title: RNA-seq of Acinetobacter.sp
design: Comparative transcriptome analysis was performed to elucidate the selenite reduction mechanism and biosynthesis of selenium nanoparticles. A total of six samples processed under the same culture conditions were prepared from culture broth without Na2SeO3 (control) and supplemented with 2 mM Na2SeO3 (selenite treatment). Total RNA from bacterial cells was extracted with the TRIzol (Invitrogen, USA) reagent, and genomic DNA was removed using DNaseI (TaKara, Dalian, China). RNA quality was analysed using a 2100 bioanalyser (Agilent, USA) and concentration was quantified with an ND-2000 spectrophotometer (NanoDrop Technologies, USA). Next, 5 g of total RNA was used to prepare RNA-seq strand-specific libraries according to the manufacturers instructions (TruSeq RNA Sample Preparation Kit, San Diego, CA). Briefly, the RiboZero rRNA removal kit (Epicenter, USA) and Fragmentation Buffer were used for rRNA removal and fragmentation, respectively. cDNA synthesis, end repair, A base addition, and connection of Illumina index connectors were performed according to the Illumina protocol. The library was then sized for 200300 bp cDNA target fragments on 2% agarose, and then amplified by 15 PCR cycles using Phusion DNA polymerase (NEB, USA). Paired-end libraries were sequenced after TBS380 (YPH-Bio, Beijing) quantification using an Illumina NovaSeq 6000 sequencer (150bp*2, Shanghai BIOZERON Co., Ltd ). Trimming and quality control of raw paired-end reads was conducted using the Trimmomatic software package (SLIDINGWINDOW:4:15 MINLEN:75). The Rockhopper software was then used to individually align the clean reads to a reference genome with orientation patterns. Computational analysis of bacterial RNA-seq data was also conducted with Rockhopper using the RNA sequencing reads generated by high-throughput sequencing technology as input. The software calculates gene expression levels using default parameters. The numbers of fragments per kilobase per million mapped reads (FPKM) were then used to calculate the expression level of each transcript to discriminate DEGs (differentially expressed genes) between samples from two different treatments. The edgeR package was used for differential expression analysis. The DEGs between the two samples were selected using the following criteria: the FDR (false discovery rate) should be less than 0.05 and the log fold change greater than 2. The Goatools library was used to perform GO (Gene Ontology) functional enrichment analysis to gain insights into the function of the DEGs, and KOBAS (KEGG Orthology Based Annotation System) was used for KEGG pathway analysis. DEGs were considered to be significantly enriched in GO terms and metabolic pathways when the Bonferroni corrected P-value was less than 0.09.