Mathematical description of bacterial traveling pulses

The Keller-Segel system has been widely proposed as a model for bacterial waves driven by chemotactic processes. Current experiments on {\em E. coli} have shown precise structure of traveling pulses. We present here an alternative mathematical description of traveling pulses at a macroscopic scale. This modeling task is complemented with numerical simulations in accordance with the experimental observations. Our model is derived from an accurate kinetic description of the mesoscopic run-and-tumble process performed by bacteria. This model can account for recent experimental observations with {\em E. coli}. Qualitative agreements include the asymmetry of the pulse and transition in the collective behaviour (clustered motion versus dispersion). In addition we can capture quantitatively the main characteristics of the pulse such as the speed and the relative size of tails. This work opens several experimental and theoretical perspectives. Coefficients at the macroscopic level are derived from considerations at the cellular scale. For instance the stiffness of the signal integration process turns out to have a strong effect on collective motion. Furthermore the bottom-up scaling allows to perform preliminary mathematical analysis and write efficient numerical schemes. This model is intended as a predictive tool for the investigation of bacterial collective motion

ANDES: Statistical tools for the ANalyses of DEep Sequencing

<p>Abstract</p> <p>Background</p> <p>The advancements in DNA sequencing technologies have allowed researchers to progress from the analyses of a single organism towards the deep sequencing of a sample of organisms. With sufficient sequencing depth, it is now possible to detect subtle variations between members of the same species, or between mixed species with shared biomarkers, such as the 16S rRNA gene. However, traditional sequencing analyses of samples from largely homogeneous populations are often still based on multiple sequence alignments (MSA), where each sequence is placed along a separate row and similarities between aligned bases can be followed down each column. While this visual format is intuitive for a small set of aligned sequences, the representation quickly becomes cumbersome as sequencing depths cover loci hundreds or thousands of reads deep.</p> <p>Findings</p> <p>We have developed ANDES, a software library and a suite of applications, written in Perl and R, for the statistical ANalyses of DEep Sequencing. The fundamental data structure underlying ANDES is the position profile, which contains the nucleotide distributions for each genomic position resultant from a multiple sequence alignment (MSA). Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.</p> <p>Conclusions</p> <p>As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common. We have provided a software package and demonstrated its application on various empirically-derived datasets. Investigators may download the software from Sourceforge at <url>https://sourceforge.net/projects/andestools</url>.</p

Spectroscopic evidence for an all-ferrous [4Fe–4S]0 cluster in the superreduced activator of 2-hydroxyglutaryl-CoA dehydratase from Acidaminococcus fermentans

The key enzyme of the fermentation of glutamate by Acidaminococcus fermentans, 2-hydroxyglutarylcoenzyme A dehydratase, catalyzes the reversible syn-elimination of water from (R)-2-hydroxyglutaryl-coenzyme A, resulting in (E)-glutaconylcoenzyme A. The dehydratase system consists of two oxygen-sensitive protein components, the activator (HgdC) and the actual dehydratase (HgdAB). Previous biochemical and spectroscopic studies revealed that the reduced [4Fe–4S]+ cluster containing activator transfers one electron to the dehydratase driven by ATP hydrolysis, which activates the enzyme. With a tenfold excess of titanium(III) citrate at pH 8.0 the activator can be further reduced, yielding about 50% of a superreduced [4Fe–4S]0 cluster in the all-ferrous state. This is inferred from the appearance of a new Mössbauer spectrum with parameters δ = 0.65 mm/s and ΔEQ = 1.51–2.19 mm/s at 140 K, which are typical of Fe(II)S4 sites. Parallel-mode electron paramagnetic resonance (EPR) spectroscopy performed at temperatures between 3 and 20 K showed two sharp signals at g = 16 and 12, indicating an integer-spin system. The X-band EPR spectra and magnetic Mössbauer spectra could be consistently simulated by adopting a total spin St = 4 for the all-ferrous cluster with weak zero-field splitting parameters D = −0.66 cm−1 and E/D = 0.17. The superreduced cluster has apparent spectroscopic similarities with the corresponding [4Fe–4S]0 cluster described for the nitrogenase Fe-protein, but in detail their properties differ. While the all-ferrous Fe-protein is capable of transferring electrons to the MoFe-protein for dinitrogen reduction, a similar physiological role is elusive for the superreduced activator. This finding supports our model that only one-electron transfer steps are involved in dehydratase catalysis. Nevertheless we discuss a common basic mechanism of the two diverse systems, which are so far the only described examples of the all-ferrous [4Fe–4S]0 cluster found in biology

Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity

Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or “brain-read”, unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90–100%) were achieved with 15–40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction

Distinct Neurobehavioural Effects of Cannabidiol in Transmembrane Domain Neuregulin 1 Mutant Mice

The cannabis constituent cannabidiol (CBD) possesses anxiolytic and antipsychotic properties. We have previously shown that transmembrane domain neuregulin 1 mutant (Nrg1 TM HET) mice display altered neurobehavioural responses to the main psychoactive constituent of cannabis, Δ9-tetrahydrocannabinol. Here we investigated whether Nrg1 TM HET mice respond differently to CBD and whether CBD reverses schizophrenia-related phenotypes expressed by these mice. Adult male Nrg1 TM HET and wild type-like littermates (WT) received vehicle or CBD (1, 50 or 100 mg/kg i.p.) for 21 days. During treatment and 48 h after withdrawal we measured behaviour, whole blood CBD concentrations and autoradiographic receptor binding. Nrg1 HET mice displayed locomotor hyperactivity, PPI deficits and reduced 5-HT2A receptor binding density in the substantia nigra, but these phenotypes were not reversed by CBD. However, long-term CBD (50 and 100 mg/kg) selectively enhanced social interaction in Nrg1 TM HET mice. Furthermore, acute CBD (100 mg/kg) selectively increased PPI in Nrg1 TM HET mice, although tolerance to this effect was manifest upon repeated CBD administration. Long-term CBD (50 mg/kg) also selectively increased GABAA receptor binding in the granular retrosplenial cortex in Nrg1 TM HET mice and reduced 5-HT2A binding in the substantia nigra in WT mice. Nrg1 appears necessary for CBD-induced anxiolysis since only WT mice developed decreased anxiety-related behaviour with repeated CBD treatment. Altered pharmacokinetics in mutant mice could not explain our findings since no genotype differences existed in CBD blood concentrations. Here we demonstrate that Nrg1 modulates acute and long-term neurobehavioural effects of CBD, which does not reverse the schizophrenia-relevant phenotypes

Raman Spectroscopy and Regenerative Medicine: A Review

The field of regenerative medicine spans a wide area of the biomedical landscape—from single cell culture in laboratories to human whole-organ transplantation. To ensure that research is transferrable from bench to bedside, it is critical that we are able to assess regenerative processes in cells, tissues, organs and patients at a biochemical level. Regeneration relies on a large number of biological factors, which can be perturbed using conventional bioanalytical techniques. A versatile, non-invasive, non-destructive technique for biochemical analysis would be invaluable for the study of regeneration; and Raman spectroscopy is a potential solution. Raman spectroscopy is an analytical method by which chemical data are obtained through the inelastic scattering of light. Since its discovery in the 1920s, physicists and chemists have used Raman scattering to investigate the chemical composition of a vast range of both liquid and solid materials. However, only in the last two decades has this form of spectroscopy been employed in biomedical research. Particularly relevant to regenerative medicine are recent studies illustrating its ability to characterise and discriminate between healthy and disease states in cells, tissue biopsies and in patients. This review will briefly outline the principles behind Raman spectroscopy and its variants, describe key examples of its applications to biomedicine, and consider areas of regenerative medicine that would benefit from this non-invasive bioanalytical tool