DESENVOLVIMENTO DE UM ALGORITMO PARA DESIGN E VALIDAÇÃO DE AMPLICONS DE RNA DE INTERFERÊNCIA PARA GENES ALTERADOS NA SÍNDROME DE RETT
Introduction: Rett syndrome (RTT) is a rare neurodevelopmental disorder affecting mainly girls, caused primarily by mutations in the MECP2 gene. This mutation leads to intellectual and motor impairments, communication difficulties, and repetitive hand movements. While several studies have investigated genes and metabolic pathways altered in RTT, few have analyzed each altered gene individually. RNA interference (RNAi) is a valuable technique for establishing genotype–phenotype correlations by silencing specific genes. However, short RNAi sequences may cause off-target effects due to nonspecific binding. To address this, the present project aimed to develop an algorithm for designing and validating RNAi amplicons to minimize off-target effects in RTT-related gene studies. Aims: Develop and implement a bioinformatics tool for the design and validation of RNAi amplicons targeting altered genes in RTT. Materials and methods: Validation rules for RNAi amplicons were collected from scientific literature and previous work from the LaBiN/LEM research group. Thirteen rules were selected—four mandatory (related to functionality) and nine desirable (related to stability and immunogenicity). The algorithm was implemented in Python using libraries such as re (for regex pattern searches), Biopython, and scikit-bio. A Bash script was created to run the Burrows-Wheeler Aligner (BWA) for mapping candidate amplicons against a reference transcriptome to detect potential off-targets. Mapping results were filtered based on a scoring threshold derived from BWA’s scoring system. A preliminary transcriptome database was compiled from multiple public sources, although its completion was postponed to a subsequent project. Validated sequences were mapped to the Homo sapiens GRCh38.p14 mRNA transcriptome (RefSeq) using BWA with a score threshold based on length and mismatches. Perfect matches in the seed region and minimal mismatches elsewhere were prioritized. The mapping identified potential off-targets in transcripts such as BDNF and genes in TrkB receptor signaling pathways. Results: The algorithm integrates three steps: amplicon design, validation, and transcriptome mapping. From genes altered in Rett syndrome (MECP2, BDNF, DLG4), 21-nt RNAi candidates were generated and tested against 13 validation rules, four mandatory for functionality and nine desirable for stability and reduced immunogenicity. Sequences failing mandatory rules were discarded; others were ranked by compliance score. Validation applied 13 rules from the literature, four of which were mandatory: first nucleotide as A or U, last nucleotide as C or G, at least four A/U in the first seven positions, and GC content between 26–62%. The remaining nine rules, including absence of long CG stretches, specific inhibitory motifs (UGU, GUCCUUCAA, UGUGU), and palindromic sequences, were desirable for stability and reduced immunogenicity. Amplicons failing mandatory rules were excluded, while compliance with desirable rules contributed to ranking scores. Final considerations: The developed algorithm effectively designs and validates RNAi amplicons, reducing off-target effects and providing detailed sequence evaluation. It offers a practical, efficient solution for planning RNAi-based gene silencing experiments in RTT research, improving specificity and supporting more accurate genotype–phenotype correlations.
Keywords: RNAi; Amplicon validation; Gene silencing; Off-target effects; Rett syndrome.
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