Satellitome Analysis of Rhodnius prolixus, One of the Main Chagas Disease Vector Species.
Eugenia E MontielFrancisco PanzeraTeresa PalomequeFrancisco PanzeraSebastián PitaPublished in: International journal of molecular sciences (2021)
The triatomine Rhodnius prolixus is the main vector of Chagas disease in countries such as Colombia and Venezuela, and the first kissing bug whose genome has been sequenced and assembled. In the repetitive genome fraction (repeatome) of this species, the transposable elements represented 19% of R. prolixus genome, being mostly DNA transposon (Class II elements). However, scarce information has been published regarding another important repeated DNA fraction, the satellite DNA (satDNA), or satellitome. Here, we offer, for the first time, extended data about satellite DNA families in the R. prolixus genome using bioinformatics pipeline based on low-coverage sequencing data. The satellitome of R. prolixus represents 8% of the total genome and it is composed by 39 satDNA families, including four satDNA families that are shared with Triatoma infestans, as well as telomeric (TTAGG)n and (GATA)n repeats, also present in the T. infestans genome. Only three of them exceed 1% of the genome. Chromosomal hybridization with these satDNA probes showed dispersed signals over the euchromatin of all chromosomes, both in autosomes and sex chromosomes. Moreover, clustering analysis revealed that most abundant satDNA families configured several superclusters, indicating that R. prolixus satellitome is complex and that the four most abundant satDNA families are composed by different subfamilies. Additionally, transcription of satDNA families was analyzed in different tissues, showing that 33 out of 39 satDNA families are transcribed in four different patterns of expression across samples.
Keyphrases
- long non coding rna
- poor prognosis
- single molecule
- genome wide
- circulating tumor
- cell free
- single cell
- nucleic acid
- small molecule
- gene expression
- randomized controlled trial
- transcription factor
- systematic review
- big data
- healthcare
- dna methylation
- high frequency
- machine learning
- social media
- dna repair
- health information
- artificial intelligence
- health insurance