Several plant growth promoting bacteria are already used on an industrial scale (Berg et al. 2014). Their effects can be direct through synthesis of certain metabolites and facilitating nutrient uptake or indirect through disease prevention. One indirect mode of disease prevention in bacteria is the production of volatile organic compounds (VOCs) that have inhibitory effects on pathogens and competitors.

1.6 Volatile organic compounds (VOCs) of bacteria as RKN control The production of volatile organic compounds (VOCs) is very common in microorganisms like fungi and bacteria (Cernava et al., 2015; Leff & Fierer, 2008). VOCs are semiochemicals that

act as “long-range” interspecific communication molecules in soil (Effmert et al., 2012) and can have promoting or inhibiting effects on other microorganisms (Cernava et al., 2015; Kusstatscher et al., 2017) and plants (Ryu et al., 2003; Ryu et al., 2004; Weise et al., 2013). The fact, that VOCs can have communicational, controlling or inhibitory effects that act inter- and/or intraspecifically, make VOCs also an interesting field of study for biological control. VOCs that have an antagonistic effect on plant pathogens can be used either by applying the VOC-producing organisms or as a biorational formulation in the field side.

The advantage of VOCs compared to secretions and excretes are the higher mobility and range within soil . Since RKN are pathogens on a microscopic scale, they may be negatively affected by bacterial VOCs produced by the abundant bacterial community.

1.7 Aims of this study The induction of giant cells within tomato roots is a dramatic manipulation of the cytological and physiological state of the host plant (Gheysen & Fenoll, 2002). This may lead to a shift of the bacterial community and an increased severity towards fungal infections. Insights to the changes in bacterial community composition present in tropical tomatoes will lead to a better understanding of the present soil-pathogen-host interactions in the RKN-disease.

The challenge in controlling the RKN-disease complex is that RKNs and fungal pathogens have to be controlled simultaneously. Therefore, bacterial isolates from RKN infected sites in Uganda will be screened for antagonistic activity. One part of this study aims to find bacterial strains that are able to produce nematicidal VOCs (nVOCs), but also to screen for antagonism against phytopathogenic fungi known to infect tomatoes (Jones et al., 2015). For a more specific treatment, the RKN present have to be identified as well.

This study is considered basic work for the development of a multi-antagonistic biological control (BCA) formulation for tomato cultivation in Uganda. Furthermore, data from microbiome analysis on the abundance of bacterial nematode-antagonists may lead to optimized screening methods for new more effective BCA-strains.

2.0 Materials and methods This study comprises experiments regarding RKNs, bacteria, PPFs and nematicidal VOCs

Fig. 3: workflow of experiments carried out in this study. PPF: plant pathogenic fungi; VOC: volatile organic compounds; TCVA: two clamp VOC assay; grey: bacterial workflow; white: nematode workflow; diagonals: VOC workflow; dots: fungal antagonist screening; red arrows: screened nematode antagonists; olive arrows: PPF antagonists.

(nVOC). To clarify the workflow, a summary is given in Fig. 3 with numbers corresponding to the chapters of this section. Three parts of the infected tomato roots were processed: microbial fraction for cultivation (→ chapter 2.2), DNA for microbiome analysis (→ chapter 2.3) and galls for breeding (→ chapter 2.9) followed by extraction of living RKNs (→ chapter 2.10). Cultivated bacteria were tested for antagonistic activity towards phytopathogenic fungi in dual cultures (→ chapter 2.5) and J2 larvae using a variation of the two-clamp VOC assay (→ chapter 2.6). 16S rDNA of found antagonists was sequenced (→ chapter 2.5) for identification. nVOCs of nematicidal strains were analyzed using GC-MS (→ chapter 2.7). Extracted RKNs were identified using morphological and molecular techniques (→ chapter 2.11).

2.1 Sampling

A B Fig. 4: Sampling sites. A: Luwero, site 1, virgin soil within natural surrounding; B: Namulonge, site 2, concrete basin isolated from surrounding soil, RKN breeding station

Roots of fruiting tomato plants (Solanum lycopersicum) with adhering soil were collected at two different sites in Uganda on April 1st 2017. Sampling site 1 (Luwero, coordinates: 0°39'20" N, 32°24'38" E) is situated 43km north-northwest of the center of Kampala (train station Kampala) and 14.8km east-southeast of Wobulenzi (www.luftlinie.org, 26.10.2017), 1187m a.s.l. Site 1 is a rural open field with black- to dark brownish-colored virgin soil; the area was cleared from natural vegetation one year before sampling (Fig. 4A). The area is slightly inclined north (approximately 5-10°). The field is still bordered with natural vegetation, even within the field the crop is mixed with native herbs, weeds, shrubs and trees. The farmer uses pesticides in unknown quantity and frequency. Tomato cultivar is ‘Rio Grande’. Plants were labeled T1-T10.

Sampling site 2 (Fig. 4B) is the RKN breeding station at the “National Crops Resources Research institute” from IITA (Namulonge, 0°31'46" N, 32°36'45" E), which is situated 24.7km north of the center of Kampala (train station Kampala) and 8.96km southeast of Bombo (Bombo market), at a height of 1170m a.s.l. The breeding station consists of RKN-infected soil within a ca. 40cm deep concrete tomato outdoor bed. There is no direct connection to surrounding soils. The soil is sandy and has a red to brownish color. Tomato cultivar is ‘Moneymaker’, no pesticides are applied. Tomatoes are labeled T12-T21.

The severity of RKN-infections of the roots was categorized with a root galling index (RGI) from 1-5, where RGI 1 means no visible infection and 5 means lethal infection. 10 plants were collected from every sampling site, preferably two plants for every RGI per site. In addition,

three soil samples were collected from every site. Roots and soil were transported in plastic bags and stored as cool as possible.

300g soil samples from both sampling sites were tested for pH, nutrient contents (K, P, Mg, organic matter) and soil type by “AGROLAB Agrar und Umwelt GmbH” (Sarstedt, Germany) to evaluate abiotic differences in soil composition of the two sampling sites.

2.2 Bacterial isolates and DNA extraction 5g of roots with adhering soil were bagmixed in sterile 50ml 0.9% NaCl. 2*2ml of the solution was transferred to Eppendorf tubes (labeled T#R where ‘R’ stands for rhizosphere). Roots were then surface-sterilized in 4% NaOCl for 3 min and washed 4 times with Aqua dest. Roots with RGI = 2-4.5 were parted in gall infected parts (T#GI) and parts without galls (T#GH). The root parts were weighed again, and then put into a plastic bag filled with another 5 ml sterile 0.9% NaCl and crushed with a mortar. 2x2ml of the resulting liquid was transferred to two 2ml Eppendorf tubes. Bulk soil samples (S1-S6) were manually mixed to crush solid crumbs. ±5g of soil was vortexed in 10ml sterile 0.9% NaCl. 2*2ml was transferred to 2*2ml Eppendorf tubes. All samples were centrifuged for 20min at 13500rpm and 4°C. Supernatant was removed; remaining pellets were stored at -20°C for amplicon analysis (chapter 2.4) until further use.

The remaining liquids of the tomato root samples (T#R, T#GI, T#GH) were diluted 10-1, 10-2 and 10-3. 100µl of the dilutions were plated on R2A plates with sterile glass beads and incubated at room temperature for 2 days. The number of CFUs in T#GH- and T#GI-samples were counted; a trend line was calculated and extrapolated to CFU/g root. A mean value for CFU/g root was calculated for every RGI was calculated. Well-defined CFUs differing in shape, size and/or color were transferred to NA plates, incubated at 30°C for one day and then stored at 4°C for further experiments. If CFUs showed signs of contamination with another strain, both seemingly different strains were brought to clean cultures and added to the experiment strains, resulting in a total number of 260 bacterial isolates.

2.3 DNA preparation for Amplicon analysis Extraction of DNA pellet was done with “FastDNA Spin Kit for soil” from the manufacturer’s instruction with some modifications to maximize DNA output. The concentration and purity

of DNA was estimated with NanoDrop 2000. Due to the high amount of contaminants, genomic DNA from bulk soil (S#) and adhering soil (T#R) were cleaned using GENECLEAN TurboTM Kit (MPBio) following the manufacturer’s instructions for genomic DNA to remove contaminants.

16S rDNA amplifications were carried out in 3*30µl reactions with the Illumina barcode universal bacterial primer set 515f-806r and PNA Mix to remove pDNA. 2µl template DNA, 1.2 µl of each primer, 6µml Taq-&GOTM Mastermix 5xC (MP Biomedicals, Eschwege Germany) and 0.45 activated PNA Mix were used for every sample except T4R, T16GH and T17GH. PCR amplification conditions are summarized in the Appendix section. Gel electrophoresis was carried out in 1xTAE buffer with 0.8% agarose using 100V for 60min. The gel was stained in 0.01% ethidium bromide for 60min. and checked under UV light. Samples with poor amplification bands were repeated another three times with 3µl template. The mentioned samples T4R, T16GH and T17GH still did not amplify, so the PCR was repeated with Q5®High- Fidelity DNA Polymerase (New England Biolabs, Frankfurt a. M., Germany) and 4µl template, which resulted in amplifications for T16GH and T17GH. T4R was repeated with Taq-&GOTM Mastermix 5 x C and 4µl template, which resulted in weak amplifications. The amplifying PCR- product was then used as 4µl template for another 6 PCRs to increase DNA concentration of this sample.

The PCR products were purified using ‘Wizard® SV Gel and PCR Clean-Up System’ (Promega, Mannheim, Germany), concentrations of high quality DNA were estimated using NanoDrop 2000. DNA solutions were pooled to equimolarity. Sequencing was carried out by LGC genomics (Berlin, Germany).

2.4 Amplicon analysis Amplicon data analysis was carried out using QIIME 2 following the protocol used in (Schwendner et al., 2017). A mapping file was created with Microsoft Excel 2010. Preprocessing, comprising joining reads, extracting barcodes from reads, demultiplexing, sequence quality control and feature table construction, was carried out by Dr. Alexander Mahnert because of limited available computing power.

The OTU table was evaluated in Excel 2010. The core microbiome of the two soils were compared on phylum and order level. The soil and rhizosphere samples from both sampling sites were compared due to the OTUs that showed >1% abundance with VENNY 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/).

Remaining reads for mitochondria and chloroplasts were filtered from the samples using QIIME 2. Core diversity metrics were calculated for all samples to compare bacterial diversity within the samples. To test for significant differences between the four habitats (soil = S, rhizosphere = R, healthy endosphere = ESH, diseased endosphere = ESD), a PERMANOVA was carried out for Bray-Curtis distance (quantitative community dissimilarity), weighted UniFrac distances and unweighted UniFrac distances. Bray-Curtis dissimilarity and unweighted UniFrac dissimilarity between habitats were visualized using EMPEROR (https://view.qiime2.org). Relative abundance of the genera identified as nematode antagonists in this study was calculated for every data set. Furthermore, the abundance of bacterial genera that are known from literature to have nematicidal effects was compared between habitats. Bacterial abundances on family level were compared between healthy and diseased root samples to visualize bacterial community shift due to RKN infection.

Bacterial genera that showed nematicidal or fungicidal activity were searched within the data to compare the abundance of potential antagonists within the different habitats.

2.5 Bacterial antagonistic activity against fungal pathogens All bacterial isolates were tested for antagonistic activity against the fungal pathogens Botrytis cinerea (Fig. 5A), Fusarium oxysporum (Fig. 5B), Fusarium verticilloides (Fig. 5C), Sclerotium rolfsii (Fig. 5D) and Verticillium dahliae (Fig. 5E).

Fungal pathogens were grown on PDA at room temperature for one week. For dual cultures B. cinerea, F. oxysporum, F. verticilloides and S. rolfsii, bacterial isolates from living cultures were streaked on Waxman agar plates (WA) in lines, four per plate with a gap in the center. Pieces of agar with living hyphae of the fungal pathogen were placed in the center (see chapter 3.6; Fig. 15). Plates were incubated at room temperature and controlled after 5 days. If bacteria colonies spread and reached the zone of other isolates, the test was repeated with the regarding isolate on an own single WA plate.

For V. dahliae, 3ml of Czapek-Dox broth was added to the living culture and mixed with a cell spreader. 100µl of the spore suspension was plated out on WA plates and dried for several minutes. Then, bacterial isolates were streaked out like for the other fungal pathogens. All tests were repeated three times. Antifungal activity was categorized to four categories: 0 (fungi overgrow bacterial colony), +1 (hyphae reach bacteria, but do not overgrow), +2 (lateral inhibition zone < 0.5cm) and +3 (lateral inhibition zone > 0.5cm). The mean category of all repeats was calculated.

Lists of bacteria that showed a strong antifungal effect (category +3) were compared using a VENNY diagram using VENN (http://bioinformatics.psb.ugent.be/webtools/Venn/). Bacteria that showed a mean antifungal activity of +3 in four tested pathogens were renewed on NA. Their DNA was isolated by transferring living colonies to ribolysing tubes containing sterile glass beads and 400µl Aqua dest. They were ribolyzed two times for 30s @ 5.0m/s and then centrifuged at 13500rpm for 5min. Supernatant was transferred to sterile 1.5ml tubes and used for BOX PCR and 16S rDNA sequencing. DNA was used for fingerprint BOX- PCR to identify clones of the same species. BOX-PCR was carried out in 25µl reactions containing 16.5µl ddH2O, 2.5µl 10µM BOX A1 primer, 5µl Taq&Go Mastermix and 1µl template.

To identify the bacterial isolates that showed different amplification band patterns in the BOX-PCR, 16S rDNA was amplified using the universal bacterial primer set 27f/1492r

Fig. 5 Phytopathogenic fungi tested in dual cultures with Ugandan bacterial isolates. A: Botrytis cinerea; B: Fusarium oxysporum; C: F. verticilloides; D: Sclerotium rolfsii; E: Verticillium dahliae.

in 30µl reactions, containing 2µl template, 1µl of each primer (10µM), 6µl Taq&Go MasterMix and 20µl ddH2O.

2.6 Screening for nVOCs-producing strains Screening for nVOC-producing strains was carried out using a variation of the two clamp VOC assay (TCVA) of Cernava et al. (2015): Bacterial isolates were streaked on 12-well plates containing NA and incubated at 30°C for 24h. Every plate had a blank well, containing only NA. Plates were placed upside down on another 12-well plate containing ~100 (100µl J2 suspension) J2 larvae of M. incognita (kindly provided by Julius Kühn- Institute, Münster, Germany) on 2% tab water agar. A silicon foil with a

5mm hole between the two Fig. 6 (up): Modified TCVA (two-clamp VOC assay, after chambers separated the two 12-well Cernava et al., 2015) for screening of nematicidal volatile organic compounds (nVOCs) (down) Assignment of J2 plates. The two plates were held larvae of M. incognita as alive (A) or dead/immobile (B) together with two clamps to provide airtight test conditions (Fig. 6). The set-up was incubated for 24h at room temperature.

Dead J2 larvae were counted at 40-fold magnification using a binocular microscope. J2 were assigned as dead if the body was straight and did not move after touching with a dissection needle (Fig. 6B). Mortality rate was calculated using percentage of dead J2 and the blank- correction with blank value of the corresponding plate blank. Bacterial strains were categorized in non-active (0-10% mortality), slightly active (>10-80%), active (>80%-95%) and highly active (>95%). Distributions of number of bacterial strains within the categories were compared between sampling sites, bacterial origin (healthy/infected root, rhizosphere) and

RGI. The test was repeated two times with the samples showing >80% nematicidal activity. The strains that showed consistent nematicidal activity were then identified by sequencing of 16S rDNA (see chapter 2.5).

2.7 GC-MS of nVOC-producing bacteria Single colonies were transferred from NA plates and streaked out with an inoculating loop on 8mL NA slope agar (1.5%) in 20mL headspace vials (75.5 x 22.5mm; Chromtech, Idstein, Germany). After 24h of incubation at 30°C, the vials were sealed with adequate crimp seals and incubated for additional 2h. Solid phase micro extraction (SPME) was performed with an automated sampler and 50/30μm Divinylbenzen/CarboxenTM/Polydimethylsiloxane (PDMS) 2cm Stableflex/SSfiber (Supelco, Bellefonte, PA, USA). Volatile compounds were extracted for 30min at 30°C, desorption time in the injector was 36 min. Compound separation and detection was performed on a system combining a GC7890A with a quadrupole MS5975C (Agilent Technologies, Waldbronn, Germany). Samples were analyzed by a (5%- phenyl)methylpolysiloxane column, 30m × 0.25mm i.d., 0.25μm film thickness (HP-5MS; Agilent Technologies, Waldbronn, Germany), followed by electron ionization (EI; 70eV) and detection (mass range 25–350). The inlet temperature was adjusted to 250°C. The following temperature program was observed: 40°C for 2min, raising to 110°C at a rate of 5°C/min, then to 280°C at 10°C /min and finally maintained at 280°C for 3min. The helium flow rate was set to 1.2mL/min. Obtained spectra were compared with NIST Mass Spectral Database 08 entries. Specific compounds were identified based on values for NIST database matches and on non- isothermal Kovats retention indices calculated after Van den Dool and Kratz (1963) using an alkane standard:

100 ∗ (푡푥 − 푡푛) RI푥 = 100 x [푛 + ] 푡푛+1 − 푡푛

Where

RIx: retention index for substance x n: number of C atoms of the reference alkane eluting directly before the compound tx: retention time of compound x tn: retention time of alkane eluting directly before substance x tn+1: retention time of alkane eluting directly after substance x

2.8 Testing nematicdal effect of single VOCs A total of 9 single compounds (purity >98%) partially found in the GC-MS samples were tested against J2 larvae in a chambered petri dish, namely decene (10en), undecene (11en), undecan- 2-on (11on), dodecene (12en), 2-methoxy-3-methyl pyrazine (2M3MP), 2,5,-dimethyl pyrazine (25DP), 5-isobutyl-2,3-dimethyl pyrazine (5I23DP), 2-ethyl-3-methyl pyrazine (2E3MP) and 2-isobutyl-3-methoxy pyrazine (2I3MP) (all Sigma-Aldrich, Darmstadt, Germany). On the one side of the petri dish, 500µl of M. javanica-suspension (~250 larvae, extraction see chapter 2.10) were placed on 8ml of 2% tab water agar. On the other side, single compounds were placed in three different concentrations (1µl, 5µl, 20µl) on a microscopic slide to prevent interactions of compounds with the petri dish material (Fig. 7). Tests were incubated at room temperature for 24h. A petri dish with 20µl distilled water was done as blank. The blank corrected mortality rate was calculated for all nine compounds.

Fig. 7: Dual-chamber test set up for testing single volatile compounds for nematicidal activity

2.9 Breeding of root-knot nematodes Four remaining infected roots per sampling site were used for breeding. Two root parts (2- 3cm) with 3-10 visible galls were put into the soil of tomato seedlings in 1cm depth. Tomato seedlings were in the fourth to fifth true leaf stadium and of several different cultivars. Seedlings were grown in an autoclaved 1:1 (volume) mix of garden soil and sand and watered as needed. Infected tomatoes were fertilized with a long-term NPK fertilizer (COMPO Tomaten Langzeit-Dünger, COMPO GmbH & CO. KG, Germany), bred for 50 days in a 16:8h photoperiod at 25±2°C. New tomato seedlings were infected with infected root parts from the former breeding generation or already extracted J2 larvae.

2.10 J2 larvae extraction

Fig. 8: Workflow of J2 extraction from infected roots. A: infected tomato plant; B: roots with visible galls gets surface sterilized and shredded; C: root suspension is washed through a sieve tower with 100µm and 25µm sieve, eggs remain at the lower 25µm sieve; D: egg suspension is aerated for 10 days; E: Baerman funnel. Hatched J2 larvae actively wander through filter paper to the lower part of the rubber hose (asterisk); F: J2 suspension for experiments or reinfection of new tomato seedlings

For extracting the nematodes, roots were cleaned from adhering soil. Infected root parts were cut and mixed in a 1.2% NaOCl solution with a hand blender for 3min. Suspension was washed with tab water through a 100µm-25µm sieve tower. Debris is removed at the 100µm sieve, eggs enrich at the 25µm sieve. Egg suspension is washed to a beaker and was aerated for 10 days to let J2 larvae hatch. The hatched larvae suspensions are placed in a Baerman funnel and incubated for 24h at room temperature. Larvae actively wander through the filter paper and enrich in lower part of the rubber hose, plant debris stays in the filter. Around 30ml J2 suspension was brought to 50ml Sarstedt tubes and horizontally stored at 4°C until use (Fig. 9).

2.11 Molecular and morphological determination of RKNs Randomly selected adult females with dauer eggs were dissected from infected tomato roots. Eggs were removed with a needle and stored at room temperature, in case monospecific stocks are needed.

DNA extraction was carried out following the method of Adam et al. (2007) with some variations. Surrounding plant material was removed with a clean dissection needle; females were transferred to 30µl worm lysis buffer (WLB, Appendix). The female was squashed with a needle, the intestines were pulled out and the cuticulae were transferred to a microscope slide with tab water. For morphological examination, photos of the perineal region were taken at 400x magnification. Intestines with WLB were transferred to sterile 0.5ml PCR tubes with another 30µl WLB and stored for 15min. at -70°C for cell breakdown. The samples were then heated for 60min at 60°C for raising proteinase K activity and then 10min at 90°C for protein denaturation. Samples were stored at -20°C until further use. PCRs were carried out using the SCAR (species characterized amplified region) primer sets for tropical RKNs described in Adam et al. (2007): 194/195 for parting temperate from tropical species (Blok et al., 1997), Far/Rar for M. arenaria, Fjav/Rjav for M. javanica (Zijlstra et al., 2000) and MI-F/MI-R for M. incognita (Meng et al., 2004). For PCR conditions, see appendix. Gel electrophoresis was carried out in 0.5x TBE 0.8% agarose for one hour at 100V.

For nematode samples that did not show amplifications with the SCAR primers, a PCR with the primer pairs D2A/D3B (Baldwin et al. 1997) for amplification of the D2-D3 expansion of the 28S rDNA and NAD5F2/NAD5R1 (Janssen et al., 2016) for NAD dehydrogenase subunit 5 sequences were used. Samples were purified using GENECLEAN TurboTM Kit (MPBio). DNA concentrations were estimated with NanoDrop 2000. They were diluted to the required concentrations for sequencing (40ng/µl). Sequencing was carried out by LGC Genomics GmbH (Berlin, Germany). Sequences were checked for quality using SeqScanner 2 and aligned using ClustalW implemented in MEGA 6. Sequences were compared to existing sequences using megablastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Reference sequences from the NCBI database for several Meloidogyne species were added to the alignment and checked for species-specific SNPs (single nucleotide polymorphisms). According to observed SNPs and data from (Janssen et al., 2016), SCAR primer amplifications and perineal pattern morphology the nematodes were assigned to a species.

3.0 Results

3.1 Physical soil composition Both soil samples were classified as loamy sand (S’l). Both sampling sites show slightly acidic soil pH (5.3 and 5.6). Sodium concentration is higher in Namulonge (site 2), all other tested nutrient parameters are higher in Luwero (site 1) (Table 5).

Table 1: Physical soil composition of the two sampling sites

Sampling site Soil type pH K [mg/kg] P [mg/kg] Mg Org. matter [%] [mg/kg] Luwero S’l 5.3 142 40 250 3.9 Namulonge S’l 5.6 213 31 196 3.2

3.2 Cultivable bacteria from tomato root endosphere The number of cultivable bacterial strains extracted from endosphere was highest in plants with moderately infected roots (RGI = 2.5, ESH: n = 5*105 CFUs/g root; ESD: n = 2.7*105 CFUs/g root) and lowest in severely infected (RGI = 4, ESH: n =744 CFUs/g root; ESD: n = 195 CFUs/g root). More bacteria could be cultured from lethal infected plants (RGI = 5, ESD: n = 8.4*103

Fig. 9: Category distribution of fungal antagonism in bacteria from Ugandan tomato. Most bacteria showed no (category 0) or only balking (1+) effect on different phytopathogenic fungi. n = 260

CFUs/g root) than from healthy plants (RGI = 1, ESH: n = 2*103 CFUs/g root). However, number of CFUs/g root was higher in healthy roots at all infection categories except for RGI = 3.

3.3 Abundant bacterial community in soil and rhizosphere On genera level, 763 OTUs were found in soil samples from Luwero and 782 in Namulonge. When comparing the core microbiome on genus level between the two soils, number of genera is reduced to 253 and 351 OTUs respectively. 188 OTUs were found in both soil samples, 65 OTUs were specific for Luwero and 163 for Namulonge. When only looking at genera with more than 1% abundance, 13 genera were found in both samples, 9 OTUs were specific for Luwero and 4 for Namulonge. However, these ‘specific’ genera are present in both soils but in abundances lower than 1%. The most abundant genera in both soils are an unidentified Planctomycetaceae (4.5% and 4.1%) and an unidentified Acidobacteria Subgroup 6 (4.2% and 3.9%).

When comparing the core rhizosphere microbiome on genus level between the two sampling sites (44 and 43 OTUs per site), 24 OTUs were found in rhizosphere from both sites. 20 were site-specific for Luwero and 19 for Namulonge. Most abundant genera are Pseudomonas (19.4% and 29.6%), Sphingobium (9.4% and 6.8%) and unidentified Enterobacteriaceae spp. (9.3% and 23.4%).

When OTUs are compared on genera level between habitats, a total of 1379 OTUs were found with 467 OTUs present in all habitats (Fig. 10). Rhizosphere has the smallest number of

Fig. 10: Venn diagramm of OTUs on genera level between soil, rhizosphere, healthy endosphere (ESH) and diseased endosphere (ESD).

habitat-specific OTUs, while ESH and ESD have more habitat-specific (n = 139 and 100) than shared (n=75) OTUs (Fig. 10).

3.4 Bacterial diversity of the tropical RKN disease complex A total of 9*107 reads were found due to Amplicon sequencing. Highest average read numbers

Fig. 11: Distribution of Phyla in soil, rhizosphere, ESH and ESD. Proteobacteria cover the main abundance in tomato associated phyla. Only phyla with >1% abundance shown. SCG: soil crenarchaeotic group (ad Thaumarchaeota) were found in soil (mean: 2.4*106 reads), followed by rhizosphere (mean: 1.8*106 reads), ESD (mean: 1.3*105 reads) and ESH (mean: 8*105 reads). When comparing OTUs of the two soils on genera level, 594 shared OTUs were found, 169 specific for Luwero and 188 specific for Namulonge. The most abundant genera in both soils are an unknown Planctomycetaceae sp. (4.54% and 4.09%), an unknown Acidobacteria Subgroup 6 (4.19% and 3.93%).

Microbiome data for the four habitats at phylum level showed a relatively balanced diverse distribution of OTUs in soil and a gradual decline in diversity from rhizosphere to endosphere (Fig. 11). Gammaproteobacteria have the highest abundance in rhizosphere and endosphere.

Pairwise Kruskal-Wallis test for Shannon’s diversity index showed significant (p < 0.05) differences in alpha diversity between all habitats except rhizosphere (median H’ = 7.34) and ESH (median H’ = 6.98; p = 0.25). Shannon diversity was highest in soil (median H’ = 10.2) and lowest in ESD (median H’ = 5.36). When visualizing beta diversity via Bray-Curtis dissimilarity of the amplicon samples, soil and rhizosphere samples are clearly clustering while healthy and diseased endosphere samples overlap. Three healthy endosphere samples (T1GH, T2GH, and

(A) (B)

Fig. 12: (A) PCoA plot for Bray-curtis dissimilarity shows clear clustering in soil (brown) in rhizosphere (blue) while diseased (red) and healthy (green) sampling points overlap. A: sample T1GH; B: sample T2GH; C: sample T3GH; A, B and C cluster with rhizosphere s samples; ring: sample T4R does not cluster with other rhizosphere samples; diamonds: T16Gh, T17GH cluster within other endosphere samples. (B): PCoA plot for unweighted UniFrac dissimilarity shows clear clustering of soil (brown), rhizosphere (blue) and endosphere. ESD and ESH show no significant difference (p = 0.446). Size of spheres positively correlates with RGI.

T3GH) cluster with the rhizosphere samples, T4R clusters with the endosphere samples. Healthy endosphere partially overlaps with rhizosphere (Fig. 12A).

Pairwise PERMANOVA showed significant differences for both Bray-Curtis dissimilarity, unweighted and weighted UniFrac dissimilarity between soil, rhizosphere, and endosphere (all p≤0.001). Differences between healthy and diseased root endosphere are significantly for Bray-Curtis (p = 0.031) and weighted UniFrac dissimilarities (p = 0.049). Unweighted UniFrac dissimilarity is not significant (Fig. 12B, p = 0.446).

The most abundant orders in soil are Planctomycetales (7.2%), Rhizobiales (6.5%) and Bacillales (6.2%) (see Supplementary data). Most abundant orders in rhizosphere are Pseudomonadales, Enterobacteriales and Burkholderiales. Abundance of Enterobacteriales and Bacillales rises in ESH, Pseudomonadales, Sphingomonadales and Cytophagales drop heavily. Abundance of Bacillales and Rhizobiales rise in ESD compared to ESH (Table 2).

When only looking on habitat-specific families above 1% abundance, four families in all habitats (Bacillaceae, Chitinophagaceae, Sphingomonadaceae, Xanthomonadaceae), 16

habitat-specific families for soil and only one for endosphere (Pasteuriaceae) and rhizosphere samples (Cytophagaceae) can be found. In soil, the families with abundance >1% cover 48% of the total abundance of bacteria, in rhizosphere 85%, in ESH 78%, in ESD 86%.

Table 2: Bacterial relative abundance (rA) shift from rhizosphere towards ESH to ESD on order level. Δ(abundance) refers to abundance changes of the order from the previous habitat (ESH to rhizosphere, ESD to ESH). Red: <-50%; green: >+50%; yellow: ±50% range. Enterobacteriales and Bacillales rise in the endosphere, but in diseased roots Enterobacteriales become less abundant. Rhizobiales abundance rises in diseased tomato roots.

rhizosphere ESH ESD order rA [%] rA [%] Δ(abundance) [%] rA [%] Δ(abundance) [%] Pseudomonadales 25.74 9.85 -61.75 -61.75 8.67 -11.92 -11.92 Enterobacteriales 17.35 32.29 86.19 +86.19 24.70 -23.52 -23.52 Burkholderiales 10.3 12.56 21.95 +21.95 10.03 -20.14 -20.14 Sphingomonadales 9.858 4.27 -56.70 -56.7 2.21 -48.17 -48.1 Rhizobiales 7.5 6.29 -16.08 -16.08 12.43 97.49 +97.49 Sphingobacteriales 6.74 3.54 -47.50 -47.5 2.18 -38.53 -38.53 Flavobacteriales 3.474 1.95 -43.93 -43.93 1.31 -32.71 -32.71 Xanthomonadales 3.345 3.57 6.59 +6.59 1.92 -46.05 -46.05 Bacillales 3.059 11.45 274.40 +274.4 27.65 141.36 +141.36 Caulobacterales 1.21 1.34 10.40 +10.4 0.64 -52.41 -52.41 Cytophagales 1.193 0.53 -55.58 -55.58 0.42 -19.87 -19.87 Methylophilales 1.179 1.76 48.95 +48.95 0.88 -50.17 -50.17 Micrococcales 1.126 1.25 10.94 +10.94 0.92 -26.04 -26.04 orders <1% rel. Abundance 7.933 9.89 24.72 +24.72 8.91 -9.97 -9.97

3.5 Differences between infected and non-infected tomato plant roots Bacterial families with >1% relative abundance cover 78 of ESH and 86% of ESD sample abundances. A few families occur in higher ratios in ESD than in ESH, while abundance of other families is negatively affected to a different extant (Fig. 13).

Fig. 13: Relative abundances of bacterial families within endosphere. Differences in abundance mainly depend on a few families: Rhizobiaceae, Pasteuriaceae and Enterobacteriaceae

When comparing families that are >1% abundant in ESH and ESD, we see an increase of Oxalobacteraceae, Comamonadaceae, Bacillaceae, Rhizobiaceae and Pasteuriaceae due to RKN infection. Abundances of Enterobacteraceae, Burkholderiaceae, Sphingomonadaceae and Xanthomonadaceae are the most negatively affected. We also see a slightly negative effect on Pseudomonadaceae (Fig 13, 14). If the abundance of families in ESH is regarded as the starting point (100%), we see a bigger impact on lower-abundant family abundances than on higher-abundant families due to RKN feeding site induction (Fig. 14).

Fig. 15: Changes of relative abundance of bacteria families due to RKN infection. Only families with >1% relative abundance shown. +: obligate nematode parasites; f: fungi antagonists; *: nematode antagonists found in this study. Rel. Family shift refers to mean abundance in ESH. Relative abundance shift has a bigger negative impact on low-abundant taxa like Sphingobacteriaceae, Caulobacteraceae and Methylophilaceae.

3.6 Antifungal isolates Most bacterial isolates showed no (0) or only balking effect (+1), but several strains showed high antagonistic effects (Fig. 15). 72 strains showed high antagonistic effects (+3) on at least one fungal pathogen. More antagonistic strains (n=30) were isolated from healthy endosphere (ESH) than from diseased endosphere (ESD, n=14) samples. Antagonists isolated from rhizosphere (n=28) were mainly active against 1-3 fungal pathogens. Antagonists of V. dahliae were only found in rhizosphere (Table 3). Isolates that only showed activity against 1-3 plant pathogens were excluded for fingerprint BOX-PCR. BOX-PCR patterns for Fig. 14 Dual cultures of bacterial the 23 isolates active against four isolates showed that isolates and phytopathogenic fungi the majority of these strains are clones of one show partially strong inhibitory effects on the fungi morphologically indistinct strain. Five different BOX-

PCR patterns could be observed. 16S rDNA sequences of these strains identified them all as members of the Bacillus amyloliquefaciens complex, namely one strains of B. amyloliquefaciens (T17GI2), one strain of B. methylotrophicus (T7GH4) and three strains of B. velezensis (T3R11, T17GI1, T21GH5) (see also chapter 3.7).

23 strains seemed to be highly antagonistic against the four plant pathogens B. cinerea, F. oxysporum,

F. verticilloides and S. rolfsii. Strains Fig. 16: Venn diagramm for high bacterial antagonism that showed high activity against the against fungal pathogens. Isolates that showed high antagonism against B. cinerea, F. oxysporum, F. other fungal pathogens were less verticilloides and S. rolfsii did not show high effects against V. dahliae. Red circle: active strains against four fungal efficient against V. dahliae (Fig. 16). pathogens. Yellow circle: most Verticillium antagonists did not show strong antagonism towards other pathogens.

3.7 Nematicidal volatile producing strains

Fig. 16: Most nematicidal active strains are found in diseased galls, independent of site. ESH: endosphere healthy ESD: endosphere diseased; R: rhizosphere; S1: Luwero; S2: Namulonge; Mbv: mortality through bacterial VOCs. In ESD, around are 36% are categorized active or very active in nVOC production The overall mean mortality through nematicidal volatiles of bacteria was similar on both sampling sites (32.06 ± 25.45% in Luwero, 30.89 ± 28.12 in Namulonge). Most bacteria showed no effect (mortality < 10%) or only slightly nematicidal effects. 43 strains were categorized as active (80-95% mortality; n = 20) or highly active (>95% mortality; n = 23).

When comparing the overall nematicidal activity between RGI, a trend toward higher mortality rates for strains from highly infected plants was noticed (Table 4). Most active and highly active strains were found in ESD samples. Around 36% of all strains from ESD are either active or highly active independent of sampling site (Fig. 17).

When repeating the TCVA another two times, only six strains showed consistently nematicidal activity (>80% mortality). 5 out of 6 strains were bacteria isolated from gall infected roots. Three of them were collected from T13, a RGI = 4.5 infected plant from Namulonge. Only one strain was isolated from ESD from Luwero. Due to sequences of 16S rDNA, the strains were identified as Pseudomonas koreensis, Comamonas sediminis, Variovorax paradoxus, two strains of Pseudomonas monteilii and Pseudomonas soli. Strains with high nematicidal activity show no high antifungal activity and vice versa (Table 6).

Table 6: Identification of strains with antagonistic properties against either RKN or fungal pathogens. NMbv: nematodemortality through bacterial VOCs; antifungal category: values ordered as values for B.cinerea-F.oxysporum-F.verticilloides-S.rolfsii-V.dahliae where 0 = no antagonism and 3 = inhibition zone > 5mm. Values for nematode antagonists and fungal antagonists with five examples of V. dahliae- antagonistic strains

Strain Best hit (ref_seq) ident NCBI Acc. Identified as Mbv Antifungal name No. category T3GI1 Pseudomonas koreensis 99% NR_025228.1 Ps. koreensis T3GI1 97.84 % 1-2-2-0-1 T1GI1 Variovorax paradoxus 99% NR_113736.1 V. paradoxus T1GI1 96.19 % 0-1-1-0-1 Comamonas sediminis 93.77 % 1-1-2-1-0 99% NR_149789.1 T13GI2 Comamonas sediminis T13GI2 T8GH4 Ps. monteilii 100% NR_114224.1 Ps. monteilii T8GH4 87.66 % 0-1-0-0-0 T13GI4 Ps. soli 100% NR_134794.1 Ps. soli T13GI4 86.68 % 2-1-2-2-2 T13GI6b Ps. monteilii 100% NR_114224.1 Ps.monteilii T13GI6b 83.54 % 0-1-2-0-1 T3R11 Bacillus velezensis 98% NR_075005.2 B. cf. velezensis T3R11 1.9 % 3-3-3-3-1 T17GI1 Bacillus velezensis 97% NR_075005.2 B. cf. velezensis T17GI1 36.56 % 3-3-3-3-1 T17GI2 B. amyloliquefaciens 3.47 % 3-3-3-3-2 99% NR_117946.1 B. amyloliquefaciens T17GI2 T7GH4a B. methylotrophicus 0 % 3-3-3-3-0 99% NR_116240.1 B. methylotrophicus T7GH4a T21GH5 B. velezensis 97% NR_075005.2 B. cf. velezensis T17GI1 0 % 3-3-3-3-1 T1R1 - - - T1R1 6.52% 2-2-2-2-3 T1R12 - - - T1R12 4.76% 2-2-2-0-3 T1R14 - - - T1R14 4.11% 2-2-2-2-3 T2R18b - - - T2R18b 0% 2-2-2-2-3 T14R6 - - - T14R6 3.07 2-2-2-1-3

3.8 Chemistry of bacterial volatiles VOCs of the bacterial strains that showed high nematicidal activity could be subdivided into alkenes, sulfuric compounds, alcohols, ketones and aldehydes. Additionally, one pyrazine could be constantly detected in the strain with the highest nematicidal effect, namely 3- methoxy-2,5-dimethyl pyrazine. 1-undecene was one of the main components of Comamonas sediminis, Ps. monteilii and Ps. soli. Except for P. monteilii T8GH4, dimethyl disulfide was the main component of the VOC bouquet, but it was also found in the blank (see Supplementary data). When comparing the overall number of MS ion counts, the total counts of bacterial isolates were up to sevenfold higher (in Ps. soli T13GI4) than in the blank (Table 7).

Figures of gas chromatogram and mass spectra of all compounds are shown in the Supplementary data section.

Table 7: results of GC-MS for the volatiles of the nVOC-producing strains (next page). Values of compounds are percentage of area of ionic counts. RI: retention index after Van den Dool &Kratz (1963)

Others: oxygen-containing compounds sulfuric compounds Alkenes

proportion ion counts/counts blank counts/counts ion proportion

unknown(Rt=12.277) substance

unknown(Rt=1.581) substance

carboximidate

Methyl(Z)-N-hydroxybenzene-

3-methoxy-2,5-dimethylpyrazine

2-undecanone

Benzaldehyde

2,3-epoxybutan

3-methylbutanol

2-methylbutanol

3-methylbutanal

2-methylbutanal

2-butanone

Acetone

CO₂

S-methyloctanethioic acid

S-methyl3-methyl-butanethioate

Methylthiolacetate

2-me-2-methylthiobutan

2,3-dimercaptopropan-1-ol

S-methylpropanethioate

Dimethyltrisulfide

Dimethyldisulfide

Dimethylsulfide

Methanethiol

1,12-tridecadiene

1-tridecene

1-dodecene

E-3-undecene

E-1,4-undecadiene

1-undecene

1-decene

1-nonene

899

1046

1288

951

510

725

728

646

656

601

502

1291

931

698

836

835

784

959

733

520

1272

1286

1186

1085

1081

1091

989 889

RI measured

1054

1291

958

734

736

649

659

601

503

1293

938

701

847

785

972

740

515

464

1279

1293

1193

1085

1093

993 892 RI NIST

(mainlib)

Jagella and Grosch, 1999 Grosch, and Jagella

Vagionas et al., 2007 et al., Vagionas

Kukic et al., 2005 et al., Kukic

Pino et al., 2005 et al., Pino

Jarunrattanasri et al., 2007 et al., Jarunrattanasri

Insausti et al., 2005 et al., Insausti

Gros et al., 2011 et al., Gros

Beaulieu & Grimm, 2001 Grimm, & Beaulieu

Liu et al., 2005 et Liu al.,

Gros et al., 2011 et al., Gros

Jarunrattanasri et al., 2007 et al., Jarunrattanasri

Bonaiti et al., 2005 et al., Bonaiti

Sojak et al., 2006 et al., Sojak

Flamini et al., 2006 et al., Flamini

Junkes et al., 2003 et Junkes al.,

Flamini et al., 2006 et al., Flamini

Flamini et al., 2006 et al., Flamini Flamini et al., 2003 et al., Flamini

Reference

813

949

602

799

739

949

838

820

741

999

809

727

877

755

680

781

926

968

745

976

871

919

913

884

932

933 868

NIST match

5.59

0.16

0.15

14.95

1.56

0.84

2.92

0.62

70.43

1.73

4.45

1.2

- - P. koreensis

T3GI1

6.02

0.96

0.27

1.5

1.7

2.66

1.2

1.44

2.88

81.69

5.39

0.3

- - V. paradoxus

T1GI1

5.17

1.19

1.83

44.44

0.55

2.03

0.38

47.12

0.78 0.68 C. sediminis

T13GI2

2.95

1.11

0.25

0.29

0.5

1.43

0.16

0.13

5.45

0.17

2.25

14.19

1.03

0.08

0.04

0.6

0.99

68.43

0.94 1.96 P. monteilii

T8GH4

7.38

0.24

0.17

1.25

1.09

2.54

73.6

1.01

2.27

17.83

- -

a P. soli

T13GI4

1.85

9.93

3.6

2.03

3.31

1.32

39.01

1.64

1.13

28.45

- 6.58a

P. monteilii a

T13GI6b

31.93

13.82

7.82

3.89

0.79

16.37

25.38

- - blank (NA)

25.00

20.00

15.00 soil rhizosphere

10.00 ESH ESD rel.abundance [%]

5.00

0.00 Bacillus ssp. Comamonas ssp. Variovorax ssp. Pseudomonas Pasteuria ssp. ssp. Fig. 17: Relative abundances of nematode antagonistic genera identified in this study within soil, rhizosphere, healthy endosphere (ESH) and diseased endosphere (ESD).

3.9 Abundance of nematicidal bacterial genera in tomato plants Within our dataset, 28 genera that include species with known nematode controlling effects could be detected (see Discussion). The relative abundance of these genera is higher in rhizosphere and endosphere than in the bulk soil samples. Mean relative abundance of nematode antagonistic genera was highest in gall-infected roots, but it does not significantly differ between rhizosphere, ESH and ESD (see Appendix).

When searching for genera with antagonists found in this study, the abundance of Bacillus spp. is highest in ESH (4.1%), Variovorax spp. is highest in ESD (0.97%), Comamonas spp. (0.3

4%) and Pseudomonas spp. (22.01%) highest in rhizosphere. (Fig 18).

When looking at the three most abundant antagonistic genera (Pseudomonas, Pasteuria, Bacillus), no trend between their abundance and RGI was visible (Fig. 20). Pseudomonas was highest in RGI=5 plants, Pasteuria highest in moderately infected plants. However, the high abundance of Pseudomonas in lethal infected plant may be due to saprophytic species, which already degrade the plant material.

Fig. 18: Differences of antagonist abundance in different severity of RKN-disease. No trends in abundance (rAESD-rAESH) of the three antagonistic genera Pasteuria, Pseudo- monas and Bacillus due to RGI is visible

3.10 Testing single compounds for nematicidal activity When testing several pyrazine derivate against J2 larvae, only three substances significantly exceeded the blank mortality after one day using 5µl and 20µl: 2-undecanone (11on), 2- methoxy-3-methyl pyrazine (2m3mp) and 2-ethyl-3-methyl pyrazine (2e3mp). Only 11on and 2m3mp exceeded the blank mortality when using 1µl compound (Fig. 20). At the highest concentration, living J2 larvae were mainly found flanked or within a pile dead individuals.

Three more compounds showed nematicidal effects using 20 µl compound after an incubation time of 4 days, namely 2-isobutyl-3methyl pyrazine (67.8%), 5-isobutyl-2,3-dimethyl pyrazine (44%) and 2-ethyl-3-methyl pyrazine (50.2%).

100.00 95.00

75.00 75.99 71.26

57.29 50.00

45.31 mortality[%] 25.00 22.32 11on 14.53 2m3mp 12.68 2e3mp 0.00 0 50 100 150 200 applied compound [nmol] Fig. 19: Single compounds that showed nematicidal activity after 24h of incubation. 11on: undecan- 2-on; 2m3mp: 2-methoxy-3-methyl pyrazine; 2e3mp: 2-ethyl-3-methyl pyrazine. Red line represents blank mortality. Compounds that showed no nematicidal activity are not shown.

3.11 Abundant nematode species Due to the perineal patterns, the extracted adult females could be assigned to either M. javanica showing the typical rounded pattern with distinct lateral lines (Fig. 21B) or members of the M. incognita-group (MIG). Perineal patterns within individuals assigned to M. incognita s. str. showed no lateral lines and a relatively high, square-shaped dorsal arch (Fig. 21A). Still there was a variance within the patterns that may indicate more than these two species. Those specimens were assigned to M. incognita. s. lat.

The molecular key diagnosis from Adam et al. (2007) did not show clear results, only 7 out of 30 nematodes could be assigned to one species due to SCAR-primer amplifications (see: Supplementary data). Other samples did not show amplification bands in the electrophoresis gel. No amplifications of M. arenaria-specific primers could be detected. The amplified samples could be assigned to either M. javanica or M. incognita.

Using the primer for 28S rDNA resulted in only weak amplification bands. Sequences from the existing bands were not sufficient in length or quality to be used as identification reference.

When comparing nematode sample sequences of NAD subunit 5 with the reference genomes of different Meloidogyne species, some sequences showed several positions with poor quality and/or SNPs. When using a neighbor-joining tree, eight samples do not cluster with the

Fig. 20: Perineal Pattern of Meloidogyne incognita (A) and egg-laying M. javanica (B) a: anus; da: dorsal arch; e: egg; l: lateral lines; p: punctuations; s: striae; t: tail terminus; v: vulva. Bar = 10µm

reference genome sequences (Fig. 22). Therefore, species-specific polymorphisms within the reference genomes were used for assigning a sample to a species.

No samples could be assigned to M. arenaria. 10 specimen of M. incognita and two MIG specimens were found on Luwero (site 1). Three specimen of M. incognita, six specimen of M. javanica and five MIG specimens were identified from Namulonge (site2).

Fig. 21: Neighbour-joining tree of NAD5 of extracted nematodes. Eight samples were unassignable on overall sequences and thus assigned using SNPs and perineal pattern.

4.0 Discussion

4.1 The microbiome of tomato roots in Uganda More OTUs were found at the breeding station in Namulonge than in the virgin soil of Luwero. Maybe the change from natural condition towards agricultural management in Luwero caused lower bacterial diversity than in Namulonge, where relatively stable conditions are provided. Still, the abiotic composition in the soil on both sampling sites is similar. Microbiome data show that the difference of the bacterial communities is habitat-specific, not site-specific. On both sampling sites unknown Planctomycetaceae are the most abundant OTU in bulk soil.

Rhizosphere and soil microbiome clearly differ in their microbial community. Especially the rhizosphere is an important transition zone between the soil and the endosphere of plants. Plants change the direct environment of their roots by secreting exudates attractant or repellant for some microorganisms, leading to a change in microbial diversity in the rhizosphere (Berg et al., 2014). The root epidermis is another borderline that only a subset of soil bacterial is able to overcome. Thus, microbiomes of rhizosphere and endosphere clearly differ both quantitatively and qualitatively in their composition (Krechel et al., 2002; Ryan et al., 2008; Tian et al., 2015).

4.2 Genera with RKN-controlling properties within Ugandan tomatoes Several bacterial genera are known to have a controlling effect on PPNs, a review is given by Tian et al. (2007). Most of the genera mentioned in this study could also be detected in our dataset (Table 8). However, their modes of action are numerous, ranging from obligate nematode parasitism in the genus Pasteuria (Viaene et al., 2013) to complex interactions like “honey-traps”-VOCs in Paenibacillus polymyxa (Cheng et al., 2017). Most nematode- controlling bacteria that have a negative impact on PPNs through toxic compounds, antibiotic substances or enzymes reducing RKN reproduction, mobility and/or invasion (Viaene et al., 2013).

Since RKNs represent a threat to global food security, there are many publications on the interaction with microorganisms. Hardly all of the genera comprising known RKN-antagonistic species could be detected in Ugandan tomato roots. That indicates a big potential for “self- defense” of the tomato plant. On the other hand, this potential seems not to be sufficient to

Table 8: Bacterial genera with known nematicidal effects (after Tian et al. 2007, additionally genera mentioned seperately); bold genera were found in Ugandan tomato roots Actinomycetes, Agrobacterium, Alcaligenes, Arthrobacter, Aureobacterium, Azotobacter, Bacillus, Beijerinckia, Brevibacillus, Burkholderia, Chromobacterium, Chryseobacterium (Krechel et al., 2002), Clavibacter, Clostridium, Comamonas, Corynebacterium, Curtobacterium, Desulforibtio, Enterobacter, Enterococcus (Z. Liu et al., 2013), Flavobacterium, Gluconobacter, Hydrogenophaga, Klebsiella, Lysinibacillus (L. L. Yang et al., 2012), Methylobacterium, Pasteuria, Paenibacillus (Siddiqui et al., 2007; Son et al., 2009), Pseudomonas, Phyllobacterium, Rhizobium, Serratia, Sphingobacterium, Staphylococcus(Z. Liu et al., 2013) , Stenotrophomonas, Streptococcus (Z. Liu et al., 2013), Streptomyces (Krechel et al., 2002), Variovorax control RKNs to a higher extent. However, regarding the fact that a big part of cultivable bacteria is potentially able to negatively affect RKNs by producing VOCs shows that the bacterial community - amongst other factors - may contribute to the overall suppressiveness of different soils. This may also explain the efficacy of PPN management methods that promote or enhance bacterial growth, for example the use of soil amendments (Thoden et al., 2011).

4.3 Bacterial community shift in tomato roots due to RKN infection Tomato is not a native crop in Africa, its origin reaches back to the tropical highlands of Peru and Venezuela and thus has a long history of cultivation and migration (Jenkins, 1948). The continuous cultivation practices in tomato breeding have led to a multiplicity of phenotypical variations, which most likely changed the interactions between tomatoes and associated microorganisms in different cultivars. Until now, only a few studies were carried out on the microbial diversity within tomato roots. Ottesen et al. (2013) found Micrococcineae, Rhizobiales (mainly Rhizobium and Mesorhizobium), Sphingobium and Acidobacteraceae the predominant groups in tomato roots (Solanum lycopersicum cv. BHN602) in Virginia, USA. Tian et al. (2015) focused on the bacterial community shift du to RKN infection in tomato roots. They found Streptomycetales, Micromonosporales and Rhizobiales as dominant groups within nematode-infected tomato roots (cv. Jiabao) in China. A decrease of abundance in Streptomycetales and Pseudomonadales while Burkholdales and Micromonosporales slightly increased in diseased roots was described as well.

The endosphere samples do not differ qualitatively, but quantitatively in their microbiome composition. Thus, RKN infections have a bigger impact on abundance than occurrence of

present bacteria. However, our results show Bacillales, Enterobacteriales and Rhizobiales to be the most dominant groups in RKN-diseased roots. Most dominant groups in healthy roots are Enterobacteriales, Burkholderiales and Bacillales. Furthermore, a higher diversity of endophytes due to RKN infection as mentioned by Tian et al. (2015) could not be confirmed, because more cultivable bacteria were counted in ESH samples and more habitat-specific OTUs were found in ESH (n=139) than in ESD (n=100). On the other hand, we focused on the healthy roots of diseased plants; therefore the uptake of microbiota from the soil through mechanical RKN-penetration of the root may still be enhanced. To be precise, our microbiome data may show the difference between mechanical penetration and giant cell induction under natural conditions. Anyway, results from the two mentioned studies heavily differ both quantitatively and qualitatively from our study. These differences most likely contribute to the difference of abundant soil microbiota. A part of the variance may arouse from a cultivar effect like mentioned for other crops (Rybakova et al., 2017; Weinert et al., 2011), but should be not that apparent at this taxonomic level.

The increase of Pateuriaceae (ad Bacillales) in root galls can easily explained, since obligate nematode parasites Pasteuria will be enriched where their nutrient basis is available. Several plants (T3, T5, T7, T8, T16, T19) showed a relatively high abundance (>20%) of Pasteuria spp. within ESD samples, but its abundance did not show a clear connection to RGI. Since the abundance of Pasteuria sp. within soil and rhizosphere is relatively low and varies between samples, its abundance in plant roots may be dependent on a successful transportation to the infection site. Pasteuria have to successfully adhere to the cuticle of RKN during J2 soil migration to reach the infection site. In general, the microbial community within the infection site of host plants may be influenced by bacteria and fungi that are able to stick to the highly glycosylated cuticle of nematodes. It seems like there are some preferences of microorganisms to bind on RKNs surface and thus they may be actively transported to the infection site (Elhady et al., 2017).

The increase of Rhizobiaceae in RKN galls seems to be a constant effect (Tian et al., 2015) and may contribute to a defense reaction of the host plant, since Rhizobium spp. are known to have a plant health-promoting effect in several ways. They are able to induce systemic resistance and increase mycorrhizal establishment, leading to reduced J2 penetrating the roots, egg masses and galls of RKN in tomato (Martinuz et al., 2013; Reimann et al., 2008).

Rhizobium and Brachyrhizobium are known to closely interact with several plant roots and to induce root tubercle in legumes. They are attracted towards the rhizodermis via chemotaxis of flavonoids (Bresinsky et al., 2008). Plant-specific flavonoids are the key driver for the rhizosphere microbiome (Weston & Mathesius, 2013) and have several physiological functions within the plant. Some flavonoids have a repellent effect on RKNs and thus directly affect nematode behavior (Wuyts et al., 2006). Flavonoid synthesis is suppressed in giant cells (Ji et al., 2013) while their synthesis is increased after feeding site induction (Hutangura et al., 1999). Most likely, the rise of Rhizobiaceae in RKN-infected roots is a consequence of higher flavonoid concentration in the surrounding tissue at the infection site. This would indicate an indirect defense mechanism in plants against root-knot nematodes. There is evidence that RKN try to counteract the effects of Rhizobium, since RKN are able to reduce nodulation in legumes (Kimenju et al., 1999). Some Rhizobium strains are known to have biocontrol potential towards RKNs, the use of some Rhizobium strains in tomato are promising (Mahdy et al., 2001). Since Rhizobium strains are enriched in galls (Hallmann et al., 2001), screening for promising plant-promoting Rhizobium strains native to Uganda could focus on RKN- diseased roots.

Enterobacteriaceae are known to include symbionts, pathogens and also species suitable for biological control like Serratia plymuthica (Kurze et al., 2001). However, the question whether there was a lower abundance at the infection site beforehand or the abundance was lowered due to RKN infection cannot be answered. It seems like Enterobacteriaceae are not competitive in the changed physiological settings at the infection site.

4.4 Potential of nVOCs-producing bacterial strains Apparently, the strains we found as nVOC producers hold a big potential as biocontrol agents. This chapter gives a short introduction to the found strains.

Pseudomonas koreensis, which was the most promising strain in nVOC production within our samples, was first isolated from Korean agricultural soil by Kwon et al., (2003). P. koreensis is known to have potential in biocontrol, because it is able to produce biosurfactants that have a negative effect on phytopathogenic oomycetes like Phytophthora infestans (Hultberg et al., 2010b) and Pythium ultimum (Hultberg et al., 2010a). It is known to promote plant growth in

Miscanthus sinensis (Panicoideae, Poaceae), promote heavy metal solubilization in the rhizosphere and heavy metal uptake by the plant. Thus it is also an interesting candidate for bioremediation (Babu et al., 2015).

Comamonas sediminis was first isolated from lagoon sediments from North Carolina, USA and described by Bang et al. (2016). It forms transparent colonies and is aerobic and gram- negative. Due to this recent first description, the available data to this organism is very scarce. However, some members of the genus Comamonas, e.g. C. acidovorans are known to have antagonistsic effects towards phytopathogenic fungi (El-Banna, 2007) or oomycetes (Liu et al., 2007).

Variovorax paradoxus is a yellowish, gram-negative, aerobic member of the Comamonadaceae and it showed signs of swarming. It is known for its large potential for bioremediation and biotechnology because of its diverse catabolic pathways (Satola et al., 2013). There are also several studies on the plant-promoting or plant-protecting effects of V. paradoxus (e.g. Chen et al., 2013; Sharp et al., 2011). The complete genome sequence of several V. paradoxus-strains is already available, strain S110 was even isolated from tomato (Han et al., 2011). However, both Variovorax spp. and Comamonas spp. were found in relatively low abundances in ESH and ESD samples (<1%). The question remains, whether they can reach abundances high enough to affect RKNs via their nVOCs. But still, these strains may inherit plant-promoting traits that are not dependent on high abundances.

The VOC assay carried out in this study is not an in planta approach. It is not sure whether the found antagonistic strains show RKN-inhibiting effects in living plants or under field conditions. Still, the relatively high mean mortality due to nematicidal VOCs on both sampling sites shows the potential of soil microorganism to produce RKN-controlling VOCs. This may partially explain the controlling effect of management methods that generally raise numbers and diversity of bacterial populations in soil like the use of soil amendments and green manures (Widmer et al., 2002). Although there are some promising results for several compounds like chicken manure (Kaplan & Noe, 1993) or chitin (Ladner et al., 2008), the main problem with those amendments is the varying composition, the high quantities needed and the partially only regional availability (Hassan et al., 2013)

4.5 Nematicidal volatiles There are VOCs known to have a negative effect on soil nematodes in general and on RKNs in particular. Nematicidal VOCs were identified from bacteria (Gu et al., 2007; Huang et al., 2010; L. L. Yang et al., 2012), fungi (Grimme et al., 2007; Z. Yang et al., 2012) and also plants (Barros et al., 2014). Biorational products seem to have a comparable or even stronger effect than the living formulation of the corresponding BCA formulation (Grimme et al., 2007). Therefore, the active components of a VOC spectrum are of special interest.

The compound 2-undecanone was used as a positive control, because it is already known to have a high nematicidal effect on M. incognita (Huang et al., 2010) and other free-living soil nematodes (Gu et al., 2007). 3-methoxy-2,5-dimethyl-pyrazine (3M25DP) was constantly found in the GC-MS spectrum of our strongest nematode antagonist Pseudomonas koreensis T3GI1. Pyrazines were tested because it is known that they have a controlling effect on bacteria (Kusstatscher et al., 2017) and fungi (Haidar et al., 2016). Since the exact compound was not available, the structurally most similar available compound 2-methoxy-3-methyl pyrazine (2M3MP) was used instead in the single compound test. Although pyrazines did not reach effectiveness of 2-undecaone, they may enhance the nematicidal effect of the volatilome.

Alkenes like undecene were continuously found in GC-MS data of nVOCs in relatively high abundances. They were found in nVOC-producing strains before (Gu et al., 2007) but were not tested as single compounds. Alkenes showed no nematicidal effect. However, this may contribute to vapor pressure and their hydrophobicity. nVOCs thus should have a higher nematicidal effect if they are hydrophilic or amphiphilic. Since J2 larvae are exposed to liquid and gas phase as well as to compounds adsorbed to soil particles when migrating through soil (Noling, 2009) alkenes may still have an effect when J2 larvae are directly exposed to them.

We also found several sulfuric compounds known to have strong odors (e.g. dimethyl sulfide) or compounds used as flavoring agent (e.g. octanethioic acid s-methylester, https://pubchem.ncbi.nlm.nih.gov) that may also be active compounds. Due to logistic reasons, sulfuric compounds could not be tested as single compounds. Nematicidal properties may arise from dimethyl disulfide, which was continuously found in highest percentages in all strains except P. monteilii T8GH4. Dimethyl disulfide is an effective nematicide (Noling, 2009)

and was meant to be an alternative for methyl bromide as a broad spectrum soil fumigant (www.arkema.com).

4.6 Managing the root-knot nematode disease complex with BCAs The approach to control both RKN and fungal pathogens seems to be very difficult, since our tests did not result in one single strain controlling both RKNs and phytopathogenic fungi. nVOC-producing strains did not show effective antagonism against fungal pathogens, and the strains antagonistic to V. dahliae were not efficient antagonists for the other fungal pathogens. Furthermore, antagonists seem to be more abundant in different habitats: Verticillium-antagonists were mainly found in rhizosphere samples, strains antagoniststic to the other fungi were mainly found in ESH-samples. For Verticillium-antagonists the question remains if they are even able to colonize the root endosphere. The use of these strains in their origin habitat may be the most effective approach for biocontrolling V. dahliae.

The most antagonistic strains for the remaining four fungal pathogens were found in healthy roots. This result is confirmed by amplicon data where Bacillus spp. has the highest relative abundance in ESH. This indicates a protecting function of these strains. Members of the Bacillus-amyloliquefaciens complex are known to have strong antagonistic effects on fungal pathogens (Chowdhury et al., 2015; Malfanova et al., 2011). Members are already successfully commercialized as biocontrol agent and biofertilizer in agriculture. Though the antimicrobial activity of several metabolites is well-studied, the main mechanism for the plant-protecting effects seems to be induced systemic resistance (ISR) in crops, which may protect them against pathogenic microbes, viruses, and nematodes (Chowdhury et al., 2015). However, Bacillus- strains found in this study clearly did not affect J2 larve of M. incognita directly through VOCs. Furthermore, they seem not to effectively inhibit growth of V. dahliae, although reported for other strains (Danielsson, Reva, & Meijer, 2007). Still, B. amyloliquefaciens is also able to control RKN via antibiotic peptides like plantazolicin (Z. Liu et al., 2013). Our findings exclude nVOCs of Bacillus strains as controlling component for RKNs, though they may have a repellent effect in vitro.

There are some biocontrol agents known to be able to control both RKN and fungal pathogens (Adam et al., 2014; Krechel et al., 2002). For example, some Pseudomonas- and Streptomyces

strains control the Verticillium dahliae-RKN pathosystem (Krechel et al., 2002). Paenibacillus strains were found to control a combination of both Fusarium- and RKN infection (Son et al., 2009). Some bacteria seem to have an indirect effect on the RKN infections, for example some Bacilllus subtilis strains known for their antifungal effect against Rhizoctonia solani reduce 86% gall formation and 96% egg production by inducing systemic resistance in tomatoes (Adam et al., 2014). Therefore it is possible, that these fungi-antagonistic strains also have an effect on RKNs, but with another mode of action than nVOCs.

Surprisingly, antagonists against fungal pathogens found are well-known antagonists. The question remains, if they are enriched from the surrounding soil or if they are a part of the core microbiome of the plant. If the latter, the higher susceptibility of tomatoes towards diseases may also be a consequence a declining bacterial diversity through breeding practices or the lack of ability to establish native antagonists within the plant tissue or rhizosphere.

4.7 Determination problems of abundant RKN species Based on the methodological approach used herein, only two different species could be clearly identified. However, the SCAR-primer method for identification (Adam et al., 2007) holds the big disadvantage of false negative results if the PCR did not work, as already mentioned by (Janssen et al., 2016). Nematode samples that did not amplify may still belong to other species than M. incognita or M. javanica, since morphological traits are inter- and intraspecifically variable Eisenback et al., 1981).

4.8 Future prospects Our data show, that different BCAs are needed for effectively controlling RKNs and PPFs. For future studies, Verticillium-antagonists within our data should be identified using 16S rDNA sequencing. The found antagonists should be tested in planta to evaluate their effects on the selected pathogens under natural and field conditions. Antagonists should be tested individually and as consortia against fungal pathogens and RKN to quantify their effect. If consortia of these strain have sufficient effects, other crops can be tested. One could start with other Solanaceae that are important for Ugandan smallholders like pepper (Capsicum

annus) or eggplant (Solanum melonegra), because antagonistic strains may act similar in phylogenetic related crops.

The biological control of the RKN-disease complex in tropical climates is possible. Using microorganism-based products for crop protection can lead to comparable results than the use of chemical pesticides, using different management methods can lead to synergistic beneficial effects (e.g. Njoronge, 2014). Uganda is a land on the borderline between subsistence and extensive agriculture. Implementing biocontrol strategies in widespread IPM programs may help to prevent health issues due to agricultural byproducts, hunger and social conflicts while simultaneously providing the economic and nutritional needs of the local people. Biocontrol of upcoming pests and pathogens during mechanization and industrialization of agriculture will –amongst other methods- help to maintain both environmental and human health in Sub-Saharan Africa.

Fig. 22: Field at the IITA research center in Namulonge, Uganda (Foto: G. Berg)

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Yang, Z., Yu, Z., Lei, L., Xia, Z., Shao, L., Zhang, K., & Li, G. (2012). Nematicidal effect of volatiles produced by Trichoderma sp. Journal of Asia-Pacific Entomology, 15(4), 647–650. https://doi.org/10.1016/j.aspen.2012.08.002

Zijlstra. C., Donkers-Venne, D. T. H. M., Fargette, M. (2000). Identification of Meloidogyne incognita, M. javanica and M. arenaria using sequence characterised amplified region (SCAR) based PCR assays. Nematology, 2, 847–53.

6.0 Acknowledgements: I would like to thank Dr. Gabriele Berg, Mag. Dr. Günther Raspotnig, Dr. Danny Coyne and project partners (IITA, CGIAR, Austrian Development Agency, TU Graz, Makerere University) for giving me the opportunity to work on this interesting field. Julian Taffner for both amicable and technical help and advice. Dr. Johannes Hallmann and Dr. Wim Bert for their additional expertise. Rafaela Araújo Guimarães, Tobija Glawogger, Nikolina Todorovic, Jelena Gagic, Ingrid Matzer and Doreen Nampamya for helping in the laboratory experiments. Alexander Mahnert, Barbara Fetz, Christin Zachow, Henry Müller, Tomislav Cernava, Monica Schneider- Trampitsch and Isabella Wrolli for additional help. My family and friends, especially Kathrin and Mia for support during the last year. Without you, this work would not have been possible in this way!

7.0 Appendix Media

R2A:

- 18.1g/l R2A Agar (Carl Roth GmbH + Co. KG, Karlsruhe, Germany)

NA:

- 15g/l Nӓhrbouillon II (sifin Diagnostics, Berlin, Germany) - 18g/l Agar-Agar, Kobe 1 (Carl Roth GmbH + Co. KG, Karlsruhe, Germany)

NB:

- 15g/l Nӓhrbouillon II (sifin Diagnostics, Berlin, Germany)

WA:

- 4g Trypton (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - 8g Glucose (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - 2,4g yeast extract (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - 4g NaCl 1 (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - 16g Agar-Agar, Kobe 1 (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - Fill up with Aqua dest. to 800ml end volume

PDA:

- 21,4g potato-glucose bouillon 1 (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - 16g Agar-Agar, Kobe1 (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) - Fill up with Aqua dest. to 800ml end volume

Czapek-Dox broth:

- 33.4g/l Czapek Dox Media (Duchefa Biochem, Harleem, Netherlands)

Worm lysis buffer (WLB) after Castagnone-Sereno et al. (1995)

o 50mM KCl o 10mM Tris pH 8,2 o 2,5 mM MgCl2 o 60µg/ml proteinase K (Roche) o 0,45% NP40 (Fisher Scientific) o 0,45% Tween 20 (Sigma-Aldrich) o 0,01% gelatine

Abbreviations:

11on undecan-2-one 2e3mp 2-ethyl-3-methyl pyrazine 2m3mp 2-methoxy-3-methzl pyrazine BCA biological control agent CFU colony forming unit ESD diseased endosphere ESH healthy endosphere gas chromatography - mass GC-MS spectrometry J2 second stage juvenile Mbv mortality through VOCs MIG Meloidogyne incognita group NA nutrition agar NaCl sodium chloride NaOCl sodium hypochloride NB nutrition broth NMbv nematicidal mortality through VOCs nVOC nematicidal volatile organic compound OTU operational taxonomic unit PCoA Principal coordinates analysis PPF phytopathogenic fungi PPN phytopathogenic nematode RGI root galling index RKN root-knot nematode SCAR species characterized amplified region SCG Soil crenarchaeotic group TCVA two clamps VOC assay VOC volatile organic compound WA Waxman agar WLB worm lysis buffer

Primer sequences used in this study:

Primer Name Sequence (5’-3’) Specificity Reference Nem_194 TTAACTTGCCAGATCGGACG Blok et al. 5S-18S ribosome region Nem_195 TCTAATGAGCCGTACGC (1997) Nem_Far TCGGCGATAGAGGTAAATGAC Zijlstra et al. M. arenaria-specific SCAR Nem_Rar TCGGCGATAGACACTACAAACT (2000) Nem_Fjav GGTGCGCGATTGAACTGAGC Zijlstra et al. M. javanica-specific SCAR Nem_Rjav CAGGCCCTTCAGTGGAACTATAC (2000) Nem_MI-F GTGAGGATTCAGCTCCCCAG Meng et al. M. incognita-specific SCAR Nem_MI-R ACGAGGAACATACTTCTCCGTCC (2004) Nem_NAD5F2 TATTTTTTGTTTGAGATATATTAG NADH dehydrogenase (Janssen et al., Nem_NAD5R1 CGTGAATCTTGATTTTCCATTTTT subunit 5 2016) Nem_D2A ACAAGTACCGTGAGGGAAAGTT D2-D3 expansion of 28S (Baldwin et al. Nem_D3B TCGGAAGGAACCAGCTACTA rDNA 1997) 515f GTGYCAGCMGCCGCGGTAA 16S rDNA 806r GGACTACHVGGGTWTCTAAT 27f AGAGTTTGATCMTGGCTCAG 16S rDNA 1492r GGTTACCTTGTTACGACTT (Rademaker & BOX A1R CTACGGCAAGGCGACGCTGACG Repetitive sequences De Bruin 1997)

Thermocycler programs for PCRs

Melting Melting PNA Primer Repeats cooling Primer 1 2 annealing annealing Elongation (grey) Elongation 515f/806r pNA 5min @ 60s @ 60s @ 10min @ hold @ 5s @ 75°C 60s @ 74°C 30x Amplicon PCR 96°C 96°C 54°C 74°C 4°C 27f/1492r 5min @ 30s @ 30s @ 5min @ hold @ 90s @ 72°C 30x 95°C 95°C 57°C 72°C 15°C 6min @ 60s @ 60s @ 16min @ hold @ - 8m @ 65°C 35 BOX PCR 95°C 95°C 53°C 65°C 10°C 2min @ 60s @ 60s @ 10min @ hold @ - 90s @ 72°C 40 NAD5_F2/R1 94°C 94°C 45°C 72°C 4°C 4min @ 60s @ 90s @ 120s @ 10min @ hold @ - 35 D2A/D3B 94°C 94°C 55°C 72°C 72°C 4°C 2min @ 30s @ 30s @ 7min @ hold @ - 90s @ 72°C 45 Nem_194/195 94°C 94°C 50°C 72°C 4°C 2min @ 30s @ 30s @ 7min @ hold @ - 60s @ 72°C 45 Nem_Far/Rar 94°C 94°C 61°C 72°C 4°C 2min @ 30s @ 30s @ 7min @ hold @ - 60s @ 72°C 45 Nem_Fjav/Rjav 94°C 94°C 64°C 72°C 4°C 2min @ 30s @ 30s @ 7min @ hold @ - 60s @ 72°C 45 Nem_MI-F/MI-R 94°C 94°C 62°C 72°C 4°C

8.0 Supplementary data

Table 8: Identification grid for adult female RKN identification. NA stands for “no amplification”, if the perineal region was lost during processing of the RKN, specimen were assigned using only molecular identification methods

Sample NAD5 Perineal origin SCAR name (assigned as) Pattern N1 Luwero NA M. cf. Incognita M. incognita N2 Luwero NA M. cf. Incognita M. incognita N3 Namulonge NA M. cf. Incognita M. incognita N4 Namulonge NA M. cf. Incognita M. incognita N5 Namulonge NA M. cf. Incognita M. incognita N6 Namulonge NA NA M. incognita N7 Namulonge NA NA M. incognita N8 Namulonge M. javanica NA M. javanica N9 Namulonge NA NA M. javanica N10 Namulonge M. javanica M. javanica M. javanica N12 Namulonge NA M. cf. javanica - N13 Namulonge M. javanica M. javanica - N15 Luwero NA M. incognita M. incognita N16 Luwero M. incognita M. incognita M. incognita N17 Luwero NA M. incognita M. incognita N18 Luwero M. incognita M. incognita M. incognita N19 Luwero NA M. incognita M. incognita N20 Luwero M. incognita M. incognita M. incognita N21 Luwero NA M. incognita M. incognita N22 Luwero NA M. incognita M. incognita N23 Luwero NA M. incognita M. incognita N24 Luwero NA M. incognita M. incognita N25 Namulonge NA NA M. incognita N26 Namulonge NA NA M. incognita N27 Namulonge NA NA M. cf. incognita N28 Namulonge NA M. javanica - N29 Namulonge NA M. javanica M. javanica N30 Namulonge NA M. javanica M. javanica

Variovorax paradoxus T1GI1

Gas chromatograms of nVOC-producing bacterial strains: Pseudomonas koreensis T3GI1

Fig. 23a: GC of the six found nVOC-producing strains. Siloxane are regarded as originating from the fiber and are not labelled. One repeat of P. monteilii T13GI6b showed bad growth, resulting in compounds that were found specidic for the blank (G,H,R,S; not shown) A: CO2; B: Methanethiol; C: Acetone; D: 2,3-epoxybutane; E: Dimethyl sulfide; F: 2-butanone; G: 3-methylbutanal; H: 2- methylbutanal; I: Methyl thiolacetate; J: 3-methylbutanol; K: 2-methylbutanol; L: Dimethyl disulfide; M: S-methyl propanethioate; N: 2,3-dimercaptopropan-1-ol; O: 2-methyl-2methylthiobutan; P: Methyl (Z)-N-hydroxybenzene-carboximidate; Q: S-methyl-3-methylbutanethioat; R: Benzaldehyde; S: Dimethyl trisulfide; T: 1,4-undecadiene; U: Octanethioic acid S-methylester; X1: unidentified (Rt = 13en 1.581); X2: unidentified hydrocarbon (Rt = 12.277); Pyr: 3-methoxy-2,5-dimethyl pyrazine; 11on: 2- undecanone; 9en: 1-nonene; 10en: 1-decene; 11en: 1-undecene; 12en: 1-dodecene; 13en: tridecene. Next page: remaining chromatograms of Ps. Monteilii T8GH4, Ps. Soli T13GI4, Ps. Monteilii T13GI6b and blank (NA).

Fig. 24a: GC of the six found nVOC-producing strains. Siloxane not labelled. A: CO2; B: Methanethiol; C: Acetone; D: 2,3-epoxybutane; E: Dimethyl sulfide; F: 2-butanone; G: 3-methylbutanal; H: 2- methylbutanal; I: Methyl thiolacetate; J: 3-methylbutanol; K: 2-methylbutanol; L: Dimethyl disulfide; M: S-methyl propanethioate; N: 2,3-dimercaptopropan-1-ol; O: 2-methyl-2methylthiobutan; P: Methyl (Z)-N-hydroxybenzene-carboximidate; Q: S-methyl-3-methylbutanethioat; R: Benzaldehyde; S: Dimethyl trisulfide; T: 1,4-undecadiene; U: Octanethioic acid S-methylester; X1: unidentified (Rt = 1.581); X2: unidentified hydrocarbon (Rt = 12.277); Pyr: 3-methoxy-2,5-dimethyl pyrazine; 11on: 2- undecanone; 9en: 1-nonene; 10en: 1-decene; 11en: 1-undecene; 12en: 1-dodecene; 13en: tridecene.

Mass spectra of single compounds found in nVOC-producing bacteria

Table 9: Mass spectra of bacterial volatiles. M+: molecular mass of compound. Only the ten highest peaks are shown.

compounds fragmentation patterns m/z (relative intensity) CO₂ 45 (13), 44 (M+, 100), 40 (34) Methanethiol 50 (4), 49 (6), 48 (M+, 82), 47 (100), 46 (15), 45 (66), 44 (22), Acetone 58 (M+, 39), 48 (14), 47 (17), 45 (19), 44 (39), 43 (100), 42 (7), 41 (3), 40 (9), 39 (5) 2,3-epoxybutan 68 (15), 58 (36), 48 (20), 47 (24), 45 (16), 44 (79), 43 (100), 42 (9), 40 (15), 39 (13) Dimethyl sulfide 62 (M+, 33), 61 (11), 58 (11), 48 (15), 47 (40), 46 (13), 45 (25), 44 (100), 43 (26), 40 (15) 2-butanone 72 (M+, 33), 57 (12), 47 (2), 45 (2), 44 (29), 43 (100), 42 (7), 41 (3), 40 (8), 39 (3), 3-methyl butanal 71 (29), 58 (63), 57 (25), 45 (12), 44 (100), 43 (70), 42 (20), 41 (86), 40 (8), 39 (38) 2-methyl butanal 58 (65), 57 (88), 56 (53), 55 (18), 44 (18), 43 (40), 42 (18), 41 (100), 40 (11), 39 (30) Methyl thiolacetate 92 (3), 90 (M+, 65), 75 (5), 48 (6), 47 (14), 46 (6), 45 (17), 44 (9), 43 (100), 42 (7) 3-methyl butanol 70 (68), 57 (24), 55 (100), 45 (18), 44 (45), 43 (65), 42 (61), 41 (56), 40 (13), 39 (24) 2-methyl butanol 70 (61), 57 (100), 56 (89), 55 (53), 44 (98), 43 (46), 42 (30), 41 (85), 40 (22), 39 (26) Dimethyl disulfide 96 (10), 94 (M+, 100), 81 (5), 79 (52), 64 (11), 61 (10), 47 (14), 46 (18), 48 (6), 45 (37) S-methyl propanethioate 104 (M+, 54), 94 (19), 79 (10), 75 (17), 61 (95), 57 (100), 48 (13), 47 (23), 46 (10), 45 (27) 2,3-dimercaptopropan-1-ol 108 (6), 106 (65), 91 (7), 61 (6), 59 (100), 58 (23), 57 (8), 47 (7), 45 (14), 44 (5) 2-me-2-methylthiobutan 118 (M+, 23), 75 (19), 71 (60), 48 (20), 47 (18), 45 (16), 44 (11), 43 (100), 41 (40), 39 (16) 1-nonene 92 (3), 90 (M+, 59), 75 (4), 48 (6), 47 (14), 46 (6), 45 (17), 44 (17), 43 (100), 42 (7), Methyl (Z)-N-hydroxybenzene- 153 (6), 152 (9), 151 (M+, 69), 135 (23), 134 (13), 133 (100), 77 (14), 75 (6), carboximidate 68 (10), 45 (6) S-methyl 3-methyl- 85 (75), 75 (20), 57 (100), 56 (8), 47 (14), 45 (12), 43 (19), 42 (8), 41 (47), butanethioate 39 (16) Benzaldehyde 107 (8), 106 (M+, 100), 105 (98), 78 (18), 77 (95), 76 (5), 74 (10), 52 (9), 51 (35), 50 (21) Dimethyl trisulfide 128 (13), 126 (M+, 100), 111 (16), 80 (14), 79 (46), 78 (7), 64 (19), 47 (20), 46 (12), 45 (33) 1-decene 83 (42), 70 (88), 69 (72), 57 (68), 56 (99), 55 (100), 43 (68), 42 (33), 41 (97), 39 (39), 3-methoxy-2,5-dimethyl 138 (M+, 100), 137 (44), 120 (32), 109 (49), 108 (16), 107 (28), 95 (17), 82 pyrazine (28), 54 (28), 42 (25) E-1,4-undecadiene 82 (48), 81 (74), 79 (46), 69 (50), 68 (67), 67 (100), 55 (64), 54 (89), 43 (37), 41 (66) E-3-undecene 97 (37), 84 (40), 83 (61), 70 (85), 69 (78), 57 (55), 56 (83), 55 (100), 43 (79), 41 (92) 1-undecene 97 (49), 84 (49), 83 (73), 70 (95), 69 (88), 57 (56), 56 (83), 55 (100), 43 (68), 41 (86) 1-dodecene 97 (50), 84 (40), 83 (65), 70 (75), 69 (79), 57 (58), 56 (82), 55 (100), 43 (81), 41 (88)

Continued Table 9: Mass spectra of bacterial volatiles. M+: molecular mass of compound. Only the ten highest peaks are shown.

compounds fragmentation patterns m/z (relative intensity) 1,12-tridecadiene 96 (43), 95 (38), 82 (57), 81 (82), 69 (38), 68 (46), 67 (100), 55 (76), 54 (60), 41 (65) 1-tridecene 97 (59), 84 (38), 83 (77), 70 (74), 69 (84), 57 (70), 56 (73), 55 (100), 43 (89), 41 (91) 2-undecanone 85 (10), 71 (35), 59 (30), 58 (100), 57 (11), 55 (11), 43 (71), 42 (6), 41 (18), 39 (6) S-methyl ester octanethioic acid 159 (12), 127 (62), 75 (10), 71 (12), 58 (32), 57 (100), 55 (24), 43 (50), 41 (31), 39 (10) Unidentified substance 50 (4), 49 (5), 48 (69), 47 (82), 46 (12), 45 (48), 44 (100), 40 (22) (Rt=1.581) unidentified substance 85 (68), 84 (22), 71 (95), 70 (25), 69 (20), 57 (100), 56 (19), 55 (25), 43 (99), (Rt=12.277) 41 (42)

Additional amplicon data

0 soil rhizosphere healthy root gall

Fig. 25: Total abundance of genera including species with known nematicidal effects (after Tian et al., 2015) do not differ between rhizosphere, healthy and diseased roots

Table 3: Most abundant bacterial orders in Ugandan soil. rA: relative abundance. We see a high alpha diversity and relatively low-abundant OTUs

order rA [%] Planctomycetales 7,20 Rhizobiales 6,50 Bacillales 6,20 Sphingobacteriales 5,46 Gemmatimonadales 4,69 unknown Acidobacteria Subgroup 6 4,05 Sphingomonadales 3,50 Gaiellales 3,42 Burkholderiales 2,79 Solirubrobacterales 2,47 Rhodospirillales 2,29 Legionellales 2,12 Xanthomonadales 2,02 Blastocatellales 1,99 Myxococcales 1,95 unknown Thaumarchaeota (SCG) 1,94 Acidimicrobiales 1,84 Chthoniobacterales 1,77 Nitrosomonadales 1,76 unknown bacteria 1,51 Thermomicrobia JG30-KF-CM45 1,25 Chloroflexi TK10 1,18 Acidobacteriales 1,09 orders <1% rel. Abundance 31,03