Yuri P. Galachyants

The Structure of Microbial Community and Degradation of Diatoms in the Deep Near-Bottom Layer of Lake Baikal

Insight into the role of bacteria in degradation of diatoms is important for understanding the factors and components of silica turnover in aquatic ecosystems. Using microscopic methods, it has been shown that the degree of diatom preservation and the numbers of diatom-associated bacteria in the surface layer of bottom sediments decrease with depth; in the near-bottom water layer, the majority of bacteria are associated with diatom cells, being located either on the cell surface or within the cell. The structure of microbial community in the near-bottom water layer has been characterized by pyrosequencing of the 16S rRNA gene, which has revealed 149 208 unique sequences. According to the results of metagenomic analysis, the community is dominated by representatives of Proteobacteria (41.9%), Actinobacteria (16%); then follow Acidobacteria (6.9%), Cyanobacteria (5%), Bacteroidetes (4.7%), Firmicutes (2.8%), Nitrospira (1.6%), and Verrucomicrobia (1%); other phylotypes account for less than 1% each. For 18.7% of the sequences, taxonomic identification has been possible only to the Bacteria domain level. Many bacteria identified to the genus level have close relatives occurring in other aquatic ecosystems and soils. The metagenome of the bacterial community from the near-bottom water layer also contains 16S rRNA gene sequences found in previously isolated bacterial strains possessing hydrolytic enzyme activity. These data show that potential degraders of diatoms occur among the vast variety of microorganisms in the near-bottom water of Lake Baikal.

Sub-Ice Microalgal and Bacterial Communities in Freshwater Lake Baikal, Russia

The sub-ice environment of Lake Baikal represents a special ecotope where strongly increasing microbial biomass causes an “ice-bloom” contributing therefore to the ecosystem functioning and global element turnover under low temperature in the world’s largest freshwater lake. In this work, we analyzed bacterial and microalgal communities and their succession in the sub-ice environment in March–April 2010–2012. It was found out that two dinoflagellate species (Gymnodinium baicalense var. minor and Peridinium baicalense Kisselew et Zwetkow) and four diatom species (Aulacoseira islandica, A. baicalensis, Synedra acus subsp. radians, and Synedra ulna) predominated in the microalgal communities. Interestingly, among all microalgae, the diatom A. islandica showed the highest number of physically attached bacterial cells (up to 67 ± 16 bacteria per alga). Bacterial communities analyzed with pyrosequencing of 16S rRNA gene fragments were diverse and represented by 161 genera. Phyla Proteobacteria, Verrucomicrobia, Actinobacteria, Acidobacteria, Bacteroidetes, and Cyanobacteria represented a core community independently on microalgal composition, although the relative abundance of these bacterial phyla strongly varied across sampling sites and time points; unique OTUs from other groups were rare.

Diversity of Archaea in Bottom Sediments of the Discharge Areas With Oil- and Gas-Bearing Fluids in Lake Baikal

ABSTRACT Using massively parallel sequencing (the Roche 454 platform) we have studied the diversity of archaeal 16S rRNA gene sequences in oxic and anoxic sediments at six sites in Lake Baikal with oil- and gas-bearing fluids discharge. Archaeal communities appeared to be represented mainly by five phyla: Euryarchaeota, Crenarchaeota, Thaumarchaeota, Bathyarchaeota (miscellaneous Crenarchaeotic group), and Woesearchaeota (deep sea hydrothermal vent group 6). Among them we detected sequences of methanogens of the orders Methanomicrobiales, Methanosarsinales, Methanococcales, as well as representatives of the following uncultured archaeal lineages: Group C3, Marine Benthic Group D, and Terrestrial Miscellaneous Group. We have also identified sequences of ammonia-oxidizing archaea of the phyla Crenarchaeota and Thaumarchaeota. Phylogenetic analysis showed the presence ANME-2d-related sequences. However, the analysis of mcrA genes libraries has not revealed typical representatives of ANME groups. Comparison of amplicon libraries 16S rRNA gene fragments from different samples proved the widespread presence of previously detected Baikal archaeal lineages, which are members of the phylum Crenarchaeota and Thaumarchaeota (formerly Group C3 of Crenarchaeota).

Metagenomic Analysis of Microbial Communities of the Sediments of the Kara Sea Shelf and the Yenisei Bay

Microbial diversity in the sediments of the Kara Sea shelf and the southern Yenisei Bay, differing in pore water mineralization, was studied using massive parallel pyrosequencing according to the 454 (Roche) technology. Members of the same phyla (Cyanobacteria, Verrucomicrobia, Actinobacteria, Proteobacteria, and Bacteroidetes) predominated in bacterial communities of the sediments, while their ratio and taxonomic composition varied within the phyla and depended on pore water mineralization. Increasing salinity gradient was found to coincide with increased share of the γ-Proteobacteria and decreased abundance of α- and β-Proteobacteria, as well as of the phyla Verrucomicrobia, Chloroflexi, Chlorobi, and Acidobacteria. Archaeal diversity was lower, with Thaumarchaeota predominant in the sediments with high and low mineralization, while Crenarchaeota predominated in moderately mineralized sediments. Microbial communities of the Kara Sea shelf and Yenisei Bay sediments were found to contain the organisms capable of utilization of a broad spectrum of carbon sources, including gaseous and petroleum hydrocarbons.

Inferring Phylogenetic Networks from Gene Order Data

Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary), sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm). Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

Co-occurrence Networks Among Bacteria and Microbial Eukaryotes of Lake Baikal During a Spring Phytoplankton Bloom

The pelagic zone of Lake Baikal is an ecological niche where phytoplankton bloom causes increasing microbial abundance in spring which plays a key role in carbon turnover in the freshwater lake. Co-occurrence patterns revealed among different microbes can be applied to predict interactions between the microbes and environmental conditions in the ecosystem. We used 454 pyrosequencing of 16S rRNA and 18S rRNA genes to study bacterial and microbial eukaryotic communities and their co-occurrence patterns at the pelagic zone of Lake Baikal during a spring phytoplankton bloom. We found that microbes within one domain mostly correlated positively with each other and are highly interconnected. The highly connected taxa in co-occurrence networks were operational taxonomic units (OTUs) of Actinobacteria, Bacteroidetes, Alphaproteobacteria, and autotrophic and unclassified Eukaryota which might be analogous to microbial keystone taxa. Constrained correspondence analysis revealed the relationships of bacterial and microbial eukaryotic communities with geographical location.

Bacterial and Archaeal Community Structure in the Surface Diatom Sediments of Deep Freshwater Lake Baikal (Eastern Siberia)

ABSTRACT Diatom sediment records of large lakes can be used to decipher the history of ancient phytoplankton. The upper layer of the sediment is an important area of remineralization of the sedimenting phytoplankton biomass. It hosts a bacterial community different from those of both the water column and deeper sediment layers. In this work, we analyzed the structure and diversity of the communities of Bacteria and Archaea in the surface sediment core containing valves of diatoms, the major producers in Lake Baikal. Pyrosequencing of the bacterial V3–V4 region of the 16 S ribosomal RNA (rRNA) and archaeal V1–V3 16 S rRNA gene regions yielded 29,168 and 36,997 reads, respectively. In total, we have identified 33 bacterial phyla; uncultured Actinobacteria were the most abundant in the upper layers, while lower sediment was dominated by Firmicutes and Alphaproteobacteria. The composition of the archaeal community changed with depth, but was generally dominated by Crenarchaeota from the classes Marine Group I and Miscellaneous Crenarchaeotic Group, as well as Euryarchaeota from the class Thermoplasmata. These dominant bacterial and archaeal taxa are presumed to participate in the destruction of buried organic matter, which eventually leads to degradation of the diatom valves.

DISTANT JOINING: A SEQUENCE SAMPLING METHOD FOR COMPLEX EVOLUTIONARY HISTORIES

Motivation: Massive parallel phylogenetic analyses allow to reconstruct phylogenetic trees for every gene in genome, typically using the set of potential homologues detected via BLAST or analogue. However, if the amount of hits is too high, the dataset should be reduced to tractable size, preferably without human intervention. Currently available methods are error-prone on at least some datasets and some of them also depend on additional data which may not be available. Results: We propose a distance-based algorithm, termed Distant Joining, for phylogenetic dataset reduction that does not require any input besides sequences themselves. It was shown to be robust to both complex evolutionary histories and large data sets. We also discuss the assumptions and limitations of different sequence sampling approaches, and provide guidelines to selection of the method for a phylomic pipeline. Availability: Proof-of-concept Python implementation is available at https://github.com/SynedraAcus/sampler under the terms of CC-BY-4.0 license. Please check README for dependencies. Supplementary information: Supplementary data are available at Journal of Bioinformatics and Genomics online.