Evaluating the Efficacy of Plant Growth Promoting Rhizobacteria In Australian Agriculture
Shelby Kathleen Berg Bachelor of Science (Hns)
A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2021 School of Agriculture and Food Sciences
Abstract Interest in the soil edaphon is rising with the appreciation that soil biological processes are a main contributor of planetary and ecosystem function, and agricultural productivity. Soils under agricultural production often accumulate crop pathogens, while crop-beneficial microbes may be present in insufficient numbers. An attractive concept is engineering microbial communities to improve crop resilience, development, growth and yield. Plant growth promoting rhizobacteria (PGPR) and other beneficial microbes that improve plant and soil function (termed ‘biostimulants’ in the following) could lower the costs for farmers and the environment. While some countries require manufacturers to provide proof-of-efficacy, the sector remains largely unregulated, including in Australia. There is doubt about the efficacy of many biostimulants because crop relevant scientific evaluations are mostly not in the public domain. As it stands, it has been argued that research has to advance knowledge of plant-microbe-soil interactions to identify suitable strategies for enabling effective biostimulants. The overarching aim of this thesis was to evaluate putative PGPR and soil-enhancing microbes across several levels of experimental control to advance this knowledge frontier for tropical crops. Two chapters (Chapters 2 and 3) report on experiments that tested putative (i) commercial biostimulants and soil enhancing products Soil-LifeTM and Nutri-Life Platform® in a sugarcane field in Queensland’s wet tropics, and (ii) PGPR Serenade® Prime, Rhizo-max, Great Land® and an isolate of Enterobacter at a commercial seedling nursery and in controlled growth cabinets. The effects of microbial inoculation were evaluated with growth and physiological responses, and with phylogenetic marker gene sequencing to analyse microbial communities in root+rhizosphere (‘root’ in the following) and soil. Microbial inoculation did not enhance yield (sugarcane crop) or growth (seedlings of watermelon, tomato, pumpkin), and Great Land®-inoculated watermelon seedlings had significantly less biomass than the un-inoculated control. In field-grown sugarcane, root and soil bacterial communities were unaffected by commercial biostimulants. However, root fungal communities showed a significant increase of Fusarium, Marasmius and Talaromyces populations, confirming that inoculation can indirectly alter microbial communities, but evaluating whether this change convey benefits was beyond scope. Microbial communities of nursery-grown seedlings were not analysed. Instead, we tested sugarcane and watermelon seedlings under well-watered and water limited (stressed) conditions to determine if inoculation with active or inactive microbes conveys benefits. Inoculation did not affect the growth of well-watered seedlings. Water-stressed seedlings grew significantly less than non-stressed seedlings, an exception were watermelon seedlings inoculated with active Rhizo-max which produced similar biomass as well-watered seedlings. Root communities of water-stressed and inoculated seedlings had altered bacterial community composition and species richness. Similar to the field experiment, taxa present in the inoculants did
Page 2 of 167 not form the dominant genera of the root communities or increase in relative abundance. Taken together, the findings show that commercial microbial products are mostly ineffective in the test systems. A third chapter (Chapter 4) reports on the screening of PGP traits of 121 culturable bacterial isolates that originated from the roots of non-inoculated field-grown sugarcane. We tested the in vitro expression of PGP traits, which often informs the selection for biostimulant products, to gain insight of trait distribution and efficacy. Sanger sequencing identified as dominant genera Serratia (43%, Enterobacteriaceae), Pseudomonas (20%, Pseudomonadaceae) and Bacillus (18%, Bacillaceae). These taxa encompass species that have been identified as PGPR previously. Screening 10 PGP traits in vitro showed that the isolates could be broadly categorised into two groups. The first group expressed one or two PGP traits to a high level, the second expressed three or more PGP traits to low or moderate levels, so that the isolates could be broadly classified into ‘specialists’ and ‘generalists’. We then tested whether in vitro activity translates to in vivo effectiveness using bioassays. The first assay used IPT-mutant Arabidopsis to evaluate cytokinin producing bacteria. Of the three cytokinin producers tested, only two affected plant growth either by producing cytokinin or affecting the cytokinin biosynthesis pathway. The second assay involved five isolates with the highest biocontrol activity in vitro and Gummy Stem Blight (GSB, Stagonosporopsis), a fungal disease of Curcurbitaceae. All isolates resulted in somewhat reduced GSB disease severity in watermelon seedlings, including a Bacillus isolate that extensively colonised fungal hyphae on the leaf surface. These findings are promising and have to be furthered to develop PGPR as an effective tool in plant production. We propose that the current focus on selecting PGPR based on in vitro tests should be expanded to screening effective root colonisation to ensure a strong ability to establish with and colonise target crops. Whether ‘specialist’ or ‘generalist’ PGPR hold promise for product development should be investigated. We discuss effective microbe delivery and the implications of inducible PGP traits on microbial survival. Taken together, the research confirms the need for improved products and their delivery, which will likely include more specific uses of optimised biostimulants for particular species and settings. It confirms that current products can affect root microbial communities, and in one instance, show efficacy. While regulators are reining in the rapidly expanding biostimulant market in some regions by demanding evidence of efficacy, similar action is needed for tropical crops. Ideally, R&D should be performed in industry relevant settings and underpinned by fundamental research on the behaviour of microbes in soil and root communities. Collaboration between government regulators, biostimulant producers, crop industries and independent research agencies is required to deliver the promise of green agriculture; in Chapter 5, we discuss the way forward.
Page 3 of 167 Declaration by author
This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.
I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.
I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.
Page 4 of 167 Publications during candidature
Berg S, Dennis PG, Paungfoo‐Lonhienne C, et al (2020) Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield. Biol Fertil Soils 56:565– 580
Publications included in this thesis
Berg S, Dennis PG, Paungfoo‐Lonhienne C, et al (2020) Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield. Biol Fertil Soils 56:565– 580
Manuscripts included in this thesis
Chapter Two Berg S, Dennis PG, Paungfoo‐Lonhienne C, et al (2020) Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield. Biol Fertil Soils 56:565– 580
Chapter Three Berg S, Dennis PG, Schmidt S, et al (2021) Examining the efficacy of commercial PGPR products for seedling production in horticulture. unpublished
Chapter Four Berg S, Dennis PG, Schmidt S, et al (2021) Screening rhizobacteria for plant growth promoting traits. unpublished
Contributor Statement of contribution Shelby Berg Conception and design (10%) Analysis and interpretation (55%) Drafting and production (60%) Susanne Schmidt Conception and design (40%) Analysis and interpretation (10%) Drafting and production (10%) Paul Dennis Conception and design (0%) Page 5 of 167 Analysis and interpretation (30%) Drafting and production (10%) Chanyarat Paungfoo-Lonhienne Conception and design (20%) Analysis and interpretation (5%) Drafting and production (5%) Jay Anderson Conception and design (0%) Analysis and interpretation (0%) Drafting and production (5%) Nicole Robinson Conception and design (10%) Analysis and interpretation (0%) Drafting and production (2.5%) Richard Brackin Conception and design (0%) Analysis and interpretation (0%) Drafting and production (2.5%) Adam Royle Conception and design (10%) Analysis and interpretation (0%) Drafting and production (2.5%) Lawrence DiBella Conception and design (10%) Analysis and interpretation (0%) Drafting and production (2.5%)
Page 6 of 167 Contributions by others to the thesis
Susanne Schmidt and Paul Dennis contributed to the ideas explored in this thesis and editing. Rebecca Martin has contributed as research assistant with DNA extractions conducted for bacterial isolates, watermelon root and soil samples. Rebecca’s honours research was conducted alongside the research of this thesis. One of Rebecca Martin’s bacteria-P mobilisation assays is included in Chapter 4 of this thesis. I assisted Rebecca with bacteria colony management, experimental design, and result interpretation. Jay Anderson and Rebecca Martin contributed to the Gummy Stem Blight experiments. Paul Dennis contributed to the taxonomic resolution and statistical analysis of microbial community data.
Statement of parts of the thesis submitted to qualify for the award of another degree
None
Research Involving Human or Animal Subjects
No animal or human subjects were involved in this research.
Page 7 of 167 Acknowledgements I would like to Acknowledge Susanne Schmidt, Paul Dennis, Nicole Robinson and Stephen Mudge for the support as supervisors and advisors during the course of this thesis. I would like to acknowledge Chanyarat Paungfoo-Lonhienne for assistance with the field experiment, Paul Dennis for his assistance with bioinformatics and statistical analysis of microbial communities. I would like to thank Chanyarat Paungfoo-Lonhienne and Yun Kit Yeoh for collection and preparation of the bacteria library and for allowed me to use it in this thesis. I would like to thank Rebecca Martin for her assistance with bacteria colony management and collaboration in phosphorous mobilisation experiments. I would also like to acknowledge TNRM, HCPSL and Wide Bay Seedlings for working alongside us to conduct field trials and testing of microbial products. I would like to thank Christine Beveridge for acting as a mentor in plant hormones and assisting with the interpretation of results. I would like to thank Rick Webb for assisting with microscopy training, sample preparation and image capture using scanning electron microscopy.
Financial support
This research was supported by an Australian Government Research Training Program Scholarship. A living stipend and operational funds scholarship was provided by Sugar Research Australia. I would also like to thank Terrain Natural Resource Management for funding the sugarcane field trial and Herbert Cane Productivity Services Limited for managing and sampling the trial (Chapter 1). In-kind support was provided from Adrian Ross and Andrew Ross, Wide Bay Seedlings.
Page 8 of 167 Keywords Rhizobacteria, beneficial microbes, bacteria, fungi, soil health, next generation fertiliser, microbial communities.
Australian and New Zealand Standard Research Classifications (ANZSRC) ANZSRC code: 060504, Microbial Ecology, 60% ANZSRC code: 050302, Soil Biology, 30% ANZSRC code: 070108, Sustainable Agricultural Development, 10%
Fields of Research (FoR) Classification FoR code: 0605, Microbiology, 50% FoR code: 0607, Plant Biology, 30% FoR code: 0701, Agriculture, Land and Farm Management, 20%
Page 9 of 167 Table of Contents Chapter 1 Introduction ...... 20
1.1 The importance of soil biology for healthy crops ...... 20
1.2 Plant growth promoting rhizobacteria ...... 24
1.2.1 Pathogen resistance ...... 26
1.2.2 Hormone production and signalling ...... 28
1.2.3 Nutrient acquisition ...... 29
1.3 Selecting putative PGPR’s ...... 31
1.4 Commercial formulations of plant growth-promoting rhizobacteria ...... 33
Chapter 2 Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield ...... 37
2.1 Abstract ...... 37
2.2 Introduction ...... 37
2.3 Methods ...... 40
2.3.1 Study site and experimental design ...... 40
2.3.2 Microbial biostimulant products ...... 41
2.3.3 Sampling of microbial products, soil and roots ...... 42
2.3.4 Analysis of bacterial communities ...... 42
2.3.5 Analysis of fungal communities ...... 43
2.3.6 Statistical analysis ...... 43
2.4 Results ...... 44
2.4.1 Sugarcane yield ...... 44
2.4.2 Composition of the microbial products ...... 45
2.4.3 Response of bacterial communities to microbial product application ...... 46
2.4.4 Response of fungal communities to microbial product application ...... 48
2.5 Discussion ...... 49
2.5.1 Sugarcane yield ...... 49
2.5.2 Composition of the microbial products ...... 49
Page 10 of 167 2.5.3 Effects of microbial product application on the composition of bacterial communities 50
2.5.4 Effects of microbial product application on the composition of fungal communities . 52
2.5.5 Considerations for product efficacy ...... 54
2.6 Conclusions ...... 56
Chapter 3 Examining the efficacy of commercial PGPR products for seedling production in horticulture 60
3.1 Introduction ...... 60
3.2 Methods ...... 62
3.2.1 Preparation of bacteria inoculum ...... 62
3.2.2 Nursery experiment ...... 64
3.2.3 Stress experiment in growth cabinet ...... 65
3.2.4 Root sampling and sequencing ...... 66
3.3 Results ...... 67
3.3.1 Nursery experiment ...... 67
3.3.2 Water stress experiment in growth cabinet ...... 69
3.3.3 Analysis of root bacterial communities ...... 70
3.4 Discussion ...... 74
3.4.1 Microbial products have variable affects in a seedling nursery ...... 75
3.4.2 Environmental stress together with microbial inoculants shift bacterial communities in the root+rhizosphere ...... 77
3.5 Conclusion ...... 81
Chapter 4 Screening rhizobacteria for plant growth promoting traits ...... 83
4.1 Introduction ...... 83
4.2 Methods ...... 87
4.2.1 Isolation and culture maintenance of bacterial isolates ...... 87
4.2.2 Phosphorus mobilising activity ...... 88
4.2.3 Biological nitrogen fixation ...... 88
4.2.4 Auxin production ...... 89
Page 11 of 167 4.2.5 Cytokinin production ...... 89
4.2.6 Biofilm and EPS production ...... 89
4.2.7 Siderophore production ...... 90
4.2.8 Antifungal activity ...... 91
4.2.9 Hydrogen cyanide (HCN) activity ...... 91
4.2.10 Protease activity ...... 92
4.2.11 Plant screening assays ...... 92
4.2.12 16s rRNA gene sequencing and analysis ...... 96
4.3 Results ...... 96
4.3.1 Nutrient acquisition assays...... 98
4.3.2 Phytohormone production ...... 100
4.3.3 Pathogen resistance and antifungal activity ...... 101
4.3.4 Biofilm production ...... 102
4.3.5 Screening of bacteria with plants ...... 103
4.3.6 Bacteria classification and phylogenetic signals ...... 110
4.4 Discussion ...... 113
4.4.1 Distribution of PGP traits and generalist/specialist bacteria ...... 114
4.4.2 Traditional plate assays allow for untapped PGP mechanisms...... 120
4.4.3 Translation from plate to plant ...... 121
4.5 Conclusion ...... 124
Chapter 5 Conclusion ...... 126
5.1 Current status of commercial microbial biostimulants...... 126
5.2 Selecting effective microorganisms: Considerations for current screening assays ...... 129
5.3 Improving PGPR delivery in agricultural and horticultural systems ...... 130
Page 12 of 167 List of Figures Figure 1 Yield of the studied sugarcane crop after 14 months of growth. Yield is expressed as a fresh weight of sugarcane harvested, b sugar yield and c commercial cane sugar (CCS, a measure of sugar content used in the Australian sugarcane industry). Treatments include recommended N fertiliser rate (full N, 130 kg N ha−1) and 75% reduced rate (75% N, 100 kg N ha−1) with (+microbes) or without inoculation using the commercial products. The two 75% N treatments were analysed for bacterial and fungal communities. Data are averages of three plots per treatment with no significant differences in cane yield, sugar yield or CCS between the four treatments (ANOVA, P ≥ 0.05). Generated using GraphPad Prism 7.0a ...... 44 Figure 2 Heatmap summarising the relative abundances of rhizosphere and bulk soil bacterial communities associated with soils receiving 100 kg/ha N (75% of recommended N fertiliser rate) without (control, blue) or with application of microbial products (microbe, red). Each line represents the relative abundance (RA) of a given operational taxonomic unit (OTU) in taxonomic order. The three left columns show the RA of each OTU in “Nutri-Life Platform®”, “Soil-Life™” and mixed products, respectively. Remaining columns are ordered by sample (rhizosphere, soil), followed by week of collection from week 2 (W2), week 15 (W15) to week 25 (W25). Generated using R 3.5.1 ...... 47 Figure 3 Heatmap of rhizosphere and soil fungal communities for plots receiving 75% of recommended N fertiliser rate (100 kg/ha N) without (control, blue) or with application of microbial products (microbe, red). Each line represents the relative abundance (RA) of a given operational taxonomic unit (OTU) in taxonomic order. The three left columns show relative abundance of each OTU within the “Nutri-Life Platform®”, “Soil-Life™” and mixed inoculum, respectively. Remaining columns are ordered by sample (root + rhizosphere, soil), followed by week of collection from week 2 (W2), week 15 (W15) to week 25 (W25). Only fungi with a relative abundance of greater than 1% are included in the figure. Generated using R 3.5.1 ...... 48 Figure 4 Four week old harvested watermelon seedlings grown at WBS. Plants from left to right follow the order of treatments listed in bar graphs below and include Control (C), Serenade® (S), Rhizomax® (R), Great Land® (G), Enterobacter N17a (N). Images in the top row include control treatments that did not receive active microbes. This includes the Control treatment receiving water and inactive microbial treatments (indicated by a “-”). Images in the bottom row include active microbial treatments (indicated by a “+”). Plant roots were washed clean of substrate and root architecture was assessed by observation...... 68 Figure 5. Shoot and root biomass of tomato (g dry weight) (A, D), capsicum (B, E) and watermelon (C, F) seedlings harvested two weeks post inoculation (A-C) and four weeks post inoculation (D-F). Microbial treatments containing active (inoculum with active microbes) or inactive (pressed and
Page 13 of 167 filtered inoculums containing no active microbes) treatments are indicated by + or – symbols. ANOVA and Tukey’s post hoc analysis evaluated statistical significance between products. Only two statistically significant differences was detected (D, F) as indicated by an asterisk (P < 0.05). 68 Figure 6 Four week old watermelon seedlings grown in growth cabinets in non-water stressed (top row) and water stressed (bottom row) conditions. Plants from left to right follow the order of treatments listed in bar graphs below and include Control (C), Inactive Serenade® (S-), Active Serenade® (S+), Inactive Rhizomax® (R-), Active Rhizomax® (R+), Inactive Great Land® (G-), Active Great Land® (G+), Inactive Enterobacter N17a (N-), Active Enterobacter N17a (N+). Plant roots were washed clean of substrate and root architecture was assessed by observation...... 69 Figure 7 Four week old sugarcane plantlets grown in non-water stressed (top row) and water stressed (bottom row) conditions. Plants from left to right follow the order of treatments listed in bar graphs below and include Control (C), Inactive Serenade® (S-), Active Serenade® (S+), Inactive Rhizomax® (R-), Active Rhizomax® (R+), Inactive Great Land® (G-), Active Great Land® (G+), Inactive Enterobacter N17a (N-), Active Enterobacter N17a (N+). Plant roots were washed clean of substrate and root architecture was assessed by observation...... 70 Figure 8. Watermelon (A-C) and sugarcane plantlets (D-F) four weeks post inoculation, grown in growth cabinets. Treatments were ‘non-water stress’ (B, C) and ‘water stress’ (E, F). Root and shoot dry weights are shown (A, D). Plants were treated with eight microbe treatments and a non- inoculated control. The three commercial products and Enterobacter N17A were delivered as inoculums with active (+) bacteria or inactive (-) bacteria. Different letters above/below the bars indicate statistical significance (P<0.05)...... 70 Figure 9. Redundancy Analysis (RDA) plots representing bacterial community composition of stressed and non-stressed watermelon root+rhizosphere. Circles symbolise active (+) bacteria treatments, triangles symbolise inactive (-) bacterial treatments. Shaded ovals represent community composition and variation across three replicate samples for each treatment. When shaded ovals are overlapping, the communities are similar and when they separate on the plot, the communities are different...... 71 Figure 10. Heatmap of root+rhizosphere communities of non-stressed and water-stressed watermelon seedlings. Eight microbe treatments were tested with active (+) or inactive (-) microbe inoculum. Each line represents the relative abundance (RA) of a given operational taxonomic unit (OTU) in taxonomic order. Only bacteria with a relative abundance of greater than 1% are included in the figure. RA is represented in colour scale ranging from the lowest relative abundance score in white (1% RA), to moderate relative abundance scores in blue (4% RA), to the highest relative abundance score in red (7% RA). Generated using R Studio 3.5.1 and Microsoft Excel...... 74
Page 14 of 167 Figure 11 Proportion of bacteria expressing ten plant growth promoting traits in plate assays. 121 microbes were screened for these PGP traits. Bacteria were isolated from the sugarcane rhizosphere grown in field conditions in North Queensland, Australia...... 97 Figure 12 Proportion of bacteria expressing a particular number of PGP traits. 121 bacterial isolates were screened for ten plant growth promoting traits and no organism exhibited more than five traits...... 98 Figure 13 Results of enzyme assays screening bacteria for P solubilising (A-B) or protease activity
(C-E). (A) Proportion of 121 bacterial isolates exhibiting phytase activity, Pi solubilising activity, both activities, or no P mobilising activity. (B) P solubilisation index of 121 bacterial isolates expressing P solubilising and phytase activity. Microbes are ranked from highest to lowest activity according to solubilisation index (see Section 1.2.2). (C, D) Proportion of bacteria screening positive for protease activity with high, low and no activity after five or ten days of growth. (E) Microbes are ranked from highest to lowest protease activity. Activity is assigned based on a calculated solubilisation index...... 99 Figure 14 Phytohormone assays included IAA and cytokinin production. (A) Salkowski assay identified IAA producing bacteria with pink colouration indicating isolates producing IAA. Each well contains one bacterial isolate with arrows indicating those that tested positive for IAA activity. (B) Pie chart with proportion of bacteria testing positive for IAA production. (C) Cucumber cotyledons placed next to bacteria cultures developing green pigment are indicators of bacterial production of cytokinin. The left cotyledon pellet represents an intermediate level of cytokinin production, the right one a high rate. (D) Pie chart with proportion of bacteria that expressed high (7%), intermediate (28%) or no (72%) of cytokinin activity...... 100 Figure 15 Microbes tested for pathogen resistance, HCN production and antifungal properties. (A) Solubilisation index of siderophore producing bacteria ranked from highest to lowest activity. Activity is assigned based on a solubilisation index (see Section 1.2.2). (B) Development of a yellow halo around bacterial colonies indicates siderophore production as the result of iron degradation by bacterially-derived siderophores. (C) Screening bacteria for HCN production with filter paper soaked in a yellow picric acid solution with colour change to light or dark brown indicating production of volatile HCN; top plate shows an isolate that with no production, the bottom plate shows a HCN-producing isolate. (D) Inhibition assays of fungal pathogen Stagonosporopsis sp. and isolates show putative antagonists suppress fungal growth (inhibition zone around bacterial colonies on the four plates on the left), while non-antagonistic bacteria were colonised by the fungus (two plates on the right)...... 101 Figure 16 Biofilm forming bacteria are identified by colour reaction (i.e. darker stain indicating more bacteria adhering to the sides of the wells) (B). Pie chart (A) and ranking (C) of relative
Page 15 of 167 activity illustrates the proportion of bacteria that screened positive for biofilm formation grouped into high, intermediate or no activity. Isolates with an absorbance equal or lower than that of the control well are shown with the grey dotted box...... 103 Figure 17 (A) Watermelon seedlings inoculated with putative fungal antagonists and GSB pathogen. Control plants (left column of growth containers) were not inoculated with bacteria, but received only water and GSB pathogen. Bacteria treatments from (second) left to right growth containers are Bacillus N16, Bacillus N22a, Pseudomonas S13, Rhizobium S22 and Bacillus T37. (B) Percentage of disease incidence by plant infection and death is shown for all treatments...... 104 Figure 18 A Re-isolation of bacteria inoculated on leaf surface of watermelon plants. Leaf cuttings were collected from the watermelon plants and plated on nutrient agar. After overnight incubation, bacteria of the same colony morphology as the original inoculated Bacillus N16 bacteria grew from the leaf cuttings (A). A small amount was subcultured onto a new plate (B) and then again subcultured and 16-streaked (C) to analyse colony morphology...... 104 Figure 19 Representative SEM images of watermelon leaves inoculated with putative bacterial antagonists and GSB pathogen. The control (A) were leaves infected with GSB but did not receiving bacteria, these had fungal hyphae covering leaf surfaces. Bacteria treatments included Bacillus T37 (B), Bacillus N22a (C,D), Pseudomonas S13 (E,F), and Bacillus N16a (G,H). Replicate plants of each treatment were sampled and pooled prior to SEM imaging...... 105 Figure 20 Arabidopsis assay screening microbes with high and no cytokinin activity. Wild type (WT) and isopentenyltrensferase (IPT) mutant plants were inoculated with bacteria isolates to evaluate growth promoting potential. High cytokinin producers from plate assays included Serratia N22b, Stenotrophomonas N31a, and Acinetobacter N18a. Bacillus R13 did not test positive for cytokinin production in plate assays and was not expected to have any significant results when inoculated on WT or IPT mutants as this was one of the bacteria isolates that did not test positive for any plant growth promoting traits...... 106 Figure 21 Dry weight of watermelon seedlings exposed to stressed and non-stressed water treatments and five different bacteria treatments including a no bacteria control, two ‘no-trait’ negative control bacterial treatments and two putative cytokinin producing microbes Serratia N22b and Stenotrophomonas N31a...... 107 Figure 22 Dry weight of watermelon plants exposed to water stressed and non-stressed treatments and six microbial treatments including a no microbe control, two ‘no-auxin producing microbes’ control treatments and three putative auxin producing microbes...... 108 Figure 23 Watermelon plants that did not receive bacteria inoculation (A,B) or that received Enterobacter N13, a putative auxin producer (C,D). Plants were grown with sufficient water supply
Page 16 of 167 (A,C) or limited water to induce stress (B,D). Before drying and weighing, plants were washed and photographed to visualise roots...... 109
Figure 24 Total dry biomass of sugarcane plantlets after four weeks growth supplied with soluble Pi and two P waste sources (MBM and Struvite) and bacterial inoculants (both with phytase and P- solubilising activity). No statistically significant differences were observed (ANOVA, post-hoc test)...... 110 Figure 25 Bacteria isolates that were identified as identical species by their phylogenetic signal equating to 0. Bacteria genera and identification code are listed and organised by phylogenetic relatedness. Testing positive for a particular trait is indicated by a coloured rectangle, testing negative is indicated by a white rectangle...... 111 Figure 26 Phylogenetic tree of bacterial isolates as sequenced by Sanger sequencing of 16S rRNA. Bacterial phyla are indicated by blue background hues within each clade. Trait data is indicated by coloured circles on rings surrounding the phylogenetic tree for each bacterium tested. Large circles denote high trait activity, small circles indicate low activity, and no circle no detectable trait. All 121 bacteria isolates are shown here including the 31 genetically similar bacteria identified by 16s rRNA gene sequencing...... 112 Figure 27 Redundancy Analysis (RDA - R package vegan) plots showing bacterial isolates tested for nine traits (BNF, as a not detected was omitted here). Family level taxonomy is highlighted in blue to represent distribution of different taxonomic groups in relation to trait expression. Red and black text denote the screened traits and bacteria isolate, respectively. Bacterial families include Flavobacteriaceae, Burkholderiaceae, Bacillaceae, Xanthomonadaceae, Enterobacteriaceae, Microbacteriaceae, Rhizobiaceae, Pseudomonadaceae, Moraxellaceae, Comamonadaceae, Micrococcaceae, and Alcaligenaceae...... 113
Page 17 of 167 List of Tables Table 1 Plant growth-promoting traits for which microbes are screened to select putative PGPRs. 32 Table 2 Overview of the microbes claimed to be present and detected in biostimulant products to the lowest taxonomical classification as specified by the manufacturers. These broad microbial groupings were quantified in roots and soil in plots treated with the biostimulant products and in untreated plots. The organisms were detected in roots and in soil in similar abundance irrespective of biostimulant treatment. For detail on organism groups, please see text ...... 46 Table 3 Experiment overview of the Wide Bay Seedling nursery experiment. For each crop, we used x trays with x seedlings to pool x pseudo replicate into one replicate, different trays constituted replicates...... 65 Table 4. Overview of bacteria listed as ingredients in biostimulant products to the lowest taxonomical classification as specified by manufacturers. These broad microbial groupings were quantified in roots+rhizosphere of inoculated and control watermelon seedlings in water stressed and non-stressed treatments...... 72 Table 5 Top 10 OTUs across the control and Rhizomax® treatments for non-stressed and water stressed watermelon seedlings. OTUs are listed with their OTU-ID, lowest taxonomical classification and relative abundance...... 73 Table 6 PGPR literature showing the number of bacteria and traits screened in previous and this study...... 84 Table 7 Overview of selected bacterial isolates that (i) expressed the highest activities and fewest traits (‘specialists’), or (ii) expressed the widest range of traits (‘generalists’). Twenty-seven of the 121 isolates, identified to genus level, are shown here with the number of traits expressed and the level of activity for each trait...... 98
Page 18 of 167 List of Abbreviations used in the thesis ACC 1-aminocyclopropane-1-carboxylic acid BTH Benzo-(1,2,3)-thidiazole-7-carbothioic acid S-methyl ester BNF Biological nitrogen fixation C Carbon CCS Commercial cane sugar (a measure of sugarcane content, Australia) DAPG 2,4-diacetylphloroglucinol DNA Deoxyribonucleic acid HCN Hydrogen cyanide IAA Indole-3-acetic acid ISR Induced systemic resistance N Nitrogen OTU Operational taxonomic units P Phosphorus PCA Phenazine1-carboxylic acid PCR Polymerase chain reaction PGP Plant growth promotion PGPR Plant growth promoting rhizobacteria SAR Systemic acquired resistance QS Quorum sensing ROS Reactive Oxygen Species SOD Superoxide Dismutase
Page 19 of 167 Chapter 1 Introduction
1.1 The importance of soil biology for healthy crops
Since the Green Revolution in the mid-20th century, food production has increased by 125% with high-yielding crop varieties, increasing use of mineral fertilisers, herbicides and pesticides, irrigation and other improvements (Khush 1999; Kush and Khush 2001; Thenkabail 2010). However, more recently crop yields are not rising proportionally to the increasing input of agrochemicals and are not meeting world food demand (Ray et al. 2013; Pii et al. 2015). Intensive agriculture is under scrutiny as it has a high footprint on the environment. Agronomic practices in many high production systems are accompanied by inefficiencies, and mineral fertiliser and other agrochemicals cause water and air pollution, soil degradation and greenhouse gas emissions (among others) (Tilman 1998; Altomare and Tringovska 2011; Tilman et al. 2011). To feed the growing human population that is predicted to reach nine billion by 2050, we need to accelerate food production (Godfray et al. 2010; Tilman et al. 2011; Ray et al. 2012; Hochman et al. 2013; Fischer et al. 2014). It is recognised at the highest level of governance (FAO, UN Environment Assembly) that to meet food demand and maintain high agricultural production in the long term, we must move towards more sustainable management practices. One aspect, which is the focus of this thesis, is the recognition that soil is a major factor that is limiting crop productivity. Soil health, specifically soil biological health, is a term used to describe soils that support large, diverse microbial communities while suppressing soil/plant pathogens and enhancing plant development (Altieri and Nicholls 2003; Pankhurst et al. 2003; Brackin et al. 2017).
Interest in soil health has increased in recent years with the appreciation of the importance of soils for food security, global ecosystem function, and environmental quality (Doran 2002). Soil formation is a slow process which is commonly surpassed by soil erosion in most agricultural land, and thus soil is considered a non-renewable resource (Amundson et al. 2015). The replacement of biodiverse ecosystems with crop monocultures, physical, chemical, and biological disruption of soil with continuous application of mineral fertilisers and pesticides has severe impacts on soil function. This can include impacts on soil microbial communities and the ability of soils to cycle nutrients, exchange gases, and store organic carbon as organic matter (Doran 2002; Amundson et al. 2015). While farmers have ways of manipulating the physical and chemical properties of soils, soil biological properties are less well understood or altered. Knowledge of, and ultimately, effective
Page 20 of 167 manipulation of, a soil’s biology therefore offers potential for improving soil health and function, and associated crop productivity. This strategy would allow growers to enhance populations of beneficial soil bacteria, such as plant growth promoting rhizobacteria (PGPR) investigated in this thesis, over deleterious bacteria populations. PGPR have been studied for several years for their growth promoting effects and were rapidly commercialised due to the wide range of benefits they provide to plant and soil health (see section 1.2).
The soil microbiome has a vast influence on the health and productivity of plant communities (Chaparro et al. 2012). The composition of the soil microbiome is primarily influenced by soil physico-chemical and environmental factors that include regolith, climate, and vegetation. Numerous studies have shown that soil pH is a major factor driving the composition of the soil microbial community (Fierer and Jackson 2006; Högberg et al. 2007; Rousk et al. 2010; Chaparro et al. 2012). Fungal and bacterial species thrive at different pH values (Högberg et al. 2007; Chaparro et al. 2012) likely due to individual species and ecotypes having evolved to survive within specific ecological niches. Some have suggested that the soil pH is a predictor for soil bacterial community structure (Lauber et al. 2009). However, whether soil pH has a direct effect on microbiome composition or indirectly shapes communities by influencing changes in other soil chemical properties (nutrient content, organic carbon pools, soil moisture, temperature, etc.) is often not investigated, although these additional chemical and environmental factors need to be taken into account (Rousk et al. 2010). Temperature, soil moisture, organic carbon pools, and nutrient content have all been proposed as drivers of microbial community composition (Egamberdiyeva and Höflich 2003; Çakmakçi et al. 2006; Högberg et al. 2007; Hayat et al. 2010; Chaparro et al. 2012; Hansen et al. 2019; Schlatter et al. 2019). There is evidence that plant communities have a strong effect on the microbial community composition (Grayston et al. 1998; Marschner et al. 2001; Wieland et al. 2001; Chaparro et al. 2014). The presence of particular microbes in agricultural soils is determined by the original pool of microbes in the natural ecosystem from which the agricultural system has been derived, with subsequent changes in response to land use change, environmental stresses/pressures and agronomic management of crops or pastures (Chaparro et al. 2012). In addition, plants enrich certain microbial taxa on and inside roots that are pre-determined by soil type and environment (Tilak et al. 2005; Yeoh et al. 2016). Microbial community composition can differ substantially between bulk soil and rhizosphere with a lower bacterial and fungal diversity in the rhizosphere, which supports the concept that plants recruit a subset of microbes from surrounding soil (Smalla et al. 2001; Kowalchuk et al. 2002; Paungfoo-Lonhienne et al. 2008, 2015; Chaparro et al. 2012; Yeoh et al. 2017). Ultimately it is not just one factor that determines the
Page 21 of 167 composition of microbial communities but rather the combination of existing and induced factors (Hansen et al. 2019).
The rhizosphere is characterised as the interface between roots and the surrounding soil (Bais et al. 2006; Lugtenberg and Kamilova 2009). The distance from the root surface that plants can influence is difficult to discern, although there is agreement that plants manipulate the microbial composition of endophytic compartments, root surface and the area directly surrounding the root surface with as many as 1011 microbial cells per gram of root present within the rhizosphere (Yang and Crowley 2000; Berendsen et al. 2012). This is highlighted by studies showing that microbial community composition can differ between different plant species that are grown in the same soil (Berendsen et al. 2012). Plant specificity could be associated with specific root exudates (Berg and Smalla 2009) which have strong influence on microbial activity and can shape the diversity and distribution of soil-borne microbes (Grayston et al. 1998; Kowalchuk et al. 2002; Marschner et al. 2004; Rudrappa et al. 2008; Berg and Smalla 2009; Huang et al. 2014; Kost et al. 2014).
Plant roots secrete numerous chemicals including sugars, organic acids, oxalates, phenolic compounds, amino acids and many secondary metabolites (van Loon 2007; Rudrappa et al. 2008; Mandal et al. 2010; Ahemad and Kibret 2014; Huang et al. 2014; Kost et al. 2014). These compounds are referred to as root exudates, also termed chemo-attractants, and are secreted by plants to target and mediate biological interactions within the rhizosphere (van Loon 2007; Ahemad and Kibret 2014; Huang et al. 2014). Lectins, for example, are a group of well-studied root exudates that are secreted by plants to communicate with and ‘attract’ biological nitrogen fixing microbes (Smit et al. 1992; Rodríguez-Navarro et al. 2007). Wieland et al. (2001) investigated the effects of multiple biotic/abiotic factors on microbial community composition. The study concluded that, of the tested parameters (i.e. plant species and developmental stage, soil type), plant species had the strongest effect in shaping microbial communities, although a limitation of this analysis was that the exudate composition of each host was not investigated (Wieland et al. 2001). Similarly, other studies have found that rhizobacteria communities are plant species-specific (Marschner et al. 2001). Grayston et al. (1998) were able to demonstrate that plant species had a substantial impact on microbial community structure and carbon use. They found that the distinct rhizosphere communities associating with each plant species used carbon sources in different ways, likely the result of differing exudate properties (Grayston et al. 1998). Other studies have also shown plant- specificity in rhizobacterial growth promotion with evidence of developmental stage, cultivar- specificity and genotype influencing microbial establishment (Chiarini et al. 1998; Kowalchuk et al. 2002; Marschner et al. 2004; Berg and Smalla 2009; Lery et al. 2011; Chaparro et al. 2014).
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Microbes are attracted to, and move towards, root exudates by means of chemotaxis and quorum sensing. Quorum sensing (QS) is a regulatory mechanism that allows bacteria to regulate gene expression, respond to their environment and coordinate group behaviour (Li and Nair 2012; Eickhoff and Bassler 2018). QS has been identified in many bacteria and found to play a key role in bacterial functions and activities including bioluminescence, nodulation, biofilm formation, antibiotic production, secondary metabolite production, chemotaxis and motility (Li and Nair 2012; Venturi and Keel 2016). Bacteria release small signals (autoinducers) as a means of measuring population density and when the signal concentration reaches a threshold, response regulators are activated (Li and Nair 2012). Signalling events are then triggered which lead to a coordinated expression of traits across an entire population and the activation of traits that microbes were not necessarily capable of when they existed as single, free-living cells (Mohan et al. 2018). This illustrates the importance of a strong and established population of microbes for a coordinated response and beneficial effect to occur. The signal concentration and therefore required population density before the threshold is met most likely differs between taxa (Czárán and Hoekstra 2009), suggesting some isolates could enhance plant growth in small population densities while others need to be present in much higher concentrations. This could also suggest why beneficial microbes that are already present in the rhizosphere are not delivering plant growth promoting effects under in situ conditions but require isolation, and re-inoculation in higher cell concentrations to deliver population densities that are capable of triggering QS expression and trait activity (Czárán and Hoekstra 2009; Mohan et al. 2018).
While it may be seen as a disadvantage to not express traits constantly, an evolutionary reason is that such activities are costly processes (Schluter et al. 2016). Trait expression from a single microbe cell is unlikely to deliver a benefit to the microbe or the surrounding biota, while expression from a larger microbial population will (Schluter et al. 2016; Moreno-Gámez et al. 2017). QS therefore ensures mitigation of unnecessary energy cost and a balance of expense and gain. It has also been suggested that cross-talk can occur between different bacteria isolates with one bacteria recognising and responding to autoinducers released by other isolates (Mohan et al. 2018). This aligns with other evidence in the scientific literature that the application of beneficial microbial products can indirectly affect native microbial communities (i.e. the inoculant abundance does not increase in the soil but rather other populations do; Ma et al., 2018). Certain bacteria may not only respond to stimuli themselves but may encourage other bacteria to respond. Aside from direct competition to perceive and respond to QS signals, some bacterial antagonists can also interrupt QS in other bacteria therefore control other microbes by inhibiting signal molecule
Page 23 of 167 production and perception (Raaijmakers et al. 2009), and both, beneficial and pathogenic bacteria can possess this trait. While QS helps control the response of the bacterial population to signalling, microbes find roots through chemotaxis (Sood 2003; Feng et al. 2018). Chemotaxis is triggered within bacteria when a chemoattractant binds to its associated chemoreceptor (Feng et al. 2018). This initiates a cascade of processes that leads to the activation of the flagellar motor and subsequent movement of the microbe in the direction of the chemoattractant gradient (Dennis et al. 2013; Feng et al. 2018). With plants, this direction leads towards the various root zones that secrete root exudates and contain microbial chemoattractants. These mechanisms not only allow microbes to respond to, and move towards chemical stimuli as a coordinated group, but also allow plants to attract beneficial microbes with particular traits to help them cope with environmental pressures.
1.2 Plant growth promoting rhizobacteria
Soil contains microbes that have no discernible effect on plant growth, pathogenic and beneficial microbial communities. Microbes that elicit beneficial effects and promote plant growth are so called ‘plant growth promoting rhizobacteria’ (PGPR) (Lugtenberg and Kamilova 2009). Over the past century, many species and strains of PGPR have been identified that substantially enhance plant growth. Different strains, while belonging to the same species, can have very different effects on plant growth, and can differ in their ability to produce certain compounds (Estrada et al. 2013). There are a number of avenues through which PGPR can enhance plant growth, however these can be generally categorised into three primary modes of action. These include indirect plant growth promotion through pathogen resistance or direct growth promotion via the release of hormones, or improving nutrient relations via the solubilisation of unavailable soil nutrients or biological nitrogen fixation (BNF) (Vessey 2003; Kloepper et al. 2004; Hayat et al. 2010; Sokolova et al. 2011; Beneduzi et al. 2012). The beneficial effects of PGPR can alleviate pathogen attack, enhance nutrient supply and improve crop growth and yield. PGPR inocula have the potential to reduce the need for mineral fertilisers and agrochemicals, which would have numerous benefits for the environment and primary producers.
However, if PGPR are already present in the soil, why should a farmer purchase and add more? It is presently believed that continuous farming of monocultures in combination with less-than-optimal management practices can lead to detrimental changes in soil microbial communities (Garside et al. 2001; Lienhard et al. 2014). Since ancient times, agriculturists realised the potential of crop rotations to ‘reset’ the microbial community composition because one crop’s pathogens were not pathogenic to the next crop. While crop pathogens have long been a focus of microbiological Page 24 of 167 research and practices, the emphasis on beneficial microbes has a much shorter history. The most prominent example is the selection and delivery of efficient N-fixing Rhizobium and Bradyrhizobium strains for legume crops; a success story that has soybeans now fixing up to 465 kg N ha-1 (Pankievicz et al. 2019). Rhizobia have been intensively researched for many years which has led to a deep understanding of rhizobia-legume interactions and successful implementation in agriculture (Robertson and Vitousek 2009; Vargas et al. 2017; Pankievicz et al. 2019).
Such depth of understanding is arguably lacking for other PGPR for reasons that fall into two broad categories, the (1) tremendous microbial diversity in soil and associated with plants that is limited by knowledge of the organisms and their autecology, and (2) the complex interactions between microbial communities, plants and environments - with genetics, spatial and temporal effects, biotic and abiotic factors contributing. The term PGPR was first introduced by Kloepper and Schroth (1978), while research on rhizobia dates to the 1890ties (Kloepper and Schroth 1978; Goswami et al. 2016). Aside from PGPR research gaining momentum comparatively recently, PGPR interactions are shrouded in complex ecological networks which make it difficult to acquire fundamental knowledge of plant-microbe associations in nature. By comparison, rhizobia-legume symbioses represent less complex, highly specialised, individual plant-microbe relationships, with many variables eliminated that appear to characterise the broader root microbial community. The rhizobia-legume symbiosis is triggered by plant proteins (lectins) and lipochitooligosaccharides, also known as Nod factors (NF), signalling molecules that result in a cascade of responses, from attracting rhizobium to root hairs to the formation of nodules (Smit et al. 1992; Rodríguez-Navarro et al. 2007; Murray 2011). Similar to rhizobia research, PGPR research has largely employed simplified systems, testing individual microbes in controlled growth conditions, such as in vitro tests or in vivo bioassays involving seedlings that are performed in artificial substrate or sterilised soil to eliminate (or limit) interactions and competition from other microbes.
While this research has benefited understanding of PGPR and their functions, there is a growing need to expand such comparatively simple individual plant-PGPR interactions and consider the complexity of biological, chemical and physical factors in real world settings (Chaparro et al. 2012). For in nature, and in agriculture, plants and PGPR interact with the vast diversity of native microbes that shape each system. Three primary mechanisms through which PGPR enhance plant growth and that form the basis for bacteria screening and selection of PGPR strains are discussed below.
Page 25 of 167 1.2.1 Pathogen resistance Some PGPR can indirectly promote plant growth by enhancing plant survival when under threat of deleterious pathogenic microbes (Lugtenberg and Kamilova 2009). PGPR accomplish this either indirectly by priming the plant for pathogen attack and stimulating plant defence pathways, or directly through competition and antagonism (van Loon 2007; Lugtenberg and Kamilova 2009; Beneduzi et al. 2012). Rhizobacteria capable of defending plants against pathogens are sought after as biocontrol agents in agriculture (Beneduzi et al. 2012). The use of PGPR could reduce the application of synthetic pesticides and reduce negative off-site impacts on the environment and humans.
Numerous bacterial strains have been identified that improve plant defence pathways via (i) secretion of chemicals and signalling molecules that trigger induced systemic resistance (ISR), or (ii) structural changes such as enhanced suberisation and lignification that impede pathogen attack (Wei et al. 1991; Vallad and Goodman 2004; Lugtenberg and Kamilova 2009; Martins et al. 2013; Park et al. 2013; Vacheron et al. 2013). PGPR that prime plants by inducing ISR to instigate biochemical, physiological, morphological and anatomical changes that improve defence could act as biological control agents against various shoot or root pathogens, including microbial and viral diseases. Two PGPR strains in genera Pseudomonas and Serattia successfully reduced the incidence of anthracnose on cucumber seedlings to the fifth leaf stage, with lesion diameter used as a measure of ISR activity, which increased over time (Liu et al. 1995). Other studies have used gene expression analysis to show ISR elicitation in various host-PGPR systems. For example, Paenibacillus polymyxa induced mild biotic stress, and increased expression of defence genes PR-1, HEL and ATVSP in A. thaliana resulting in the initiation of systemic resistance (Timmusk and Wagner 1999). A combination of PGPR that delivers induced systemic resistance (ISR) with a benzo-(1,2,3)-thidiazole-7-carbothioic acid S-methyl ester (BTH – an inducer of systemic acquired resistance in plants) is more efficient at suppressing soft rot incidence in tobacco than either defence elicitors were individually (Park et al. 2013). The PR-1a defence gene was most highly expressed in this treatment, possibly indicating that PGPR and BTH work synergistically to induce systemic resistance against pathogens. PGPR are also known to induce structural and physiological changes within plant tissue to enhance defence.
A recent study found that the application of various PGPR strains increased the content of phenolics and led to lignin accumulation in cotton plants in the presence and absence of the bacterial wilt pathogen Curtobacterium flaccumfaciens pv. flaccumfaciens (Cff) (Martins et al. 2013). This change in the plant’s physiology and structure could be the result of a priming effect initiated by Page 26 of 167 PGPR that reduced the spread of the pathogen in vitro. Cff is a seed-transmitted disease however, and consequently the potential for direct antagonism alongside ISR could have caused pathogen resistance (Martins et al. 2013). Various other studies have reported PGPR strains that induce plant physiological change as a result of ISR, including that PGPR elicit plant resistance to abiotic stresses, including drought, salt, heavy metal, and others, through ISR (Timmusk and Wagner 1999; Yang et al. 2009; Bhattacharyya and Jha 2012). Other reported mechanisms of resistance occur through direct antagonism with pathogenic microbes via niche competition and the production of antimicrobial compounds (Burmølle et al. 2006; Haggag and Timmusk 2008; Beneduzi et al. 2012).
PGPR that efficiently colonise or form biofilms on roots can be effective antagonists against deleterious microbes via niche competition (Haggag and Timmusk 2008; Beneduzi et al. 2012). The formation of bacterial films along the root surface can extensively cover root-binding sites making it difficult for pathogen colonisation as well as act as a nutrient sink and depleting the rhizosphere of exudates which would benefit pathogen growth (Haggag and Timmusk 2008). Colonisation of the root surface is imperative for the initial stages of pathogenesis, and PGPR capable of covering root colonising sites make pathogenic infection challenging (Danhorn and Fuqua 2007; Beneduzi et al. 2012). Kamilova et al. (2005) showed that bacteria mutants with impaired motility do not effectively control pathogens. The authors also found that some bacteria used root exudates more efficiently than others and were consequently more efficient root colonisers (Kamilova et al. 2005). These results demonstrated that the bacteria of interest (Pseudomonas fluorescence) could act as a biocontrol agent via niche and nutrient competition. Some rhizobacteria can also release various secondary metabolites that possess antimicrobial activity and enhance nutrient acquisition, giving them a competitive advantage against pathogens. Various identified antimicrobial compounds include polyketides, lipopeptides, siderophores, plantazolicins, bacteriocins, lytic acid and others (Scholz et al. 2011; Beneduzi et al. 2012). Siderophores are a group of well-studied metabolites produced by various PGPR strains that chelate iron (and other essential metal cations), thus reducing access to essential nutrients for pathogens. Various studies have found PGPR strains to be efficient biocontrol agents against soil-borne pathogens via competition for iron (Kurek and Jaroszuk-Scisel 2003; Vassilev et al. 2006; Beneduzi et al. 2012). Others have also reported that the plant hormone Indole-3-acetic acid (IAA) is involved in plant defences. Bacteria that produce IAA have been shown to reduce fungal spore germination and mycelial growth as well as interact with other plant derived enzymes to stimulate plant defence reactions (Vassilev et al. 2006). Overall, PGPR are capable of producing a large array of phytohormones and signalling compounds, which not only aid in pathogen resistance, but additionally enhance plant growth promotion and vigour, as discussed below.
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1.2.2 Hormone production and signalling Compounds that regulate plant physiological processes are termed phytohormones and are produced endogenously within plants to serve various important functions (Arshad and Frankenberger 1998). Phytohormones include auxins, cytokinins, ethylene and gibberellins (Arshad and Frankenberger 1998; Cassán et al. 2014). Lateral root formation, nutrient signalling, and plant stress response pathways are three of the many physiological processes that hormones regulate (Malamy 2005; Ingram and Malamy 2010). Some rhizobacteria exogenously produce phytohormones to modulate root architecture and plant growth (Bhattacharyya and Jha 2012; Ahemad and Kibret 2014; Cassán et al. 2014). Microbial production of IAA (an auxin compound) has been widely studied for its ability to modulate plant physiological processes and influence plant-microbe interactions (Spaepen et al. 2007; Ahemad and Kibret 2014).
IAA modifies plant cell metabolism to promote growth, regulate root morphology and lateral root formation, and is involved in various other processes - ranging from environmental stress response to vascular development (Malamy 2005; Ingram and Malamy 2010; Cassán et al. 2014). Bacteria that produce different concentrations of IAA, and mutant strains deficient in IAA encoding genes, have been investigated for their PGP effects and show deleterious and beneficial effects on plants (Spaepen et al. 2007, 2008; Ingram and Malamy 2010; Gumiere et al. 2014). Gumiere et al. (2014) show that bacteria strains that produce low concentrations of IAA, negatively impact the growth of lettuce (Lactuca sativa) while strains releasing higher concentrations of IAA elicited beneficial effects in the same host. The observed increase in plant biomass as a response to higher IAA levels may have been a plant stress response or evidence for other effective metabolites released by bacteria (Gumiere et al. 2014). Similarly to auxin, other microbially derived phytohormones such as cytokinin, ethylene and gibberellins also impact root system architecture with various endogenous and exogenous hormone balances having adverse effects on plant growth and development (Vacheron et al. 2013).
A well-known growth-promoting function of some PGPR strains is their ability to produce the enzyme 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase which catabolises ethylene (Glick 2005; Vacheron et al. 2013). Ethylene regulates physiological processes including stress response, seed germination and early plant development (Glick 2003). As plants mature, ethylene levels increase and negatively impact growth. It is known that high intracellular ethylene concentrations can lead to a decrease in plant growth and crop yield (Glick 2003; Bhattacharyya and Jha 2012). PGPR with ACC deaminase activity can modulate plant ethylene levels by Page 28 of 167 catabolising ACC produced by plants and preventing re-uptake (Glick 2005). This can lead to PGPR promoting nodule formation, modifying root architecture to proliferate in soil and enhance nutrient acquisition, as well as increasing root plasticity in response to harsh environmental conditions and abiotic stresses (Shahzad et al. 2010; Glick 2014). PGPR with this mode of action promote plant growth when affected stresses including waterlogging, drought, salt and heavy metals (Glick 2005, 2014; Yang et al. 2009; Bhattacharyya and Jha 2012). Three strains of Bacillus and Ochrobactrum reduced the impact of heavy metal toxicity on rice, potentially acting as plant growth promoters as a result of ACC deaminase activity (Pandey et al. 2013). The reduction of internal ethylene levels via PGPR-derived ACC deaminase activity can also benefit plant photosynthetic capabilities and fitness (Goh et al. 2013). When ethylene levels are reduced as a result of ACC deaminase activity, chlorophyllase activity is also reduced while light capture is enhanced (Shimokawa et al. 1978). This has been demonstrated by situations where PGPR inoculation subsequently leads to an increase in chlorophyll content and photosynthetic efficiency (Bal et al. 2013; Goh et al. 2013).
Some PGPR promote plant growth indirectly via the stimulation of other beneficial microbes and symbiotic fungi. Legume-rhizobia symbiosis and associations with arbuscular mycorrhiza (AM) and ecto-mycorrhiza (EM) are known to promote root foraging and plant growth. Various PGPR strains of Pseudomonas, Bacillus and others stimulate and promote the mutualistic symbiotic relationships of plants and microbial organisms via the production of hormones and signalling molecules (Vessey 2003; Krey et al. 2013). PGPR derived hormones, signalling molecules and secondary metabolites are considered beneficial for agriculture as agents that can augment crop yield, increase crop tolerance to abiotic stresses, and, as discussed below, promote lateral root and root hair formation to assist anchoring and nutrient acquisition (Glick 2003).
1.2.3 Nutrient acquisition PGPR can directly enhance plant nutrient absorption through modification of root architecture and solubilisation of unavailable nutrient forms. For example, ancient landscapes are deficient in phosphorus (P) which is a major driver of ecosystem productivity (Lambers et al. 2010; Li et al. 2013). Indeed, most soils are considered to have low P availability, although there are generally pools of insoluble and organic P that are not readily accessible by plants (Singh and Satyanarayana 2011; Li et al. 2013). As fertiliser P is readily fixed in the topsoil and becoming unavailable to crops, it would be beneficial to enable access to these P reserves (Gamalero and Glick 2011). Some PGPR strains produce organic acids and enzymes, such as phosphatase and phytase, which render
Page 29 of 167 organic and inorganic forms of P available to plants (Vessey 2003; Lugtenberg and Kamilova 2009; Hayat et al. 2010; Krey et al. 2013).
Many PGPR including members of Enterobacter, Rhizobium, Bacillus and Pseudomonas, promote plant growth by solubilising calcium phosphate (Krey et al. 2013; Vacheron et al. 2013). In pot experiments, these PGPR increased soluble P concentrations in soil and enhanced P availability to plants. In field studies, PGPR-inoculated maize had enhanced growth relative to un-inoculated plants under nutrient limiting conditions (Krey et al. 2013). Certain bacteria release extracellular phytases to degrade phytate, which is a common form of organic P in soils (Li et al. 2007, 2013; Lim et al. 2007). Three bacteria strains with high phytase activity were individually applied to poplar and mason pine seedlings (Li et al. 2013). After 150 days of growth, a significant increase in shoot growth was observed for all bacterial treatments (Li et al. 2013). PGPR were not screened for the production of other metabolites, such as phytohormones, and there were no control treatments with plant accessible P, thus it can only be speculated that the observed growth-promoting effect is the result of phytate degradation. Li et al. (2013) acknowledge the limitations that are inherent to many PGPR studies, including their own, with appropriate controls often missing and causalities remaining unclear. In a more controlled study, IIdriss et al. (2002) showed that a strain of Bacillus amyloliquefaciens significantly promoted the growth of maize seedlings under limiting P conditions with the sole source of P being phytate. Their control treatment was the filtrate of isogenic mutant PGPR strains incapable of producing extracellular phytases which showed no plant growth stimulation, confirming that the PGP effect was microbial phytase (Idriss et al. 2002). Similarly, other studies have investigated the growth promotion of plants via the application of phytate- mineralising and phosphate-solubilising bacteria which resulted in increased grain yield, biomass and P uptake (Lim et al. 2007; Hariprasad and Niranjana 2009; Singh and Satyanarayana 2011; Yazdani et al. 2011; Metin et al. 2012; Kaur and Reddy 2013; Krey et al. 2013). Thus, aside from siderophore production, which aids in the breakdown and acquisition of iron and BNF (discussed below), P mobilisation by microbes offers a further effect of PGPR that improves plant nutrition.
Biological nitrogen fixation (BNF) involves the conversion of nitrogen (N2) gas by prokaryotic organisms into reactive N that plants can use via the activity of nitrogenase (Ahemad and Kibret 2014). Plant inoculation with BNF microbes significantly increased N uptake and plant biomass (Zahran 1999; Kelemu et al. 2011; Paungfoo-Lonhienne et al. 2014). BNF microbes can act within the rhizosphere and rhizoplane as free-living microbes or as endophytes that use chemical signalling to communicate with and colonise roots (or stems) to form nodules in legumes and certain non- legume species (Triplett 1996; Antoun et al. 1998; José et al. 2013; Ahemad and Kibret 2014).
Page 30 of 167 Applications of BNF bacteria to legume crops can enhance nodule formation and N uptake (Bhattacharyya and Jha 2012). Improvement in plant growth of non-nodule forming crops after application with BNF bacteria has been observed (Antoun et al. 1998; Martinez De Oliveira et al. 2006; Taulé et al. 2012; Santi et al. 2013). In field conditions, Rhizobium populations can increase around the roots of non-legume crops, including maize and wheat (Antoun et al. 1998). Such results are particularly important for agricultural systems where N availability often limits crop growth and the extensive use of synthetic N fertiliser causes N pollution (Whan et al. 2010; Robinson et al. 2011), so that increasing the presence of PGPR with BNF properties could benefit crop systems (Dini-Andreote and van Elsas 2013). Taken together, there is now ample evidence that PGPR have much potential for agriculture via multiple avenues of crop growth promotion. The key question that underpins the bridging from fundamental in-vitro research to practical application is how do we find, select and formulate microbes that will convey PGP efficiently and consistently for a particular crop or, ideally, a suite of crops, and growth conditions?
1.3 Selecting putative PGPR’s
PGPR studies commonly begin with the selection of a soil and crop of interest, isolation of culturable bacteria and screening of individual strains for desirable PGP traits. One limitation in isolating and testing potential PGPR is that only culturable strains are evaluated and these represent only a small proportion of the soil bacterial communities, with the majority of bacteria being resistant to laboratory culture and are thus unattainable for testing and formulation as inocula (Tringe et al. 2005). A second limitation is that ‘desirable’ traits differ from study to study, depending on the research focus and preferred outcome. For example, if the interest is on improving P acquisition, N acquisition or pathogen resistance, researchers may specifically screen for P solubilising bacteria, BNF bacteria or microbes with antibiotic/antifungal activity respectively (Wei et al. 1991; Viswanathan and Samiyappan 2001; Oliveira et al. 2002; Sundara et al. 2002; Beneduzi et al. 2012; Metin et al. 2012; Krey et al. 2013). Other studies may screen bacteria for a range of PGP traits to select putative PGPR that are capable of enhancing plant growth via multiple mechanisms (Piromyou et al. 2011; Bakthavatchalu et al. 2012; Hussain et al. 2013; Anwar et al. 2014; Bhatt and Vyas 2014; Dinesh et al. 2015). Traits for which microbes have been screened (Table 1) are discussed in recent reviews (Zahir et al. 2003; van Loon 2007; Lugtenberg and Kamilova 2009; Ahemad and Kibret 2014). These traits can be active simultaneously, to different levels of efficiency, or independently at different stages of plant development (Anwar et al. 2014). When selecting microbes for testing, one question in particular should be considered: is it best to select microbes that express high activity of one particular mode of action, or should microbes be Page 31 of 167 selected that exhibit multiple PGP traits, even if the activity of each trait is not as high? This is a major knowledge gap in PGPR research (Vacheron et al. 2016). The answer to this question may differ from system to system and could depend on whether microbes are inoculated individually or as a consortium. It is an important question to consider when thinking about which microbes the plant would recruit, and in regards to microbe consortia – what approach will lead to microbial synergism as opposed to antagonism?
Table 1 Plant growth-promoting traits for which microbes are screened to select putative PGPRs. Mode of Action Trait References Pathogen Resistance ISR Production of lipopolysaccharides, some siderophores including (van Loon 2007) pseudobactins and pyochelin. Production of volatile compounds including 2,3-butanediol. Antifungal/Antibiotic Synthesis of hydrolytic enzymes (cellulase, chitinase, pectinase, (Bakthavatchalu et al. activity protease, lipase) that can lyse pathogenic fungal cells. Plate 2012; Beneduzi et al. assays screening for siderophore production, production of 2012) antimicrobial metabolites (e.g. 2,4-diacetylphloroglucinal (DAPG), phenazine1-carboxylic acid (PCA), phloroglucinols, pyrrolnitrin, pyoluteorin, cyclic lipopeptides), hydrocyanic acid production. Production of lactic acid and of lytic agents (e.g. lysozymes), various exotoxins and bacteriocins (cloacins, marcescins, megacins), hydrogen cyanide (HCN) Phytohormone Production IAA Bacteria production of IAA leading to growth stimulation of (Glick 2012) roots and shoots Gibberellin Bacteria production of gibberellin leading to enhanced root branching, root hair growth and biomass Cytokinin Bacteria production of cytokinins potentially enhancing plant (Hayat et al. 2010; Glick resistance to abiotic stresses 2012) ACC deaminase Production of ACC deaminase to modulate plant ethylene levels, (Glick 2012; Bal et al. reduce internal ethylene levels leading to growth promoting 2013) effects primarily when influenced by abiotic stresses Nutrient Acquisition N fixation Screening for bacteria with nifH genes to identify isolates that (Taulé et al. 2012; can hydrolyse atmospheric N2, Screen bacteria for ammonia Estrada et al. 2013; production in liquid cultures (formation of brown/yellow Dinesh et al. 2015) precipitate when grown in peptone broth with Nessler’s reagent), Screen for acetylene reduction activity P solubilisation Screen bacteria for production of organic acids and (Singh and solubilisation of organic/insoluble P forms using liquid (changes Satyanarayana 2011; in pH/colour) or plate assays (observing visible halos around Bakthavatchalu et al. bacteria colonies on plates made of organic/insoluble P source) 2012; Rani et al. 2012) Bacterial Colonisation Biofilm formation Screening bacteria for their ability to form biofilms via liquid (Burmølle et al. 2006; cultures and staining, screening bacteria for colonisation of root Ren et al. 2014a) surfaces using microscopy Exo-polysaccharides Production of exo-polysaccharides to select for bacteria that may (Meneses et al. 2011) be efficient colonisers via compactness and ability to adhere to surfaces
Following the inoculation of soil or growth medium, it is often difficult to determine which mechanisms of PGP are active and deliver enhanced growth. This is even more difficult to discern Page 32 of 167 with a consortium of multi-trait PGPRs. The traits which each microbe employs may depend on the nature of its interaction with other microbial species, the plant host and environment (Ahmad et al. 2008b; Berg and Smalla 2009). Microbes may synergistically enhance one another, allowing all traits to flourish, or they may compete with one another with the end growth promoting result simply being the work of one species. It is often assumed that when a microbial formulation produces higher plant biomass and enhanced growth relative to individual inoculations that this is the result of microbial synergism. However, this may not always be the case. Despite the fact that justification of including each microbe within a mixed inoculant is difficult, many commercial inoculants containing a number of species currently exist on the market.
1.4 Commercial formulations of plant growth-promoting rhizobacteria
Interest in PGPR is rapidly growing as more industries are recognising the potential of PGPR for enhanced crop performance and are investing in biological products. The interest of agricultural industries to use PGPR as biostimulants, biofertilisers, biopesticides including biofungicides is on the rise, and is reflected by the increasing number of commercial PGPR products available today (Lucy et al. 2004; Berg 2009; Bhattacharyya and Jha 2012). The biostimulant industry worth $US 2.9 billion in 2017, is predicted to increase to $US 5.4 billion by 2022 (Research and Market 2017) however, many products are often sold without appropriate scientific testing, and this results in products having variable effects between laboratory, glasshouse and field conditions.
This variability can be the result of a number of factors ranging from (un)successful root colonisation due to host specificity or environmental factors including temperature, water availability and soil chemistry (Egamberdiyeva and Höflich 2003; Lucy et al. 2004; Çakmakçi et al. 2006; Egamberdiyeva 2007), as well as an inability to compete with native microbes (Bashan 1998). In some European countries, such as Switzerland, Netherlands and Germany, efficacy of products must be demonstrated, and only one or two microbes are formulated into a product for use for one crop and in particular, environmental settings. In an unregulated market such as Australia, a cocktail of microbes are often formulated into products and advertised with spurious claims and lacking information on mode of action. For example, a product called ‘Concentrated Mother Earth’ may sound good to a non-scientist and attract buyers, but unless products have proven PGP efficacy, they are useless for primary producers. This is relevant because farmers chose such products as a comparatively cheap alternative to more fundamental changes that are required for improving soil health and crop productivity (e.g. machinery for maintaining the same wheel tracks,
Page 33 of 167 organic soil amendments such as composts, crop rotations, cover crops, agrochemicals including fertilisers, and others).
Microbial biostimulants are imported from other countries including overseas, as is occurring in Australia, opening up the potential import of pathogens. When concerns were raised with the Australian Quarantine Inspection Service (AQIS), it was explained that producers of biostimulants fill in forms where they state that the microbes contained within their products are not crop pathogens. Without proof that all crops (and genotypes) have been tested, manufacturers provide a lip service rather than proof. This pseudo-scientific approach also exempts native flora from tests despite vulnerability to introduced microbes that has been experience in many countries. In the case of the highly endemic flora of Australia, allowing microbial products untested into the country is ludicrous, especially in the light of generally tight border controls that aim to restrict the introduction of foreign biota.
In one of the products tested in this thesis, this problem is clearly illustrated. Brazilian research had identified five strains of diazotrophs that efficiently fix nitrogen and promote sugarcane growth. Herbaspirillum rubrisubalbicans, one of the species used in this PGPR formulation, is a known plant pathogen causing mottled stripe disease in some sugarcane genotypes and other crops including sorghum (Baldani et al. 1996; James and Olivares 1997; James et al. 1997). H. rubrisubalbicans causes disease symptoms in sugarcane varieties from regions that have received high-N fertiliser applications, which includes Australian sugarcane varieties (Baldani et al. 1996). Another major caveat is that manufacturers do not disclose a full list of components (biotic or abiotic) of their products, and/or provide detailed instruction on delivery methods (Bashan et al. 2016). In our study, insufficient information was delivered to growers on microbial inoculant delivery methods and application rates until after a negative effect was observed on plant growth. Following this the microbial inoculant manufacturer delivered detailed information on application rates that followed a very different format to what was printed on the label or on website reference guides. Following an experiment with the correct application rates when no beneficial effects were observed, the manufacturer then explained that these products would not work on horticultural crops that are well watered and maintained as they are ‘happy’ plants. While this aligns with what we expect for PGPR, that they are not likely to have strong effects on non-stressed plants, this is contradictory to the initial claims made by the manufacturer that the nurseries plants would benefit from application of the biostimulant. This leads growers to feel that no matter what they do, they do it wrong when the product is ineffective. Regulation is therefore a must if the aim is to advance biostimulants for crop production.
Page 34 of 167
Advancing our understanding of how these products affect not only plants, but native microbial communities will aid farmers in making judicious decisions when faced with a plethora of product choices. Rigorous testing of product efficacy and environmental effects should fall at the feet of the manufacturer, and carried out by independent researchers. However, efficacy regulations do not require extensive field-testing and consequently there are major knowledge gaps in the effects these products have on native soil microbial communities. The primary aims of this thesis therefore are to
i) evaluate the efficacy of commercial biostimulants for PGP in industry-relevant settings, ii) investigate how biostimulants alter soil and root communities, iii) assess 121 rhizosphere isolates with in vitro and in vivo screening for PGP traits, iv) discuss the findings for next steps to advance biostimulant technology for crops.
We first investigated the effects of two commercial biostimulants in broad acre sugarcane farming. We evaluated their ability to enhance sugarcane biomass and sugar yield as well as the effects product inoculation had on native microbial communities. Using phylogenetic marker gene sequencing, we aimed to determine whether the biostimulant products were able to directly (inoculated microbes increased within the root/soil samples) or indirectly (other genera that were not apart of the commercial formulations increased in abundance) manipulate soil and root bacterial and fungal communities. We found no effects on cane biomass or sugar yield across treatments and soil bacterial and fungal communities remained unchanged in response to microbial product application. Root fungal communities showed increases in a few genera however none correlated to inoculated groups and the effect they have on plant growth is yet to be determined. Microbial formulations that deliver beneficial effects in lab and glasshouse conditions often do not have the same effect in field conditions where there are many uncontrollable variables (Valverde et al. 2006; Chin-A-Woeng and Lugtenberg 2008; Naveed et al. 2014; Etesami et al. 2015; Bashan et al. 2016; Parnell et al. 2016). Field soils have much more complex native microbial communities making it difficult for liquid cultures to compete and establish in the long term (Berendsen et al. 2012; Lee et al. 2016). We therefore decided to investigate the efficacy of commercial biostimulants in a more controlled industry setting.
A local seeding nursery had a selection of microbial products they wanted to use. We used three commercial inoculants (manufactured by a range of companies from global to a smaller local business) on multiple horticultural crops as well as sugarcane plantlets. Given the more controlled environment and the less microbially complex substrate we hypothesised that the products were
Page 35 of 167 more likely to survive, associate with plants and deliver growth promoting effects in a nursery environment. Our first experiment was conducted at the nursery and after finding no consistent beneficial effects of any products we further tested them in a growth-cabinet experiment with stressed and non-stressed plants. By the manufacturers’ recommendation, these products are more likely to interact with and therefore promote the growth of stressed plants. We again found inconsistent effects however, for both sugarcane and watermelon plantlets in the stressed and non- stressed treatments. While one product reduced plant growth relative to the un-inoculated controls most others displayed no difference in biomass to controls. This led us to ask the question, how are microbes selected and do the existing screening methods provide a good method for selection of microbes that are likely to deliver a growth promoting effect on plants? Members of our research team (Chanyarat Paungfoo-Lonhienne, Yun Kit Yeoh) had previously isolated a library of bacteria from a sugarcane rhizosphere in central Queensland (Yeoh et al. 2016). After serial dilutions, plating and sub-culturing we had a total of 121 pure bacteria cultures. These microbes were Sanger sequenced for taxonomy and screened for 10 different plant growth promoting traits. A selection of microbes with very high activity (according to plate assays) and no activity were then tested on plants. We found very inconsistent results with no obvious relationship between in vitro high activity microbes and in vivo plant growth promoting effects. Our study highlights the need for more rigorous selection of microbes, placing less emphasis on top performers in artificial plate assays and more focus on the consideration of selecting isolates that will have competitive colonisation advantages or removing the need for plant-microbe cross-talk. We propose that liquid formulations are less likely to deliver un-contaminated, viable microbial cells that are protected enough to survive within complex soil microbiomes and establish for long term effects. We finally discuss the need for strict regulations that call for rigorous field efficacy testing and what the future of microbial inoculants looks like with an emphasis on innovative formulations.
Page 36 of 167 Chapter 2 Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield
Berg, S., Dennis, P.G., Paungfoo-Lonhienne, C. et al. Effects of commercial microbial biostimulants on soil and root microbial communities and sugarcane yield. Biol Fertil Soils 56, 565–580 (2020). https://doi.org/10.1007/s00374-019-01412-4
2.1 Abstract
Ameliorating biological attributes of agricultural soils is desirable, and one avenue is introducing beneficial microbes via commercial biostimulant products. Although gaining popularity with farmers, scientific evaluation of such products in field-grown crops is often lacking. We tested two microbial products, Soil-Life™ and Nutri-Life Platform®, in a commercial sugarcane crop by profiling bacterial and fungal communities in soil and roots using high throughput phylogenetic marker gene sequencing. The products, one predominantly consisting of Lactobacillus and the other of Trichoderma, were applied as a mixture as per manufacturers’ instructions. Additives included in the formulations were not listed, and plots that did not receive the product mixture were the controls. The compositions of bacterial communities of soil and sugarcane roots, sampled 2, 5 and 25 weeks after application, were unaffected by the products. Soil fungal communities were also unaffected, but sugarcane roots had a greater relative abundance of three unidentified taxa in genera Marasmius, Fusarium and Talaromyces in the treated plots. Sugarcane yield was similar across all treatments that included a 25% lower nitrogen fertiliser rate. Further research must examine if the altered root fungal community is a consistent feature of the tested products, and if it conveys benefits. We conclude that putative biostimulants can be evaluated by analysing the composition of microbial communities. DNA profiling should be complemented by cost-benefit analysis to build a public information base documenting the effects of microbial biostimulants. Such knowledge will assist manufacturers in product development and farmers in making judicious decisions on product selection, to ensure that the anticipated benefits of microbial biostimulants are realised for broad acre cropping.
2.2 Introduction
Soils support critical ecosystem services with the provision of food and clean water (Baveye et al. 2016). To maximise agricultural productivity, soils require significant intervention, which often Page 37 of 167 leads to undesirable environmental impacts and a reduction in soil quality over time (Doran and Zeiss 2000; Doran 2002). While agronomic options are available to alleviate soil physicochemical constraints, managing soil biological properties represents a challenge (Brackin et al. 2017). Soil organisms strongly influence plant health and growth. Beneficial plant-microbe interactions reduce pest and pathogen pressure (so-called biocontrol), mobilise nutrients, improve root architecture and modulate stress responses (Pii et al. 2015). Agronomic methods to improve soil biological properties include (i) crop rotations to reduce the presence of pathogens and pests associated with monoculture cropping, (ii) incorporation of organic materials (crop residues, composts, manures) that stimulate functionally diverse soil biological communities and (iii) inoculating soil or crop with chemical (e.g. phytohormones) and/or microbial (i.e. bacteria and fungi, the focus of this study) biostimulants. The last approach has encouraged the rise of an industry worth $US 2.9 billion in 2017, that is predicted to increase to $US 5.4 billion by 2022 (Research and Market 2017).
The use of plant growth promoting (PGP) microbes has a scientific basis and long-standing practice in the coating of legume seeds with nitrogen (N) fixing Rhizobium and Bradyrhizobium bacteria (Murray 2011; Chen et al. 2015; Vargas et al. 2017). Microbial biostimulants appear justified when considering the benefits of modern legume symbioses that have been optimised with the selection of efficient bacterial strains and efficient delivery to substantially improve N fixation rates and legume yields (Graham and Vance 2003; Batista et al. 2015; Parnell et al. 2016). Commercial products of PGP microbes have been in use since the 1940s, and many crops now receive PGP microbes as soil or plant applications aimed at benefitting shoots or roots (reviewed by Parnell et al. 2016). Sugarcane, for example, is frequently inoculated with diazotrophic Gluconacetobacter diazotrophicus, Herbaspirillum, Azospirillum and Burkholderia in Brazil and elsewhere (Mendes et al. 2007; Pedula et al. 2016), but modes of action are much less well established in sugarcane than in legumes (Robinson et al. 2013; Figueiredo et al. 2017).
PGP microbes can enhance plant performance via three primary modes of action: improved pathogen resistance, access to nutrients and beneficial hormones (Tabassum et al. 2017). The rising interest in microbial biostimulants (also termed biofertilisers, biocontrol agents, biopesticides) is driven by the notion that they can improve crop yield as a green alternative to agrochemicals. Subsequently, commercial products, especially those containing PGP rhizobacteria (PGPR), are increasingly used (Lucy et al. 2004; Berg 2009; Bhattacharyya and Jha 2012). A second type of product aims to improve soil function. For example, Soil-Life™ (tested here) is marketed as a soil activator, claiming to enhance soil quality as well as crop growth and yield. It is often unclear which mechanism a particular microbial product may deliver, and there is agreement in the
Page 38 of 167 scientific community that research is warranted (reviewed by Parnell et al. 2016; Finkel et al. 2017). Beyond PGP mechanisms, the complete composition of products is also often lacking from product labels and not outlined in sufficient depth in the scientific literature (Bashan et al. 2016).
Limitations to the effectiveness of microbial biostimulant products include that microbe species and strains (‘ecotypes’) can strongly diverge in their plant growth promoting (PGP) effects in response to growth environments, crop species and crop cultivar (Chiarini et al. 1998; do Amaral et al. 2016). Much of the research demonstrating effects and identifying mechanisms of microbial biostimulants has occurred in laboratory and glasshouse, excluding or minimising the effects of competing native soil microbes, unfavourable environmental conditions, transport and storage of the microbial products and field application. The interest of farmers and the rapidly expanding product market is fuelled by the potential benefits and the desire for sustainable intensification of cropping, but independent scientific evaluations in real-world settings are rare. Yet this is important since the cost of such products can be considerable and a lack of beneficial effects (or even undesirable effects) jeopardises the usefulness of the technology. Major discrepancies in the efficiency of beneficial bacteria have been reported between laboratory, glasshouse and field-grown plants (Valverde et al. 2006; Chin-A-Woeng and Lugtenberg 2008; Naveed et al. 2014; Etesami et al. 2015; Bashan et al. 2016; Parnell et al. 2016), and government agencies increasingly demand field testing with scientifically valid methodologies to ensure that products deliver on their promises (EPPO 2012a, b, c).
Bacterial taxa often used as microbial biostimulants include members of Acetobacter, Agrobacterium, Azospirillum, Azotobacter, Bacillus, Burkholderia, Enterobacter, Frankia, Pseudomonas, Rhizobia, Serratia and Streptomyces (Saharan and Nehra 2011). Beneficial fungal taxa include Trichoderma and Gliocladium (Raaijmakers et al. 2009), and arbuscular mycorrhizal (AM) fungi (Glomeromycota) (Barea et al. 2005; Bonfante and Genre 2010). While these are generally considered beneficial genera, not all species or strains (ecotypes) are beneficial. Broad- scale categorisation into beneficial and detrimental organisms is unwarranted since the same taxon can benefit or harm different plant hosts (or have no quantifiable effect). For example, while numerous Pseudomonas species are PGP organisms, some negatively impact plants (Mehnaz 2013; Reis and Teixeira 2015). Crop cultivar specificity occurs with Herbaspirillum rubrisubalbicans, which causes mottled stripe disease in some sugarcane cultivars, while other cultivars harbour the organism as an endophytic diazotroph (Baldani et al. 1996; Boddey et al. 2003). Similarly, different strains of a Burkholderia species, a common bacterial genus of soil and roots (Yeoh et al. 2017), can be detrimental or beneficial to different plant hosts (Coenye and Vandamme 2003). In addition
Page 39 of 167 to the genetic makeup of the organisms, environmental factors can modulate effects; for example, grown with high amounts of N fertiliser, sugarcane is susceptible to H. rubrisubalbicans as a pathogen (James and Olivares 1997).
The results of microbial product testing in commercial cropping is less commonly reported in the peer-reviewed scientific literature than experiments in controlled settings that demonstrate benefits of particular microbes. Reasons for such underreporting include the high costs of field experimentation, variable or inconclusive results in field settings and, in the absence of regulations that demand proof of efficacy, the practice of extrapolating results from controlled to field settings. PGP microbes have to establish within the biologically complex soil matrix, and compete with native microbes (Dutta and Podile 2010). Product efficacy can be compromised inter alia by an inability of microbes to establish in soil due to unfavourable abiotic conditions, unsuccessful colonisation of host roots and competition from native soil organisms (Egamberdiyeva and Höflich 2003; Lucy et al. 2004; Çakmakçi et al. 2006; Egamberdiyeva 2007).
To address the need for validation of microbial products in broad acre cropping, we tested two bacteria- and fungi-containing products that are used in sugarcane production in tropical Australia. We investigated if (i) manufacturer-specified microbes were present in the products, (ii) application of the products enhances sugarcane yield, (iii) the microbes colonise soil or sugarcane roots/rhizosphere, and (iv) the products alter microbial communities. We used next-generation DNA sequencing to characterise bacterial and fungal communities three times over the crop season and determined sugarcane yield to quantify the effects.
2.3 Methods
2.3.1 Study site and experimental design The research was performed at a commercial sugarcane farm in Australia’s Wet Tropics near Ingham (− 18.651844, 146.119511), on a sandy loam soil. The crop was rain-fed (1890 mm annual rainfall) and consisted of plant cane of commercial variety Q250. The block had previously been fallowed (grasses and mixed broadleaf weeds) for 6 months following a former cane crop. Plots within the field were established in a factorial design with each plot consisting of four rows of sugarcane (30 m long, 1.8 m row spacing) and five replicated plots per treatment. The experiment consisted of four treatments that reflect the desire of sugarcane growers to reduce N fertiliser rates and partially achieve this goal with microbial biostimulants. The treatments were (1) recommended
Page 40 of 167 N rate (130 kg N ha−1), (2) reduced N rate (75%, 100 kg N ha−1), (3) recommended N rate + microbial products and (4) reduced N rate + microbial products.
Fertiliser was applied at the planting of sugarcane. A di-ammonium phosphate (DAP) starter was used in all plots with 46 kg N, 40 kg P and 15 kg S per hectare. At the same time, a urea and muriate potash mixture was applied as a side dressing. Full N-plots received an additional 85 kg N ha−1 and 105 kg K ha−1 alongside the side dressing. Reduced-N plots received an additional 57 kg ha−1 N and 105 kg ha−1 K. One of the microbial products tested here (Soil-Life™, see below) had been applied in the preceding year but manufacturer instructions stipulate annual re-application. Neither product listed any additives that were included in the formulation, with only the bacteria and fungi included as the composition.
After 14 months of growth (July 2015 to September 2016), sugarcane was hand-harvested by collecting 10 stalks from the middle two rows of all replicate plots of all treatments. Stalks were weighed and processed at the Victoria mill (Wilmar Sugar) to obtain three yield measures: tonnes of cane, sugar yield and commercial cane sugar yield (CCS, recoverable sugar content). Yield per hectare was estimated by multiplying the 10-stalk weight by the total number of stalks in each plot. Wet weight measurement for biomass follows sugarcane industry protocols. Due to the large size of sugarcane (over 4 m tall and 5 cm or more in diameter), stalks would need to be cut for drying which leaks sugar (~ 10% of stalk wet weight) which introduces error. Previous research has shown that wet weights correlate well with dry weights.
2.3.2 Microbial biostimulant products The commercial products Soil-Life™ (ActivFert 2017) and Nutri-Life Platform® (Nutri-Tech Solutions® 2017) were blended and applied as mixed inoculum, as recommended by the manufacturers. Briefly, 20 L Soil-Life™ was blended with 20 L molasses and 160 L rainwater, and then incubated for 1 month in a shaded 1000-L shuttle prior to use—a procedure referred to by the manufacturers as the extension process. Inoculum was applied at a rate of 185 L per hectare consisting of (i) 20 L of extended Soil-Life™ solution, (ii) 150 g of Nutri-Life Platform® (powder formulation) and (iii) 165 L of water. This was applied with 100 mm deep sub-surface coulters. The inoculum was applied approximately 1 month after fertiliser application when the crop was ~ 1 m tall. We use the term ‘microbial product’ to denote the mixture of Soil-Life™ and Nutri-Life Platform® that was applied to the treated plots.
Page 41 of 167 2.3.3 Sampling of microbial products, soil and roots Samples of the Soil-Life™, Nutri-Life Platform® and mixed ‘microbial product’ were collected for microbiome analyses. The Nutri-Life Platform® sample was collected immediately after opening the supplier package. The Soil-Life™ sample was collected after the extension process, and the mixed ‘microbial product’ was sampled immediately after the extended Soil-Life™ and freshly opened Nutri-Life Platform® products were combined.
Soil and sugarcane roots were sampled 2, 15 and 25 weeks after application of the microbial product in five replicate plots from the reduced N treatment without (control plots) and with the microbial product applied (treated plots). At each time point, two plants from the centre two rows of each plot were sampled. Root and soil samples were collected from the upper 15 cm of soil at the row shoulder. Sections of thick white roots, as well as darker fibrous roots with adhering soil, were collected to obtain samples of the microbial communities present in roots, rhizoplane and rhizosphere (‘root microbial communities’ in the following). Roots from both plants were pooled into one container for sub-sampling and analysis. Bulk soil was sampled from several places surrounding the soil from which roots were sampled, sieved (2 mm) and pooled into a separate container for analysis of soil microbial communities. Three technical replicates were included for each treatment and time point. Samples were kept cool for transport and stored at − 20 °C prior to DNA extraction.
2.3.4 Analysis of bacterial communities DNA was extracted from soil and root samples with MP Biomedicals Fast DNA SPIN Kit (MP Biomedicals, LLC, Santa Ana, CA, USA). PCR amplification and sequencing was performed by the Australian Genome Research Facility (AGRF). Bacterial 16S rRNA genes were PCR amplified using the primers 803F (5’-ATTAGATACCCTGGTAGTC-3′) and 1392wR (5’- ACGGGCGGTGWGTRC-3′), which were modified on the 5’end to contain the i5 and i7 Illumina Nextera adaptor sequences, respectively. A PCR was also conducted with no template to act as a negative control. The thermocycling conditions were 95 °C for 3 min, then 30 cycles of 95 °C for 30 s, 55 °C for 45 s, 72 °C for 90 s, then 72 °C for 10 min. Raw sequences were trimmed using Seqtk (version 1.0) and processed using QIIME (version 1.8; Caporaso et al. 2010), USEARCH (version 8.0.1623; Edgar 2010; Edgar et al. 2011) and UPARSE (Edgar et al. 2011) software. Sequences were quality filtered using USEARCH. Full-length duplicate sequences were removed, and sequences were sorted by abundance. Singletons or unique reads in the data set were discarded. Sequences were clustered followed by chimera filtering using the ‘rdp_gold’ database for reference (using the USEARCH software). Reads were mapped back to OTU’s with a minimum identity of Page 42 of 167 97%. Finally, taxonomy was assigned using QIIME with the Greengenes database (version 13_8 Dated Aug 2013; DeSantis et al. 2006). Relative abundance was calculated and used to illustrate the composition of the commercial products, soil and root microbial communities. The number of reads per sample was rarefied to 10,000 and the numbers of observed (Sobs) and predicted OTUs (Chao1) as well as the Simpson’s Diversity Index were calculated for each sample.
2.3.5 Analysis of fungal communities DNA was extracted from soil and root samples using MO BIO PowerSoil DNA isolation kits. PCR amplification and sequencing was performed by AGRF. PCR amplicons were generated using the primers ITS1F (5’-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5’- GCTGCGTTCTTCATCGATGC-3′), and the AmpliTaq Gold 360 master mix solution (Life Technologies, Australia). Sequences were modified on the 5′ end to contain the i5 and i7 Illumina Nextera adaptor sequences, respectively. PCR was ran with the thermocycling conditions 95 °C for 7 min, then 35 cycles of 94 °C for 30 s, 55 °C for 45 s, 72 °C for 60 s, then 72 °C for 7 min. A secondary PCR was performed to index amplicons using TaKaRa Taq DNA polymerase (Clontech). Amplicons were measured by fluorometry (Invitrogen Picogreen) and normalised. The equimolar pool was measured by qPCR (KAPA) followed by Illumina MiSeq sequencing (San Diego, CA, USA) with 2 × 300 base pairs paired-end chemistry.
PEAR (version 0.9.5; Zhang et al. 2014) was used to assemble paired-end reads by aligning the forward and reverse reads. Sequences were trimmed from the raw reads using Seqtk (version 1.0) and processed using QIIME (version 1.8; Caporaso et al. 2010), USEARCH (version 8.0.1623; Edgar 2010; Edgar et al. 2011) and UPARSE (Edgar et al. 2011) software. Sequences were quality filtered using USEARCH. Full-length duplicate sequences were removed, and sequences were sorted by abundance. Singletons or unique reads in the data set were also discarded. Sequences were then clustered followed by chimera filtering using Unite database for reference (using the USEARCH software). Reads were mapped back to OTU’s with a minimum identity of 97%. Taxonomy was assigned using QIIME with the Unite database (version 7; Kõljalg et al. 2005). The number of reads per sample was rarefied to 40,000 and the numbers of observed (Sobs) and predicted OTUs (Chao1) as well as the Simpson’s Diversity Index were calculated for each sample.
2.3.6 Statistical analysis To determine whether the addition of N fertiliser and microbial products significantly influenced sugarcane yield and the alpha diversity indices, we used two-way analysis of variance (ANOVA). The effects of these parameters on community composition were assessed using Permutational Page 43 of 167 Multivariate Analysis of Variance (PERMANOVA; Anderson 2017). All PERMANOVA analyses were performed using Hellinger transformed OTU abundances (Legendre and Gallagher 2001). All analyses were implemented using R 3.5.1 (R Core Team 2013) and the ‘vegan’ R package (Philip 2003).
2.4 Results
2.4.1 Sugarcane yield Sugarcane biomass yield was similar with approximately 120 t/ha in all treatments irrespective of N fertiliser rate or microbial product application (ANOVA and Tukey HSD, P > 0.05, Figure 1). Similarly, sugar yield (17 t/ha) and CCS (14) were unaffected by the microbial product with no statistically significant differences across treatments.
Figure 1 Yield of the studied sugarcane crop after 14 months of growth. Yield is expressed as a fresh weight of sugarcane harvested, b sugar yield and c commercial cane sugar (CCS, a measure of sugar content used in the Australian sugarcane industry). Treatments include recommended N fertiliser rate (full N, 130 kg N ha−1) and 75% reduced rate (75% N, 100 kg N ha−1) with (+microbes) or without inoculation using the commercial products. The two 75% N treatments were analysed for bacterial and fungal communities. Data are averages of three plots per treatment with no significant differences in cane yield, sugar yield or CCS between the four treatments (ANOVA, P ≥ 0.05). Generated using GraphPad Prism 7.0a
Page 44 of 167 2.4.2 Composition of the microbial products According to the manufacturer, Soil-Life™ comprises three bacterial groups (lactic acid bacteria (i.e. Lactobacillus), Actinomycetes and photosynthetic bacteria), and fungi including yeasts and unspecified fungi (Table 2). Phylogenetic marker gene sequencing indicated that Lactobacillus represented 99.9% of the bacteria in Soil-Life™. In addition, yeasts, non-specified fungi, Actinomycetes and photosynthetic bacteria (Rhodosporillaceae, Chloroflexaceae, Cyanophyta, Heliobacteria) were detected albeit at < 1% relative abundance (Figure 2 & Figure 3).
According to the manufacturer, Nutri-Life Platform® contains AM fungi (Glomus intraradices, Glomus etunicatum, Glomus aggregatum and Glomus mosseae), four strains of Trichoderma, as well as Azospirillum sp., Bacillus sp., Pseudomonas sp. and Streptomyces cellulosae bacteria. Phylogenetic marker gene sequencing indicated that Hypocrea was the most abundant genera of fungi in the Nutri-Life Platform® (85.8%, Figure 3; note Hypocrea in the sexual stage of the life cycle with its anamorph (asexual stage) Trichoderma (Kubicek et al. 2008) which is the claimed primary constituent of the Nutri-Life Platform® product). The fungal genera with the highest relative abundances in the product included members of the Hypocrea/Trichoderma, Thermoascus, Talaromyces and Candida (relative abundances of 86, 9.3, 2.4 and 2.3% respectively). No AM fungi (Glomeromycota) were detected.
Page 45 of 167 Table 2 Overview of the microbes claimed to be present and detected in biostimulant products to the lowest taxonomical classification as specified by the manufacturers. These broad microbial groupings were quantified in roots and soil in plots treated with the biostimulant products and in untreated plots. The organisms were detected in roots and in soil in similar abundance irrespective of biostimulant treatment. For detail on organism groups, please see text Affected by Detected in Detected Detected Relative Abundance Claimed soil microbe product in roots in soil treatment Roots Soil Soil-LifeTM Lactobacillus Yes Yes Yes No <0.1% <0.1% Actinomycetes Yes Yes Yes No <60% <30% Photosynthetic bacteria Yes Yes Yes No <5% <5% Yeasts Yes Yes Yes No <1% <1% Fungi See comments in the text Nutri-Life Platform® Mycorrhizas No Yes Yes No <0.1% <0.1% (Glomeromycota) Trichoderma Yes Yes Yes No <3.0% <3.0% Azospirillum Yes Yes Yes No <0.2% <0.1% Bacillus Yes Yes Yes No <10% <10% Pseudomonas Yes Yes Yes No <0.1% <0.1% Streptomyces cellulosae* Yes Yes Yes No <40% <5% *Members of the Streptomyces genus were identified but the sequencing methodology applied here is insufficiently sensitive to assign taxa to species level, so we cannot confirm that S. celluloae was detected.
2.4.3 Response of bacterial communities to microbial product application Bacterial communities in soil and roots differed significantly (P < 0.05) (Figure 2). Soil communities were dominated by Gaiellaceae, Bacillus and Streptomyces (7–9%, 4–6% and 4–6% across sampling times) while roots were dominated by Streptomyces (10–40% across sampling times, Figure 2). Treating soil with the microbial product (i.e. the mixture of Soil-Life™ and Nutri- Life Platform®) did not result in a significant difference in the composition of bacterial communities in soil or roots (Figure 2).
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Figure 2 Heatmap summarising the relative abundances of rhizosphere and bulk soil bacterial communities associated with soils receiving 100 kg/ha N (75% of recommended N fertiliser rate) without (control, blue) or with application of microbial products (microbe, red). Each line represents the relative abundance (RA) of a given operational taxonomic unit (OTU) in taxonomic order. The three left columns show the RA of each OTU in “Nutri-Life Platform®”, “Soil-Life™” and mixed products, respectively. Remaining columns are ordered by sample (rhizosphere, soil), followed by week of collection from week 2 (W2), week 15 (W15) to week 25 (W25). Generated using R 3.5.1 Page 47 of 167 2.4.4 Response of fungal communities to microbial product application Fungal communities of soil and roots differed significantly (P < 0.05). Similar to bacteria, plants can shape their rhizosphere microbiome by attracting particular fungi from the bulk soil (Houlden et al. 2008; Zhang et al. 2008; Paungfoo-Lonhienne et al. 2015; Zhou et al. 2018; Huang et al. 2019). Soil fungal communities were dominated by Capnodiales (relative abundance 20–30% across sampling times) while root communities were dominated by members of the Nectriaceae and Marasmius (relative abundances 10–40% and 10–80%, respectively, across sampling times) (Figure 3). Inoculation did not affect the composition of soil fungal communities, while root fungal communities differed significantly (P < 0.05). The plots treated with the microbial product had larger relative abundances of Marasmius, Fusarium and Talaromyces populations compared to roots in the untreated control. We detected OTUs identified to the Glomeromycota phylum in soil and root samples; however, they were in very small relative abundance and were unaffected by microbial product application.
Figure 3 Heatmap of rhizosphere and soil fungal communities for plots receiving 75% of recommended N fertiliser rate (100 kg/ha N) without (control, blue) or with application of microbial products (microbe, red). Each line represents the relative abundance (RA) of a given operational taxonomic unit (OTU) in taxonomic Page 48 of 167 order. The three left columns show relative abundance of each OTU within the “Nutri-Life Platform®”, “Soil-Life™” and mixed inoculum, respectively. Remaining columns are ordered by sample (root + rhizosphere, soil), followed by week of collection from week 2 (W2), week 15 (W15) to week 25 (W25). Only fungi with a relative abundance of greater than 1% are included in the figure. Generated using R 3.5.1
2.5 Discussion
2.5.1 Sugarcane yield Of most interest to agricultural producers is if microbial products benefit their crops, manifested as improved yields and/or reduced need for inputs such as fertilisers and pesticides. We quantified sugarcane tonnage and sugar yield in replicate plots in a commercial crop, and found that the application of microbial product at full or reduced N fertiliser rates had no measurable effect on these yield parameters. Application of the microbial product was, however, associated with a shift in three fungal OTUs in roots, suggesting indirect effects of the product application that did not translate into detectable yield increases in our study. For a comprehensive analysis of benefits, multiyear yield experimentation is needed, and is indeed a requirement in countries that demand proof of efficacy prior to registering commercial products (EPPO 2012c). A multiyear analysis was beyond the scope of our study, and interpretation focusses therefore on the methodology of microbial community characterisation as a tool to substantiate potential effects of microbial products.
2.5.2 Composition of the microbial products The organisms present in both products broadly aligned with manufacturer product labels. All microbes claimed to be present were identified in the Soil-Life™ product. However, Lactobacillus was the dominant bacterial taxon, accounting for approximately 99%, with all other taxa present at very low relative abundance (< 1%). An identified problem with small populations of target microbes is that they may be unable to establish, outcompete native microbes and survive when applied to soil (Parnell et al. 2016). A second issue is that some microbial groups present in the product are extremely diverse which makes it difficult to gauge potential benefits. For example, ‘photosynthetic bacteria’ contain many taxa. Soil-Life™ also lists ‘unspecified fungi’ as an ingredient, which is uninformative given that there are ~ 400,000 known fungal species in 18,500 genera (Bensch 2016), and an estimated 5 million fungal species globally. Fungi are prevalent soil organisms, associate with roots, and range from beneficial to highly pathogenic to crops. Our analysis detected 440 fungal genera with 45 OTUs with > 1% relative abundance (Figure 3).
Page 49 of 167 Similarly to fungi, the diversity of bacteria is immense with only 5000 bacteria species described so far out of an estimated 1 billion species (Nielsen et al. 2015). With such broad classification, it is difficult to ascertain potential mechanisms as species and strains can be beneficial, neutral and/or pathogenic. For example, Burkholderia species/strains isolated from soil can benefit plant growth, while others are primary plant pathogens, and some are beneficial for one plant host but pathogenic for another (Coenye and Vandamme 2003; Paungfoo-Lonhienne et al. 2016).
We identified all microbes claimed to be present in the Nutri-Life Platform® product except AM fungi (Glomeromycota) although Glomeromycota were detected in root and soil. Whether the lack of detection in the product is indeed due to an absence of mycorrhizal spores in the product requires further investigation. It is not trivial to generate AM spores for microbial products (Singh et al. 2014b), and even if mycorrhizal fungi are present in a product and associate with a target crop, their abundance in roots or soil does not necessarily increase in response to product application. Insufficient quantities of propagules and soil conditions that are unfavourable to the AM symbioses (e.g. high soil N and phosphorus levels; Treseder 2004) can prevent an increase in the presence of AM fungi.
More generally, DNA analysis of organisms has to be accompanied by biological tests, since the presence of DNA may not translate into viable organisms. There is additionally the consideration that different marker genes and classification databases can yield different results as outlined by Xue et al. (2019). The UNITE database, which we used here, is considered best for identifying the highest number of taxa but also has the highest number of unclassified OTUs, denoting AM fungi may have been present that were not classified. An argument against this notion would be that AM fungi have received considerable attention, including DNA-based identification. Our study highlights that product quality should be confirmed in each batch, and that quantitative information on each type of organism on the product label will assist consumer decision making. The onus should be on manufacturers to ensure presence and viability of the claimed microbes, and to communicate suitable methods of storage and handling to customers.
2.5.3 Effects of microbial product application on the composition of bacterial communities The application of microbial products to soil did not directly or indirectly influence the composition of soil or root bacterial communities. Microbes compete for niches, for example by being efficient root colonisers (e.g. biofilm formers), producing antibiotic compounds that directly impact the growth of other microbes, or by depleting resources that are essential for other microbes, and thereby indirectly reducing the presence of native bacteria and fungi (Gamalero and Glick 2011). Page 50 of 167 Many microbial biostimulant products include only one or very few taxa, especially in regulated markets such as EU countries. Other products, including the products tested here, have a mixed- culture fermentation step (so-called extension). These microbial products aim to deliver a rich mixture of microbes (‘effective microorganisms’), commonly including Lactobacillus, photosynthetic bacteria, yeasts and Actinomycetes (Calvo et al. 2014; Lamont et al. 2017), all of which are included in Soil-Life™. Similarly, Lamont et al. (2017) found that Lactobacillus commonly dominates fermented cultures. Microbes are often fermented with organic materials and molasses so that microbes enhance the efficiency of mineral and organic fertilisers (Calvo et al. 2014). Multiyear trials testing these ‘effective microorganism products’ have detected variable effects and inconsistent impacts on yield in temperate crops (Calvo et al. 2014; Lamont et al. 2017), confirming that there is limited evidence and understanding of the mechanisms through which crop growth is enhanced with such products (Cóndor Golec et al. 2007). Evidence supporting the importance of each individual microbial component to the efficacy of the overall product is also lacking for most mixed-culture products.
Lactobacillus dominated the Soil-Life™ and (mixed) microbial products but this did not translate to an increased abundance in soil or roots. This is in line with manufacturer claims that Lactobacillus does not directly enhance plant growth but delivers indirect benefits by stimulating other microorganisms. Our analysis detected no evidence that Lactobacillus enhanced the populations of other bacteria within the taxonomic resolution of our study, but may have contributed to the observed increase in three fungal taxa in the root microbial community. While certain Lactobacillus strains possess antifungal activity (Kim 2005; Gerbaldo et al. 2012), there is currently no published record of direct stimulation of root fungal communities by Lactobacillus.
Lactobacillus species produce lactic acid from monosaccharides, inhabit the human gut, are used in food preservation (König and Fröhlich 2009; Lamont et al. 2017), commonly occur in composts and silage and decompose organic materials (Lamont et al. 2017). While there is little evidence that Lactobacillus, when applied directly to plants, enhances growth, it may enhance nutrient cycling or stimulate other soil microbes. Inoculation of composts with microbial consortia dominated by Lactobacillus can increase yield and nutrient uptake of different crops (Lamont et al. 2017), although mechanisms remain unclear.
Actinomycete species, a minor component in Soil-Life™, are abundant in soil and have been studied for their PGP potential (Franco-Correa et al. 2010). There is evidence that members of this bacterial order can enhance P mobilization, fix N2, produce PGP hormones (such as IAA), release
Page 51 of 167 siderophores, hydrolytic enzymes and antimicrobial compounds to boost plant growth and protect against pathogens (Jog et al. 2016). Many commercial microbial products include Actinomycetes with a wide array of potential benefits reported, including biocontrol (Doumbou et al. 2002). Actinomycetes had relative abundances ranging from 20 to 30% and 40 to 60% in soil and roots, respectively, which were unaffected by the products.
Photosynthetic bacteria are a third bacterial group present in Soil-Life™. These organisms include Cyanobacteria, Rhodosporillaceae and Chloroflexaceae which can perform incomplete photosynthesis anaerobically and grow well in mixed liquid cultures with other fermenting bacteria and yeasts (Higa and Parr 1994). These organisms are often included in mixed fermentation biostimulant products due to their ability to fix atmospheric CO2 and N2, and their hydrogen metabolism (Kobayashi and Haque 1971; Higa and Parr 1994; Bothe et al. 2010; Hallenbeck and Liu 2016). Photosynthetic bacteria can detoxify soils by metabolising toxic compounds such as hydrogen sulphide or amines, and improve soil fertility (Kobayashi and Haque 1971; Higa and Parr 1994). In our study, the collective relative abundance of all microbes that classify as photosynthetic bacteria was < 5% across soil and root samples of all treatments. Whether this is a sufficiently high abundance to deliver benefits to soil and crops is unknown.
The Nutri-Life Platform® contains a number of bacterial genera including Azospirillum, Bacillus, Pseudomonas and Streptomyces. These are well-known PGP genera with demonstrated potential to enhance plant vigour and growth, and are common ingredients of microbial products (Glick 2012; Cote et al. 2015). These organisms encompass a wide range of PGP traits to deliver benefits via hormone production, nutrient mobilisation and pathogen resistance (van Loon 2007; Dutta and Podile 2010; Trabelsi and Mhamdi 2013). Members of these genera were identified in all soils and roots (Table 2), with no detectable change in abundance in response to product application.
2.5.4 Effects of microbial product application on the composition of fungal communities There was no quantifiable effect on soil fungal communities, but root-associated populations of Marasmius, Fusarium and Talaromyces increased in product-treated plots (discussed below). Fungi applied with the products include four Trichoderma strains (the main fungus present in Nutri-Life Platform®), a genus reported to convey benefits to several crops including tomato, mustard and bean (Ghorbanpour et al. 2018). Various species of Trichoderma have modes of action that include out-competing pathogens for space and nutrients, parasitising pathogenic fungi, antimicrobial compounds and inducing pathogen resistance in plants (Ghorbanpour et al. 2018). Application of the product to soil did not, however, significantly change the relative abundance of Trichoderma Page 52 of 167 (< 3% relative abundance in roots and soil). Arbuscular mycorrhizas (AM) are desirable plant symbiotic fungi and were detected in soil and roots in low relative abundance (< 0.1%) without discernible increase following product treatment. Possible reasons include insufficient or no viable AM spores in Nutri-Life Platform® or unfavourable conditions for AM fungi. AM spores can become nonviable in high temperatures which may occur during storage, compromising carrier materials and microbe viability (Bashan et al. 2014). Yeasts were a component of Soil-Life™ and are commonly included in ‘effective microbe’ formulations due to several PGP benefits (Higa and Parr 1994; Nutaratat et al. 2014; Fu et al. 2016; Sarabia et al. 2018). Yeasts can mobilise nutrients (P, siderophores) (Sarabia et al. 2018), regulate hormones (IAA production, 1-aminocyclopropane- 1-carboxylate (ACC) deaminase activity) and protect from pathogens by producing hydrogen cyanide, cell-wall degrading enzymes, catalase and ammonia (Nutaratat et al. 2014). While yeasts were identified in soil and roots, their relative abundance was small (< 1%). Marasmius was present in soil of all plots (< 1% relative abundance), and roots in the treated plots had an increased relative abundance in a specific population of Marasmius (OTU_32, Figure 3) while roots in control plots had an increased abundance of a different Marasmius population (OTU_30, Figure 3). These populations are not annotated in the database and interactions with sugarcane are unknown. Marasmius contains decomposer species but also crop pathogens (Ferreira Gregorio et al. 2006) with Marasmius sacchari the cause of root rot disease in sugarcane (Singh et al. 2017).
Similarly, it is unclear whether Fusarium OTU_21 that increased statistically significantly from 12 to 17% relative abundance in roots of treated plots are beneficial genera, neutral and/or pathogenic organisms. Fusarium wilt disease, for example, is caused by soilborne pathogenic Fusarium oxysporum strains that invade roots and vascular tissue of economically important crops (including tomato, banana, brassicas, cucurbits), causing wilting and eventually death (Fravel et al. 2003). F. oxysporum is widespread in soils and nonpathogenic strains compete with, and act as, biocontrol agents against pathogenic F. oxysporum strains (Mandeel and Baker 1991). Whether the Fusarium OTU_21 includes biocontrol strains remains to be investigated.
At week 25, the largest abundance change in response to product application occurred in Talaromyces (OTU_15) which increased in relative abundance from 5 to 17% from week 2 to 25, but in control plots remained at 5% throughout. Talaromyces represents the sexual reproductive stage (teleomorphic) of Penicillium (Yilmaz et al. 2014). The genus Talaromyces contains PGP species including some that emit volatiles that promote plant growth or act as strong biocontrol agents against fungal pathogens including Verticillium, Rhizoctonia and Sclerotinia (Yamagiwa et al. 2011; Yilmaz et al. 2014).
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Future research could verify or refute efficacy and mechanisms of the three fungal OTU’s that increased in abundance in response to product application. Taken together, our findings suggest that the effects of the microbial product on root fungal communities are indirect since the fungal genera that increased in abundance in roots were not primary constituents of the products. Further research should examine if the altered fungal OTUs have PGP or biocontrol function for sugarcane. The comparatively large increase in the abundance of Talaromyces makes this taxon a priority for further investigation.
2.5.5 Considerations for product efficacy The claimed benefits of the tested microbial products include improving soil physical and biological health, stimulating beneficial microbial populations, increasing crop yield, root growth, plant establishment, drought tolerance and reducing fertiliser requirements. We did not test all claims but focussed on microbial communities in soil and roots, and quantified sugarcane yield. As outlined above, despite changes in root fungal communities, no quantifiable benefit on yield was detected, but longer-term and multi-site research is needed for a full evaluation of the microbial products to quantify effects. That sugarcane yields were similar with full and a 25% reduced N rate is perhaps unsurprising considering that the full N fertiliser rate exceeds crop N needs and factors in N losses from soil that are inevitable with current fertilisers. Similar to our previous research (Yeoh et al. 2016), abundance of N fixing bacteria did not increase in soil or roots with lower N fertiliser supply. The Nutri-Life Platform® product claims to improve N fixation with Azospirillum, a genus that includes free-living N fixing species (Zhang et al. 1997; Steenhoudt and Vandereyden 2000) but this was not substantiated here. Many studies in Brazil have shown that inoculation with Azospirillum can significantly enhance crop growth and grain yield, primarily for cereal crops; however, this was reliant on seed inoculation and stringent seed storage procedures (Diaz-Zorita et al. 2015; Garcia et al. 2017). Limitations of our study include that identification of microbes was restricted to genus level, which is a current restriction of the 16S rRNA marker gene sequencing technique. The 16S rRNA method has been widely adopted to obtain a snapshot of microbial community composition and is useful for taxonomic classification of organisms, but is insufficiently sensitive to characterise microbial assemblages at a functional level (Hamady and Knight 2009). Meta-genomic analysis requiring longer DNA-reads characterises the function of microbial communities, but this technique is much more costly. High-throughput methods, such as pyrosequencing, offer an affordable and rapid approach to obtain a snapshot of bacterial and fungal communities. Sequencing with Illumina MiSeq (used here) has a low error rate and a high resolution so that microbes that occur in very small relative abundances, as low as 0.00001% in our Page 54 of 167 study, are detected (Loman et al. 2012). It is possible that some microorganisms present in the sampled soil and roots were not identified with this method, but they would have been present in minute abundance relative to the quantified microbial populations. However, microbes occurring in very small abundances can impact plant health and growth. For example, as few as seven propagules of F. oxysporum f. sp. apii per gram of soil were required to cause vascular discolouration in celery (Elmer and Lacy 1987), which is a minor proportion of the soil microbial community with 108 bacterial cells g−1 soil reported in temperate soils (Raynaud and Nunan 2014). A recent study by Wei et al. (2019) also showed that rare taxa that exist in low relative abundances can play pivotal roles in the mineralisation of P under P-limiting conditions.
Our experiment was limited to 1 year, which may have been insufficient time to stimulate changes in soil biology and manifest quantifiable benefits, although the product manufacturers do not stipulate a timeline for effects. Longer-term interactions between microbial products, lower N fertiliser use and a range of yield and soil parameters have to be investigated to conclusively evaluate microbial biostimulant products. Previous reports have shown that application of microbial suspensions to soil can shift native microbial communities for short periods, but that the community composition often returns to its original state within several weeks postinoculation (Bauoin et al. 2009; Qiao et al. 2017). A glasshouse study using non-sterilised natural soils showed that inoculation with PGPR can change native microbial community composition in soil or roots for several months (Thokchom et al. 2017). As outlined above, however, there is a dearth of field studies that examine root, rhizosphere and soil communities in longer-term field experimentation.
The Nutri-Life Platform® product sheet lists 143 crops that benefit from inoculation, sugarcane included, but, to the best of our knowledge, no scientific evidence for those claims is publicly available. In light of research demonstrating that individual microbes often have very specific interactions with plant hosts and benefit only a subset of crops (Alves et al. 2015; do Amaral et al. 2016), as well as plant genotype influencing PGP effects of microbes, such claims may overstate the effects of microbial products. For example, of 21 Herbaspirillum strains tested on two maize genotypes, only one strain promoted the growth of both maize genotypes under field conditions (Alves et al. 2015). Aside from plant genotype, abiotic factors influence PGPR success, making product efficacy difficult to maintain across different soils and climates (Tabassum et al. 2017). As a result, industry stakeholders are often sceptical about microbial biostimulants including their effect as biocontrol agents. For example, four out of five commercial microbial products tested for their ability to control soilborne plant pathogens in glasshouse conditions showed large variability in efficacy (Koch 1999). Most field trials evaluating commercial microbial products in Australia
Page 55 of 167 have examined Rhizobium products for legumes and advanced the selection and manufacture of effective microbial products. In contrast, evaluation of commercial biostimulants beyond Rhizobium has not had much attention past small-scale testing in pot trials with most studies occurring in India and South America (Figueiredo et al. 2017). More generally, we have often observed that in field trials, two variables are changed simultaneously, e.g. crops grown with 25% less fertiliser with added microbial product are compared to a fully fertilised crop. Such experimentation does not distinguish between the effects of a lower fertiliser dose and microbial product, and can lead to false claims. Overall, we find little information available in Australia about the tests that have been performed on various crops and the outcomes of such tests, which may be a feature of the microbial biostimulant market more generally (Parnell et al. 2016). The responsibility of product testing and confirmation of efficacy should fall to the manufacturers. Insufficient regulation on product efficacy in unregulated markets often prevents rigorous scientific evaluation and optimisation of biostimulant products for particular crops and farming systems. EU countries are addressing this by legislating that the efficacy of products has to be demonstrated in scientifically validated field trials (EPPO 2012c). In regulated markets, commercial products are often formulated with a single microbial strain for a particular context (i.e. crop, soil, climate). In contrast, in unregulated markets such as Australia and the USA, products are often mixtures of numerous microbial taxa and recommended for many crops. We anticipate that in the future the commercial microbial biostimulant industry will consolidate the product market with effective products for particular crops and growth situations. There is evidence that single-microbial strain products may not establish and survive well under field conditions (Tabassum et al. 2017), and that mixed-culture products enable a pre-established synergistic microbial community that enhances competitive ability (Trabelsi and Mhamdi 2013; Burmølle et al. 2014). For both types of products, thorough classification of each active constituent should be a priority to reduce the risk of inadvertent pathogen release. We found that product labels are often not informative, lacking detail on microbial composition (including on sub-species/strain), recommended storage and application rates (Bashan et al. 2016). If microbial biostimulants are to contribute to the sustainable intensification of farming, collaboration between regulators, manufacturers, scientists and end-users is an obvious next step.
2.6 Conclusions
Microbial biostimulants and biocontrol agents are receiving much attention with promising discoveries over recent decades and a desire to improve agricultural practices to reduce the environmental footprint of farming. Fuelled by the accelerating ability to analyse microbial DNA in Page 56 of 167 environmental samples, it is now possible to characterise microbial communities taxonomically and functionally. Our study was motivated by the bottleneck that has limited the translation of fundamental research into field-effective products, as identified by sugarcane industry stakeholders. Multiple factors need to be considered for product efficacy. These include (i) do microbes benefit the target crop directly (host specificity) or indirectly (improved soil function including soil biology), (ii) are the introduced microbes viable at the time of application (suitable transport, storage, formulation), (iii) is the application technique appropriate (seeds coating, liquid sub/surface application to soil, timing) to deliver viable microbes and (vi) do introduced microbes effectively compete with native microbes and survive soil biotic and abiotic conditions? Many farmers see microbial products as a tool to improve identified soil constraints, but there has to be clarity on what such products can deliver. We agree with others that microbial products have potential to complement current agronomic practices, but that that the aims must be realistic and backed up by science to ensure quantifiable benefits. Our study confirms the potential of microbial community profiling to ascertain if biological inoculants influence native microbial communities directly and indirectly. Next steps require longer-term field evaluations together with an analysis of functional changes of the microbial communities. Metagenomics now allow closer examination of microbial function and will benefit understanding of the mechanisms. Research and development of beneficial microbes can then integrate into comprehensive analyses of soil and crop health, connecting physical, chemical and biological attributes of crop systems.
2.7 Final Conclusions This experiment was conducted in collaboration with Terrain Natural Resource Management (TNRM) and Herbert Cane Productivity Services Limited (HCPSL). The design of this experiment was limited by the available funding, field site, sampling and time constraints. The funding body and growers identified their primary aim as testing the products of interest in the way they were designed to be delivered, therefore products were applied together as a mixed inoculum and not tested separately (as per the manufacturer’s instructions). Additionally, the growers primary objective was to evaluate whether the biostimulant products contained the microbes they claimed, whether they ended up in the soil, and if application of these biostimulants could increase crop yield and subsequently allow for reduced N fertiliser rates.
The limitations of this chapter are testimony to the constraints that all growers experience in their endeavour to find a product that works for their crop. Growers are faced with several biostimulants, clever marketing, unreasonable product claims, and limited space to test products on or knowledge of how to test them effectively. The products tested in this chapter (Soil-LifeTM and Nutri-Life Page 57 of 167 Platform®) both stated broad claims for crop improvement. For the case of Soil- LifeTM, some of these claims included, improved soil quality (physical, chemical and biological), improved crop quality, yield and growth, enhanced germination and plant establishment, as well as drought resistance. Nutri-Life Platform® promises to reduce fertiliser rates due to enhanced nutrient uptake, improved nitrogen fixation, enhanced root zone and root resilience. Several are these claims are broad and difficult to interpret making it hard for growers to know what to expect. Aside from this however both companies marketed these products to sugarcane growers across Queensland insisting that they would enhance establishment, crop growth and yield while reducing fertiliser rates. As one of the primary claims made by both products was the ability to enhance yield and reduce fertiliser rates, this was the focus of our experimental design. This therefore led to a full and reduced N treatment, with and without biostimulant application. Root samples were collected from the reduced N treatments only (with and without biostimulant application) to reduce sequencing costs.
As outlined in the Methods (section 2.3.1) the yield was calculated using a 10-stalk estimation method and wet weights which adheres to industry protocols. The foundation of this research was that it needed to benefit the growers in their endeavour to understand how biostimulant products work, how they affect the soil, and if they can enhance yields with reduced N input. The methods used here to compare yields amongst treatments aligns effectively with these goals while keeping the focus on the microbial communities which was the primary aim of this thesis. Ultimately it is required that multi-year trials are conducted for ratoon crops such as sugarcane and future studies could incorporate water limited yield potentials to investigate whether these biostimulant products can assist, long term, in reducing the gap between potential and actual yield.
One observation worth noting is that the absence of effect on sugarcane yield may be due to residual N from previous harvests or N fertiliser rates that were too high, even in the reduced N treatment, to deliver a noticeable effect. The latter is more likely as Nitrate is an incredibly mobile nutrient and is rapidly lost from plant/soil systems. A consideration for future experiments could be to lower N rates further than a 25% reduction as standard rates in Australian sugarcane cropping systems are relatively high compared to other parts of the world (Yeoh et al. 2016). Only 50% of applied N fertiliser is typically used by the crop suggesting fertiliser rates could potentially be lower without impacting yield (Robinson et al. 2011). Despite this, a shift in fungal communities was observed following product application with Fusarium, Talaromyces, and Marasmius OTUs increasing in relative abundance. It was outside the scope of this study to investigate whether these OTUs were beneficial, neutral, or pathogenic organisms however no evidence of disease was
Page 58 of 167 observed in the crop during the growth period. Further study is required to evaluate the functional traits of these groups. One of the limitations of this study was that the field site we had access to had been treated with Soil-LifeTM in the previous year. The results indicate however that this did not appear to have a strong impact on our study as Lactobacillus counts were in very low relative abundance in all treatment plots and were unaffected by microbial product application despite being in high abundance in the product itself. While there were some limitations in this study, we have highlighted the strengths of utilising community profiling techniques to evaluate PGPR formulations and effects on soil communities. This study highlights the challenges growers are faced with when making judicious decisions about biostimulant products and the limitations they experience when testing them.
Page 59 of 167 Chapter 3 Examining the efficacy of commercial PGPR products for seedling production in horticulture
3.1 Introduction
Agricultural soils often accumulate crop-specific pathogens, while microbes that benefit particular crops and production systems may be absent or have a suboptimal presence (Liu et al. 2014; Zhou et al. 2017; Arafat et al. 2019). There is considerable scope to enhance the presence of beneficial microbes in the soil and rhizosphere microbiome to boost crop resilience, speed of development, growth, and yield (Bhattacharyya and Jha 2012; Tabassum et al. 2017). Biotechnology that enables crop systems with lower costs for primary producers and a reduced environmental footprint, is now a major focus of agro-industries. Avenues are sought to enable the action of beneficial microbes to facilitate the phasing out of environmentally harmful and/or costly agrochemicals including pesticides. The growing interest in bacterial inocula by global agro-industries (e.g. Syngenta, DuPont, Bayer) that specialist companies (e.g. Koppert) have produced for several decades is testimony for the desire to develop agro-production systems that harness microbial-driven, ecological processes. Microbe-based products that promise a green and clean future for agriculture are now a $US 2.9 billion dollar industry (Research and Market 2017). Having previously tested commercial microbial products in the field setting of commercial sugarcane production and unable to detect statistically significant crop growth promoting effects over one crop cycle (Chapter Two), I aimed here to investigate the efficacy of microbial products in a more controlled, yet industry- relevant setting. A local seedling nursery (Wide Bay Seedlings, WBS) is interested in reducing chemical inputs while still maintaining high quality seedling growth and vigour and was our industry partner for this study. A secondary aim of the nursery operations is to evaluate if PGPR can prime seedlings for field transplantation, e.g. by enhancing resilience against disease agents encountered in field soils. WBS has previously had problems with root vigour in winter months. A sizeable root system is likely to improve seedling resilience and growth after transplantation to the field (Ruzzi and Aroca 2015a). In the research performed here, therefore we studied the effects of putative PGPR in the commercial nursery environment and in controlled growth cabinets. Specifically, we examined growth and biomass allocation of different horticultural species when inoculated with several commercial and non-commercial biological inocula.
The benefits of ‘bio-priming’ seeds or seedlings include the indirect protection against pathogens by initiating internal plant defence mechanisms, or the direct control of soil-borne microbial pathogens,
Page 60 of 167 for example via competition for resources (Finkel et al. 2017; Gorawala and Mandhatri 2017). Bio- priming is most commonly done by inoculating seeds or seedlings with single microbial suspensions (Raj et al. 2004; Kumari et al. 2018). For example, seeds that were soaked with a Pseudomonas fluorescence suspension and air dried prior to sowing showed improved germination rates and up to 22% increased biomass (compared to non-inoculated seedlings) under field conditions. These ‘bio-primed’ seeds also displayed enhanced resistance to downy mildew when grown in the field with a 75% reduction in disease incidence for one of the tested isolates (Raj et al. 2004). Another mechanism of PGPR that benefits horticultural crops is improving root architecture, which in turn enhances anchorage, water and nutrient uptake, and resilience following field transplantation (Ruzzi and Aroca 2015b; Kumar and Meena 2019). In the setting of the seedling nursery here, certain crops, especially watermelon, have inhibited root growth during winter months, which translates to a longer growth period and associated economic penalty at the nursery and a higher rate of seedling loss when transplanted to the field. Approximately 20% of watermelon seedlings can be lost at the nursery during winter months due to reduced root growth (Adrian Ross, WBS, pers. comm.).
Growth substrates used by nurseries commonly avoid a high presence of microbes by using near- sterile substrates to minimise the introduction of pathogens. Currently, the benefits of the plant microbiome are neither fully understood nor quantified, and the efficacy of PGPR often remains unclear (Finkel et al. 2017). The growth substrate used by WBS is a sterilised peat-vermiculite mixture with an initially low presence of microbes, which is contrasted by microbe-rich field soils that are encountered by seedlings at planting. Identifying the microbes that accumulate in the growth substrate and rhizosphere of seedlings over a ~ 30 day nursery growth period has, to the best of our knowledge, not been studied at WBS or any other Australian nursery. This is despite the problems that the shift from a potentially low microbial presence in nursery growth substrate, to a high microbial presence and diversity after field transplantation can cause problems for plant growth and survival (Zhang et al. 2019). This is particularly important in fields that routinely undergo monoculture cropping as crop-specific pathogens can proliferate in the soil over time (Liu et al. 2014; Zhou et al. 2017; Arafat et al. 2019).
Seeds and roots treated with PGPR prior to transplantation in field soil improved plant resistance to the common ‘bacterial speck’ disease (Pseudomonas syringae pv. tomato) in tomato seedlings (Ji et al. 2006). A more recent study found that bio-priming seedlings with PGPR in nursery substrates can have multiple beneficial effects after transplantation to the field (Zhang et al., 2019). Pepper (Capsicum annuum L.) seedlings primed with Bacillus velezensis NJAU-Z9 had significantly higher
Page 61 of 167 yield (up to 24% more than non-inoculated seedlings), and populations of several PGPR genera, including Sphingomonas, Sphingopyxis, Lysobacter, Streptomyces and Pseudomonas, proliferated in the rhizosphere. Zhang et al. (2019) found that NJAU-Z9 populations were maintained in the rhizosphere after field transfer from seedling to flowering and mature plant phase, representing approximately 60% of the total bacterial population. Overall, the focus of more recent research on the effects of PGPR is less on pre-inoculation of seedling roots in nurseries prior to field transfer and more focused on seed priming for improved field establishment.
Transplantation is often stressful for seedlings as environmental conditions can dramatically change from the protected nursery environment to the field, and an adequate root system generally underpins plant survival and vigour (Leskovar and Stoffella 1995; Davis and Jacobs 2005; Koevoets et al. 2016). Stresses impacting plant vigour during this process include handling, uprooting and altered resource availability that together can affect the plants’ immune system and increase vulnerability to pathogen attack. Soil pathogens can infect weakened plants including via damaged tissues (Yadeta and Thomma 2013). Priming seedlings in the nursery with PGPR can therefore generate a competitive advantage, reduce the acclimation period and accelerate growth (Ji et al. 2006; Akbar et al. 2019; Zhang et al. 2019).
Here, we tested multiple commercial microbial products for their efficacy to enhance growth and alleviate abiotic stress. We examined if microbial biostimulants as live or dead organisms enhance seedling survival and deliver growth-promoting effects of significant horticultural crops in controlled growth environments, including commercial nursery and growth cabinet. The specific aims were to investigate whether (i) commercial biostimulants are effective in the controlled settings of a commercial seedling nursery, (ii) commercial products benefit stressed plants more than non-stressed plants, and (iii) adding putative biostimulants alters the composition of bacterial communities in the rhizosphere.
3.2 Methods
3.2.1 Preparation of bacteria inoculum The experiment included four microbial formulations, three commercial products and one bacterial isolate that we have identified as an Enterobacter species (isolate N17a). The Enterobacter was isolated from a sugarcane rhizosphere (Burdekin region, North Queensland, Australia) and tested positive for phosphorus mobilising activity and cytokinin production (see Chapters 2 and 4). In earlier experiments with maize and tomato seedlings, Enterobacter (N17a) enhanced shoot and root Page 62 of 167 biomass, as well as root branching (data not shown). This work was conducted across 2013-2014 as a part of undergraduate study and an honours thesis (Advancing the efficacy of Plant Growth Promoting Rhizobacteria (PGPR) to benefit root architecture and nutrition of nursery crops, Shelby Berg 2014, unpublished). The nursery owner selected three commercial microbial products, with each product claiming to enhance plant growth, root growth and branching, and protect against pathogens. The products were Serenade® Prime (Bacillus subtilis strain QST 713, Bayer Crop Science), Rhizo-max® (Bacillus amyloliquefaciens strain pm414, Biofilm Crop Protection) and Great Land® (Lactobacillus and Acetobacter, Terragen). The experiment consisted of a no-microbe control and eight bacterial treatments which included the four bacterial inoculants applied both as an active (i.e. microbes delivered from the product) and an inactive treatment (i.e. pressed microbe filtrate, see below).
The active treatment refers to a suspension of active microbes, termed here ‘active +’ microbial inoculum. For the commercial products, we used the product within a month of arrival, prepared and applied as per manufacturers’ instructions. An inactive microbe treatment for each inoculant was included to investigate whether the potential beneficial effects were the result of active bacteria or rather microbe-derived biochemical constituents. This inoculum of inactive microbes was prepared by passing samples through a French press. This lyses bacterial cells as they are exposed to extreme pressure (1000 kPa, for 3 minutes, 5 repeated passes). The pressed samples were then passed through a 0.22 µm filter to ensure that any surviving microbes were removed. The sample therefore contained the chemical components of the commercial products (i.e. the carrier liquid) as well as bacteria-derived compounds that are produced by the bacteria while in suspension, and/or are released from lysed bacteria.
Microbial products were diluted and applied at rates recommended by the manufacturers. This included an aqueous solution with a suspension of either 5% Serenade® (50ml/1L stock solution), 5% Rhizomax® (50 ml/1L stock solution) or 2% Great Land® (20 ml/1L stock solution). Enterobacter inoculum was generated from an overnight culture of one Enterobacter N17a colony grown to mid-log phase in nutrient broth at 28℃ with shaking. After centrifugation (3,500 rpm for 8 10 min) the pellet was resuspended in sterile distilled water to an OD600 = 0.3-0.5 (~10 CFU/ml). 100 ml of the suspension was mixed with 900 ml of sterile distilled water to generate 1 L stock inoculum. Each seedling received 2 ml of the bacterial inoculum. No-microbe control seedlings received 2 ml of sterile distilled water. The solutions were pipetted onto the substrate at the base of the stem to ensure that it percolated directly over the roots. Quality control checks were conducted for all active and inactive inoculums. 200 µl of all ‘active’ inoculums were pipetted onto nutrient
Page 63 of 167 agar, spread and incubated for one week (30℃) to ensure that growth occurred, which confirmed that all products contained active microbes for the active “+” treatments. 200 µl of inactive/pressed inoculum was also pipetted onto nutrient agar to ensure that active cells had been removed, and no growth occurred on agar plates in these treatments.
3.2.2 Nursery experiment Three crop species were tested in the nursery trial: Lycopersicon esculentum Mill. (tomato), Capsicum annuum L. (capsicum) and Citrullus lanatus (Thunb.) Matsum. & Nakai (watermelon). The experiment was conducted at the commercial nursery ‘Wide Bay Seedlings’, located in Pioneers Rest, south-east Queensland, Australia (http://www.wbseedlings.com.au/). The standard peat-vermiculite mixture was used as the growth substrate, and seeds were germinated within seedling wells (2 x 2 x 6 cm) in the dark until hypocotyls emerged. Trays were subsequently moved to the glasshouse and four days later were inoculated with the bacterial inoculum (see details below). Seedlings received the nursery standard water and fertiliser treatment, which comprises daily checks for moisture levels and sprinkler systems to gently deliver fertigation solutions topically. Fertiliser treatment commenced after the first true leaf appeared on seedlings using ‘Gonzo’ liquid fertiliser (a liquid fertiliser was used according to standard practice of WBS). The liquid fertiliser was applied at a rate of 40, 10 and 80 ml for watermelon, capsicum and tomato seedlings respectively, according to nursery practice. The chemical breakdown and composition of this liquid treatment is proprietary information held by Wide Bay Seedlings and was not provided for the purposes of this thesis. As the primary aim of this study was to test the efficacy of commercial PGPR products in a nursery environment under nursery practices, the nurseries fertiliser treatment was used for the first experiment. Additional controlled nutrient solutions were used in following growth cabinet experiments. The glasshouse had automated roof systems to ensure optimum moisture and sun levels for the seedlings.
Each treatment consisted of five true replicates. Seedlings within one seedling tray were treated as pseudo-replicates and we established true replicates across different trays (five separate trays in total for each treatment). Per seedling tray, eight plants were inoculated and marked. At two and four weeks post inoculation, four plants were harvested from within one tray. This aligned with the nurseries normal seedling turnaround times, which is approximately 30 days depending on the crop. Root and shoots were separated, rinsed and dried in the oven for 2 days to quantify dry weight. The pseudo-replicates were averaged to give a biomass measure for one true replicate (see Table 3). Root and shoot biomass were our primary measure of plant performance followed by visual inspection of root system architecture. When a seedling was removed from a well, a seedling from a Page 64 of 167 separate (non-inoculated) tray was placed into the well to avoid edge effects. All trays were randomised within the glasshouse. Root samples were not collected for sequencing in this experiment as root systems were too small upon harvest to collect enough material for biomass measurements and DNA collection. Root collection and sequencing was carried out in the growth cabinet experiment (see section 3.2.4). Data were analysed with ANOVA and Tukey’s post hoc test using R Studio 3.5.1. Table 3 Experiment overview of the Wide Bay Seedling nursery experiment. For each crop, we used x trays with x seedlings to pool x pseudo replicate into one replicate, different trays constituted replicates. Number of pseudo Number of Number of Total number of replicates sampling times Treatment Replicates plants inoculated (seedlings within a (2 and 4 weeks post (seedling trays) per treatment tray) inoculation) Control 5 4 2 40 Serenade® - 5 4 2 40 Serenade® + 5 4 2 40 Rhizomax® - 5 4 2 40 Rhizomax® + 5 4 2 40 Great Land® - 5 4 2 40 Great Land® + 5 4 2 40 N17a - 5 4 2 40 N17a + 5 4 2 40 Total number of plants inoculated and harvested 360
3.2.3 Stress experiment in growth cabinet To investigate whether commercial products have a larger effect with stressed plants, we conducted a second experiment using watermelon and a Saccharum L. hybrid (sugarcane cultivar Q186), the original source of the Enterobacter N17a species used. The same bacterial treatments and growth substrate were used in this experiment as previously outlined in the nursery trial. A larger seedling pot was used in this experiment due to the addition of a larger tissue culture based plant (5 x 5 x 5cm). Seedling pots were watered to water holding capacity (approximately 10 ml) with deionised water before plastic bags were placed over each seedling well to reduce the risk of contamination. Seedlings were placed into a growth cabinet with a photoperiod of 16 h of light, day/night temperatures of 23℃, and an average of 100 µmol m-2s-1 growth light.
The microbial inoculants were prepared in the same way as for the nursery experiment to include an active and an inactive microbe inoculum for each bacteria product (see above). For each plant species and all microbial treatments, we also compared water stressed (droughted) and non-stressed plants. Watermelon seedlings were germinated in the dark and transplanted to soil. After 5 days, seedlings were inoculated with microbial inoculum, or in the case of the control, deionised tap water, which was also used for watering throughout the experiment. Tissue cultured sugarcane Page 65 of 167 plantlets that were approximately 5 cm tall and grown on half-MS media were transplanted to soil. After five days, plants were inoculated with microbial inoculum or water in the control. After one week, plants received a second inoculation of 2 ml microbial inoculum (prepared the same way as described above; water for control plants) as per manufacturers’ recommendation.
Non-stressed plants were watered every 2 days to field capacity, approximately 10 ml using de- ionised water, and received nutrient solution once per week. The nutrient solution consisted of
NH4NO3 4mM; CaCl2 1.5 mM; MgSO4 1.5 mM; KNO3 1.5 mM; KH2PO4 1.5 mM; MnSO4 10 μM;
NaFe-EDTA 18 μM, and micronutrients as H3BO3 45 μM; MnCl2.2H2O 4.5 μM; CuCl2.2H2O 315 nM; ZnSO4 750 nM and (NH4)6MO7O24.4H2O 15 nM. The pH was adjusted to approximately 6.0 using periodic additions of KOH and HCl. Water stressed plants received water every three days which included the weekly application of nutrient solution. To monitor plant stress, a porometer (Steady state diffusion porometer SC-1, Decagon Devices, Inc.) was used to measure transpiration rates (mmol m-2 s-1). Measurements were conducted inside the growth cabinet on fully expanded leaves prior to watering to ensure plants in the non-stress treatment were indeed not water stressed. Throughout the experiment, the transpiration rates of stressed and non-stressed plants were <100 or ~200 mmol m-2 s-1, respectively. Rates of <100 mmol m-2 s-1 is considered water stressed for several dicot species without carrying the risk of major cell death (Sibomana et al. 2013; Elansary et al. 2020). After four weeks of growth, plants were harvested. Root and shoots were separated, rinsed and dried in the oven for 2 days to quantify dry weight. Root and shoot biomass were our primary measure of plant performance followed by visual inspection of root system architecture. Watermelon roots were sampled for sequencing of bacterial communities (see below).
3.2.4 Root sampling and sequencing Roots were sampled for sequencing at the final harvest after four weeks of growth for the growth cabinet stress experiment only. Root samples could not be collected for the nursery experiment due to low root biomass. A small section of root was taken from the tip and branching root zones. Samples were stored at -80°C before DNA extraction. Methods of DNA extraction, pyrosequencing and bioinformatics were carried out as outlined in Chapter 2 Materials and Methods for bacteria isolation and community profiling. Briefly, DNA was extracted with MP Biomedicals Fast DNA SPIN Kit (MP Biomedicals, LLC, Santa Ana, CA, USA). PCR amplification and sequencing was performed by the Australian Genome Research Facility (AGRF). Bacterial 16S rRNA genes were PCR amplified using the primers 803F (5’-ATTAGATACCCTGGTAGTC-3’) and 1392wR (5’- ACGGGCGGTGWGTRC-3’), which were modified on the 5’end to contain the i5 and i7 Illumina Nextera adaptor sequences, respectively. Thermocycling conditions were 95C for 3 min; then 30 Page 66 of 167 cycles of 95C for 30 s; 55C for 45 s; 72C for 90 s; then 72C for 10 min. Raw sequences were trimmed using Seqtk (version 1.0) and processed using QIIME (version 1.8; Caporaso et al. 2010), USEARCH (version 8.0.1623; Edgar 2010; Edgar et al. 2011), and UPARSE (Edgar et al. 2011) software. Sequences were quality filtered using USEARCH. Full-length duplicate sequences were removed, and sequences were sorted by abundance. Singletons or unique reads in the data set were discarded. Sequences were clustered followed by chimera filtering using the “rdp_gold” database for reference (using USEARCH software). Reads were mapped back to OTU’s with a minimum identity of 97%. Finally, taxonomy was assigned using QIIME with the Greengenes database (version 13_8 Dated Aug 2013; DeSantis et al. 2006). Relative abundance was calculated and used to illustrate the composition of the commercial products, soil and root microbial communities. The number of reads per sample was rarefied to 10,000 and the numbers of observed (Sobs) and predicted OTUs (Chao1) as well as the Simpson’s Diversity Index were calculated for each sample.
3.3 Results
3.3.1 Nursery experiment Overall, we did not observe growth enhancing effects in microbe treatment at the first or second harvest, two and four weeks post inoculation (Figure 5A-C; Figure 5D-F). At the first harvest, capsicum and watermelon seedlings had similar root and shoot biomass in all treatments, except tomato seedlings inoculated with active Serenade® product, which had significantly less root biomass (P < 0.05) relative to the non-inoculated control (Figure 5A). Root architecture appeared to be very similar as all seedlings were still very small. At the second harvest, tomato seedlings with active Enterobacter N17A had significantly reduced root biomass compared to the control (Figure 5D), and watermelon seedlings treated with active Great Land® inoculum had reduced root biomass and significantly lower shoot biomass compared to all other treatments (Figure 5F). Root architecture was similar across all plant hosts and PGPR treatments at week four harvest, except watermelon treated with active Great Land®, which had significantly (P < 0.05) smaller root systems with limited lateral root formation (Figure 4). Five of the 14 harvested watermelon plants (five true replicates with multiple pseudo replicates harvested) in the Great Land® treatment had stunted root growth. Watermelon root systems across the rest of the treatments were short and sparse but had some lateral root development.
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Figure 4 Four week old harvested watermelon seedlings grown at WBS. Plants from left to right follow the order of treatments listed in bar graphs below and include Control (C), Serenade® (S), Rhizomax® (R), Great Land® (G), Enterobacter N17a (N). Images in the top row include control treatments that did not receive active microbes. This includes the Control treatment receiving water and inactive microbial treatments (indicated by a “-”). Images in the bottom row include active microbial treatments (indicated by a “+”). Plant roots were washed clean of substrate and root architecture was assessed by observation.
Figure 5. Shoot and root biomass of tomato (g dry weight) (A, D), capsicum (B, E) and watermelon (C, F) seedlings harvested two weeks post inoculation (A-C) and four weeks post inoculation (D-F). Microbial treatments containing active (inoculum with active microbes) or inactive (pressed and filtered inoculums containing no active microbes) treatments are indicated by + or – symbols. ANOVA and Tukey’s post hoc analysis evaluated statistical significance between products. Only two statistically significant differences was detected (D, F) as indicated by an asterisk (P < 0.05).
Page 68 of 167 3.3.2 Water stress experiment in growth cabinet All plants in the water stressed treatments visually appeared stressed with slight wilting and yellowing of leaves (Figure 8). Water stress reduced the growth of watermelon seedlings with significant differences in shoot biomass between non-stressed and stressed plants for almost all treatments (Figure 8). The only stressed watermelon plants that did not exhibit a significant reduction in shoot biomass were those treated with active Rhizomax®. Water-stressed watermelon roots were small irrespective of treatment however, some differences were observed in root architecture and root biomass of non-stressed watermelon plants (Figure 6). Seedlings treated with active Serenade®, Rhizomax® and Enterobacter N17a had denser roots, with more root hairs that bound substrate more strongly relative to control seedlings. These roots had approximately double the biomass of controls and a significant increase was identified for active Serenade® (Figure 8).
Figure 6 Four week old watermelon seedlings grown in growth cabinets in non-water stressed (top row) and water stressed (bottom row) conditions. Plants from left to right follow the order of treatments listed in bar graphs below and include Control (C), Inactive Serenade® (S-), Active Serenade® (S+), Inactive Rhizomax® (R-), Active Rhizomax® (R+), Inactive Great Land® (G-), Active Great Land® (G+), Inactive Enterobacter N17a (N-), Active Enterobacter N17a (N+). Plant roots were washed clean of substrate and root architecture was assessed by observation.
All three commercial products enhanced the shoot and root growth of non-stressed sugarcane plants relative to the control plants (ranging from 52-70% increased shoot biomass and 35-64% increased root biomass), however, the non-stressed (water) control sugarcane plants had very low biomass potentially indicating an unidentified stress imposed. Sugarcane roots were generally small, short and sparse across all water stress and bacteria treatments (Figure 7). In both non-stressed and water stressed conditions, active Serenade® treated sugarcane plantlets had the densest roots with strong branching and root hairs. The growth substrate was difficult to wash from these roots.
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Figure 7 Four week old sugarcane plantlets grown in non-water stressed (top row) and water stressed (bottom row) conditions. Plants from left to right follow the order of treatments listed in bar graphs below and include Control (C), Inactive Serenade® (S-), Active Serenade® (S+), Inactive Rhizomax® (R-), Active Rhizomax® (R+), Inactive Great Land® (G-), Active Great Land® (G+), Inactive Enterobacter N17a (N-), Active Enterobacter N17a (N+). Plant roots were washed clean of substrate and root architecture was assessed by observation.
Figure 8. Watermelon (A-C) and sugarcane plantlets (D-F) four weeks post inoculation, grown in growth cabinets. Treatments were ‘non-water stress’ (B, C) and ‘water stress’ (E, F). Root and shoot dry weights are shown (A, D). Plants were treated with eight microbe treatments and a non-inoculated control. The three commercial products and Enterobacter N17A were delivered as inoculums with active (+) bacteria or inactive (-) bacteria. Different letters above/below the bars indicate statistical significance (P<0.05).
3.3.3 Analysis of root bacterial communities Bacterial communities associated with watermelon roots from the water experiment were characterised using 16S rRNA gene amplicon sequencing. Inoculation and water treatment together
Page 70 of 167 significantly influenced the composition of bacterial communities (P = 0.002). When considering the two separately, both the microbial treatments and water stress had significant effects on rhizosphere communities (P = 0.002, P = 0.001). The non-water stressed treatment had less variation across samples, which is represented by smaller circles with less overlap (Figure 9). The bacterial communities associated with roots of water stressed plants overlapped more across the bacteria treatments, yet statistically significant differences were observed. For most microbial treatments, active (+) and inactive (-) bacterial inocula separated from each other and the control (Figure 9) indicating different compositions of the microbial community.
Figure 9. Redundancy Analysis (RDA) plots representing bacterial community composition of stressed and non-stressed watermelon root+rhizosphere. Circles symbolise active (+) bacteria treatments, triangles symbolise inactive (-) bacterial treatments. Shaded ovals represent community composition and variation across three replicate samples for each treatment. When shaded ovals are overlapping, the communities are similar and when they separate on the plot, the communities are different.
The microbial inoculants were claimed to contain active Bacillus subtilis (Serenade®), Bacillus amilolyquefaciens (Rhizo-max), Lactobacillus and Acetobacter sp. (Great Land®), or Enterobacter N17a. The inoculated microbes were not the dominant microbes in the roots+rhizosphere and occurred in small relative abundances, ranging from 0.2 to 0.7% (Table 4). Microbes with >1% relative abundance were included in the heatmap and had OTU counts ranging from 270 to 1,780. Nutrient agar plate cultures taken from commercial microbial products were sequenced and the bacteria genera representing the dominant culturable microbe in the biostimulant products were identified as Serratia (Serenade® and Rhizo-max) and Pseudomonas (Great Land®).
Page 71 of 167 Table 4. Overview of bacteria listed as ingredients in biostimulant products to the lowest taxonomical classification as specified by manufacturers. These broad microbial groupings were quantified in roots+rhizosphere of inoculated and control watermelon seedlings in water stressed and non-stressed treatments. Affected by Product and main addition of active Relative Abundance in Detected in roots bacteria listed and/or inactive Root+Rhizosphere inoculum Serenade® Bacillus subtilis* Yes No <0.05% Rhizomax® Bacillus amyloliquefaciens* Yes No <0.05% Great Land® Lactobacillus No No 0% Acetobacter No No 0% N17a Enterobacter Yes No <0.07% *Members of the Bacillus genus were identified, but the methodology applied here only allows assigning taxa to genus level, so that we cannot confirm if B. subtilis or B. amyloliquefaciens were present.
Root+rhizosphere communities of non-stressed and water-stressed watermelon seedlings differed significantly (Figure 10). This included notable differences in the relative abundance of bacteria across different treatments and between active and inactive bacterial inoculants. Pseudolabrys (OTU 12) was more dominant in non-stressed than stressed roots+rhizospheres and occurred in higher relative abundance (ranging from 2.5-5% RA in non-stressed and 1-3% in stressed treatments) across all treatments including controls. The water-stressed treatments were primarily dominated by Massilia and Streptomyces (OTU 5 and 14), however, Streptomyces populations increased substantially in all Great Land® treatments. Rhodanobacter and Rhizobiales (OTUs 31, 4, 7, 52, 12) were consistently in high relative abundance across all treatments in the stressed and non- stressed groups. A Myxococcales OTU (OTU 592) was identified as the dominant genus (5.1% RA compared to <1% RA in other treatments) in the non-stressed, active Rhizomax® inoculated root+rhizosphere and was in a lower relative abundance (<3%) in all other treatments. In root+rhizosphere of water-stressed watermelon treated with active Rhizomax®, most of the dominant genera were present across many of the other treatments including the controls (Table 5). An OTU that stood out was OTU 21 identified as a Rhizobium (1.6% RA). This OTU was still present in all other treatments but in much lower relative abundances (<0.6% RA in other treatments).
Page 72 of 167 Table 5 Top 10 OTUs across the control and Rhizomax® treatments for non-stressed and water stressed watermelon seedlings. OTUs are listed with their OTU-ID, lowest taxonomical classification and relative abundance. Non-stressed Rhizomax® Control Inactive (-) Active (+) Otu52 Rhizomicrobium 2.75 Otu3 Rhodanobacter 2.99 Otu592 Myxococcales 5.15 Otu3 Rhodanobacter 2.72 Otu12 Pseudolabrys 2.99 Otu12 Pseudolabrys 3.71 Otu12 Pseudolabrys 2.49 Otu52 Rhizomicrobium 2.64 Otu17 Opitutus 2.86 Otu4 Bradyrhizobium 2.14 Otu7 Devosia 2.62 Otu11 Myxococcales 2.83 Otu7 Devosia 1.75 Otu4 Bradyrhizobium 2.25 Otu23 Opitutus 2.54 Otu25 Caulobacter 1.53 Otu25 Caulobacter 2.09 Otu7 Devosia 2.34 Otu17 Opitutus 1.49 Otu98 Zoogloea 1.97 Otu52 Rhizomicrobium 2.26 Otu23 Opitutus 1.44 Otu6 Cellulomonas 1.79 Otu4 Bradyrhizobium 1.73 Otu8 Rhizomicrobium 1.44 Otu15 uncultured 1.45 Otu25 Caulobacter 1.63 Otu6 Cellulomonas 1.37 Otu8 Rhizomicrobium 1.37 Otu3 Rhodanobacter 1.43 Stressed Rhizomax® Control Inactive (-) Active (+) Otu3 Rhodanobacter 3.41 Otu14 Massilia 6.60 Otu5 Streptomyces 4.99 Otu12 Pseudolabrys 3.25 Otu5 Streptomyces 5.47 Otu3 Rhodanobacter 3.48 Otu14 Massilia 2.95 Otu3 Rhodanobacter 3.03 Otu14 Massilia 3.23 Otu25 Caulobacter 2.67 Otu25 Caulobacter 2.30 Otu7 Devosia 2.68 Otu7 Devosia 2.43 Otu18 Massilia 2.30 Otu4 Bradyrhizobium 2.16 Otu5 Streptomyces 1.91 Otu9 Pseudomonas 1.94 Otu18 Massilia 2.15 Otu4 Bradyrhizobium 1.90 Otu7 Devosia 1.92 Otu12 Pseudolabrys 2.05 Otu52 Rhizomicrobium 1.56 Otu12 Pseudolabrys 1.69 Otu25 Caulobacter 2.01 Otu23 Opitutus 1.44 Otu4 Bradyrhizobium 1.65 Otu6 Cellulomonas 1.82 Otu19 Flavobacterium 1.44 Otu52 Rhizomicrobium 1.33 Otu21 Rhizobium 1.67 *OTUs highlighted in blue represent OTUs that have a high relative abundance across all of the treatments. **OTUs highlighted in yellow represent OTUs that are unique within the list of top genera across all of the treatments.
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Figure 10. Heatmap of root+rhizosphere communities of non-stressed and water-stressed watermelon seedlings. Eight microbe treatments were tested with active (+) or inactive (-) microbe inoculum. Each line represents the relative abundance (RA) of a given operational taxonomic unit (OTU) in taxonomic order. Only bacteria with a relative abundance of greater than 1% are included in the figure. RA is represented in colour scale ranging from the lowest relative abundance score in white (1% RA), to moderate relative abundance scores in blue (4% RA), to the highest relative abundance score in red (7% RA). Generated using R Studio 3.5.1 and Microsoft Excel.
3.4 Discussion
Across both experiments, commercial biostimulants had variable effects on the tested plant hosts, with most tests showing no noticeable plant growth promoting effects in the experimental settings. We tested only some of the wide-ranging claims made by the manufacturers of the products, including (i) growth promotion via root growth stimulation, (ii) plant growth promotion via enhanced biomass, and (iii) enhanced stress tolerance. Under the experimental conditions tested here, most commercial products did not live up to the manufacturer claims, with only one product alleviating water stress of one plant species. By assessing bacterial communities in root+rhizosphere, to investigate if the inoculated microbes are present and/or affect the composition of the bacterial community, we found that active and inactive microbial inocula shifted
Page 74 of 167 communities in different ways. However, such altered microbial community only led to growth promoting effects in one of the active microbial inoculum treatments.
Several factors influence microbial biostimulant efficacy to deliver plant growth promoting effects (Egamberdiyeva and Höflich 2003; Lucy et al. 2004; Çakmakçi et al. 2006; Egamberdiyeva 2007). Some factors that may have influenced product efficacy in the system tested here include survivability in soil, establishment with plant host, leaching of the product from pots, and lack of environmental stimuli required for initiation of plant-microbe communication (Parnell et al. 2016; Berg et al. 2020). These factors are discussed below in relation to the two experimental systems.
3.4.1 Microbial products have variable affects in a seedling nursery Of the three plant hosts tested within the commercial seedling nursery, none exhibited growth promotion effects as result of microbial biostimulant inoculation within four weeks. Plants were housed for the same amount of time as per the nurseries usual practice (approximately 28 days with some variation of cultivars and seedling development) before they are delivered to commercial growers and outplanting. By the fourth week of growth, the only significant effects observed were undesirable effects. N17a+ (active inoculum Enterobacter) treated tomato root biomass and G+ (active inoculum Great Land®) treated watermelon shoot biomass which were significantly reduced relative to control plants. Additionally, stunted root systems with less lateral root formation were observed in G+ treated watermelon seedlings. Given that this stunting effect was observed in the active microbe treatments and not the inactive treatments, this suggests a direct negative affect of the Enterobacter N17a and Lactobacillus-Acetobacter (Great Land®) inoculants on the growth of tomato and watermelon. A number of studies have identified PGPR isolates that can stunt plant growth in some hosts. Timmusk and Wagner (1999) found that following inoculation of Arabidopsis thaliana with Pseudomonas polymyxa plants had enhanced drought tolerance and biocontrol in stress conditions, while in the absence of any biotic or abiotic stress, inoculation led to a 30% reduction in plant growth and root stunting (Timmusk and Wagner 1999; Timmusk et al. 2005). They suggested that this was because the Pseudomonas isolate altered plant gene expression and triggered both biotic and drought stress defence pathways.
Plants were continuously watered every day with standard nursery practice. As trays were held on standing tables with no base, excess water but also nutrients can leach out of the small pots. The microbial inoculant therefore may have leached out of the base of the pot as they are watered frequently and have a small ratio of growth substrate:roots. Whether the lack of growth promoting effects was due to microbial leaching was not investigated as the roots were not sequencing from Page 75 of 167 this experiment. The product was applied in line with manufacturers’ advice, and the microbial inoculant market is currently dominated by liquid formulations as they are faster and cheaper to manufacture, higher microbial concentrations are attainable, and delivery to crops is easy (Lee et al. 2016). However, formulations such as this are more exposed to environmental stress (such as nutrient depletion, hypoxia, abiotic stresses and microbial competition) and can have reduced efficacy relative to other, more innovative, carrier substances such as encapsulated cells or pelletised formulations (Herrmann and Lesueur 2013; Lee et al. 2016). An alternative delivery that could reduce or avoid leaching are pellet or other solid formulations, sometimes formulated with fertilisers or as seed coating.
A study by Hynes et al. (2001) tested three Rhizobium leguminosarum formulations (liquid, peat powder and peat granules) on pea plants and survivability in soils. Two field tests at different sites showed that the liquid formulation was more susceptible to environmental conditions, leading to a decline in soil populations at one of the sites (Hynes et al. 2001). Tittabutr et al. (2007) evaluated the interactions between a range of additives (including Polyvinyl pyrrolidone PVP-40T, Polyethylene glycol, Polyvinyl alcohol, Gum Arabic, Sodium alginate, and Tapioca Flour) and several microbial strains over a six-month period. It was identified that for optimum performance of PGPR, in terms of survivability in soil and soybean field performance, specific additives were needed for individual species or strains of microbes (Tittabutr et al. 2007). Mixed microbial liquid formulations therefore pose an added complexity for selecting appropriate additives for improved cell viability.
An alternative explanation to leaching is that the microbes of the products may not have interacted with or established in the rhizosphere of the plants. This initial communication step is crucial in beneficial plant-microbe interactions and takes place via complex plant signalling involving root exudation and bacteria cell-cell signalling, also termed quorum sensing (Danhorn and Fuqua 2007; Huang et al. 2014). Different bacteria can not only have host specific interactions, but also unique root colonisation patterns. Bilal et al. (1993) shows that attachment to the roots was imperative to long term colonisation and bacteria differed where they colonised roots, how quickly they established on roots, and how long they survived. Different microbes have different mechanisms for how they respond to environmental stimuli and attach to plant roots, and the effectiveness of these mechanisms can vary from lab to field and in the presence of microbial competitors (Bilal et al. 1993; Berg and Smalla 2009). A 2014 review outlines the factors that affect the ability of PGPR to sufficiently colonise plant roots, including environmental factors (biotic and abiotic), the influence of flagella and pili, and cell density (Podile et al. 2014).
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The claims by the manufacturers encompassed broad statements including general enhanced plant growth, root growth, nutrient uptake, and stress alleviation. Manufacturers additionally confirmed (Bayer Crop Sciences representative, pers. comm.) that it is unlikely we would see growth promoting effects in an environment that receives adequate water and nutrient supply as was supplied in the first experiment. This does align with the evaluation of the scientific literature of PGPR, so that we would not necessarily expect plant-microbe relationships and beneficial effects to occur when growth conditions are optimal. However, this feedback was contradictory to initial information received by the nursery owner as companies were encouraged to use the products irrespective of presence/absence of stresses (Adrian Ross, WBS, pers. comm.). Note, the initial discussions did not yield information of how much product should be used for nursery seedlings, highlighting the uncertainty surrounding product use. Our first test in the seedling nursery used a concentration based on our lab tests with the bacteria isolated from rhizospheres, but application of this concentration of product killed the seedlings. The company representative’s feedback was that we used an excessive amount of product. When growers receive feedback such as this, it becomes challenging for them to know what path to take next and they can often lose faith in biostimulant products all together. With the feedback that product efficacy will only manifest itself in stressful conditions, we conducted a second experiment to test the hypothesis that bacterial products benefit seedlings that are exposed to a stressful growth environment.
3.4.2 Environmental stress together with microbial inoculants shift bacterial communities in the root+rhizosphere While plants within a commercial nursery environment (such as the one at WBS) are unlikely to experience water stress, there is still premise for using microbes to alleviate stress as a method of priming seedlings for unexpected stressful events such as cold temperatures during winter and pathogen attacks among others. In our study, specific interest was on root growth. Robust root systems aid with field transplantation and establishment, thereby reducing the risk of seedling loss encouraging speedier growth (Koevoets et al. 2016). As the products did not stipulate which type of stress (or stresses) are likely to promote plant growth in the presence of microbes, we selected water stress as it can be easily monitored and adjusted by monitoring leaf transpiration rates.
Only one beneficial response of seedlings was observed following product application; active Rhizomax® treated watermelon seedlings under water stress had a biomass equivalent to non- stressed seedlings. All other stressed plants had significantly (P<0.05) lower biomass than non- stressed counterparts. With sugarcane plantlets, it was difficult to establish if microbial inoculation Page 77 of 167 had an effect due to the low biomass of non-stressed control seedlings. This is likely due to the variable and somewhat compromised nature of tissue-culture derived seedlings that are transplanted from an initial media source and carry a considerable hormone ‘burden’ due to generation from callus. Active Rhizomax® treated watermelon showed stress alleviation, while inactive Rhizomax® did not. This is evidence that active bacteria inoculated to the root system are causing this effect. This effect may have been due to direct influences of the Bacillus isolate present within the product or indirect stimulations of other microbes within the rhizosphere. Given that Bacillus OTU's were identified across all of the treatments but did not increase in relative abundance in the root environment as a result of product application, it is likely that the effect was a result of indirect bacterial influences. However, to confirm this notion and exclude the possibility that is was a particular Bacillus species or stain, further research is needed. More detailed taxonomic resolution of microbes is now possible using full length sequencing platforms and meta-genomics (Johnson et al. 2019), but such analysis was outside the scope of this project.
Microbes that enhance plant growth can do this via direct and indirect mechanisms. Indirect mechanisms may refer to antagonism against other pathogenic bacteria/fungi or stimulation of other beneficial bacteria or fungi. For example, in the presence of oligotrophic bacteria Sphingobacterium sp., Variovorax sp., and Roseomonas sp., population densities of the more difficult to culture Acidobacteria were altered in bulk and rhizosphere soils (Kalam et al. 2017). They observed an increase in Acidobacteria cell numbers in tomato rhizospheres up to 60 days following the inoculation of the three PGPR isolates, however could not identify whether plant growth promotion effects were the result of the overall inoculation or specifically the increased presence of Acidobacteria.
Changes in root architecture, increased lateral root branching and denser root systems, were observed with active microbe (+) treatments including non-stressed watermelon treated with Serenade® (B. subtilis), Rhizomax® (B. amyloliquefaciens) and Enterobacter N17a, as well as water-stressed sugarcane treated with Serenade®. This effect on watermelon and sugarcane root morphology may have been the result of inoculation with active bacteria given that rhizosphere community composition differed between bacterial treatments and between active and inactive treatments. There is substantial evidence in the scientific literature that many species of Bacillus and Enterobacter can modify root system architecture. PGPR are able to do this via the production of growth hormones such as auxin or by modification of root morphogenesis signalling pathways (Ambreetha et al. 2018). B. amyloliquefaciens colonises the roots of banana seedlings (observed via SEM) which leads to increased root length, secondary root formation and root biomass (Gamez et
Page 78 of 167 al. 2019). B. subtilis strain 101 was also able to modify the root architecture of tomato plants, though the mode of action was expected to be IAA production this was not tested fully (Felici et al. 2008). Bacillus, among other PGPR isolates, can influence complex and intricate pathways in plant gene expression in order to bring about changes in root morphology and architecture. B. altitudinis modulated expression of genes responsible for primary and lateral root growth thereby leading to alterations in root structure following inoculation (Ambreetha et al. 2018). Enterobacter strains have similarly been found to produce phytohormones such as IAA, including E. asburiae which enhance total root biomass and lateral root development of maize and rice seedlings (Ogbo and Okonkwo 2012).
Both stress and bacterial treatment affected rhizosphere community composition in our study. The non-stressed watermelon plantlets had more significant shifts in bacteria community composition and species richness compared to water stressed watermelon. The overlap of water stressed communities may indicate that the environmental pressure decreased community complexity leading to loss of vulnerable groups and increases in well-adapted or possibly plant recruited groups. As stresses increase, the pool of microbes that persist well within the given conditions also decreases thus leading to proliferation of particular microbes. This could be partially attributed to plant affects and recruitment as it is well known that plants shape microbial communities however this is unlikely in this study as the communities between treatment and control plots overlapped. We observed that for all inocula the active and inactive treatments diverged suggesting that the application of the active bacteria does affect the rhizosphere community composition, providing the underlying mechanism for PGP effects. However, only in one situation (out of 16 plant x inoculum combinations) could we detect efficacy; the active Rhizomax® treatment was the only treatment to benefit watermelon by alleviating stress to match non-stressed biomass and seedling appearance. However, the dominant bacterial genera in this treatment were also present in high abundances in all other treatments. One exception was a Rhizobium OTU which did not occur in the highest abundance but was notably higher in stressed, Rhizomax®+ treated watermelon than any other treatment. The rhizosphere community of stressed Rhizomax®+ treated watermelon was similar to that of stressed Serenade®+ treated watermelon however no growth promotion or stress alleviation was identified for this treatment. It is possible that growth promotion as a result of bacteria inoculation can occur with the smallest shifts in rhizosphere community composition, and this cannot be resolved with the taxonomic tools provided by short-read 16sRNA.
Rhizobium can increase resistance to stress in legumes, but this is not as well understood in non- legume plants (Ahmad et al. 2013; Egamberdieva et al. 2013; Wang et al. 2016). The mechanisms
Page 79 of 167 by which Rhizobium can increase plant stress tolerance include alteration in anti-oxidative enzymes (e.g. POD, SOD, CAT), ACC-deaminase production and general plant stimulation through nutrient availability and phytohormone production (Bhattacharjee et al. 2012; Wang et al. 2016). These mechanisms are not always reliant on the formation and development of root nodules and some evidence in the literature suggests that Rhizobium are not only beneficial for legumes but sometimes wider crop varieties. Rhizobium is a constituent of the core microbiome of plant roots (Yeoh et al. 2017), which further supports the notion that Rhizobium could be a PGPR beyond legumes.
This study has shown that even under tightly controlled conditions than in broadacre cropping (Chapter 2), growth promoting effects from commercial microbial inoculants are variable and mostly undetectable. In order to detect a growth promoting effect, we had to expose seedlings to stress in controlled conditions. This can be expected given that some plant-microbe relationships are dependent upon the presence of a particular environmental stress or stimulus. PGPR are known to prepare plants for abiotic stresses indirectly by (induced systemic resistance (ISA), synthesis of defence chemicals, and the production of enzymes such as POX or superoxide dismutase (SOD) that mitigate the production of toxic molecule such as reactive oxygen species (ROS) in plants (Goswami et al. 2016). Even if microbes do not directly enhance plant growth under benign growth conditions, the ability of a microbe to suppress abiotic stress, via phytohormone regulation, is important as this is reported to be the primary cause of crop loss globally (Goswami et al. 2016).
The products used here were marketed for use in commercial nursery settings and should therefore have provided quantifiable benefits when used in such an environment. Whether benefits would be seen following transplantation to a field environment is yet to be determined and was outside the scope of our study. There was certainly no indication from the manufacturers that the products would be most or only useful after transplantation however further work is needed to investigate this. While we did observe shifts in microbial communities, it is unknown whether these shifts would benefit or hinder plants following transplantation to the field. It may be of benefit to the nursery to prime plants with PGPR for field transfer and establishment rather than targeting enhanced plant growth within a nursery that provides optimal growth conditions. Our findings within this commercial setting make it clear that there needs to be a shift in commercial microbial products from a blanket approach (i.e. all crops, all growth stages, all growing conditions, many benefits) to targeted products. That this is possible has been long demonstrated with the success of the legume-Rhizobium symbiosis. There is no doubt that plant-microbe interactions are complex and that they are modulated by biotic and abiotic factors. While it is challenging to identify suitable PGPR for specific applications, this is what is needed to achieve consistent and reliable results in
Page 80 of 167 agricultural and horticultural industries. Promisingly, fundamental in-vitro research is deepening the understanding of plant-microbe interactions (Finkel et al. 2017) and this knowledge now has to be translated and advanced for the manufacture of PGPR that have targeted mechanisms suitable for particular conditions.
Additionally, the formulations and delivery of commercial PGPR products needs to be improved to deliver effective reliable results. The rhizobium-legume symbiosis has been well studied and the mechanisms behind plant growth promotion are so well understood that there is a clear protocol for growers to follow from seed and microbial storage, to inoculation and maintenance so that growth promotion is all but guaranteed. Innovations in microbial formulation is only recently emerging with exciting advancements in carrier substances. This process is complex as multiple factors need to be considered when developing biological carrier formulations including mass production capability, cost factors, crop delivery and an appropriate balance between the protection and effective release of microbes for plant stimulation (Belen Lobo et al. 2019). Some of the formulation types that currently exist regularly on the market include liquid, powder and pelletised/beaded microbes (Bashan 1998; Bashan et al. 2014). Additional to this, raw carrier substances such as biochar, manures, and bagasse are also being investigated for PGPR delivery (Bashan et al. 2014). It is likely that PGPR efficacy and establishment will rely on effective formulations that are designed and targeted for each microbe, crop system and desired mode of action.
3.5 Conclusion
The key findings of this study were that both microbial inoculants and environmental pressures can shift rhizosphere communities of nursery plants, however these shifts do not necessarily lead to quantifiable growth promoting effects. Growers are now more than ever interested in protecting their crops and improving plant vigour without the use of agrochemicals and fertilisers that harm humans or the environment. There has been a shift in focus from getting the highest yields at the cost of plant and soil health to a more holistic view of more sustainable systems for the future. It is difficult however for growers to identify reliable, scientifically supported products from ineffective ones. Tools such as community profiling can start to shed light on the effectiveness of microbial formulations in shifting rhizosphere communities and our comparison of active and inactive bacterial treatments highlights a gap that is missing from many PGPR studies. Understanding the mechanisms behind which PGPR operate and the plant and environmental stimuli required to
Page 81 of 167 deliver a beneficial response is imperative for growers to have confidence in the purpose of biological products such as those tested here.
Page 82 of 167 Chapter 4 Screening rhizobacteria for plant growth promoting traits
4.1 Introduction
The plant’s root system is a hotspot of metabolic activity with the release of a vast array of root exudates, microbial activity, and conversions that enable nutrient uptake by roots. Among the millions of bacteria that form the root microbiome there are neutral, deleterious and beneficial organisms (Raaijmakers et al. 2009; Doornbos et al. 2012). Many techniques have been developed to attempt to sort root-associated microbes into beneficial microorganisms, so-called plant growth promoting rhizobacteria (PGPR), and pathogenic, or ‘non-performing’ bacteria with the aim to boost plant performance and resource use. The environment can strongly determine rhizosphere microbial communities and by extension, microbially derived traits (Nicolitch et al. 2016; Wood et al. 2018). For example, in a nutrient depleted soil where phosphorus reserves are locked away in insoluble forms that plants cannot readily access, phosphorus (P)-mobilising microbes offer an advantage for plants that harbour such bacteria in their root microbiome. A 2016 study along a natural toposequence in the forest Long Term Observatory (LTO) of Montiers-sur-Saulx (France), found that as the soil nutrient status declined from enriched to poor, the presence of P mobilising microbes increased as a result of plant selection (Nicolitch et al. 2016). However, plants are not always able to successfully recruit microbes with advantageous traits in the presence of nutrient limiting conditions. A study investigating an N supplemented sugarcane field found that diazotrophs were not enriched in the rhizosphere as expected when N fertilizer rate was reduced (Yeoh et al. 2016). To assist with the balance of reduced chemical inputs and crop nutrient supplementation and protection, many growers and researches have turned to PGPR delivery, however, selecting efficient PGPR has proven to be difficult. As shown in the previous two chapters of this thesis, commercially available PGPR products can have variable effects in the environments they are marketed to work for. PGPR formulations that exist in unregulated markets are often promised to work for a broad range of crop-soil systems and often do not meet the claims. Ultimately the problem starts with the selection and formulation of PGPR products. As there is a plethora of amazing research providing fast, cost effective screening tools it is easy for manufacturers to perform these and bottle the microbes that had effects on agar plates. It appears as though for many commercial products the broader complexities of ensuring microbial survival, efficacy and growth promotion in field relevant settings is lacking. This intern begs the question, are the existing plate screening methods effective in identifying putative PGPR for field use, or are they not as reliable as we may think?
Page 83 of 167 The majority of PGPR studies have screened bacteria libraries from root communities for comparatively few traits of interest as these studies typically target a particular trait to deliver a specific outcome, or, alternatively, have selected a small number of bacteria to screen for a broader range of traits (Table 6). Comparatively fewer studies have investigated a broad range of traits of a large library of bacteria. However, such approach could assist understanding the functional characteristics of the (culturable) bacterial community of a given environment and the traits that provide a potential selective advantage for plants within a particular niche. By exploring which traits dominate a root’s bacterial population, we may begin to understand what plants are selecting for when they recruit microbes in response to particular soil and environmental conditions. Additionally, and this was the focus here, we can begin to investigate the abundance of ‘specialist’ and ‘generalist’ bacteria which is well known in other macroscopic and microscopic environments and ecological framework (Sriswasdi et al. 2017; Chen et al. 2020; Mills et al. 2020), to see if it holds true for sugarcane rhizospheres. Both generalists and specialists offer advantages for growers, as some microbes may be more flexible or adaptable (generalists), while others are specialised to outcompete native microbes in targeted systems (specialists). Often studies do not explore whether the microbes they are studying are specialists or generalists which may have an impact on how these bacteria perform once they are in the field.
Table 6 PGPR literature showing the number of bacteria and traits screened in previous and this study. Number of Number of traits Number of Main Findings Reference bacteria combinations 9 4 36 2 bacteria promoted plant growth Majeed et al., 2015 8 isolates expressed 3-5 traits 10 5 50 No plant screening Kamble and Naphade, 2016 All isolates expressed 3-5 traits 21 4 84 No plant screening Haiyambo, Chimwamurombe 3 phosphate solubilizers, 4 siderophore and Reinhold-Hurek, 2015 producers, 8 IAA producing isolates and 5 nitrogen-fixers 30 3 90 15 bacteria promoted plant growth Hussain et al., 2013 30 P mobilisers, 17 ACC deaminase producers, 12 IAA producers 26 4 104 2 bacteria promoted plant growth Prasad, Gupta and Raghuwansh, 17 P mobilisers, 12 HCN producers, 17 2017 IAA producers, 10 catalase producers, 15 7 105 1 bacterium expressed all traits and Bakthavatchalu, Shivakumar and promoted plant growth Sullia, 2012 82; 27 1; 7 82; 162 27 bacteria of 82 were N fixers Mazumdar et al. 2020 Of 27 N fixers 12 produced IAA, 22 solubilized DCP, 6 solubilized TCP, 14 solubilized zinc phosphate and 16 solubilized zinc carbonate 35 5 175 16 bacteria with multiple PGP traits Rani, Arundhathi and Reddy, enhanced seed germination 2012 3 bacteria with all traits, 7 IAA producers, 6 phosphate solubilizers, 5 antagonists, 8 enzyme producers 10 bacteria expressed 4 traits each 1 bacteria promoted plant growth 38 5 190 No plant screening Guerrieri et al. 2020 18 bacteria mobilized P, 29 N fixers, 72 6 432 No plant screening Ahmad, Ahmad and Khan, 2008 11 bacteria with high multi-trait activity Correlation between taxonomy and specific traits observed Page 84 of 167 160 3 480 2 bacteria promoted plant growth Pontes et al., 2015 13 bacteria with all traits, 118 IC synthesises, 91 siderophore producers, 28 tricalcium phosphate solubilisers 66 12 792 No plant screening El-Sayed et al., 2014 10 bacteria exhibited >9 traits 100 9 900 2 bacteria promoted plant growth, Dinesh et al., 2015 41 IAA producers, 41 NH3 producers, 74 citrate utilisers, 33 VP, 9 methyl red test (conversion of glucose to lactate/acetate/formate), 18 starch hydrolysis, 33 succinic acid producers, 33 HCN producers, 46 oxidase, 27 protease, 14 cellulase, 11 pectinase, 12 amylase 121 11 1331 7 bacteria promoted plant growth, this study 4 bacteria with 5 traits, 18 bacteria with 4 traits, 41 bacteria with 3 traits, 33 bacteria with 2 traits, 23 bacteria with 1 trait, 2 bacteria with 0 traits
PGPR trait screening methods using plate or similar assays have been widely used for a number of years to select for putative PGPRs. Many studies have found that only a portion of the microbes that test positive in plate assays lead to growth promoting effects in glasshouse experiments and an even smaller proportion in field tests (Ahmad et al. 2008b; Baliyan et al. 2018). Pontes et al. (2015) screened 160 isolates for three PGPR traits, and while many microbes tested positive for various traits, only two were confirmed to have PGPR effects following testing with plants. This implies that putative PGPR may not be effective with plants and confirm that a 2-step approach is required with PGPR trait screening and host plant testing. A successful example from a recent study employed a new screening method termed the ‘field first strategy’ with the aim to select PGPR based on their survivability prior to in vitro screening (Baliyan et al. 2018). The authors tested 62 bacteria isolates in field conditions (using chickpea, Cicer arietinum) and showed that only two isolates delivered promising plant growth promotion potential. This study was only conducted over a short 30-day period for a chickpea crop which can be grown for up to 200 days (GRDC 2018), and neither was the long term survival of the microbes in soil was evaluated (Baliyan et al. 2018). While field first strategies are a desirable method for selecting field competent PGPR, this method is more costly, time consuming, and unrealistic for many researchers and manufacturers.
Such discrepancy between performance in single organism assays and the performance of crops in the field is not unexpected given the leap from sterile, artificial and controlled environments involving one organism (or two in the case of biocontrol assays), to field environments where many organisms interact in a complex environmental setting. Promisingly, a recent study showed that a subset of soil microbial taxa is consistently recruited to roots of land plants including lycophytes, ferns, gymnosperms and angiosperms (Yeoh et al. 2017). This may indicate that a comparatively narrow suite of bacteria taxa forms associations with roots rather than the majority of soil bacteria that have an abundance in bulk soil of between 101 and 1010 per gram of soil (Kalayu 2019) and very high Page 85 of 167 biodiversity (Berendsen et al. 2012; Raynaud and Nunan 2014; Yeoh et al. 2017). Our focus here was to test bacteria, isolated from the roots of field-grown sugarcane, in well-established plate assays and then evaluate how these taxa impact on the performance of sugarcane, tomato and watermelon.
There is no guarantee that the selected PGPR can established with new host plants, in different environmental conditions, and with a selected set of microbes. This begs the question as to whether using plate assays to screen hundreds of microbes is beneficial or whether it is a wasted effort as has been suggested by some authors (Ahmad et al. 2008b; Dinesh et al. 2015; Pontes et al. 2015; Baliyan et al. 2018). Primary reasons that microbes do not deliver PGP effects in field conditions include that (i) the target microbes do not survive or establish in a different soil, (ii) plants and microbes do not communicate effectively, and (iii) microbes do not express the desired traits (Ahmad et al. 2008b; Pontes et al. 2015; Baliyan et al. 2018). The first factor can be overcome by isolating microbes from the environment and crop rhizosphere of interest to ensure that the microbes can survive in the specific soil, resist being outcompeted by native microbes, and interact with the plant from which they were isolated (Baliyan et al. 2018). Guaranteeing that microbe-plant communication and subsequent root colonisation occurs following inoculation is, however, much harder to achieve. This can be due to reasons that include competition with other bacteria, bacteria not able to survive in a specific environment, and the absence of triggers required for trait expression (Ahmad et al. 2008b).
An initial screening for microbes that have superior colonisation capacity and PGP traits may be a way to overcome this limitation. With the rise of molecular tools, meta-genomics and -transcriptomics are increasingly used as alternative means of identifying microbes that express particular traits of interest. Target genes and their expression can be identified and used to select species of interest (Saleh-Lakha et al. 2005; Combes-Meynet et al. 2011; Thaweenut et al. 2011). However, sequencing for multiple traits in a large library of bacteria remains costly and does not guarantee that every microbe with the potential to express a given trait will do so when inoculated on a plant. Here, we screened a large library of microbes to investigate their expression of key PGP traits and ability to express those traits when grown with plants.
Previously, we isolated bacteria from a sugarcane rhizosphere in a broadacre cropping system located in tropical North Queensland, Australia (Yeoh et al. 2016). Over 200 bacterial strains were isolated and 121 of those have been regularly sub-cultured and maintained on nutrient agar plates. These 121 bacteria were screened for ten PGP traits here. To the best of our knowledge, this project is the first to screen over 100 microbes from a tropical plant rhizosphere for a breadth of PGP traits spanning hormone production, pathogen resistance and nutrient relations. A motivation for choosing sugarcane
Page 86 of 167 is the possibility that it obtains a portion of its nitrogen from BNF which has had much attention in Brazil and elsewhere but lacks molecular evidence (reviewed in Robinson et al. 2013). Most foundational research has focussed on diazotrophic bacteria inhabiting the shoot apoplast (e.g. Dong Zhongmin et al. 1994), rather than roots. A second reason is that sugarcane growers want to improve nutrient use efficiencies and environmental resilience of their crop, and transition from agro- chemicals (e.g. fungicides) to biological alternatives. A Burkholderia isolate colonised the roots of Australian sugarcane with biofilm-generated micro-anaerobic conditions opening the possibility that BNF occurs in the rhizosphere (Paungfoo‐Lonhienne et al. 2014). Further, there was no evidence that sugarcane roots recruit diazotrophic bacteria in low N soil in Australia (Yeoh et al. 2016), which could imply that inoculation with appropriate bacteria is essential for BNF in rhizosphere and root to be a realistic avenue for improving N efficiency of sugarcane.
Here we screened the 121 bacterial isolates for ten PGP traits in mostly gnotobiotic plate assays. This bacterial library was collected by Chanyarat Paungfoo-Lonhienne and Yun Kit Yeoh in a predecessor study investigating N inputs on microbial populations and the presence of BNF genes (Yeoh et al. 2016). The microbes were taxonomically identified using full length 16s rRNA gene sequencing. We aimed to investigate (i) whether type or number of traits is linked to microbial taxonomy, (ii) which traits are most widely expressed, (iii) whether specialists and generalist bacteria exist and if this can be linked to taxonomy, and (iv) if traits and their magnitude in assays translates to effects on plants when inoculated with single isolates.
4.2 Methods
4.2.1 Isolation and culture maintenance of bacterial isolates All microbes were isolated from a sugarcane rhizosphere (variety Q208) near Ayr, North Queensland, Australia. Methods for bacteria isolation are described by Paungfoo-Lonhienne et al. (2014). Chanyarat Paungfoo-Lonhienne and Yun Kit Yeoh collected and prepared the bacterial library for an earlier study and I was given permission to use the same microbes for this thesis. The Enterobacter isolate used in the preceding experimental chapter was also one of the isolates collected by these UQ researchers. Briefly, root samples with adhering soil were collected from multiple replicate crops of a sugarcane trial site. After mixing in a sterile phosphate-buffered saline solution, the filtered solution was dilute and plated on R2A minimal media. After 5 days of incubation at 28C bacteria were subcultured onto different agar media sources and streaked to pure cultures and maintained by re- streaking and storing at -5C. Bacteria codes include letters signifying the media sources they were
Page 87 of 167 cultured to and include Nutrient Agar (N), R2A minimal media (R), Sabouraud agar (S), and Tryptic Soy Agar (T). A total of 121 pure bacteria cultures were sequenced via 16S rRNA sequencing and screened for PGP traits.
4.2.2 Phosphorus mobilising activity Phosphorus is a key nutrient for plant growth and several studies have shown growth promotion by microbially derived enzymes (phosphatase and phytase) that mobilise potentially plant-unavailable P sources (Metin et al. 2012; Kaur and Reddy 2013; Krey et al. 2013). Phosphorus (P) mobilisation capacity was assessed by screening the bacteria isolates using two insoluble P sources, inorganic phosphate and organic phytic acid. National Botanical Research Institutes Phosphate (NBRIP) agar medium was used to assess bacterial mobilisation of calcium bound phosphate (Ca3(PO4)2), and Phytase specific medium (PSM) to assess bacterial mobilisation of phytic acid (See Appendix A, Table A-1 for media protocol). A solubilisation (hydrolysis) zone (i.e. clearing zone in media) around the bacteria colony indicated mobilisation of the insoluble phosphorus forms. To quantify P mobilisation, the width of the hydrolysis zone was divided by the width of the bacteria colony to generate a solubilisation index (Singh et al. 2014a; Anwar et al. 2016).
4.2.3 Biological nitrogen fixation Nitrogen fixing bacteria are one of the most widely studied PGPR in the scientific literature for legumes and non-legumes. Most commonly, members of the Rhizobium genus are well known nitrogen fixers in legumes, however a number of diazotrophs in other genera such as Acetobacter, Burkholderia, and Herbaspirillum have been identified that promote the growth of several horticultural and agriculturally significant crops by boosting N supply and subsequently plant biomass (Dong Zhongmin et al. 1994; Triplett 1996; Knoth et al. 2014; Schultz et al. 2014; Paungfoo- Lonhienne et al. 2016). The N-free semi-solid media technique was used to screen bacteria for BNF activity and identify putative diazotrophs (Baldani et al. 2014). All 121 microbes were inoculated in both JNFb (Appendix A, Table A-2) and LGI (Appendix A, Table A-3) media. Each medium contains different carbon sources and pH to target a wider range of bacteria. Ten ml of media was poured into small vials, the media was then inoculated with a single colony of bacteria and incubated for 10 days at 30C. The formation of a pellicle (a type of biofilm) suggested BNF activity (Appendix A, Figure A-1).
Page 88 of 167 4.2.4 Auxin production Auxin (IAA) is a phytohormone that modulates root system development and, when produced by PGPR, enhances plant growth (Ortíz-Castro et al. 2009). Microbes were screened for their ability to produce IAA using Salkowski reagent (Ehmann 1977; Rani et al. 2012). To prepare bacteria cultures, 900 ml of nutrient broth was autoclaved. Once cooled, 100 ml of water supplemented with 100 mg of tryptophan (0.1% L-tryptophan) was filter-sterilised and mixed through. Ten ml of this nutrient broth solution was then poured into individual falcon tubes. The broth was inoculated with bacteria and incubated overnight, in the dark, with shaking at 30C and 180 rpm. Bacteria cultures were then centrifuged at 10,000 rpm for 10 min. One ml of supernatant was mixed with 2 ml of Salkowski reagent (11 ml of 0.5 M ferric chloride and 540 ml of 35% perchloric acid were mixed well and stored in a brown glass bottle) and incubated for approximately 25 min. After 25 minutes, the tubes were compared with development of a pink-red colour from the original yellow solution indicating IAA production.
4.2.5 Cytokinin production Cytokinin is a phytohormone which modulates several physiological processes in plants including cell division and apical dominance, and plays a critical role in plant response to abiotic/biotic stresses (Chernyad’ev 2009; Hussain and Hasnain 2009). Cytokinin production by bacteria was identified using a cucumber leaf plate assay (Hussain and Hasnain 2009). M9 media supplemented with 20% glucose, 0.2% casamino acid, 2 pg/l biotin and 0.7% agar was autoclaved and poured into plates. Bacteria were streaked across one end of the plate and left to grow in the dark for 72 hours at 28C. Five etiolated cucumber cotyledons excised from 5 day old seedlings were removed and placed onto plates so that they were sitting within 5 mm of culture but not in direct contact with bacteria colonies. Plates were incubated for a further 24 hours in the dark at approximately 25C followed by exposure to light for 3 hours at 25C. White leaves indicated no cytokinin production, while green leaves indicated bacteria has produced cytokinin.
4.2.6 Biofilm and EPS production Biofilm formation involves masses of bacteria cells, often comprised of multi-species conglomerates, maintaining a critical mass in specific locations (e.g. on roots) by encasing themselves in a self- produced extracellular polymeric substance matrix (Danhorn and Fuqua 2007; Haggag and Timmusk 2008; Burmølle et al. 2014; Ren et al. 2014a). Biofilm formation assists bacteria with establishment and survival as well as aiding plants in the protection against phytopathogen colonisation (Danhorn and Fuqua 2007; Haggag and Timmusk 2008). A 96-well plate assay for biofilm quantification was
Page 89 of 167 adapted from Ren et al. (2014a) to identify putative biofilm forming bacteria. Briefly, 5 ml of tryptic soy broth (TSB) was inoculated with bacteria and incubated overnight, with shaking at 24C. Then, 500 µl of overnight culture was added to 4 ml of TSB and incubated, with shaking (250 rpm) for a further 2-5 hours until spectrophotometer OD600 reached 0.5 at 24C. Each well of a 96-well tissue culture plate was then inoculated with 160 µl of liquid bacteria culture. Control wells were inoculated with 160 µl of TSB that had not been inoculated with bacteria. The plate was then incubated overnight with shaking (200 rpm) at 24C. After approximately 24 hours, 160 µl of solution was removed from each well and the wells washed three times with phosphate buffered saline solution. 180 µl of an aqueous 1% (w/v) crystal violet solution was added to each well and the plate incubated for a further 25 min. The wells were then washed again three times with phosphate buffered saline solution before 200 µl of 96% ethanol was pipetted into each well. The plate was incubated for approximately 30 min to allow the stain to dissolve into the ethanol before the absorbance was measured using a spectrophotometer at 590 nm. EPS production was additionally assessed by manually testing bacteria cultures on nutrient agar for “compactness” and “ropiness” as described by Ricciardi et al. (1997). This is a simple method that is used to observe the structure and texture of the bacteria culture without adding time for experimental set up. While all biofilm producing bacteria likely produce EPS, EPS producing bacteria can benefit plants in other ways than biofilm production. EPS can assist with cell attachment, cell survival, as well as rhizobia nodulation and infection thread formation (Gage 2004; Danhorn and Fuqua 2007; Haggag 2007; Jog et al. 2016; Ma et al. 2018).
4.2.7 Siderophore production Siderophores are low-molecular-mass molecules that have a high affinity for, and chelate, iron in the soil, thereby enhancing plant iron nutrition and inhibiting phytopathogen establishment (Lewis et al. 2018; Nutaratat et al. 2014; de Souza et al. 2015). We used the Chrome Azurol S (CAS) agar assay to quantify siderophore production (Nutaratat et al. 2014; Anwar et al. 2016). Two solutions were prepared for the CAS media including a blue dye solution and a mixture solution. The blue dye solution was prepared by dissolving 0.18 g of CAS in 150 ml of water (dye solution one), 0.0081 g of FeCl3.6H2O in 30 ml of 10 mM HCl (dye solution two) and 0.219 g of HDTMA in 40 ml of water (dye solution three). Dye solution one was then mixed with 9 ml of dye solution two. Once homogenised, solution three was added to develop a dye with a bright blue colour. This dye solution was then autoclaved and left to cool to room temperature.
CAS agar was prepared by first adding 100 ml of Minimal Media 9 (MM9) salt solution stock to 750 ml of water. 32.24 g of piperazine-N,N’-bis(2-ethanesulfonic acid, i.e. PIPES), was then added slowly while stirring. When PIPES dissolves, it reduces pH and it will not dissolve below a pH of 5. Page 90 of 167 Therefore pH 6 was maintained by adding NaOH. Once dissolved, the pH was adjusted to approximately 6.8. (Note, the mixture cannot exceed a pH of 6.8 or the media will turn green and be ineffective). Fifteen gram of agar was then added to the mixture, stirred and autoclaved. The mixture was allowed to cool to approximately 50C before 30 ml of sterile casamino acid solution (9 g of casamino acid solution in 81 ml of water, extracted with 3% 8-hydroxyquinoline in chloroform and filter sterilised) and 10 ml of sterile 20% glucose solution (20 g of glucose in 100 ml of water, filter sterilised) was added. Finally, 100 ml of the blue dye solution was slowly added to the mixture while stirring. Once homogenised, the media was poured into plates and allowed to set. Bacteria were then streaked onto the media and the plates incubated for 10 days in the dark at 28C. With the media being a green-blue colour, the development of a yellow halo around the bacteria colony indicates siderophore production (as described in 1.2.2 Phosphorus mobilising activity).
4.2.8 Antifungal activity Two common fungal pathogens of agricultural and horticultural crops in Queensland, Fusarium oxysporum and Stagonosporopsis sp. (Gummy Stem Blight; GSB) were selected to screen bacteria for antifungal properties. A cutting of the respective fungal culture was placed on a PDA plate for antifungal screening. Bacteria were screened against GSB using the dual culture method whereby a single colony of the bacteria isolate was streaked across the agar at the opposite end of the petri dish to the fungal pathogen culture (Singh et al. 2014a). F. oxysporum assays were prepared similarly but with liquid culture whereby an 8 mm well was cut out of the agar and 200 µl of 24 hour microbial broth was inoculated into the well. Plates were incubated for 5 days at 28C in the dark. An inhibition zone around the bacterial colony (i.e. area where fungal hyphae do not grow) indicates antifungal activity.
4.2.9 Hydrogen cyanide (HCN) activity Hydrogen cyanide is a secondary metabolite produced commonly by Pseudomonads and Bacillus species that can protect plants by inhibiting the growth of phytopathogenic fungi (Blumer and Haas 2000; Rani et al. 2012). A plate assay by Kaur and Reddy (2013) was used to assess HCN production by bacteria. Briefly, nutrient agar amended with 4.4 g/l glycine was autoclaved and poured into petri dishes. Once set, bacteria was streaked onto agar. A Whatman filter paper No. 1 was soaked in a picric acid solution (0.5% picric acid with 2% sodium carbonate). A cutting of filter paper was then placed on the lid of the petri dish plate. Plates were sealed with parafilm with the filter paper facing down and incubated at 30C for 10 days. The development of a light to dark brown colour indicated HCN production.
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4.2.10 Protease activity Proteases are a class of protein-degrading enzymes, including proteins in cell walls, that are produced by bacteria and that can inhibit the growth of phytopathogens (Bakthavatchalu et al. 2012; Dinesh et al. 2015; Moustaine et al. 2017; Simpson et al. 2017). Protease activity was assessed by screening bacteria on skim milk agar. Nutrient agar was amended with 3% skim milk and autoclaved (Bakthavatchalu et al. 2012; Dinesh et al. 2015). Once poured and cooled, bacteria were streaked onto the agar and incubated for 10 days at 30ºC. A solubilisation zone (clearing zone) in the medium around the bacteria colony indicated protease activity. To quantify protease activity, the width of the hydrolysis zone was divided by the width of the bacteria colony to give a solubilisation index. This hydrolysis zone was measured at 24 hours, 5 days and 10 days post inoculation of bacteria.
4.2.11 Plant screening assays Four PGP traits were selected for further screening in plant assays to evaluate how high activity in artificial plate selection protocols translate to plant growth effects. The selected traits included antifungal activity, cytokinin production, auxin production and P mobilising activity. These traits were selected as they represent the traits that were most commonly expressed and span from nutrient mobilisation to plant defence and phytohormone production. The top performing microbes for each of these trait assays were tested on plants along with “no microbe” and “negative control microbes” (i.e. those did not test positive in the respective plate assays) controls.
4.2.11.1 Antifungal activity against Gummy Stem Blight The 18 bacteria that screened positive for antagonistic activity against F. oxysporum were further screened against cultures of Gummy Stem Blight (Stagonosporopsis sp., isolated from an infected watermelon field in Giru, North QLD). The isolates that tested positive for antagonistic activity against Gummy Stem Blight (GSB), as shown by inhibition zones of fungi surrounding bacteria cultures, were selected for plant assays. Bacteria inoculum was prepared as described in Chapter 3. Briefly, bacteria isolates were inoculated in nutrient broth, grown overnight to mid-log phase and 8 then centrifuged and resuspended in nutrient solution, to an OD600 = 0.3-0.5 (~10 CFU/ml), to remove the nutrient broth. This was done to ensure live bacteria cells were added and no other nutrient or chemical variables that may affect results. While this may have removed secondary metabolites produced by the bacteria into the culture, we ensured live cells were applied, and confirmed that living cells were maintained on the leaf for several days prior to GSB application. This gave the microbes time to establish on the leaf surface and produce secondary metabolites. This solution made up the microbial inoculant and was applied by pipetting onto the leaf surface. The inoculant was placed on Page 92 of 167 the leaf as this is the avenue in which it infects plants. As we had not tested the bacteria isolates for induced systemic resistance (ISR) activity, we aimed to test direct antagonism against gummy stem cells on the leaf surface. After the bacteria solution was applied to leaves, plants were grown in growth cabinets for a further 6 days before inoculation with GSB as liquid inoculum onto the leaf surface. Plants were grown for a further 9 days after which a visual inspection was performed to evaluate plant health and GSB infection. The control plants acted as a benchmark for plant health and the presence/absence of leaf senescence and stem droopiness were observed. Following the visual inspection, leaf samples were taken, surface sterilised and plated on nutrient agar to re-isolate bacteria. This was done to investigate if the inoculated bacteria survived on the leaf surface for the growth period. We took additional leaf samples for scanning electron microscope (SEM) imaging. Leaf samples were cut down into small segments and placed in fixative for processing (Paungfoo- Lonhienne et al. 2010, 2016).
4.2.11.2 Cytokinin activity on transgenic Arabidopsis After identifying putative cytokinin producers, we conducted an in vitro assay with Arabidopsis to investigate whether these microbes produce cytokinin when grown with plants. Wild type (WT) and isopentenyl transferase (IPT) mutant Arabidopsis were used. Arabidopsis with mutation in IPT genes are unable to synthesise their own cytokinin however can still perceive cytokinin if applied externally (Sun et al. 2003). The bacteria treatments included a no microbe control, a negative control using a microbial isolate which was identified as a non-cytokinin producer from the cucumber plate assay (Bacillus R13), and the three top cytokinin producers (Serratia N22b, Stenotrophomonas N31a and Acinetobacter N18a). Arabidopsis seeds were surface sterilised and germinated on half-strength Murashige Skoog (MS) medium agar for 4 days. Four Arabidopsis plants were transplanted to new MS agar plates. IPT mutant plants and WT plants were transplanted onto separate plates. Ten treatments included five bacteria treatments (no microbes, a negative control microbes, three putative cytokinin producing microbes) and plant types (WT, IPT). Each treatment consisted of five replicate plates with four pseudo-replicates on each plate. After transplantation of Arabidopsis plants, a single bacteria colony from a pure 16-streaked culture was inoculated onto the agar plate, streaked in a line at the base of the plate under the Arabidopsis roots. The bacteria line was drawn below the roots so that it was not close to touching the plant. Plates were placed on a slight angle in the growth cabinet so that condensation did not pool on the agar surface causing the bacteria to contaminate across the entire plate. Plants were grown for a further 7 days before being evaluated. Plants were visually evaluated but were too small for biomass quantification.
Page 93 of 167 Two of the top cytokinin producers were also tested on stressed and non-stressed watermelon seedlings to evaluate if their ability to produce cytokinin may reduce plant senescence when exposed to water stress. Watermelon were chosen due to their easy germination and quick growth. Watermelon seedlings were germinated in the dark for 3 days before being transplanted to the nursery soil substrate used previously (Chapter 3). After 5 days, seedlings were inoculated with microbial suspensions or a water only control. Microbial suspensions were set up as described previously, by inoculating nutrient broth with one colony of bacteria and grown overnight to mid-log phase (OD600) in a 28℃ incubator while shaking 200 rpm. The suspension was centrifuged, and the pellet resuspended in an equal volume of nutrient solution. Bacterial solutions were checked with the plate counting method to ensure it contained approximately 108 cells ml-1. Five ml of bacterial solution, or water for the control, was pipetted onto the soil near the stem base. Non-stressed plants were watered daily and received nutrient solution (see Chapter 3 Section 1.2.3 for solution composition) once per week. Stressed plants received water only every three days, including the weekly application of nutrient solution, avoiding death-causing water limitation. To monitor water stress, a porometer (Steady state diffusion porometer SC-1, Decagon Devices, Inc.) was used to measure transpiration rates (mmol m-2 s-1). Measurements were conducted inside the growth cabinet before watering with values of non-stressed plants having rates of ~200 mmol m-2 s-1 and water-stressed plants <100 mmol m-2 s-1. After 2 weeks of growth, plants were harvested by separating roots and shoots. Biomass was dried in the oven for 2 days to quantify dry weight.
4.2.11.3 Auxin production on stressed and non-stressed watermelon seedlings Top auxin producers were screened on stressed and non-stressed watermelon seedlings to evaluate growth promotion effects. Auxin producers have often been shown to enhance the growth of stressed and non-stressed plants (Ambreetha et al. 2018; Raheem et al. 2018). Watermelon seedlings were germinated in the dark and transplanted to soil as described above (Section 1.2.11.2). After five days, they were inoculated with microbial suspensions or water control. Microbial suspensions were set up by inoculating nutrient broth with one colony of bacteria and grown overnight to mid-log phase in a 28℃ incubator while shaking (200 rpm). The suspension was centrifuged, and the pellet resuspended 8 in an equal volume of nutrient solution to an OD600 = 0.3-0.5 (~10 CFU/ml) as described previously. Five ml of solution (or water) was pipetted onto the soil near the stem base. Stress treatments were applied as described above in Chapter 4, Section 4.2.11.2 as well as in the preceding Chapter 3, Section 1.2.3.
Page 94 of 167 4.2.11.4 Phosphorus mobilisation of organic phosphorus wastes This assay was conducted in collaboration with student Rebecca Martin and formed a part of her Honours thesis (Martin 2017). Rebecca and I worked collaboratively to maintain and store the bacteria library and conduct a P mobilisation assay using organic wastes to be reported on in her honours thesis (Phosphate Solubilising Bacteria and Recycled Wastes for Sustainable Cropping, Rebecca Martin 2017, unpublished) and this PhD thesis. Micro-propagated sugarcane plantlets (cultivar Q237) were provided by Sugar Research Australia (SRA) for plant growth experiments. Tissue culture plantlets were maintained on half strength Murashige and Skoog medium for seven days before growth experiments commenced. We grew the plants in culture jars (400 ml) fitted with a ten mm opening in the lid covered with multiple layers of breathable micropore tape to allow gas exchange while maintaining a sterile environment (Paungfoo‐Lonhienne et al. 2014). Agar growth medium (400 ml) was added to each jar, containing nutrients at a final concentration per 1 l distilled water: NH4NO3 4mM; CaCl2 1.5 mM; MgSO4 1.5 mM; KNO3 1.5 mM; KH2PO4 1.5 mM; MnSO4 10
μM; NaFe-EDTA 18 μM, H3BO3 45 μM; MnCl2 2H2O 4.5 μM; CuCl2 2H2O 315 nM; ZnSO4 750 nM and (NH4)6MO7O24 4H2O 15 nM. Media was supplemented with 4 g of agar and pH adjusted to 6 before autoclaving at 121°C for 20 minutes.
Four phosphorus treatments were used including a no P control, inorganic P, two P waste products, inorganic struvite and organic meat and bone meal (MBM). Inorganic P (Pi), added as KH2PO4, was excluded from the above nutrient solution for no P treatments, and was replaced with MBM and struvite at a rate equivalent to 1.5 mM P l-1 for recycled P treatments. Detailed analysis on the chemical composition of waste products was not performed, rather we estimated the P concentration of the wastes using theoretical values. For struvite, P concentration was calculated using the molecular weight of magnesium ammonium phosphate (MAP), which is 0.37g l-1. As the chemical composition of MBM is variable, concentration was calculated based on 5% P content assuming that the P fraction is mainly present as apatite, a total of 0.75g l-1 was added.
The two most efficient bacterial isolates from P solubilisation assays were selected for plant growth experiments. Bacteria were grown to mid-log phase in nutrient broth for 16 h at 30°C and shaken at 200 rpm. Samples were centrifuged at 5000 g for 5 min, washed and re-suspended in sterile water. Bacterial solutions containing approximately 108 cell ml−1 (confirmed using the plate counting method) were used for inoculation (Paungfoo‐Lonhienne et al. 2014). Bacteria solutions were applied by immersing roots in bacterial solution for 5 min before they were transferred to culture jars. Roots of plants not receiving inoculants were soaked in a nutrient solution without bacteria for the same time period.
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Plantlets were kept in a growth cabinet at 28°C with 12 h light and dark cycles and light intensity at 400 μmol m-2 s-1 for four weeks (Sevilla et al. 2001). The experiment was set up in a randomised block design with five biological replicates per treatment. Total biomass was measured after drying plant materials at 70°C for 48 h and weighing root and shoots separately.
4.2.12 16s rRNA gene sequencing and analysis To assign taxonomy for each bacteria culture we performed full length 16s rRNA gene sequencing. DNA was extracted using the DNeasy microbial kit (QIAGEN). Primers 27F and 1492 R were used along with Phire green DNA polymerase in the PCR. PCR product was purified with ExoSap and then sent to AGRF for Sanger sequencing. Bacteria specific primers 27F (5’- GAGTTTGATCCTGGCTCAG), 803F (5’-ATTAGATACCCTGGTAGTC-3’) and 1492R (5′- GGTACCTTGTTACGACTT) were included for sequencing to improve classification. Isolates were identified to genus level by comparison to sequences in GenBank database (BLAST), defined by 99% similarity to strain 16S rRNA sequences. Sequencing was carried out by Australian Genome Research Facility Ltd (AGRF).
4.3 Results
A total of 121 microbial isolates were screened in separate assays for ten PGP traits including P mobilisation, protease activity, cytokinin production, siderophore production, biofilm formation, cyanide production, antifungal activity, IAA production, exopolysaccharide production and BNF.
Page 96 of 167 80% 70% 67% 60% 55% 50%
40% 35% 34% 32% 30% 21% 20% 12% 10% 2% 2% 0% Microbes expressing PGPR traits PGPR expressing Microbes 0%
Figure 11 Proportion of bacteria expressing ten plant growth promoting traits in plate assays. 121 microbes were screened for these PGP traits. Bacteria were isolated from the sugarcane rhizosphere grown in field conditions in North Queensland, Australia.
Phosphorus mobilisation was the most widely expressed trait with 67%, 81 isolates testing positive for the ability to mobilise either phytate or calcium-phosphate (Figure 11). Of these 81 isolates, 20 had ‘high’ P activity with a solubilisation index >3 (0 indicating no solubilisation, 5 the highest solubilisation zone). Over 30% of isolates tested positive for protease, cytokinin, biofilm, or siderophore production, and >20% produced HCN. Less than 20% of isolates had antifungal activity, auxin or EPS production, and none were diazotrophic in the test conditions screened here.
Most microbes expressed three or fewer PGP traits (Figure 12). Of the 121 bacteria screened, only two isolates had no activity across any of the traits screened and only four isolates expressed activity in five traits. We could categorise the isolates into ‘specialists’ and ‘generalists’ in this cultured community. Microbes that had the highest activity for a given trait generally only expressed one or two traits of the ten traits tested. The microbes that expressed four traits (and some that expressed three traits), generally had low activity in these traits and none were ‘top performers’ in any of the tested traits. Table 7 shows 27 specialist and generalist isolates identified to genus level.
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Figure 12 Proportion of bacteria expressing a particular number of PGP traits. 121 bacterial isolates were screened for ten plant growth promoting traits and no organism exhibited more than five traits.
Table 7 Overview of selected bacterial isolates that (i) expressed the highest activities and fewest traits (‘specialists’), or (ii) expressed the widest range of traits (‘generalists’). Twenty-seven of the 121 isolates, identified to genus level, are shown here with the number of traits expressed and the level of activity for each trait. ‘Specialists’ ‘Generalists’ ID # Traits ID # Traits Traits Traits Burkholderia S7 3 P, H-, PR Enterobacter R31 5 P, C, A-, B-, PR Serratia N60 3 P, A-, PR Pseudomonas T10 5 P, C, S, H-, PR Serratia R62 2 P, PR Bacillus T34b 5 P, S, B-, H-, PR Serratia N66 2 P, PR Chryseobacterium T58 5 P, C, S, B-, PR Chryseobacterium N25 2 S, PR Rhizobium R37 4 P, C, S, B- Pseudomonas T31 3 S, H-, PR Pseudomonas S13 4 AF, B, H-, P Chryseobacterium N29 1 S Bacillus T37 4 C, S, AF, PR Chryseobacterium N31b 1 S Enterobacter S27 4 P, A-, C, S Serratia N22b 1 C Serratia N70 4 P, S, AF, PR Stenotrophomonas N31a 2 C, P Stenotrophomonas N46 1 PR Serratia N38 3 PR, P, B- Serratia N36 2 PR, P Bacillus N22a 2 A, AF Bacillus N16 3 AF, P, PR Serratia N22b 1 C Stenotrophomonas N31a 2 P, C Acinetobacter N18a 3 P, C, PR P = P-mobilising; H = HCN; PR = Proteolytic; C = Cytokinin, S = Siderophore, B = Biofilm; AF = Antifungal; A = Auxin; = high activity; = low activity; - = intermediate activity with no clear high/low distinction
4.3.1 Nutrient acquisition assays 67% of microbes had some P mobilising activity (Figure 13). Most microbes showing P mobilising activity were able to mobilise organic (phytic acid) and inorganic (Ca-bound Pi) sources. While four and five isolates appear to specialise in the mobilisation of either organic or inorganic P sources, four
Page 98 of 167 isolates stood out having high phytase and high Pi solubilisation activities (Burkholderia S7, and Serratia N60, N66 and R62).
Figure 13 Results of enzyme assays screening bacteria for P solubilising (A-B) or protease activity (C-E). (A) Proportion of 121 bacterial isolates exhibiting phytase activity, Pi solubilising activity, both activities, or no P mobilising activity. (B) P solubilisation index of 121 bacterial isolates expressing P solubilising and phytase activity. Microbes are ranked from highest to lowest activity according to solubilisation index (see Section 1.2.2). (C, D) Proportion of bacteria screening positive for protease activity with high, low and no activity after five or ten days of growth. (E) Microbes are ranked from highest to lowest protease activity. Activity is assigned based on a calculated solubilisation index.
After 10 days of growth, 56% of bacteria showed protease activity with 40% having high activity (solubilisation index of > 4) and 17% with lower activity (solubilisation index < 4). Protease activity was compared to P mobilising activity to see if the two traits occurred in the same organisms, but his was not confirmed (Figure 13). However, several microbes stood out having high protease, phytase and Pi solubilising activity all belonging to the genus Serratia (N60, N66, N68, N70, and N76).
To identify diazotrophs, two types of N-free semisolid medium were used but none of the isolates formed a pellicle, indicating that none were diazotrophs. The assay was repeated three times to ensure validity of results.
Page 99 of 167 4.3.2 Phytohormone production Auxin (IAA) production was quantified using Salkowski reagent with the development of a pink colour indicating the production of IAA. Only three microbes were identified as putative IAA producers (Figure 14).
Figure 14 Phytohormone assays included IAA and cytokinin production. (A) Salkowski assay identified IAA producing bacteria with pink colouration indicating isolates producing IAA. Each well contains one bacterial isolate with arrows indicating those that tested positive for IAA activity. (B) Pie chart with proportion of bacteria testing positive for IAA production. (C) Cucumber cotyledons placed next to bacteria cultures developing green pigment are indicators of bacterial production of cytokinin. The left cotyledon pellet represents an intermediate level of cytokinin production, the right one a high rate. (D) Pie chart with proportion of bacteria that expressed high (7%), intermediate (28%) or no (72%) of cytokinin activity.
Of the 42 microbes that expressed some level of cytokinin production, only eight had strong cytokinin production (Figure 14) as indicated by the development of green pigment in cotyledons placed beside bacterial cultures (Figure 14); the majority of microbes did not produce cytokinin as indicated by the cotyledons remaining white after incubation next to bacteria (not shown).
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4.3.3 Pathogen resistance and antifungal activity A total of 39 microbes of 121 screened developed a hydrolysis zone on CAS agar indicating the mobilisation of bound iron complexes via bacterial siderophore production for pathogen resistance and competition (Figure 15A, B). Of these, 12 had high siderophore activity (solubilisation zone > 5). The most efficient siderophore producer was Chryseobacterium N25 (not shown) which produced a small colony but large (30 mm) orange halos around the colony thereby mobilising iron.
Figure 15 Microbes tested for pathogen resistance, HCN production and antifungal properties. (A) Solubilisation index of siderophore producing bacteria ranked from highest to lowest activity. Activity is assigned based on a solubilisation index (see Section 1.2.2). (B) Development of a yellow halo around bacterial colonies indicates siderophore production as the result of iron degradation by bacterially-derived siderophores. (C) Screening bacteria for HCN production with filter paper soaked in a yellow picric acid solution with colour change to light or dark brown indicating production of volatile HCN; top plate shows an isolate that with no production, the bottom plate shows a HCN-producing isolate. (D) Inhibition assays of fungal pathogen Stagonosporopsis sp. and isolates show putative antagonists suppress fungal growth (inhibition zone around bacterial colonies on the four plates on the left), while non-antagonistic bacteria were colonised by the fungus (two plates on the right).
Page 101 of 167 Antifungal activity was assessed by inoculating a culture of Fusarium oxysporum onto the centre of a plate and bacteria cultures in the outer parts of the plate. Direct antagonism by bacteria was recorded when a clear inhibition zone was observed for fungal growth near or around the bacterial colonies. Only 11 microbes showed clear inhibition zones against F. oxysporum. The 11 microbes displaying activity against F. oxysporum were also screened against Gummy Stem Blight (GSB) in plate assays (Figure 15D). We identified five bacteria that inhibited GSB, and these were selected for further testing on plants.
Of the 121 microbes screened 26 isolates (21%) tested positive for HCN production. This was indicated by the colour change assay of picric acid-soaked filter paper (Figure 15C) as bacteria release volatile HCN which triggers a colour change in the filter paper (Kaur and Reddy 2013).
4.3.4 Biofilm production 34% of microbes screened positive for biofilm formation (Figure 16), with eight isolates having high biofilm formation potential as indicated by darker stain (see Section 1.2.6). Only two bacteria isolates (Chryseobacterium N34 and N49) appeared to exhibit the EPS phenotype via compact and rope-like colonies (Ricciardi et al. 1997).
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Figure 16 Biofilm forming bacteria are identified by colour reaction (i.e. darker stain indicating more bacteria adhering to the sides of the wells) (B). Pie chart (A) and ranking (C) of relative activity illustrates the proportion of bacteria that screened positive for biofilm formation grouped into high, intermediate or no activity. Isolates with an absorbance equal or lower than that of the control well are shown with the grey dotted box.
4.3.5 Screening of bacteria with plants 4.3.5.1 Antifungal activity against Gummy Stem Blight All seedlings treated with the five bacteria isolates that suppressed F. oxysporum and GSB appeared healthier than non-inoculated controls (Figure 17A, B). To confirm Koch’s postulate, isolates from inoculated leaf cuttings were plated onto nutrient agar and displayed the same colony morphology as the bacteria that were initially inoculated onto leaves (Figure 18).
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0 Plants with Disease Incidence (%) Incidence Disease Plantswith Control N16 N22a S13 S22 T37
Figure 17 (A) Watermelon seedlings inoculated with putative fungal antagonists and GSB pathogen. Control plants (left column of growth containers) were not inoculated with bacteria, but received only water and GSB pathogen. Bacteria treatments from (second) left to right growth containers are Bacillus N16, Bacillus N22a, Pseudomonas S13, Rhizobium S22 and Bacillus T37. (B) Percentage of disease incidence by plant infection and death is shown for all treatments.
Figure 18 A Re-isolation of bacteria inoculated on leaf surface of watermelon plants. Leaf cuttings were collected from the watermelon plants and plated on nutrient agar. After overnight incubation, bacteria of the same colony morphology as the original inoculated Bacillus N16 bacteria grew from the leaf cuttings (A). A small amount was subcultured onto a new plate (B) and then again subcultured and 16-streaked (C) to analyse colony morphology.
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Figure 19 Representative SEM images of watermelon leaves inoculated with putative bacterial antagonists and GSB pathogen. The control (A) were leaves infected with GSB but did not receiving bacteria, these had fungal hyphae covering leaf surfaces. Bacteria treatments included Bacillus T37 (B), Bacillus N22a (C,D), Pseudomonas S13 (E,F), and Bacillus N16a (G,H). Replicate plants of each treatment were sampled and pooled prior to SEM imaging.
Page 105 of 167 4.3.5.2 Cytokinin activity on transgenic IPT mutant Arabidopsis Both types of Arabidopsis (wild type (WT) and isopentenyl transferase (IPT) mutant) in the control treatment (no bacteria inoculation) had long primary roots. Plants that were inoculated with Acinetobacter N18a (high cytokinin producer) and Bacillus R13 (non cytokinin producer), also resembled this root morphology and we observed no obvious effects on shoot or root growth (Figure 20). WT plants inoculated with Serratia N22b (high cytokinin producer) had stunted primary roots with enhanced lateral root growth and obvious yellowing/senescence relative to other plants. In contrast, IPT plants inoculated with Serratia N22b more resembled the controls and had long primary roots. Both WT and IPT Arabidopsis plants inoculated with Stenotrophomonas N31a (high cytokinin producer) exhibited stunting of primary root growth and enhanced lateral root formation.
Figure 20 Arabidopsis assay screening microbes with high and no cytokinin activity. Wild type (WT) and isopentenyltrensferase (IPT) mutant plants were inoculated with bacteria isolates to evaluate growth promoting potential. High cytokinin producers from plate assays included Serratia N22b, Stenotrophomonas N31a, and Acinetobacter N18a. Bacillus R13 did not test positive for cytokinin production in plate assays and was not expected to have any significant results when inoculated on WT or IPT mutants as this was one of the bacteria isolates that did not test positive for any plant growth promoting traits.
Watermelon seedlings exposed to drought stress or growing in well-watered (non-stress) conditions, did not exhibit visual sign or quantifiable benefit as a result of inoculation with putative cytokinin producing bacteria (Figure 21). However, in non-stressed plants one of the ‘no trait activity’ microbes (T32) caused significantly reduced shoot biomass relative to the ‘no microbes’ control treatment. All treatments, with the exception of plants treated with Bacillus T32, had significantly reduced shoot biomass in stressed plants relative to non-stressed plants. There was no significant difference across the stressed plant treatments in shoot or root dry weight.
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Figure 21 Dry weight of watermelon seedlings exposed to stressed and non-stressed water treatments and five different bacteria treatments including a no bacteria control, two ‘no-trait’ negative control bacterial treatments and two putative cytokinin producing microbes Serratia N22b and Stenotrophomonas N31a.
4.3.5.3 Effect of auxin production on stressed and non-stressed watermelon seedlings There was no visual or quantitative growth enhancement following inoculation with putative auxin producing bacteria (Figure 22). All treatments, with the exception of non-cytokinin Bacillus T32 which inhibited the biomass production of seedlings also in non-stressed, resulted in significantly reduced shoot biomass of non-stressed compared to stressed plants. There was no significant difference across any of the stressed plant treatments. Seedlings inoculated with Bacillus R13 or Enterobacter N13 had roots protruding out of the base of the pots in the non-stressed treatment. When washed, the roots were longer and denser than any other treatment (Figure 23) and were harder to wash free of substrate. While Enterobacter N13-treated seedlings had somewhat more root biomass this was a significant increase relative to controls.
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Figure 22 Dry weight of watermelon plants exposed to water stressed and non-stressed treatments and six microbial treatments including a no microbe control, two ‘no-auxin producing microbes’ control treatments and three putative auxin producing microbes.
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Figure 23 Watermelon plants that did not receive bacteria inoculation (A,B) or that received Enterobacter N13, a putative auxin producer (C,D). Plants were grown with sufficient water supply (A,C) or limited water to induce stress (B,D). Before drying and weighing, plants were washed and photographed to visualise roots.
4.3.5.4 Phosphorus mobilisation from phosphorus wastes Isolates Enterobacter N17a and Serratia N70 were selected for plant growth experiments based on their observed superior solubilisation ability and growth on Pi, struvite and MBM. Analysis of plant biomass (tissue-culture generated sugarcane plantlets), after four weeks of growth revealed that plants generally grew more above-ground biomass and had underdeveloped root systems, irrespective of microbial inoculant or P source. Plants supplied with inorganic P (Pi, struvite) generally had slightly higher biomass compared to those receiving no P but there were no statistically significant differences due to variability in the bacterial treatments (Figure 24).
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Figure 24 Total dry biomass of sugarcane plantlets after four weeks growth supplied with soluble Pi and two P waste sources (MBM and Struvite) and bacterial inoculants (both with phytase and P-solubilising activity). No statistically significant differences were observed (ANOVA, post-hoc test).
Plantlet biomass of bacterial treatments were generally similar to uninoculated plantlets when supplied with P waste products, suggesting that there were no positive plant growth responses elicited from simultaneous application of the two amendments (p = 0.074). We took air samples taken from the culture jars on the final day to check the CO2 concentration, but found no significant differences in CO2 concentration between inoculated and un-inoculated treatments (p = 0.168). This indicates that bacterial respiration had no measureable effect on CO2 concentration in the growth containers
(as increased CO2 concentrations could have boosted plant growth).
4.3.6 Bacteria classification and phylogenetic signals Bacteria were assigned taxonomy following Sanger sequencing and blasting in the NCBI database (Figure 26). The most dominant genera were Serratia followed by Pseudomonas and Bacillus (43%, 20% and 18%, respectively). After calculating phylogenetic signal for each microbe, we identified that multiple bacteria had a phylogenetic signal of 0 indicating that they were the same strain. This collapses the 121 isolates to 90 distinct bacteria strains. We compared the ‘double ups’ to one another to investigate whether they performed similarly in the various trait assays to see if they can be treated as replicate tests (Figure 25). We found that while there were similarities, some replicates tested positive for traits while others tested negative. In the case of Bacillus, replicates consistently did not test positive for certain traits (P and antifungal activity, IAA, HCN and EPS production), but there were discrepancies across some replicates for other traits. Some replicates tested positive for biofilm, proteolytic, cytokinin and siderophore activity while others did not (Figure 25). Using Pagel’s
Page 110 of 167 Lambda, we identified significant phylogenetic signal in multiple traits indicating that they are more likely to occur within certain taxonomical clades. The traits exhibiting significant phylogenetic signal included phytase activity (p = 1.18e-6), P-mobilising activity (p = 2.22e-5), proteolytic activity (p = 0.027), siderophore production (p = 0.032), HCN production (p = 1.5e-17) and EPS production (p = 0.012). A Redundancy Analysis plot showed the distribution of isolates and their taxonomic classification (family level) in relation to trait expression (Figure 27). Isolates in the Burkholderiaceae were associated with an expression of phytase activity while isolates in the Flavobacteriaceae were associated with proteolytic and siderophore activity.
Figure 25 Bacteria isolates that were identified as identical species by their phylogenetic signal equating to 0. Bacteria genera and identification code are listed and organised by phylogenetic relatedness. Testing positive for a particular trait is indicated by a coloured rectangle, testing negative is indicated by a white rectangle.
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Figure 26 Phylogenetic tree of bacterial isolates as sequenced by Sanger sequencing of 16S rRNA. Bacterial phyla are indicated by blue background hues within each clade. Trait data is indicated by coloured circles on rings surrounding the phylogenetic tree for each bacterium tested. Large circles denote high trait activity, small circles indicate low activity, and no circle no detectable trait. All 121 bacteria isolates are shown here including the 31 genetically similar bacteria identified by 16s rRNA gene sequencing.
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Figure 27 Redundancy Analysis (RDA - R package vegan) plots showing bacterial isolates tested for nine traits (BNF, as a not detected was omitted here). Family level taxonomy is highlighted in blue to represent distribution of different taxonomic groups in relation to trait expression. Red and black text denote the screened traits and bacteria isolate, respectively. Bacterial families include Flavobacteriaceae, Burkholderiaceae, Bacillaceae, Xanthomonadaceae, Enterobacteriaceae, Microbacteriaceae, Rhizobiaceae, Pseudomonadaceae, Moraxellaceae, Comamonadaceae, Micrococcaceae, and Alcaligenaceae.
4.4 Discussion
PGPR have been studied by agricultural scientists for decades using the plate assays described here and others (Lugtenberg and Kamilova 2009; Agrawal et al. 2015). Arguably, selecting PGPR with desirable traits is a promising approach when searching for proficient PGPR for specific plant growth environments (e.g. cytokinin producers for drought environments, P mobilisers for soil in which P sources are locked up). However, upscaling from the proficiency of microbes of delivering a desirable
Page 113 of 167 trait in sterile plate assays to plant growth promotion in field conditions is not necessarily reliable (Ahmad et al. 2008b; Finkel et al. 2017). First, such approach can lead to the selection of microbes that are incapable of establishing with host plants under field conditions and second, it is possible that other, better PGP traits remain unknown or untapped (Finkel et al. 2017). Gaining better understanding of the presence and intensity of PGP traits in a culturable bacterial community, including the presence of specialists and generalists, as well as their taxonomic classification, could provide insight into which microbes plants are selecting in a given environment. Selecting microbes based on their presence in the rhizosphere, the expression of key traits that plants have selected for, and efficiency in colonising the rhizosphere and roots will assist in enhancing the establishment of such microbial communities and ensure that PGP effects are realised in real world conditions. These are several factors that are often not considered by manufacturers when formulating PGPR products. This thesis highlights that performance in traditional in-vitro plate assays alone is not sufficient in identifying effective PGPR for field use.
4.4.1 Distribution of PGP traits and generalist/specialist bacteria 4.4.1.1 Sugarcane preferentially selects for both generalist and specialist bacteria One aim of this Thesis was to evaluate the distribution of PGP traits across the culturable microbial community in the sugarcane rhizosphere. We screened a comparatively large number of isolates for 10 PGP traits (or enabling traits such as biofilm production) to identify generalities, which led to the conclusion that a proportion of isolates stands out as either being specialists or generalists. We identified PGP specialists (‘specialists’ in the following) as top performing microbes for a particular PGP trait and these organisms did not exhibit other PGP traits (or only at low level in the test conditions). PGP generalists (‘generalists’ in the following) expressed the largest number of traits and typically did not have high activity in any of them. The greatest number of traits that any isolate expressed was five, with all others expressing between 1-5 traits. A recent study screening PGPR from a tomato rhizosphere found that only two bacteria isolates were able to express all of the five tested traits however these traits were expressed at lower levels relative to other isolates which expressed only one property but at a much higher level (Guerrieri et al. 2020).
Vacheron et al. (2016) investigated culturable Pseudomonas populations from a maize rhizosphere and found that plants preferentially selected Pseudomonas expressing 1-5 PGP traits. This selection of microbes with fewer traits was not associated with particular combinations of traits or taxonomical groups (Vacheron et al. 2016). Determining which type of PGPR (generalist, multi-trait expressing bacteria with low to moderate expression versus specialist bacteria expressing one or few traits strongly) will be most effective when aiming to improve the rhizosphere microbial community is Page 114 of 167 difficult to discern with the current knowledge basis. It is recognised that microbes acting as PGPR specialists may be more efficient for particular traits than generalists (Dykhuizen and Davies 1980; Vacheron et al. 2016). A number of studies have investigated the preferential selection of generalist microbes by plants as it is assumed that the expression of multiple traits has a cumulative beneficial effect and thus provides greater benefits for the hosts (Vacheron et al. 2016).
Almario (2014) evaluated Pseudomonas isolates for their ability to protect plants against Pythium and Fusarium disease incidence and found that microbes expressing all three of the biocontrol PGP traits (T3SS genes controlling lytic enzyme and siderophore secretion, ACC deaminase, DAPG) were most effective as biocontrol agents. This suggests that generalists expressing a range of traits within a particular PGP category (such as pathogen resistance in the example above) may be most effective, as they are targeted in their purpose, (i.e. reducing pathogen infection) while also exhibiting a range of traits to enhance their efficacy, such as T3SS, DAPG and ACC-deaminase (Almario et al. 2014).
Evidence suggests that generalist microbes, capable of surviving in a broad range of environments due to flexible metabolic processes, that make up ‘core microbiomes’ (e.g. Bacillaceae, Flavobacteriaceae, Rhizobiaceae, Pseudomonadaceae identified as generalists in our study and core taxa by Yeoh et al. 2015) may be the best adapted to survive and persist especially in the face of stochastic environmental pressures (Lindh et al. 2016; Sriswasdi et al. 2017). It would be a reasonable expectation that specialist microbes would outcompete generalists and deliver PGP effects when optimal conditions are present such as when a particular nutrient is lacking or a pathogen is present (Chen et al. 2020; Mills et al. 2020). However, ensuring environmental conditions are favourable for specialist microbial survival in horticultural and agricultural fields remains a challenge. Clearly, to move forward and realise effective PGPR inoculants, research is needed that integrates the characterisation of individual bacteria and bacterial communities (e.g. using functional screening, meta-genomics, meta-transcriptomics), understanding of how plants recruit bacteria to their rhizosphere (plants genetics and function), and the integration of both knowledges across diverse host and environmental conditions that can be subject to rapid and strong change. This will require interdisciplinary and systems research that integrate microbiology, plant science and agronomy. Additionally, products that are developed to incorporate specialists must be marketed appropriately to the targeted environment. This is difficult however as specialists in particular traits (e.g. HCN production) may only be effective in specific circumstances (e.g. on one pathogen species or pathogen type such as soil pathogens), and could still be misused by growers if this is not made clear.
Page 115 of 167 The 18 generalist isolates in our study belonged to six genera (Burkholderia, Serratia, Chryseobacterium, Pseudomonas, Stenotrophomonas, Acinetobacter). The six specialist isolates were in genera Enterobacter, Pseudomonas, Bacillus, Chryseobacterium, Rhizobium, and Serratia. Thus, while Pseudomonas, Chryseobacterium and Serratia contained both specialists and generalists, three genera each contained only generalists (Burkholderia, Stenotrophomonas, Acinetobacter) or specialists (Enterobacter, Bacillus, Rhizobium). Yeoh et al. (2017) showed that Bacillus and Rhizobium have a ubiquitous presence in rhizospheres across a wide range of plant taxa, but it was not investigated if these organisms can be categorised as PGP generalists or specialists. It remains difficult to select generalist versus specialist PGPR for the purpose of inoculation and associated ‘rhizosphere engineering’ and predict which organisms are likely to survive, persist and deliver benefits as evidence exists for both (Vacheron et al. 2016; Guerrieri et al. 2020). Future work should focus on the identification of generalist and specialist microbes and investigate in more detail their survivability with consistent vs stochastic environmental conditions.
The classification of generalist versus specialist organisms originates in ecological theory and there is good supports for macro-organisms, while little research has been conducted in this context with rhizosphere microorganisms (Dykhuizen and Davies 1980; Lindh et al. 2016; Chen et al. 2020). Here, we also have to consider that the traits examined are specific to PGP traits that boost host vigour and survival in agricultural soils. It is conceivable that the recruitment of generalist or specialist microbes by the plant is reliant on environmental conditions and associated pressures and that an efficient rhizosphere microbiome is made up of both generalists and specialists. Such categorisation should be evaluated further as it may hold clues for identifying effective PGPR and microbial engineering of the rhizosphere. Interestingly, bacterial specialists in the human gut microbiome have a smaller genome than generalists (Walter and Ley 2011). Examining the respective bacteria in our study for their genome size could be a logical next step, followed by testing specialist-generalist combinations to advance fundamental understanding of rhizosphere communities and how the bacteria function in communities.
4.4.1.2 A wide variety of traits are expressed in sugarcane rhizospheres
We found that phytase activity, Pi mobilising activity, proteolytic activity, siderophore production, HCN production, and EPS production had significant phylogenetic signal indicating association with particular taxonomical groups. Our analysis (phylogenetic tree, RDA) showed that isolates in the Enterobacteriaceae and Burkholderiaceae were associated with P-mobilising and protease activity, Flavobacteriaceae with siderophore and EPS production, and Pseudomonadaceae with HCN production. The remaining four traits did not exhibit significant phylogenetic signal indicating that Page 116 of 167 they were not associated with particular taxonomical groups. Many microbes from the groups listed here have been previously associated with these traits in the scientific literature and were selected as PGPR for use in plant growth studies as discussed below.
The most commonly expressed traits in our study were phosphorus mobilising activity (organic and inorganic sources) with phytase and Pi solubilising activity, as well as protease activity. Most microbes that exhibited P mobilising activity had phytase and Pi solubilising capacity with different degrees of efficiency. Few microbes expressed only one of the tested P mobilising traits and four microbes had very strong activity in both. Having been isolated from a sugarcane rhizosphere, the results may indicate preferential selection of crops towards P mobilising bacteria over N fixing bacteria as we did not identify any culturable diazotrophs. Yeoh et al. (2015) performed the initial research on the microbes of our study and detected Nif-gene expression in rhizosphere bacteria indicative of BNF but no difference between replete or limiting amounts of N fertiliser as was expected. The authors concluded that these findings could be the result of differences in sugarcane varieties, N application rates (which are quite high in Australia) as well as the agronomic history of sugarcane production. Additionally, the present study either did not use appropriate isolation media to target diazotrophs or diazotrophs have indeed a low abundance compared to P-mobilisers in Australian sugarcane rhizospheres.
Our findings confirm previous findings (see below) that phosphorus solubilising bacteria (PSM) are often widespread with 67% of isolates having a form of P mobilising activity and 16% to a high level. The environment from which the microbes were isolated may explain this finding because P fertiliser can become unavailable to plants when bound to soil, particularly as iron or aluminium phosphates that are prevalent in acidic sugarcane soils including the ones in our study (Yeoh et al. 2016). The abundance of P mobilising bacteria can vary dramatically between different soil environments comprising as little as 1% to as high as 50% of the microbial community and more active in the rhizosphere (Kalayu 2019). In grassland soils following N addition, the abundance of P-mobilising bacteria decreased, however phosphatase activity increased creating a shift in the dominant P process from mobilisation of P minerals to organic sources via microbially derived acid phosphatase (Widdig et al. 2019). Another study found that long term N inputs altered microbial community composition of multiple field sites in China and reduced microbial P mobilisation capacity while also delivering a significant increase in phytase activity, similarly suggesting a shift in P mobilisation strategies (Dai et al. 2020). This is consistent with the idea that N inputs can increase microbial biomass and phosphatase production, depleting mineral P resources and leaving organic P as the primary source for possible exploitation by phytase producing PGPR (Widdig et al. 2019). We did not investigate P
Page 117 of 167 mobilising bacteria populations in sugarcane soils but identified that the culturable community was dominated by bacteria with the capacity to mobilise either inorganic phosphorus (via the release of acid phosphatase) or phytate (via the release of phytase organic acids). As N inputs can alter P cycling and create shifts towards certain P mobilising strategies (Marklein and Houlton 2012), this may explain the widespread presence of phosphatase and phytase activity given that these microbes were isolated from sugarcane soils with high nitrogen inputs.
There is wide range and diversity of microbes that are reported to mobilise soil phosphorus of which Enterobacter and Burkholderia are commonly reported (Vassilev et al. 1997; Sharma et al. 2013; Borham et al. 2017; Widdig et al. 2019). A 2011 review outlined a number of research articles reporting phosphatase and phytase activity among different taxa including both Burkholderia and Enterobacter (Singh and Satyanarayana 2011). A recent study found that Burkholderia was repeatedly identified in high abundance within various N and P amended soils (Widdig et al. 2019). Protease, while being an enzyme produced by these taxa similar to phytase and phosphatase, is primarily involved in the hydrolyzation of pathogen cell walls and depolymerisation of nitrogen (Simpson et al. 2017; Novo et al. 2018). Several strains of Enterobacter have previously been shown to express both P mobilising and Protease activity (Guo et al. 2020). Protease activity has also been previously identified in several Burkholderia strains which is a taxa well known for its Nitrogen mobilising and antimicrobial potential (Kooi et al. 2005).
Members of the Flavobacteriaceae family, most notably Flavobacterium, are well known pathogens of aquatic organisms as well as strong plant growth promoters (Guan et al. 2013). The ability of this bacterial genus to access iron resources by producing siderophores has been well studied. A study screening 40 strains of Flavobacterium found that 36 strains were capable of siderophore production as determined by the CAS agar assay used in this study (Møller et al. 2005). More recent studies using transcriptome analysis and complete genome sequencing have confirmed evidence of siderophore production among many bacteria that express other pathogen resistance traits, as well as the capacity for biofilm formation and exopolysaccharide production. Biofilm production was not among the traits that had a significant phylogenetic signal, however two isolates Chryseobacterium T49 and Chryseobacterium T58 did test positive for biofilm production in our study as shown in Figure 26 (Cai et al. 2019; Jeong et al. 2019). Chryseobacterium was identified as the most common Flavobacteriaceae among the cultured microbes isolated here which has also been repeatedly shown to produce siderophores among other antimicrobial traits. Full genome sequencing of a Chryseobacterium showed strong evidence for conserved siderophore gene clusters among several species (Dahal et al. 2020). Another study investigating multi-trait PGPR identified a strain of
Page 118 of 167 Chryseobacterium capable of producing siderophores along with ACC deaminase, IAA, HCN, and protease (Bhise et al. 2017). Evidence also exists for exopolysaccharide production by Chryseobacterium with a recent study highlighting the ability of rhizobacteria to alter their EPS production to protect plants against heavy metal toxicity (Kowalkowski et al. 2019).
We found here that HCN production was widely expressed amongst the Pseudomonas isolates which is consistent with many other articles investigating PGP traits of Pseudomonas populations. HCN production has been identified as an important trait of PGP Pseudomonas in the suppression of root rot fungi (Deepika et al. 2019). A study investigating the antifungal (AF) activity of Pseudomonas chlororaphis against Sclerotinia sclerotiorum found that HCN mutants had reduced AF activity relative to non-mutants capable of producing HCN (Nandi et al. 2017). Further, it was demonstrated that when the hcnA gene that encodes a membrane-bound HCN synthase complex is inactivated in Pseudomonas mutants, they were unable to produce HCN and had reduced AF activity relative to wild type controls (Strano et al. 2017). Compared to the different forms of P mobilisation, the three antifungal traits tested here did not overlap as strongly. Microbes that expressed HCN production did not consistently produce siderophores or exhibit antifungal activity. This could be explained by the fact that many other antifungal modes of action exist (ISR, ASR, production of other antimicrobial compounds, etc.) that were not tested here. Additionally, the specialist/generalist argument could explain why some microbes exhibit only one mode of action and trait while others exhibit many mode of actions/traits that are not necessarily constrained within one category (i.e. pathogen resistance strategies).
Based on the culturable community isolated here, the sugarcane crop from which the bacteria were isolated primarily attracted and selected for a wide range of members in the Enterobacteriaceae and Burkholderiaceae capable of expressing phosphorus mobilising and protease activities. The predecessor study which comprehensively sequenced the bacterial communities of the sugarcane rhizosphere and bulk soil similarly found that Enterobacter and Burkholderia OTUs were enriched in the rhizosphere relative to the bulk soil (Yeoh et al. 2016). Many of the 121 isolates identified in this study were also enriched in the sugarcane rhizosphere according to community profiling data except for Serratia, Klebsiella, Acinetobacter, Micrococcus, Achromobacter, and Leifsonia. If these bacteria were present in the rhizosphere samples collected, they were below detection limit. This highlights that isolating culturable communities and sequencing soil profiles can provide different information about the composition of microbiomes. When considering formulation of PGPR for commercial purposes, culturable communities are often the focus. This is because targeting unculturable microbes is more difficult, for example using chemotaxis assays that attract particular
Page 119 of 167 organisms towards a chemical source. It has been proposed that such approach is however promising, especially with view of the development of new assays allowing fast screening of chemo-attractants (Bhattacharjee et al. 2012; Dennis et al. 2013; Feng et al. 2019).
4.4.2 Traditional plate assays allow for untapped PGP mechanisms In principle, a major benefit of plate assays such as those used in our study is the ease of which large bacteria libraries can be screened for a desired trait with minimal resources. Results for plate assays can be obtained within 2 hours to 5 days depending on the test being performed, meaning it is a high- throughput method for fast evaluation of putative PGPR isolates. However, screening a library of microbes and identifying isolates that were, according to their 16S rRNA sequence, identified as the same strain, we found that microbes did not consistently test positive for the same traits. Some microbes that tested positive for a trait in one or two of the replicate assays may not have tested positive in the third assay. There were also discrepancies observed in the double up isolates where, for example, two Pseudomonas cultures that were identified as the same strain delivered completely different results in the trait screening with the first (T4) expressing proteolytic, HCN and biofilm activity while the other culture (T24) expressed phytase, P-mobilising and cytokinin activity (Figure 25). There is recent evidence to suggest that 16S rRNA sequencing using the short read method applied in our study does not allow distinguishing between strains, suggesting the isolates may still be genetically distinct (Johnson et al. 2019).
A 2017 review discussing PGPR modes of action outlined that current phenotype-based screening methods rather predict the known (traits we know many microbes can express) than allow us to identify new, untapped mechanisms (Finkel et al. 2017). These authors also suggests that plate assays often fail to quantify the magnitude of PGP activity that could be delivered in field-grown plants. They postulated that genome sequencing provides a better, more time effective alternative for identifying PGP-associated genes. However, a counterargument is that genome sequencing is still a costly technique (often unavailable to researchers in developing economies) and, even if a microbe possesses the genes required for a given PGP trait, the phenotype requires specific conditions and environmental stimuli to be functional in the field (Ahmad et al. 2008b; Finkel et al. 2017). Many research scientists continue to advocate that traditional screening methods provide the strongest most effective tool in identifying effective bacteria (Lyddiard et al. 2016). This is arguably an important consideration given that many functional traits/characteristics identified using metagenomics may not be accessible for in-vitro tests or commercialisation.
Page 120 of 167 Identifying top performing PGPR isolates would require multiple cycles of trait screening to select bacteria, however many studies perform only one screening round and rely on the initial outcome to assess PGPR potential. As stated earlier, all of the screening tests performed here have been used for numerous years in PGPR research, but it appear that advances are needed to improve testing. Screening methods are rarely innovated and are not often used in conjunction with other methods to confirm functionality or reliability such as metagenomics or field survivability. As explained by Joseph Kloepper, a leading PGPR expert, it would be ideal to test microbes in the field conditions under the stresses one would want to select for to identify putative PGPR, and to then dive into the mechanisms (Joseph Kloepper, PGPR Workshop 2015 Leiden, Belgium, pers. comm.). However, this may be unrealistic, time consuming and costly, and better selection procedures are needed to select PGPR with high performance in the field. A missing link in PGPR research is the systematic testing of plants in real world conditions, including quantification of plant stress and associated physiological indicators, to advance knowledge of the plant-rhizosphere-soil-environment system. Additionally, to this and desired PGPR traits, there are a suite of other bacteria characteristics that are required for effective commercialisation. They need to undergo testing for large scale production, survivability in inoculum materials (whether it be liquid, pellets, dried formulations, etc.), shelf-life storage, and so on. Effective strains must not only have an effect in the field, but survive long enough to make it there.
4.4.3 Translation from plate to plant In our experiments, we identified some specialist PGPR isolates which had effects in assays with host plants, however most of the specialist bacteria (in plate assays) did not deliver growth promoting effects when inoculated on host plants. The main traits tested in plant experiments were P mobilisation, cytokinin production, auxin production, and anti-fungal activity to include the three main PGP strategies of nutrient mobilisation, phytohormone production, and pathogen resistance.
The most effective plant screening assay was anti-fungal activity where we identified four microbes that alleviated pathogen infection and plant death by 60-80%. Using SEM, we observed colonisation of bacteria on the leaf surface and directly colonising fungal mycelia. Bacillus N16 was the microbe with the highest colonisation of leaves and fungal hyphae, despite it not being the isolate with the highest rate of disease suppression in plate and plant assays. Pathogen control by beneficial microbes is one of the most broadly studied interactions, due to the widespread and severe impacts that crop pathogens can have on horticultural and agricultural industries, and desire to find alternatives to harmful agro-chemicals. PGPR can alleviate pathogen infection via a number of direct and indirect mechanisms depending on the bacteria and pathogen of interest. Bacteria colonisation of fungal Page 121 of 167 mycelia may indicate direct antibiotic activity or may act as a mode of transportation using the hyphae to enter plant tissue (Benoit et al. 2015; Kaewkham et al. 2016). For example, Kaewkham et al. (2016) screened two PGPR isolates for their ability to suppress gummy stem blight (GSB) incidence in cucumber. Bacillus subtilis QST713 displayed effective inhibition of GSB mycelial growth in plate assays and reduced disease incidence when applied to cucumbers as a seed treatment and as a foliar spray (Kaewkham et al. 2016). This strain of B. subtilis is the same strain used in the Serenade® Prime product described in Chapter 3 however we found no direct benefits to plant health in our experimental design. This may have been due to a difference in the environmental stress that was imposed (pathogen pressure compared to the water stress tested in this thesis) or the crops that were used in our experiment did not form a relationship with this strain of B. subtilis. Similarly to Kaewkham’s study, commercial biocontrol product Rhapsody® with active agent Bacillus subtilis used as a foliar spray prior to pathogen incidence significantly reduced disease severity in cucumbers (Punja et al. 2019). While bacteria cell attachment by Bacillus to GSB hyphae appears not to have been described in the scientific literature, there is evidence that bacteria attach to fungal hyphae including Aspergillus niger (Benoit et al. 2015; Kovács 2019).
We evaluated cytokinin production via two assays, first in a plate assay with WT and IPT mutant Arabidopsis, and second in a pot experiment with stressed and non-stressed watermelon seedlings. In the first assay, we found that two isolates Serratia N22b and Stenotrophomonas N31a altered root architecture. While WT plants inoculated with Serratia exhibited root stunting, IPT plants resembled control plants with long primary roots. One explanation is that bacteria produce cytokinin so that WT plants inoculated with Serratia receive excess hormone as both bacteria and plant produce the hormone, while IPT plants receive a normal level of cytokinin as only bacteria generate cytokinin, not the plant (Chernyad’ev 2009; Hönig et al. 2018). Alternatively, Serratia may induce cytokinin biosynthesis so that WT plants over-produce cytokinin, leading to the root stunting effect (Kudoyarova et al. 2019). IPT plants have a mutation blocking their cytokinin biosynthesis pathway which may explain why they resembled control plants (Plants with no bacteria) as cytokinin biosynthesis was not enhanced by Serratia.
Both WT and IPT plants inoculated with Stenotrophomonas exhibited stunted primary root growth and enhanced lateral root formation. This may suggest large production of cytokinin by Stenotrophomonas or, alternatively, the release of a compound that enhances the cytokinin receptor pathway. This would cause the plant to perceive cytokinin at a higher rate, leading to stunting effects. There is evidence of cytokinin production in Stenotrophomonas however, the broader effects in context of phytoremediation and stress tolerance are more commonly reported. A study that isolated
Page 122 of 167 70 bacteria from the rhizosphere of Coleus forskohlii (a herb that grows on mountain slopes in India, China, etc.) found that a Stenotrophomonas maltophilia strain was the most effective in producing cytokinin and had the potential for alleviating biotic and abiotic stresses (Patel and Saraf 2017). Evidence for cytokinin production in the genera Serratia is not reported with the exception of one study that screened a large bacteria library from a Saskatchewan soil (Canada) for their ability to grow on cytokinin N6-benzyladenine (BA) media. They found a single isolate of Serratia proteamaculans that was able to grow on BA and use cytokinin as a C source (Taylor et al. 2006).
In the second assay, we screened Serratia N22b and Stenotrophomonas N31a on stressed (i.e. water limited) and non-stressed (well-watered) plants to determine the ability of the microbes to manipulate cytokinin production/pathways for plant defence priming. This was done alongside auxin producing microbes as both phytohormones have been associated with increased plant growth and stress response (Arkhipova et al. 2007; Ambreetha et al. 2018; Raheem et al. 2018). Cytokinins play key roles in multiple functional pathways, controlling hormone balance and gene expression in plants as well as plant defence and stress response (Arkhipova et al. 2007; Liu et al. 2013). In the system tested here, we found that the top performing microbes for phytohormone production did not alleviate stress response or enhance growth when inoculated on plants. Similarly, the microbes selected for plate screening with the highest phosphorus activity did not lead to enhanced phosphorus acquisition in plant assays. As discussed previously, translation from laboratory tests to field investigations often leads to variable and unreliable PGP effects. Next steps research, which was beyond the scope of our study, has to investigate whether the lack of PGP effects is due to a lack of (i) colonisation and persistence of the target microbes, (ii) trait expression, (iii) both, or (iv) other reasons that include that the trait is not the primary need to avoid inhibited plant performance.
There is extreme variation in the success of plate to plant assays both in this experiment and reported in the literature. Many microbes that are screened in PGPR research do not lead to PGP effects when tested with plants or in field conditions. This is likely due either the inability of plate assays to identify top performers or the lack of plant-microbe communication following soil inoculation. As we have shown here, when microbes are screened multiple times for the same traits, different results can be achieved for trait activity. The microbes that were identified as being genetically identical exhibited very different results for trait screening, therefore leading to a lack of confidence in traditional screening techniques. Additionally, lack of PGP effects may result from a fault in the plant-microbe communication and establishment phase as microbes must first survive in the soil and associate with plants to deliver PGP effects. Microbes that perform well in plate assays are not necessarily the groups that will form the strongest associations with plants as we have seen here. For PGP to occur microbes
Page 123 of 167 need to be exposed to the correct conditions in the field in order to (i) express traits and (ii) have a competitive advantage relative to surrounding native microbes to be able to effectively colonise the rhizosphere and survive long term. More focus should be placed on selection of effective colonisers as well as improved methods of delivery to ensure that microbes are protected from biotic and abiotic influences. If the need for plant-microbe communication could be removed, top performing microbes could be better candidates for PGPR selection however further innovation is needed in this space to create microbial delivery tools that work consistently and reliably as we have seen for the rhizobium- legume system. Another possibility could be the cultivation of microbial suspensions with specific media to encourage the production of hormones/enzymes of interests, and lysis of bacterial cells to ensure all microbially derived compounds are extracted prior to soil inoculation. If these beneficial microbially derived compounds could be applied routinely then we would no longer rely on microbial survival, plant communication, or triggers for bacterial inducible traits. The use of living microbes offers advantages for single dose applications which is of particular benefit to large crops such as sugarcane. However, more care needs to be taken to ensure triggers for inducible traits are delivered (e.g. a particular phosphorus source is delivered to stimulate P mobilisation strategy shifts) early on in the inoculation phase to ensure PGPR survival.
4.5 Conclusion Plate assays are a popular avenue to identify PGPR due to their high-throughput and affordable nature. Putative PGPRs can be selected for quickly so that not all isolated microbes need to be tested in plant systems. These assays are therefore useful for helping researchers gain a broad understanding of trait activity and functional capacity within the culturable population of plant rhizospheres, however plate assays do not guarantee selection of effective PGPR, as shown in our study where microbes with high trait activity in plate assays did not translate to high activity in plant assays. We propose that this is likely due to either (i) the inability of plate assays to reliably identify ‘top performers’ that deliver PGP effects for plants, (ii) that microbes require specific environmental conditions to elicit trait expression which is not achieved in the field or, (iii) the selection of inefficient colonisers. We identified that among the culturable microbes isolated there was a combination of both specialist and generalist organisms and the most widely expressed trait was P mobilising activity. We recommend that future work should critically evaluate specialists and generalists to ensure that microbes are used effectively. While specialists offer avenues for highly targeted systems, generalists may offer competitive advantage for survival due to flexible metabolic activity. It is important for researchers and product developers to understand whether they are utilising specialists or generalists to know how the microbes can be most effectively targeted and in what systems they are likely to deliver effects. Additionally, improved delivery methods must be considered for PGPR inoculation Page 124 of 167 to facilitate the survival of microbes in the absence of favourable environmental conditions and this is another major caveat in PGPR research that is only recently receiving attention. Furthermore, the expectation that field-inoculated PGPR will effectively colonise plant roots and survive long term in the absence of the particular environmental conditions required to elicit competitive advantage for those microbes may be unrealistic. PGPR delivery as a ‘just in case’ strategy is likely the reason most commercial PGPR products do not survive in the soil long enough to proliferate and associate with plants. As discussed in Chapter Two, there are reports of commercial products entering the market comprised of individual isolates that can elicit strong PGP effects (Berg et al. 2020). The best examples being Rhizobia for legume growth and Trichoderma for pathogen control. Good research has provided a strong foundation for effective PGPR in these products however this is lacking in the Australian market with less rigorous regulations for new products entering the commercial space.
Page 125 of 167 Chapter 5 Conclusion
5.1 Current status of commercial microbial biostimulants
There has been a vast growth in the use of microbial biostimulants for crop growth and protection following the transition to ‘green’ agricultural practices (Berg et al. 2020). In recent years, growers have become more interested in green agricultural practices, aiming to use more sustainable amendments over chemical fertilizers and pesticides. Harnessing the benefits of the soil and rhizosphere microbial communities has therefore been a major focus of agricultural scientists (Hartman et al. 2018). PGPR have offered an impressive and exciting possibility for growers with their proven abilities to act as biocontrol agents, enhance plant nutrition, growth and resilience (Guerrieri et al. 2020). The major caveat in PGPR research that has proven difficult to overcome, is the inconsistent delivery of plant growth promoting traits when scaling up from successful plate assays and, glasshouse experiment to field trial (Ahmad et al. 2008b; Baliyan et al. 2018). Manufacturers in highly regulated markets, including European countries, have addressed this problem by developing single microbe inocula designed for targeted crops. Products in these markets have undergone a minimum of 10 years testing and rigorous field evaluation (EPPO 2012b, a, c). While more R&D is needed to develop products with this approach, the microbial inoculants offer more targeted and reliable results. This level of rigour and quality control is presently lacking in many countries, including Australia. This has led to a growing biostimulant market that is saturated with products which are insufficiently (or not) scientifically tested and which make broad and unrealistic claims. Crop growers therefore have difficulty making judicious decisions about which product will work best for them and often fall prey to advertisement. Many products, including the ones tested here, are marketed for too many crops and crop systems (e.g. Nutri-Life Platform® is marketed for 143 crops) rather than specific ones. Given the plethora of research indicating that PGPR can be highly specific and only associate and convey benefits for particular crops that trigger PGP traits, it is unsurprising that such products have variable and inconsistent effects when used in field conditions (Grayston et al. 1998; Guerrieri et al. 2020).
We have shown here that several commercial microbial inoculants in the Australian market failed to deliver growth promoting effects in real-world agricultural and horticultural settings. It was only when the test system was simplified to a laboratory setting with the application of an environmental pressure (water stress), that a growth promoting effect was observed for one treatments (out of 82 plant-microbe combinations tested) (Chapters 2 and 3). Manufacturers in unregulated countries are
Page 126 of 167 not only avoiding rigorous testing expectations but are placing responsibility onto growers to get the products to work. When queried by the nursery owner why the products did no improve seedling growth in the commercial setting, manufacturers either advised that the seedlings were “too happy” to stimulate beneficial effects (Bayer representative, pers. comm.), or that the product had not been applied correctly (but not providing information on how products should be applied correctly). A lack of interest in providing appropriate advice could be due to a number of reasons that include that the companies know that their products only work in some situations or that sales are successful irrespective. Aside from a lack of field evidence for PGP potential, there is also a major gap in understanding of how microbial inoculants affect native microbial communities. Whether products that contain isolates from outside Australia could cause a biosecurity issue has not been explored to the best of our knowledge. Raising this with an APVMA (Australian Pesticides and Veterinary Medicines Authority) representative showed that such products are not on the radar of the Australian authorities which otherwise have strict guidelines aimed at protecting Australia from imported biota.
Focussing on the science of biostimulants, the rhizosphere microbiome is a complex consortium of bacteria that (i) exist naturally within a given soil system (due to chemical and environmental factors) and (ii) are selectively recruited by plant hosts (Berendsen et al. 2012; Raynaud and Nunan 2014; Yeoh et al. 2017; Kalayu 2019). Engineering this microbiome is difficult and microbial inoculants can have unexpected effects on rhizosphere community composition (Berg et al. 2020). As we have shown in this thesis, microbes that are applied to soil, do not always proliferate and survive but, in some cases, may increase the relative abundance of other, possibly unwanted groups. While we saw significant shifts in rhizosphere microbial communities following the application of inoculants, none of the added OTUs increased. How these changes affected soil function or the fitness of plant was outside the scope of this thesis. However there remains an undeniable caveat in biostimulant regulations that manufacturers are not expected to prove efficacy of these products for engineering rhizosphere communities to support the bacteria of interest. The scientific literature lists reasons as to why bacteria applied in field conditions, particularly in liquid formulations, do not deliver plant growth promoting effects reliably (Lee et al. 2016; Guerrieri et al. 2020). These reasons include (i) a lack of plant-microbe communication leading to no colonisation or competitive advantage over other microbes, (ii) non-ideal environmental pressures such as temperature, moisture, and soil chemistry, and (iii) lack of biotic/abiotic triggers required for activation of PGP traits. There are ways to navigate these limitations such as through (i) improved PGPR formulations, (ii) selection of microbes from native habitat, and (iii) engineered activation of PGP traits, however, this responsibility falls at the feet of the manufacturer and can be circumnavigated
Page 127 of 167 in unregulated markets. Product efficacy remains difficult to ascertain even in regulated markets with so many variables affecting PGPR success.
Another strategy that scientists have investigated is the formulation of artificial microbial consortia for enhanced survival following delivery to soil systems. Some studies have shown that PGPRs when combined together in a consortium offer not only enhanced survival, but improved efficacy for plant growth promotion (Ren et al. 2014b; Vacheron et al. 2016; Finkel et al. 2017). The limitation with this however is that microbes within consortiums will still compete with one another for resources and evidence that each individual isolate brings added benefits to the plant and bacteria community as a whole is required (Burmølle et al. 2014). A number of products in the Australian market have included mixtures of microbes from bacteria to yeasts that supposedly work together to enhance plant growth, but with evidence lacking. Many products in unregulated markets even fail to identify each microbial additive to species level, with some listed only by family level taxonomy. Improvement in molecular diagnostics and declining costs associated with high- throughput sequencing will assist research to improve taxonomic resolution to species and strain level as well as characterisation of function. Ultimately, for the majority of microbial inoculants currently occupying the commercial market, stricter regulations are required to ensure product efficacy in field conditions. Targeted products with more sophisticated delivery techniques are sorely needed in Australia. In order to bridge from fundamental research to real world application more experimentation in industry-relevant, field conditions are necessary. Methods such as the “field first strategy” described in Chapter 4 could offer a way forward in selection of field competent PGPR (Baliyan et al. 2018). However further engineering of activation strategies for PGP traits is required to ensure PGPR potential is unlocked (Guerrieri et al. 2020). Ideally it should be easy for growers to select products that deliver one or more desired effects for their crops (e.g. enhanced crop nutrition in soils where are nutrients are out of reach for crops, or biocontrol in pathogen-affected soils). In the current situation, it is up to growers to sort quality products from ‘snake oil’. In our experience with sugarcane growers and nursery producers, practitioners fall into two categories: those who are enthusiastic about the opportunities of biostimulants and who use them frequently, and those who are cynical and have either stopped using biostimulants or have never used them. Clearly, there is need for regulation and incentivisation with push from regulators and pull from the biostimulant industry as well as crop industries.
Page 128 of 167 5.2 Selecting effective microorganisms: Considerations for current screening assays
Plate screening assays have been the bread and butter of PGPR selection for decades. They have provided a cost-effective, high-throughput, fast screening tool for identifying putative PGPR in a large bacteria library (Vacheron et al. 2013, 2016; Finkel et al. 2017). Plate assays have previously been preferred as a simple method of bacteria selection in comparison to high-cost genome sequencing techniques. However, as more research has been conducted, it has become more obvious that many of the microbes that elicit trait activity on plates, do not express the same trait activity under field conditions. Many studies have found that only small percentages of bacteria that test positive in plate assays, deliver benefits to plants. As stated in a recent review, “plate assays help us identify the known” as we screen for a specific known trait while endless possibilities of untapped, unscreened PGP mechanisms may await for us to explore (Finkel et al. 2017). As shown in this thesis, many microbes that not only show discrepancies in trait expression between plate and pot assays, can also give different results when plate assays are repeated. This begs the question do plate assays accurately identify “top performers”? Top performing microbes, i.e. bacteria with the highest level of trait activity in plate assays, have been targeted in the majority of PGPR studies, following the logic that the bacteria with the highest trait activity in plate assays would have the highest trait activity when tested with plants. However, it is reasonable to expect that specialist microbes with high trait activity may require very specific environmental conditions or ‘triggers’ for these PGP modes of actions to be stimulated. It has been reviewed that generalist microbes, with more flexible metabolic processes may offer advantages in persistence and colonisation (Lindh et al. 2016; Sriswasdi et al. 2017). We identified that both specialists and generalist microbes exist within the culturable community of sugarcane roots and more research is needed to ascertain if either category provides a competitive advantage as inocula for crops.
One way forward that could benefit from plate assays as screening tool, is to put a larger emphasis on the selection of effective colonisers by screening for EPS and biofilm producing microbes. Microbes that are able to colonise in large numbers, produce agglutination proteins and extracellular polysaccharides for the formation of dense biofilms have a competitive advantage over other native organisms. Biofilms also allow for enhanced protection following colonisation both to the hosts root systems and other bacteria colonising the rhizosphere (Burmølle et al. 2006, 2014; Ren et al. 2014b). It appear that the majority of studies screening for nutrient mobilising, phytohormone producing or pathogen controlling bacteria often do not evaluate a microbe’s ability to effectively colonise root surfaces. It is reasonable to assume that any microbe isolated from the target rhizosphere, would have the ability to be attracted to and move toward the roots, however effective Page 129 of 167 establishment and colonisation may not be a trait of all microbes (Burmølle et al. 2006; Checcucci et al. 2017; Baliyan et al. 2018; Yi et al. 2018). Similarly, it is not a guarantee that microbes, once established, will express desired PGP traits, however it is a step towards enhanced survivability and long-term persistence of microbes in the rhizosphere. Additionally to this, as discussed earlier, research is needed to determine whether single microbes or bacteria consortia, trait specialists or generalists, have advantages as PGPR inoculants. The answer to this may differ from system to system.
Another consideration for future studies aiming to isolate effective PGPR is that no one screening method is superior to the other and should be used in conjunction. Using plate assays in conjunction with metagenomics and transcriptomics may offer a stronger method for identification of putative PGPR (Yi et al. 2016; Finkel et al. 2017). Metagenomics is important for understanding and unlocking the DNA potential of a microbial community while transcriptomics, or metatranscriptomics, help to identify which genes (traits) are actually being expressed (Aguiar- pulido et al. 2016; Li et al. 2019). Here, we used short-read sequencing to acquire a snapshot of microbial community composition as our aim was to investigate whether inoculation of particular bacterial genera increased the abundance of the same genera in soil. However, the sequencing used here was not robust enough to determine whether the Fusarium OTU with increased abundance was a pathogenic or non-pathogenic strain. Long read sequencing, that better allows for this delineation, is becoming more accessible for researchers and is essential for future studies in order to delve deeper into understanding subtle shifts in microbial community composition and function (Yi et al. 2016; Mantere et al. 2019). Given the widespread distribution of PGPR research both through time and across the world, a global database of tested bacteria strains to which industry, government, and research agencies contribute could offer a strong path forward in consolidating effective microbes. This would create accountability for manufacturers with transparency of experiments and effects. With a database of inoculants and the parameters around which they have been tested, growers could better select products that are suitable for their needs.
5.3 Improving PGPR delivery in agricultural and horticultural systems
One of the fundamental pillars upon which PGPR efficacy still rests is that of formulation for crop delivery. PGPR formulation was long not considered in a wide range of options as liquid formulations dominated the research and commercial space. Liquid formulations have gained the attention they have due to mass production potential, ease of application, and cost-effective nature (Tittabutr et al. 2007; Herrmann and Lesueur 2013). This remains the most common formulation Page 130 of 167 type in the market to date despite the fact that one of the most successful examples of PGPR is that of Rhizobium which is routinely coated on legume seeds as a freeze-dried powder (Vanelsas and Heijnen 1990; Lee et al. 2016). One of the major limitations of liquid formulations is that following application to soils, microbes disperse quickly through the substrate and are more exposed to biotic and abiotic pressures (Hynes et al. 2001; Tittabutr et al. 2007). The bacteria must communicate with plant roots and establish around the rhizosphere before being outcompeted by native microbes or succumbing to other environmental stresses. Bacterial counts are often seen to decrease with liquid formulations and multiple applications are required to elicit beneficial effects which poses problems for large crops such as sugarcane (Hynes et al. 2001). One avenue that has gained momentum is the use of endophytic bacteria. While root endophytes have been shown to elicit strong benefits in crops and may have enhanced survival potential, this would dramatically reduce the pool of microbes from which PGPR can be drawn (Triplett 1996; Gaiero et al. 2013). Ensuring that we are able to deliver the required amount of bacteria endogenously in host plants offers difficulty similar to that of rhizosphere microbes and their requirement to colonise the root surface. There is evidence of successful seed inoculation in the case of Rhizobia colonisation in legume nodules however research has shown that nodules are colonised with a wide range of microbes (Checcucci et al. 2017). To guarantee plant growth promoting effects, colonisation and competitiveness is crucial. Alternatively, removing the need for plants and microbes to communicate all together may be more effective.
Time-released systems such as encapsulation in alginate beads or pelletised organics offers strong potential for PGPR delivery and has gained more interest in recent years. Alginate beads have been used in both the agriculture and food sciences to store, protect, and deliver bacterial cells (Bashan and Gonzalez 1999; Vassileva et al. 1999; Jain et al. 2010; Minaxi 2011; Sohail et al. 2012). With sophisticated engineering, alginate beads can also provide a time-released system whereby particular pH levels and chemical triggers in the soil lead to the expulsion of stored cells. Pelletisation of bacteria inside organic compounds may offer another delivery method that not only allows bacteria to be delivered to root systems but can also house them within the pellet matrix. A suite of organic compounds can be used for pellet formation ranging from biochar to manures (Krey et al. 2013; Saxena et al. 2013). By utilising particular materials, we could further enhance the efficacy of the selected microbes by directly supplying PGP trait triggers. For example, supplying an efficient P mobilising bacterium pelletised within an appropriate organic P pellet would trigger the bacteria’s P mobilisation strategies, providing a competitive advantage for microbial survival when introduced to the soil. Pelletisation additionally offers protection against native antagonists as well as a nutrient sink to attract roots for effective interactions.
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There is no doubt that PGPR offers great potential as a sustainable method of enhancing plant nutrition, growth and resilience. However, despite the plethora of research supporting the benefits of PGPR when used in cropping systems, there remains a pool of unreliable and ineffective biostimulants in the Australian market. Aside from the lack of regulations encouraging rigorous scientific testing, some crucial gaps remain in PGPR research and understanding. We propose that future PGPR screening should involve a combination of traditional plate assays, metagenomics, and field first strategies (where possible) with a heavier focus on identifying generalists and specialists, effective colonisers, and PGP trait triggers. Delivery methods need to evolve past liquid formulations and pelletisation in alternative fertiliser materials offers a way to (i) recycle organic compounds, (ii) reduce chemical fertiliser application, (iii) easily apply microbes, (iv) protect encapsulated bacteria, (v) simultaneously supply a PGP trait trigger to stimulate bacteria growth, survival, and trait expression, and (vi) attract roots for more effective interactions. Additionally, it is imperative that microbial formulations are designed with specific cropping systems in mind. Microbes should ideally be isolated from the cropping system of interest to guarantee plant-microbe communication is attainable. While conserved microbiomes do exist and there is the potential for PGPR inoculants with a wider host range (Yeoh et al. 2017; Schlatter et al. 2019), developing products that work on 143 different crops is unrealistic and gives growers false hope. With rigorous scientific testing in targeted systems, and an emphasis on microbial survival and effective delivery, the promise of green agriculture in support of the Sustainable Development Goals may be realised for crops globally.
5.4 The Future of PGPR Research and Commercialisation
The future of PGPR research and commercialisation forms a complex and contentious discussion. Research in this field has persisted for so many decades that it is difficult to make recommendations without risking repetition or redundance. Traditional screening assays have been utilised for fundamental research and in-vitro studies for so long due to their high-throughput nature, cost effectiveness, and ease of application. However, while providing a basis for in depth understanding of microbial characteristics, time and time again these methods have led to the development of inoculums that do not hold up in field testing. Conversely field first strategies and metagenomic techniques offer exciting opportunities at massive costs. Cost of funding, land, time, and resources have prevented many scientists from using these techniques. Despite the fact that they can provide information and understanding that is non-existent in plate screening assays, major caveats still exist in using these methods as a sole means of PGPR selection. Page 132 of 167
Throughout this thesis we have highlighted that traditional screening assays do not always allow for the identification of field effective PGPR. We have shown there are several commercially available microbial inoculants that are marketed for systems they do not perform well in. This highlights the lack of regulation that exists in what would otherwise be considered a well-developed country. Governments that require multi-year field testing and proof of efficacy are in the minority and growers are required to have in depth understanding of microbiology to properly evaluate whether a product could work for their cropping system.
We have additionally highlighted that PGPR can fit within specialist and generalist categories and that this is an important consideration when formulating effective biostimulant products. Manufacturers must consider whether they are aiming to develop a product that can be metabolically flexible, adapting and surviving to promote plant growth under stochastic conditions, or whether they are developing targeted specialists for crops with specific problems. Additionally, consideration must be taken when selecting which PGPR traits are important for the crop of interest. Research in the past has focused on targeting particular traits without first investigating the array of traits the crop had initially selected for. Aside from plant growth promoting traits, industry relevant traits must also be taken into consideration. To effectively develop a product for use in agriculture, bacteria isolates must be accessible in a cost-effective manner, they must be easily cultivated for large-scale commercialisation, and have the capacity to survive long term within a formulation that is not their optimal niche.
Future research ultimately requires more holistic approaches than what has been used previously. Research has previously taken one of two paths to either i) use traditional screening assays or ii) use metagenomics or transcriptomics. Taking a holistic approach to use these techniques in conjunction with one another may offer a deeper understanding while still targeting traits that are accessible for in-vitro studies and large-scale commercialisation. There must also be more focus on generalists and specialists in future studies to investigate the importance of these categories in developing effective PGPR. Is it feasible to develop enough specialist products that target all of the cropping systems in the world? Do generalists actually survive in the soil longer under changing environmental conditions? Are single isolate formulations, which have previously dominated the biostimulant market in regulated countries, the way forward or is a multi-species mixture of generalists and specialists the best option for community survival and effective delivery?
Page 133 of 167 While there has been a lot of advancement in microbial delivery with biochar and pelletisation products reaching the market, more research is needed to effectively deliver microbes to crops in a way that matches their performance in the field. Crops that have delayed nutrient uptake such as sugarcane may require time controlled PGPR release. Crops that are not capable of communicating with microbes consistently may require pelletised matrices that protect bacteria cells and remove the need for plant-microbe communication. We need to have a better understanding of these questions and a clear view of our aims in PGPR formulation in order to bridge the gap from simple systems to complex microbiomes.
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Page 165 of 167 Appendix A Media Recipes
Table A-1 PSM and NBRIP media protocol. Chemicals are added in g per L and the pH adjustment required prior to autoclaving is shown. Chemical PSM (g/L) NBRIP (g/L) D-glucose 10 10 Na-phytate 4 - CaPO4 - 5 CaCl2.2H2O 2 - NH4NO3 5 - KCl 0.5 0.2 MgSO4.H2O 0.5 0.25 FeSO4.7H2O 0.1 - MnSO4.H2O 0.1 - MgCl2.6H2O - 5 (NH4)2SO4 - 0.1 Agar 15 15 pH 7 7
Table A-2 Chemical list for JNFb media. Chemicals must be added in the given sequence to avoid precipitation of iron or of other salts (Baldani et al. 2014). Chemical g L-1 Malic Acid 5 K2HPO4 0.6 KH2PO4 1.8 MgSO4.7H2O 0.2 NaCl 0.1 CaCl2.2H2O 0.02 2 ml of Micronutrient Solution (g L-1) CuSO4.5H2O 0.04 ZnSO4.7H2O 0.12 H3BO3 1.4 Na2MoO4.2H2O 1 MnSO4.H2O 1.175
Bromothymol blue 2 ml (5 g L-1 in 0.2 N KOH) FeEDTA 4 ml (Solution 16.4 g L-1)
Vitamin solution 1 ml (biotin, 10 mg; pyridoxal-HCl, 20 mg. Dissolve in hot water bath. Complete to 100 ml by adding distilled water)
KOH 4.5 pH (adjust with KOH) 5.8 Agar 1.8
Page 166 of 167 Table A-3 Chemical list for LGI media. Chemicals must be added in the given sequence to avoid precipitation of iron or of other salts (Baldani et al. 2014). Chemical g L-1 Malic Acid 5 K2HPO4 0.2 KH2PO4 0.6 MgSO4.7H2O 0.2 NaCl 0.02 CaCl2.2H2O 0.002 2 ml of Micronutrient Solution (g L-1) CuSO4.5H2O 0.04 ZnSO4.7H2O 0.12 H3BO3 1.4 Na2MoO4.2H2O 1 MnSO4.H2O 1.175
Bromothymol blue 5 ml (5 g L-1 in 0.2 N KOH) FeEDTA 4 ml (Solution 16.4 g L-1)
Vitamin solution 1 ml (biotin, 10 mg; pyridoxal-HCl, 20 mg. Dissolve in hot water bath. Complete to 100 ml by adding distilled water)
pH (adjust with KOH) 6.0 – 6.2 Agar 1.6 to 1.8
Figure A-1 Pellicle formation on N-free semi solid media after 10 days of incubation. Pellicle formation is indicated by the black arrows. Here, Herbaspirillum seropedicae was inoculated in JNFb (A) and Azospirillum amazonense was inoculated in LGI media (B) for pellicle observation (Baldani et al. 2014)
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