A comparative description of mitochondrial DNA differentiation in selected avian and other vertebrate genera

Levels of mitochondrial DNA (mtDNA) sequence divergence between species within each of several avian (Anas, Aythya, Dendroica, Melospiza, and Zonotrichia) and nonavian (Lepomis and Hyla) vertebrate genera were compared. An analysis of digestion profiles generated by 13-18 restriction endonucleases indicates little overlap in magnitude of mtDNA divergence for the avian versus nonavian taxa examined. In 55 interspecific comparisons among the avian congeners, the fraction of identical fragment lengths (F) ranged from 0.26 to 0.96 (F = 0.46), and, given certain assumptions, these translate into estimates of nucleotide sequence divergence (p) ranging from 0.007 to 0.088; in 46 comparisons among the fish and amphibian congeners, F values ranged from 0.00 to 0.36 (F = 0.09), yielding estimates of P greater than 0.070. The small mtDNA distances among avian congeners are associated with protein-electrophoretic distances (D values) less than approximately 0.2, while the mtDNA distances among assayed fish and amphibian congeners are associated with D values usually greater than 0.4. Since the conservative pattern of protein differentiation previously reported for many avian versus nonavian taxa now appears to be paralleled by a conservative pattern of mtDNA divergence, it seems increasingly likely that many avian species have shared more recent common ancestors than have their nonavian taxonomic counterparts. However, estimates of avian divergence times derived from mtDNA- and protein-calibrated clocks cannot readily be reconciled with some published dates based on limited fossil remains. If the earlier paleontological interpretations are valid, then protein and mtDNA evolution must be somewhat decelerated in birds. The empirical and conceptual issues raised by these findings are highly analogous to those in the long-standing debate about rates of molecular evolution and times of separation of ancestral hominids from African apes.      

Pyramidal Neurons Derived from Human Pluripotent Stem Cells Integrate Efficiently into Mouse Brain Circuits In Vivo

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Human milk fractions inhibit the adherence of diffusely adherent Escherichia coli (DAEC) and enteroaggregative E. coli (EAEC) to HeLa cells

Binding to a specific receptor is an essential step for most enteropathogens to initiate an intestinal infection. We analyzed the inhibitory effect of human milk and its protein components on adhesion of two diarrheagenic Escherichia coli strains, diffusely adherent E. coli (DAEC) and enteroaggregative E. coli (EAEC), to HeLa cells. Defatted milk, whey proteins, immunoglobulin and non-immunoglobulin fractions, in concentrations lower than usually found in whole milk, inhibited both DAEC and EAEC adhesion, indicating that human milk components may contribute to the defense of the infants against enteropathogens.     

Challenges for simultaneous nitrification, denitrification, and phosphorus removal in microbial aggregates: mass transfer limitation and nitrous oxide production

The microbial community composition and activity was investigated in aggregates from a lab-scale bioreactor, in which nitrification, denitrification and phosphorus removal occurred simultaneously. The biomass was highly enriched for polyphosphate accumulating organisms facilitating complete removal of phosphorus from the bulk liquid; however, some inorganic nitrogen still remained at the end of the reactor cycle. This was ascribed to incomplete coupling of nitrification and denitrification causing NO(3)(-) accumulation. After 2 h of aeration, denitrification was dependent on the activity of nitrifying bacteria facilitating the formation of anoxic zones in the aggregates; hence, denitrification could not occur without simultaneous nitrification towards the end of the reactor cycle. Nitrous oxide was identified as a product of denitrification, when based on stored PHA as carbon source. This observation is of critical importance to the outlook of applying PHA-driven denitrification in activated sludge processes.     

Glucokinase gene mutations: structural and genotype-phenotype analyses in MODY children from South Italy

Background

Maturity onset diabetes of the young type 2 (or GCK MODY) is a genetic form of diabetes mellitus provoked by mutations in the glucokinase gene (GCK).

Methodology/Principal Findings

We screened the GCK gene by direct sequencing in 30 patients from South Italy with suspected MODY. The mutation-induced structural alterations in the protein were analyzed by molecular modeling. The patients'' biochemical, clinical and anamnestic data were obtained. Mutations were detected in 16/30 patients (53%); 9 of the 12 mutations identified were novel (p.Glu70Asp, p.Phe123Leu, p.Asp132Asn, p.His137Asp, p.Gly162Asp, p.Thr168Ala, p.Arg392Ser, p.Glu290X, p.Gln106_Met107delinsLeu) and are in regions involved in structural rearrangements required for catalysis. The prevalence of mutation sites was higher in the small domain (7/12: ∼59%) than in the large (4/12: 33%) domain or in the connection (1/12: 8%) region of the protein. Mild diabetic phenotypes were detected in almost all patients [mean (SD) OGTT = 7.8 mMol/L (1.8)] and mean triglyceride levels were lower in mutated than in unmutated GCK patients (p = 0.04).

Conclusions

The prevalence of GCK MODY is high in southern Italy, and the GCK small domain is a hot spot for MODY mutations. Both the severity of the GCK mutation and the genetic background seem to play a relevant role in the GCK MODY phenotype. Indeed, a partial genotype-phenotype correlation was identified in related patients (3 pairs of siblings) but not in two unrelated children bearing the same mutation. Thus, the molecular approach allows the physician to confirm the diagnosis and to predict severity of the mutation.     

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane’s inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite–soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons.     

Measuring Symmetry,Asymmetry and Randomness in Neural Network Connectivity

Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.