The deep-sea brines of the Red Sea include some of the most extreme and unique envi-ronments on Earth. They combine high salinities with increases in temperature, heavy metals, hydrostatic pressure, and anoxic conditions, creating unique settings for thriving populations of novel extremophiles. Despite a recent increase of studies focusing on these unusual biotopes, their viral communities remain unexplored. The current survey explores four metagenomic datasets obtained from different brine–seawater interface samples, focusing specifically on the diversity of their viral communities. Data analysis confirmed that the particle-attached viral communities present in the brine–seawater interfaces were diverse and generally dominated by Caudovirales, yet appearing distinct from sample to sample. With a level of caution, we report the unexpected finding of Phycodnaviridae, which infects algae and plants, and trace amounts of insect-infecting Iridoviridae. Results from Kebrit Deep revealed stratification in the viral communities present in the interface: the upper-interface was enriched with viruses associated with typical marine bacteria, while the lower-interface was enriched with haloviruses and halophages. These results provide first insights into the unexplored viral communities present in deep-sea brines of the Red Sea, representing one of the first steps for ongoing and future sampling efforts and studies.

Covering a quarter of the world’s tropical coastlines and being one of the most threat-ened ecosystems, mangroves are among the major sources of terrestrial organic matter to oceans and harbor a wide microbial diversity. In order to protect, restore, and better understand these ecosystems, researchers have extensively studied their microbiology, yet few surveys have focused on their fungal communities. Our lack of knowledge is even more pronounced for specific fungal populations, such as the ones associated with the rhizosphere. Likewise, the Red Sea gray man-groves (Avicennia marina) remain poorly characterized, and understanding of their fungal commu-nities still relies on cultivation-dependent methods. In this study, we analyzed metagenomic datasets from gray mangrove rhizosphere and bulk soil samples collected in the Red Sea coast, to obtain a snapshot of their fungal communities. Our data indicated that Ascomycota was the dominant phylum (76%–85%), while Basidiomycota was less abundant (14%–24%), yet present in higher numbers than usually reported for such environments. Fungal communities were more stable within the rhizosphere than within the bulk soil, both at class and genus level. This finding is consistent with the intrinsic patchiness in soil sediments and with the selection of specific microbial communities by plant roots. Our study indicates the presence of several species on this mycobiome that were not previously reported as mangrove-associated. In particular, we detected representatives of several commercially-used fungi, e.g., producers of secreted cellulases and anaerobic producers of cellulosomes. These results represent additional insights into the fungal community of the gray mangroves of the Red Sea, and show that they are significantly richer than previously reported.

In mammalian cells, transcribed enhancers (TrEns) play important roles in the initiation of gene expression and maintenance of gene expression levels in a spatiotemporal manner. One of the most challenging questions is how the genomic characteristics of enhancers relate to enhancer activities. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers' DNA code in a more systematic way. To address this problem, we developed a novel computational framework, Transcribed Enhancer Landscape Search (TELS), aimed at identifying predictive cell type/tissue-specific motif signatures of TrEns. As a case study, we used TELS to compile a comprehensive catalog of motif signatures for all known TrEns identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that combinations of different short motifs characterize in an optimized manner cell type/tissue-specific TrEns. Our study is the first to report combinations of motifs that maximize classification performance of TrEns exclusively transcribed in one cell type/tissue from TrEns exclusively transcribed in different cell types/tissues. Moreover, we also report 31 motif signatures predictive of enhancers' broad activity. TELS codes and material are publicly available at http://www.cbrc.kaust.edu.sa/TELS.
Enhancer Elements, Genetic ; Genomics ; methods ; Humans ; Nucleotide Motifs ; Sequence Analysis, DNA ; methods ; Transcription, Genetic