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Hadal zones account for the deepest 45% of oceanic depth range and play an important role in ocean biogeochemical cycles. As the least-explored aquatic habitat on earth, further investigation is still required to fully elucidate the microbial taxonomy, ecological significance, metabolic diversity, and adaptation in hadal environments. In this study, a novel strain Lsc_1132T was isolated from sediment of the Mariana Trench at 10,954 m in depth. Strain Lsc_1132T contains heterogenous 16S rRNA genes, exhibiting the highest sequence similarities to the type strains of Neobacillus drentensis LMG 21831T, Neobacillus dielmonensis, Neobacillus drentensis NBRC 102427T, Neobacillus rhizosphaerae, and Neobacillus soli NBRC 102451T, with a range of 98.60–99.10% identity. The highest average nucleotide identity (ANI), the highest digital DNA-DNA hybridization (DDH) values, and the average amino acid identity (AAI) with Neobacillus sp. PS3-40 reached 73.5%, 21.4%, and 75.54%, respectively. The major cellular fatty acids of strain Lsc_1132T included iso-C15:0, Summed Feature 3 (C16:1ω6c and/or C16:1ω7c), iso-C17:0, anteiso-C15:0, and iso-C17:1ω5c. The respiratory quinone of strains Lsc_1132T was MK-7. The G + C content of the genomic DNA was 40.9%. Based on the GTDB taxonomy and phenotypic data, strain Lsc_1132T could represent a novel species of a novel genus, proposed as Aliineobacillus hadale gen. nov. sp. nov. (type strain Lsc_1132T = MCCC 1K09620T). Metabolically, strain Lsc_1132T demonstrates a robust carbohydrate metabolism with many strain-specific sugar transporters. It also has a remarkable capacity for metabolizing amino acids and carboxylic acids. Genomic analysis reveals a streamlined genome in the organism, characterized by a significant loss of orthologous genes, including those involved in cytochrome c synthesis, aromatic compound degradation, and polyhydroxybutyrate (PHB) synthesis, which suggests its adaptation to low oxygen levels and oligotrophic conditions through alternative metabolic pathways. In addition, the reduced number of paralogous genes in strain Lsc_1132T, together with its high protein-coding gene density, may further contribute to streamlining its genome and enhancing its genomic efficiency. This research expands our knowledge of hadal microorganisms and their metabolic strategies for surviving in extreme deep-sea environments. [ABSTRACT FROM AUTHOR]

Archaeal isoprenoid glycerol dibiphytanyl glycerol tetraethers (GDGTs) preserved in sediments are popular tools for the reconstruction of past temperature in the global ocean. Whereas the most common GDGTs have been well studied through environmental and culture studies, their hydroxylated version (OH-GDGTs) is just emerging as a new proxy. Some empirical evidence suggests that the distribution of OH-GDGTs may capture sea surface temperature variability. However, the effects of additional environmental factors on OH-GDGT distributions have not been rigorously tested, and evidence suggests that salinity, sea ice, seasonality, terrestrial input, and water depth may be additional factors in some settings. In this study, we analyzed the distribution of OH-GDGTs in modern and Holocene marine sediment from the North Iceland Shelf. By statistically comparing the biomarker datasets against a collection of modern instrumental and paleoceanographic records, we separated which environmental variables may be controlling OH-GDGT-derived proxies around Iceland. In contrast to prevailing theory, we found that nitrate concentrations and water-column stratification are best correlated to OH-GDGT distributions, and not temperature. These results hold important implications for the application of OH-GDGT proxies in high-latitude oceans, particularly in highly stratified locations, as well as for future studies on the biological sources and functionality of these lipids. Given the current complexity of proxy interpretation, we urge caution in the current application of OH-GDGTs as a tool in paleotemperature reconstructions. [ABSTRACT FROM AUTHOR]

Deep-sea environments, as relatively unexplored extremes within the Earth's biosphere, exhibit notable distinctions from terrestrial habitats. To thrive in these extreme conditions, deep-sea actinomycetes have evolved unique biochemical metabolisms and physiological capabilities to ensure their survival in this niche. In this study, five actinomycetes strains were isolated and identified from the Mariana Trench via the culture-dependent method and 16S rRNA sequencing approach. The antimicrobial activity of Microbacterium sp. B1075 was found to be the most potent, and therefore, it was selected as the target strain. Molecular networking analysis via the Global Natural Products Social Molecular Networking (GNPS) platform identified 25 flavonoid compounds as flavonoid secondary metabolites. Among these, genistein was purified and identified as a bioactive compound with significant antibacterial activity. The complete synthesis pathway for genistein was proposed within strain B1075 based on whole-genome sequencing data, with the key gene being CHS (encoding chalcone synthase). The expression of the gene CHS was significantly regulated by high hydrostatic pressure, with a consequent impact on the production of flavonoid compounds in strain B1075, revealing the relationship between actinomycetes' synthesis of flavonoid-like secondary metabolites and their adaptation to high-pressure environments at the molecular level. These results not only expand our understanding of deep-sea microorganisms but also hold promise for providing valuable insights into the development of novel pharmaceuticals in the field of biopharmaceuticals. [ABSTRACT FROM AUTHOR]

In this paper we study point processes on the complex plane and illustrate their uses in several statistical areas, where the quantities of interest requiring estimation involve Fourier expansions. In particular, for any problem where we can describe a quantity in terms of its Fourier expansion, we propose modeling the coefficients of the expansion using a point process on the complex plane. We utilize the Poisson complex point process and model its intensity function using log-linear and mixture models. The proposed models are exemplified via applications to general density approximation, via modeling of the characteristic function, and time series analysis, via modeling of the spectral density. [ABSTRACT FROM AUTHOR]