Multi-omics analyses of tomato rhizosphere and leaves treated with a biowaste-derived biostimulant extract

Multi-omics analyses of tomato rhizosphere and leaves treated with a biowaste-derived biostimulant extract | 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 data-descriptor Multi-omics analyses of tomato rhizosphere and leaves treated with a biowaste-derived biostimulant extract Daniela Bona, Claudia Maria Oliveira Longa, Paolo Sonego, Oscar Giovannini, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9556034/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 The urgent need for sustainable agriculture, aimed at preserving soil fertility while meeting the food demands of a growing global population, is driving the development of biowaste-derived products. These products represent a cornerstone of the circular economy, transforming organic waste into high-value biostimulants that enhance plant productivity and stress tolerance. This study presents a comprehensive multi-omics investigation into the biostimulant effects of a humic-like extract derived from biowaste, tested on pot-grown tomato plants under controlled conditions at two different dosages. The adopted approach integrates rhizosphere eDNA metabarcoding and leaf RNA sequencing (RNA-seq) to capture the simultaneous response of the soil microbial community and the plant transcriptome. These molecular surveys were complemented by physiological parameter measurements and nutrient analysis of both soil and leaves. This holistic characterization provides a robust scientific foundation to assess the efficacy of biowaste extracts, elucidating their impact on plant growth and providing novel insights into their integrated mechanisms of action within the plant-soil system. Figures Figure 1 Figure 2 Background & Summary The growing global population and the consequent intensification of agriculture pose urgent challenges in achieving more sustainable food systems for meeting increasing demand without causing environmental degradation. Agriculture currently faces several pressures, including reduced productivity driven by climate change and global warming, declining soil fertility, irrigation constraints, and an increasing demand for fertilizer 1,2 . According to the Food and Agriculture Organization (FAO) of the United Nations, global agricultural production must increase by approximately 60% by 2050, relative to 2006 levels, to satisfy future food demand 3 . In this context, biostimulants have emerged as a promising solution for enhancing agricultural sustainability and addressing the pressure on crops, improving the resistance to abiotic stresses 4 . Biostimulants are defined by the European Biostimulant Industry Council (EBIC) as "a product which stimulates plant nutrition processes independently of the product’s nutrient content, with the sole aim of improving one or more of the following characteristics of the plant or the plant rhizosphere: nutrient use efficiency, tolerance to abiotic stress, quality traits, or the availability of confined nutrients in soil or rhizosphere" (EBIC, 2016). Biostimulants include a wide range of substances, such as microbial inoculants, humic substances, amino acids, and seaweed extracts 5 . Plant biostimulants based on humic substances (HS) enhance nutrient use efficiency, improve crop quality, and increase tolerance to abiotic stressors such as drought and salinity without functioning as traditional fertilizers 6,7 . Among these, HS are highly complex and heterogeneous, and their chemical composition varies depending on their origin and formation conditions, including both hydrophilic portions, such as -OH and -COOH groups, and hydrophobic portions 8 . They are typically classified based on solubility: fulvic acids remain soluble at all pH levels, while humic acids precipitate under strongly acidic conditions (pH < 2). Their structure includes numerous oxygen-containing functional groups, such as phenols and carboxylic acids, which interact with minerals and nutrients in soil (Kim et al., 2014; Zanin et al., 2018). HS act as biostimulants through several mechanisms linked to their supramolecular structure. They enhance plant growth by improving nutrient uptake, either by increasing Fe and P availability to the plant 9,10 or by stimulating nitrate uptake through the activation of root H + ATP-ase pumps 11,12 . They stimulate root development, often increasing root biomass by more than 20% and promoting root formation through hormone-like activity 13 . Beyond the plant itself, HS can alter the plant microbiome by favoring the recruitment of beneficial microorganisms in the rhizosphere 14 . However, most commercial humic products are currently derived from non-renewable sources such as peat, coal, and leonardite. To find more sustainable production methods, recent research has focused on valorizing organic waste as a renewable source of humic substances. In particular, the HS extraction from biowaste was recently investigated for the development of multifunctional, non-toxic nanocomposites with potential applications in crop nutrition and plant stimulation 15–19 . Renewable sources, such as agro-industrial processing waste or animal husbandry byproducts, can produce highly bioactive materials that can replace non-renewable ones. The organic composition of recently produced alkaline hydrolysates of biowaste suggests their use as plant biostimulants, but the mechanisms underlying their effect on plants and soil are still poorly understood. Here, a multi-omics approach was employed to assess the biostimulant potential of a humic-like extract sourced from biowaste. The experimental design combined leaf transcriptomics (RNA-seq) and soil eDNA metabarcoding to capture the interplay between plant gene expression and rhizosphere microbial dynamics. Despite the increasing number of single-omic studies, this work represents one of the first attempts to bridge these two methodologies in a unified biostimulant evaluation 20 . The molecular data were complemented by physiological measurements at the whole-plant scale and chemical analyses of the soil and leaf nutrient content. The aim of the study was to determine the biostimulant mechanism of action both on plant growth and metabolism and rhizosphere microbial communities. Two identical but independent experiments were performed with tomato plants grown under controlled conditions ( Figure 1 ). Transcriptomic, environmental metagenomic, physiological and chemical analyses were performed at two time-points, 3 and 6 weeks. The data collected within this study will provide a valuable baseline for future research, serving as a comparative benchmark for testing other biostimulants and contributing to overcoming problems such as inconsistent formulations and protocols, which currently hinder their widespread adoption. Furthermore, this type of molecular-level study is essential to unraveling the complex mechanisms of action behind plant biostimulants, moving beyond phenotypic observations to a deeper understanding of the biological pathways they activate. Raw data are available under BioProject PRJNA1425279 (NCBI SRA) and processed data under Zenodo DOI 10.5281/zenodo.19336337. Methods Description of the biowaste extract The humic-like biowaste extract (hereafter referred as E1) has been obtained at pilot scale by the extraction of organic compounds from a composted digestate under alkaline conditions (KOH). The digestate derived from the thermophilic anaerobic treatment of the organic fraction of municipal solid waste (OFMSW) at full scale was then sent to the second phase of aerobic composting. The organic product obtained has been chemically characterized in an external laboratory (pH, organic matter and nutrients) (Table 1). Among the 7 doses preliminarily tested, D3 and D4 have been identified as suitable for agronomic purposes (Table 2). Table 1: Biowaste extract E1 characterization Parameter Value Unit Method pH 11.24 ± 0.74 – UNI EN 13037:2012 Dry Matter content (DM) 1.59 ± 0.46 % UNI EN 13040:2002 Total Organic Carbon (TOC) 28.1 ±7.0 % DM DM 21/12/2000 Italian Law n° 21; 26/01/2001 Humic and Fulvic Acids (HA+HF) 7.34 % DM Methods for fertilizers analysis. Italian Law n° 29, 04/02/1991 Total N 4.5 ±1.1 % DM UNI 10780:1998 Appendix P (P 2 O 5) ) 2.47 ±0.73 % DM UNI EN 13657:2004 + UNI EN ISO 11885:2009 K ( K 2 O) 9.8 ±2.9 % DM UNI EN 13657:2004 + UNI EN ISO 11885:2009 Table 2: Applied doses of biowaste extract E1 and composition of each dose Doses DM TOC HA + HF Unit t/ha kg/ha kg/ha kg/ha D3 45.41 703.91 380.11 51.67 D4 11 178.56 96.42 5.46 Plant material and growth conditions Tomato plants ( Solanum lycopersicum L., cv. Moneymaker) were grown under controlled greenhouse conditions at the plant pathology greenhouse of the Fondazione Edmund Mach (San Michele all’Adige, Italy, Figure 1 ). Seeds were sown individually in 70 mL plug trays using a commercial technical substrate (Semina, Tercomposti s.p.a., Calvisano, Italy). Seedlings were grown under controlled conditions for 15 days (25 °C; 70 ± 5% relative humidity [RH]; 16 h light/8 h dark photoperiod); then 30 plants were transplanted into 5.5 L pots filled with 4.6 kg of field soil collected in a local orchard (GPS coordinates N 46.187713, E 11.104974) and sieved at 10-mm granulometry. The soil was classified as a sandy loam soil (51.9% sand, 37.1% loam, 11% clay) with a soil organic matter content of 2.6%. After transplanting, plants were trained vertically on a string support and axillary shoots were not removed (no desuckering), allowing full evaluation of canopy development. Tomato cultivation was carried out in a greenhouse under controlled conditions (25 °C; 70% RH; 16 h light/8 h dark photoperiod) for six weeks. Experimental design and water and nutrient management The experimental setup consisted of 30 plants divided into two independent groups (15 plants each) designated for two distinct destructive sampling timepoints: i) week 3 (Group T1) and ii) week 6 (Group T2, Figure 1 ). Within each group, plants were assigned to three conditions, in five biological replicates (plants): a reference control and two treatments, using two doses (D3 and D4) of biostimulant E1. The entire experimental trial was independently replicated twice under the same conditions, starting in November 2024 and February 2025, respectively. A strict water management protocol was implemented to maintain the substrate at field capacity (FC) while entirely avoiding leachate production, thereby ensuring precise control over water and nutritional inputs. Fertigation was applied at 7, 14, 21, 28, 35 days post-transplanting (d.p.t.) by supplying a nutrient solution (0.9 g/L NPK UltraSol 13.5.30+2MgO) up to FC. The nutrient solution was adjusted to pH 6.5 using HNO 3 , achieving an electrical conductivity (EC) of 1650 µS/cm. Biostimulant treatments (D3 and D4) were applied at 2, 9, 23, 30 d.p.t. by delivering 100 mL of the respective formulation to the root zone. Two hours post-application, irrigation was completed with water up to FC. The control plants received exclusively water up to FC. At 16 and 37 d.p.t., solely water up to FC was supplied to all plants. Finally, supplementary irrigation with water up to FC was performed for all treatments at 4, 11, 18, 25, 32, 39 d.p.t.. Biometric and physiological assessments Non-destructive growth parameters, including plant height, stem diameter at the first true leaf, number of leaves with central vein length over 3 cm, length of the central vein of the compound leaf, phenological stage, and canopy characteristics, were monitored every 10–15 days throughout the crop cycle 21 . The physiological status of the plants was assessed by quantifying the content of epidermal polyphenols (anthocyanins and flavonols) and chlorophyll at 3 and 6 weeks post-transplanting (21 and 42 d.p.t, respectively). Measurements were carried out using a Dualex Scientific optical leaf clip sensor (Pessl Instruments, Austria) on five randomly selected representative leaves per plant 22 . Destructive biomass analyses were conducted at week 3 for Group T1 and at week 6 for Group T2. At both sampling dates, the total epigeal biomass was evaluated. Fresh weight, excluding the root system, was recorded immediately after harvest. Subsequently, samples were subjected to a forced drying process at 25 °C for 20 days in order to quantify the dry weight. Sample preparation for leaf transcriptomic and rhizosphere communities metabarcoding The fourth leaf from the apex was collected at the two time points, three weeks (T1) and six weeks (T2), from tomato plants grown in the three conditions, in 5 biological replicates ( Figure 1 ). They represent young but fully expanded leaves. Samples were immediately flash-frozen in liquid nitrogen and stored at -80°C. A total of 60 samples were processed. Rhizosphere soil was collected from the same tomato plants used for the transcriptomic analysis. In addition, one bulk soil sample was collected at the beginning of each independent experiment (T0), prior to plant transplanting, as a reference. Plants were removed from their pots and the roots were gently shaken to discard excess soil particles; the rhizosphere soil was then obtained by vigorously shaking the roots into a plastic bag. To ensure the purity of the soil, it was sieved through a 2 mm sieve to remove any debris. An aliquot of this soil was collected in 50 mL tube, crio-lyophilized and stored at room temperature until DNA extraction. Rhizosphere metabarcoding sequencing and data processing Total genomic DNA was extracted from 250 mg of soil using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol. For the prokaryotic community, the V4–V5 hypervariable regions of the 16S rRNA gene were amplified using the primer pair 515F (5′-GTGCCAGCMGCCGCGGTAA-3’) and 909R (5′-CCCCGYCAATTCMTTTRAGT-3’) (Huang et al., 2014). For the fungal community, the ITS1 region was targeted using the primers ITS1-F (5′-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS2-R (5′-GCTGCGTTCTTCATCGATGC-3’) 23,24 . Libraries were prepared according to the Illumina 16S Metagenomic Sequencing Library Preparation guide. Sequencing was performed on the Illumina MiSeq platform using v3 chemistry (2 × 300 bp paired-end reads). Raw reads were processed with MICCA (v.1.7.2) software 25 . Specifically, the paired end reads were merged with a minimum overlap length of 100 and a maximum number of allowed mismatches of 32. Primers were trimmed using Cutadapt v.1.18 26 , and merged reads shorter than 100 bp with an expected error rate higher than 0.25% were removed. Filtered sequences were clustered into Amplicon Sequence Variants (ASV) using the UNOISE 27 algorithm available in MICCA utilizing VSEARCH 28 . Taxonomic assignment was carried out using the RDP classifier v. 2.14 29 update to training set n.19 30 for the 16S and against the fungal UNITE + INSD v.8.3 database for the ITS 31,32 . Plant transcriptomic sequencing and data processing Frozen tomato leaf samples were ground using a MixerMill (Retsch). Total RNA was extracted from 50 mg of powder using the Spectrum Plant Total RNA kit (Sigma), following the manufacturer's instructions, and then subjected to TURBO DNAse (Invitrogen) digestion to remove any contaminating DNA. The purified total RNA was used to construct mRNA libraries using the KAPA Stranded mRNA-Seq Kit (Roche, Basel, Switzerland), following the manufacturer’s instructions. RNA integrity and library size distribution were assessed using the Agilent 4150 TapeStation system (Agilent Technologies, Santa Clara, CA, USA). Libraries were quantified via RT-qPCR to ensure optimal loading concentrations. Sequencing was performed on the Illumina NovaSeq X Plus platform, generating 2 × 150 bp paired-end reads. Reads were pre-processed using fastp 33 (version 0.23.4) with default parameters, retaining on average 97.6% of input reads. They were aligned using STAR 34 (version 2.7.11b) with default parameters to the latest tomato reference genome (S. lycopersicum v5.0, assembly SL5.0, ITAG5.0 annotation), available via Phytozome JGI (https://phytozome-next.jgi.doe.gov/info/Slycopersicum_ITAG5_0). Aligned reads were subsequently assigned to genomic features using featureCounts 35 (version 2.0.3) from the Subread package, employing the ITAG5.0 gene annotation and default parameters for paired-end, reverse-stranded counting. Gene-level raw counts were obtained for all annotated features, providing the basis for downstream quantitative analyses. In addition to raw counts, normalized expression values were calculated as counts per million (CPM) to account for differences in library size across samples, facilitating direct comparison of gene expression levels. Soil chemical analysis Bulk soil was collected from the area surrounding the roots, from the same plants’ pots used for the omics analyses. The soil was air-dried and sieved (<2mm). For organic C and total N quantification, the samples were also ground <0.02mm. A CN analyzer (Primacs SNC100-IC-E, Skalar, Breda, The Netherlands) was used for total N quantification after combustion at 1200°C (Dumas method, ISO13878:1998). Temperature-dependent differentiation of organic carbon fractions (TOC400 released up to 400°C and ROC measured at 600°C) was carried out using a temperature-gradient method after dry combustion in the presence of oxygen (UNI EN 17505:2024). Total organic carbon was calculated as the sum of these two fractions. Soil reaction (in water 1:2,5) was measured with a pH-meter (Inolab level 2, WTW, Weilheim, Germany). Exchangeable K and Mg were quantified by spectrometry with an Optima 8300 ICP-OES (PerkinElmer, MA, USA) after ammonium acetate 1M pH 7 extraction. Cation exchange capacity (CEC) was determined using BaCl 2 solution buffered at pH 8.1 (ISO 13536:1995). Available (Olsen)-P was solubilized in sodium bicarbonate and quantified with a spectrophotometer (ONDA V10, Giorgio Bormac, Modena, Italy) after color development at high temperature (ISO 11263:1994). Leaf nutrient analysis The leaf ground powder remaining after RNA extraction was freeze-dried and submitted to an external laboratory (Ecoopera, www.ecoopera.coop) for the analysis of macronutrients (nitrogen, calcium, magnesium, potassium, and phosphorus). Analytical methods are reported in the data table. A total of 15 samples from T1 (3 weeks) of the first experiment and 15 samples from T2 (6 weeks) of the second experiment were selected for this analysis. Data Record The raw RNAseq data of the 60 tomato leaf samples and the raw metabarcoding sequencing data of the 60 samples of tomato rhizosphere and 2 T0 soil samples were deposited at SRA NCBI under the accession BioProject PRJNA1425279. BioSample accessions, experimental groups, internal sample IDs, and SRA run identifiers for each biological replicate are available as Supplementary Table 1 . Transcriptomic data (raw counts), normalized data (Counts per Million, CPM), as well as phenotypical data, soil analysis and leaf nutrient analysis can be downloaded from Zenodo, 10.5281/zenodo.19336337. Technical Validation A total of 60 RNA-seq libraries were generated from 30 tomato leaf samples across two independent experimental trials. Sequencing on the Illumina NovaSeq X Plus platform (2 × 150 bp paired-end) yielded approximately 438.8 Gb of raw data. The total number of raw reads, pre-processed clean reads, uniquely mapped reads and successfully assigned sequences is shown in Figure 2a (data in Supplementary Table 2) . Sequencing output is summarized as raw read pairs and library quality is described by four fastp-derived metrics: base-call error rate, the proportions of bases achieving Phred scores ≥ 20 (Q20) and ≥ 30 (Q30), and GC content ( Supplementary Table 3 ). To assess global transcriptome homogeneity across libraries, variance-stabilizing transformation (VST) 36 was applied to raw count data using blind dispersion estimation 37 . Euclidean distances were computed on the transposed VST expression matrix and visualized as a symmetric heatmap, with rows and columns ordered by complete-linkage hierarchical clustering of the distance matrix ( Figure 2b ). A total of 124 Amplicon libraries were generated from 60 rhizosphere soil and 2 bulk soil samples. Sequencing on the Illumina MiSeq Platform (2 x 300 bp paired-end) yielded approximately 18.66 Million raw reads. On average, 78.3% of the clean reads were clustered into Amplicon Sequence Variants (ASV) (range: 68.0-88.6%, Supplementary Table 4 ). Declarations Data availability The raw sequencing data can be retrieved at SRA NCBI under the accession BioProject PRJNA1425279. The RNAseq normalized data, the plant physiological data, and soil and leaf chemical data can be downloaded from Zenodo, 10.5281/zenodo.19336337 under CC-BY 4.0 licence. Code Availability Supplementary Table 5 details all the software and versions used in this study for transcriptomics and metagenomics. Unless specific parameter details are provided, the programs were utilized with their default parameters. Acknowledgements We would like to thank our colleagues at FEM Roberta Valentini for support in the greenhouse and Daniela Nicolini for RNA extraction. Author Contributions S.S., D.Bo., C.M.L.O., S.P., P.S., M.P. and O.G. conceived the work and designed the experiments; O.G., D.Bo., C.M.O.L. and S.P. set up the experiments and collected the samples; O.G. did physiological measurements; E.S. and S.L. did DNA and RNA library preparation; P.S. and M.P. did formal analyses and data curation; D.Be. performed soil chemical analyses; S.S. acquired funding and coordinated the project. S.S., D.Bo., C.M.L.O., S.P., P.S., M.P. and O.G. drafted and then revised the manuscript. All the authors approved the final version of the manuscript. Funding This work is part of the project PERCIVAL - Processi di EstRazione di bioprodotti da sCarti agroIndustriali e VALorizzazione in cascata (ARS01_00869) co-funded by the Italian Ministry of University and Research (MUR) under the National Operational Programme on Research and Innovation 2014-2020. Competing Interests The authors declare no competing interests. References Velten, S., Leventon, J., Jager, N. & Newig, J. What is sustainable agriculture? A systematic review. Sustain. 7 , 7833–7865 (2015). Robinson, G. M. Global sustainable agriculture and land management systems. Geogr. Sustain. 5 , 637–646 (2024). McKenzie, F. C. & Williams, J. Sustainable food production: constraints, challenges and choices by 2050. Food Secur. 7 , 221–233 (2015). Khoulati, A. et al. 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Supplementary Files SupplementaryTable1.csv Supplementary Table 1: Description of the samples analyzed by RNAseq and eDNA metabarcoding. SupplementaryTable2.csv Supplementary Table 2: Reads processing and mapping. SupplementaryTable3.csv Supplementary Table 3: RNAseq stats. SupplementaryTable4.csv Supplementary Table 4: eDNA sequencing stats. SupplementaryTable5.csv Supplementary Table 5: List of the software used for the transcriptomic and metagenomic analyses. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9556034","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":631190439,"identity":"e840b830-ff3e-4ea6-a6ce-7259fbf4d7ce","order_by":0,"name":"Daniela Bona","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Bona","suffix":""},{"id":631190440,"identity":"6c6175b6-9b59-4381-8b98-157d08042cda","order_by":1,"name":"Claudia Maria Oliveira Longa","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"Maria Oliveira","lastName":"Longa","suffix":""},{"id":631190441,"identity":"6ea97dba-5597-4006-a19e-4f486004965d","order_by":2,"name":"Paolo Sonego","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Sonego","suffix":""},{"id":631190442,"identity":"9c0d9d47-7dcc-4f35-b2e7-647dcf28b9ea","order_by":3,"name":"Oscar Giovannini","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Giovannini","suffix":""},{"id":631190443,"identity":"a4ef85b8-d0c7-423f-8f17-546e4f69300e","order_by":4,"name":"Daniela Bertoldi","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Bertoldi","suffix":""},{"id":631190444,"identity":"2402eeda-c9df-435f-8bec-a51779b59bc9","order_by":5,"name":"Erika Stefani","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Stefani","suffix":""},{"id":631190445,"identity":"cdc99e7b-efb6-48a3-91be-f0e57eaf1963","order_by":6,"name":"Simone Larger","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Larger","suffix":""},{"id":631190446,"identity":"2f8b1477-751b-4bdb-a241-d1a7b558114e","order_by":7,"name":"Massimo Pindo","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Pindo","suffix":""},{"id":631190447,"identity":"fc1e67b7-b87e-4305-864b-3fe39df89c40","order_by":8,"name":"Stefania Pilati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie2SMUvEMBTHXzmoS47b5EnO61dIOdAOil8lRWiXEwQXp5ISiEvE9TY/g5ubKYVOfgi7KygH4oGCOaKDQ+oqmN+SR+DH///gAQQCf5RnALRPVBs7zLYEwMj9eIghWn4pwgDHOTFO8TtOcVgFcu0Uf0xyLRuxvs0SoFKY9VtW6u22X51CVvkU1sV5fXmPqZg2otEcTzQt5nQ5UIzFZP9hrDASmAtDrHJH+d6IDCiJmrzUHwqPNkrzzrEktHwdVKAjkbQp+UZpbQondDGcwroilbsKj5XdpZ0WmGq6OKOE4Y7wFZNtXz+p6vCKXvT940GV2GI3K3JeTbzFvol/FmG/CjB4IYFAIPDP+QSee08YvQqSNgAAAABJRU5ErkJggg==","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":true,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Pilati","suffix":""},{"id":631190448,"identity":"808a66f0-724b-4dec-a384-d13fc7bd730a","order_by":9,"name":"Silvia Silvestri","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Silvestri","suffix":""}],"badges":[],"createdAt":"2026-04-28 15:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9556034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9556034/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108070201,"identity":"e00ac91c-d5fb-450b-9a1c-c8d3c2480142","added_by":"auto","created_at":"2026-04-29 05:48:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":450390,"visible":true,"origin":"","legend":"\u003cp\u003eDescription of the experiment. From the top: i) greenhouse conditions, irrigation and fertilizing management; ii) experiment time-line: sowing, transplanting, growth, treatments and sampling; iii) experimental design; iv) on the left, nucleic acid extractions, sequencing and data repository; on the right: physiological measurements taken on the plants at the two sampling time-points and soil and leaves chemical analyses of nutrients content. Created in BioRender (2026).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/a5e5d1fbc24e4d692f39d135.png"},{"id":108070204,"identity":"7daad83a-88c2-44fe-97a3-51faf4df797b","added_by":"auto","created_at":"2026-04-29 05:48:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":466525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality control on RNAseq data. \u003c/strong\u003ea. Read retention across the RNA-seq processing pipeline. Stacked bar charts show read pairs (millions) partitioned by fate at each pipeline stage for all 60 libraries across Experiment 1 (left) and Experiment 2 (right): reads removed by fastp (Trimmed-out, light blue), multi-mapping or unmapped reads (Multi/unmapped, orange), uniquely\u003cstrong\u003e \u003c/strong\u003emapped but unassigned reads (Unique not assigned, grey), and reads successfully assigned to a genomic feature (Assigned to feature, green). b. Unsupervised hierarchical clustering of RNA-seq samples based on sample-to-sample distances. Color intensity encodes pairwise dissimilarity (dark blue = low distance, i.e. high similarity; white = high distance). Annotation bars indicate Condition (control and two doses of E1), Experiment (1 or 2), and Time point (T1 and T2) for each sample.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/e68f9828aa48f74d8c13a58e.png"},{"id":108490791,"identity":"e66d1614-7f3f-44fc-aa9b-cc7a3fc79135","added_by":"auto","created_at":"2026-05-05 09:48:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/463193ef-8d7b-4e21-996f-6b2753d5db23.pdf"},{"id":108182135,"identity":"c4375314-337f-496f-8ea9-de3b4d4a248f","added_by":"auto","created_at":"2026-04-30 08:59:10","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1: Description of the samples analyzed by RNAseq and eDNA metabarcoding.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/dfa979406a774447e2d04492.csv"},{"id":108070203,"identity":"b35864dd-8067-47d6-a9ab-2cecd3376d16","added_by":"auto","created_at":"2026-04-29 05:48:40","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2: Reads processing and mapping.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/9c96ed5bb60eae30f73415d2.csv"},{"id":108070206,"identity":"30428ecb-fa19-48ff-8df0-017d1fc4c5ec","added_by":"auto","created_at":"2026-04-29 05:48:40","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3: RNAseq stats.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable3.csv","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/8a0691fd034545b49111df3c.csv"},{"id":108070205,"identity":"9d19c9ac-7402-4d81-8632-d1c155ef945d","added_by":"auto","created_at":"2026-04-29 05:48:40","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 4: eDNA sequencing stats.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable4.csv","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/f68fc7117079764378ef08a4.csv"},{"id":108181863,"identity":"1da8fe15-22e3-4230-ad44-ca83d0b2bc87","added_by":"auto","created_at":"2026-04-30 08:58:58","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 5: List of the software used for the transcriptomic and metagenomic analyses.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable5.csv","url":"https://assets-eu.researchsquare.com/files/rs-9556034/v1/ec3ffa3372f3cdefdeba00f1.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics analyses of tomato rhizosphere and leaves treated with a biowaste-derived biostimulant extract","fulltext":[{"header":"Background \u0026 Summary ","content":"\u003cp\u003eThe growing global population and the consequent intensification of agriculture pose urgent challenges in achieving more sustainable food systems for meeting increasing demand without causing environmental degradation. Agriculture currently faces several pressures, including reduced productivity driven by climate change and global warming, declining soil fertility, irrigation constraints, and an increasing demand for fertilizer \u003csup\u003e1,2\u003c/sup\u003e. According to the Food and Agriculture Organization (FAO) of the United Nations, global agricultural production must increase by approximately 60% by 2050, relative to 2006 levels, to satisfy future food demand \u003csup\u003e3\u003c/sup\u003e. In this context, biostimulants have emerged as a promising solution for enhancing agricultural sustainability and addressing the pressure on crops, improving the resistance to abiotic stresses \u003csup\u003e4\u003c/sup\u003e. Biostimulants are defined by the European Biostimulant Industry Council (EBIC) as \u0026quot;a product which stimulates plant nutrition processes independently of the product\u0026rsquo;s nutrient content, with the sole aim of improving one or more of the following characteristics of the plant or the plant rhizosphere: nutrient use efficiency, tolerance to abiotic stress, quality traits, or the availability of confined nutrients in soil or rhizosphere\u0026quot; (EBIC, 2016). Biostimulants include a wide range of substances, such as microbial inoculants, humic substances, amino acids, and seaweed extracts \u003csup\u003e5\u003c/sup\u003e. Plant biostimulants based on humic substances (HS) enhance nutrient use efficiency, improve crop quality, and increase tolerance to abiotic stressors such as drought and salinity without functioning as traditional fertilizers \u003csup\u003e6,7\u003c/sup\u003e. Among these, HS are highly complex and heterogeneous, and their chemical composition varies depending on their origin and formation conditions, including both hydrophilic portions, such as -OH and -COOH groups, and hydrophobic portions\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e. They are typically classified based on solubility: fulvic acids remain soluble at all pH levels, while humic acids precipitate under strongly acidic conditions (pH \u0026lt; 2). Their structure includes numerous oxygen-containing functional groups, such as phenols and carboxylic acids, which interact with minerals and nutrients in soil (Kim et al., 2014; Zanin et al., 2018). HS act as biostimulants through several mechanisms linked to their supramolecular structure. They enhance plant growth by improving nutrient uptake, either by increasing Fe and P availability to the plant\u0026nbsp;\u003csup\u003e9,10\u003c/sup\u003e or by stimulating nitrate uptake through the activation of root H\u003csup\u003e+\u003c/sup\u003e ATP-ase pumps\u0026nbsp;\u003csup\u003e11,12\u003c/sup\u003e. They stimulate root development, often increasing root biomass by more than 20% and promoting root formation through hormone-like activity\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Beyond the plant itself, HS can alter the plant microbiome by favoring the recruitment of beneficial microorganisms in the rhizosphere\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;However, most commercial humic products are currently derived from non-renewable sources such as peat, coal, and leonardite. To find more sustainable production methods, recent research has focused on valorizing organic waste as a renewable source of humic substances. In particular, the HS extraction from biowaste was recently investigated for the development of multifunctional, non-toxic nanocomposites with potential applications in crop nutrition and plant stimulation\u0026nbsp;\u003csup\u003e15\u0026ndash;19\u003c/sup\u003e. Renewable sources, such as agro-industrial processing waste or animal husbandry byproducts, can produce highly bioactive materials that can replace non-renewable ones. The organic composition of recently produced alkaline hydrolysates of biowaste suggests their use as plant biostimulants, but the mechanisms underlying their effect on plants and soil are still poorly understood.\u003c/p\u003e\n\u003cp\u003eHere, a multi-omics approach was employed to assess the biostimulant potential of a humic-like extract sourced from biowaste. The experimental design combined leaf transcriptomics (RNA-seq) and soil eDNA metabarcoding to capture the interplay between plant gene expression and rhizosphere microbial dynamics. Despite the increasing number of single-omic studies, this work represents one of the first attempts to bridge these two methodologies in a unified biostimulant evaluation \u003csup\u003e20\u003c/sup\u003e. The molecular data were complemented by physiological measurements at the whole-plant scale and chemical analyses of the soil and leaf nutrient content. The aim of the study was to determine the biostimulant mechanism of action both on plant growth and metabolism and rhizosphere microbial communities. Two identical but independent experiments were performed with tomato plants grown under controlled conditions (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Transcriptomic, environmental metagenomic, physiological and chemical analyses were performed at two time-points, 3 and 6 weeks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data collected within this study will provide a valuable baseline for future research, serving as a comparative benchmark for testing other biostimulants and contributing to overcoming problems such as inconsistent formulations and protocols, which currently hinder their widespread adoption. Furthermore, this type of molecular-level study is essential to unraveling the complex mechanisms of action behind plant biostimulants, moving beyond phenotypic observations to a deeper understanding of the biological pathways they activate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRaw data are available under BioProject PRJNA1425279 (NCBI SRA) and processed data under Zenodo DOI 10.5281/zenodo.19336337.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDescription of the biowaste extract\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe humic-like biowaste extract (hereafter referred as E1) has been obtained at pilot scale by the extraction of organic compounds from a composted digestate under alkaline conditions (KOH). The digestate derived from the thermophilic\u003cem\u003e\u0026nbsp;\u003c/em\u003eanaerobic treatment of the organic fraction of municipal solid waste (OFMSW) at full scale was then sent to the second phase of aerobic composting. \u0026nbsp;The organic product obtained has been chemically characterized in an external laboratory (pH, organic matter and nutrients) (Table 1). Among the 7 doses preliminarily tested, D3 and D4 have been identified as suitable for agronomic purposes (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Biowaste extract E1 characterization\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e11.24 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUNI EN 13037:2012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eDry Matter content (DM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.59 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUNI EN 13040:2002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eTotal Organic Carbon (TOC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e28.1 \u0026plusmn;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e% DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDM 21/12/2000 Italian Law n\u0026deg; 21; 26/01/2001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eHumic and Fulvic Acids (HA+HF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e% DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMethods for fertilizers analysis. \u0026nbsp; Italian Law n\u0026deg; 29, 04/02/1991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eTotal N\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e4.5 \u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e% DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUNI 10780:1998 Appendix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eP (P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5)\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.47 \u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e% DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUNI EN 13657:2004 + UNI EN ISO 11885:2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eK ( K\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e9.8 \u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e% DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUNI EN 13657:2004 + UNI EN ISO 11885:2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Applied doses of biowaste extract E1 and composition of each dose\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTOC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eHA + HF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003et/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ekg/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ekg/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ekg/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e45.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e703.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e380.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e51.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e178.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e96.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlant material and growth conditions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTomato plants (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e L., cv. Moneymaker) were grown under controlled greenhouse conditions at the plant pathology greenhouse of the Fondazione Edmund Mach (San Michele all\u0026rsquo;Adige, Italy, \u003cstrong\u003eFigure 1\u003c/strong\u003e). Seeds were sown individually in 70 mL plug trays using a commercial technical substrate (Semina, Tercomposti s.p.a., Calvisano, Italy). Seedlings were grown under controlled conditions for 15 days (25 \u0026deg;C; 70 \u0026plusmn; 5% relative humidity [RH]; 16 h light/8 h dark photoperiod); then 30 plants were transplanted into 5.5 L pots filled with 4.6 kg of field soil collected in a local orchard (GPS coordinates N 46.187713, E 11.104974) and sieved at 10-mm granulometry. The soil was classified as a sandy loam soil (51.9% sand, 37.1% loam, 11% clay) with a soil organic matter content of 2.6%. After transplanting, plants were trained vertically on a string support and axillary shoots were not removed (no desuckering), allowing full evaluation of canopy development. Tomato cultivation was carried out in a greenhouse under controlled conditions (25 \u0026deg;C; 70% RH; 16 h light/8 h dark photoperiod) for six weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExperimental design and water and nutrient management\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental setup consisted of 30 plants divided into two independent groups (15 plants each) designated for two distinct destructive sampling timepoints: i) week 3 (Group T1) and ii) week 6 (Group T2, \u003cstrong\u003eFigure 1\u003c/strong\u003e). Within each group, plants were assigned to three conditions, in five biological replicates (plants): a reference control and two treatments, using two doses (D3 and D4) of biostimulant E1. The entire experimental trial was independently replicated twice under the same conditions, starting in November 2024 and February 2025, respectively.\u0026nbsp;A strict water management protocol was implemented to maintain the substrate at field capacity (FC) while entirely avoiding leachate production, thereby ensuring precise control over water and nutritional inputs. Fertigation was applied at 7, 14, 21, 28, 35 days post-transplanting (d.p.t.) by supplying a nutrient solution (0.9 g/L NPK UltraSol 13.5.30+2MgO) up to FC. The nutrient solution was adjusted to pH 6.5 using HNO\u003csub\u003e3\u003c/sub\u003e, achieving an electrical conductivity (EC) of 1650 \u0026micro;S/cm. Biostimulant treatments (D3 and D4) were applied at 2, 9, 23, 30 d.p.t. by delivering 100 mL of the respective formulation to the root zone. Two hours post-application, irrigation was completed with water up to FC. The control plants received exclusively water up to FC. At 16 and 37 d.p.t., solely water up to FC was supplied to all plants. Finally, supplementary irrigation with water up to FC was performed for all treatments at 4, 11, 18, 25, 32, 39 d.p.t..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiometric and physiological assessments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-destructive growth parameters, including plant height, stem diameter at the first true leaf, number of leaves with central vein length over 3 cm, length of the central vein of the compound leaf, phenological stage, and canopy characteristics, were monitored every 10\u0026ndash;15 days throughout the crop cycle \u003csup\u003e21\u003c/sup\u003e. The physiological status of the plants was assessed by quantifying the content of epidermal polyphenols (anthocyanins and flavonols) and chlorophyll at 3 and 6 weeks post-transplanting (21 and 42 d.p.t, respectively). Measurements were carried out using a Dualex Scientific optical leaf clip sensor (Pessl Instruments, Austria) on five randomly selected representative leaves per plant \u003csup\u003e22\u003c/sup\u003e. Destructive biomass analyses were conducted at week 3 for Group T1 and at week 6 for Group T2. At both sampling dates, the total epigeal biomass was evaluated. Fresh weight, excluding the root system, was recorded immediately after harvest. Subsequently, samples were subjected to a forced drying process at 25 \u0026deg;C for 20 days in order to quantify the dry weight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample preparation for leaf transcriptomic and rhizosphere communities metabarcoding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fourth leaf from the apex was\u0026nbsp;collected at the two time points, three weeks (T1) and six weeks (T2), from tomato plants grown in the three conditions, in 5 biological replicates (\u003cstrong\u003eFigure 1\u003c/strong\u003e). They represent young but fully expanded leaves. Samples were immediately flash-frozen in liquid nitrogen and stored at -80\u0026deg;C. A total of 60 samples were processed. Rhizosphere soil was collected from the same tomato plants used for the transcriptomic analysis. In addition, one bulk soil sample was collected at the beginning of each independent experiment (T0), prior to plant transplanting, as a reference. Plants were removed from their pots and the roots were gently shaken to discard excess soil particles; the rhizosphere soil was then obtained by vigorously shaking the roots into a plastic bag. To ensure the purity of the soil, it was sieved through a 2 mm sieve to remove any debris. An aliquot of this soil was collected in 50 mL tube, crio-lyophilized and stored at room temperature until DNA extraction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRhizosphere metabarcoding sequencing and data processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal genomic DNA was extracted from 250 mg of soil using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), following the manufacturer\u0026rsquo;s protocol. For the prokaryotic community, the V4\u0026ndash;V5 hypervariable regions of the 16S rRNA gene were amplified using the primer pair 515F (5\u0026prime;-GTGCCAGCMGCCGCGGTAA-3\u0026rsquo;) and 909R (5\u0026prime;-CCCCGYCAATTCMTTTRAGT-3\u0026rsquo;) (Huang et al., 2014). For the fungal community, the ITS1 region was targeted using the primers ITS1-F (5\u0026prime;-CTTGGTCATTTAGAGGAAGTAA-3\u0026rsquo;) and ITS2-R (5\u0026prime;-GCTGCGTTCTTCATCGATGC-3\u0026rsquo;) \u003csup\u003e23,24\u003c/sup\u003e. Libraries were prepared according to the Illumina 16S Metagenomic Sequencing Library Preparation guide. Sequencing was performed on the Illumina MiSeq platform using v3 chemistry (2 \u0026times; 300 bp paired-end reads). Raw reads were processed with MICCA (v.1.7.2) software \u003csup\u003e25\u003c/sup\u003e. Specifically, the paired end reads were merged with a minimum overlap length of 100 and a maximum number of allowed mismatches of 32. Primers were trimmed using Cutadapt v.1.18 \u003csup\u003e26\u003c/sup\u003e, and merged reads shorter than 100 bp with an expected error rate higher than 0.25% were removed. Filtered sequences were clustered into Amplicon Sequence Variants (ASV) using the UNOISE \u003csup\u003e27\u003c/sup\u003e algorithm available in MICCA \u0026nbsp;utilizing VSEARCH \u003csup\u003e28\u003c/sup\u003e. Taxonomic assignment was carried out using the RDP classifier v. 2.14 \u003csup\u003e29\u003c/sup\u003e update to training set n.19 \u003csup\u003e30\u003c/sup\u003e for the 16S and against the fungal UNITE + INSD v.8.3 database for the ITS \u003csup\u003e31,32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlant transcriptomic sequencing and data processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrozen tomato leaf samples were ground using a MixerMill (Retsch). Total RNA was extracted from 50 mg of powder using the Spectrum Plant Total RNA kit (Sigma), following the manufacturer\u0026apos;s instructions, and then subjected to TURBO DNAse (Invitrogen) digestion to remove any contaminating DNA. The purified\u0026nbsp;total RNA was used to construct mRNA libraries using the KAPA Stranded mRNA-Seq Kit (Roche, Basel, Switzerland), following the manufacturer\u0026rsquo;s instructions. RNA integrity and library size distribution were assessed using the Agilent 4150 TapeStation system (Agilent Technologies, Santa Clara, CA, USA). Libraries were quantified via RT-qPCR to ensure optimal loading concentrations. Sequencing was performed on the Illumina NovaSeq X Plus platform, generating 2 \u0026times; 150 bp paired-end reads.\u003c/p\u003e\n\u003cp\u003eReads were pre-processed\u0026nbsp;using fastp \u003csup\u003e33\u003c/sup\u003e (version 0.23.4) with default parameters, retaining on average 97.6% of input reads. They were aligned using STAR \u003csup\u003e34\u003c/sup\u003e (version 2.7.11b) with default parameters to the latest tomato reference genome (S. lycopersicum v5.0, assembly SL5.0, ITAG5.0 annotation), available via Phytozome JGI (https://phytozome-next.jgi.doe.gov/info/Slycopersicum_ITAG5_0). Aligned reads were subsequently assigned to genomic features using featureCounts \u003csup\u003e35\u003c/sup\u003e (version 2.0.3) from the Subread package, employing the ITAG5.0 gene annotation and default parameters for paired-end, reverse-stranded counting. Gene-level raw counts were obtained for all annotated features, providing the basis for downstream quantitative analyses. In addition to raw counts, normalized expression values were calculated as counts per million (CPM) to account for differences in library size across samples, facilitating direct comparison of gene expression levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSoil chemical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBulk soil was collected from the area surrounding the roots, from the same plants\u0026rsquo; pots used for the omics analyses. The soil was air-dried and sieved (\u0026lt;2mm). For organic C and total N quantification, the samples were also ground \u0026lt;0.02mm. A CN analyzer (Primacs SNC100-IC-E, Skalar, Breda, The Netherlands) was used for total N quantification after combustion at 1200\u0026deg;C (Dumas method, ISO13878:1998). Temperature-dependent differentiation of organic carbon fractions (TOC400 released up to 400\u0026deg;C and ROC measured at 600\u0026deg;C) was carried out using a temperature-gradient method after dry combustion in the presence of oxygen (UNI EN 17505:2024). Total organic carbon was calculated as the sum of these two fractions. \u0026nbsp;Soil reaction (in water 1:2,5) was measured with a pH-meter (Inolab level 2, WTW, Weilheim, Germany). Exchangeable K and Mg were quantified by spectrometry with an Optima 8300 ICP-OES (PerkinElmer, MA, USA) after ammonium acetate 1M pH 7 extraction. Cation exchange capacity (CEC) was determined using BaCl\u003csub\u003e2\u003c/sub\u003e solution buffered at pH 8.1 (ISO 13536:1995). Available (Olsen)-P was solubilized in sodium bicarbonate and quantified with a spectrophotometer (ONDA V10, Giorgio Bormac, Modena, Italy) after color development at high temperature (ISO 11263:1994).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLeaf nutrient analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe leaf ground powder remaining after RNA extraction was freeze-dried and submitted to an external laboratory (Ecoopera, www.ecoopera.coop) for the analysis of macronutrients (nitrogen, calcium, magnesium, potassium, and phosphorus). Analytical methods are reported in the data table. A total of 15 samples from T1 (3 weeks) of the first experiment and 15 samples from T2 (6 weeks) of the second experiment were selected for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Record\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw RNAseq data of the 60 tomato leaf samples and the raw metabarcoding sequencing data of the 60 samples of tomato rhizosphere and 2 T0 soil samples were deposited at SRA NCBI under the accession BioProject PRJNA1425279. BioSample accessions, experimental groups, internal sample IDs, and SRA run identifiers for each biological replicate are available as \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e. Transcriptomic data (raw counts), normalized data (Counts per Million, CPM), as well as\u0026nbsp;phenotypical data, soil analysis and leaf nutrient analysis can be downloaded from Zenodo, 10.5281/zenodo.19336337.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnical Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 60 RNA-seq libraries were generated from 30 tomato leaf samples across two independent experimental trials. Sequencing on the Illumina NovaSeq X Plus platform (2 \u0026times; 150 bp paired-end) yielded approximately 438.8 Gb of raw data. The total number of raw reads, pre-processed clean reads, uniquely mapped reads and successfully assigned sequences is shown in \u003cstrong\u003eFigure 2a\u0026nbsp;\u003c/strong\u003e(data in \u003cstrong\u003eSupplementary Table 2)\u003c/strong\u003e. Sequencing output is summarized as raw read pairs and library quality is described by four fastp-derived metrics: base-call error rate, the proportions of bases achieving Phred scores \u0026ge; 20 (Q20) and \u0026ge; 30 (Q30), and GC content (\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e). To assess global transcriptome homogeneity across libraries, variance-stabilizing transformation (VST) \u003csup\u003e36\u003c/sup\u003e was applied to raw count data using blind dispersion estimation \u003csup\u003e37\u003c/sup\u003e. Euclidean distances were computed on the transposed VST expression matrix and visualized as a symmetric heatmap, with rows and columns ordered by complete-linkage hierarchical clustering of the distance matrix (\u003cstrong\u003eFigure 2b\u003c/strong\u003e). A total of 124 Amplicon libraries were generated from 60 rhizosphere soil and 2 bulk soil samples. Sequencing on the Illumina MiSeq Platform (2 x 300 bp paired-end) yielded approximately 18.66 Million raw reads. On average, 78.3% of the clean reads were clustered into Amplicon Sequence Variants (ASV) (range: 68.0-88.6%, \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data can be retrieved at SRA NCBI under the accession BioProject PRJNA1425279. The RNAseq normalized data, the plant physiological data, and soil and leaf chemical data can be downloaded from Zenodo, 10.5281/zenodo.19336337 under CC-BY 4.0 licence.\u003cem\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 5\u003c/strong\u003e details all the software and versions used in this study for transcriptomics and metagenomics. Unless specific parameter details are provided, the programs were utilized with their default parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank our colleagues at FEM Roberta Valentini for support in the greenhouse and Daniela Nicolini for RNA extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.S., D.Bo., C.M.L.O., S.P., P.S., M.P. and O.G. conceived the work and designed the experiments; O.G., D.Bo., C.M.O.L. and S.P. set up the experiments and collected the samples; O.G. did physiological measurements; E.S. and S.L. did DNA and RNA library preparation; P.S. and M.P. did formal analyses and data curation; D.Be. performed soil chemical analyses; S.S. acquired funding and coordinated the project. S.S., D.Bo., C.M.L.O., S.P., P.S., M.P. and O.G. drafted and then revised the manuscript. All the authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of the project PERCIVAL - Processi di EstRazione di bioprodotti da sCarti agroIndustriali e VALorizzazione in cascata (ARS01_00869) co-funded by the Italian Ministry of University and Research (MUR) under the National Operational Programme on Research and Innovation 2014-2020.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVelten, S., Leventon, J., Jager, N. \u0026amp; Newig, J. What is sustainable agriculture? A systematic review. \u003cem\u003eSustain.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 7833\u0026ndash;7865 (2015).\u003c/li\u003e\n\u003cli\u003eRobinson, G. M. 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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 550 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-9556034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9556034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The urgent need for sustainable agriculture, aimed at preserving soil fertility while meeting the food demands of a growing global population, is driving the development of biowaste-derived products. These products represent a cornerstone of the circular economy, transforming organic waste into high-value biostimulants that enhance plant productivity and stress tolerance. This study presents a comprehensive multi-omics investigation into the biostimulant effects of a humic-like extract derived from biowaste, tested on pot-grown tomato plants under controlled conditions at two different dosages. The adopted approach integrates rhizosphere eDNA metabarcoding and leaf RNA sequencing (RNA-seq) to capture the simultaneous response of the soil microbial community and the plant transcriptome. These molecular surveys were complemented by physiological parameter measurements and nutrient analysis of both soil and leaves. This holistic characterization provides a robust scientific foundation to assess the efficacy of biowaste extracts, elucidating their impact on plant growth and providing novel insights into their integrated mechanisms of action within the plant-soil system.","manuscriptTitle":"Multi-omics analyses of tomato rhizosphere and leaves treated with a biowaste-derived biostimulant extract","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 05:48:35","doi":"10.21203/rs.3.rs-9556034/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":"39570514-f488-4705-bf56-b06ee424aadb","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-09T09:20:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T14:11:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-05T14:10:19+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T09:25:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 05:48:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9556034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9556034","identity":"rs-9556034","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}