**2. Materials and methods**

#### **2.1. Plant material and growth conditions**

*A. thaliana* genotypes (*hen1‐1, hyl1‐2, rdr6‐15*, Col-0 and Ler-0) were germinated in soil main‐ tained at 21°C day/20°C night, with 9 h of light (120 µE m−2s1 ) and 60% of relative humidity. Selected *A. thaliana* accession Ler-0 was described to be susceptible to *C. higginsianum* infection, while Col‐0 showed intermediate resistance [31]. Ler-0 is the wild-type genetic background of *hen1‐1* mutants; all other mutants have a Col-0 genetic background. One week after germina‐ tion, seedlings were individually transferred to 33-mL Jiffy pellets and kept in the same con‐ ditions until the infections. *Z. mays* (variety Jubilee, West Coast Seeds, www.westcoastseeds. com) was cultured in a soil-free plant growth system as described by Ref. [32].

#### **2.2. Pathogen and pest cultivation and inoculation**

*C. higginsianum* IMI34 349061-GFP [26] was cultured on potato dextrose agar (PDA) in a growth chamber under permanent light at 25°C. For infections, a fungal spore suspension of 106 spores mL−1 was prepared from 2-week-old cultures. Four- to five-week-old *A. thaliana* plants were drop-inoculated with 5 µL of the spore suspension. The plants were then incu‐ bated in darkness for 16 h at 25°C and 100% relative humidity. Post incubation, the growth condition of the plants was changed to long day (16 h/8h day/night cycle at 25°C). Control plants were treated only with sterile water. *C. graminicola* M1.001 was cultivated on PDA under permanent light at 25°C; infection assays were performed on 12‐day‐old maize plants as previously described [32].

#### **2.3. Quantification of fungal growth**

*In planta* fungal growth of *C. higginsianum* was measured every 24 h post infection for 4 days. The infection sites of the green fluorescent protein-expressing fungal strain were illuminated using a Nikon C-SHG1 UV lamp. Images were captured using a Nikon DS-L1 camera and the pictures were further analysed with the help of ImageJ (http://rsbweb.nih.gov/ij/) and Adobe Photoshop CS3 (http://labs.adobe.com). The area of fungal growth was measured in pixels and converted to mm2 .

#### **2.4. Hormone quantification**

together with a defective setup of chemical defences. Moreover, to better understand the role of sRNA during infection with *Colletotrichum* spp., we performed an miRNA expression pro‐ filing to obtain a deeper insight into adaptations of the sRNA transcriptome in different *C. graminicola*-infected maize tissues. The miRNA profiling demonstrated that the vast majority of altered miRNAs were targeting genes that are not directly linked to antifungal-defence pathways, suggesting that antifungal-defence responses are not regulated by specifically

This chapter provides a multi‐omics analysis of sRNA‐mediated antifungal plant reactions on a phenotypic, metabolomic as well as transcriptomic point of view. Altogether, our data pro‐ pose a rather indirect defensive role of sRNAs in calibrating metabolomic and transcriptomic balances during antifungal responses against *Colletotrichum* spp. Future putative applications

*A. thaliana* genotypes (*hen1‐1, hyl1‐2, rdr6‐15*, Col-0 and Ler-0) were germinated in soil main‐

Selected *A. thaliana* accession Ler-0 was described to be susceptible to *C. higginsianum* infection,

) and 60% of relative humidity.

of sRNA-based fungal control strategies will be commented.

**Figure 2.** *Zea mays* leaf (left) and root (right) infected with *Colletotrichum graminicola*.

tained at 21°C day/20°C night, with 9 h of light (120 µE m−2s1

induced miRNAs.

12 Plant Engineering

**2. Materials and methods**

**2.1. Plant material and growth conditions**

For hormone analysis, salicylic acid, jasmonic acid and abscisic acid were quantified simultane‐ ously from leaf material using UHPLC-MS/MS as described [32]. Hormone measurements were performed 4 days post *C. higginsianum* infection. To analyse each Arabidopsis accession, three independent biological replicates per sample were generated, each replicate a pool of five plants.

#### **2.5. Metabolomic profiling**

For metabolomic analysis, 4-week-old Arabidopsis plants were infected with *C. higginsianum*. Metabolites were isolated and analysed 4 dpi as described [32]. Six technical replicates for each treatment were analysed, and each replicate consisted of a pool of four plants.

#### **2.6. Gene expression analysis**

Confirmation of down-regulation of maize genes putatively targeted by miRNAs was con‐ ducted as described [32], using ZmGAPc as normalizing gene. Primer sequences are as fol‐ lows: ZmATPS\_fw: tcgtattaatgctggtgcaaac, ZmATPS\_rev: ctctgtggggtggctcat; ZmSAT\_fw: ttataaaaaccctgttcttctgctc, ZmSAT\_rev: aggacaccttcctcaagaacc; ZmGAPc\_fw: gcatcaggaaccct‐ gaggaa, ZmGAPc\_rev: catgggtgcatctttgcttg.

### **2.7. Deep sequencing and Northern blotting of maize sRNAs**

For sRNA library preparation, six biological replicates were pooled and total RNA was iso‐ lated using Trizol (Invitrogen, www.invitrogen.com); 10 µg of total RNA was further pro‐ cessed using an Illumina-Solexa deep-sequencing approach at FASTERIS (http://www. fasteris.com). The expression of selected miRNAs was further analysed using sRNA Northern blotting techniques as described [33].

### **2.8. Identification and quantification of conserved miRNAs**

To identify conserved maize miRNAs, sequences of 4677 mature plant miRNAs were down‐ loaded from miRBase (release 18.0, November 2011). Identical miRNA sequences identified in different species or duplicated loci in a genome were collapsed, resulting in a non-redun‐ dant list consisting of 2228 unique miRNAs. Sequences belonging to the same miRNA family were further analysed by multiple alignment using ClustalW (www.clustal.org) and classified in subgroups to distinguish bona fide mature miRNAs from misannotated miRNA\* forms or sequences generated from different regions of the same precursor. This non-redundant library was then applied to screen the small RNA libraries. All the small RNA reads in the range of 20–24 nt in size, and which are represented and represented by at least two reads in a library were aligned to the 1772 unique miRNAs derived from miRBase. For the screening, a maximum of three mismatches was allowed and up to 2 nt overhanging nucleotides at the 5' and/or 3' end. Alignments were performed using SeqMap [34]. The output was filtered and reformatted with custom PERL scripts, classifying the identified miRNAs according to miRBase.

#### **2.9. Target prediction of maize miRNAs**

Putative targets of maize miRNAs were identified using the psRNATarget web server (http:// bioinfo3.noble.org/miRU2/) against *Z. mays* DFCI Gene index (version 19) and *Z. mays* PlantGDB genomic project. Default settings were applied.

#### **2.10. Statistical analysis**

Variances of quantified levels of metabolites and fungal growth for multiple groups were analysed by a one-way analysis of variance (ANOVA); a *P*‐value of <0.05 was considered significant. The Mann–Whitney *U*-test was used to compare significant differences between two sample groups. All statistical analysis was performed using Sigma Plot 11.0 (http://www. sigmaplot.com).
