4.3.2. Gene Expression Analysis

Gene expression analysis was monitored at 12, 24, 48, and 72 h in leaves of tomato after transplantation and exposure to biosolids and Forl. The relative gene expression analysis of *GLUA*, *CHI3*, *PR1-a*, *LOX*, and *AOC* genes (Table 5), associated with enhanced resistance against pathogen infection in tomato plants, was performed using the reversetranscription quantitative polymerase chain reaction (RT-qPCR). Total RNA from tomato leaves was extracted using the Monarch Total RNA miniprep kit (New England Biolabs Inc., Ipswich, MA, USA) in three biological replications at each time point and treatment. RNA quality and quantity were assessed using the UV-Vis Spectrophotometer Q5000 (Quawell Technology Inc., San Jose, CA, USA) and optically with gel electrophoresis in 1.5% agarose gel. Pooling of biological replicates was performed equimolarly with 1 μg per replicate at a working concentration of 100 ng μL<sup>−</sup>1.


**Table 5.** Primers used in RT-qPCR of the 5 genes (*GLUA, CHI3, PR1-a, LOX,* and *AOC*) associated with response mechanisms to pathogens and the 3 housekeeping genes (*β-actin, CyOXID,* and *Gapdh*).

> The cDNA was prepared with 1 μg pooled RNA using the PrimeScript™ Reverse Transcriptase kit (TAKARA BIO Inc., Kusatsu, Shiga, Japan) and random hexamers according to the manufacturers' instructions. Relative gene expression was assessed by real-time quantitative reverse transcriptase PCR (RT-qPCR) performed on a Rotor-Gene 6000 real-time 5-Plex HRM PCR Thermocycler (Corbett Research, Sydney, Australia) using the Rotor-Gene Q software version 2.0.2 (Corbett Life Science, Cambridge, UK) and melt curve analysis. The reaction mixtures were prepared in a total volume of 20 μL per reaction

consisting of 50 ng cDNA, 1× PCR buffer, 0.5 μM forward and reverse primers, 0.2 mM dNTPs, 1.5 mM SYTO™ 9 Green Fluorescent Nucleic Acid Stain (Invitrogen, Eugene, OR, USA), and 1 U Kapa Taq DNA polymerase (Kapa Biosystems, Wilmington, MA, USA). The amplification was performed according to the following thermal cycling conditions: initial denaturation at 95 ◦C for 2 min, followed by 35 cycles of 95 ◦C for 10 s, 54 ◦C for 25 s, and 72 ◦C for 30 s. Fluorescence was acquired at the end of each PCR cycle. Melting curve analysis was performed at temperature range between 65–95 ◦C and in increments of 0.3 ◦C every 2 s; fluorescence was measured at the end of each increment step.

The tomato gene-specific primers (Table 5) *GLUA* [35], *CHI3* [35,37], *PR1*-*a*, *LOX* [35], and *AOC* [7,36,109] were used for the relative gene expression analysis. The housekeeping genes encoding for actin (*β-actin*) [35], mitochondrion cytochrome oxidase subunit I (*Cy-OXID*) [110], and Glyceraldehyde-3-phosphate-dehydrogenase (*Gapdh*) [51] were used as reference genes for normalization in tomato. Data analysis was carried out with relative quantification in three technical replicates for each pool, using the 2−ΔΔCT method [111], and data normalization was achieved using the expression levels of the reference genes.

#### *4.4. Characterization of the Soil Substrate Microbiome Using 16S Sequencing*

Soil substrate samples near the roots were collected from the same pots that the leaf tissue was collected, at 12 and 72 h after treatment application, in 3 biological replications. The total microbial genomic DNA was extracted with the NucleoSpin Soil, Mini kit (Macherey-Nagel GmbH & Co.KG., Düren, Germany). The DNA quantity and quality were assessed using the UV-Vis Spectrophotometer Q5000 (Quawell Technology Inc., San Jose, CA, USA). The 16S rRNA libraries were constructed after amplification of the 16S subunit of the prokaryotic ribosomal 16S RNA gene (16S rRNA), using primers that amplify between the V3 and V4 regions of the gene for each sample. The 2 × 300 bp paired-end reads were generated using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA).

For the identification and quantification of the detected microorganisms, as well as the comparison between the samples, bioinformatics analysis was performed using Mothur programs (1.44.1) and R packages (4.0.3) [112], Phyloseq (1.34.0), ggplot2 (3.3.2), DESeq2 (1.28.1), and vegan (2.5.6). The raw sequencing data were processed to remove low-quality sequences as well as DNA adapters. Pure sequences were grouped into Operational Taxonomic Unit (OTUs) with a sequence similarity rate of 97%. The classification of OTUs was characterized by SILVA multiple sequencing (SILVA alignment Release 132, 8517 bacteria, 147 archaea, and 2516 eukarya sequences). The Mothur analysis was performed following the Standard Operating Procedure (SOP) for the Mothur Metagenomics analysis as described in Schloss et al. [113].

Variation of microbial communities within a single soil substrate or between the soil substrates was found using alpha and beta diversity, respectively. More specifically, the αdiversity was evaluated by rarefaction curves measuring the Shannon, Chao1, Abundancebased Coverage Estimator (ACE), and Simpson diversity indices. The β-diversity was assessed by non-metric multidimensional scaling (NMDS) to define the structure of the microbiome. Canonical correspondence analysis (CCA) was used to relate species abundance to the treatment and time variables. A graphic interpretation of the main principal axes by tri-plot on the two dimensions was obtained with the Phyloseq software. The functional potential of the detected microbial communities was predicted based on the 16s rRNA marker using the PICRUSt software (2.3.0\_b) [114]. The functional prediction was based on the unique OTU sequences and the biom file produced by Mothur (1.44.11). The analysis returned the relative abundance of the predicted EC codes and their related pathways description. The statistical analysis and final visualization of the results were performed using the Statistical Analysis of Metagenomic Profiles (STAMP) software (2.1.3) [115].
