See the published version of this preprint at BMC Genomics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Enoch Ng'oma
University of Missouri Columbia Ellis Library
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6741-7922
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear. Results: To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, and 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, P adj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (FDR P adj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing many cellular and nuclear processes (Fisher exact test, P adj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5 , Rheb , and Sirt2 showed significant elevation in expression, many key genes such as InR , chico , most insulin peptide genes, and the nutrient-sensing pathways were not observed. Conclusions: Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.
Keywords
Differential gene expression, diet effects, gene co-expression, gene set enrichment, multiparent population, Drosophila melanogaster
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile8pathwayGenesDE.csv
- Additionalfile3DEGslrt.interaction0.05.csv
- Additionalfile2DEGslrt.treatment0.05.csv.csv
- Additionalfile7GOEnrichWGCNAResamp.csv
- Additionalfile1Figures.docx
- Additionalfile11SampleInfoBatchInfo.xlsx
- Additionalfile12SampleInfoRNA.xlsx
- Additionalfile6WCGNAeigengenes.csv.csv
- Additionalfile9DEGsunderQTL.csv
- Additionalfile5GSEAsigPathwayssigGOTerms.xlsx
- Additionalfile4Simulation.docx
- Additionalfile10DietInfo.docx
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Genomics.
Version 4
Posted 09 Jan, 2020
No community comments so far
Submission checks complete
On 08 Jan, 2020
Editorial decision: Accept
On 08 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 03 Jan, 2020
Editor assigned
On 27 Dec, 2019
Submission checks complete
On 26 Dec, 2019
Editor invited
On 26 Dec, 2019
No comments provided
Editorial decision: Minor revision
On 18 Dec, 2019
Review #2 received
Received 14 Dec, 2019
Review #1 received
Received 25 Nov, 2019
Reviewer #2 agreed
On 16 Nov, 2019
Reviewer #1 agreed
On 13 Nov, 2019
Reviewers invited
Invitations sent on 13 Nov, 2019
Editor assigned
On 06 Nov, 2019
Submission checks complete
On 05 Nov, 2019
Editor invited
On 05 Nov, 2019
No comments provided
Editorial decision: Major revision
On 07 Oct, 2019
Review #2 received
Received 10 Sep, 2019
Review #1 received
Received 06 Sep, 2019
Reviewer #2 agreed
On 21 Aug, 2019
Reviewer #1 agreed
On 20 Aug, 2019
Reviewers invited
Invitations sent on 20 Aug, 2019
Submission checks complete
On 05 Aug, 2019
Editor invited
On 05 Aug, 2019
Editor assigned
On 30 Jul, 2019
First submitted
On 29 Jul, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
Learn more about our company.
See the published version of this preprint at BMC Genomics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Enoch Ng'oma
University of Missouri Columbia Ellis Library
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6741-7922
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Genomics.
Version 4
Posted 09 Jan, 2020
No community comments so far
Submission checks complete
On 08 Jan, 2020
Editorial decision: Accept
On 08 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 03 Jan, 2020
Editor assigned
On 27 Dec, 2019
Submission checks complete
On 26 Dec, 2019
Editor invited
On 26 Dec, 2019
No comments provided
Editorial decision: Minor revision
On 18 Dec, 2019
Review #2 received
Received 14 Dec, 2019
Review #1 received
Received 25 Nov, 2019
Reviewer #2 agreed
On 16 Nov, 2019
Reviewer #1 agreed
On 13 Nov, 2019
Reviewers invited
Invitations sent on 13 Nov, 2019
Editor assigned
On 06 Nov, 2019
Submission checks complete
On 05 Nov, 2019
Editor invited
On 05 Nov, 2019
No comments provided
Editorial decision: Major revision
On 07 Oct, 2019
Review #2 received
Received 10 Sep, 2019
Review #1 received
Received 06 Sep, 2019
Reviewer #2 agreed
On 21 Aug, 2019
Reviewer #1 agreed
On 20 Aug, 2019
Reviewers invited
Invitations sent on 20 Aug, 2019
Submission checks complete
On 05 Aug, 2019
Editor invited
On 05 Aug, 2019
Editor assigned
On 30 Jul, 2019
First submitted
On 29 Jul, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Genomics.
Background: Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear. Results: To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, and 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, P adj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (FDR P adj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing many cellular and nuclear processes (Fisher exact test, P adj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5 , Rheb , and Sirt2 showed significant elevation in expression, many key genes such as InR , chico , most insulin peptide genes, and the nutrient-sensing pathways were not observed. Conclusions: Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile8pathwayGenesDE.csv
- Additionalfile3DEGslrt.interaction0.05.csv
- Additionalfile2DEGslrt.treatment0.05.csv.csv
- Additionalfile7GOEnrichWGCNAResamp.csv
- Additionalfile1Figures.docx
- Additionalfile11SampleInfoBatchInfo.xlsx
- Additionalfile12SampleInfoRNA.xlsx
- Additionalfile6WCGNAeigengenes.csv.csv
- Additionalfile9DEGsunderQTL.csv
- Additionalfile5GSEAsigPathwayssigGOTerms.xlsx
- Additionalfile4Simulation.docx
- Additionalfile10DietInfo.docx
Loading...