Dog bites man or man bites dog? The enigma of the amino acid conjugations

The proposition posed is that the value of amino acid conjugation to the organism is not, as in the traditional view, to use amino acids for the detoxication of aromatic acids. Rather, the converse is more likely, to use aromatic acids that originate from the diet and gut microbiota to assist in the regulation of body stores of amino acids, such as glycine, glutamate, and, in certain invertebrates, arginine, that are key neurotransmitters in the central nervous system (CNS). As such, the amino acid conjugations are not so much detoxication reactions, rather they are homeostatic and neuroregulatory processes. Experimental data have been culled in support of this hypothesis from a broad range of scientific and clinical literature. Such data include the low detoxication value of amino acid conjugations and the Janus nature of certain amino acids that are both neurotransmitters and apparent conjugating agents. Amino acid scavenging mechanisms in blood deplete brain amino acids. Amino acids glutamate and glycine when trafficked from brain are metabolized to conjugates of aromatic acids in hepatic mitochondria and then irreversibly excreted into urine. This process is used clinically to deplete excess nitrogen in cases of urea cycle enzymopathies through excretion of glycine or glutamine as their aromatic acid conjugates. Untoward effects of high-dose phenylacetic acid surround CNS toxicity. There appears to be a relationship between extent of glycine scavenging by benzoic acid and psychomotor function. Glycine and glutamine scavenging by conjugation with aromatic acids may have important psychosomatic consequences that link diet to health, wellbeing, and disease.

Analysis of key molecules of the innate immune system in mammary epithelial cells isolated from marker-assisted and conventionally selected cattle

Mastitis is the most prevalent infectious disease in dairy herds. Breeding programs considering mastitis susceptibility were adopted as approaches to improve udder health status. In recent decades, conventional selection criteria based on phenotypic characteristics such as somatic cell score in milk have been widely used to select animals. Recently, approaches to incorporate molecular information have become feasible because of the detection of quantitative trait loci (QTL) affecting mastitis resistance. The aims of the study were to explore molecular mechanisms underlying mastitis resistance and the genetic mechanisms underlying a QTL on Bos taurus chromosome 18 found to influence udder health. Primary cell cultures of mammary epithelial cells from heifers that were selected for high or low susceptibility to mastitis were established. Selection based on estimated pedigree breeding value or on the basis of marker-assisted selection using QTL information was implemented. The mRNA expression of 10 key molecules of the innate immune system was measured using quantitative real-time PCR after 1, 6, and 24 h of challenge with heat-inactivated mastitis pathogens (Escherichia coli and Staphylococcus aureus) and expression levels in the high and low susceptibility groups were compared according to selection criteria. In the marker-assisted selection groups, mRNA expression in cells isolated from less-susceptible animals was significantly elevated for toll-like receptor 2, tumor necrosis factor-alpha, IL-1beta, IL-6, IL-8, RANTES (regulated upon activation, normal t-cell expressed and secreted), complement factor C3, and lactoferrin. In the estimated pedigree breeding value groups, mRNA expression was significantly elevated only for V-rel reticuloendotheliosis viral oncogene homolog A, IL-1 beta, and RANTES. These observations provide first insights into genetically determined divergent reactions to pathogens in the bovine mammary gland and indicate that the application of QTL information could be a successful tool for the selection of animals resistant to mastitis.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Nucleon and pion structure with lattice QCD simulations at physical value of the pion mass

We present results on the nucleon scalar, axial, and tensor charges as well as on the momentum fraction, and the helicity and transversity moments. The pion momentum fraction is also presented. The computation of these key observables is carried out using lattice QCD simulations at a physical value of the pion mass. The evaluation is based on gauge configurations generated with two degenerate sea quarks of twisted mass fermions with a clover term. We investigate excited states contributions with the nucleon quantum numbers by analyzing three sink-source time separations. We find that, for the scalar charge, excited states contribute significantly and to a less degree to the nucleon momentum fraction and helicity moment. Our result for the nucleon axial charge agrees with the experimental value. Furthermore, we predict a value of 1.027(62) in the MS¯¯¯¯¯ scheme at 2 GeV for the isovector nucleon tensor charge directly at the physical point. The pion momentum fraction is found to be ⟨x⟩π±u−d=0.214(15)(+12−9) in the MS¯¯¯¯¯ at 2 GeV.