Bt Research 2025, Vol.16, No.4, 136-146 http://microbescipublisher.com/index.php/bt 139 analysis. First, quality control software (such as FastQC) is required to evaluate the quality of original sequencing reads and perform low-quality read truncation. The clean reads are then aligned to the Bt reference genome, which is usually done using the efficient alignment software HISAT2. HISAT2 uses hierarchical indexes and graph models to support sequence alignments containing a large number of alternative splicing or mutations, and also has good alignment performance for Bt-rich genomes. The obtained sam/bam files can be used for transcript assembly and quantification. If the research focuses on mRNA expression, the reads counts of each gene can be counted directly based on gene annotation; you can also choose assembly software such as StringTie for transcript assembly to discover new transcript variants or unannotated genes. For prokaryotes such as Bt, due to the absence of complex alternative splicing, transcriptome assembly is mainly used to verify UTR regions or polycistron transcription units, etc. Tools such as StringTie have been updated continuously in recent years. For example, the latest version of StringTie2 supports mixed assembly of long-read and short-read data, improving the accuracy of transcript reconstruction. After transcription assembly and quantification were completed, differential expression analysis software such as DESeq2 or edgeR was used to normalize the expression amount of genes under different conditions and test the difference significance. DESeq2 is based on a negative binomial distribution model and strictly standardizes sequencing depth and biological repetitions. It is one of the classic tools for RNA-Seq difference analysis. 4.2 Analysis of expression profile of Bt toxingene Through transcriptome sequencing, the expression dynamics of insecticidal toxin genes in Bt strains can be analyzed, thereby understanding the regulatory rules of toxin synthesis. Many studies have focused on transcriptional changes in cry toxin genes during the Bt growth and development cycle. For example, a comparative transcriptome analysis was conducted on the high-virulent Bt strain Bt4.0718 at different stages of growth (medium logarithmic phase, stable phase, etc.), and the results revealed a panoramic view of gene expression during bud cell formation and accompanying cell crystal formation. During the logarithmic growth phase, Bt expresses a large number of challenge factors such as metabolic enzymes and lysozymes. In the late stage of spore and crystal formation, the transcription of major crystal protein genes such as cry1Aa and cry2Aa has reached a peak, becoming the most actively expressed gene (Chen et al., 2022). The same study also compared the expression differences of different virulent Bt strains, and found that high strains express more types of cry and vip3A toxin genes than low strains at all stages of growth, and still maintain a higher toxin gene transcription level during the transition to the stability phase. In addition, RNA-Seq can also be used to analyze the expression of toxin genes under specific environmental conditions. For example, Bt was cultured in nutrient-rich and barren medium, and compared its transcriptome, it was found that pBt regulates secondary metabolism and stress-responsive genes during nutritional deficiency, while the expression of major insecticidal toxin genes was advanced (Wang et al., 2023). This suggests that environmental factors can affect the toxin synthesis process of Bt. 4.3 Analysis of differentially expressed genes and functional enrichment After obtaining a list of differentially expressed genes (DEGs), further functional enrichment analysis is often required to mine the significance of these genes at the biological process or pathway level. For Bt transcriptome studies, commonly used methods include gene ontology (GO) enrichment analysis and KEGG pathway enrichment analysis. Through GO enrichment, it is possible to understand what types of biological processes or molecular functions are involved in upregulated or downregulated genes. KEGG enrichment analysis helps locate the metabolic or signaling pathways to which different genes belong. For example, a study found that a batch of genes upregulated by Bt under nutritional deficiency is significantly enriched in the dichotomy signaling system and secondary metabolic pathways, suggesting that Bt may respond to lack of nutrient stress by enhancing environmental signal perception and activating antiretrograde metabolism (Wu et al., 2018). During enrichment analysis, statistical tests and mapping can be completed in R language with bioinformatics tools such as ClusterProfiler or TopGO package. These tools can conveniently output indicators such as enrichment entries, p-values, enrichment factors, etc., and can be visualized as bubble charts, bar charts, etc., to visually display the distribution of differential genes in various functional categories.
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