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Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting.


ABSTRACT: Filarial nematodes can cause debilitating diseases in humans. They have complicated life cycles involving an insect vector and mammalian hosts, and they go through a number of developmental molts. While whole genome sequences of parasitic worms are now available, very little is known about transcription factor (TF) binding sites and their cognate transcription factors that play a role in regulating development. To address this gap, we developed a novel motif prediction pipeline, Emotif Alpha, that integrates ten different motif discovery algorithms, multiple statistical tests, and a comparative analysis of conserved elements between the filarial worms Brugia malayi and Onchocerca volvulus, and the free-living nematode Caenorhabditis elegans. We identified stage-specific TF binding motifs in B. malayi, with a particular focus on those potentially involved in the L3-L4 molt, a stage important for the establishment of infection in the mammalian host. Using an in vitro molting system, we tested and validated three of these motifs demonstrating the accuracy of the motif prediction pipeline.

SUBMITTER: Grote A 

PROVIDER: S-EPMC7337397 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting.

Grote Alexandra A   Li Yichao Y   Liu Canhui C   Voronin Denis D   Geber Adam A   Lustigman Sara S   Unnasch Thomas R TR   Welch Lonnie L   Ghedin Elodie E  

PLoS neglected tropical diseases 20200623 6


Filarial nematodes can cause debilitating diseases in humans. They have complicated life cycles involving an insect vector and mammalian hosts, and they go through a number of developmental molts. While whole genome sequences of parasitic worms are now available, very little is known about transcription factor (TF) binding sites and their cognate transcription factors that play a role in regulating development. To address this gap, we developed a novel motif prediction pipeline, Emotif Alpha, th  ...[more]

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