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Analysis Pipeline of PIWI‐Interacting RNA (piRNA) in an Inflammatory Parasitic Infectious Disease
Author(s) -
Cooley Ayorinde,
Rayford Kayla,
Arun Ashutosh,
Rachakonda Girish,
Kleschenko Yuliya,
Villalta Fernando,
Lima Maria,
Nde Pius,
Pratap Siddharth
Publication year - 2021
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2021.35.s1.04309
Subject(s) - piwi interacting rna , biology , computational biology , transposable element , genetics , microrna , rasirna , genome , gene
Small noncoding RNAs (sncRNA) have been implicated as major contributors to the pathogenesis of various diseases. PIWI‐Interacting RNA (piRNA) are a type of sncRNA. with several regulatory roles across the genome. They are primarily expressed from piRNA clusters within the genome, where they go on to regulate and maintain genomic stability. They are capable of silencing transposable elements and inducing epigenetic changes. Some studies also suggest that piRNAs can regulate gene transcription. This gene regulatory mechanism is thought to be similar to that of miRNAs. These sncRNA play important roles in regulating gene expression by forming complexes with Argonaute proteins to recognize specific target sequences. The specific goal of this project was to develop a method of computationally analyzing piRNA in the context of their diverse functions and relate them to biological responses occurring in disease. To accomplish this, we created a pipeline to analyze dysregulation of piRNA expression and predict how these changes contribute to Trypanosoma cruzi ‐induced cardiac pathology. We used primary human cardiac myocytes (PHCM) challenged by T. cruzi infection and computationally assessed the impact of piRNA during inflammatory response. Small RNA from PHCM challenged by T. cruzi infection was extracted and sequenced. The piRNA annotation program Piano was used to predict known piRNAs via a support vector machine algorithm with transposon interaction informatics. The NOISeq method was used to determine differentially expressed piRNAs. Prediction of binding interactions between coding transcripts and piRNAs was done by adapting the miRanda and RNA22 algorithms using strict pairing thresholds. Predicted target transcripts were then mapped to enriched KEGG pathways using WEBGESTALT. Mapped genes were then queried for biological interaction network construction with the GeneMANIA algorithm. In combination, these analyses were used to produce biological interaction networks involving piRNAs and predicted target genes related to inflammation, specifically pro‐inflammatory AP‐1 transcription factor genes. We demonstrate a method of ascertaining the dynamics of changes in piRNA expression using this disease model, from which more directed biological inquiries can subsequently be made.