Biogeography of Rhizobium radiobacter and distribution of associated temperate phages in deep subseafloor sediments

Bacteriophages might be the main ‘predators'' in the marine deep subsurface and probably have a major impact on indigenous microbial communities. To identify their function within this habitat, we have determined their abundance and distribution along the sediment columns of two continental margin and two open ocean sites that were recovered during Leg 201 of the Ocean Drilling Program. For all investigated sites, viral abundance followed the total cell numbers with a virus-to-cell ratio between 1 and 10 in the upper 100 mbsf (meters below seafloor). An increasing ratio of about 20 in deeper layers indicated an ongoing viral production in up to 11 Ma old sediments. We have used Rhizobium radiobacter as the most frequently isolated organism from the deep subsurface with a high in situ abundance to identify the frequency of associated rhizobiophages. In this study, 16S rRNA gene copies of R. radiobacter accounted for up to 5.6% of total bacterial 16S rRNA genes (average: 0.75%) as detected by quantitative PCR. A distinctive distribution was identified for R. radiobacter as indicated by a site-specific arrangement of genetically similar populations. Whole genome information of rhizobiophage RR1-A was used to generate a primer system for quantitative PCR specifically targeting the prophage antirepressor gene, indicative for temperate phages. The quantification of this gene within various sediment horizons showed a contribution of temperate phages of up to 14.3% to the total viral abundance. Thus, the high amount of temperate phages within the sediments and among all investigated isolates indicates that lysogeny is the main viral proliferation mode in deep subsurface populations.     

Prokaryotic community composition and biogeochemical processes in deep subseafloor sediments from the Peru Margin

The community compositions of Bacteria and Archaea were investigated in deep, sub-seafloor sediments from the highly productive Peru Margin (ODP Leg 201, sites 1228 and 1229, c. 25 km apart) down to nearly 200 m below the seafloor using taxonomic (16S rRNA) and functional (mcrA and dsrA) gene markers. Bacterial and archaeal groups identified from clone libraries of 16S rRNA gene sequences at site 1229 agreed well with sequences amplified from bands excised from denaturing gradient gel electrophoresis (DGGE) depth profiles, with the exception of the Miscellaneous Crenarchaeotic Group (MCG). This suggested that the prokaryotic community at site 1228, obtained from DGGE profiling alone, was reliable. Sites were dominated by Bacteria in the Gammaproteobacteria, Chloroflexi (green non-sulphur bacteria) and Archaea in the MCG and South African Gold Mine Euryarchaeotic Group, although community composition changed with depth. The candidate division JS1 was present throughout both sites but was not dominant. The populations identified in the Peru Margin sediments consisted mainly of prokaryotes found in other deep subsurface sediments, and were more similar to communities from the Sea of Okhotsk (pelagic clays) than to those from the low organic carbon Nankai Trough sediments. Despite broad similarities in the prokaryotic community at the two sites, there were some differences, as well as differences in activity and geochemistry. Methanogens (mcrA) within the Methanosarcinales and Methanobacteriales were only found at site 1229 (4 depths analysed), whereas sulphate-reducing prokaryotes (dsrA) were only found at site 1228 (one depth), and these terminal-oxidizing prokaryotes may represent an active community component present at low abundance. This study clearly demonstrates that the deep subsurface sediments of the Peru Margin have a large diverse and metabolically active prokaryotic population.     

Microorganisms persist at record depths in the subseafloor of the Canterbury Basin

The subsurface realm is colonized by microbial communities to depths of >1000 meters below the seafloor (m.b.sf.), but little is known about overall diversity and microbial distribution patterns at the most profound depths. Here we show that not only Bacteria and Archaea but also Eukarya occur at record depths in the subseafloor of the Canterbury Basin. Shifts in microbial community composition along a core of nearly 2 km reflect vertical taxa zonation influenced by sediment depth. Representatives of some microbial taxa were also cultivated using methods mimicking in situ conditions. These results suggest that diverse microorganisms persist down to 1922 m.b.sf. in the seafloor of the Canterbury Basin and extend the previously known depth limits of microbial evidence (i) from 159 to 1740 m.b.sf. for Eukarya and (ii) from 518 to 1922 m.b.sf. for Bacteria.     

Analysis of DGGE profiles to explore the relationship between prokaryotic community composition and biogeochemical processes in deep subseafloor sediments from the Peru Margin

The aim of this work was to relate depth profiles of prokaryotic community composition with geochemical processes in the deep subseafloor biosphere at two shallow-water sites on the Peru Margin in the Pacific Ocean (ODP Leg 201, sites 1228 and 1229). Principal component analysis of denaturing gradient gel electrophoresis banding patterns of deep-sediment Bacteria, Archaea, Euryarchaeota and the novel candidate division JS1, followed by multiple regression, showed strong relationships with prokaryotic activity and geochemistry (R(2)=55-100%). Further correlation analysis, at one site, between the principal components from the community composition profiles for Bacteria and 12 other variables quantitatively confirmed their relationship with activity and geochemistry, which had previously only been implied. Comparison with previously published cell counts enumerated by fluorescent in situ hybridization with rRNA-targeted probes confirmed that these denaturing gradient gel electrophoresis profiles described an active prokaryotic community.     

Alteration of a Submarine Basaltic Glass under Environmental Conditions Conducive for Microorganisms: Growth Patterns of the Microbial Community and Mechanism of Palagonite Formation

In basaltic glass from the southern Mid-Atlantic-Ridge conducive environmental conditions for biogenic weathering resulted in excellent preserved microbial morphologies on glass surfaces. The distinct glass interface and open spaces between palagonite sheet and glass indicate a dissolution-reprecipitation mechanism of glass alteration potentially supported by microorganisms. On internal fracture surfaces, branching channels with widths at 20–30 μm containing longish structures with targeted dissolution of the glass by growing tips were observed. Alteration resulted in enrichment of Fe, Ti, P, and K in palagonite in amorphous mineral forms.     

A New Frontier for Palaeobiology: Earth's Vast Deep Biosphere

Diverse micro‐organisms populate a global deep biosphere hosted by rocks and sediments beneath land and sea, containing more biomass than any other biome except forests. This paper reviews an emerging palaeobiological archive of these dark habitats: microfossils preserved in ancient pores and fractures in the crust. This archive, seemingly dominated by mineralized filaments (although rods and coccoids are also reported), is presently far too sparsely sampled and poorly understood to reveal trends in the abundance, distribution, or diversity of deep life through time. New research is called for to establish the nature and extent of the fossil record of Earth's deep biosphere by combining systematic exploration, rigorous microanalysis, and experimental studies of both microbial preservation and the formation of abiotic pseudofossils within the crust. It is concluded that the fossil record of Earth's largest microbial habitat may still have much to tell us about the history of life, the evolution of biogeochemical cycles, and the search for life on Mars.     

MUFOLD‐SS: New deep inception‐inside‐inception networks for protein secondary structure prediction

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception‐inside‐inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD‐SS. The input to MUFOLD‐SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio‐chemical properties of amino acids, PSI‐BLAST profile, and HHBlits profile. MUFOLD‐SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD‐SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD‐SS outperformed the best existing methods and other deep neural networks significantly. MUFold‐SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html .     

Intraseasonal characterization of glass eel migration in the River Tiber: space and time dynamics

Glass eel ( Anguilla anguilla ) upstream migration was studied in the River Tiber estuary to obtain a better understanding of spatial and temporal migration dynamics within the season of ascent. Using data from glass eel fisheries, time series analysis of daily catches per unit of effort revealed a fortnightly cycle that can be related to invasion waves possibly corresponding to tidal currents. The amplitude of these waves appeared to correspond to the tidal area of the estuary. Furthermore, glass eels apparently had a delay in this area before resuming upstream migration.     

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae

ABSTRACT

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 ?uorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.

Abbreviations: Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.