Basking in the sun: how mosses photosynthesise and survive in Antarctica

PnOPR6 from Antarctic moss mediates JA-ABA crosstalk and enhances abiotic stress tolerance in Arabidopsis

Jasmonates (JAs) and abscisic acid (ABA) are vital plant hormones that are integral to the plant's response mechanisms against various abiotic stresses. These hormones also function in an antagonistic manner to regulate seed germination and dormancy. However, little is known about the molecular mechanism underlying the interaction between ABA and JA signaling. Here, seven 12-oxo-phytodienoic acid reductase genes (PnOPR1-7), a key enzyme in the JA biosynthesis pathway, were identified in the Antarctic moss Pohlia nutans transcriptome, and their expressions in response to abiotic stress were examined. Among these, PnOPR6 expression levels rose most under cold and UV-B stresses. Transgenic Arabidopsis overexpressing PnOPR6 demonstrated increased tolerance to salt, cold, dehydration, glucose, and ABA, but also greater sensitivity to methyl jasmonate (MeJA) during seed germination or early root growth. Furthermore, in the transgenic Arabidopsis, PnOPR6 suppressed the expression of genes involved in the ABA pathway and ABI3/5-responsive JA receptor COI1. Additionally, phytohormone metabolomics investigations revealed a significant rise in JA precursor (OPDA, OPC-6, and OPC-4), JA, and its derivative 12-OH-JA in PnOPR6-overexpressing line. Moreover, the accumulation of flavonoid in Arabidopsis was enhanced by heterologous expression of PnOPR6. These findings imply that PnOPR6 functions as a signaling regulator, improving plant resistance to abiotic stress through flavonoid accumulation and JA-ABA antagonistic crosstalk, therefore aiding P. nutans in adjusting to polar climates.

The Establishment of a Terrestrial Macroalga Canopy Impacts Microbial Soil Communities in Antarctica

Prasiola is a genus of foliose green algae that forms extensive cryptogamic canopies that contribute to the greening of ice-free areas in the Antarctic tundra. To better understand the impact of Prasiola canopy establishment on colonization in these areas, this study compared the taxonomic and functional structures of bacterial and fungal communities in adjacent soils with and without extensive Prasiola colonization. DNA metabarcoding was employed to analyze the microbial community structure in these soils and in the canopy. Additionally, a phylogenetic study of Prasiola samples was conducted to characterize the taxonomic composition of the analyzed canopies, revealing the presence of Prasiola crispa (Lightfoot) Kützing and P. antarctica Kützing. Key soil attributes were assessed to examine the canopy's influence. Higher pH and carbon, nitrogen, and organic matter contents were found in Prasiola-covered soils than in bare soils. Furthermore, Prasiola canopy establishment not only influenced abiotic soil properties but also shaped soil microbial community structure and its functions. For instance, while Actinobacteriota predominated in bacterial communities both within the Prasiola canopy and beneath it, Bacteroidota dominated in the bare soil. Despite significant variability across soil types, fungal communities showed a trend of higher abundances in certain Ascomycetes, such as Helotiales, Hypocreales, or Xylariales, in soils beneath Prasiola compared to bare soils. Regarding functional diversity, covered soils exhibited a statistically significant lower potential for bacterial methanogenesis and autotrophic CO2 fixation compared to bare soils. Finally, lichenized fungi, plant pathogens, and fungal wood saprotrophs tended to be more abundant in covered soils.

Photostasis and photosynthetic adaptation to polar life

Photostasis is the light-dependent maintenance of energy balance associated with cellular homeostasis in photoautotrophs. We review evidence that illustrates how photosynthetic adaptation in polar photoautrophs such as aquatic green algae, cyanobacteria, boreal conifers as well as terrestrial angiosperms exhibit an astonishing plasticity in structure and function of the photosynthetic apparatus. This plasticity contributes to the maintenance of photostasis, which is essential for the long-term survival in the seemingly inhospitable Antarctic and Arctic habitats. However, evidence indicates that polar photoautrophic species exhibit different functional solutions for the maintenance of photostasis. We suggest that this reflects, in part, the genetic diversity symbolized by inherent genetic redundancy characteristic of polar photoautotrophs which enhances their survival in a thermodynamically challenging environment.

Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.