Biomarkers Reveal Diverse Microbial Communities in Black Smoker Sulfides from Turtle Pits (Mid-Atlantic Ridge,Recent) and Yaman Kasy (Russia,Silurian)

The steep biogeochemical gradients near deep sea hydrothermal vents provide various niches for microbial life. Here we present biosignatures of such organisms enclosed in a modern and an ancient hydrothermal sulfide deposit (Turtle Pits, Mid-Atlantic Ridge, Recent; Yaman Kasy, Russia, Silurian). In the modern sulfide we found high amounts of specific bacterial and archaeal biomarkers with δ¹³C values between ?8 and ?37‰ VPDB. Our data indicate the presence of thermophilic members of the autotrophic Aquificales using the reductive tricarboxylic acid (rTCA) cycle as well as of methanogenic and chemolithoheterotrophic Archaea. In the ancient sample, most potential biomarkers of thermophiles were obscured by compounds derived from allochthonous organic matter (OM), except for an acyclic C₄₀ biphytane and its C₃₉ breakdown product. Both samples contained high amounts of unresolved complex mixtures (UCM) of hydrocarbons. Apparently, OM in the sulfides had to withstand high thermal stress, indicated by highly mature hopanes, steranes, and cheilanthanes with up to 41 carbon atoms.     

Organic Carbon Degradation in Anoxic Organic-Rich Shelf Sediments: Biogeochemical Rates and Microbial Abundance

Identifying and explaining bottlenecks in organic carbon mineralization and the persistence of organic matter in marine sediments remain challenging. This study aims to illuminate the process of carbon flow between microorganisms involved in the sedimentary microbial food chain in anoxic, organic-rich sediments of the central Namibian upwelling system, using biogeochemical rate measurements and abundances of Bacteroidetes, Gammaproteobacteria, and sulfate-reducing bacteria at two sampling stations. Sulfate reduction rates decreased by three orders of magnitude in the top 20 cm at one sampling station (280 nmol cm^?3 d^?1 – 0.1 nmol cm^?3 d^?1) and by a factor of 7 at the second station (65 nmol cm^?3 d^?1 – 9.6 nmol cm^?3 d^?1). However, rates of enzymatic hydrolysis decreased by less than a factor of three at both sampling stations for the polysaccharides laminarin (23 nmol cm^?3 d^?1– 8 nmol cm^?3 d^?1 and 22 nmol cm^?3 d^?1– 10 nmol cm^?3 d^?1) and pullulan (11 nmol cm^?3 d^?1– 4 nmol cm^?3 d^?1 and 8 nmol cm^?3 d^?1– 6 nmol cm^?3 d^?1). Increasing imbalance between carbon turnover by hydrolysis and terminal oxidation with depth, the steep decrease in cell specific activity of sulfate reducing bacteria with depth, low concentrations of volatile fatty acids (less than 15 μM), and persistence of dissolved organic carbon, suggest decreasing bioavailability and substrate limitation with depth.     

Lipid Segregation and Membrane Budding Induced by the Peripheral Membrane Binding Protein Annexin A2

The formation of dynamic membrane microdomains is an important phenomenon in many signal transduction and membrane trafficking events. It is driven by intrinsic properties of membrane lipids and integral as well as membrane-associated proteins. Here we analyzed the ability of one peripherally associated membrane protein, annexin A2 (AnxA2), to induce the formation of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂)-rich domains in giant unilamellar vesicles (GUVs) of complex lipid composition. AnxA2 is a cytosolic protein that can bind PI(4,5)P₂ and other acidic phospholipids in a Ca²⁺-dependent manner and that has been implicated in cellular membrane dynamics in endocytosis and exocytosis. We show that AnxA2 binding to GUVs induces lipid phase separation and the recruitment of PI(4,5)P₂, cholesterol and glycosphingolipids into larger clusters. This property is observed for the full-length monomeric protein, a mutant derivative comprising the C-terminal protein core domain and for AnxA2 residing in a heterotetrameric complex with its intracellular binding partner S100A10. All AnxA2 derivatives inducing PI(4,5)P₂ clustering are also capable of forming interconnections between PI(4,5)P₂-rich microdomains of adjacent GUVs. Furthermore, they can induce membrane indentations rich in PI(4,5)P₂ and inward budding of these membrane domains into the lumen of GUVs. This inward vesiculation is specific for AnxA2 and not shared with other PI(4,5)P₂-binding proteins such as the pleckstrin homology (PH) domain of phospholipase Cδ1. Together our results indicate that annexins such as AnxA2 can efficiently induce membrane deformations after lipid segregation, a mechanism possibly underlying annexin functions in membrane trafficking.     

Rule-based modeling and simulations of the inner kinetochore structure

Background

Combinatorial complexity is a central problem when modeling biochemical reaction networks, since the association of a few components can give rise to a large variation of protein complexes. Available classical modeling approaches are often insufficient for the analysis of very large and complex networks in detail. Recently, we developed a new rule-based modeling approach that facilitates the analysis of spatial and combinatorially complex problems. Here, we explore for the first time how this approach can be applied to a specific biological system, the human kinetochore, which is a multi-protein complex involving over 100 proteins.

Results

Applying our freely available SRSim software to a large data set on kinetochore proteins in human cells, we construct a spatial rule-based simulation model of the human inner kinetochore. The model generates an estimation of the probability distribution of the inner kinetochore 3D architecture and we show how to analyze this distribution using information theory. In our model, the formation of a bridge between CenpA and an H3 containing nucleosome only occurs efficiently for higher protein concentration realized during S-phase but may be not in G1. Above a certain nucleosome distance the protein bridge barely formed pointing towards the importance of chromatin structure for kinetochore complex formation. We define a metric for the distance between structures that allow us to identify structural clusters. Using this modeling technique, we explore different hypothetical chromatin layouts.

Conclusions

Applying a rule-based network analysis to the spatial kinetochore complex geometry allowed us to integrate experimental data on kinetochore proteins, suggesting a 3D model of the human inner kinetochore architecture that is governed by a combinatorial algebraic reaction network. This reaction network can serve as bridge between multiple scales of modeling. Our approach can be applied to other systems beyond kinetochores.     

Postembryonic lineages of the Drosophila brain: I. Development of the lineage-associated fiber tracts

Neurons of the Drosophila central brain fall into approximately 100 paired groups, termed lineages. Each lineage is derived from a single asymmetrically-dividing neuroblast. Embryonic neuroblasts produce 1,500 primary neurons (per hemisphere) that make up the larval CNS followed by a second mitotic period in the larva that generates approximately 10,000 secondary, adult-specific neurons. Clonal analyses based on previous works using lineage-specific Gal4 drivers have established that such lineages form highly invariant morphological units. All neurons of a lineage project as one or a few axon tracts (secondary axon tracts, SATs) with characteristic trajectories, thereby representing unique hallmarks. In the neuropil, SATs assemble into larger fiber bundles (fascicles) which interconnect different neuropil compartments. We have analyzed the SATs and fascicles formed by lineages during larval, pupal, and adult stages using antibodies against membrane molecules (Neurotactin/Neuroglian) and synaptic proteins (Bruchpilot/N-Cadherin). The use of these markers allows one to identify fiber bundles of the adult brain and associate them with SATs and fascicles of the larval brain. This work lays the foundation for assigning the lineage identity of GFP-labeled MARCM clones on the basis of their close association with specific SATs and neuropil fascicles, as described in the accompanying paper (Wong et al., 2013. Postembryonic lineages of the Drosophila brain: II. Identification of lineage projection patterns based on MARCM clones. Submitted.).