Transcriptomic-based Therapeutic Target Discovery Service for Infectious Disease Research

It is essential for research to keep pace with the constant evolution of pathogens in order to discover the determinants of drug resistance, immune evasion, and virulence in the study of infectious diseases. Transcriptomics research can help in the study of infectious diseases by analyzing changes in gene expression profiles to discover new therapeutic targets. Creative Biolabs can provide a full range of transcriptomics services to assist clients in accelerating research whether it is for vaccine development, antibiotic resistance, new viruses, or immune evasion.

Transcriptomic Analysis Strategies in Infectious Disease Research

Transcriptome sequencing is effective in analyzing global gene expression levels in infectious diseases and analyzing transcriptomes in depth, discovering novel transcripts and isoforms, as well as assembling previously unstudied transcriptomes to identify possible therapeutic targets. These analyses are involved as follows.

Gene expression profiling. By analyzing changes in gene expression profiles before and after infection, genes associated with the pathogenesis of infectious diseases can be identified, providing important clues for therapeutic target discovery.
Cytokine analysis. Cytokines are important regulatory factors in the immune system and have a significant impact on the pathogenesis of infectious diseases. The changes in cytokine expression during infection are analyzed to enable a better knowledge of the immune response mechanism of infectious diseases.
Drug screening. Known drugs are screened for new drugs that can be used to treat infectious diseases. By analyzing the effects of drugs on gene expression profiles, the mechanism of action and potential therapeutic effects of drugs can be determined.
Bioinformatics analysis. By analyzing genomic, transcriptomic, and proteomic data, more in-depth insights are provided. This will help to better comprehend the pathogenesis of infectious diseases and provide more possibilities for therapeutic target discovery.

Transcriptomics Analysis Process

Step 1. Data preprocessing

The raw RNA sequencing data are preprocessed using standardized analysis. First, quality control of the raw RNA-seq sequences is performed to remove low-quality sequences and splice sequences. Then, the eluted sequences are aligned to the reference genome. Finally, estimate gene expression based on the gene-encoded information.

Step 2. Differential gene analysis

Calculate the expression of different genes in the sample. This analysis calculates the log2 fold change and p-value of the gene. log2 fold change value indicates the degree of difference in the expression of the gene in the two sets of samples, while the p-value represents whether the difference is significant or not. Genes with a p-value less than 0.01 are defined as differentially expressed genes.

Step 3. GO/KEGG enrichment analysis

GO analysis is able to annotate and categorize the biological functions of differentially expressed genes at different levels, such as cellular and molecular, etc. KEGG analysis is able to annotate the signaling pathways and metabolic pathways of differentially expressed genes. GO/KEGG results with a p-value less than 0.01 are considered significant.

Step 4. Screening for therapeutic targets

Among all differentially expressed genes, those with potential roles in the disease treatment process, such as those related to immune response, inflammatory response, etc., are selected first. Then, based on the results of GO/KEGG analysis, those functional items related to infectious diseases are selected, and genes with significant differential expression are further selected. These genes are considered as potential therapeutic targets.

Types of Transcriptome Sequencing

Various types of samples such as total RNA, mRNA, ds-cDNA, and cultured cells/cell sediment are acceptable.

Sequencing Type

Standard transcriptome sequencing. Standard transcriptome sequencing includes eukaryotic transcriptome (mRNA) sequencing and prokaryotic transcriptome (mRNA) sequencing, which can comprehensively and rapidly obtain almost all transcripts and gene sequences of a particular cell or tissue of a species in a certain state, and can be used to study gene structure and gene function, variable splicing and de novo transcript prediction.
lncRNA sequencing. lncRNA sequencing distinguishes whether a transcript is from a positive or negative strand and provides information about gene expression on a particular strand. This sequencing technology helps to analyze the specific expression of alleles and is very important for gene function analysis.
Small RNA sequencing. Small RNA sequencing enables the analysis of small RNAs and microRNAs that normally regulate genes at the transcriptional and post-transcriptional stages, and have been shown to regulate a wide range of biological processes, including development, cell differentiation, and apoptosis.
Single-cell transcriptome sequencing. Quantification of gene expression levels in single cells by single-cell sequencing.

Fig. 1 Comparison of single-cell RNA sequencing and bulk RNA sequencing.¹

Starting from experimental design, and standardized experimental procedures to professional bioinformatics analysis, Creative Biolabs aims to provide transcriptome analysis solutions for your target discovery in infectious disease research. Please contact us if you are interested and wish to learn more.

Reference:

Zou, Ju, et al. "Liver in infections: a single-cell and spatial transcriptomics perspective." Journal of biomedical science 30.1 (2023): 53.

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