Temperature-Associated Anatomical, Photosynthetic, and Putative GC–MS Metabolomic Profiles in the Arid Species Nitraria sibirica Pall. and Nitraria schoberi L.

Dariga Dairbekova, Nina V. Terletskaya, Aigerim Mamirova, Nazym K. Korbozova, Elvira A. Shadenova, Aizhan Mussayeva

Metabolites, 2026

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DOI 10.3390/metabo16090646
Data confidence 85 high
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Abstract

Backgrounds: The present study investigated temperature-associated responses of microclones of two halophytic shrubs, Nitraria sibirica Pall. and Nitraria schoberi L., exposed to low positive (+3 °C) and elevated (+30 °C) temperatures for 72 h. Methods: Temperature-associated responses were investigated by evaluation of leaf anatomy, chlorophyll fluorescence, and GC–MS-based metabolite profiling. Results: The results revealed shared and species-specific responses, including changes in leaf anatomical dimensions, maintenance or adjustment of PSII-related fluorescence parameters, and treatment-dependent shifts in the relative composition and diversity of putatively annotated metabolite classes. Metabolomic profiling was performed in both in vitro microclones and soil-acclimatised microclones. In vitro microclones showed more pronounced shifts in the number and relative representation of detected compound classes under temperature exposure, whereas soil-acclimatised microclones demonstrated comparatively greater compositional stability. Comparative analysis of the datasets revealed temperature-associated patterns across anatomical, photosynthetic, and relative metabolomic responses; however, these patterns were interpreted descriptively rather than as direct causal mechanisms. Some detected compounds or compound classes have been associated with biological relevance in previous studies; however, their identity, abundance, and activity require targeted validation. Conclusions: Overall, the findings provide a descriptive experimental dataset on temperature-associated anatomical, chlorophyll fluorescence, and relative GC–MS profile changes in Nitraria microclones and identify candidate metabolite classes for future targeted validation.