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In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds

BACKGROUND: High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selectio...

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Autores principales: Gakii, Consolata, Bwana, Billiah Kemunto, Mugambi, Grace Gathoni, Mukoya, Esther, Mireji, Paul O., Rimiru, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255069/
https://www.ncbi.nlm.nih.gov/pubmed/34249514
http://dx.doi.org/10.7717/peerj.11691
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author Gakii, Consolata
Bwana, Billiah Kemunto
Mugambi, Grace Gathoni
Mukoya, Esther
Mireji, Paul O.
Rimiru, Richard
author_facet Gakii, Consolata
Bwana, Billiah Kemunto
Mugambi, Grace Gathoni
Mukoya, Esther
Mireji, Paul O.
Rimiru, Richard
author_sort Gakii, Consolata
collection PubMed
description BACKGROUND: High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem. METHODOLOGY: In this study we analyzed the transcriptome of Glossina morsitans morsitans (Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes. RESULTS AND DISCUSSION: Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent.
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spelling pubmed-82550692021-07-09 In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds Gakii, Consolata Bwana, Billiah Kemunto Mugambi, Grace Gathoni Mukoya, Esther Mireji, Paul O. Rimiru, Richard PeerJ Bioinformatics BACKGROUND: High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem. METHODOLOGY: In this study we analyzed the transcriptome of Glossina morsitans morsitans (Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes. RESULTS AND DISCUSSION: Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent. PeerJ Inc. 2021-07-01 /pmc/articles/PMC8255069/ /pubmed/34249514 http://dx.doi.org/10.7717/peerj.11691 Text en © 2021 Gakii et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Gakii, Consolata
Bwana, Billiah Kemunto
Mugambi, Grace Gathoni
Mukoya, Esther
Mireji, Paul O.
Rimiru, Richard
In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
title In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
title_full In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
title_fullStr In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
title_full_unstemmed In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
title_short In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
title_sort in silico-driven analysis of the glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255069/
https://www.ncbi.nlm.nih.gov/pubmed/34249514
http://dx.doi.org/10.7717/peerj.11691
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