*3.5. Transcriptomics Analysis*

RNA sequencing (RNA-seq) experiment was performed on 18 samples collected at 3 h, 6 h, and 12 h after inoculation in treated medium with glucose addition (+G) and control group (−G), respectively. Total RNA was extracted using the plant RNA extraction kit (TaKaRa, Tokyo, Japan), and contaminating DNA was removed with RNase-free DNase I. The assessment of RNA concentration and quality was performed by Nano-Drop (ND1000 UV–Vis spectrophotometer; NanoDrop Technologies, Wilmington, Delaware, USA) and Agilent 2100 Bio analyzer (Agilent Technologies, Inc., Palo Alto, CA, USA). After purification, poly-A containing mRNA molecules were fragmented and reverse-transcribed into cDNA, then adaptor sequences were ligated to cDNA molecules. The products were purified and enriched with PCR amplification. The PCR yield was quantified by QuantiFluor® dsDNA System, and samples were pooled together to form the final library. The pooled samples were subjected to cluster generation and sequenced using an Illumina HiSeq. Raw reads were trimmed and clipped with SeqPrep (https://github.com/jstjohn/SeqPrep, accessed on 20 January 2021) and Sickle (https://github.com/najoshi/sickle, accessed on 20 January 2021) software by filtering the low-quality reads, reads with adaptors and reads with unknown bases (N bases more than 5%) to ge<sup>t</sup> clean reads. The RNA-seq data were deposited in the Gene Expression Omnibus under accession number GSE168959.

De novo transcriptome assembly was performed with Trinity software (Version v2.8.5, https://github.com/trinityrnaseq/trinityrnaseq, accessed on 20 January 2021). The assembled transcriptome sequences were annotated using major databases (Non-Redundant Protein Sequence Database (NR), Swiss-Prot, Pfam protein family database, Clusters of Orthologous Groups (COG), Gene Ontology database (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)), and statistics on its annotations were carried out in each database. RSEM software was used to calculate gene expression levels from RNA-seq data, and the expression profiles for all genes were normalized with the standardized transcript abundance measure "transcripts per million" (TPM) [48]. R studio (Version 4.0.2., Lucent Technologies Inc. (formerly AT&T Bell Laboratories), Murray Hill, NJ, USA) software was used to perform statistics: Pearson correlation between all samples was calculated using cor function, and PCA analysis was performed with all samples using princomp function. Finally, differential expressed genes (DEGs) were identified between samples and clustered with DEseq2 (Version 1.24.0, http://bioconductor.org/packages/stats/bioc/DESeq2/, accessed on 16 March 2021) software using the parameters as |log2FC| (Fold Change) ≥ 1.00 and adjusted *p* value (padj) < 0.05.

#### *3.6. Weighted Gene Co-Expression Network (WGCNA)*

For investigating correlations between genes and glucose-induced trophic transition, a global weighted gene co-expression network (WGCNA) were constructed as follows (https: //horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/, accessed on 16 March 2021): (1) genes and samples were first filtered by the variation efficient and hierarchical clustering analysis; (2) co-expressed gene modules were identified from the network using the function blockwise Modules; (3) modules with higher Pearson correlation coefficient were selected to perform the hub gene analysis.

## *3.7. Metabolomics Analysis*

The cells were collected by centrifugation and quenched rapidly with liquid nitrogen. Cells were homogenized by a tissue lyser at 30 Hz for 1 min, and the adapter set of the tissue lyser was pre-chilled at −80 ◦C overnight before use. The metabolites were extracted by pre-chilled methanol: acetonitrile: H2O (2:1:1, *v/v/v*) after homogenized using a tissue lyser. The supernatant was collected and vacuum-dried using Centri Vap benchtop vacuum concentrator and re-suspended in methanol: H2O (1:9, *v/v*). Twenty microliters of each sample was taken and mixed into a quality control (QC) sample for evaluation of reproducibility and stability of the analysis.

Metabolomics analysis was performed with an UPLC (waters, USA) in-line with Q Exactive mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipping with a BEH C18 column (1.7 μm, 2.1 mm × 100 mm, Waters, USA). The eluents were H2O containing 0.1% (*v/v*) formic acid (A) and methanol containing 0.1% (*v/v*) formic acid (B) for positive ion mode (ESI+), and H2O containing 10 mM ammonium formate (A) and 95% methanol containing 10 mM ammonium formate (B) for negative ion mode (ESI-). The linear gradient was as follow: 0–1 min, 98% A; 1–9 min, 98% A to 2% A; 9–12 min, 2% A; 12–12.1 min, 2% A to 98% A and maintained until 15 min. The flow rate was at 350 μL/min, and injection volume was 5 μL. MS data was recorded over the m/z range 70–1050. The flow rate of sheath gas was 40, and aux gas flow rate was 10. The spray voltage was 3.8 kV for positive ion mode and 3.2 kV for negative ion mode. The capillary temperature was 320 ◦C and the aux gas heater temperature was 350 ◦C. Data reprocessing was performed with Compound Discoverer 3.0 (Thermo Fisher Scientific, USA) and MetaX (http://metax.genomics.cn, accessed on 16 March 2021) [49].
