Larger volumes of sea ice have been thawing in the Central Arctic Ocean (CAO) during the last decades than during the past 800,000 years. Brackish brine (fed by meltwater inside the ice) is an expanding sympagic habitat in summer all over the CAO. We report for the first time the structure of bacterial communities in this brine. They are composed of psychrophilic extremophiles, many of them related to phylotypes known from Arctic and Antarctic regions. Community structure displayed strong habitat segregation between brackish ice brine (IB; salinity 2.4-9.6) and immediate sub-ice seawater (SW; salinity 33.3-34.9), expressed at all taxonomic levels (class to genus), by dominant phylotypes as well as by the rare biosphere, and with specialists dominating IB and generalists SW. The dominant phylotypes in IB were related to Candidatus Aquiluna and Flavobacterium, those in SW to Balneatrix and ZD0405, and those shared between the habitats to Halomonas, Polaribacter and Shewanella. A meta-analysis for the oligotrophic CAO showed a pattern with Flavobacteriia dominating in melt ponds, Flavobacteriia and Gammaproteobacteria in solid ice cores, Flavobacteriia, Gamma- and Betaproteobacteria, and Actinobacteria in brine, and Alphaproteobacteria in SW. Based on our results, we expect that the roles of Actinobacteria and Betaproteobacteria in the CAO will increase with global warming owing to the increased production of meltwater in summer. IB contained three times more phylotypes than SW and may act as an insurance reservoir for bacterial diversity that can act as a recruitment base when environmental conditions change.

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Protein sequences are highly dimensional and present one of the main problems for the optimization and study of sequence-structure relations. The intrinsic degeneration of protein sequences is hard to follow, but the continued discovery of new protein structures has shown that there is convergence in terms of the possible folds that proteins can adopt, such that proteins with sequence identities lower than 30% may still fold into similar structures. Given that proteins share a set of conserved structural motifs, machine-learning algorithms can play an essential role in the study of sequence-structure relations. Deep-learning neural networks are becoming an important tool in the development of new techniques, such as protein modeling and design, and they continue to gain power as new algorithms are developed and as increasing amounts of data are released every day. Here, we trained a deep-learning model based on previous recurrent neural networks to design analog protein structures using representations learning based on the evolutionary and structural information of proteins. We test the capabilities of this model by creating de novo variants of an antifungal peptide, with sequence identities of 50% or lower relative to the wild-type (WT) peptide. We show by in silico approximations, such as molecular dynamics, that the new variants and the WT peptide can successfully bind to a chitin surface with comparable relative binding energies. These results are supported by in vitro assays, where the de novo designed peptides showed antifungal activity that equaled or exceeded the WT peptide.

Although crucial for the addition of new nitrogen in marine ecosystems, dinitrogen (N2) fixation remains an understudied process, especially under dark conditions and in polar coastal areas, such as the West Antarctic Peninsula (WAP). New measurements of light and dark N2 fixation rates in parallel with carbon (C) fixation rates, as well as analysis of the genetic marker nifH for diazotrophic organisms, were conducted during the late summer in the coastal waters of Chile Bay, South Shetland Islands, WAP. During six late summers (February 2013 to 2019), Chile Bay was characterized by high NO3− concentrations (~20 µM) and an NH4+ content that remained stable near 0.5 µM. The N:P ratio was approximately 14.1, thus close to that of the Redfield ratio (16:1). The presence of Cluster I and Cluster III nifH gene sequences closely related to Alpha-, Delta- and, to a lesser extent, Gammaproteobacteria, suggests that chemosynthetic and heterotrophic bacteria are primarily responsible for N2 fixation in the bay. Photosynthetic carbon assimilation ranged from 51.18 to 1471 nmol C L−1 d−1, while dark chemosynthesis ranged from 9.24 to 805 nmol C L−1 d−1. N2 fixation rates were higher under dark conditions (up to 45.40 nmol N L−1 d−1) than under light conditions (up to 7.70 nmol N L−1 d−1), possibly contributing more than 37% to new nitrogen-based production (≥2.5 g N m−2 y−1). Of all the environmental factors measured, only PO43- exhibited a significant correlation with C and N2 rates, being negatively correlated (p < 0.05) with dark chemosynthesis and N2 fixation under the light condition, revealing the importance of the N:P ratio for these processes in Chile Bay. This significant contribution of N2 fixation expands the ubiquity and biological potential of these marine chemosynthetic diazotrophs. As such, this process should be considered along with the entire N cycle when further reviewing highly productive Antarctic coastal waters and the diazotrophic potential of the global marine ecosystem.