The Last Month of Szent-Györgyi in Groningen

Albert (von) Szent-Györgyi started his studies on biological oxidation processes – which also resulted in the discovery of vitamin C, for which he received the Nobel Price in 1937 – in the Laboratory of Physiology of the University in Groningen in 1922–1926. These studies were later continued in Cambridge (UK) and Szeged (Hungary). When he had already received the invitation as well as the financial means to come and work in Cambridge, he still did experiments in Groningen to find out whether the adrenal extract, isolated by him and later found to be a major source of vitamin C, contained the hormone essential for the survival of cats whose adrenals were removed. He was rather upset by the negative results of this experiment, judging by the recollections of a former student of his. This history constitutes an interesting example of the difference between serendipitous discovery and planned invention.     

Identifying Optimal Models to Represent Biochemical Systems

Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an “optimal” reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model.     

A diverse uncultivated microbial community is responsible for organic matter degradation in the Black Sea sulphidic zone

Organic matter degradation in marine environments is essential for the recycling of nutrients, especially under conditions of anoxia where organic matter tends to accumulate. However, little is known about the diversity of the microbial communities responsible for the mineralization of organic matter in the absence of oxygen, as well as the factors controlling their activities. Here, we determined the active heterotrophic prokaryotic community in the sulphidic water column of the Black Sea, an ideal model system, where a tight coupling between carbon, nitrogen and sulphur cycles is expected. Active microorganisms degrading both dissolved organic matter (DOM) and protein extracts were determined using quantitative DNA stable isotope probing incubation experiments. These results were compared with the metabolic potential of metagenome-assembled genomes obtained from the water column. Organic matter incubations showed that groups like Cloacimonetes and Marinimicrobia are generalists degrading DOM. Based on metagenomic profiles the degradation proceeds in a potential interaction with members of the Deltaproteobacteria and Chloroflexi Dehalococcoidia. On the other hand, microbes with small genomes like the bacterial phyla Parcubacteria, Omnitrophica and of the archaeal phylum Woesearchaeota, were the most active, especially in protein-amended incubations, revealing the potential advantage of streamlined microorganisms in highly reduced conditions.     

Natranaerofaba carboxydovora gen. nov., sp. nov., an extremely haloalkaliphilic CO-utilizing acetogen from a hypersaline soda lake representing a novel deep phylogenetic lineage in the class ‘Natranaerobiia’

An anaerobic enrichment with CO from sediments of hypersaline soda lakes resulted in a methane-forming binary culture, whereby CO was utilized by a bacterium and not the methanogenic partner. The bacterial isolate ANCO1 forms a deep-branching phylogenetic lineage at the level of a new family within the class ‘Natranaerobiia’. It is an extreme haloalkaliphilic and moderate thermophilic acetogen utilizing CO, formate, pyruvate and lactate as electron donors and thiosulfate, nitrate (reduced to ammonia) and fumarate as electron acceptors. The genome of ANCO1 encodes a full Wood–Ljungdahl pathway allowing for CO oxidation and acetogenic conversion of pyruvate. A locus encoding Nap nitrate reductase/NrfA ammonifying nitrite reductase is also present. Thiosulfate respiration is encoded by a Phs/Psr-like operon. The organism obviously relies on Na-based bioenergetics, since the genome encodes for the Na⁺-Rnf complex, Na⁺-F1F0 ATPase and Na⁺-translocating decarboxylase. Glycine betaine serves as a compatible solute. ANCO1 has an unusual membrane polar lipid composition dominated by diethers, more common among archaea, probably a result of adaptation to multiple extremophilic conditions. Overall, ANCO1 represents a unique example of a triple extremophilic CO-oxidizing anaerobe and is classified as a novel genus and species Natranaerofaba carboxydovora in a novel family Natranaerofabacea.     

A Morpho-Density Approach to Estimating Neural Connectivity

Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF) describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining a greater understanding of the relationship between neural morphology and network connectivity.