Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies
Gene Regulatory Networks (GRNs) modulate the traits of an organism. Perturbation experiments which were employed to identify Trait-influencing Genes (TGs) are limited to only a few genes at once, and inadequate to identify the TGs of complex traits like disease resistance. Network modelling techn...
| Autor principal: | |
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| Formato: | Tesis |
| Lenguaje: | Inglés |
| Publicado: |
University of Ibadan
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/125996 |
| _version_ | 1855513111311679488 |
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| author | Olagunju, T.A. |
| author_browse | Olagunju, T.A. |
| author_facet | Olagunju, T.A. |
| author_sort | Olagunju, T.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Gene Regulatory Networks (GRNs) modulate the traits of an organism. Perturbation experiments
which were employed to identify Trait-influencing Genes (TGs) are limited to only a few genes at
once, and inadequate to identify the TGs of complex traits like disease resistance. Network modelling
techniques for complex systems such as GRNs can provide a holistic system view to overcome the
limitation of identifying TGs of complex traits with perturbation experiments when applied to
genome-wide Next Generation Sequencing (NGS) data. This study was therefore designed to mine
and identify disease-responsive genes from GRN using network perturbation technique.
Small Ribonucleic Acid (sRNA) Profiles (sRNAP) from computationally annotated NGS data were
combined with Gene Co-expression (GC) data and used to construct two-layer GRN Models
(GRNMs). Node removal perturbation was applied to the GRNMs, and the Percentage Network
Density Change (PNDC) was recorded as the network robustness measure and perturbation response.
Model validation was done in three stages. The sRNAP was compared with two Published Profiles
(PP) using number of Conserved sRNAs (CsRNAs), Cleaned Raw Sequences (CRS), Host sRNAs
(HsRNAs) and Pathogen-derived sRNAs (PsRNAs) as parameters. The GRNMs were validated using
F-Score and p-value from Analysis of Variance (ANOVA). Well-defined Gene Ontology (GO)
annotation was used for biological interpretation of the results. The model was applied to GC data
containing 3,146 genes, and NGS data comprising 383,105,237 sequences from five Cassava
genotypes labelled as A, B, C, D and E.
An automated computational pipeline was developed to annotate the NGS data across all the dataset
and produced the sRNAP comprising 25,214 sRNAs and 16,436 genes involved in 105,515
interactions, used for constructing the two-layer differential GRNMs. The PNDC for ten differential
GRNMs AB, AC, AD, AE, BC, BD, BE, CD, CE and DE were, -0.0086, 0.3140, -0.1315, -0.2204, -
0.1519, 0.0649, -1.6422, -0.0895, -0.6397 and -0.3999, respectively, indicating AB as the most robust
and BE the least to node removal perturbation. The CsRNAs for sRNAP was 144 contrasted with 114
and 118 in the two PPs, while the CRS for sRNAP was 97.09 compared to 87.46 and 65.70 % in PP.
The HsRNAs in the sRNAP ranged from 71.14 to 89.00, but were 65.90 to 73.51 and 66.90 to 70.69
% in PP. The PsRNAs range was 9.87 to 23.56, while 4.00 to 17.00 and 7.34 to 12.65 % were
reported in the two PPs. The F-Score for the randomly rewired GRNMs was between 4.49 at 0.03 pvalue
and 1934.00 at <2e-16 p-value, while it was between 5.26 at 0.02 p-value and 728.9 at <2e-16 pvalue
for the randomly relabelled GRNMs, suggesting that the GRNMs were truly representative of
the underlying biological network. The GO annotation revealed that the perturbed nodes which
resulted in reduced network robustness were disease-responsive genes.
The developed perturbation simulation model identified disease-responsive genes obtained through
the reduced network robustness measures validated by gene ontology. This knowledge could be useful
in reprogramming the gene regulatory network to obtain desirable traits. |
| format | Tesis |
| id | CGSpace125996 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Ibadan |
| publisherStr | University of Ibadan |
| record_format | dspace |
| spelling | CGSpace1259962023-02-15T07:29:04Z Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies Olagunju, T.A. networks density genes genotypes genomes pathogens ribonucleic acid diseases Gene Regulatory Networks (GRNs) modulate the traits of an organism. Perturbation experiments which were employed to identify Trait-influencing Genes (TGs) are limited to only a few genes at once, and inadequate to identify the TGs of complex traits like disease resistance. Network modelling techniques for complex systems such as GRNs can provide a holistic system view to overcome the limitation of identifying TGs of complex traits with perturbation experiments when applied to genome-wide Next Generation Sequencing (NGS) data. This study was therefore designed to mine and identify disease-responsive genes from GRN using network perturbation technique. Small Ribonucleic Acid (sRNA) Profiles (sRNAP) from computationally annotated NGS data were combined with Gene Co-expression (GC) data and used to construct two-layer GRN Models (GRNMs). Node removal perturbation was applied to the GRNMs, and the Percentage Network Density Change (PNDC) was recorded as the network robustness measure and perturbation response. Model validation was done in three stages. The sRNAP was compared with two Published Profiles (PP) using number of Conserved sRNAs (CsRNAs), Cleaned Raw Sequences (CRS), Host sRNAs (HsRNAs) and Pathogen-derived sRNAs (PsRNAs) as parameters. The GRNMs were validated using F-Score and p-value from Analysis of Variance (ANOVA). Well-defined Gene Ontology (GO) annotation was used for biological interpretation of the results. The model was applied to GC data containing 3,146 genes, and NGS data comprising 383,105,237 sequences from five Cassava genotypes labelled as A, B, C, D and E. An automated computational pipeline was developed to annotate the NGS data across all the dataset and produced the sRNAP comprising 25,214 sRNAs and 16,436 genes involved in 105,515 interactions, used for constructing the two-layer differential GRNMs. The PNDC for ten differential GRNMs AB, AC, AD, AE, BC, BD, BE, CD, CE and DE were, -0.0086, 0.3140, -0.1315, -0.2204, - 0.1519, 0.0649, -1.6422, -0.0895, -0.6397 and -0.3999, respectively, indicating AB as the most robust and BE the least to node removal perturbation. The CsRNAs for sRNAP was 144 contrasted with 114 and 118 in the two PPs, while the CRS for sRNAP was 97.09 compared to 87.46 and 65.70 % in PP. The HsRNAs in the sRNAP ranged from 71.14 to 89.00, but were 65.90 to 73.51 and 66.90 to 70.69 % in PP. The PsRNAs range was 9.87 to 23.56, while 4.00 to 17.00 and 7.34 to 12.65 % were reported in the two PPs. The F-Score for the randomly rewired GRNMs was between 4.49 at 0.03 pvalue and 1934.00 at <2e-16 p-value, while it was between 5.26 at 0.02 p-value and 728.9 at <2e-16 pvalue for the randomly relabelled GRNMs, suggesting that the GRNMs were truly representative of the underlying biological network. The GO annotation revealed that the perturbed nodes which resulted in reduced network robustness were disease-responsive genes. The developed perturbation simulation model identified disease-responsive genes obtained through the reduced network robustness measures validated by gene ontology. This knowledge could be useful in reprogramming the gene regulatory network to obtain desirable traits. 2021-12 2022-12-14T15:40:33Z 2022-12-14T15:40:33Z Thesis https://hdl.handle.net/10568/125996 en Limited Access University of Ibadan Olagunju, T.A. (2021). Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies. Ibadan, Nigeria: University of Ibadan, (279p.). |
| spellingShingle | networks density genes genotypes genomes pathogens ribonucleic acid diseases Olagunju, T.A. Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies |
| title | Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies |
| title_full | Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies |
| title_fullStr | Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies |
| title_full_unstemmed | Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies |
| title_short | Development of a multi-layer gene regulatory network perturbation simulation model for host-pathogen interaction studies |
| title_sort | development of a multi layer gene regulatory network perturbation simulation model for host pathogen interaction studies |
| topic | networks density genes genotypes genomes pathogens ribonucleic acid diseases |
| url | https://hdl.handle.net/10568/125996 |
| work_keys_str_mv | AT olagunjuta developmentofamultilayergeneregulatorynetworkperturbationsimulationmodelforhostpathogeninteractionstudies |