Abstract
In a time with decreasing biodiversity, especially among insects, a detailed understanding about specific resource utilization strategies is crucial. The physiological and behavioural responses to host switches in phytophagous insects are poorly understood. Earlier studies indicate that a host plant switch might be associated with distinctive molecular and physiological responses in different lineages. Expanding the assessment of such associations across Lepidoptera will reveal if there are general patterns in adaptive responses, or if each switch event is more of a unique character. We investigated host plant preference, fitness consequences, effects on expression profiles and gut microbiome composition in two common wood white (Leptidea sinapis) populations with different host plant preferences from the extremes of the species distribution area (Sweden and Catalonia). Our results show that female Catalonian wood whites lack preference for either host plant (Lotus corniculatus or L. dorycnium), while Swedish females laid significantly more eggs on L. corniculatus. Individuals from both populations reared on L. dorycnium had longer developmental times and smaller body size as adults. This indicates that both environmental and genetic factors determine the choice to use a specific host plant. Gene expression analysis revealed a more pronounced response to host plant in the Catalonian compared to the Swedish population. In addition, host plant treatment resulted in a significant shift in microbiome community structure in the Catalonian population. Together, this suggests that population specific plasticity associated with local conditions underlies host plant utilisation in wood whites.
| Originalsprache | Englisch |
|---|---|
| Zeitschrift | Molecular Ecology |
| Jahrgang | 30 |
| Ausgabenummer | 2 |
| Seiten (von - bis) | 499-516 |
| Seitenumfang | 18 |
| ISSN | 0962-1083 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 01.2021 |
Fördermittel
This work was supported by a junior research grant (VR 2013-4508) and a project research grant (VR 2019-04791) from the Swedish Research Council to NB. The authors acknowledge support from the National Genomics Infrastructure in Stockholm and Uppsala funded by the Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, for assistance with massively parallel sequencing and the bioinformatics support team (WABI). The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala University partially funded by the Swedish Research Council through grant agreement no. 2016-07213. AK and HB acknowledge computational support from the OMICS compute cluster at the University of L?beck. HB acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany`s Excellence Strategy ? EXC 22167-390884018. RV acknowledges support from project PID2019-107078GB-I00/AEI/10.13039/501100011033. We thank Luis Leal for support with the gene expression analysis and Jesper Boman and Lars H??k for helpful comments on a previous version of this manuscript. This work was supported by a junior research grant (VR 2013‐4508) and a project research grant (VR 2019‐04791) from the Swedish Research Council to NB. The authors acknowledge support from the National Genomics Infrastructure in Stockholm and Uppsala funded by the Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, for assistance with massively parallel sequencing and the bioinformatics support team (WABI). The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala University partially funded by the Swedish Research Council through grant agreement no. 2016‐07213. AK and HB acknowledge computational support from the OMICS compute cluster at the University of Lübeck. HB acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany`s Excellence Strategy – EXC 22167‐390884018. RV acknowledges support from project PID2019‐107078GB‐I00/AEI/10.13039/501100011033. We thank Luis Leal for support with the gene expression analysis and Jesper Boman and Lars Höök for helpful comments on a previous version of this manuscript.
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 3 – Gesundheit und Wohlergehen
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SDG 9 – Industrie, Innovation und Infrastruktur
Strategische Forschungsbereiche und Zentren
- Forschungsschwerpunkt: Infektion und Entzündung - Zentrum für Infektions- und Entzündungsforschung Lübeck (ZIEL)
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