## 2 Materials and Methods
### Sample Preparation and Analysis for Transcriptome Sequencing  
All cultured broth was centrifuged at 1700\(\times\)_g_ for 5 min to separate the fermentation supernatant from the cell pellet. All solvents and samples were filtered through 0.22 \(\upmu\)m filters before injection. Subsequently, the adenosine content in the fermentation broth was determined using the HPLC method [14]. An Ultimate AQ-C18 column (250 mm \(\times\) 4.6 mm i.d., 5 \(\upmu\)m) from Welchmat (Shanghai, China) was used. The column temperature was maintained at 30 \({}^{\circ}\)C. All samples were separated using a mobile phase consisting of 0.4% phosphoric acid (A) and methanol (B). The linear gradient conditions were 90% A and 10% B for 25 min. The flow rate was set at 1.0 mL/min, and the injection volume was 20 \(\upmu\)L. The detection wavelength was set at 257 nm. An adenosine standard was purchased from Sigma-Aldrich Co., Ltd., serial dilutions were prepared at concentrations ranging from 0.1 mg/L to 100 mg/L, and the adenosine yield was quantified by comparing chromatographic peak areas against the standard curve.  
Cell pellets collected at 60 h of cultivation with three individual biological repeats for each group were flash-frozen in liquid nitrogen to prepare for transcriptome sequencing, which was performed by Magigene Co., Ltd. (Shenzhen, Guangdong Province, China) using an Illumina sequencing platform. Raw sequencing reads underwent quality control (QC), and adapter/low-quality sequences were filtered using Trimmomatic to obtain clean data [15]. Ribosomal RNA-depleted sequencing reads were aligned to the reference _Bacillus subtilis_ 168 genome (RefSeq: GCF_000009045.1) using Bowtie 2 [16].  
Transcript quantification was performed using RNA-Seq by Expectation Maximization (RSEM) to obtain transcript-level read counts, followed by conversion to gene-level read counts with the R 4.3.0 software and the R package tiximport [17]. Gene expression levels were quantified using the TPM (transcripts per million) method [18]. The formula for TPM is as follows:  
TPM = actual gene counts/total counts of clean reads \(\times\) 10\({}^{6}\)  
Differential gene expression analysis was performed using DESeq2, with the basal medium-cultured sample as the control, to compare transcriptional abundance between the control and treatment groups [19]. We considered segments with a fold change of \(\geq\)2 and a false discovery rate (FDR) of <0.05 in our comparison as significantly differentially expressed genes (DEGs).  
DEGs were functionally annotated by mapping to Gene Ontology (GO) terms using WEGO ([[http://wego.genomics.org.cn](http://wego.genomics.org.cn)]([http://wego.genomics.org.cn](http://wego.genomics.org.cn))) [20]. These terms were summarized and classified by their function and location. Then, GO terms that were significantly enriched compared with the background genome were verified through a hypergeometric test, and _p_-values were calculated using the following formula:  
\[P=1-\sum_{i=0}^{m=1}\frac{\binom{M}{i}\binom{N-M}{n-i}}{\binom{N}{n}}\]where \(N\) represents the number of all genes with GO annotations, \(M\) is the total number of genes annotated to the specific GO terms, and \(m\) is the number of DEGs in \(M\). The \(p\)-value was FDR-corrected, and an FDR of \(\leq\)0.05 was used as the threshold [21].  
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted using Cluster Profiler, quantifying DEGs enriched at different pathway levels [22]. The method for calculating the \(p\)-value was the same as that used in the GO analysis [23]. Both GO and KEGG analyses covered the entire genome.