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Novosphingobium fuchskuhlense sp nov., isolated from the north-east basin of Lake Grosse Fuchskuhle
(2013)
A yellow pigmented, Gram-negative, rod-shaped bacterium designated FNE08-7(T) was isolated from subsurface water of the north-east basin of the bog lake Grosse Fuchskuhle (Brandenburg, Germany). A first analysis of the nearly full-length 16S rRNA gene sequence analysis including environmental 16S rRNA gene sequences derived from freshwater ecosystems showed that strain FNE08-7(T) is the first cultured representative, to our knowledge, of the freshwater tribe Novo-A2. Further analysis indicates highest 16S rRNA gene sequence similarities to the type strains of Novosphingobium stygium (98.0%) and Novosphingobium taihuense (97.4%) and between 94.0% and 96.9% sequence similarity to other members of the genus Novosphingobium. Reconstruction of phylogenetic trees showed that strain FNE08-7(T) formed a distinct cluster with the type strains of N. stygium and N. taihuense supported by high bootstrap values. DNA DNA hybridization of strain FNE08-7(T) with N. stygium SMCC B0712(T) and N. taihuense DSM 17507(T) revealed low similarity values of 18.4% (reciprocal: 11.4%) and 23.1% (reciprocal: 54.2%), respectively. The predominant fatty acid of the isolate is C-18:1 omega 7c (56.4%) and two characteristic 2-hydroxy fatty acids, C-14:0 2-OH (16.5%) and C-15:0 2-OH (3.3%) occur. Ubiquinone Q-10 is the major respiratory quinone. The predominant polar lipids are phosphatidylethanolamine, phosphatidylmethylethanolamine, phosphatidylglycerol, sphingoglycolipid, phosphatidylcholine and minor amounts of diphosphatidylglycerol. Spermidine is the predominant polyamine. Characterization by genotypic, chemotaxonomic and phenotypic analysis indicate that strain FNE08-7(T) represents a novel species of the genus Novosphingobium within the Alphaproteobacteria. Therefore, we propose the species Novosphingobium fuchskuhlense sp. nov., with FNE08-7(T) (=DSM 25065(T)=CCM 7978(T)=CCUG 61508(T)) as the type strain.
Permafrost-affected ecosystems including peat wetlands are among the most obvious regions in which current microbial controls on organic matter decomposition are likely to change as a result of global warming. Wet tundra ecosystems in particular are ideal sites for increased methane production because of the waterlogged, anoxic conditions that prevail in seasonally increasing thawed layers. The following doctoral research project focused on investigating the abundance and distribution of the methane-cycling microbial communities in four different polygons on Herschel Island and the Yukon Coast. Despite the relevance of the Canadian Western Arctic in the global methane budget, the permafrost microbial communities there have thus far remained insufficiently characterized. Through the study of methanogenic and methanotrophic microbial communities involved in the decomposition of permafrost organic matter and their potential reaction to rising environmental temperatures, the overarching goal of the ensuing thesis is to fill the current gap in understanding the fate of the organic carbon currently stored in Artic environments and its implications regarding the methane cycle in permafrost environments. To attain this goal, a multiproxy approach including community fingerprinting analysis, cloning, quantitative PCR and next generation sequencing was used to describe the bacterial and archaeal community present in the active layer of four polygons and to scrutinize the diversity and distribution of methane-cycling microorganisms at different depths. These methods were combined with soil properties analyses in order to identify the main physico-chemical variables shaping these communities. In addition a climate warming simulation experiment was carried-out on intact active layer cores retrieved from Herschel Island in order to investigate the changes in the methane-cycling communities associated with an increase in soil temperature and to help better predict future methane-fluxes from polygonal wet tundra environments in the context of climate change. Results showed that the microbial community found in the water-saturated and carbon-rich polygons on Herschel Island and the Yukon Coast was diverse and showed a similar distribution with depth in all four polygons sampled. Specifically, the methanogenic community identified resembled the communities found in other similar Arctic study sites and showed comparable potential methane production rates, whereas the methane oxidizing bacterial community differed from what has been found so far, being dominated by type-II rather than type-I methanotrophs. After being subjected to strong increases in soil temperature, the active-layer microbial community demonstrated the ability to quickly adapt and as a result shifts in community composition could be observed. These results contribute to the understanding of carbon dynamics in Arctic permafrost regions and allow an assessment of the potential impact of climate change on methane-cycling microbial communities. This thesis constitutes the first in-depth study of methane-cycling communities in the Canadian Western Arctic, striving to advance our understanding of these communities in degrading permafrost environments by establishing an important new observatory in the Circum-Arctic.
Intermittent riverine resuspension effects on phosphorus transformations and heterotrophic bacteria
(2013)
Intermittent riverine resuspension (IRR), a common phenomenon, was applied to investigate its effects on sedimentary resources availability and bacteria in the water column. This lab experiment used organic-rich lowland river sediment in a newly designed erosion chamber, the Benthic Water Column Simulator, generating well-defined ratios of shear velocity u* to turbulence intensity. Eight consecutive resuspension events, 1-8, were initiated at u* = 1.1 cm s(-1). Sedimentary and phosphorus entrainment decreased from 20.4 g m(-2) h(-1) and 111.6 mg m(-2) h(-1) at event 1 to 1.31 g m(-2) h(-1) and 18.7 mg m(-2) h(-1) at event 8, suggesting an exhaustion of particulate and dissolved sediment constituents. Entrainment of particle-associated (PA) bacteria (132.7 x 10(9)-251.1 x 10(9) cells m(-2) h(-1)) was strongly correlated to that of particles. Free-living (FL) bacteria (-27.6 x 10(9)-36.4 x 10(9) cells m(-2) h(-1)) were fractionally entrained. Numbers of PA bacteria remained low after each event, whereas those of FL bacteria strongly increased 5-15 h after an event because of growth due to increased availability of dissolved organic carbon and inorganic nutrients following each event. FL bacteria community structure also changed during IRR. The systematic changes over consecutive IRR cycles show a strong effect in all considered parameters that elude the typical single-event, steady-state experiments. IRR should thus be considered in two respects: experimental protocols on riverine water quality should be revised. In ecosystem modeling, IRR should be considered to better predict extent and effect of resuspension. Only IRR adequately reflects the natural interplay between hydrodynamics and organisms in rivers.
Time hierarchies, arising as a result of interactions between system's components, represent a ubiquitous property of dynamical biological systems. In addition, biological systems have been attributed switch-like properties modulating the response to various stimuli across different organisms and environmental conditions. Therefore, establishing the interplay between these features of system dynamics renders itself a challenging question of practical interest in biology. Existing methods are suitable for systems with one stable steady state employed as a well-defined reference. In such systems, the characterization of the time hierarchies has already been used for determining the components that contribute to the dynamics of biological systems. However, the application of these methods to bistable nonlinear systems is impeded due to their inherent dependence on the reference state, which in this case is no longer unique. Here, we extend the applicability of the reference-state analysis by proposing, analyzing, and applying a novel method, which allows investigation of the time hierarchies in systems exhibiting bistability. The proposed method is in turn used in identifying the components, other than reactions, which determine the systemic dynamical properties. We demonstrate that in biological systems of varying levels of complexity and spanning different biological levels, the method can be effectively employed for model simplification while ensuring preservation of qualitative dynamical properties (i.e., bistability). Finally, by establishing a connection between techniques from nonlinear dynamics and multivariate statistics, the proposed approach provides the basis for extending reference-based analysis to bistable systems.
The unicellular green alga Chlamydomonas reinhardtii is a long-established model organism for studies on photosynthesis and carbon metabolism-related physiology. Under conditions of air-level carbon dioxide concentration [CO2], a carbon concentrating mechanism (CCM) is induced to facilitate cellular carbon uptake. CCM increases the availability of carbon dioxide at the site of cellular carbon fixation. To improve our understanding of the transcriptional control of the CCM, we employed FAIRE-seq (formaldehyde-assisted Isolation of Regulatory Elements, followed by deep sequencing) to determine nucleosome-depleted chromatin regions of algal cells subjected to carbon deprivation. Our FAIRE data recapitulated the positions of known regulatory elements in the promoter of the periplasmic carbonic anhydrase (Cah1) gene, which is upregulated during CCM induction, and revealed new candidate regulatory elements at a genome-wide scale. In addition, time series expression patterns of 130 transcription factor (TF) and transcription regulator (TR) genes were obtained for cells cultured under photoautotrophic condition and subjected to a shift from high to low [CO2]. Groups of co-expressed genes were identified and a putative directed gene-regulatory network underlying the CCM was reconstructed from the gene expression data using the recently developed IOTA (inner composition alignment) method. Among the candidate regulatory genes, two members of the MYB-related TF family, Lcr1 (Low-CO2 response regulator 1) and Lcr2 (Low-CO2 response regulator 2), may play an important role in down-regulating the expression of a particular set of TF and TR genes in response to low [CO2]. The results obtained provide new insights into the transcriptional control of the CCM and revealed more than 60 new candidate regulatory genes. Deep sequencing of nucleosome-depleted genomic regions indicated the presence of new, previously unknown regulatory elements in the C. reinhardtii genome. Our work can serve as a basis for future functional studies of transcriptional regulator genes and genomic regulatory elements in Chlamydomonas.
Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.
Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism.
Robustness of biochemical systems has become one of the central questions in systems biology although it is notoriously difficult to formally capture its multifaceted nature. Maintenance of normal system function depends not only on the stoichiometry of the underlying interrelated components, but also on the multitude of kinetic parameters. Invariant flux ratios, obtained within flux coupling analysis, as well as invariant complex ratios, derived within chemical reaction network theory, can characterize robust properties of a system at steady state. However, the existing formalisms for the description of these invariants do not provide full characterization as they either only focus on the flux-centric or the concentration-centric view. Here we develop a novel mathematical framework which combines both views and thereby overcomes the limitations of the classical methodologies. Our unified framework will be helpful in analyzing biologically important system properties.
Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into k segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.