Sorghum rhizosphere bacteriome studies to pinpoint, isolate and assess plant beneficial bacteria

Sorghum rhizosphere bacteriome studies to pinpoint, isolate and assess plant beneficial bacteria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sorghum rhizosphere bacteriome studies to pinpoint, isolate and assess plant beneficial bacteria Chandan Kumar, Alfonso Esposito, Iris Bertani, Samson Musonerimana, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4643586/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In the intricate relationship between plants and microorganisms, plant growth-promoting bacteria (PGPB) play a vital role in the rhizosphere. This study focuses on designing synthetic bacterial consortia using key bacterial strains mapped and isolated from the sorghum rhizosphere microbiome. Results A large set of samples of the rhizosphere bacteriome of Sorghum bicolor was analyzed across various genotypes and geographical locations. We assessed the taxonomic composition and structure of the sorghum root-associated bacterial community using 16S rRNA gene amplicon profiling, identifying key taxa and core-bacterial components. A set of 321 bacterial strains was then isolated, and three multi-strain consortia were designed by combining culturable and unculturable microbiome-derived information. Subsequently, co-existence and plant-growth promoting ability of three consortia were tested both in vitro and in planta . In growth-chamber and in-field experiments demonstrated that bacterial Consortia 3 promoted plant growth in growth-chamber conditions while Consortia 1 and 2 performed better in field-plot experiments. Despite these differences, 16S rRNA gene profiling confirmed the stable colonization of the inoculated consortia in the sorghum rhizosphere without significant alterations to the overall bacterial community. Conclusions This study aims at translating microbiome knowledge into applications by designing and testing microbiome-based multi-strain bacterial consortia in promoting sorghum growth. Sorghum bicolor Rhizosphere Keystone bacteria Core microbiome bacteria 16S rRNA gene Synthetic consortia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Microbes are important players in every ecological niche and recently they are being widely studied for their ability to promote plant growth and health. The rhizosphere is the microbial habitat of the soil around the root [ 1 ] and plant roots recruit their rhizomicrobiome from the soil (highest microbial biodiversity on earth). The complex dynamics of microbial interactions and signaling taking place within the rhizosphere remain still largely unknown [ 2 – 4 ]. Multiple factors influence plant microbiome composition, diversity and abundance such as the soil physiochemical properties [ 5 , 6 ], plant compartment [ 7 ], host genome [ 8 ], plant immune system [ 9 ], plant development stage and season [ 10 ] and agricultural practice. Plants possess a “common core” rhizosphere microbiome, suggesting that the plant plays a role in customizing and maintaining the composition of its bacterial community [ 11 , 12 ]. The “core microbiome” is based on the prevalence and persistence of a particular set of bacteria across diverse samples and conditions/treatments, indicating a stable association with the host [ 13 ]. On the other hand, keystone taxa in microbial ecology are also gaining importance; this refers to microorganisms that influence the structure and function of a microbial community, despite their abundance. These taxa possibly play an important role in maintaining the stability and diversity of the ecosystem occupying significant positions in microbial networks having several interactions with other members of the community. Alterations of their abundance or even their absence can have effects on the overall community structure and function [ 14 – 16 ]. Discovering the rhizosphere core microbiome and keystone taxa holds the potential to create microbiome-based solutions that tackle agronomic challenges and promote sustainable agriculture [ 17 , 18 ]. The use of plant growth-promoting bacteria (PGPB) in agriculture is gaining importance in recent years as a way to reduce the use of chemical fertilizers thus protecting the soil structure, preventing environmental damage and promoting a more sustainable way to enhance crop productivity [ 19 , 20 ]. However, the use of microbial inoculants in agriculture face challenges in competing with native microbes and adapting to environmental changing conditions [ 21 , 22 ]. The use of microbial consortia, comprising of multiple strains might therefore be advantageous compared to the use of single strains to effectively colonize plant-associated environments and enhance seed development and plant growth [ 21 , 23 ]. In particular, the diversity within these consortia allows for a broader range of metabolic capabilities. Each bacterial strain may specialize in distinct nutrient uptake pathways or metabolic processes, thus collectively enhancing the consortium's ability to utilize various resources available in the plant environment. Moreover, synergistic interactions may involve cross-feeding, where one strain metabolizes a substrate into a form that can be utilized by another strain, promoting mutual growth and survival. Furthermore, the presence of multiple strains within bacterial consortia can enhance their resilience to environmental stresses. By harnessing a broader array of genetic diversity, consortia are better equipped to withstand fluctuations in environmental conditions, such as changes in temperature, moisture levels, or nutrient availability. This resilience is crucial for the long-term establishment and persistence of bacterial communities in dynamic plant-associated environments. Sorghum ( Sorghum bicolor) serves as a staple food in the diets of millions of people in developing countries, providing essential energy, protein, vitamins, and minerals. This is particularly evident in sub-Saharan Africa, as well as certain areas of Asia and Latin America. It is the world's fifth-most important cereal crop after rice, wheat, maize, and barley [ 24 , 25 ]. Unlike the other major crops, sorghum is adapted to a wide range of climates being a resilient crop in hot and arid environments [ 26 ]. Several recent studies have been performed on the rhizosphere microbiome of sorghum evidencing that it is influenced by a set of factors including plant genotype, pathogens, plant developmental stage, soil properties, salt and drought stress as well as agricultural soil practices [ 27 – 33 ]. The aim of this study was firstly to pinpoint via microbiome approach a set of key beneficial rhizosphere bacterial strains (core and keystone bacterial strains) associated with the sorghum rhizosphere, regardless of host-plant genotype, location, climate, and soil type and secondly, to isolate them and create tailored bacterial consortia to be used to promote sorghum growth. To fulfill this purpose, sorghum plants grown in Ethiopia, Burundi, and Italy were sampled and subjected to rhizosphere 16S rRNA gene sequencing analysis. In addition to these samples, sorghum microbiome samples collected in the USA were also reanalyzed and integrated into a single pipeline, aiming to enhance the significance of the obtained results. A list of the most stably associated microbiome members has been drawn and a set of samples has also been used to isolate, classify and establish a working bacterial culture collection of sorghum rhizosphere-associated strains. A set of key bacterial candidates have then been investigated for their PGP activities in vitro and in planta . Based on their co-growth capabilities and the complementarity of their PGP abilities, we designed three consortia that were subsequently tested in planta both in plant growth-chamber and in open field conditions. This study leverages data obtained from microbiomes to design sorghum-beneficial multi-strain consortia with higher performance and greater efficiency for sorghum growth and productivity. Methods Rhizosphere sample collection from Italy, Ethiopia and Burundi locations The rhizosphere samples were collected from sorghum grown in different geographical locations: Italy (coordinate: 45.981558 N, 13.198167 E), Ethiopia (coordinate: 9°03'49.8"N 40°52'29.2"E, 9°05'30.9"N 37°02'45.1"E, 8°40'13.3"N 39°29'14.8"E and 7°50'11.6"N 38°41'41.2"E) and Burundi (coordinate:4°00'03.3"S 30°04'26.2"E). In Italy 46 sorghum genotypes (Table S1 ) were selected as a subset of the broad genetic diversity sorghum association panel (SAP) United States [ 27 , 34 , 35 ]. Seeds were surface sterilized with 10% bleach for 10 min followed by five washes with sterilized water and then pre-germinated in the plant growth room to ensure uniformity in growth. The pre-germinated seeds were then placed in a homogenized soil mixture and grown in controlled conditions (25 o C, 16 h photoperiod) for two weeks in a growth chamber. Watering was performed every 24h for the first three days and every three days starting from the fourth day onward. After 14 days, uniformly grown seedlings were transplanted to an experimental field in three replicates at ERSA (Agenzia Regionale per lo Sviluppo Rurale) center at Pozzuolo del Friuli, Italy. Three plants of each of the 46-sorghum genotype were transplanted in each field block, with a separation of 20 cm between each plant and 40 cm between each block. After nine weeks, root samples were collected from the 414 plants (three plants from each block of the same genotype). Additionally, nine soil samples (three from each block) were collected using a shovel, with each sample individually dipped 20 cm into a 50 ml Falcon tube containing removal buffer (6.75g KH2PO4, 8.75g of K2HPO4, and 1 ml of Triton X-100, in 1L). The rhizospheres were separated from the root as previously described [ 36 ]. The recovered rhizospheres of the three plants with the same genotype and collected from the same field block were pooled together as a technical replicate and stored at -80 o C in 20% glycerol for further DNA isolation and bacterial isolation. The same procedure was followed for collecting rhizosphere samples from sorghum plants in Burundi and Ethiopia grown in agricultural fields as part of regular farming practices. A total of 16 sorghum rhizosphere DNA samples were collected from Burundi and 30 samples were collected from Ethiopia at the age of nine weeks post transplantation. In Burundi, rhizosphere samples were collected from four different sorghum varieties: IESH22005, IESH29068, ICSR93034, and Gambela. In Ethiopia, rhizosphere samples were collected from five different sorghum varieties: Gambella 1107, Chiro, Meko-1, Dinkinash and Merera. In summary, rhizosphere samples for DNA isolation were uniformly collected from all geographical locations, at the same plant age (pre-maturation stage), and following identical procedures. DNA extraction, library preparation and sequencing DNA was extracted from the sorghum rhizosphere compartment using DNeasy® PowerSoil® Kit from Qiagen (Cat. No. 12888-100). The DNA quality and quantity was measured by using the Nanodrop device (Thermo Scientific, Wilmington, DE, USA). The 16S rRNA gene amplicon libraries were prepared for each DNA sample following the manufacturer’s protocol (Illumina Inc., San Diego, CA, USA). Amplification of V3- V4 hypervariabe region of the 16S rRNA gene was performed by using long PCR primer [ 37 ] incorporating the Illumina adaptor sequences (Forward Primer 5’- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG and Reverse Primer 5’- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTAT CTAATCC). Following the first amplification, a cleaning step was performed using the AMPure XP bead clean-up (A63880l; Beckman Coulter Inc., Brea, CA, USA). Next steps for the library construction have been performed with a second PCR to attach dual index and Illumina sequencing adapters using the Nextera XT Index Kit; followed by a final AMPure XP bead clean-up. Amplicons size, integrity, and purity were checked using the Bioanalyzer equipment (Agilent Inc., Santa Clara, CA, USA) and the library concentration was measured by fluorimetric quantification using Qubit 2 (Invitrogen Inc., Carlsbad, CA, USA). Libraries were adjusted to a final concentration of 4nM in 5ul of 10mM Tris pH 8.5 and submitted for sequencing using 2x250 bp paired end reads on Illumina Miseq. Amplicon bioinformatics data analysis Comprehensive analysis of a large dataset was conducted using the fastq files generated from samples collected in Italy (141), Ethiopia (30), and Burundi (16), as mentioned above. Additionally, fastq files from 600 16S rRNA gene libraries were obtained from publicly available sources, specifically focusing on rhizosphere samples cultivated in the United States. These sequences were sourced from [ 27 ], and were downloaded from the NCBI BioProject ID PRJNA612320. All the 787 fastq files were imported into qiime2 [ 38 ] and quality filtering, denoising and amplicon sequence variants detection have been done using DADA2 [ 39 ] and taxonomic assignment to each Amplicon Sequence Variants ASVs was carried out based on Silva database (release 138) [ 40 ]. Diversity parameters were calculated at a rarefaction depth of 1385 reads. ASVs that have been classified as a chloroplast and mitochondria were filtered out. The obtained dataset was imported into R using qiime2R package [ 41 ], and the subsequent analysis was done using phyloseq [ 42 ], vegan [ 43 ] and microbiome R package [ 44 ]. Alpha and Beta diversity were measured using Shannon and Unifrac unweighted distance respectively. The prevalence of each taxon was determined at ASVs and Genus level. We considered as MPT (most prevalent taxa) or “core microbiome” the genera occurring (i) in more than 80% of the total samples with a relative abundance > 0.5% per sample and also (ii) present in > 70% of samples from each location, regardless to their relative abundance. [ 13 , 45 ]. To infer co-occurrence using the fastspar [ 46 ], we exported the table at the genus level, filtered to retain the genera occurring in at least 5 samples, and computed the SparCC value [ 47 ] to estimate the co-occurrence of bacterial genera among the samples. Correlations with a p-value > 0.05 and those in the range − 0.3–0.3 were eliminated. A bottleneck analysis was performed to identify pivotal nodes within the microbial community structure. This analysis entails constructing a network in which nodes represent various taxa or microbial entities, and edges denote interactions or relationships between them, such as co-occurrence patterns [ 48 ]. Cytoscape was used to compute the statistics and depict the generated network [ 49 ]. According to [ 14 , 50 , 51 ], keystone bacterial taxa were considered the nodes presenting the highest betweenness centrality and node degree. The network analysis was performed either using the whole dataset or restricted to the Italian samples. For monitoring the presence and co-existence of the 8 strains used in the designed consortia, gene community sequencing was employed both in the co-existence/compatibility experiment ( in vitro ) and in planta tests (plant growth room and open field). All fastq files were imported into qiime2 and processed following the same pipeline as describe above. For the samples derived from the compatibility experiment in plant growth room the diversity parameters were calculated at a rarefaction depth of 4100 reads, and the number of reads per sample ranged from 14387 to 72399 with an average of 44035.37 reads. For the samples derived from the sorghum rhizosphere persistence experiment in field, diversity parameters were calculated at a rarefaction depth of 900 reads, and the number of reads per sample ranged from 16 to 29136 with an average of 12649,08 reads. For the evaluation of the consortia compatibility and ability of the 8 selected isolates to colonize the sorghum rhizosphere a customized dataset of 16S rRNA gene sequences from the 8 isolates used in this study was created and used for the taxonomic assignment. For clustering and comparing the ASVs based on sequence similarity, the cd-hit program was used, setting the parameter of sequence similarity equal to 98% for two sequences to be considered part of the same cluster. All the ASVs not matching with any of the 16S rRNA gene sequences of the customised dataset were removed from the analysis. The obtained dataset was imported in R using the package qiime2R [ 38 ] and the analysis and graphs were generated using phyloseq and vegan packages in R [ 42 , 43 ]. Isolation of culturable collection of sorghum rhizosphere-associated bacteria A large set (321 strains) of culturable bacteria associated with the sorghum root microbiome have been isolated using aliquots of the rhizospheric fractions from Italy samples which were stored at -80 o C with 20% glycerol. Different dilutions of the samples were plated in various solid media; Tryptic Soy Agar TSA, (TSA-supplemented with NaCl, CuSo4 and and pH 4.5), NBRIP, KBA, YEM Congo red, Jonson medium (JM). All the media compositions used are presented in Table S2. The plates were incubated at 30 o C for 2–5 days and pure single colonies showing distinct colony morphology were picked up independently and streaked on 1/5 TSA plates to ensure the purity of the colonies. The pure colony culture was then stored at -80 o C in 1/5 Tryptic Soy Broth (TSB) and 20% glycerol. Colony PCR was performed after boiling (10’ at 98 o C) a colony suspension in 50 ul of sterile H2O to amplify the complete 16S rRNA gene by using primer fD1Funi 16S (5’- AGAGTTTGATCCTGGCTCAG-3’) and rP2Runi 16S (5’-ACGGCTACCTTGTTAGGACTT-3’). PCR products were purified by using PCR Clean-Up purification kit (Euroclone S.p.A). The sequencing was performed with the primer 518F (5’- CCAGCAGCCGCGGTAATACG-3’) and 907R (5’- CCGTCAATTCMTTTRAGTTT-3’) and the stains were identified by BLAST analysis at NCBI ( https://www.ncbi.nlm.nih.gov ). Co-growth tests of sorghum rhizospheric most prevalent and keystone strains and strategy for designing multi-strain consortia From the bacterial strains identified as sorghum-MPT and keystone using the 16S rRNA gene profiling approach, we carefully chose those that could be cultured/grown under laboratory conditions and exhibited the highest 16S rRNA gene sequence homology (99%) with the ASVs identified as sorghum-MPT and keystone. A set of eight bacterial strains was carefully selected ( Pantoea sp., Enterobacter sp., Pseudomonas sp., Bacillus sp., Rhizobium sp., Streptomyces sp., Paenibacillus sp. and Paraburkholderia sp.) and based on the genomic characterization and in vitro PGP features, three consortia were designed as follows: Consortium 1( Pantoea dispersa SRG11 , Enterobacter asburiae SRG25 and Pseudomonas chlororaphis subsp. aurantiaca SRG 32 ), Consortium 2 ( Priestia megaterium SRG70, Rhizobium wenxiniae SRG248 and Streptomyces sp. SRG181 ) and Consortium 3 ( Paenibacillus illinoisensis SRG287 and Paraburkholderia sp. SRG18 ). In order to perform the first set of compatibility growth experiments, the eight bacterial sorghum MPT and keystone rhizospheric strains were independently grown in liquid media (TSB), washed twice and resuspended in phosphate-buffered-saline (PBS) solution. An equal amount of each strain (1.00E + 08 CFU/ml) was then pooled in a PBS solution in order to form the three different consortia. This mixed solution containing the strains in a comparable amount was used as inoculum for in vitro and in planta compatibility tests as described here below. In all these conditions, each bacterial-strain consortium was monitored and assayed using gene amplicon sequencing and analysis. Co-growth on agar plate media an aliquot of 100µl of each strain from the mixed inoculum in PBS, as described above, was spotted on TSA plates and was incubated at 30 o C. Samples were collected after 96 hours, DNA was purified and used for 16S rRNA gene library preparation and sequencing. As a control, all eight strains were mixed in equal amount and DNA was purified immediately (0 hour). In planta co-growth sorghum seeds were surface sterilized, as described above and then submerged in each consortium-inoculum for 30 minutes. The seeds were then transferred to a 50ml Falcon tube containing semi-solid agar (0.4% agar) with ½ Hoagland solution. The plants were grown in a growth chamber under controlled conditions, including a photoperiod of 16 hours of light and 8 hours of darkness, as well as a humidity level of 80%. After nine days post-inoculation, the rhizosphere was collected and DNA was extracted for 16S amplicon microbiome determination. In growth chamber and in field plant growth promotion experiments by applying multi-strain bacterial consortia For the in planta experiments performed in the growth-chamber, surface-sterilized sorghum seeds were carefully submerged in each consortium-inoculum for 30 minutes. As control, sorghum seeds were submerged in 1xPBS without the bacterial suspensions. After inoculation, both the treated and no-treated seeds as control were transferred to the soil; seeds were inserted 1 cm deep and covered with soil. In brief, the soil was prepared using a mixture of garden soil, perlite/vermiculite, and sand in a specified ratio (1:1:5). After seven days post-germination and root development, a second furrow inoculation was performed using an aliquot of 500 µl of the three consortia and 1xPBS for control was applied near the roots. After the second inoculation, five plants were collected for rhizosphere DNA extractions and 16S rRNA gene amplicon library preparation and ten plants were collected for plant agronomic parameters detection. The experiments involved two time points, TP1: 25 days post-seed inoculation (includes 10 days after furrow inoculation), TP2: 60 days post-seed inoculation (includes 50 days after furrow inoculation). For the in field experiments, the same seed-inoculum procedure was followed, as described above. The soil was prepared in three blocks, each receiving a different percentage of chemical fertilizer (Nitrogen, P2O5, K2O followed by N (Urea) at 4/5 leaf stage plant growth): Block 1: 100% chemical fertilizer, Block 2: 50% chemical fertilizer, Block 3: 0% chemical fertilizer (Figure S1 ). Both the treated and control seeds were transferred to the soil in each block. An aliquot of 2.5 ml of each consortium and 1xPBS for control was applied near the roots using furrow inoculation. Five plants were collected for rhizosphere at TP1: 40 days post-seed inoculation (includes 20 days after furrow inoculation), TP2: 80 days post-seed inoculation (includes 60 days after furrow inoculation) and ten mature sorghum plants were collected for plant agronomic parameters determination (shoot dry biomass, panicle dry weight, seeds dry weight and seeds count) analysis at days 115 post-inoculation (includes 95 days after furrow inoculation). At each time point the total DNA was extracted, 16S rRNA gene amplicon library prepared and microbiome monitored. Whole Genome sequencing of the eight bacterial strains isolates forming the three multi-strain consortia The eight bacterial strains used in the consortia studied here were whole genome sequenced. Each strain was cultured overnight in TS broth at 30°C. Following cultivation, the cultures were centrifuged at 6000 RPM for 5 minutes, the cells were collected and DNA was subsequently extracted using the Norgen Biotek Corp. Bacterial Genomic DNA Isolation Kit. The complete genomes were sequenced with the Illumina NovaSeq 6000 platform using 150bp paired-end reads and following the tagmentation Illumina Nextera XT protocol (Illumina Inc., San Diego, CA, USA). The assembly was performed with Unicycler v. 0.5.0 and the assembly statistics were recorded with QUAST v. 5.2.0 [ 52 ]. The assembled genomes were uploaded in the Integrated Microbial Genomes and Metagenomes (IMG/M) database and automatically annotated, using annotation pipeline IMG Annotation Pipeline v.4.16.6 [ 53 ]. Functional annotation and phylogenetic characterization were performed by DFAST [ 54 ] ran from https://dfast.ddbj.nig.ac.jp/ . Results Experimental rationale for the design of sorghum beneficial bacterial consortia In order to develop a set of potential multi-strain bacterial consortia that promotes the growth and health of sorghum ( Sorghum bicolor ), an investigation into the rhizosphere bacterial community was performed to identify key and core bacterial members. We hypothesized that these might represent bacterial species with significant roles within the sorghum rhizosphere community. A total of 787 rhizosphere samples were analyzed from sorghum plants at the same plant stage, from different genotypes and very different geographical locations. This large sampling size provides a dataset across different soil environments, climate conditions and agricultural management practices. Samples from Ethiopia, Burundi and Italy were obtained through the collection of sorghum rhizosphere and subsequent DNA purification conducted in this study, while another portion of samples from the USA was retrieved from a publicly available dataset as explained in the Materials and Methods section. 16S rRNA gene amplicon analysis assessed the structure of the sorghum root-associated community and was used to identify likely keystone and core bacterial components. A culturable bacterial collection was then isolated consisting of 321 strains and a set was selected based on the bioinformatically identified most prevalent taxa (MPT) and keystones members and used for the design of bacterial consortia. The multi-strain consortia were then tested for co-existence/compatibility in vitro and in planta set-ups in order to determine whether the bacterial strains selected can colonize and persist together. Finally, three bacterial consortia were designed combining culturable and microbiome results and tested for plant growth promotion in plant growth room and in field experiments. The rationale of the experimental design is presented in Fig. 1 . Sorghum rhizosphere-associated bacterial community diversity and structure across different geographical locations. In order to assess the composition of the sorghum rhizosphere across different geographical locations, 16S amplicon community analysis was conducted on sorghum plants cultivated in Italy, Ethiopia, Burundi, and USA. After quality check and removal of reads classified as chloroplasts and mitochondria, the number of reads ranged from 1531 to 2,21,339 with an average of 85327.8 reads per Amplicon Sequence Variant (ASV). The alpha diversity, assessed by the Shannon index, ranged from 1.32289 to 6.19555 with an average of 4.72737 and showed a few significant variations among different geographical locations (Fig. 2 A). Specifically, sorghum plants cultivated in Burundi harbored the highest biodiverse bacterial community, whereas those grown in Italy generally presented a less diverse community. The analysis of the beta diversity showed that sorghum plants grown in the different geographical areas hosted a different bacterial community, as they form a distinct cluster on the first axes of the NMDS plot based on their growing locations (Fig. 2 B). Notably, samples from Italy and USA, clustered separately, while samples from Burundi and Ethiopia formed two partially overlapping groups (Fig. 2 B), suggesting some similarities in the bacterial community structure, likely influenced by more comparable environmental conditions. To summarize, significative differences in both alpha and beta parameters were noted according to geographical location, emphasizing that the primary factor influencing the structural composition of the bacterial community is the geographical growing location. Most prevalent taxa (MPT) or “core” bacterial members and keystone taxa are persistent across different geographical locations. In order to map a “common-core” group of bacterial members most adapted to the sorghum rhizosphere, we quantified the sorghum most prevalent taxa (MPT) or “core” microbiome. At genus level it was empirically decided to define as MPT the genera that (i) were detected in at least the 80% of the total number of samples with > 0.5% relative abundance, and (ii) present in > 70% of the samples from each location, regardless to their relative abundance. A shortlist of fourteen candidate MPT/core included genera such as Bacillus, Blastococcus, Candidatus_Koribacter, Devosia, Haliangium, Massilia, Niastella, Paenibacillus, Paraburkholderia, Ramlibacter, Rhizobium group, Solirubrobacter, Sphingomonas , and Streptomyces , as presented in Fig. 3 A. With the aim of identifying interactions within all members of the sorghum microbiome, co-occurrence networks were generated for each geographical location, considering only genera occurring in at least 5 samples, and excluding either the correlations with SparCC values in the range of − 0.3–0.3 or non-significant values. We focused on the co-occurrence network resulting from the Italian samples, as our goal was to create multi-strain beneficial consortia using a bacterial culture collection generated from the rhizosphere samples collected in Italy (see below). A bottleneck analysis was performed, and it showed 50 taxa as nodes in the network and these could give a disproportionately significant impact on the community structure [ 48 ]. Among the 50 nodes in the network, three genera— Pantoea , Enterobacter , and Pseudomonas , were identified as highly interconnected with other nodes in the resident community (Fig. 3 B). To summarize, the 16S rRNA gene community analysis provided us with a set of persistent and interconnecting bacterial candidates; these were considered worthy candidates to be tested as bioinocula for sorghum growth and health. Isolation and characterization of a set of culturable bacterial strains from Sorghum bicolor rhizosphere microbiome for designing beneficial multi-strain consortia. A collection of bacterial strains was isolated from the sorghum rhizosphere in order to test whether the taxa identified bioinformatically as MPT/core and keystones (see above) are impactful for the health and performance of the sorghum plants (see above). A large set of 321 culturable bacterial strains was isolated from sorghum rhizosphere as described in the Materials and Methods section. The collection includes strains of Bacillus (71 isolates), Enterobacter (71), Pseudomonas (26), Priestia (23), Pantoea (19), Peribacillus (8), Paenibacillus (7) and Citrobacter (5); the complete list is available in Table S3. Based on the available bacterial culturable collection and the preceding bacteriome analysis, we selected eight bacterial strains for the formulation of three consortia; these included three strains possibly defined as keystone taxa and five strains from the core-microbiome having the highest 16S sequence homology with the ASVs detected in the microbiome analysis. Specifically, Consortium 1 was designed based on the results of the co-occurrence network and consisted of three strains belonging to the Pantoea, Enterobacter , and Pseudomonas genera (see above and Fig. 3 B). On the other hand, among the 14 MPT/core taxa identified, we were able to isolate the following five taxa; Priestia, Rhizobium group, Streptomyces, Paenibacillus and Paraburkholderia ; Consortia 2 and 3 were designed based on these results (Fig. 3 A). Consortium 2 consisted of three strains belonging to Streptomyces, Bacillus , and Rhizobium group selected based on their relative abundance > 0.5% (sum of all samples abundance from each location) and present in > 80% of samples. Consortium 3 consisted of Paenibacillus and Paraburkholderia selected based on their presence > 70% of samples without taking in consideration their relative abundance. In order to obtain genetic information on the eight selected strains forming the three consortia, their whole genomes were sequenced. The genomic features are presented in Table S4. Additionally, a set of in vitro plant growth-promoting (PGP) features of all eight strains were tested, including protease and lipase production, phosphate solubilization ability, EPS (exopolysaccharide) production, diazotroph growth ability, swimming and swarming motility, IAA (indole-3-acetic acid) production and biofilm formation; results are presented in Fig. 4 A. The complementarity of the PGP features was also used as a criterion to design the three consortia, e.g. in Consortium 1 Pseudomonas chlororaphis subsp. aurantiaca SRG 32 was the only one being able to produce lipase and protease enzymes, while Enterobacter asburiae SRG25 was the only one able to synthesize IAA in vitro , and Pantoea dispersa SRG11 and Enterobacter asburiae SRG25 were solely able to solubilize phosphate and displayed EPS production. By designing bacterial consortia with a wider spectrum of metabolic capabilities most likely results in a more stable multi-strain consortium. Compatibility growth experiments revealed that the bacterial strains mixed in each consortium are co-colonizing It was of interest to determine whether the strains of the three consortia were able to co-exist both in vitro and in planta set-ups. As detailed in the Materials and Method section, we performed compatibility tests both in vitro (on solid media) and in planta (seed inoculation) and we targeted the persistence and presence of each strain by 16S rRNA gene amplicon sequencing. Figure 4 B depicts which bacterial strains were detected in co-spotting on solid media after 94 hrs of incubation. Importantly, at the inoculation starting point (i.e. 0 hrs), all bacterial strains were detected in equal abundance suggesting that the starting inoculum was well mixed and the technique used allows to unequivocally distinguish each strain (Fig. 4 B). In vitro , all the strains were detected, according to each consortium composition; however, they were present in different abundances, i.e. in Consortium 1 Enterobacter asburiae SRG25 was predominant, while Pantoea dispersa SRG11 was detected in low relative abundance, probably due to the limitation of nutrients or growth limitation caused by the predominance of Enterobacter asburiae SRG25 cells. Similarly, Streptomyces sp. SRG181 in Consortium 2 was detected in lower abundance compared to Priestia megaterium SRG70 and Rhizobium wenxiniae SRG248 . On the other hand, in Consortium 3 both Paenibacillus illinoisensis SRG287 and Paraburkholderia sp. SRG18 were present in equal abundance. Figure 4 C depicts which bacterial strains were detected in planta co-growth; for this experiment, as described in the Materials and Methods section, surface-sterilized sorghum seeds were inoculated with each of the three consortia. Rhizosphere samples were then collected, DNA extracted and 16S rRNA gene amplicon sequencing was performed. The results indicated a richer and more diverse community persisted in planta attributed to the background of bacterial strains originating from the seeds. However, all the bacterial strains inoculated were detected in higher abundance compared to the "background strains”, suggesting that the three designed consortia were capable of persisting and also performing well in the sorghum rhizosphere. Specifically, Consortium 1 exhibited a well-balanced distribution in the relative abundance of each of its three components ( Pantoea dispersa SRG11 , Enterobacter asburiae SRG25 and Pseudomonas chlororaphis subsp. aurantiaca SRG32 ). In planta experiments of the three consortia in plant growth room and in field plots It was now of interest to perform in planta assays in order to determine the potential plant growth promoting properties of the three microbiome-based consortia and further evaluate their co-colonization and persistence in a more pertinent environment. The three consortia have been studied both in plant growth room and field experiments by 16S rRNA gene community sequencing analysis and via several agronomic parameters. Plant growth-chamber experiments Sorghum seeds were inoculated independently with the three different consortia and shoot and root height as well dry biomass were scored after 25 (time-point 1) and 60 (time-point 2) days (Fig. 5 A-D). The inoculated consortia displayed a positive effect on plant growth, compared to the uninoculated control (Figure S2). In particular, Consortium 3 displayed a significant increase nearly 40% in both plant shoot and root length (Fig. 5 A) and almost a 50% increase in plant shoot and root dry biomass (Fig. 5 B) at 25 days (time-point 1) compared to the uninoculated control. Consortium 3 had also an increase in shoot and root length as well as biomass at 60 days (Fig. 5 C-D), compared to the uninoculated plants. It was then of interest to assess the persistence and the efficiency in root colonization of the three consortia. Figure 5 E-F depicts that the three consortia do not cause any significant shifts or changes in the biodiversity of the rhizobacteriome compared to the uninoculated plants. The alpha diversity ranged 1.32289 to 6.19555 with an average of 5.72737 (Fig. 5 E). We observed a significative clustering based on the two time-points, which is a physiological aspect due to the stabilization of the bacterial community as the plant grows (Fig. 5 F). On the other hand, targeting specifically the strains used in the three consortia, we were able to detect all the bacterial strains inoculated, with some exceptions (Fig. 5 G). In particular, in Consortium 1, all the three strains were detected; however, as observed in the compatibility test (see above and Fig. 4 B-C), Pantoea dispersa SRG11 was present in a lower relative abundance compared to Enterobacter asburiae SRG25 and Pseudomonas chlororaphis subsp. aurantiaca SRG 32 . This result can be due to a technical bias or a metabolic/inter-strains interaction mechanism. Consortia 2 exhibited similar abundance of Priestia megaterium SRG70 and Rhizobium wenxiniae SRG248 at both time points, while Streptomyces sp. SRG181 showed an increase in abundance at 60 days. Consortia 3 displayed consistent detection of Paenibacillus illinoisensis SRG287 and Paraburkholderia sp. SRG18 at both time-points. All these results suggest that the Consortium 3 was the best performer as it resulted in an increase in all the agronomic parameter tested. Moreover, the 16S rRNA gene profiling approach demonstrated that all the strains were able to co-persist. In field experiments In order to further evaluate the impact of the three consortia in open field, a field trial experiment was designed as follows: the land was partitioned into three blocks under varying conditions, as detailed in Materials and Methods section and Figure S1 , supplemented with 100%, 50% or 0% chemical fertilizer. Sorghum seeds were inoculated independently with the three consortia and these were then also added via a second furrow inoculation which was conducted in close proximity to the roots. All the 16S rRNA gene community studies were performed at two time-points while all the agronomic parameters (shoot dry biomass, panicle dry weight, seeds dry weight and seeds counts) were measured at 115 days, representing the maturation stage of the sorghum plants. In contrast with the results obtained in the plant growth room experiments, Consortium 3 showed no differences in all the parameters and conditions tested compared to the uninoculated plants (Fig. 6 A, B and C). On the other hand, Consortium 2 displayed an increase in shoot biomass, panicle and seeds dry weight compared to the uninoculated plants and in all the different conditions of chemical fertilization (Fig. 6 A, B and C). Consortium 1performed better than the control in shoot dry biomass and panicle dry weight under 50% and no fertilizer application (Fig. 6 A and B). Grain quality, as indicated by seed count, remained uniform across treatments and different fertilizer application (Fig. 6 D). The rhizosphere community analysis revealed that the consortia supplementation had minimal impact on alpha diversity in the different conditions (Fig. 6 E), with a slight and not significant decrease in biodiversity at 80 days (time-point 2). The alpha diversity ranged 1.66339 to 6.08117 with an average of 4.51931. Beta diversity parameters showed no clustering based on the agrochemical treatments or consortia application, while confirmed a distinct cluster formation based on the time point (Fig. 6 F). Targeting specifically the strains used in the three consortia, we observed differences in the detection of the bacterial strains based on the chemical treatments and time-points (Fig. 6 G). Specifically, the 3-strain members of Consortium 1 were always detected in all the conditions and time-points, however, in different abundances. In particular, Pseudomonas chlororaphis subsp. aurantiaca SRG 32 was more abundant at TP1, while Enterobacter asburiae SRG25 was more present at 60 days (time-point 2). In Consortium 2, Pantoea dispersa SRG11 was the only one detected in all the conditions tested, while Rhizobium wenxiniae SRG248 and Streptomyces sp. SRG181 were undetected at 25 days under 100% of fertilizer. In Consortium 3, only Paenibacillus illinoisensis SRG287 was detected, while Paraburkholderia sp. SRG18 was undetected in all the conditions tested. The observed decline in activity of this consortium in field conditions could be attributed to the possible competition from the natural community, which may have outperformed Paraburkholderia sp. SRG18 . This outcome may offer an explanation for the reduced consortium activity. It was concluded that Consortium 2 significantly promoted sorghum plant growth under the conditions tested, especially when no fertilizer was applied. Discussion Plant Growth-Promoting Bacteria (PGPB) are emerging as important components in sustainable agricultural practices, however the challenges faced by individual microbial strains, such as competition with native microbes and difficulties in adapting to environmental changes is prompting researchers to explore alternative strategies [ 55 – 57 ]. This study involves analyzing the rhizosphere samples collected from a considerable number (787) of sorghum plants grown in different geographic and environmental conditions while maintaining uniform plant age. This approach allowed a robust and comprehensive analysis of the sorghum rhizosphere bacterial community, especially focusing on the prevalent and key taxa associated with sorghum in order to then isolate potentially beneficial strains to be used in sustainable agriculture approaches. At present, there are a large number of studies providing a description of the total microbiome of plants and crops [ 27 , 28 , 30 , 32 , 58 , 59 ], which contrasts with the scarcity of efforts directed towards leveraging this information for the development of efficient plant probiotics. There is then a notable gap in translating microbiome insights into practical applications for plant health, which might be attributed to several factors, such as the intricate relationship between the microbiome and plant health that is not yet fully understood, or the practical implementation of microbiome research into agricultural practices, as scalability, consistency, and environmental adaptability. This study aims to begin to use the information obtained from microbiomes to design multi-strain probiotics for sorghum that have greater efficacy and adaptability to the specific sorghum environment. The 16S rRNA gene amplicon community analysis noted higher alpha diversity in samples from Burundi and Ethiopia compared to those from Italy and USA suggesting geographical variations in the bacterial communities associated with sorghum plant species in these locations. Similarly, the beta diversity analysis indicates distinct profiles across the diverse geographical regions. This allowed us to focus on a set of bacteria consistently associated with sorghum roots "core-microbiome" regardless of the growth location, environmental and physical conditions. These bacteria likely have adapted to provide advantages to sorghum and confer benefits. Understanding the role of “core microbiome” and keystone taxa on plant health has been extensively discussed [ 13 – 15 , 60 ], however a practical validations of such finding lags behind [ 1 ]. Our study contributes in moving towards designing beneficial bacterial consortia based on keystone taxa and core microbiome insights. A core set of fourteen bacterial genera forming the most prevalent taxa in the microbial communities associated with Sorghum bicolor across different geographical locations was mapped (Fig. 3 A). Strains belonging to these genera likely have functional roles in promoting the growth and health of sorghum plants, potentially contributing to aspects such as nutrient cycling, pathogen resistance, or other beneficial interactions. Keystones bacterial species on the other hand are likely to influence the composition and structure of the community acting on the type and abundance of the other members [ 18 , 61 , 62 ]. Co-occurrence network analysis revealed as likely keystone taxa the genera Pantoea, Enterobacter , and Pseudomonas in the microbial community associated with sorghum in Italy. These taxa are highly interconnected (Fig. 3 B) and may have significant roles in shaping the structure and function of the rhizosphere community, possibly contributing to ecosystem stability or specific interactions beneficial for the plant. The number of bacterial strains was reduced by combining information obrained from the bioinformatic analysis and the strains that were isolated and purified under the growth conditions used. The eight candidate strains were subsequently phenotypically and genomically characterized and used to create multi-strain consortia. From the core microbiome and correlation studies, three bacterial consortia were designed and their co-existence and impact on sorghum growth tested in vitro and in planta . Results evidenced that the Consortium 3 performed better in plant growth room and Consortia 1 and 2 performed better in the open field experiments. In addition, 16S rRNA gene amplicon profiling showed that the application of these consortia does not have a significant impact on the bacterial community and in several cases confirmed the colonization and the establishment in the rhizosphere of the inoculated consortia. The reasons for the differential effects of the consortia in the plant growth room versus field conditions can be due to multiple factors. Plant growth room environments provide controlled conditions (e.g. temperature, humidity, UV radiation exposure and nutrient availability) that may favor specific bacterial strains or interactions, allowing Consortium 3 to exhibit enhanced performance. It's possible that Paenibacillus and Burkholderia (Consortium 3) form a functional pair, but they may not be able to operate effectively in more complex conditions, such as when they're exposed to competition, predation, or environmental stress. This hypothesis is supported by the fact that we couldn't detect Paraburkholderia sp. SRG18 in the field 16S microbiome, further suggesting that the presence of both strains and their synergistic effect is crucial for improving the growth of sorghum plants. On the other hand, open field conditions introduce additional environmental variables such as soil composition, moisture levels, and microbial competition, which may have favored Consortia 1 and 2. It's also possible that the strains used in consortia 1 and 2 don't have a direct effect on the plant, or they may exhibit functional redundancy in simplified conditions such as in the growth chamber, making them unnecessary. However, in a more complex condition, their presence and ability to utilize various nutrient sources, as well as their complementarity, become important in improving the health and growth of sorghum plants. This approach provides valuable insights into the performance of the consortia in growth chamber and in field conditions, highlighting differences among consortia and the potential impact on plant growth. However, it's important to consider the limitations that this approach presents, such as the need to test the two most promising consortia in various pedoclimatic and environmental conditions. For example, testing the consortia in Burundi, Ethiopia, and the USA to see if they have a universal effect would be necessary, as well as testing whether the proposed approach is applicable also to other crop types. Conclusions This study performed experiments alongside the concept of designing synthetic microbiome-based bacterial consortia to promote sorghum growth and productivity. Multi-strain bacterial consortia can improve both the colonization of the strains in the plant microbiome and the variety of beneficial effects the total community of microbes can provide to the plant [ 63 , 64 ]. There is an increasing need for research that delves into the underlying mechanisms of how microbial communities, especially synthetic ones, influence the growth and fitness of host plants in agricultural settings [ 4 ]. There is now an urgent need to translate plant microbiome research into effective solutions for a more sustainable agriculture. Declarations Ethical declarations This article does not contain any studies with human participants or animals performed by any of the authors Conflict of interests The authors declare that they have no conflict of interests. Author Contribution C.K. and V.V. conceived the study. C.K., I.B., C.B. and V.V.designed the experiments. C.K. and I.B. performed the experiments. C.K., A.E., S.P. and C.B. analyzed the data. S.M., M.J.M., K.T., D.C.D. and L.D. collected samples and managed the field trial experiments. C.K., C.B. and V.V drafted the MS, and all authors contributed to the revision of the manuscript. Acknowledgement The authors gratefully thank ERSA (Regional Agency for Agricultural Development; Via Sabbatini, 5-3, 33050 Pozzuolo del Friuli, UD, Italia), and in particular Dr. P. Tonello and Dr. C. Cattivello, for providing spaces, consultancy and crop managing for the open field experiments. Dr. Steffen Windpassinger from Department of Plant Breeding, University Giessen, Giessen, Germany for sending us sorghum seeds. CK is beneficially of an ICGEB pre-doctoral fellowship. Data Availability The genomes of eight strain used in the consortia are publicly available in the IntegratedMicrobial Genomes (IMG, https://img.jgi.doe.gov) database under IMG GOLD study ID Gs0164287. 16S rRNA data were submitted to the NCBI under the submission code PRJNA1124253. References Zhang J, Cook J, Nearing JT, Zhang J, Raudonis R, Glick BR, Langille MG, Cheng Z. Harnessing the plant microbiome to promote the growth of agricultural crops. Microbiol Res. 2021;245:126690. 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Genetic Engineering and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Alfonso","middleName":"","lastName":"Esposito","suffix":""},{"id":323393193,"identity":"78d931ae-b473-4fd8-b390-3e1a0aba154a","order_by":2,"name":"Iris Bertani","email":"","orcid":"","institution":"International Centre for Genetic Engineering and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Iris","middleName":"","lastName":"Bertani","suffix":""},{"id":323393194,"identity":"ecd543d0-04ac-472b-8fb9-6c5fb5d211cb","order_by":3,"name":"Samson Musonerimana","email":"","orcid":"","institution":"Institut des Sciences Agronomiques du Burundi","correspondingAuthor":false,"prefix":"","firstName":"Samson","middleName":"","lastName":"Musonerimana","suffix":""},{"id":323393195,"identity":"d60a077b-5cec-40d1-8b57-03cd790c1c49","order_by":4,"name":"Mulissa Midekssa","email":"","orcid":"","institution":"Bio and Emerging Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Mulissa","middleName":"","lastName":"Midekssa","suffix":""},{"id":323393196,"identity":"84077e5f-0630-4994-ba9b-ca22e03eb3b1","order_by":5,"name":"Kassahun Tesfaye","email":"","orcid":"","institution":"Bio and Emerging Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Kassahun","middleName":"","lastName":"Tesfaye","suffix":""},{"id":323393197,"identity":"61ec8fa4-52c2-4c8d-bb36-3d379845be59","order_by":6,"name":"Devin Derr","email":"","orcid":"","institution":"Plant Gene Expression Center","correspondingAuthor":false,"prefix":"","firstName":"Devin","middleName":"","lastName":"Derr","suffix":""},{"id":323393198,"identity":"db8eacc6-1634-4f6d-b644-55845e5267e6","order_by":7,"name":"Lara Donaldson","email":"","orcid":"","institution":"International Center for Genetic Engineering and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Lara","middleName":"","lastName":"Donaldson","suffix":""},{"id":323393199,"identity":"d07d1644-bf97-417e-ae2d-22c154c32c62","order_by":8,"name":"Silvano Piazza","email":"","orcid":"","institution":"International Centre for Genetic Engineering and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Silvano","middleName":"","lastName":"Piazza","suffix":""},{"id":323393200,"identity":"7f2bb613-d43a-4449-a522-13a2ade0c718","order_by":9,"name":"Cristina Bez","email":"","orcid":"","institution":"International Centre for Genetic Engineering and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Bez","suffix":""},{"id":323393201,"identity":"c70e0ae3-2457-4837-a720-28ad9cfd7ec1","order_by":10,"name":"Vittorio Venturi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZjApAefz8DMwMD7Ar4WZsQGu5QBQi2QDA7MBAWtAWhjgWhgMDjCwCeDTYHCc//iDj3ss5Bgkko89/th2T8b4RnYacwHDNnmcWg4zMzbOeCZhzCCRlm5wsK2Yx+xG7rbHMxhuGzbg0CLZzMzYzHNAIrGB54yZxMG2BJCW7cY8DLcZidBy/htYi/GM3G3SQC32uLTwM8O0sPewgbUYSEC0JOLRYjhzxgEJYzb2NjOJM+cSeCTOvN1sPMPgdjIuLWz8Bx98+HCgTg6o95lERVmCPX977sbHBRW3bXFpQehF5jAzEIhMTMBMqoZRMApGwSgY1gAA5INQsjcwPiQAAAAASUVORK5CYII=","orcid":"","institution":"Université Mohammed VI Polytechnique","correspondingAuthor":true,"prefix":"","firstName":"Vittorio","middleName":"","lastName":"Venturi","suffix":""}],"badges":[],"createdAt":"2024-06-26 15:00:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4643586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4643586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60991754,"identity":"0e3585b1-d53d-4502-a4f9-9cc95788f5d6","added_by":"auto","created_at":"2024-07-24 11:21:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":712211,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental rationale employed in this study aimed at identifying keystone and core microbiome components, with the ultimate goal of designing specific consortia to enhance sorghum growth. See text for details.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/3ec9c3654677525c872131f0.png"},{"id":60991753,"identity":"b8f3807c-6427-4237-9570-87878fdc37d8","added_by":"auto","created_at":"2024-07-24 11:21:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258667,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity diversity and structure across different geographical locations. A) Shannon diversity values, represented as boxplots colored according to the growing country. Significant diversity at the Wilcoxon test are defined as *p\u0026lt;0.05 and **p\u0026lt;0.01. B) Non-metric multidimensional scaling (NMDS) resulting from the unifrac distance matrix calculated on the ASVs-by-samples matrix. The dots are colored and shaped according to the country.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/e143fe7f6d4c379e8ca8a761.png"},{"id":60992337,"identity":"8cc19c68-53f7-40f1-8a9d-d9edb374d43b","added_by":"auto","created_at":"2024-07-24 11:29:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1072103,"visible":true,"origin":"","legend":"\u003cp\u003eMost prevalent taxa (MPT) members and keystone taxa. A) Boxplots showing the relative abundance of the genera found in at least the 80% of the samples. The boxplots are colored according to the geographical location of origin. B) Correlation network inferred using the SparCC Correlations between ASVs, with SparCC values in the range of -0.3 to 0.3. The three red nodes represent the keystone taxa.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/5fbc519094fc29c7679006ff.png"},{"id":60991759,"identity":"e3efcac7-5f43-4d20-9884-970c650c50aa","added_by":"auto","created_at":"2024-07-24 11:21:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":571631,"visible":true,"origin":"","legend":"\u003cp\u003eA) Phylogenetic tree based on the 16S rRNA gene of the eight strains selected for the three consortia design and representation of their PGP activities. B) Bubble plot showing the relative abundance of each bacterial strain (y-axis) used in vitro co-spotting growth experiments after 94 hrs, as obtained from 16S rRNA gene sequencing analysis. The samples are arranged and subplot according to each specific consortium i.e. C1, C2, C3 (x-axis). C) Bubble plot showing the relative abundance of each bacterial strain (y-axis) used in planta compatibility growth experiments after 15 dpi. Control denotes samples collected immediately after co-inoculum setup.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/e60f7e056e84b9c8b3ad945e.png"},{"id":60992338,"identity":"7696d9a0-b92f-4cc0-b81f-2a938f8df294","added_by":"auto","created_at":"2024-07-24 11:29:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":531454,"visible":true,"origin":"","legend":"\u003cp\u003eIn \u003cem\u003eplanta\u003c/em\u003e experiments of the three consortia conducted in the plant growth room. Ten plants were used in each group of treatments plant agronomical phenotype. To assess statistical significance, ANOVA test was used for multigroup comparison analysis. All data are presented as means ± standard error of the mean (SEM), and GraphPad Prism 9.2 (GraphPad Software, Inc.) was used for statistical analysis. *P \u0026lt; 0.05 **P \u0026lt; 0.005, ***P \u0026lt; 0.0005, ****P \u0026lt; 0.0005 as indicated. The error bars indicate standard deviations. A) plant height at 25 days post inoculation. B) plant dry biomass at 25 days post inoculation. C) plant height at 60 days post inoculation. D) plant dry biomass at 60 days post inoculation. E) Shannon diversity represented as boxplots colored according to the Consortia. F) Non-metric multidimensional scaling (NMDS) resulting from the unifrac distance. The dots are colored according to the Consortia and their shape represents the two time points. G) Detection of the strains inoculated in each Consortia. The barcharts are colored according to strains present in each Consortia.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/8f12237b29eb04c85c3c5d24.png"},{"id":60991756,"identity":"f7f91af0-af75-416d-9d72-ae2aa050c1ed","added_by":"auto","created_at":"2024-07-24 11:21:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":756703,"visible":true,"origin":"","legend":"\u003cp\u003eIn \u003cem\u003eplanta\u003c/em\u003e experiments of the three consortia conducted in field. Ten plants were used in each group of treatments plant agronomical phenotype. To assess statistical significance, ANOVA test was used for multigroup comparison analysis. All data are presented as means ± standard error of the mean (SEM), and GraphPad Prism 9.2 (GraphPad Software, Inc.) was used for statistical analysis. *P \u0026lt; 0.05 **P \u0026lt; 0.005, ***P \u0026lt; 0.0005, ****P \u0026lt; 0.0005 as indicated. The error bars indicate standard deviations. A) Shoot dry biomass of mature sorghum plants. B) Panicle dry biomass of mature sorghum plants. C) Seeds dry biomass of mature sorghum plants. D) Seeds count per 100 seeds in gm. E) Shannon diversity represented as boxplots colored according to the Consortia. F) Non-metric multidimensional scaling (NMDS) resulting from the bray distance. The dots are colored according to the Consortia and their shape represents time points. G) Detection of the strains inoculated in each Consortia. The barcharts are colored according to strains present in each Consortia.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/b949196d86342a95a2eb9113.png"},{"id":64650669,"identity":"ce02e260-dc95-487b-83df-93cab8b53f5a","added_by":"auto","created_at":"2024-09-17 05:24:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4083439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/f7040758-155d-42f4-ab4c-364c77d3c06b.pdf"},{"id":60991760,"identity":"2bbf83b5-9ea9-4294-81ed-675296c7f5af","added_by":"auto","created_at":"2024-07-24 11:21:33","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":3273641,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsKumaretal.docx","url":"https://assets-eu.researchsquare.com/files/rs-4643586/v1/c8df4abff5b736e92c120360.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sorghum rhizosphere bacteriome studies to pinpoint, isolate and assess plant beneficial bacteria","fulltext":[{"header":"Background","content":"\u003cp\u003eMicrobes are important players in every ecological niche and recently they are being widely studied for their ability to promote plant growth and health. The rhizosphere is the microbial habitat of the soil around the root [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and plant roots recruit their rhizomicrobiome from the soil (highest microbial biodiversity on earth). The complex dynamics of microbial interactions and signaling taking place within the rhizosphere remain still largely unknown [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Multiple factors influence plant microbiome composition, diversity and abundance such as the soil physiochemical properties [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], plant compartment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], host genome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], plant immune system [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], plant development stage and season [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and agricultural practice.\u003c/p\u003e \u003cp\u003ePlants possess a \u0026ldquo;common core\u0026rdquo; rhizosphere microbiome, suggesting that the plant plays a role in customizing and maintaining the composition of its bacterial community [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The \u0026ldquo;core microbiome\u0026rdquo; is based on the prevalence and persistence of a particular set of bacteria across diverse samples and conditions/treatments, indicating a stable association with the host [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. On the other hand, keystone taxa in microbial ecology are also gaining importance; this refers to microorganisms that influence the structure and function of a microbial community, despite their abundance. These taxa possibly play an important role in maintaining the stability and diversity of the ecosystem occupying significant positions in microbial networks having several interactions with other members of the community. Alterations of their abundance or even their absence can have effects on the overall community structure and function [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Discovering the rhizosphere core microbiome and keystone taxa holds the potential to create microbiome-based solutions that tackle agronomic challenges and promote sustainable agriculture [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of plant growth-promoting bacteria (PGPB) in agriculture is gaining importance in recent years as a way to reduce the use of chemical fertilizers thus protecting the soil structure, preventing environmental damage and promoting a more sustainable way to enhance crop productivity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the use of microbial inoculants in agriculture face challenges in competing with native microbes and adapting to environmental changing conditions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The use of microbial consortia, comprising of multiple strains might therefore be advantageous compared to the use of single strains to effectively colonize plant-associated environments and enhance seed development and plant growth [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In particular, the diversity within these consortia allows for a broader range of metabolic capabilities. Each bacterial strain may specialize in distinct nutrient uptake pathways or metabolic processes, thus collectively enhancing the consortium's ability to utilize various resources available in the plant environment. Moreover, synergistic interactions may involve cross-feeding, where one strain metabolizes a substrate into a form that can be utilized by another strain, promoting mutual growth and survival. Furthermore, the presence of multiple strains within bacterial consortia can enhance their resilience to environmental stresses. By harnessing a broader array of genetic diversity, consortia are better equipped to withstand fluctuations in environmental conditions, such as changes in temperature, moisture levels, or nutrient availability. This resilience is crucial for the long-term establishment and persistence of bacterial communities in dynamic plant-associated environments.\u003c/p\u003e \u003cp\u003eSorghum (\u003cem\u003eSorghum bicolor)\u003c/em\u003e serves as a staple food in the diets of millions of people in developing countries, providing essential energy, protein, vitamins, and minerals. This is particularly evident in sub-Saharan Africa, as well as certain areas of Asia and Latin America. It is the world's fifth-most important cereal crop after rice, wheat, maize, and barley [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Unlike the other major crops, sorghum is adapted to a wide range of climates being a resilient crop in hot and arid environments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Several recent studies have been performed on the rhizosphere microbiome of sorghum evidencing that it is influenced by a set of factors including plant genotype, pathogens, plant developmental stage, soil properties, salt and drought stress as well as agricultural soil practices [\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31 CR32\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was firstly to pinpoint via microbiome approach a set of key beneficial rhizosphere bacterial strains (core and keystone bacterial strains) associated with the sorghum rhizosphere, regardless of host-plant genotype, location, climate, and soil type and secondly, to isolate them and create tailored bacterial consortia to be used to promote sorghum growth.\u003c/p\u003e \u003cp\u003eTo fulfill this purpose, sorghum plants grown in Ethiopia, Burundi, and Italy were sampled and subjected to rhizosphere 16S rRNA gene sequencing analysis. In addition to these samples, sorghum microbiome samples collected in the USA were also reanalyzed and integrated into a single pipeline, aiming to enhance the significance of the obtained results. A list of the most stably associated microbiome members has been drawn and a set of samples has also been used to isolate, classify and establish a working bacterial culture collection of sorghum rhizosphere-associated strains. A set of key bacterial candidates have then been investigated for their PGP activities \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein planta\u003c/em\u003e. Based on their co-growth capabilities and the complementarity of their PGP abilities, we designed three consortia that were subsequently tested \u003cem\u003ein planta\u003c/em\u003e both in plant growth-chamber and in open field conditions. This study leverages data obtained from microbiomes to design sorghum-beneficial multi-strain consortia with higher performance and greater efficiency for sorghum growth and productivity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eRhizosphere sample collection from Italy, Ethiopia and Burundi locations\u003c/p\u003e \u003cp\u003eThe rhizosphere samples were collected from sorghum grown in different geographical locations: Italy (coordinate: 45.981558 N, 13.198167 E), Ethiopia (coordinate: 9\u0026deg;03'49.8\"N 40\u0026deg;52'29.2\"E, 9\u0026deg;05'30.9\"N 37\u0026deg;02'45.1\"E, 8\u0026deg;40'13.3\"N 39\u0026deg;29'14.8\"E and 7\u0026deg;50'11.6\"N 38\u0026deg;41'41.2\"E) and Burundi (coordinate:4\u0026deg;00'03.3\"S 30\u0026deg;04'26.2\"E). In Italy 46 sorghum genotypes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were selected as a subset of the broad genetic diversity sorghum association panel (SAP) United States [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Seeds were surface sterilized with 10% bleach for 10 min followed by five washes with sterilized water and then pre-germinated in the plant growth room to ensure uniformity in growth. The pre-germinated seeds were then placed in a homogenized soil mixture and grown in controlled conditions (25\u003csup\u003eo\u003c/sup\u003eC, 16 h photoperiod) for two weeks in a growth chamber. Watering was performed every 24h for the first three days and every three days starting from the fourth day onward. After 14 days, uniformly grown seedlings were transplanted to an experimental field in three replicates at ERSA (Agenzia Regionale per lo Sviluppo Rurale) center at Pozzuolo del Friuli, Italy. Three plants of each of the 46-sorghum genotype were transplanted in each field block, with a separation of 20 cm between each plant and 40 cm between each block. After nine weeks, root samples were collected from the 414 plants (three plants from each block of the same genotype). Additionally, nine soil samples (three from each block) were collected using a shovel, with each sample individually dipped 20 cm into a 50 ml Falcon tube containing removal buffer (6.75g KH2PO4, 8.75g of K2HPO4, and 1 ml of Triton X-100, in 1L). The rhizospheres were separated from the root as previously described [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The recovered rhizospheres of the three plants with the same genotype and collected from the same field block were pooled together as a technical replicate and stored at -80\u003csup\u003eo\u003c/sup\u003eC in 20% glycerol for further DNA isolation and bacterial isolation. The same procedure was followed for collecting rhizosphere samples from sorghum plants in Burundi and Ethiopia grown in agricultural fields as part of regular farming practices. A total of 16 sorghum rhizosphere DNA samples were collected from Burundi and 30 samples were collected from Ethiopia at the age of nine weeks post transplantation. In Burundi, rhizosphere samples were collected from four different sorghum varieties: IESH22005, IESH29068, ICSR93034, and Gambela. In Ethiopia, rhizosphere samples were collected from five different sorghum varieties: Gambella 1107, Chiro, Meko-1, Dinkinash and Merera. In summary, rhizosphere samples for DNA isolation were uniformly collected from all geographical locations, at the same plant age (pre-maturation stage), and following identical procedures.\u003c/p\u003e \u003cp\u003eDNA extraction, library preparation and sequencing\u003c/p\u003e \u003cp\u003eDNA was extracted from the sorghum rhizosphere compartment using DNeasy\u0026reg; PowerSoil\u0026reg; Kit from Qiagen (Cat. No. 12888-100). The DNA quality and quantity was measured by using the Nanodrop device (Thermo Scientific, Wilmington, DE, USA). The 16S rRNA gene amplicon libraries were prepared for each DNA sample following the manufacturer\u0026rsquo;s protocol (Illumina Inc., San Diego, CA, USA). Amplification of V3- V4 hypervariabe region of the 16S rRNA gene was performed by using long PCR primer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] incorporating the Illumina adaptor sequences (Forward Primer 5\u0026rsquo;- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG and Reverse Primer 5\u0026rsquo;- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTAT CTAATCC). Following the first amplification, a cleaning step was performed using the AMPure XP bead clean-up (A63880l; Beckman Coulter Inc., Brea, CA, USA). Next steps for the library construction have been performed with a second PCR to attach dual index and Illumina sequencing adapters using the Nextera XT Index Kit; followed by a final AMPure XP bead clean-up. Amplicons size, integrity, and purity were checked using the Bioanalyzer equipment (Agilent Inc., Santa Clara, CA, USA) and the library concentration was measured by fluorimetric quantification using Qubit 2 (Invitrogen Inc., Carlsbad, CA, USA). Libraries were adjusted to a final concentration of 4nM in 5ul of 10mM Tris pH 8.5 and submitted for sequencing using 2x250 bp paired end reads on Illumina Miseq.\u003c/p\u003e \u003cp\u003eAmplicon bioinformatics data analysis\u003c/p\u003e \u003cp\u003eComprehensive analysis of a large dataset was conducted using the fastq files generated from samples collected in Italy (141), Ethiopia (30), and Burundi (16), as mentioned above. Additionally, fastq files from 600 16S rRNA gene libraries were obtained from publicly available sources, specifically focusing on rhizosphere samples cultivated in the United States. These sequences were sourced from [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and were downloaded from the NCBI BioProject ID PRJNA612320.\u003c/p\u003e \u003cp\u003eAll the 787 fastq files were imported into qiime2 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and quality filtering, denoising and amplicon sequence variants detection have been done using DADA2 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and taxonomic assignment to each Amplicon Sequence Variants ASVs was carried out based on Silva database (release 138) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Diversity parameters were calculated at a rarefaction depth of 1385 reads. ASVs that have been classified as a chloroplast and mitochondria were filtered out. The obtained dataset was imported into R using qiime2R package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the subsequent analysis was done using phyloseq [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], vegan [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and microbiome R package [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Alpha and Beta diversity were measured using Shannon and Unifrac unweighted distance respectively. The prevalence of each taxon was determined at ASVs and Genus level.\u003c/p\u003e \u003cp\u003eWe considered as MPT (most prevalent taxa) or \u0026ldquo;core microbiome\u0026rdquo; the genera occurring (i) in more than 80% of the total samples with a relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.5% per sample and also (ii) present in \u0026gt;\u0026thinsp;70% of samples from each location, regardless to their relative abundance. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To infer co-occurrence using the fastspar [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], we exported the table at the genus level, filtered to retain the genera occurring in at least 5 samples, and computed the SparCC value [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] to estimate the co-occurrence of bacterial genera among the samples. Correlations with a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and those in the range \u0026minus;\u0026thinsp;0.3\u0026ndash;0.3 were eliminated. A bottleneck analysis was performed to identify pivotal nodes within the microbial community structure. This analysis entails constructing a network in which nodes represent various taxa or microbial entities, and edges denote interactions or relationships between them, such as co-occurrence patterns [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Cytoscape was used to compute the statistics and depict the generated network [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. According to [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], keystone bacterial taxa were considered the nodes presenting the highest betweenness centrality and node degree. The network analysis was performed either using the whole dataset or restricted to the Italian samples.\u003c/p\u003e \u003cp\u003eFor monitoring the presence and co-existence of the 8 strains used in the designed consortia, gene community sequencing was employed both in the co-existence/compatibility experiment (\u003cem\u003ein vitro\u003c/em\u003e) and \u003cem\u003ein planta\u003c/em\u003e tests (plant growth room and open field). All fastq files were imported into qiime2 and processed following the same pipeline as describe above. For the samples derived from the compatibility experiment in plant growth room the diversity parameters were calculated at a rarefaction depth of 4100 reads, and the number of reads per sample ranged from 14387 to 72399 with an average of 44035.37 reads. For the samples derived from the sorghum rhizosphere persistence experiment in field, diversity parameters were calculated at a rarefaction depth of 900 reads, and the number of reads per sample ranged from 16 to 29136 with an average of 12649,08 reads.\u003c/p\u003e \u003cp\u003eFor the evaluation of the consortia compatibility and ability of the 8 selected isolates to colonize the sorghum rhizosphere a customized dataset of 16S rRNA gene sequences from the 8 isolates used in this study was created and used for the taxonomic assignment. For clustering and comparing the ASVs based on sequence similarity, the cd-hit program was used, setting the parameter of sequence similarity equal to 98% for two sequences to be considered part of the same cluster. All the ASVs not matching with any of the 16S rRNA gene sequences of the customised dataset were removed from the analysis. The obtained dataset was imported in R using the package qiime2R [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and the analysis and graphs were generated using phyloseq and vegan packages in R [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIsolation of culturable collection of sorghum rhizosphere-associated bacteria\u003c/p\u003e \u003cp\u003eA large set (321 strains) of culturable bacteria associated with the sorghum root microbiome have been isolated using aliquots of the rhizospheric fractions from Italy samples which were stored at -80\u003csup\u003eo\u003c/sup\u003eC with 20% glycerol. Different dilutions of the samples were plated in various solid media; Tryptic Soy Agar TSA, (TSA-supplemented with NaCl, CuSo4 and and pH 4.5), NBRIP, KBA, YEM Congo red, Jonson medium (JM). All the media compositions used are presented in Table S2. The plates were incubated at 30\u003csup\u003eo\u003c/sup\u003eC for 2\u0026ndash;5 days and pure single colonies showing distinct colony morphology were picked up independently and streaked on 1/5 TSA plates to ensure the purity of the colonies. The pure colony culture was then stored at -80\u003csup\u003eo\u003c/sup\u003eC in 1/5 Tryptic Soy Broth (TSB) and 20% glycerol. Colony PCR was performed after boiling (10\u0026rsquo; at 98\u003csup\u003eo\u003c/sup\u003eC) a colony suspension in 50 ul of sterile H2O to amplify the complete 16S rRNA gene by using primer fD1Funi 16S (5\u0026rsquo;- AGAGTTTGATCCTGGCTCAG-3\u0026rsquo;) and rP2Runi 16S (5\u0026rsquo;-ACGGCTACCTTGTTAGGACTT-3\u0026rsquo;). PCR products were purified by using PCR Clean-Up purification kit (Euroclone S.p.A). The sequencing was performed with the primer 518F (5\u0026rsquo;- CCAGCAGCCGCGGTAATACG-3\u0026rsquo;) and 907R (5\u0026rsquo;- CCGTCAATTCMTTTRAGTTT-3\u0026rsquo;) and the stains were identified by BLAST analysis at NCBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCo-growth tests of sorghum rhizospheric most prevalent and keystone strains and strategy for designing multi-strain consortia\u003c/p\u003e \u003cp\u003eFrom the bacterial strains identified as sorghum-MPT and keystone using the 16S rRNA gene profiling approach, we carefully chose those that could be cultured/grown under laboratory conditions and exhibited the highest 16S rRNA gene sequence homology (99%) with the ASVs identified as sorghum-MPT and keystone. A set of eight bacterial strains was carefully selected (\u003cem\u003ePantoea\u003c/em\u003e sp., \u003cem\u003eEnterobacter\u003c/em\u003e sp., \u003cem\u003ePseudomonas\u003c/em\u003e sp., \u003cem\u003eBacillus\u003c/em\u003e sp., \u003cem\u003eRhizobium\u003c/em\u003e sp., \u003cem\u003eStreptomyces\u003c/em\u003e sp., \u003cem\u003ePaenibacillus\u003c/em\u003e sp. and \u003cem\u003eParaburkholderia\u003c/em\u003e sp.) and based on the genomic characterization and \u003cem\u003ein vitro\u003c/em\u003e PGP features, three consortia were designed as follows: Consortium 1(\u003cem\u003ePantoea dispersa SRG11\u003c/em\u003e, \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e and \u003cem\u003ePseudomonas chlororaphis subsp. aurantiaca SRG 32\u003c/em\u003e), Consortium 2 (\u003cem\u003ePriestia megaterium SRG70, Rhizobium wenxiniae SRG248\u003c/em\u003e and \u003cem\u003eStreptomyces sp. SRG181\u003c/em\u003e) and Consortium 3 (\u003cem\u003ePaenibacillus illinoisensis SRG287\u003c/em\u003eand \u003cem\u003eParaburkholderia sp. SRG18\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eIn order to perform the first set of compatibility growth experiments, the eight bacterial sorghum MPT and keystone rhizospheric strains were independently grown in liquid media (TSB), washed twice and resuspended in phosphate-buffered-saline (PBS) solution. An equal amount of each strain (1.00E\u0026thinsp;+\u0026thinsp;08 CFU/ml) was then pooled in a PBS solution in order to form the three different consortia. This mixed solution containing the strains in a comparable amount was used as inoculum for \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein planta\u003c/em\u003e compatibility tests as described here below. In all these conditions, each bacterial-strain consortium was monitored and assayed using gene amplicon sequencing and analysis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCo-growth on agar plate media\u003c/strong\u003e \u003cp\u003ean aliquot of 100\u0026micro;l of each strain from the mixed inoculum in PBS, as described above, was spotted on TSA plates and was incubated at 30\u003csup\u003eo\u003c/sup\u003eC. Samples were collected after 96 hours, DNA was purified and used for 16S rRNA gene library preparation and sequencing. As a control, all eight strains were mixed in equal amount and DNA was purified immediately (0 hour).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIn planta co-growth\u003c/strong\u003e \u003cp\u003esorghum seeds were surface sterilized, as described above and then submerged in each consortium-inoculum for 30 minutes. The seeds were then transferred to a 50ml Falcon tube containing semi-solid agar (0.4% agar) with \u0026frac12; Hoagland solution. The plants were grown in a growth chamber under controlled conditions, including a photoperiod of 16 hours of light and 8 hours of darkness, as well as a humidity level of 80%. After nine days post-inoculation, the rhizosphere was collected and DNA was extracted for 16S amplicon microbiome determination.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn growth chamber and in field plant growth promotion experiments by applying multi-strain bacterial consortia\u003c/p\u003e \u003cp\u003eFor the \u003cem\u003ein planta\u003c/em\u003e experiments performed in the growth-chamber, surface-sterilized sorghum seeds were carefully submerged in each consortium-inoculum for 30 minutes. As control, sorghum seeds were submerged in 1xPBS without the bacterial suspensions. After inoculation, both the treated and no-treated seeds as control were transferred to the soil; seeds were inserted 1 cm deep and covered with soil. In brief, the soil was prepared using a mixture of garden soil, perlite/vermiculite, and sand in a specified ratio (1:1:5). After seven days post-germination and root development, a second furrow inoculation was performed using an aliquot of 500 \u0026micro;l of the three consortia and 1xPBS for control was applied near the roots. After the second inoculation, five plants were collected for rhizosphere DNA extractions and 16S rRNA gene amplicon library preparation and ten plants were collected for plant agronomic parameters detection. The experiments involved two time points, TP1: 25 days post-seed inoculation (includes 10 days after furrow inoculation), TP2: 60 days post-seed inoculation (includes 50 days after furrow inoculation).\u003c/p\u003e \u003cp\u003eFor the in field experiments, the same seed-inoculum procedure was followed, as described above. The soil was prepared in three blocks, each receiving a different percentage of chemical fertilizer (Nitrogen, P2O5, K2O followed by N (Urea) at 4/5 leaf stage plant growth): Block 1: 100% chemical fertilizer, Block 2: 50% chemical fertilizer, Block 3: 0% chemical fertilizer (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Both the treated and control seeds were transferred to the soil in each block. An aliquot of 2.5 ml of each consortium and 1xPBS for control was applied near the roots using furrow inoculation. Five plants were collected for rhizosphere at TP1: 40 days post-seed inoculation (includes 20 days after furrow inoculation), TP2: 80 days post-seed inoculation (includes 60 days after furrow inoculation) and ten mature sorghum plants were collected for plant agronomic parameters determination (shoot dry biomass, panicle dry weight, seeds dry weight and seeds count) analysis at days 115 post-inoculation (includes 95 days after furrow inoculation). At each time point the total DNA was extracted, 16S rRNA gene amplicon library prepared and microbiome monitored.\u003c/p\u003e \u003cp\u003eWhole Genome sequencing of the eight bacterial strains isolates forming the three multi-strain consortia\u003c/p\u003e \u003cp\u003eThe eight bacterial strains used in the consortia studied here were whole genome sequenced. Each strain was cultured overnight in TS broth at 30\u0026deg;C. Following cultivation, the cultures were centrifuged at 6000 RPM for 5 minutes, the cells were collected and DNA was subsequently extracted using the Norgen Biotek Corp. Bacterial Genomic DNA Isolation Kit. The complete genomes were sequenced with the Illumina NovaSeq 6000 platform using 150bp paired-end reads and following the tagmentation Illumina Nextera XT protocol (Illumina Inc., San Diego, CA, USA). The assembly was performed with Unicycler v. 0.5.0 and the assembly statistics were recorded with QUAST v. 5.2.0 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The assembled genomes were uploaded in the Integrated Microbial Genomes and Metagenomes (IMG/M) database and automatically annotated, using annotation pipeline IMG Annotation Pipeline v.4.16.6 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Functional annotation and phylogenetic characterization were performed by DFAST [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] ran from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dfast.ddbj.nig.ac.jp/\u003c/span\u003e\u003cspan address=\"https://dfast.ddbj.nig.ac.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eExperimental rationale for the design of sorghum beneficial bacterial consortia\u003c/p\u003e \u003cp\u003eIn order to develop a set of potential multi-strain bacterial consortia that promotes the growth and health of sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e), an investigation into the rhizosphere bacterial community was performed to identify key and core bacterial members. We hypothesized that these might represent bacterial species with significant roles within the sorghum rhizosphere community. A total of 787 rhizosphere samples were analyzed from sorghum plants at the same plant stage, from different genotypes and very different geographical locations. This large sampling size provides a dataset across different soil environments, climate conditions and agricultural management practices. Samples from Ethiopia, Burundi and Italy were obtained through the collection of sorghum rhizosphere and subsequent DNA purification conducted in this study, while another portion of samples from the USA was retrieved from a publicly available dataset as explained in the Materials and \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section. 16S rRNA gene amplicon analysis assessed the structure of the sorghum root-associated community and was used to identify likely keystone and core bacterial components. A culturable bacterial collection was then isolated consisting of 321 strains and a set was selected based on the bioinformatically identified most prevalent taxa (MPT) and keystones members and used for the design of bacterial consortia. The multi-strain consortia were then tested for co-existence/compatibility \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein planta\u003c/em\u003e set-ups in order to determine whether the bacterial strains selected can colonize and persist together. Finally, three bacterial consortia were designed combining culturable and microbiome results and tested for plant growth promotion in plant growth room and in field experiments. The rationale of the experimental design is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSorghum rhizosphere-associated bacterial community diversity and structure across different geographical locations.\u003c/p\u003e \u003cp\u003eIn order to assess the composition of the sorghum rhizosphere across different geographical locations, 16S amplicon community analysis was conducted on sorghum plants cultivated in Italy, Ethiopia, Burundi, and USA. After quality check and removal of reads classified as chloroplasts and mitochondria, the number of reads ranged from 1531 to 2,21,339 with an average of 85327.8 reads per Amplicon Sequence Variant (ASV). The alpha diversity, assessed by the Shannon index, ranged from 1.32289 to 6.19555 with an average of 4.72737 and showed a few significant variations among different geographical locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Specifically, sorghum plants cultivated in Burundi harbored the highest biodiverse bacterial community, whereas those grown in Italy generally presented a less diverse community. The analysis of the beta diversity showed that sorghum plants grown in the different geographical areas hosted a different bacterial community, as they form a distinct cluster on the first axes of the NMDS plot based on their growing locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, samples from Italy and USA, clustered separately, while samples from Burundi and Ethiopia formed two partially overlapping groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting some similarities in the bacterial community structure, likely influenced by more comparable environmental conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo summarize, significative differences in both alpha and beta parameters were noted according to geographical location, emphasizing that the primary factor influencing the structural composition of the bacterial community is the geographical growing location.\u003c/p\u003e \u003cp\u003eMost prevalent taxa (MPT) or \u0026ldquo;core\u0026rdquo; bacterial members and keystone taxa are persistent across different geographical locations.\u003c/p\u003e \u003cp\u003eIn order to map a \u0026ldquo;common-core\u0026rdquo; group of bacterial members most adapted to the sorghum rhizosphere, we quantified the sorghum most prevalent taxa (MPT) or \u0026ldquo;core\u0026rdquo; microbiome. At genus level it was empirically decided to define as MPT the genera that (i) were detected in at least the 80% of the total number of samples with \u0026gt;\u0026thinsp;0.5% relative abundance, and (ii) present in \u0026gt;\u0026thinsp;70% of the samples from each location, regardless to their relative abundance. A shortlist of fourteen candidate MPT/core included genera such as \u003cem\u003eBacillus, Blastococcus, Candidatus_Koribacter, Devosia, Haliangium, Massilia, Niastella, Paenibacillus, Paraburkholderia, Ramlibacter, Rhizobium\u003c/em\u003e group, \u003cem\u003eSolirubrobacter, Sphingomonas\u003c/em\u003e, and \u003cem\u003eStreptomyces\u003c/em\u003e, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith the aim of identifying interactions within all members of the sorghum microbiome, co-occurrence networks were generated for each geographical location, considering only genera occurring in at least 5 samples, and excluding either the correlations with SparCC values in the range of \u0026minus;\u0026thinsp;0.3\u0026ndash;0.3 or non-significant values. We focused on the co-occurrence network resulting from the Italian samples, as our goal was to create multi-strain beneficial consortia using a bacterial culture collection generated from the rhizosphere samples collected in Italy (see below). A bottleneck analysis was performed, and it showed 50 taxa as nodes in the network and these could give a disproportionately significant impact on the community structure [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Among the 50 nodes in the network, three genera\u0026mdash;\u003cem\u003ePantoea\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e, were identified as highly interconnected with other nodes in the resident community (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo summarize, the 16S rRNA gene community analysis provided us with a set of persistent and interconnecting bacterial candidates; these were considered worthy candidates to be tested as bioinocula for sorghum growth and health.\u003c/p\u003e \u003cp\u003eIsolation and characterization of a set of culturable bacterial strains from \u003cem\u003eSorghum bicolor\u003c/em\u003e rhizosphere microbiome for designing beneficial multi-strain consortia.\u003c/p\u003e \u003cp\u003eA collection of bacterial strains was isolated from the sorghum rhizosphere in order to test whether the taxa identified bioinformatically as MPT/core and keystones (see above) are impactful for the health and performance of the sorghum plants (see above). A large set of 321 culturable bacterial strains was isolated from sorghum rhizosphere as described in the Materials and \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section. The collection includes strains of \u003cem\u003eBacillus\u003c/em\u003e (71 isolates), \u003cem\u003eEnterobacter\u003c/em\u003e (71), \u003cem\u003ePseudomonas\u003c/em\u003e (26), \u003cem\u003ePriestia\u003c/em\u003e (23), \u003cem\u003ePantoea\u003c/em\u003e (19), \u003cem\u003ePeribacillus\u003c/em\u003e (8), \u003cem\u003ePaenibacillus\u003c/em\u003e (7) and \u003cem\u003eCitrobacter\u003c/em\u003e (5); the complete list is available in Table S3. Based on the available bacterial culturable collection and the preceding bacteriome analysis, we selected eight bacterial strains for the formulation of three consortia; these included three strains possibly defined as keystone taxa and five strains from the core-microbiome having the highest 16S sequence homology with the ASVs detected in the microbiome analysis.\u003c/p\u003e \u003cp\u003eSpecifically, Consortium 1 was designed based on the results of the co-occurrence network and consisted of three strains belonging to the \u003cem\u003ePantoea, Enterobacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e genera (see above and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). On the other hand, among the 14 MPT/core taxa identified, we were able to isolate the following five taxa; \u003cem\u003ePriestia, Rhizobium\u003c/em\u003e group, \u003cem\u003eStreptomyces, Paenibacillus\u003c/em\u003e and \u003cem\u003eParaburkholderia\u003c/em\u003e; Consortia 2 and 3 were designed based on these results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Consortium 2 consisted of three strains belonging to \u003cem\u003eStreptomyces, Bacillus\u003c/em\u003e, and \u003cem\u003eRhizobium\u003c/em\u003e group selected based on their relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.5% (sum of all samples abundance from each location) and present in \u0026gt;\u0026thinsp;80% of samples. Consortium 3 consisted of \u003cem\u003ePaenibacillus\u003c/em\u003e and \u003cem\u003eParaburkholderia\u003c/em\u003e selected based on their presence\u0026thinsp;\u0026gt;\u0026thinsp;70% of samples without taking in consideration their relative abundance.\u003c/p\u003e \u003cp\u003eIn order to obtain genetic information on the eight selected strains forming the three consortia, their whole genomes were sequenced. The genomic features are presented in Table S4. Additionally, a set of \u003cem\u003ein vitro\u003c/em\u003e plant growth-promoting (PGP) features of all eight strains were tested, including protease and lipase production, phosphate solubilization ability, EPS (exopolysaccharide) production, diazotroph growth ability, swimming and swarming motility, IAA (indole-3-acetic acid) production and biofilm formation; results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The complementarity of the PGP features was also used as a criterion to design the three consortia, e.g. in Consortium 1 \u003cem\u003ePseudomonas chlororaphis subsp. aurantiaca SRG 32\u003c/em\u003e was the only one being able to produce lipase and protease enzymes, while \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e was the only one able to synthesize IAA \u003cem\u003ein vitro\u003c/em\u003e, and \u003cem\u003ePantoea dispersa SRG11\u003c/em\u003e and \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e were solely able to solubilize phosphate and displayed EPS production. By designing bacterial consortia with a wider spectrum of metabolic capabilities most likely results in a more stable multi-strain consortium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompatibility growth experiments revealed that the bacterial strains mixed in each consortium are co-colonizing\u003c/p\u003e \u003cp\u003eIt was of interest to determine whether the strains of the three consortia were able to co-exist both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein planta\u003c/em\u003e set-ups. As detailed in the Materials and Method section, we performed compatibility tests both \u003cem\u003ein vitro\u003c/em\u003e (on solid media) and \u003cem\u003ein planta\u003c/em\u003e (seed inoculation) and we targeted the persistence and presence of each strain by 16S rRNA gene amplicon sequencing. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB depicts which bacterial strains were detected in co-spotting on solid media after 94 hrs of incubation. Importantly, at the inoculation starting point (i.e. 0 hrs), all bacterial strains were detected in equal abundance suggesting that the starting inoculum was well mixed and the technique used allows to unequivocally distinguish each strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). \u003cem\u003eIn vitro\u003c/em\u003e, all the strains were detected, according to each consortium composition; however, they were present in different abundances, i.e. in Consortium 1 \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e was predominant, while \u003cem\u003ePantoea dispersa SRG11\u003c/em\u003e was detected in low relative abundance, probably due to the limitation of nutrients or growth limitation caused by the predominance of \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e cells. Similarly, \u003cem\u003eStreptomyces sp. SRG181\u003c/em\u003e in Consortium 2 was detected in lower abundance compared to \u003cem\u003ePriestia megaterium SRG70\u003c/em\u003e and \u003cem\u003eRhizobium wenxiniae SRG248\u003c/em\u003e. On the other hand, in Consortium 3 both \u003cem\u003ePaenibacillus illinoisensis SRG287\u003c/em\u003e and \u003cem\u003eParaburkholderia sp. SRG18\u003c/em\u003e were present in equal abundance.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC depicts which bacterial strains were detected \u003cem\u003ein planta\u003c/em\u003e co-growth; for this experiment, as described in the Materials and \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section, surface-sterilized sorghum seeds were inoculated with each of the three consortia. Rhizosphere samples were then collected, DNA extracted and 16S rRNA gene amplicon sequencing was performed. The results indicated a richer and more diverse community persisted \u003cem\u003ein planta\u003c/em\u003e attributed to the background of bacterial strains originating from the seeds. However, all the bacterial strains inoculated were detected in higher abundance compared to the \"background strains\u0026rdquo;, suggesting that the three designed consortia were capable of persisting and also performing well in the sorghum rhizosphere. Specifically, Consortium 1 exhibited a well-balanced distribution in the relative abundance of each of its three components (\u003cem\u003ePantoea dispersa SRG11\u003c/em\u003e, \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e and \u003cem\u003ePseudomonas chlororaphis subsp. aurantiaca SRG32\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eIn planta experiments of the three consortia in plant growth room and in field plots\u003c/p\u003e \u003cp\u003eIt was now of interest to perform \u003cem\u003ein planta\u003c/em\u003e assays in order to determine the potential plant growth promoting properties of the three microbiome-based consortia and further evaluate their co-colonization and persistence in a more pertinent environment. The three consortia have been studied both in plant growth room and field experiments by 16S rRNA gene community sequencing analysis and via several agronomic parameters.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlant growth-chamber experiments\u003c/h2\u003e \u003cp\u003eSorghum seeds were inoculated independently with the three different consortia and shoot and root height as well dry biomass were scored after 25 (time-point 1) and 60 (time-point 2) days (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). The inoculated consortia displayed a positive effect on plant growth, compared to the uninoculated control (Figure S2). In particular, Consortium 3 displayed a significant increase nearly 40% in both plant shoot and root length (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and almost a 50% increase in plant shoot and root dry biomass (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) at 25 days (time-point 1) compared to the uninoculated control. Consortium 3 had also an increase in shoot and root length as well as biomass at 60 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D), compared to the uninoculated plants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt was then of interest to assess the persistence and the efficiency in root colonization of the three consortia. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F depicts that the three consortia do not cause any significant shifts or changes in the biodiversity of the rhizobacteriome compared to the uninoculated plants. The alpha diversity ranged 1.32289 to 6.19555 with an average of 5.72737 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). We observed a significative clustering based on the two time-points, which is a physiological aspect due to the stabilization of the bacterial community as the plant grows (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). On the other hand, targeting specifically the strains used in the three consortia, we were able to detect all the bacterial strains inoculated, with some exceptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). In particular, in Consortium 1, all the three strains were detected; however, as observed in the compatibility test (see above and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C), \u003cem\u003ePantoea dispersa SRG11\u003c/em\u003e was present in a lower relative abundance compared to \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e and \u003cem\u003ePseudomonas chlororaphis subsp. aurantiaca SRG 32\u003c/em\u003e. This result can be due to a technical bias or a metabolic/inter-strains interaction mechanism. Consortia 2 exhibited similar abundance of \u003cem\u003ePriestia megaterium SRG70\u003c/em\u003e and \u003cem\u003eRhizobium wenxiniae SRG248\u003c/em\u003e at both time points, while \u003cem\u003eStreptomyces\u003c/em\u003e sp. \u003cem\u003eSRG181\u003c/em\u003e showed an increase in abundance at 60 days. Consortia 3 displayed consistent detection of \u003cem\u003ePaenibacillus illinoisensis SRG287\u003c/em\u003e and \u003cem\u003eParaburkholderia\u003c/em\u003e sp. \u003cem\u003eSRG18\u003c/em\u003e at both time-points.\u003c/p\u003e \u003cp\u003eAll these results suggest that the Consortium 3 was the best performer as it resulted in an increase in all the agronomic parameter tested. Moreover, the 16S rRNA gene profiling approach demonstrated that all the strains were able to co-persist.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eIn field experiments\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIn order to further evaluate the impact of the three consortia in open field, a field trial experiment was designed as follows: the land was partitioned into three blocks under varying conditions, as detailed in Materials and \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, supplemented with 100%, 50% or 0% chemical fertilizer. Sorghum seeds were inoculated independently with the three consortia and these were then also added via a second furrow inoculation which was conducted in close proximity to the roots. All the 16S rRNA gene community studies were performed at two time-points while all the agronomic parameters (shoot dry biomass, panicle dry weight, seeds dry weight and seeds counts) were measured at 115 days, representing the maturation stage of the sorghum plants.\u003c/p\u003e \u003cp\u003eIn contrast with the results obtained in the plant growth room experiments, Consortium 3 showed no differences in all the parameters and conditions tested compared to the uninoculated plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B and C). On the other hand, Consortium 2 displayed an increase in shoot biomass, panicle and seeds dry weight compared to the uninoculated plants and in all the different conditions of chemical fertilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B and C). Consortium 1performed better than the control in shoot dry biomass and panicle dry weight under 50% and no fertilizer application (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and B). Grain quality, as indicated by seed count, remained uniform across treatments and different fertilizer application (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe rhizosphere community analysis revealed that the consortia supplementation had minimal impact on alpha diversity in the different conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), with a slight and not significant decrease in biodiversity at 80 days (time-point 2). The alpha diversity ranged 1.66339 to 6.08117 with an average of 4.51931. Beta diversity parameters showed no clustering based on the agrochemical treatments or consortia application, while confirmed a distinct cluster formation based on the time point (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Targeting specifically the strains used in the three consortia, we observed differences in the detection of the bacterial strains based on the chemical treatments and time-points (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Specifically, the 3-strain members of Consortium 1 were always detected in all the conditions and time-points, however, in different abundances. In particular, \u003cem\u003ePseudomonas chlororaphis subsp. aurantiaca SRG 32\u003c/em\u003e was more abundant at TP1, while \u003cem\u003eEnterobacter asburiae SRG25\u003c/em\u003e was more present at 60 days (time-point 2). In Consortium 2, \u003cem\u003ePantoea dispersa SRG11\u003c/em\u003e was the only one detected in all the conditions tested, while \u003cem\u003eRhizobium wenxiniae SRG248\u003c/em\u003e and \u003cem\u003eStreptomyces sp. SRG181\u003c/em\u003e were undetected at 25 days under 100% of fertilizer. In Consortium 3, only \u003cem\u003ePaenibacillus illinoisensis SRG287\u003c/em\u003e was detected, while \u003cem\u003eParaburkholderia sp. SRG18\u003c/em\u003e was undetected in all the conditions tested. The observed decline in activity of this consortium in field conditions could be attributed to the possible competition from the natural community, which may have outperformed \u003cem\u003eParaburkholderia sp. SRG18\u003c/em\u003e. This outcome may offer an explanation for the reduced consortium activity. It was concluded that Consortium 2 significantly promoted sorghum plant growth under the conditions tested, especially when no fertilizer was applied.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePlant Growth-Promoting Bacteria (PGPB) are emerging as important components in sustainable agricultural practices, however the challenges faced by individual microbial strains, such as competition with native microbes and difficulties in adapting to environmental changes is prompting researchers to explore alternative strategies [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study involves analyzing the rhizosphere samples collected from a considerable number (787) of sorghum plants grown in different geographic and environmental conditions while maintaining uniform plant age. This approach allowed a robust and comprehensive analysis of the sorghum rhizosphere bacterial community, especially focusing on the prevalent and key taxa associated with sorghum in order to then isolate potentially beneficial strains to be used in sustainable agriculture approaches. At present, there are a large number of studies providing a description of the total microbiome of plants and crops [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which contrasts with the scarcity of efforts directed towards leveraging this information for the development of efficient plant probiotics. There is then a notable gap in translating microbiome insights into practical applications for plant health, which might be attributed to several factors, such as the intricate relationship between the microbiome and plant health that is not yet fully understood, or the practical implementation of microbiome research into agricultural practices, as scalability, consistency, and environmental adaptability. This study aims to begin to use the information obtained from microbiomes to design multi-strain probiotics for sorghum that have greater efficacy and adaptability to the specific sorghum environment.\u003c/p\u003e \u003cp\u003eThe 16S rRNA gene amplicon community analysis noted higher alpha diversity in samples from Burundi and Ethiopia compared to those from Italy and USA suggesting geographical variations in the bacterial communities associated with sorghum plant species in these locations. Similarly, the beta diversity analysis indicates distinct profiles across the diverse geographical regions. This allowed us to focus on a set of bacteria consistently associated with sorghum roots \"core-microbiome\" regardless of the growth location, environmental and physical conditions. These bacteria likely have adapted to provide advantages to sorghum and confer benefits. Understanding the role of \u0026ldquo;core microbiome\u0026rdquo; and keystone taxa on plant health has been extensively discussed [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], however a practical validations of such finding lags behind [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Our study contributes in moving towards designing beneficial bacterial consortia based on keystone taxa and core microbiome insights. A core set of fourteen bacterial genera forming the most prevalent taxa in the microbial communities associated with \u003cem\u003eSorghum bicolor\u003c/em\u003e across different geographical locations was mapped (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Strains belonging to these genera likely have functional roles in promoting the growth and health of sorghum plants, potentially contributing to aspects such as nutrient cycling, pathogen resistance, or other beneficial interactions. Keystones bacterial species on the other hand are likely to influence the composition and structure of the community acting on the type and abundance of the other members [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Co-occurrence network analysis revealed as likely keystone taxa the genera \u003cem\u003ePantoea, Enterobacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e in the microbial community associated with sorghum in Italy. These taxa are highly interconnected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and may have significant roles in shaping the structure and function of the rhizosphere community, possibly contributing to ecosystem stability or specific interactions beneficial for the plant. The number of bacterial strains was reduced by combining information obrained from the bioinformatic analysis and the strains that were isolated and purified under the growth conditions used. The eight candidate strains were subsequently phenotypically and genomically characterized and used to create multi-strain consortia.\u003c/p\u003e \u003cp\u003eFrom the core microbiome and correlation studies, three bacterial consortia were designed and their co-existence and impact on sorghum growth tested \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein planta\u003c/em\u003e. Results evidenced that the Consortium 3 performed better in plant growth room and Consortia 1 and 2 performed better in the open field experiments. In addition, 16S rRNA gene amplicon profiling showed that the application of these consortia does not have a significant impact on the bacterial community and in several cases confirmed the colonization and the establishment in the rhizosphere of the inoculated consortia. The reasons for the differential effects of the consortia in the plant growth room versus field conditions can be due to multiple factors. Plant growth room environments provide controlled conditions (e.g. temperature, humidity, UV radiation exposure and nutrient availability) that may favor specific bacterial strains or interactions, allowing Consortium 3 to exhibit enhanced performance. It's possible that \u003cem\u003ePaenibacillus\u003c/em\u003e and \u003cem\u003eBurkholderia\u003c/em\u003e (Consortium 3) form a functional pair, but they may not be able to operate effectively in more complex conditions, such as when they're exposed to competition, predation, or environmental stress. This hypothesis is supported by the fact that we couldn't detect \u003cem\u003eParaburkholderia\u003c/em\u003e sp. SRG18 in the field 16S microbiome, further suggesting that the presence of both strains and their synergistic effect is crucial for improving the growth of sorghum plants. On the other hand, open field conditions introduce additional environmental variables such as soil composition, moisture levels, and microbial competition, which may have favored Consortia 1 and 2. It's also possible that the strains used in consortia 1 and 2 don't have a direct effect on the plant, or they may exhibit functional redundancy in simplified conditions such as in the growth chamber, making them unnecessary. However, in a more complex condition, their presence and ability to utilize various nutrient sources, as well as their complementarity, become important in improving the health and growth of sorghum plants.\u003c/p\u003e \u003cp\u003eThis approach provides valuable insights into the performance of the consortia in growth chamber and in field conditions, highlighting differences among consortia and the potential impact on plant growth. However, it's important to consider the limitations that this approach presents, such as the need to test the two most promising consortia in various pedoclimatic and environmental conditions. For example, testing the consortia in Burundi, Ethiopia, and the USA to see if they have a universal effect would be necessary, as well as testing whether the proposed approach is applicable also to other crop types.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study performed experiments alongside the concept of designing synthetic microbiome-based bacterial consortia to promote sorghum growth and productivity. Multi-strain bacterial consortia can improve both the colonization of the strains in the plant microbiome and the variety of beneficial effects the total community of microbes can provide to the plant [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. There is an increasing need for research that delves into the underlying mechanisms of how microbial communities, especially synthetic ones, influence the growth and fitness of host plants in agricultural settings [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. There is now an urgent need to translate plant microbiome research into effective solutions for a more sustainable agriculture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical declarations\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors\u003c/p\u003e \u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.K. and V.V. conceived the study. C.K., I.B., C.B. and V.V.designed the experiments. C.K. and I.B. performed the experiments. C.K., A.E., S.P. and C.B. analyzed the data. S.M., M.J.M., K.T., D.C.D. and L.D. collected samples and managed the field trial experiments. C.K., C.B. and V.V drafted the MS, and all authors contributed to the revision of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully thank ERSA (Regional Agency for Agricultural Development; Via Sabbatini, 5-3, 33050 Pozzuolo del Friuli, UD, Italia), and in particular Dr. P. Tonello and Dr. C. Cattivello, for providing spaces, consultancy and crop managing for the open field experiments. Dr. Steffen Windpassinger from Department of Plant Breeding, University Giessen, Giessen, Germany for sending us sorghum seeds. CK is beneficially of an ICGEB pre-doctoral fellowship.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe genomes of eight strain used in the consortia are publicly available in the IntegratedMicrobial Genomes (IMG, https://img.jgi.doe.gov) database under IMG GOLD study ID Gs0164287. 16S rRNA data were submitted to the NCBI under the submission code PRJNA1124253.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang J, Cook J, Nearing JT, Zhang J, Raudonis R, Glick BR, Langille MG, Cheng Z. Harnessing the plant microbiome to promote the growth of agricultural crops. Microbiol Res. 2021;245:126690.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes R, Garbeva P, Raaijmakers JM. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. 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ISME J. 2018;12:728\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sorghum bicolor, Rhizosphere, Keystone bacteria, Core microbiome bacteria, 16S rRNA gene, Synthetic consortia ","lastPublishedDoi":"10.21203/rs.3.rs-4643586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4643586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the intricate relationship between plants and microorganisms, plant growth-promoting bacteria (PGPB) play a vital role in the rhizosphere. This study focuses on designing synthetic bacterial consortia using key bacterial strains mapped and isolated from the sorghum rhizosphere microbiome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA large set of samples of the rhizosphere bacteriome of \u003cem\u003eSorghum bicolor \u003c/em\u003ewas analyzed across various genotypes and geographical locations. We assessed the taxonomic composition and structure of the sorghum root-associated bacterial community using 16S rRNA gene amplicon profiling, identifying key taxa and core-bacterial components. A set of 321 bacterial strains was then isolated, and three multi-strain consortia were designed by combining culturable and unculturable microbiome-derived information. Subsequently, co-existence and plant-growth promoting ability of three consortia were tested both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein planta\u003c/em\u003e. In growth-chamber and in-field experiments demonstrated that bacterial Consortia 3 promoted plant growth in growth-chamber conditions while Consortia 1 and 2 performed better in field-plot experiments. Despite these differences, 16S rRNA gene profiling confirmed the stable colonization of the inoculated consortia in the sorghum rhizosphere without significant alterations to the overall bacterial community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims at translating microbiome knowledge into applications by designing and testing microbiome-based multi-strain bacterial consortia in promoting sorghum growth.\u003c/p\u003e","manuscriptTitle":"Sorghum rhizosphere bacteriome studies to pinpoint, isolate and assess plant beneficial bacteria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 11:21:28","doi":"10.21203/rs.3.rs-4643586/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f523cfa2-1154-438f-a5c4-a2d8ea1d275b","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-17T05:07:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 11:21:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4643586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4643586","identity":"rs-4643586","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}